Aussie AI

Transformer Architectures

  • Last Updated 25 January, 2026
  • by David Spuler, Ph.D.

The Transformer was itself a major architectural advance in 2017. Since then, numerous modified Transformer architectures have been tested, and many ways to optimize Transformers have been found. Discussion of the various major architectural changes is given below; see also Transformer code optimizations, inference optimization techniques, and a very long list of Transformer optimizations

Global Transformer Architecture Changes

Since the introduction of the vanilla Transformer in 2017, researchers have been searching for optimizations up and down the Transformer's tech stack.

Global Transformer optimizations: Some of the architectural-level optimizations to Transformer inference engines include:

Depth-wise optimizations (layers):

  • Layer pruning / early exit: cutting short the layers in the encoder and/or decoder is a successful optimization strategy; see layer pruning and early exit inference.
  • Shallow decoder architecture. This idea for modifying the Transformer's decoder architecture uses layer pruning in the decoder to achieve a "deep encoder/shallow decoder" architecture, as reported in several research papers, such as Kasai et al. (2021) and Hsu et al. (2020); see shallow decoder architectures, and also depth pruning.

Width-wise optimizations (attention heads):

  • Attention head pruning: not all of the attention heads are important, esp. in the decoder (numerous research papers; see head pruning). There's some irony here when you consider the title of the original 2017 Transformer paper!
  • Flash Attention: Of all the various attention optimizations, Flash Attention (Dao et al., June 2022), and particularly Flash Attention 2 (Dao, July 2023), seems to have emerged as the most popular. See attention optimization methods.
  • Smaller Attention Head Components. It is possible to use more simplified attention head components, esp. in the decoder, as in Kasai et al. (2021); see approximate attention heads and head pruning. Another method is "weight sharing" for attention heads (or "fused" heads), such as in Zhai et al. (2023).

Lengthwise optimizations (input sequences):

Too Much of a Smart Thing: Just like in Highlander, there can be only one. No, wait, that's incorrect! It's called "multi-AI" or "ensemble" AI:

  • Ensemble architectures. Most architectures with two or more Transformers are aiming to achieve more advanced reasoning (usually at a worse speed), but the "big-small" dual architecture aims to improve inference speed by sending common queries to the smaller model. See enemble architectures.

Component-Level Transformer Architecture Changes

Attention heads are addressed above under width pruning, and layers are depth pruning, but various other Transformer components can be optimized:

Normalization optimizations:

Activation function optimizations:

Decoder algorithms:

  • Faster decoding algorithms. Research in Transformers includes beam search decoding and greedy decoding. There's also aggressive decoding, speculative decoding and collaborative decoding.
  • Speculative decoding (supervised dual decoding). A parallelization method whereby the decoding occurs in the small model to generate possible tokens. A larger model has the smaller model running ahead, and it confirms or vetos the suggested tokens, which is basically the same plot as Terminator II. No, but, I'm just checking if you're reading this stuff like an AI, rather than scanning and skipping like a human. If the small model is usually correct, this speeds up the overall process compared to only running a large model. This is similar to Big-Little architectures, but differs because both models are still running. See speculative decoding.

Feed-Fordware Network optimizations:

  • FFN Pruning. Simplified decoders, with FFN removed, as in Kasai et al. (2021), although this may be dependent on the use case; see "FFN pruning" section.

MatMul optimizations: Also known as GEMM and various other dumb names. It's matrix multiplication and vector dot product like you did in High School.

Softmax optimizations: Occurs less frequently than MatMul, but Softmax can still be optimized:

Positional encoding optimizations: Not usually considered a bottleneck, but even the PE can be optimized:

But wait, there's more. And there are more ways to optimize. Refer to the complete list of Transformer optimizations.

Survey Papers on Transformer Architectures

Several papers have surveyed the literature for the latest Transformer ideas:

Decoder-Only Architectures

Decoder-only architectures are the modern version of Transformers, such as GPT. It was discovered that the encoder in the older encoder-decoder Transformers from 2017 was not needed, and was actually an inefficiency. Decoder-only models are faster, and need fewer weights.

Research on the decoder-only transformer architectures:

  • Sathya Krishnan Suresh, Shunmugapriya P, 24 Apr 2024 (v2), Towards smaller, faster decoder-only transformers: Architectural variants and their implications, https://arxiv.org/abs/2404.14462 Code: https://github.com/SkAndMl/gpt-variations (Focuses on three new variants of decoder-only Transformer architectures: ParallelGPT (p-gpt), LinearlyCompressedGPT (lc-gpt), and ConvCompressedGPT (cc-gpt).)
  • Sachin Mehta, Mohammad Hossein Sekhavat, Qingqing Cao, Maxwell Horton, Yanzi Jin, Chenfan Sun, Iman Mirzadeh, Mahyar Najibi, Dmitry Belenko, Peter Zatloukal, Mohammad Rastegari, 22 Apr 2024, OpenELM: An Efficient Language Model Family with Open-source Training and Inference Framework, Apple Research, https://arxiv.org/abs/2404.14619 Code: https://huggingface.co/apple/OpenELM
  • Jesse Roberts, 2 Feb 2024 (v3), How Powerful are Decoder-Only Transformer Neural Models? https://arxiv.org/abs/2305.17026
  • Georgy Tyukin, 2 Apr 2024, Enhancing Inference Efficiency of Large Language Models: Investigating Optimization Strategies and Architectural Innovations, Masters Thesis, Data Science and Machine Learning, University College London., https://arxiv.org/abs/2404.05741 (Reviews various model compression and inference optimization techniques, and specifically analyzes layer skipping and sublayer skipping, such as attention head pruning and FFN/MLP pruning.)
  • Thomas Wang, Adam Roberts, Daniel Hesslow, Teven Le Scao, Hyung Won Chung, Iz Beltagy, Julien Launay, Colin Raffel, Apr 2022, What Language Model Architecture and Pretraining Objective Work Best for Zero-Shot Generalization? https://arxiv.org/abs/2204.05832
  • Urvashi Khandelwal, Kevin Clark, Dan Jurafsky, Lukasz Kaiser, 21 May 2019, Sample Efficient Text Summarization Using a Single Pre-Trained Transformer, https://arxiv.org/abs/1905.08836
  • Peter J. Liu, Mohammad Saleh, Etienne Pot, Ben Goodrich, Ryan Sepassi, Łukasz Kaiser, Noam Shazeer, Jan 2018, GENERATING WIKIPEDIA BY SUMMARIZING LONG SEQUENCES, ICLR 2018 https://arxiv.org/pdf/1801.10198.pdf
  • Peter J Liu, Mohammad Saleh, Etienne Pot, Ben Goodrich, Ryan Sepassi, Lukasz Kaiser, and Noam Shazeer. 2018. Generating Wikipedia by Summarizing Long Sequences. In Proceedings of the 6th International Conference on Learning Representations (ICLR). https://arxiv.org/abs/1801.10198
  • Yumo Bai, Feb 3, 2024 Why are most LLMs decoder-only? Dive into the rabbit hole of recent advancement in Large Language Models, https://medium.com/@yumo-bai/why-are-most-llms-decoder-only-590c903e4789
  • M Fujitake, 2023 DTrOCR: Decoder-only Transformer for Optical Character Recognition, https://arxiv.org/pdf/2308.15996.pdf
  • Benjamin Bergner, Andrii Skliar, Amelie Royer, Tijmen Blankevoort, Yuki Asano, Babak Ehteshami Bejnordi, 26 Feb 2024, Think Big, Generate Quick: LLM-to-SLM for Fast Autoregressive Decoding, https://arxiv.org/abs/2402.16844
  • Mengwei Xu, Wangsong Yin, Dongqi Cai, Rongjie Yi, Daliang Xu, Qipeng Wang, Bingyang Wu, Yihao Zhao, Chen Yang, Shihe Wang, Qiyang Zhang, Zhenyan Lu, Li Zhang, Shangguang Wang, Yuanchun Li, Yunxin Liu, Xin Jin, Xuanzhe Liu, 16 Jan 2024, A Survey of Resource-efficient LLM and Multimodal Foundation Models, https://arxiv.org/abs/2401.08092 Project: https://github.com/UbiquitousLearning/Efficient_Foundation_Model_Survey
  • Meta, July 23, 2024, Introducing Llama 3.1: Our most capable models to date, https://ai.meta.com/blog/meta-llama-3-1/
  • Yiheng Liu, Hao He, Tianle Han, Xu Zhang, Mengyuan Liu, Jiaming Tian, Yutong Zhang, Jiaqi Wang, Xiaohui Gao, Tianyang Zhong, Yi Pan, Shaochen Xu, Zihao Wu, Zhengliang Liu, Xin Zhang, Shu Zhang, Xintao Hu, Tuo Zhang, Ning Qiang, Tianming Liu, Bao Ge, 6 Jan 2024 (v2), Understanding LLMs: A Comprehensive Overview from Training to Inference, https://arxiv.org/abs/2401.02038
  • Hao Zhou, Chengming Hu, Ye Yuan, Yufei Cui, Yili Jin, Can Chen, Haolun Wu, Dun Yuan, Li Jiang, Di Wu, Xue Liu, Charlie Zhang, Xianbin Wang, Jiangchuan Liu, 17 May 2024, Large Language Model (LLM) for Telecommunications: A Comprehensive Survey on Principles, Key Techniques, and Opportunities, https://arxiv.org/abs/2405.10825
  • Andrea Matarazzo, Riccardo Torlone, 3 Jan 2025, A Survey on Large Language Models with some Insights on their Capabilities and Limitations, https://arxiv.org/abs/2501.04040 (Broad survey with many LLM topics covered from history to architectures to optimizations.)
  • Tong Xiao, Jingbo Zhu, 16 Jan 2025, Foundations of Large Language Models, https://arxiv.org/abs/2501.09223 (Huge 230 page paper on many topics such as training, prompting, alignment, and long context.)
  • Ailiang Lin, Zhuoyun Li, Kotaro Funakoshi, 31 Jul 2025, Causal2Vec: Improving Decoder-only LLMs as Versatile Embedding Models, https://arxiv.org/abs/2507.23386
  • Beilong Tang, Bang Zeng, Ming Li, 16 Aug 2025, LauraTSE: Target Speaker Extraction using Auto-Regressive Decoder-Only Language Models, https://arxiv.org/abs/2504.07402
  • Hamed Firooz, Maziar Sanjabi, Adrian Englhardt, Aman Gupta, Ben Levine, Dre Olgiati, Gungor Polatkan, Iuliia Melnychuk, Karthik Ramgopal, Kirill Talanine, Kutta Srinivasan, Luke Simon, Natesh Sivasubramoniapillai, Necip Fazil Ayan, Qingquan Song, Samira Sriram, Souvik Ghosh, Tao Song, Vignesh Kothapalli, Xiaoling Zhai, Ya Xu, Yu Wang, and Yun Dai, 23 Aug 2025, 360Brew: A Decoder-only Foundation Model for Personalized Ranking and Recommendation, https://arxiv.org/abs/2501.16450
  • Nayan Sanjay Bhatia, Pranay Kocheta, Russell Elliott, Harikrishna S. Kuttivelil, Katia Obraczka, 13 Oct 2025, Indoor Localization using Compact, Telemetry-Agnostic, Transfer-Learning Enabled Decoder-Only Transformer, https://arxiv.org/abs/2510.11926
  • Xinnan Dai, Chung-Hsiang Lo, Kai Guo, Shenglai Zeng, Dongsheng Luo, Jiliang Tang, 24 Sep 2025, Uncovering Graph Reasoning in Decoder-only Transformers with Circuit Tracing, https://arxiv.org/abs/2509.20336
  • Marko Karbevski, Antonij Mijoski, 27 Oct 2025, Key and Value Weights Are Probably All You Need: On the Necessity of the Query, Key, Value weight Triplet in Decoder-Only Transformers, https://arxiv.org/abs/2510.23912
  • Haoyin Yan, Chengwei Liu, Shaofei Xue, Xiaotao Liang, Zheng Xue, 23 Oct 2025, UniSE: A Unified Framework for Decoder-only Autoregressive LM-based Speech Enhancement, https://arxiv.org/abs/2510.20441
  • Paloma Garc\'ia-de-Herreros and Philipp Slusallek and Dietrich Klakow and Vagrant Gautam, 6 Oct 2025, Decoding Partial Differential Equations: Cross-Modal Adaptation of Decoder-only Models to PDEs, https://arxiv.org/abs/2510.05278

Encoder-Decoder Architectures

Encoder-decoder Transformers are the older architecture from 2017. Decoder-only architectures have largely superceded this version, but it is still used in some use cases such as machine translation (foreign language translation).

Research on encoder-decoder architectures:

  • Tianyu He, Xu Tan, Yingce Xia, Di He, Tao Qin, Zhibo Chen, Tie-Yan Liu, 2018, Layer-Wise Coordination between Encoder and Decoder for Neural Machine Translation, Advances in Neural Information Processing Systems 31 (NeurIPS 2018) https://papers.nips.cc/paper/2018/hash/4fb8a7a22a82c80f2c26fe6c1e0dcbb3-Abstract.html
  • Nadeem Vidhya Mar 11, 2021, Encoders-Decoders, Sequence to Sequence Architecture, Analytics Vidhya, Medium, https://medium.com/analytics-vidhya/encoders-decoders-sequence-to-sequence-architecture-5644efbb3392
  • Yumo Bai, Feb 3, 2024 Why are most LLMs decoder-only? Dive into the rabbit hole of recent advancement in Large Language Models, https://medium.com/@yumo-bai/why-are-most-llms-decoder-only-590c903e4789
  • Benjamin Bergner, Andrii Skliar, Amelie Royer, Tijmen Blankevoort, Yuki Asano, Babak Ehteshami Bejnordi, 26 Feb 2024, Think Big, Generate Quick: LLM-to-SLM for Fast Autoregressive Decoding, https://arxiv.org/abs/2402.16844
  • João Monteiro, Étienne Marcotte, Pierre-André Noël, Valentina Zantedeschi, David Vázquez, Nicolas Chapados, Christopher Pal, Perouz Taslakian, 23 Apr 2024, XC-Cache: Cross-Attending to Cached Context for Efficient LLM Inference, https://arxiv.org/abs/2404.15420
  • Mengwei Xu, Wangsong Yin, Dongqi Cai, Rongjie Yi, Daliang Xu, Qipeng Wang, Bingyang Wu, Yihao Zhao, Chen Yang, Shihe Wang, Qiyang Zhang, Zhenyan Lu, Li Zhang, Shangguang Wang, Yuanchun Li, Yunxin Liu, Xin Jin, Xuanzhe Liu, 16 Jan 2024, A Survey of Resource-efficient LLM and Multimodal Foundation Models, https://arxiv.org/abs/2401.08092 Project: https://github.com/UbiquitousLearning/Efficient_Foundation_Model_Survey
  • kipply's blog, 2023-03-30, Transformer Taxonomy (the last lit review), https://kipp.ly/transformer-taxonomy/ (Papers for all the Transformer architectures and milestone papers for the major optimization improvements on them.)
  • Yiheng Liu, Hao He, Tianle Han, Xu Zhang, Mengyuan Liu, Jiaming Tian, Yutong Zhang, Jiaqi Wang, Xiaohui Gao, Tianyang Zhong, Yi Pan, Shaochen Xu, Zihao Wu, Zhengliang Liu, Xin Zhang, Shu Zhang, Xintao Hu, Tuo Zhang, Ning Qiang, Tianming Liu, Bao Ge, 6 Jan 2024 (v2), Understanding LLMs: A Comprehensive Overview from Training to Inference, https://arxiv.org/abs/2401.02038
  • Hao Zhou, Chengming Hu, Ye Yuan, Yufei Cui, Yili Jin, Can Chen, Haolun Wu, Dun Yuan, Li Jiang, Di Wu, Xue Liu, Charlie Zhang, Xianbin Wang, Jiangchuan Liu, 17 May 2024, Large Language Model (LLM) for Telecommunications: A Comprehensive Survey on Principles, Key Techniques, and Opportunities, https://arxiv.org/abs/2405.10825
  • Anjali Shah, Kshitiz Gupta, Jiahong Liu and Haohang Huang, Dec 11, 2024, NVIDIA TensorRT-LLM Now Accelerates Encoder-Decoder Models with In-Flight Batching, https://developer.nvidia.com/blog/nvidia-tensorrt-llm-now-accelerates-encoder-decoder-models-with-in-flight-batching/
  • Andrea Matarazzo, Riccardo Torlone, 3 Jan 2025, A Survey on Large Language Models with some Insights on their Capabilities and Limitations, https://arxiv.org/abs/2501.04040 (Broad survey with many LLM topics covered from history to architectures to optimizations.)
  • Tong Xiao, Jingbo Zhu, 16 Jan 2025, Foundations of Large Language Models, https://arxiv.org/abs/2501.09223 (Huge 230 page paper on many topics such as training, prompting, alignment, and long context.)
  • Wenji Fang, Jing Wang, Yao Lu, Shang Liu, Zhiyao Xie, 6 Aug 2025, GenEDA: Towards Generative Netlist Functional Reasoning via Cross-Modal Circuit Encoder-Decoder Alignment, https://arxiv.org/abs/2504.09485
  • Zixi Li, 11 Sep 2025, TreeGPT: Pure TreeFFN Encoder-Decoder Architecture for Structured Reasoning Without Attention Mechanisms, https://arxiv.org/abs/2509.05550
  • Giuseppina Carannante, Nidhal C.Bouaynaya, Dimah Dera, Hassan M. Fathallah-Shaykh, and Ghulam Rasool, 1 Oct 2025, SUPER-Net: Trustworthy Image Segmentation via Uncertainty Propagation in Encoder-Decoder Networks, https://arxiv.org/abs/2111.05978
  • Swadhin Das, Raksha Sharma, 28 Oct 2025, MsEdF: A Multi-stream Encoder-decoder Framework for Remote Sensing Image Captioning, https://arxiv.org/abs/2502.09282
  • Marianne Arriola, Yair Schiff, Hao Phung, Aaron Gokaslan, Volodymyr Kuleshov, 26 Oct 2025, Encoder-Decoder Diffusion Language Models for Efficient Training and Inference, https://arxiv.org/abs/2510.22852
  • Yubo Zhang, Jeremy Johnston, and Xiaodong Wang, 27 Sep 2025, An Encoder-Decoder Network for Beamforming over Sparse Large-Scale MIMO Channels, https://arxiv.org/abs/2510.02355
  • Wenfeng Feng, Hongxiang Wang, Jianlong Wang, Xin Zhang, Jingjing Zhao, Yueyue Liang, Xiang Chen, Duokui Han, 16 Oct 2025, EDIT: Enhancing Vision Transformers by Mitigating Attention Sink through an Encoder-Decoder Architecture, https://arxiv.org/abs/2504.06738

Encoder-Only Architectures

Encoder-only architectures lack a decoder, and are only used where the output is not a full text sequence. This makes sense in models where the output can be an embedding vector. Most modern LLMs are not using this architecture.

Research on encoder-only architectures:

  • Ting Hu, Christoph Meinel, Haojin Yang, 2024, A flexible BERT model enabling width- and depth-dynamic inference, Computer Speech & Language 4 April 2024, 101646, https://www.sciencedirect.com/science/article/pii/S0885230824000299 (Dual pruning method with layerwise "neural grafting" that gives dynamic width models, and combined with early exit on the depth dimension.)
  • Yehui Tang, Yunhe Wang, Jianyuan Guo, Zhijun Tu, Kai Han, Hailin Hu, Dacheng Tao, 5 Feb 2024. A Survey on Transformer Compression. https://arxiv.org/abs/2402.05964 (Model compression survey paper with focus on pruning, quantization, knowledge distillation, and efficient architecture design.)
  • Mengwei Xu, Wangsong Yin, Dongqi Cai, Rongjie Yi, Daliang Xu, Qipeng Wang, Bingyang Wu, Yihao Zhao, Chen Yang, Shihe Wang, Qiyang Zhang, Zhenyan Lu, Li Zhang, Shangguang Wang, Yuanchun Li, Yunxin Liu, Xin Jin, Xuanzhe Liu, 16 Jan 2024, A Survey of Resource-efficient LLM and Multimodal Foundation Models, https://arxiv.org/abs/2401.08092 Project: https://github.com/UbiquitousLearning/Efficient_Foundation_Model_Survey
  • Yiheng Liu, Hao He, Tianle Han, Xu Zhang, Mengyuan Liu, Jiaming Tian, Yutong Zhang, Jiaqi Wang, Xiaohui Gao, Tianyang Zhong, Yi Pan, Shaochen Xu, Zihao Wu, Zhengliang Liu, Xin Zhang, Shu Zhang, Xintao Hu, Tuo Zhang, Ning Qiang, Tianming Liu, Bao Ge, 6 Jan 2024 (v2), Understanding LLMs: A Comprehensive Overview from Training to Inference, https://arxiv.org/abs/2401.02038
  • Hao Zhou, Chengming Hu, Ye Yuan, Yufei Cui, Yili Jin, Can Chen, Haolun Wu, Dun Yuan, Li Jiang, Di Wu, Xue Liu, Charlie Zhang, Xianbin Wang, Jiangchuan Liu, 17 May 2024, Large Language Model (LLM) for Telecommunications: A Comprehensive Survey on Principles, Key Techniques, and Opportunities, https://arxiv.org/abs/2405.10825
  • Benjamin Warner, Antoine Chaffin, Benjamin Clavié, Orion Weller, Oskar Hallström, Said Taghadouini, Alexis Gallagher, Raja Biswas, Faisal Ladhak, Tom Aarsen, Nathan Cooper, Griffin Adams, Jeremy Howard, Iacopo Poli, 19 Dec 2024 (v2)], Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference, https://arxiv.org/abs/2412.13663 (Encoder-only BERT model updated with modern optimizations including Flash attention, bias removal, RoPE, pre-norm, and GeGLU, a GELU varaint, hybrid local-global attention, and zero padding removal.)
  • Andrea Matarazzo, Riccardo Torlone, 3 Jan 2025, A Survey on Large Language Models with some Insights on their Capabilities and Limitations, https://arxiv.org/abs/2501.04040 (Broad survey with many LLM topics covered from history to architectures to optimizations.)
  • Tong Xiao, Jingbo Zhu, 16 Jan 2025, Foundations of Large Language Models, https://arxiv.org/abs/2501.09223 (Huge 230 page paper on many topics such as training, prompting, alignment, and long context.)
  • Francesco Pappone, Ruggero Marino Lazzaroni, Federico Califano, Niccol\`o Gentile, Roberto Marras, 16 Sep 2025, Shaping Explanations: Semantic Reward Modeling with Encoder-Only Transformers for GRPO, https://arxiv.org/abs/2509.13081

Hybrid Transformer Architectures

Transformer architectures have been merged with aspects of previous neural network theory to create hybrid architectures. Examples include:

  • Vision Transformer (ViT)
  • Transformer-RNN hybrid architectures
  • Transformer-CNN hybrid architectures

Research papers on hybrid transformer architectures:

Innovative New Transformer Architecture Research Papers

Since the original Transformer paper in 2017, and various other Transformer milestone papers, there have been numerous architectural variations proposed to alleviate efficiency or accuracy concerns. Research papers on specific modifications to the Transformer architecture include:

That's a Lot of BERTs!

BERT was an early 2019 Transformer architecture that was significantly innovative. Since then, there have been a great many variants of "BERT" (e.g. FastBERT, MobileBERT, DistilBERT, etc.). Research papers on variants of BERT include:

  • Jacob Devlin, Ming-Wei Chang, Kenton Lee, Kristina Toutanova, BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding, May 2019, https://arxiv.org/abs/1810.04805, Code: https://github.com/google-research/bert (The one BERT to rule them all.)
  • Zhiqing Sun, Hongkun Yu, Xiaodan Song, Renjie Liu, Yiming Yang, and Denny Zhou. MobileBERT: a compact task-agnostic BERT for resource-limited devices. arXiv preprint arXiv:2004.02984, 2020. https://arxiv.org/abs/2004.02984
  • Weijie Liu, Peng Zhou, Zhe Zhao, Zhiruo Wang, Haotang Deng, and Qi Ju. FastBERT: a self-distilling BERT with adaptive inference time. arXiv preprint arXiv:2004.02178, 2020. https://arxiv.org/abs/2004.02178
  • Ji Xin, Raphael Tang, Jaejun Lee, Yaoliang Yu, and Jimmy Lin. DeeBERT: Dynamic early exiting for accelerating BERT inference. arXiv preprint arXiv:2004.12993, 2020. https://arxiv.org/abs/2004.12993
  • Victor Sanh, Lysandre Debut, Julien Chaumond, and Thomas Wolf. DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter. arXiv preprint arXiv:1910.01108, 2019, https://arxiv.org/abs/1910.01108
  • Forrest N Iandola, Albert E Shaw, Ravi Krishna, and Kurt W Keutzer. SqueezeBERT: What can computer vision teach NLP about efficient neural networks? arXiv preprint arXiv:2006.11316, 2020. https://arxiv.org/abs/2006.11316
  • Daoyuan Chen, Yaliang Li, Minghui Qiu, Zhen Wang, Bofang Li, Bolin Ding, Hongbo Deng, Jun Huang, Wei Lin, and Jingren Zhou. AdaBERT: Task-adaptive BERT compression with differentiable neural architecture search. arXiv preprint arXiv:2001.04246, 2020. https://arxiv.org/abs/2001.04246
  • Lu Hou, Zhiqi Huang, Lifeng Shang, Xin Jiang, Xiao Chen, and Qun Liu. Dynabert: Dynamic bert with adaptive width and depth. arXiv preprint arXiv:2004.04037, 2020. https://arxiv.org/abs/2004.04037
  • Zejian Liu, Fanrong Li, Gang Li, and Jian Cheng. EBERT: Efficient BERT Inference with Dynamic Structured Pruning. In Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, pages 4814– 4823, 2021. https://aclanthology.org/2021.findings-acl.425/
  • Liu, M. Ott, N. Goyal, J. Du, M. Joshi, D. Chen, O. Levy, M. Lewis, L. Zettlemoyer, and V. Stoyanov, “Roberta: A robustly optimized BERT pretraining approach,” CoRR, 2019. https://arxiv.org/abs/1907.11692
  • Z. Jiang, W. Yu, D. Zhou, Y. Chen, J. Feng, and S. Yan, “Convbert: Improving BERT with span-based dynamic convolution,” in NeurIPS, 2020, https://arxiv.org/abs/2008.02496
  • H. Bao, L. Dong, S. Piao, and F. Wei, “BEit: BERT pre-training of image transformers,” in International Conference on Learning Representations, 2022. https://arxiv.org/abs/2106.08254
  • Xiaoqi Jiao, Yichun Yin, Lifeng Shang, Xin Jiang, Xiao Chen, Linlin Li, Fang Wang, Qun Liu, Oct 2020, TinyBERT: Distilling BERT for Natural Language Understanding, https://arxiv.org/abs/1909.10351
  • Mengwei Xu, Wangsong Yin, Dongqi Cai, Rongjie Yi, Daliang Xu, Qipeng Wang, Bingyang Wu, Yihao Zhao, Chen Yang, Shihe Wang, Qiyang Zhang, Zhenyan Lu, Li Zhang, Shangguang Wang, Yuanchun Li, Yunxin Liu, Xin Jin, Xuanzhe Liu, 16 Jan 2024, A Survey of Resource-efficient LLM and Multimodal Foundation Models, https://arxiv.org/abs/2401.08092 Project: https://github.com/UbiquitousLearning/Efficient_Foundation_Model_Survey
  • Tong Xiao, Jingbo Zhu, 16 Jan 2025, Foundations of Large Language Models, https://arxiv.org/abs/2501.09223 (Huge 230 page paper on many topics such as training, prompting, alignment, and long context.)
  • Kester Wong, Sahan Bulathwela and Mutlu Cukurova, 19 Jul 2025, Exploring Human-AI Complementarity in CPS Diagnosis Using Unimodal and Multimodal BERT Models, https://arxiv.org/abs/2507.14579
  • Kester Wong, Sahan Bulathwela and Mutlu Cukurova, 19 Jul 2025, Explainable Collaborative Problem Solving Diagnosis with BERT using SHAP and its Implications for Teacher Adoption, https://arxiv.org/abs/2507.14584
  • Qiyao Xue, Yuchen Dou, Ryan Shi, Xiang Lorraine Li, Wei Gao, 1 Aug 2025, MMBERT: Scaled Mixture-of-Experts Multimodal BERT for Robust Chinese Hate Speech Detection under Cloaking Perturbations, https://arxiv.org/abs/2508.00760
  • Tianpei Lu, Bingsheng Zhang, Lekun Peng, Bowen Zheng, Lichun Li, Kui Ren, 3 Aug 2025, Privacy-Preserving Inference for Quantized BERT Models, https://arxiv.org/abs/2508.01636
  • Zijian Zhao, Fanyi Meng, Zhonghao Lyu, Hang Li, Xiaoyang Li, Guangxu Zhu, 3 Aug 2025, CSI-BERT2: A BERT-inspired Framework for Efficient CSI Prediction and Classification in Wireless Communication and Sensing, https://arxiv.org/abs/2412.06861
  • Tai Vu, Robert Yang, 14 Aug 2025, BERT-VQA: Visual Question Answering on Plots, https://arxiv.org/abs/2508.13184
  • Minh Tran, Jeffery C. Chan, Min Li Huang, Maya Kansara, John P. Grady, Christine E. Napier, Subotheni Thavaneswaran, Mandy L. Ballinger, David M. Thomas, Frank P. Lin, 21 Aug 2025, A Robust BERT-Based Deep Learning Model for Automated Cancer Type Extraction from Unstructured Pathology Reports, https://arxiv.org/abs/2508.15149
  • Kun Liu, Tuozhen Liu, Feifei Wang, and Rui Pan, 13 Aug 2025, A BERT-based Hierarchical Classification Model with Applications in Chinese Commodity Classification, https://arxiv.org/abs/2508.15800
  • Daniel Frees, Aditri Bhagirath, Moritz Bolling, 25 Aug 2025, Exploring Efficient Learning of Small BERT Networks with LoRA and DoRA, https://arxiv.org/abs/2508.17586
  • Suramya Jadhav, Abhay Shanbhag, Amogh Thakurdesai, Ridhima Sinare, Ananya Joshi, Raviraj Joshi, 24 Aug 2025, MahaParaphrase: A Marathi Paraphrase Detection Corpus and BERT-based Models, https://arxiv.org/abs/2508.17444
  • Mayur Shirke, Amey Shembade, Pavan Thorat, Madhushri Wagh, Raviraj Joshi, 2 Sep 2025, Comparative Study of Pre-Trained BERT and Large Language Models for Code-Mixed Named Entity Recognition, https://arxiv.org/abs/2509.02514
  • John Hawkins and Aditya Pramar and Rodney Beard and Rohitash Chandra, 2 Oct 2025, NLP Methods for Detecting Novel LLM Jailbreaks and Keyword Analysis with BERT, https://arxiv.org/abs/2510.01644
  • Maike Behrendt, Stefan Sylvius Wagner, Stefan Harmeling, 14 Oct 2025, MaxPoolBERT: Enhancing BERT Classification via Layer- and Token-Wise Aggregation, https://arxiv.org/abs/2505.15696
  • Yunzhi Liu, Haokai Tan, Rushi Kanjaria, Lihuan Li, Flora D. Salim, 23 Oct 2025, Classical Feature Embeddings Help in BERT-Based Human Mobility Prediction, https://arxiv.org/abs/2510.20275
  • Kozhin muhealddin Awlla, Hadi Veisi, Abdulhady Abas Abdullah, 20 Sep 2025, KuBERT: Central Kurdish BERT Model and Its Application for Sentiment Analysis, https://arxiv.org/abs/2509.16804
  • Mahamodul Hasan Mahadi, Md. Nasif Safwan, Souhardo Rahman, Shahnaj Parvin, Aminun Nahar and Kamruddin Nur, 14 Oct 2025, Ethic-BERT: An Enhanced Deep Learning Model for Ethical and Non-Ethical Content Classification, https://arxiv.org/abs/2510.12850
  • Zishuo Xu, Yuhong Gu, Dezhong Yao, 27 Sep 2025, WARBERT: A Hierarchical BERT-based Model for Web API Recommendation, https://arxiv.org/abs/2509.23175
  • Zijian Zhao, Sen Li, 26 Sep 2025, Triple-BERT: Do We Really Need MARL for Order Dispatch on Ride-Sharing Platforms?, https://arxiv.org/abs/2510.03257
  • Runze Xia, Yupeng Ji, Yuxi Zhou, Haodong Liu, Teng Zhang, Piji Li, 13 Oct 2025, From Reasoning LLMs to BERT: A Two-Stage Distillation Framework for Search Relevance, https://arxiv.org/abs/2510.11056
  • Noor Ul Zain, Mohsin Raza, Ahsan Adeel, 9 Oct 2025, Single layer tiny Co$^4$ outpaces GPT-2 and GPT-BERT, https://arxiv.org/abs/2510.08404
  • Ana Ozaki, Roberto Confalonieri, Ricardo Guimar\~aes, Anders Imenes, 6 Oct 2025, Extracting PAC Decision Trees from Black Box Binary Classifiers: The Gender Bias Case Study on BERT-based Language Models, https://arxiv.org/abs/2412.10513
  • Michael Li, Nishant Subramani, 15 Oct 2025, Echoes of BERT: Do Modern Language Models Rediscover the Classical NLP Pipeline?, https://arxiv.org/abs/2506.02132

Next-Generation Architectures

What comes after Transformers? Maybe the answer is: more Transformers! Certainly, the newer multi-modal Transformers are gaining momentum, and there are other advanced Transformers:

  • Vision Transformer (ViT)
  • Multimodal transformer
  • Ensemble architectures (multi-AI, such as MoE)
  • Agent architectures (e.g., function calling, autonomous agents)
  • Advanced RAG architectures
  • Tool Augmented Language Models (TALM)
  • Retrieval Augment Language Models (RALM)
  • Compound AI architectures

However, there are some alternatives to Transformers that have been gathering steam. Here are a few newer architectures already being worked on:

  • State Space Models (SSMs)
  • RWKV (Transformer-RNN hybrid)
  • Mamba (a type of SSM)
  • Graph Neural Networks and Knowledge Graph extensions
  • S4 Hyena architecture
  • Spiking Neural Networks (SNNs) and Spiking Transformers
  • Weightless Neural Networks (WNNs)
  • Liquid Neural Networks (LNNs)
  • Hybrid Transformer-RNN architectures
  • Hybrid Transformer-CNN architectures

Research papers on next-gen architectures:

RWKV Architecture

The RWKV architecture is a hybrid Transformer-RNN architecture. Research papers on RWKV include:

  • Jean Mercat, Igor Vasiljevic, Sedrick Keh, Kushal Arora, Achal Dave, Adrien Gaidon, Thomas Kollar, 10 May 2024, Linearizing Large Language Models, https://arxiv.org/abs/2405.06640 Code: https://github.com/TRI-ML/linear_open_lm
  • Yehui Tang, Yunhe Wang, Jianyuan Guo, Zhijun Tu, Kai Han, Hailin Hu, Dacheng Tao, 5 Feb 2024. A Survey on Transformer Compression. https://arxiv.org/abs/2402.05964 (Model compression survey paper with focus on pruning, quantization, knowledge distillation, and efficient architecture design.)
  • Tiancheng Gu, Kaicheng Yang, Xiang An, Ziyong Feng, Dongnan Liu, Weidong Cai, Jiankang Deng, 11 Jun 2024. RWKV-CLIP: A Robust Vision-Language Representation Learner, https://arxiv.org/abs/2406.06973 Code: https://github.com/deepglint/RWKV-CLIP
  • Xinji Mai, Zeng Tao, Junxiong Lin, Haoran Wang, Yang Chang, Yanlan Kang, Yan Wang, Wenqiang Zhang, 27 Jun 2024, From Efficient Multimodal Models to World Models: A Survey, https://arxiv.org/abs/2407.00118 (A survey of multimodal models with coverage of many optimization techniques.)
  • 18 Apr 2024 (v2), The Efficiency Spectrum of Large Language Models: An Algorithmic Survey, Tianyu Ding, Tianyi Chen, Haidong Zhu, Jiachen Jiang, Yiqi Zhong, Jinxin Zhou, Guangzhi Wang, Zhihui Zhu, Ilya Zharkov, Luming Liang, https://arxiv.org/abs/2312.00678
  • Joanne Chen, July 23, 2024, What’s Next After Transformers, https://foundationcapital.com/whats-next-after-transformers/
  • Théodor Lemerle, Harrison Vanderbyl, Vaibhav Srivastav, Nicolas Obin, Axel Roebel, 30 Oct 2024, Lina-Speech: Gated Linear Attention is a Fast and Parameter-Efficient Learner for text-to-speech synthesis, https://arxiv.org/abs/2410.23320 https://theodorblackbird.github.io/blog/demo_lina/
  • Akul Datta, 5 Nov 2024, The Evolution of RWKV: Advancements in Efficient Language Modeling, https://arxiv.org/abs/2411.02795
  • From Transformers to the Future: An In-Depth Exploration of Modern Language Model Architectures H Xu, Z Bi, H Tseng, X Song, P Feng, https://osf.io/n8r5j/download
  • Wonkyo Choe, Yangfeng Ji, Felix Lin, 14 Dec 2024, RWKV-edge: Deeply Compressed RWKV for Resource-Constrained Devices, https://arxiv.org/abs/2412.10856
  • Haoyang Li, Yiming Li, Anxin Tian, Tianhao Tang, Zhanchao Xu, Xuejia Chen, Nicole Hu, Wei Dong, Qing Li, Lei Chen, 27 Dec 2024, A Survey on Large Language Model Acceleration based on KV Cache Management, https://arxiv.org/abs/2412.19442 (Huge survey of all KV cache optimization methods.)
  • Xiaoran Liu, Ruixiao Li, Mianqiu Huang, Zhigeng Liu, Yuerong Song, Qipeng Guo, Siyang He, Qiqi Wang, Linlin Li, Qun Liu, Yaqian Zhou, Xuanjing Huang, Xipeng Qiu, 24 Feb 2025, Thus Spake Long-Context Large Language Model, https://arxiv.org/abs/2502.17129 (Impressive survey of many techniques to improve efficiency and accuracy of long context processing in both inference and training, covering text, video and multimodal models.)
  • Sicheng Chen, Tianyi Zhang, Dankai Liao, Dandan Li, Low Chang Han, Yanqin Jiang, Yueming Jin, Shangqing Lyu, 5 Mar 2025, PathRWKV: Enabling Whole Slide Prediction with Recurrent-Transformer, https://arxiv.org/abs/2503.03199
  • Liu Xiao, Li Zhiyuan, Lin Yueyu, 27 Apr 2025, WuNeng: Hybrid State with Attention, https://arxiv.org/abs/2504.19191
  • Xiao Wang, Haiyang Wang, Shiao Wang, Qiang Chen, Jiandong Jin, Haoyu Song, Bo Jiang, Chenglong Li, 6 Aug 2025, RGB-Event based Pedestrian Attribute Recognition: A Benchmark Dataset and An Asymmetric RWKV Fusion Framework, https://arxiv.org/abs/2504.10018

State Space Models (SSMs)

  • Badri Narayana Patro, Vijay Srinivas Agneeswaran, 24 Apr 2024, Mamba-360: Survey of State Space Models as Transformer Alternative for Long Sequence Modelling: Methods, Applications, and Challenges, https://arxiv.org/abs/2404.16112
  • 8 Jun 2024 (v2), A Survey on Efficient Inference for Large Language Models, Zixuan Zhou, Xuefei Ning, Ke Hong, Tianyu Fu, Jiaming Xu, Shiyao Li, Yuming Lou, Luning Wang, Zhihang Yuan, Xiuhong Li, Shengen Yan, Guohao Dai, Xiao-Ping Zhang, Yuhan Dong, Yu Wang, https://arxiv.org/abs/2404.14294
  • Karan Goel, August 27, 2024, The On‑Device Intelligence Update https://cartesia.ai/blog/2024-08-27-on-device (On-device state space models.)
  • Nicolas Stellwag, 2024, Structured State Space Models, https://nicolasstellwag.com/download/structured_SSMs.pdf
  • Jinhao Li, Jiaming Xu, Shan Huang, Yonghua Chen, Wen Li, Jun Liu, Yaoxiu Lian, Jiayi Pan, Li Ding, Hao Zhou, Guohao Dai, 6 Oct 2024, Large Language Model Inference Acceleration: A Comprehensive Hardware Perspective, https://arxiv.org/abs/2410.04466
  • Cong Guo, Feng Cheng, Zhixu Du, James Kiessling, Jonathan Ku, Shiyu Li, Ziru Li, Mingyuan Ma, Tergel Molom-Ochir, Benjamin Morris, Haoxuan Shan, Jingwei Sun, Yitu Wang, Chiyue Wei, Xueying Wu, Yuhao Wu, Hao Frank Yang, Jingyang Zhang, Junyao Zhang, Qilin Zheng, Guanglei Zhou, Hai (Helen)Li, Yiran Chen, 8 Oct 2024. A Survey: Collaborative Hardware and Software Design in the Era of Large Language Models, https://arxiv.org/abs/2410.07265
  • Xin Dong, Yonggan Fu, Shizhe Diao, Wonmin Byeon, Zijia Chen, Ameya Sunil Mahabaleshwarkar, Shih-Yang Liu, Matthijs Van Keirsbilck, Min-Hung Chen, Yoshi Suhara, Yingyan Lin, Jan Kautz, Pavlo Molchanov, 20 Nov 2024, Hymba: A Hybrid-head Architecture for Small Language Models, https://arxiv.org/abs/2411.13676
  • Yash Akhauri, Safeen Huda, Mohamed S. Abdelfattah, 26 Nov 2024, Attamba: Attending To Multi-Token States, https://arxiv.org/abs/2411.17685
  • Rui Pan, Zhuang Wang, Zhen Jia, Can Karakus, Luca Zancato, Tri Dao, Ravi Netravali, Yida Wang, 28 Nov 2024, Marconi: Prefix Caching for the Era of Hybrid LLMs, https://arxiv.org/abs/2411.19379 (Prefix caching applied to hybrid SSM-Transformer LLMs.)
  • Haoyang Li, Yiming Li, Anxin Tian, Tianhao Tang, Zhanchao Xu, Xuejia Chen, Nicole Hu, Wei Dong, Qing Li, Lei Chen, 27 Dec 2024, A Survey on Large Language Model Acceleration based on KV Cache Management, https://arxiv.org/abs/2412.19442 (Huge survey of all KV cache optimization methods.)
  • Jonas Ulmen, Ganesh Sundaram, and Daniel G\"orges, 14 Aug 2025, Learning State-Space Models of Dynamic Systems from Arbitrary Data using Joint Embedding Predictive Architectures, https://arxiv.org/abs/2508.10489
  • Xiaochun Lei, Siqi Wu, Weilin Wu, Zetao Jiang, 24 Jul 2025, MambaNeXt-YOLO: A Hybrid State Space Model for Real-time Object Detection, https://arxiv.org/abs/2506.03654
  • Sen Lu, Xiaoyu Zhang, Mingtao Hu, Eric Yeu-Jer Lee, Soohyeon Kim, Wei D. Lu, 18 Jul 2025, State Space Models Naturally Produce Traveling Waves, Time Cells, and Scale to Abstract Cognitive Functions, https://arxiv.org/abs/2507.13638
  • Saptarshi Mitra, Rachid Karami, Haocheng Xu, Sitao Huang, Hyoukjun Kwon, 19 Jul 2025, Characterizing State Space Model (SSM) and SSM-Transformer Hybrid Language Model Performance with Long Context Length, https://arxiv.org/abs/2507.12442
  • A. Quadir, M. Tanveer, 8 Aug 2025, Hypergraph Neural Network with State Space Models for Node Classification, https://arxiv.org/abs/2508.06587
  • Yizhuo Wu, Francesco Fioranelli, Chang Gao, 27 Jul 2025, RadMamba: Efficient Human Activity Recognition through Radar-based Micro-Doppler-Oriented Mamba State-Space Model, https://arxiv.org/abs/2504.12039
  • Shiva Raja, Cansu Demirkiran, Aakash Sarkar, Milos Popovic, Ajay Joshi, 29 Jul 2025, Systolic Array-based Accelerator for State-Space Models, https://arxiv.org/abs/2507.21394
  • Yifan Yu, Shengjie Xiu, Daniel P. Palomar, 30 Jul 2025, Robust Filtering and Learning in State-Space Models: Skewness and Heavy Tails Via Asymmetric Laplace Distribution, https://arxiv.org/abs/2507.22343
  • Hiroki Sakamoto and Kazuhiro Sato, 30 Jul 2025, Compression Method for Deep Diagonal State Space Model Based on $H^2$ Optimal Reduction, https://arxiv.org/abs/2507.10078
  • Julian Lemmel, Manuel Kranzl, Adam Lamine, Philipp Neubauer, Radu Grosu, Sophie Neubauer, 1 Aug 2025, Online Fine-Tuning of Carbon Emission Predictions using Real-Time Recurrent Learning for State Space Models, https://arxiv.org/abs/2508.00804
  • Joshua Dimasaka, Christian Gei{\ss}, Emily So, 2 Aug 2025, GraphVSSM: Graph Variational State-Space Model for Probabilistic Spatiotemporal Inference of Dynamic Exposure and Vulnerability for Regional Disaster Resilience Assessment, https://arxiv.org/abs/2508.01310
  • Federico Arangath Joseph, Kilian Konstantin Haefeli, Noah Liniger and Caglar Gulcehre, 3 Aug 2025, HiPPO-Prophecy: State-Space Models can Provably Learn Dynamical Systems in Context, https://arxiv.org/abs/2407.09375
  • Yiyi Wang, Jian'an Zhang, Hongyi Duan, Haoyang Liu, Qingyang Li, 5 Aug 2025, Rethinking Selectivity in State Space Models: A Minimal Predictive Sufficiency Approach, https://arxiv.org/abs/2508.03158
  • Leon G\"otz, Marcel Kollovieh, Stephan G\"unnemann, Leo Schwinn, 5 Aug 2025, Efficient Time Series Processing for Transformers and State-Space Models through Token Merging, https://arxiv.org/abs/2405.17951
  • Yuannuo Feng, Wenyong Zhou, Yuexi Lyu, Hanjie Liu, Zhengwu Liu, Ngai Wong, Wang Kang, 16 Aug 2025, HPD: Hybrid Projection Decomposition for Robust State Space Models on Analog CIM Hardware, https://arxiv.org/abs/2508.11935
  • Chenhui Xu, Dancheng Liu, Yuting Hu, Jiajie Li, Ruiyang Qin, Qingxiao Zheng, Jinjun Xiong, 16 Aug 2025, Sub-Sequential Physics-Informed Learning with State Space Model, https://arxiv.org/abs/2502.00318
  • Zhihao Zhan, Jianan Zhao, Zhaocheng Zhu, Jian Tang, 16 Aug 2025, Overcoming Long-Context Limitations of State-Space Models via Context-Dependent Sparse Attention, https://arxiv.org/abs/2507.00449
  • Hongfan Gao, Wangmeng Shen, Xiangfei Qiu, Ronghui Xu, Jilin Hu and Bin Yang, 19 Aug 2025, SSD-TS: Exploring the Potential of Linear State Space Models for Diffusion Models in Time Series Imputation, https://arxiv.org/abs/2410.13338
  • Peiming Li, Ziyi Wang, Yulin Yuan, Hong Liu, Xiangming Meng, Junsong Yuan, Mengyuan Liu, 20 Aug 2025, UST-SSM: Unified Spatio-Temporal State Space Models for Point Cloud Video Modeling, https://arxiv.org/abs/2508.14604
  • Trinayan Baruah, Kaustubh Shivdikar, Sara Prescott, and David Kaeli, 25 Aug 2025, Characterizing the Behavior of Training Mamba-based State Space Models on GPUs, https://arxiv.org/abs/2508.17679
  • Eric Alsmann, Martin Lange, 25 Aug 2025, The Computational Complexity of Satisfiability in State Space Models, https://arxiv.org/abs/2508.18162
  • Xavier Gonzalez, Leo Kozachkov, David M. Zoltowski, Kenneth L. Clarkson, Scott W. Linderman, 22 Aug 2025, Predictability Enables Parallelization of Nonlinear State Space Models, https://arxiv.org/abs/2508.16817
  • Siddharth Chaudhary, Bennett Browning, 20 Aug 2025, Hydra: A 1.6B-Parameter State-Space Language Model with Sparse Attention, Mixture-of-Experts, and Memory, https://arxiv.org/abs/2508.15099
  • Behnoush Khavari, Mehran Shakerinava, Jayesh Khullar, Jerry Huang, Fran\c{c}ois Rivest, Siamak Ravanbakhsh, Sarath Chandar, 10 Aug 2025, Parity Requires Unified Input Dependence and Negative Eigenvalues in SSMs, https://arxiv.org/abs/2508.07395
  • Cong Ma, Kayvan Najarian, 4 Sep 2025, Rethinking the long-range dependency in Mamba/SSM and transformer models, https://arxiv.org/abs/2509.04226
  • Pradeep Singh, Balasubramanian Raman, 4 Sep 2025, Echo State Networks as State-Space Models: A Systems Perspective, https://arxiv.org/abs/2509.04422
  • Destiny Okpekpe, Antonio Orvieto, 26 Aug 2025, When recalling in-context, Transformers are not SSMs, https://arxiv.org/abs/2508.19029
  • Ruben Solozabal, Velibor Bojkovic, Hilal AlQuabeh, Kentaro Inui, Martin Tak\'a\v{c}, 28 Aug 2025, Uncovering the Spectral Bias in Diagonal State Space Models, https://arxiv.org/abs/2508.20441
  • John T. Halloran, Manbir Gulati, Paul F. Roysdon, 29 Aug 2025, Mamba State-Space Models Are Lyapunov-Stable Learners, https://arxiv.org/abs/2406.00209
  • Stefan-Alexandru Jura, Mihai Udrescu, Alexandru Topirceanu, 29 Aug 2025, Quantum-Optimized Selective State Space Model for Efficient Time Series Prediction, https://arxiv.org/abs/2509.00259
  • Huaicheng Zhang, Ruoxin Wang, Chenlian Zhou, Jiguang Shi, Yue Ge, Zhoutong Li, Sheng Chang, Hao Wang, Jin He and Qijun Huang, 3 Sep 2025, S2M2ECG: Spatio-temporal bi-directional State Space Model Enabled Multi-branch Mamba for ECG, https://arxiv.org/abs/2509.03066
  • Tengjie Zheng, Haipeng Chen, Lin Cheng, Shengping Gong, Xu Huang, 3 Sep 2025, Recursive Gaussian Process State Space Model, https://arxiv.org/abs/2411.14679
  • Takashi Morita, 8 Sep 2025, Emergence of the Primacy Effect in Structured State-Space Models, https://arxiv.org/abs/2502.13729
  • Ibne Farabi Shihab, Sanjeda Akter, Anuj Sharma, 5 Sep 2025, Efficient Unstructured Pruning of Mamba State-Space Models for Resource-Constrained Environments, https://arxiv.org/abs/2505.08299
  • Zhi Qin Tan, Xiatian Zhu, Owen Addison, Yunpeng Li, 15 Sep 2025, U-Mamba2: Scaling State Space Models for Dental Anatomy Segmentation in CBCT, https://arxiv.org/abs/2509.12069
  • Junzhi She, Xunkai Li, Rong-Hua Li, Guoren Wang, 17 Sep 2025, State Space Models over Directed Graphs, https://arxiv.org/abs/2509.13735
  • Aakash Lahoti, Tanya Marwah, Ratish Puduppully, Albert Gu, 14 Oct 2025, Chimera: State Space Models Beyond Sequences, https://arxiv.org/abs/2510.12111
  • JingChuan Guan, Tomoyuki Kubota, Yasuo Kuniyoshi, Kohei Nakajima, 1 Oct 2025, Memory Determines Learning Direction: A Theory of Gradient-Based Optimization in State Space Models, https://arxiv.org/abs/2510.00563
  • Hyun-kyu Ko, Youbin Kim, Jihyeon Park, Dongheok Park, Gyeongjin Kang, Wonjun Cho, Hyung Yi, Eunbyung Park, 1 Oct 2025, Gather-Scatter Mamba: Accelerating Propagation with Efficient State Space Model, https://arxiv.org/abs/2510.00862
  • Jared Boyer, T. Konstantin Rusch, Daniela Rus, 30 Sep 2025, Learning to Dissipate Energy in Oscillatory State-Space Models, https://arxiv.org/abs/2505.12171
  • Youngju Yoo, Jiaheng Hu, Yifeng Zhu, Bo Liu, Qiang Liu, Roberto Mart\'in-Mart\'in, Peter Stone, 24 Sep 2025, RoboSSM: Scalable In-context Imitation Learning via State-Space Models, https://arxiv.org/abs/2509.19658
  • Yangchao Wu, Zongyue Qin, Alex Wong, Stefano Soatto, 27 Oct 2025, STree: Speculative Tree Decoding for Hybrid State-Space Models, https://arxiv.org/abs/2505.14969
  • Shengkun Tang and Liqun Ma and Haonan Li and Mingjie Sun and Zhiqiang Shen, 23 Oct 2025, Bi-Mamba: Towards Accurate 1-Bit State Space Models, https://arxiv.org/abs/2411.11843
  • Yuxin Chang, Alex Boyd, Cao Xiao, Taha Kass-Hout, Parminder Bhatia, Padhraic Smyth, Andrew Warrington, 23 Oct 2025, Deep Continuous-Time State-Space Models for Marked Event Sequences, https://arxiv.org/abs/2412.19634
  • Shenwei Kang, Xin Zhang, Wen Liu, Bin Li, Yujie Liu, Bo Gao, 22 Sep 2025, DA-Mamba: Dialogue-aware selective state-space model for multimodal engagement estimation, https://arxiv.org/abs/2509.17711
  • Minxiao Wang, Runze Yan, Carol Li, Saurabh Kataria, Xiao Hu, Matthew Clark, Timothy Ruchti, Timothy G. Buchman, Sivasubramanium V Bhavani, Randall J. Lee, 19 Sep 2025, Estimating Clinical Lab Test Result Trajectories from PPG using Physiological Foundation Model and Patient-Aware State Space Model -- a UNIPHY+ Approach, https://arxiv.org/abs/2509.16345
  • Shweta Verma, Abhinav Anand, Mira Mezini, 21 Sep 2025, CodeSSM: Towards State Space Models for Code Understanding, https://arxiv.org/abs/2505.01475
  • Paul Schwerdtner, Jules Berman, Benjamin Peherstorfer, 27 Oct 2025, Hankel Singular Value Regularization for Highly Compressible State Space Models, https://arxiv.org/abs/2510.22951
  • Anooshka Bajaj, Deven Mahesh Mistry, Sahaj Singh Maini, Yash Aggarwal, Zoran Tiganj, 26 Oct 2025, Beyond Semantics: How Temporal Biases Shape Retrieval in Transformer and State-Space Models, https://arxiv.org/abs/2510.22752
  • Yonatan Slutzky, Yotam Alexander, Noam Razin, Nadav Cohen, 15 Oct 2025, The Implicit Bias of Structured State Space Models Can Be Poisoned With Clean Labels, https://arxiv.org/abs/2410.10473
  • Aleksandar Terzi\'c, Nicolas Menet, Michael Hersche, Thomas Hofmann, Abbas Rahimi, 26 Sep 2025, Structured Sparse Transition Matrices to Enable State Tracking in State-Space Models, https://arxiv.org/abs/2509.22284
  • Qiyu Chen and Guozhang Chen, 26 Sep 2025, Aligning Inductive Bias for Data-Efficient Generalization in State Space Models, https://arxiv.org/abs/2509.20789
  • Makram Chahine, Philipp Nazari, Daniela Rus, T. Konstantin Rusch, 3 Oct 2025, The Curious Case of In-Training Compression of State Space Models, https://arxiv.org/abs/2510.02823
  • Shuntaro Suzuki, Shunya Nagashima, Masayuki Hirata, Komei Sugiura, 17 Oct 2025, Cortical-SSM: A Deep State Space Model for EEG and ECoG Motor Imagery Decoding, https://arxiv.org/abs/2510.15371
  • Tengjie Zheng, Jilan Mei, Di Wu, Lin Cheng, Shengping Gong, 17 Oct 2025, Recursive Inference for Heterogeneous Multi-Output GP State-Space Models with Arbitrary Moment Matching, https://arxiv.org/abs/2510.15390
  • Youjin Wang, Yangjingyi Chen, Jiahao Yan, Jiaxuan Lu and Xiao Sun, 28 Sep 2025, MemMamba: Rethinking Memory Patterns in State Space Model, https://arxiv.org/abs/2510.03279
  • Shakson Isaac, Yentl Collin, Chirag Patel, 5 Oct 2025, SSM-CGM: Interpretable State-Space Forecasting Model of Continuous Glucose Monitoring for Personalized Diabetes Management, https://arxiv.org/abs/2510.04386
  • Mark Obozov, Makar Baderko, Stepan Kulibaba, Nikolay Kutuzov, Alexander Gasnikov, 5 Oct 2025, Exploring Applications of State Space Models and Advanced Training Techniques in Sequential Recommendations: A Comparative Study on Efficiency and Performance, https://arxiv.org/abs/2408.05606
  • Sungjun Cho, Changho Shin, Suenggwan Jo, Xinya Yan, Shourjo Aditya Chaudhuri, Frederic Sala, 23 Oct 2025, LLM-Integrated Bayesian State Space Models for Multimodal Time-Series Forecasting, https://arxiv.org/abs/2510.20952
  • Mohamad Hakam Shams Eddin, Yikui Zhang, Stefan Kollet, Juergen Gall, 24 Oct 2025, RiverMamba: A State Space Model for Global River Discharge and Flood Forecasting, https://arxiv.org/abs/2505.22535
  • Lanhu Wu, Zilin Gao, Hao Fei, Mong-Li Lee, Wynne Hsu, 23 Sep 2025, LEAF-Mamba: Local Emphatic and Adaptive Fusion State Space Model for RGB-D Salient Object Detection, https://arxiv.org/abs/2509.18683
  • Zhengbo Zhou, Dooman Arefan, Margarita Zuley, Shandong Wu, 21 Oct 2025, $\Delta$t-Mamba3D: A Time-Aware Spatio-Temporal State-Space Model for Breast Cancer Risk Prediction, https://arxiv.org/abs/2510.19003
  • Riccardo Zattra, Giacomo Baggio, Umberto Casti, Augusto Ferrante, Francesco Ticozzi, 15 Oct 2025, Context-Selective State Space Models: Feedback is All You Need, https://arxiv.org/abs/2510.14027
  • Kartikay Agrawal, Abhijeet Vikram, Vedant Sharma, Vaishnavi N., Ayon Borthakur, 16 Oct 2025, SHaRe-SSM: An Oscillatory Spiking Neural Network for Target Variable Modeling in Long Sequences, https://arxiv.org/abs/2510.14386
  • Felix Koch, Marcel Wever, Fabian Raisch, Benjamin Tischler, 16 Oct 2025, State-Space Models for Tabular Prior-Data Fitted Networks, https://arxiv.org/abs/2510.14573
  • Eran Malach, Omid Saremi, Sinead Williamson, Arwen Bradley, Aryo Lotfi, Emmanuel Abbe, Josh Susskind, Etai Littwin, 16 Oct 2025, To Infinity and Beyond: Tool-Use Unlocks Length Generalization in State Space Models, https://arxiv.org/abs/2510.14826
  • Hiroki Sakamoto, Kazuhiro Sato, 16 Oct 2025, A Deep State-Space Model Compression Method using Upper Bound on Output Error, https://arxiv.org/abs/2510.14542

Hyena Architecture

  • Pierre-David Letourneau, Manish Kumar Singh, Hsin-Pai Cheng, Shizhong Han, Yunxiao Shi, Dalton Jones, Matthew Harper Langston, Hong Cai, Fatih Porikli, 16 Jul 2024, PADRe: A Unifying Polynomial Attention Drop-in Replacement for Efficient Vision Transformer, https://arxiv.org/abs/2407.11306
  • 18 Apr 2024 (v2), The Efficiency Spectrum of Large Language Models: An Algorithmic Survey, Tianyu Ding, Tianyi Chen, Haidong Zhu, Jiachen Jiang, Yiqi Zhong, Jinxin Zhou, Guangzhi Wang, Zhihui Zhu, Ilya Zharkov, Luming Liang, https://arxiv.org/abs/2312.00678
  • From Transformers to the Future: An In-Depth Exploration of Modern Language Model Architectures H Xu, Z Bi, H Tseng, X Song, P Feng, https://osf.io/n8r5j/download
  • Yifei Wang, Wenbin Wang, Yong Luo, 12 Sep 2025, DyKen-Hyena: Dynamic Kernel Generation via Cross-Modal Attention for Multimodal Intent Recognition, https://arxiv.org/abs/2509.09940

Mamba Architecture

The Mamba architecture is an advanced AI architecture based on the State Space Model (SSM) architecture. Research papers on Mamba include:

  • Jean Mercat, Igor Vasiljevic, Sedrick Keh, Kushal Arora, Achal Dave, Adrien Gaidon, Thomas Kollar, 10 May 2024, Linearizing Large Language Models, https://arxiv.org/abs/2405.06640 Code: https://github.com/TRI-ML/linear_open_lm
  • Badri Narayana Patro, Vijay Srinivas Agneeswaran, 24 Apr 2024, Mamba-360: Survey of State Space Models as Transformer Alternative for Long Sequence Modelling: Methods, Applications, and Challenges, https://arxiv.org/abs/2404.16112
  • Zeyu Wang, Chen Li, Huiying Xu, Xinzhong Zhu, 9 Jun 2024, Mamba YOLO: SSMs-Based YOLO For Object Detection, https://arxiv.org/abs/2406.05835
  • Mehmet Hamza Erol, Arda Senocak, Jiu Feng, Joon Son Chung, 5 Jun 2024, Audio Mamba: Bidirectional State Space Model for Audio Representation Learning, https://arxiv.org/abs/2406.03344
  • Zhengcong Fei, Mingyuan Fan, Changqian Yu, Debang Li, Youqiang Zhang, Junshi Huang, 3 Jun 2024, Dimba: Transformer-Mamba Diffusion Models, https://arxiv.org/abs/2406.01159
  • Radar AI, Mar 2024, An Introduction to the Mamba LLM Architecture: A New Paradigm in Machine Learning, https://www.datacamp.com/tutorial/introduction-to-the-mamba-llm-architecture
  • Albert Gu, Tri Dao, 31 May 2024 (v2), Mamba: Linear-Time Sequence Modeling with Selective State Spaces, https://arxiv.org/abs/2312.00752
  • Xiaogang Jia, Qian Wang, Atalay Donat, Bowen Xing, Ge Li, Hongyi Zhou, Onur Celik, Denis Blessing, Rudolf Lioutikov, Gerhard Neumann, 12 Jun 2024, MaIL: Improving Imitation Learning with Mamba, https://arxiv.org/abs/2406.08234
  • Marko Vidrih, Jun 7, 2024, Mamba-2 is Out: Can it replace Transformers? https://vidrihmarko.medium.com/mamba-2-is-out-can-it-replace-transformers-6cfb3372ea39
  • Albert Gu, Tri Dao, State Space Duality (Mamba-2) Part I - The Model, May 31, 2024, https://goombalab.github.io/blog/2024/mamba2-part1-model/
  • azhar, Dec 29, 2023, Decoding Mamba: The Next Big Leap in AI Sequence Modeling, https://medium.com/ai-insights-cobet/decoding-mamba-the-next-big-leap-in-ai-sequence-modeling-ef3908060cb8
  • Waleffe, Roger ; Byeon, Wonmin ; Riach, Duncan ; Norick, Brandon ; Korthikanti, Vijay ; Dao, Tri ; Gu, Albert ; Hatamizadeh, Ali ; Singh, Sudhakar ; Narayanan, Deepak ; Kulshreshtha, Garvit ; Singh, Vartika ; Casper, Jared ; Kautz, Jan ; Shoeybi, Mohammad ; Catanzaro, Bryan, June 2024, An Empirical Study of Mamba-based Language Models, https://arxiv.org/abs/2406.07887 https://ui.adsabs.harvard.edu/abs/2024arXiv240607887W/abstract
  • 8 Jun 2024 (v2), A Survey on Efficient Inference for Large Language Models, Zixuan Zhou, Xuefei Ning, Ke Hong, Tianyu Fu, Jiaming Xu, Shiyao Li, Yuming Lou, Luning Wang, Zhihang Yuan, Xiuhong Li, Shengen Yan, Guohao Dai, Xiao-Ping Zhang, Yuhan Dong, Yu Wang, https://arxiv.org/abs/2404.14294
  • Xinji Mai, Zeng Tao, Junxiong Lin, Haoran Wang, Yang Chang, Yanlan Kang, Yan Wang, Wenqiang Zhang, 27 Jun 2024, From Efficient Multimodal Models to World Models: A Survey, https://arxiv.org/abs/2407.00118 (A survey of multimodal models with coverage of many optimization techniques.)
  • Pierre-David Letourneau, Manish Kumar Singh, Hsin-Pai Cheng, Shizhong Han, Yunxiao Shi, Dalton Jones, Matthew Harper Langston, Hong Cai, Fatih Porikli, 16 Jul 2024, PADRe: A Unifying Polynomial Attention Drop-in Replacement for Efficient Vision Transformer, https://arxiv.org/abs/2407.11306
  • 18 Apr 2024 (v2), The Efficiency Spectrum of Large Language Models: An Algorithmic Survey, Tianyu Ding, Tianyi Chen, Haidong Zhu, Jiachen Jiang, Yiqi Zhong, Jinxin Zhou, Guangzhi Wang, Zhihui Zhu, Ilya Zharkov, Luming Liang, https://arxiv.org/abs/2312.00678
  • Haohao Qu, Liangbo Ning, Rui An, Wenqi Fan, Tyler Derr, Xin Xu, Qing Li, 2 Aug 2024, A Survey of Mamba, https://arxiv.org/abs/2408.01129
  • Jingwei Zuo, Maksim Velikanov, Dhiya Eddine, Ilyas Chahed, Younes Belkada, Guillaume Kunsch, August 12, 2024, Welcome FalconMamba: The first strong attention-free 7B model, https://huggingface.co/blog/falconmamba
  • Jamba Team, 22 Aug 2024, Jamba-1.5: Hybrid Transformer-Mamba Models at Scale, https://arxiv.org/abs/2408.12570
  • Danlong Yuan, Jiahao Liu, Bei Li, Huishuai Zhang, Jingang Wang, Xunliang Cai, Dongyan Zhao, 9 Aug 2024 (v2), ReMamba: Equip Mamba with Effective Long-Sequence Modeling, https://arxiv.org/abs/2408.15496
  • From Transformers to the Future: An In-Depth Exploration of Modern Language Model Architectures H Xu, Z Bi, H Tseng, X Song, P Feng, https://osf.io/n8r5j/download
  • Shengkun Tang, Liqun Ma, Haonan Li, Mingjie Sun, Zhiqiang Shen, 18 Nov 2024, Bi-Mamba: Towards Accurate 1-Bit State Space Models, https://arxiv.org/abs/2411.11843
  • Thanaphon Suwannaphong, Ferdian Jovan, Ian Craddock, Ryan McConville, 12 Dec 2024, Optimising TinyML with Quantization and Distillation of Transformer and Mamba Models for Indoor Localisation on Edge Devices, https://arxiv.org/abs/2412.09289
  • Mingjia Shi, Yuhao Zhou, Ruiji Yu, Zekai Li, Zhiyuan Liang, Xuanlei Zhao, Xiaojiang Peng, Tanmay Rajpurohit, Shanmukha Ramakrishna Vedantam, Wangbo Zhao, Kai Wang, Yang You, 17 Dec 2024, Faster Vision Mamba is Rebuilt in Minutes via Merged Token Re-training, https://arxiv.org/abs/2412.12496
  • HF, December 18, 2024, Bamba: Inference-Efficient Hybrid Mamba2 Model, https://huggingface.co/blog/bamba
  • Haoyang Li, Yiming Li, Anxin Tian, Tianhao Tang, Zhanchao Xu, Xuejia Chen, Nicole Hu, Wei Dong, Qing Li, Lei Chen, 27 Dec 2024, A Survey on Large Language Model Acceleration based on KV Cache Management, https://arxiv.org/abs/2412.19442 (Huge survey of all KV cache optimization methods.)
  • Zhenxuan Yu, Yutaka Matsuo, Takeshi Kojima, Yusuke Iwasawa, Jan 2025, Slender-Mamba: Fully Quantized Mamba in 1.58 Bits From Head to Toe, Proceedings of the 31st International Conference on Computational Linguistics, pages 4715–4724, January 19–24, 2025, Association for Computational Linguistics, https://aclanthology.org/2025.coling-main.316.pdf
  • Zukang Xu, Yuxuan Yue, Xing Hu, Zhihang Yuan, Zixu Jiang, Zhixuan Chen, Jiangyong Yu, Chen Xu, Sifan Zhou, Dawei Yang, 23 Jan 2025, MambaQuant: Quantizing the Mamba Family with Variance Aligned Rotation Methods, https://arxiv.org/abs/2501.13484
  • Xiaoran Liu, Ruixiao Li, Mianqiu Huang, Zhigeng Liu, Yuerong Song, Qipeng Guo, Siyang He, Qiqi Wang, Linlin Li, Qun Liu, Yaqian Zhou, Xuanjing Huang, Xipeng Qiu, 24 Feb 2025, Thus Spake Long-Context Large Language Model, https://arxiv.org/abs/2502.17129 (Impressive survey of many techniques to improve efficiency and accuracy of long context processing in both inference and training, covering text, video and multimodal models.)
  • Jiyong Kim, Jaeho Lee, Jiahao Lin, Alish Kanani, Miao Sun, Umit Y. Ogras, and Jaehyun Park, 14 Aug 2025, eMamba: Efficient Acceleration Framework for Mamba Models in Edge Computing, https://arxiv.org/abs/2508.10370
  • Farnoush Bayatmakou, Reza Taleei, Nicole Simone, Arash Mohammadi, 23 Jul 2025, Mammo-Mamba: A Hybrid State-Space and Transformer Architecture with Sequential Mixture of Experts for Multi-View Mammography, https://arxiv.org/abs/2507.17662
  • Hanwen Liu, Yifeng Gong, Zuwei Yan, Zeheng Zhuang, Jiaxuan Lu, 21 Jul 2025, MSGM: A Multi-Scale Spatiotemporal Graph Mamba for EEG Emotion Recognition, https://arxiv.org/abs/2507.15914
  • Osama Hardan, Omar Elshenhabi, Tamer Khattab, Mohamed Mabrok, 15 Jul 2025, Flatten Wisely: How Patch Order Shapes Mamba-Powered Vision for MRI Segmentation, https://arxiv.org/abs/2507.13384
  • Andrew H. Zhang, Alex He-Mo, Richard Fei Yin, Chunlin Li, Yuzhi Tang, Dharmendra Gurve, Veronique van der Horst, Aron S. Buchman, Nasim Montazeri Ghahjaverestan, Maged Goubran, Bo Wang, Andrew S. P. Lim, 9 Aug 2025, Mamba-based Deep Learning Approach for Sleep Staging on a Wireless Multimodal Wearable System without Electroencephalography, https://arxiv.org/abs/2412.15947
  • Ze Rong, ZiYue Zhao, Zhaoxin Wang, Lei Ma, 26 Jul 2025, FaRMamba: Frequency-based learning and Reconstruction aided Mamba for Medical Segmentation, https://arxiv.org/abs/2507.20056
  • Baijiong Lin, Weisen Jiang, Pengguang Chen, Shu Liu, and Ying-Cong Chen, 26 Jul 2025, MTMamba++: Enhancing Multi-Task Dense Scene Understanding via Mamba-Based Decoders, https://arxiv.org/abs/2408.15101
  • Yizhuo Wu, Francesco Fioranelli, Chang Gao, 27 Jul 2025, RadMamba: Efficient Human Activity Recognition through Radar-based Micro-Doppler-Oriented Mamba State-Space Model, https://arxiv.org/abs/2504.12039
  • Aotao Wang, Haikuo Shao, Shaobo Ma, Zhongfeng Wang, 28 Jul 2025, FastMamba: A High-Speed and Efficient Mamba Accelerator on FPGA with Accurate Quantization, https://arxiv.org/abs/2505.18975
  • Hui Liu, Chen Jia, Fan Shi, Xu Cheng, Mengfei Shi, Xia Xie, Shengyong Chen, 31 Jul 2025, LIDAR: Lightweight Adaptive Cue-Aware Fusion Vision Mamba for Multimodal Segmentation of Structural Cracks, https://arxiv.org/abs/2507.22477
  • Alice Zhang, Chao Li, 29 Jul 2025, Adaptive State-Space Mamba for Real-Time Sensor Data Anomaly Detection, https://arxiv.org/abs/2503.22743
  • Jiaxuan Lu, Yuhui Lin, Junyan Shi, Fang Yan, Dongzhan Zhou, Yue Gao, Xiaosong Wang, 4 Aug 2025, Hypergraph Mamba for Efficient Whole Slide Image Understanding, https://arxiv.org/abs/2505.17457
  • Meng Zhou, Farzad Khalvati, 5 Aug 2025, ClinicalFMamba: Advancing Clinical Assessment using Mamba-based Multimodal Neuroimaging Fusion, https://arxiv.org/abs/2508.03008
  • Siyi Lu, Run Liu, Dongsheng Yang, Lei He, 8 Aug 2025, ME$^3$-BEV: Mamba-Enhanced Deep Reinforcement Learning for End-to-End Autonomous Driving with BEV-Perception, https://arxiv.org/abs/2508.06074
  • Kaichuan Kong, Dongjie Liu, Xiaobo Jin, Zhiying Li, Guanggang Geng, Jian Weng, 6 Aug 2025, MambaITD: An Efficient Cross-Modal Mamba Network for Insider Threat Detection, https://arxiv.org/abs/2508.05695
  • Zineddine Bettouche, Khalid Ali, Andreas Fischer, Andreas Kassler, 7 Aug 2025, HiSTM: Hierarchical Spatiotemporal Mamba for Cellular Traffic Forecasting, https://arxiv.org/abs/2508.09184
  • Xi Xuan, Zimo Zhu, Wenxin Zhang, Yi-Cheng Lin, Tomi Kinnunen, 12 Aug 2025, Fake-Mamba: Real-Time Speech Deepfake Detection Using Bidirectional Mamba as Self-Attention's Alternative, https://arxiv.org/abs/2508.09294
  • Honggang Jia, Nan Cheng, Xiucheng Wang, Conghao Zhou, Ruijin Sun, Xuemin (Sherman) Shen, 28 Jul 2025, RadioMamba: Breaking the Accuracy-Efficiency Trade-off in Radio Map Construction via a Hybrid Mamba-UNet, https://arxiv.org/abs/2508.09140
  • Haolong Chen, Liang Zhang, Zhengyuan Xin, Guangxu Zhu, 17 Aug 2025, STM3: Mixture of Multiscale Mamba for Long-Term Spatio-Temporal Time-Series Prediction, https://arxiv.org/abs/2508.12247
  • Jun Zeng, Yannan Huang, Elif Keles, Halil Ertugrul Aktas, Gorkem Durak, Nikhil Kumar Tomar, Quoc-Huy Trinh, Deepak Ranjan Nayak, Ulas Bagci, Debesh Jha, 17 Aug 2025, SRMA-Mamba: Spatial Reverse Mamba Attention Network for Pathological Liver Segmentation in MRI Volumes, https://arxiv.org/abs/2508.12410
  • NVIDIA: Aarti Basant, Abhijit Khairnar, Abhijit Paithankar, Abhinav Khattar, Adi Renduchintala, Adithya Renduchintala, Aditya Malte, Akhiad Bercovich, Akshay Hazare, Alejandra Rico, Aleksander Ficek, Alex Kondratenko, Alex Shaposhnikov, Ali Taghibakhshi, Amelia Barton, Ameya Sunil Mahabaleshwarkar, Amy Shen, Andrew Tao, Ann Guan, Anna Shors, Anubhav Mandarwal, Arham Mehta, Arun Venkatesan, Ashton Sharabiani, Ashwath Aithal, Ashwin Poojary, Ayush Dattagupta, Balaram Buddharaju, Banghua Zhu, Barnaby Simkin, Bilal Kartal, Bita Darvish Rouhani, Bobby Chen, Boris Ginsburg, Brandon Norick, Brian Yu, Bryan Catanzaro, Charles Wang, Charlie Truong, Chetan Mungekar, Chintan Patel, Chris Alexiuk, Christian Munley, Christopher Parisien, Dan Su, Daniel Afrimi, Daniel Korzekwa, Daniel Rohrer, Daria Gitman, et al. (161 additional authors not shown), 20 Aug 2025, NVIDIA Nemotron Nano 2: An Accurate and Efficient Hybrid Mamba-Transformer Reasoning Model, https://arxiv.org/abs/2508.14444
  • Trinayan Baruah, Kaustubh Shivdikar, Sara Prescott, and David Kaeli, 25 Aug 2025, Characterizing the Behavior of Training Mamba-based State Space Models on GPUs, https://arxiv.org/abs/2508.17679
  • Cong Ma, Kayvan Najarian, 4 Sep 2025, Rethinking the long-range dependency in Mamba/SSM and transformer models, https://arxiv.org/abs/2509.04226
  • Mustafa Munir, Alex Zhang, Radu Marculescu, 4 Sep 2025, VCMamba: Bridging Convolutions with Multi-Directional Mamba for Efficient Visual Representation, https://arxiv.org/abs/2509.04669
  • Haosong Liu, Xiancheng Zhu, Huanqiang Zeng, Jianqing Zhu, Jiuwen Cao, and Junhui Hou, 5 Sep 2025, Exploring Non-Local Spatial-Angular Correlations with a Hybrid Mamba-Transformer Framework for Light Field Super-Resolution, https://arxiv.org/abs/2509.04824
  • Zichuan Yang and Yongzhi Wang, 26 Aug 2025, EVM-Fusion: An Explainable Vision Mamba Architecture with Neural Algorithmic Fusion, https://arxiv.org/abs/2505.17367
  • John T. Halloran, Manbir Gulati, Paul F. Roysdon, 29 Aug 2025, Mamba State-Space Models Are Lyapunov-Stable Learners, https://arxiv.org/abs/2406.00209
  • Anuraj Maurya, 29 Aug 2025, Scaling Legal AI: Benchmarking Mamba and Transformers for Statutory Classification and Case Law Retrieval, https://arxiv.org/abs/2509.00141
  • Chengyuan Ma, Peng Jia, Hongyue Guo, and Wenming Yang, 2 Sep 2025, ESTM: An Enhanced Dual-Branch Spectral-Temporal Mamba for Anomalous Sound Detection, https://arxiv.org/abs/2509.02471
  • Saarang Panchavati, Corey Arnold, William Speier, 2 Sep 2025, Mentality: A Mamba-based Approach towards Foundation Models for EEG, https://arxiv.org/abs/2509.02746
  • Huaicheng Zhang, Ruoxin Wang, Chenlian Zhou, Jiguang Shi, Yue Ge, Zhoutong Li, Sheng Chang, Hao Wang, Jin He and Qijun Huang, 3 Sep 2025, S2M2ECG: Spatio-temporal bi-directional State Space Model Enabled Multi-branch Mamba for ECG, https://arxiv.org/abs/2509.03066
  • Hongjun Xu, Junxi Xia, Weisi Yang, Yueyuan Sui, Stephen Xia, 5 Sep 2025, MambaLite-Micro: Memory-Optimized Mamba Inference on MCUs, https://arxiv.org/abs/2509.05488
  • Yinuo Wang and Gavin Tao, 16 Aug 2025, LocoMamba: Vision-Driven Locomotion via End-to-End Deep Reinforcement Learning with Mamba, https://arxiv.org/abs/2508.11849
  • Junxiong Wang, Wen-Ding Li, Daniele Paliotta, Daniel Ritter, Alexander M. Rush, Tri Dao, 6 Sep 2025, M1: Towards Scalable Test-Time Compute with Mamba Reasoning Models, https://arxiv.org/abs/2504.10449
  • Ibne Farabi Shihab, Sanjeda Akter, Anuj Sharma, 5 Sep 2025, Efficient Unstructured Pruning of Mamba State-Space Models for Resource-Constrained Environments, https://arxiv.org/abs/2505.08299
  • NVIDIA: Aaron Blakeman, Aarti Basant, Abhinav Khattar, Adithya Renduchintala, Akhiad Bercovich, Aleksander Ficek, Alexis Bjorlin, Ali Taghibakhshi, Amala Sanjay Deshmukh, Ameya Sunil Mahabaleshwarkar, Andrew Tao, Anna Shors, Ashwath Aithal, Ashwin Poojary, Ayush Dattagupta, Balaram Buddharaju, Bobby Chen, Boris Ginsburg, Boxin Wang, Brandon Norick, Brian Butterfield, Bryan Catanzaro, Carlo del Mundo, Chengyu Dong, Christine Harvey, Christopher Parisien, Dan Su, Daniel Korzekwa, Danny Yin, Daria Gitman, David Mosallanezhad, Deepak Narayanan, Denys Fridman, Dima Rekesh, Ding Ma, Dmytro Pykhtar, Dong Ahn, Duncan Riach, Dusan Stosic, Eileen Long, Elad Segal, Ellie Evans, Eric Chung, Erick Galinkin, Evelina Bakhturina, Ewa Dobrowolska, Fei Jia, Fuxiao Liu, Gargi Prasad, Gerald Shen, Guilin Liu, et al. (148 additional authors not shown), 5 Sep 2025, Nemotron-H: A Family of Accurate and Efficient Hybrid Mamba-Transformer Models, https://arxiv.org/abs/2504.03624
  • Shucong Li and Zhenyu Liu and Zijie Hong and Zhiheng Zhou and Xianghai Cao, 9 Sep 2025, DEPF: A UAV Multispectral Object Detector with Dual-Domain Enhancement and Priority-Guided Mamba Fusion, https://arxiv.org/abs/2509.07327
  • Diego Fajardo-Rojas, Levente Baljer, Jordina Aviles Verdera, Megan Hall, Daniel Cromb, Mary A. Rutherford, Lisa Story, Emma C. Robinson, Jana Hutter, 8 Sep 2025, PUUMA (Placental patch and whole-Uterus dual-branch U-Mamba-based Architecture): Functional MRI Prediction of Gestational Age at Birth and Preterm Risk, https://arxiv.org/abs/2509.07042
  • Zhifang Gong, Shuo Gao, Ben Zhao, Yingjing Xu, Yijun Yang, Shenghong Ju, Guangquan Zhou, 16 Sep 2025, CECT-Mamba: a Hierarchical Contrast-enhanced-aware Model for Pancreatic Tumor Subtyping from Multi-phase CECT, https://arxiv.org/abs/2509.12777
  • Shriyank Somvanshi and Pavan Hebli and Gaurab Chhetri and Subasish Das, 14 Sep 2025, Tabular Data with Class Imbalance: Predicting Electric Vehicle Crash Severity with Pretrained Transformers (TabPFN) and Mamba-Based Models, https://arxiv.org/abs/2509.11449
  • Lokesh Antony Kadiyala, Amir Mirzaeinia, 14 Sep 2025, Mamba Outpaces Reformer in Stock Prediction with Sentiments from Top Ten LLMs, https://arxiv.org/abs/2510.01203
  • Nikolai Lund K\"uhne, Jesper Jensen, Jan {\O}stergaard, Zheng-Hua Tan, 2 Oct 2025, Exploring Resolution-Wise Shared Attention in Hybrid Mamba-U-Nets for Improved Cross-Corpus Speech Enhancement, https://arxiv.org/abs/2510.01958
  • Nikolai Lund K\"uhne, Jesper Jensen, Jan {\O}stergaard, Zheng-Hua Tan, 2 Oct 2025, MambAttention: Mamba with Multi-Head Attention for Generalizable Single-Channel Speech Enhancement, https://arxiv.org/abs/2507.00966
  • Hongkang Li, Songtao Lu, Xiaodong Cui, Pin-Yu Chen, Meng Wang, 1 Oct 2025, Can Mamba Learn In Context with Outliers? A Theoretical Generalization Analysis, https://arxiv.org/abs/2510.00399
  • Hyun-kyu Ko, Youbin Kim, Jihyeon Park, Dongheok Park, Gyeongjin Kang, Wonjun Cho, Hyung Yi, Eunbyung Park, 1 Oct 2025, Gather-Scatter Mamba: Accelerating Propagation with Efficient State Space Model, https://arxiv.org/abs/2510.00862
  • Peng Lu, Jerry Huang, Qiuhao Zeng, Xinyu Wang, Boxing Wang, Philippe Langlais, Yufei Cui, 23 Sep 2025, Mamba Modulation: On the Length Generalization of Mamba, https://arxiv.org/abs/2509.19633
  • Penghao Wang, Yuhao Zhou, Mengxuan Wu, Panpan Zhang, Zhangyang Wang, Kai Wang, 23 Oct 2025, Data Efficient Any Transformer-to-Mamba Distillation via Attention Bridge, https://arxiv.org/abs/2510.19266
  • Mingzheng Zhang, Jinfeng Gao, Dan Xu, Jiangrui Yu, Yuhan Qiao, Lan Chen, Jin Tang, and Xiao Wang, 19 Oct 2025, EMRRG: Efficient Fine-Tuning Pre-trained X-ray Mamba Networks for Radiology Report Generation, https://arxiv.org/abs/2510.16776
  • Jing Yang, Sirui Wang, Chao Wu, Fan Fan, 19 Oct 2025, Schr\"odinger Bridge Mamba for One-Step Speech Enhancement, https://arxiv.org/abs/2510.16834
  • Yovin Yahathugoda, Davide Prezzi, Piyalitt Ittichaiwong, Vicky Goh, Sebastien Ourselin, and Michela Antonelli, 20 Oct 2025, MambaX-Net: Dual-Input Mamba-Enhanced Cross-Attention Network for Longitudinal MRI Segmentation, https://arxiv.org/abs/2510.17529
  • Shenwei Kang, Xin Zhang, Wen Liu, Bin Li, Yujie Liu, Bo Gao, 22 Sep 2025, DA-Mamba: Dialogue-aware selective state-space model for multimodal engagement estimation, https://arxiv.org/abs/2509.17711
  • Tianyi Chen, Pengxiao Lin, Zhiwei Wang, Zhi-Qin John Xu, 22 Sep 2025, Achilles' Heel of Mamba: Essential difficulties of the Mamba architecture demonstrated by synthetic data, https://arxiv.org/abs/2509.17514
  • Chengsheng Zhang, Linhao Qu, Xiaoyu Liu and Zhijian Song, 21 Sep 2025, ME-Mamba: Multi-Expert Mamba with Efficient Knowledge Capture and Fusion for Multimodal Survival Analysis, https://arxiv.org/abs/2509.16900
  • Yinuo Wang, Yuanyang Qi, Jinzhao Zhou, and Gavin Tao, 22 Sep 2025, HuMam: Humanoid Motion Control via End-to-End Deep Reinforcement Learning with Mamba, https://arxiv.org/abs/2509.18046
  • Zhuoxuan Li, Jieyuan Pei, Tangwei Ye, Zhongyuan Lai, Zihan Liu, Fengyuan Xu, Qi Zhang, Liang Hu, 27 Oct 2025, GTR-Mamba: Geometry-to-Tangent Routing for Hyperbolic POI Recommendation, https://arxiv.org/abs/2510.22942
  • Junsoo Oh, Wei Huang, Taiji Suzuki, 15 Oct 2025, Mamba Can Learn Low-Dimensional Targets In-Context via Test-Time Feature Learning, https://arxiv.org/abs/2510.12026
  • Chang Liu, Bohao Zhao, Jingtao Ding, Huandong Wang, Yong Li, 26 Sep 2025, Mamba Integrated with Physics Principles Masters Long-term Chaotic System Forecasting, https://arxiv.org/abs/2505.23863
  • Yingfa Chen, Xinrong Zhang, Shengding Hu, Xu Han, Zhiyuan Liu, Maosong Sun, 26 Sep 2025, Stuffed Mamba: Oversized States Lead to the Inability to Forget, https://arxiv.org/abs/2410.07145
  • Jiarui Jiang, Wei Huang, Miao Zhang, Taiji Suzuki, Liqiang Nie, 28 Sep 2025, Trained Mamba Emulates Online Gradient Descent in In-Context Linear Regression, https://arxiv.org/abs/2509.23779
  • Md Mozaharul Mottalib, Thao-Ly T. Phan, Rahmatollah Beheshti, 28 Sep 2025, HyMaTE: A Hybrid Mamba and Transformer Model for EHR Representation Learning, https://arxiv.org/abs/2509.24118
  • Chunhao Lu, Qiang Lu, Meichen Dong and Jake Luo, 15 Oct 2025, End-to-End Multi-Modal Diffusion Mamba, https://arxiv.org/abs/2510.13253
  • Anna Tegon, Thorir Mar Ingolfsson, Xiaying Wang, Luca Benini, Yawei Li, 17 Oct 2025, FEMBA: Efficient and Scalable EEG Analysis with a Bidirectional Mamba Foundation Model, https://arxiv.org/abs/2502.06438
  • Saqib Qamar, Mohd Fazil, Parvez Ahmad, Shakir Khan, Abu Taha Zamani, 17 Oct 2025, UNet with Self-Adaptive Mamba-Like Attention and Causal-Resonance Learning for Medical Image Segmentation, https://arxiv.org/abs/2505.15234
  • Baher Mohammad, Magauiya Zhussip, Stamatios Lefkimmiatis, 6 Oct 2025, Speak, Edit, Repeat: High-Fidelity Voice Editing and Zero-Shot TTS with Cross-Attentive Mamba, https://arxiv.org/abs/2510.04738
  • Jos\'e Medina, Amnir Hadachi, Paul Honeine, and Abdelaziz Bensrhair, 6 Oct 2025, Mamba base PKD for efficient knowledge compression, https://arxiv.org/abs/2503.01727
  • Shubhi Bansal, Sreeharish A, Madhava Prasath J, Manikandan S, Sreekanth Madisetty, Mohammad Zia Ur Rehman, Chandravardhan Singh Raghaw, Gaurav Duggal, and Nagendra Kumar, 10 Oct 2025, A Comprehensive Survey of Mamba Architectures for Medical Image Analysis: Classification, Segmentation, Restoration and Beyond, https://arxiv.org/abs/2410.02362
  • I Chiu, Yu-Tung Liu, Kuan-Chen Wang, Hung-Yu Wei, Yu Tsao, 13 Oct 2025, Robust Photoplethysmography Signal Denoising via Mamba Networks, https://arxiv.org/abs/2510.11058
  • Zhenjie Mao, Yuhuan Yang, Chaofan Ma, Dongsheng Jiang, Jiangchao Yao, Ya Zhang, Yanfeng Wang, 11 Oct 2025, SaFiRe: Saccade-Fixation Reiteration with Mamba for Referring Image Segmentation, https://arxiv.org/abs/2510.10160
  • Lanhu Wu, Zilin Gao, Hao Fei, Mong-Li Lee, Wynne Hsu, 23 Sep 2025, LEAF-Mamba: Local Emphatic and Adaptive Fusion State Space Model for RGB-D Salient Object Detection, https://arxiv.org/abs/2509.18683
  • Rong Chao, Wen-Huang Cheng, Moreno La Quatra, Sabato Marco Siniscalchi, Chao-Han Huck Yang, Szu-Wei Fu, Yu Tsao, 7 Oct 2025, An Investigation of Incorporating Mamba for Speech Enhancement, https://arxiv.org/abs/2405.06573
  • Sebastian Raschka, PhD, Dec 18, 2025 (updated), The Big LLM Architecture Comparison: From DeepSeek V3 to Mistral 3 Large: A Look At Modern LLM Architecture Design, https://magazine.sebastianraschka.com/p/the-big-llm-architecture-comparison

Knowledge Graph AI Architectures

Knowledge graphs represent structured information as a graph, usually a Directed Acyclic Graph (DAG). This additional structural information can improve LLM results, but it is not easy to integrate graph-structured data into the sequential text sequences expected by an LLM. One particular usage of Knowledge Graphs is to extend RAG architectures, called a "RAG Graph" architecture.

Research papers on Knowledge Graphs in AI include:

  • Shenzhe Zhu, 6 May 2024, Exploring knowledge graph-based neural-symbolic system from application perspective, https://arxiv.org/abs/2405.03524 (Integrate knowledge graph and symbolic reasoning into neural networks.)
  • GG Klager, March 12, 2024, Is GPT fit for KGQA? Masters Thesis, Department of Information Systems & Operations Management, Vienna University of Economics and Business, https://aic.ai.wu.ac.at/~polleres/supervised_theses/Gerhard_Klager_MSc_2024.pdf
  • Louis-François Bouchard, Aug 12, 2024, When to Use GraphRAG, https://louisbouchard.substack.com/p/when-to-use-graphrag
  • Bhaskarjit Sarmah, Benika Hall, Rohan Rao, Sunil Patel, Stefano Pasquali, Dhagash Mehta, 9 Aug 2024, HybridRAG: Integrating Knowledge Graphs and Vector Retrieval Augmented Generation for Efficient Information Extraction, https://arxiv.org/abs/2408.04948
  • Dr. Ashish Bamania, Aug 2024, ‘MedGraphRAG’ Is A Complete Game Changer For AI In Medicine A deep-dive into how RAG, GraphRAG, and MedGraphRAG work and how they significantly improve the performance of LLM responses in Medicine, https://levelup.gitconnected.com/medgraphrag-is-a-complete-game-changer-for-ai-in-medicine-c6b41b0effd6
  • Junde Wu, Jiayuan Zhu, Yunli Qi, 8 Aug 2024, Medical Graph RAG: Towards Safe Medical Large Language Model via Graph Retrieval-Augmented Generation, https://arxiv.org/abs/2408.04187 Code: https://github.com/MedicineToken/Medical-Graph-RAG/tree/main
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  • Bowen Wang, Zhouqiang Jiang, Yasuaki Susumu, Shotaro Miwa, Tianwei Chen, Yuta Nakashima, 25 Aug 2025, Taming the Untamed: Graph-Based Knowledge Retrieval and Reasoning for MLLMs to Conquer the Unknown, https://arxiv.org/abs/2506.17589
  • Zahra Zehtabi Sabeti Moghaddam, Zeinab Dehghani, Maneeha Rani, Koorosh Aslansefat, Bhupesh Kumar Mishra, Rameez Raja Kureshi, Dhavalkumar Thakker, 3 Sep 2025, Explainable Knowledge Graph Retrieval-Augmented Generation (KG-RAG) with KG-SMILE, https://arxiv.org/abs/2509.03626
  • Kishor Datta Gupta, Mohd Ariful Haque, Hasmot Ali, Marufa Kamal, Syed Bahauddin Alam, and Mohammad Ashiqur Rahman, 4 Sep 2025, Continuous Monitoring of Large-Scale Generative AI via Deterministic Knowledge Graph Structures, https://arxiv.org/abs/2509.03857
  • Shanglin Wu, Lihui Liu, Jinho D. Choi, Kai Shu, 31 Aug 2025, Improving Factuality in LLMs via Inference-Time Knowledge Graph Construction, https://arxiv.org/abs/2509.03540
  • Zhaoyan Gong, Juan Li, Zhiqiang Liu, Lei Liang, Huajun Chen, Wen Zhang, 4 Sep 2025, RTQA : Recursive Thinking for Complex Temporal Knowledge Graph Question Answering with Large Language Models, https://arxiv.org/abs/2509.03995
  • Farnoosh Hashemi, Laks V.S. Lakshmanan, 4 Sep 2025, KRAFT: A Knowledge Graph-Based Framework for Automated Map Conflation, https://arxiv.org/abs/2509.04684
  • Zhangding Liu, Neda Mohammadi, and John E. Taylor, 5 Sep 2025, FloodVision: Urban Flood Depth Estimation Using Foundation Vision-Language Models and Domain Knowledge Graph, https://arxiv.org/abs/2509.04772
  • Xiaoxiong Zhang, Zhiwei Zeng, Xin Zhou, Zhiqi Shen, 5 Sep 2025, Low-Dimensional Federated Knowledge Graph Embedding via Knowledge Distillation, https://arxiv.org/abs/2408.05748
  • Nitin Nagesh Kulkarni, Bryson Wilcox, Max Sawa, Jason Thom, 25 Aug 2025, PKG-DPO: Optimizing Domain-Specific AI systems with Physics Knowledge Graphs and Direct Preference Optimization, https://arxiv.org/abs/2508.18391
  • Honghao Fu, Junlong Ren, Qi Chai, Deheng Ye, Yujun Cai, Hao Wang, 26 Aug 2025, VistaWise: Building Cost-Effective Agent with Cross-Modal Knowledge Graph for Minecraft, https://arxiv.org/abs/2508.18722
  • Rikuto Kotoge, Ziwei Yang, Zheng Chen, Yushun Dong, Yasuko Matsubara, Jimeng Sun, Yasushi Sakurai, 28 Aug 2025, ExPath: Targeted Pathway Inference for Biological Knowledge Bases via Graph Learning and Explanation, https://arxiv.org/abs/2502.18026
  • Tingxuan Xu, Jiarui Feng, Justin Melendez, Kaleigh Roberts, Donghong Cai, Mingfang Zhu, Donald Elbert, Yixin Chen, Randall J. Bateman, 28 Aug 2025, Addressing accuracy and hallucination of LLMs in Alzheimer's disease research through knowledge graphs, https://arxiv.org/abs/2508.21238
  • Dongzhuoran Zhou, Yuqicheng Zhu, Xiaxia Wang, Yuan He, Jiaoyan Chen, Steffen Staab, Evgeny Kharlamov, 29 Aug 2025, Evaluating Knowledge Graph Based Retrieval Augmented Generation Methods under Knowledge Incompleteness, https://arxiv.org/abs/2504.05163
  • Brian Wang, Mani Srivastava, 30 Aug 2025, SIGMUS: Semantic Integration for Knowledge Graphs in Multimodal Urban Spaces, https://arxiv.org/abs/2509.00287
  • Jiasheng Xu, Mingda Li, Yongqiang Tang, Peijie Wang, Wensheng Zhang, 1 Sep 2025, Towards Open-World Retrieval-Augmented Generation on Knowledge Graph: A Multi-Agent Collaboration Framework, https://arxiv.org/abs/2509.01238
  • Sergio Consoli, Pietro Coletti, Peter V. Markov, Lia Orfei, Indaco Biazzo, Lea Schuh, Nicolas Stefanovitch, Lorenzo Bertolini, Mario Ceresa, Nikolaos I. Stilianakis, 2 Sep 2025, An Epidemiological Knowledge Graph extracted from the World Health Organization's Disease Outbreak News, https://arxiv.org/abs/2509.02258
  • Susana Nunes, Samy Badreddine, Catia Pesquita, 2 Sep 2025, Rewarding Explainability in Drug Repurposing with Knowledge Graphs, https://arxiv.org/abs/2509.02276
  • Haimei Pan, Jiyun Zhang, Qinxi Wei, Xiongnan Jin, Chen Xinkai, Jie Cheng, 25 Aug 2025, Robotic Fire Risk Detection based on Dynamic Knowledge Graph Reasoning: An LLM-Driven Approach with Graph Chain-of-Thought, https://arxiv.org/abs/2509.00054
  • Yu Liu, Yanan Cao, Xixun Lin, Yanmin Shang, Shi Wang, Shirui Pan, 1 Sep 2025, Enhancing Large Language Model for Knowledge Graph Completion via Structure-Aware Alignment-Tuning, https://arxiv.org/abs/2509.01166
  • Madan Krishnamurthy, Surya Saha, Pierrette Lo, Patricia L. Whetzel, Tursynay Issabekova, Jamed Ferreris Vargas, Jack DiGiovanna, Melissa A Haendel, 1 Sep 2025, Enabling Down Syndrome Research through a Knowledge Graph-Driven Analytical Framework, https://arxiv.org/abs/2509.01565
  • Zihao Li, Dongqi Fu, Mengting Ai, Jingrui He, 1 Sep 2025, APEX$^2$: Adaptive and Extreme Summarization for Personalized Knowledge Graphs, https://arxiv.org/abs/2412.17336
  • Siyuan Li, Ruitong Liu, Yan Wen, Te Sun, Andi Zhang, Yanbiao Ma, Xiaoshuai Hao, 30 Aug 2025, Flow-Modulated Scoring for Semantic-Aware Knowledge Graph Completion, https://arxiv.org/abs/2506.23137
  • Qurat Ul Ain and Mohamed Amine Chatti and Jean Qussa and Amr Shakhshir and Rawaa Alatrash and Shoeb Joarder, 5 Sep 2025, An Optimized Pipeline for Automatic Educational Knowledge Graph Construction, https://arxiv.org/abs/2509.05392
  • Rawaa Alatrash and Mohamed Amine Chatti and Nasha Wibowo and Qurat Ul Ain, 5 Sep 2025, Inferring Prerequisite Knowledge Concepts in Educational Knowledge Graphs: A Multi-criteria Approach, https://arxiv.org/abs/2509.05393
  • Mengxue Yang, Chun Yang, Jiaqi Zhu, Jiafan Li, Jingqi Zhang, Yuyang Li, Ying Li, 8 Sep 2025, SLiNT: Structure-aware Language Model with Injection and Contrastive Training for Knowledge Graph Completion, https://arxiv.org/abs/2509.06531
  • Manit Baser, Dinil Mon Divakaran, Mohan Gurusamy, 6 Sep 2025, ThinkEval: Practical Evaluation of Knowledge Leakage in LLM Editing using Thought-based Knowledge Graphs, https://arxiv.org/abs/2506.01386
  • Hamid Ahmad, Heiko Paulheim, Rita T. Sousa, 9 Sep 2025, Bio-KGvec2go: Serving up-to-date Dynamic Biomedical Knowledge Graph Embeddings, https://arxiv.org/abs/2509.07905
  • Andrey Sakhovskiy, Elena Tutubalina, 9 Sep 2025, BALI: Enhancing Biomedical Language Representations through Knowledge Graph and Language Model Alignment, https://arxiv.org/abs/2509.07588
  • Fernando Spadea and Oshani Seneviratne, 8 Sep 2025, Avoiding Over-Personalization with Rule-Guided Knowledge Graph Adaptation for LLM Recommendations, https://arxiv.org/abs/2509.07133
  • Hudson de Martim, 9 Sep 2025, Modeling the Diachronic Evolution of Legal Norms: An LRMoo-Based, Component-Level, Event-Centric Approach to Legal Knowledge Graphs, https://arxiv.org/abs/2506.07853
  • Mingyang Li, Viktor Schlegel, Tingting Mu, Warren Del-Pinto, Goran Nenadic, 4 Sep 2025, Structured Information Matters: Explainable ICD Coding with Patient-Level Knowledge Graphs, https://arxiv.org/abs/2509.09699
  • Vaibhav Chaudhary, Neha Soni, Narotam Singh, Amita Kapoor, 11 Sep 2025, Fusing Knowledge and Language: A Comparative Study of Knowledge Graph-Based Question Answering with LLMs, https://arxiv.org/abs/2509.09272
  • Julia Gastinger, Christian Meilicke, Heiner Stuckenschmidt, 11 Sep 2025, CountTRuCoLa: Rule Confidence Learning for Temporal Knowledge Graph Forecasting, https://arxiv.org/abs/2509.09474
  • Vadim Zadykian, Bruno Andrade and Haithem Afli, 11 Sep 2025, Towards Explainable Job Title Matching: Leveraging Semantic Textual Relatedness and Knowledge Graphs, https://arxiv.org/abs/2509.09522
  • Junhong Lin, Song Wang, Xiaojie Guo, Julian Shun, Yada Zhu, 18 Sep 2025, Temporal Reasoning with Large Language Models Augmented by Evolving Knowledge Graphs, https://arxiv.org/abs/2509.15464
  • Arvindh Arun, Sumit Kumar, Mojtaba Nayyeri, Bo Xiong, Ponnurangam Kumaraguru, Antonio Vergari, Steffen Staab, 19 Sep 2025, SEMMA: A Semantic Aware Knowledge Graph Foundation Model, https://arxiv.org/abs/2505.20422
  • Mengzheng Yang, Yanfei Ren, David Osei Opoku, Ruochang Li, Peng Ren, Chunxiao Xing, 22 Aug 2025, DSRAG: A Domain-Specific Retrieval Framework Based on Document-derived Multimodal Knowledge Graph, https://arxiv.org/abs/2509.10467
  • Haodi Ma, Dzmitry Kasinets, Daisy Zhe Wang, 15 Sep 2025, Transformer-Based Multimodal Knowledge Graph Completion with Link-Aware Contexts, https://arxiv.org/abs/2501.15688
  • Alberto Cattaneo, Stephen Bonner, Thomas Martynec, Edward Morrissey, Carlo Luschi, Ian P Barrett, Daniel Justus, 18 Sep 2025, The Role of Graph Topology in the Performance of Biomedical Knowledge Graph Completion Models, https://arxiv.org/abs/2409.04103
  • Chenjun Li, Laurin Lux, Alexander H. Berger, Martin J. Menten, Mert R. Sabuncu, Johannes C. Paetzold, 17 Sep 2025, Fine-tuning Vision Language Models with Graph-based Knowledge for Explainable Medical Image Analysis, https://arxiv.org/abs/2503.09808
  • Michael Kishelev, Pranab Bhadani, Wanying Ding, Vinay Chaudhri, 9 Sep 2025, JEL: A Novel Model Linking Knowledge Graph entities to News Mentions, https://arxiv.org/abs/2509.08086
  • Przemys{\l}aw Stok{\l}osa, Janusz A. Starzyk, Pawe{\l} Raif, Adrian Horzyk, Marcin Kowalik, 9 Sep 2025, Associative Knowledge Graphs for Efficient Sequence Storage and Retrieval, https://arxiv.org/abs/2411.14480
  • Siyuan Li, Yan Wen, Ruitong Liu, Te Sun, Ruihao Zhou, Jingyi Kang, Yunjia Wu, 10 Sep 2025, Context-Driven Knowledge Graph Completion with Semantic-Aware Relational Message Passing, https://arxiv.org/abs/2506.23141
  • Minh Pham Dinh, Munira Syed, Michael G Yankoski, Trenton W. Ford, 17 Sep 2025, DAVIS: Planning Agent with Knowledge Graph-Powered Inner Monologue, https://arxiv.org/abs/2410.09252
  • Luca Cotti, Idilio Drago, Anisa Rula, Devis Bianchini and Federico Cerutti, 1 Oct 2025, OntoLogX: Ontology-Guided Knowledge Graph Extraction from Cybersecurity Logs with Large Language Models, https://arxiv.org/abs/2510.01409
  • Madina Bekbergenova (ICN), Lucas Pradi (ICN), Benjamin Navet (ICN), Emma Tysinger (ICN), Franck Michel (WIMMICS), Matthieu Feraud (ICN), Yousouf Taghzouti (ICN, WIMMICS), Yan Zhou Chen, Olivier Kirchhoffer (UNIGE), Florence Mehl (SIB), Martin Legrand (ICN), Tao Jiang (ICN), Marco Pagni (SIB), Soha Hassoun, Jean-Luc Wolfender (UNIGE), Wout Bittremieux (UA), Fabien Gandon (WIMMICS, Laboratoire I3S - SPARKS), Louis-F\'elix Nothias (CNRS, UniCA, ICN), 2 Oct 2025, MetaboT: AI-based agent for natural language-based interaction with metabolomics knowledge graphs, https://arxiv.org/abs/2510.01724
  • Jinwoo Kim, Xingyue Huang, Krzysztof Olejniczak, Kyungbin Min, Michael Bronstein, Seunghoon Hong, \.Ismail \.Ilkan Ceylan, 1 Oct 2025, Flock: A Knowledge Graph Foundation Model via Learning on Random Walks, https://arxiv.org/abs/2510.01510
  • Can Lin, Zhengwang Jiang, Ling Zheng, Qi Zhao, Yuhang Zhang, Qi Song, Wangqiu Zhou, 25 Sep 2025, RJE: A Retrieval-Judgment-Exploration Framework for Efficient Knowledge Graph Question Answering with LLMs, https://arxiv.org/abs/2510.01257
  • Mohamad Al Mdfaa, Svetlana Lukina, Timur Akhtyamov, Arthur Nigmatzyanov, Dmitrii Nalberskii, Sergey Zagoruyko, Gonzalo Ferrer, 1 Oct 2025, VL-KnG: Visual Scene Understanding for Navigation Goal Identification using Spatiotemporal Knowledge Graphs, https://arxiv.org/abs/2510.01483
  • Basem Rizk, Joel Walsh, Mark Core and Benjamin Nye, 1 Oct 2025, From Videos to Indexed Knowledge Graphs -- Framework to Marry Methods for Multimodal Content Analysis and Understanding, https://arxiv.org/abs/2510.01513
  • Felix Brei and Lorenz B\"uhmann and Johannes Frey and Daniel Gerber and Lars-Peter Meyer and Claus Stadler and Kirill Bulert, 2 Oct 2025, ARUQULA -- An LLM based Text2SPARQL Approach using ReAct and Knowledge Graph Exploration Utilities, https://arxiv.org/abs/2510.02200
  • Yongkang Xiao, Sinian Zhang, Yi Dai, Huixue Zhou, Jue Hou, Jie Ding, Rui Zhang, 2 Oct 2025, DrKGC: Dynamic Subgraph Retrieval-Augmented LLMs for Knowledge Graph Completion across General and Biomedical Domains, https://arxiv.org/abs/2506.00708
  • Bohui Zhang, Yuan He, Lydia Pintscher, Albert Mero\~no Pe\~nuela, Elena Simperl, 2 Oct 2025, Schema Generation for Large Knowledge Graphs Using Large Language Models, https://arxiv.org/abs/2506.04512
  • Zhangchi Qiu, Linhao Luo, Shirui Pan, Alan Wee-Chung Liew, 2 Oct 2025, Reasoning over User Preferences: Knowledge Graph-Augmented LLMs for Explainable Conversational Recommendations, https://arxiv.org/abs/2411.14459
  • Samuel Abedu, SayedHassan Khatoonabadi, Emad Shihab, 2 Oct 2025, Synergizing LLMs and Knowledge Graphs: A Novel Approach to Software Repository-Related Question Answering, https://arxiv.org/abs/2412.03815
  • Yuechun Yu, Han Ying, Haoan Jin, Wenjian Jiang, Dong Xian, Binghao Wang, Zhou Yang, Mengyue Wu, 14 Oct 2025, MedKGEval: A Knowledge Graph-Based Multi-Turn Evaluation Framework for Open-Ended Patient Interactions with Clinical LLMs, https://arxiv.org/abs/2510.12224
  • Rita T. Sousa and Heiko Paulheim, 13 Oct 2025, Improving Knowledge Graph Embeddings through Contrastive Learning with Negative Statements, https://arxiv.org/abs/2510.11868
  • Chengyu Li and Debo Cheng and Guixian Zhang and Yi Li and Shichao Zhang, 14 Oct 2025, Toward Fair Graph Neural Networks Via Dual-Teacher Knowledge Distillation, https://arxiv.org/abs/2412.00382
  • Yurun Chen, Xavier Hu, Yuhan Liu, Ziqi Wang, Zeyi Liao, Lin Chen, Feng Wei, Yuxi Qian, Bo Zheng, Keting Yin, Shengyu Zhang, 14 Oct 2025, Graph2Eval: Automatic Multimodal Task Generation for Agents via Knowledge Graphs, https://arxiv.org/abs/2510.00507
  • Ran Liu, Yuan Fang, Xiaoli Li, 1 Oct 2025, FusionAdapter for Few-Shot Relation Learning in Multimodal Knowledge Graphs, https://arxiv.org/abs/2510.00894
  • Evan Heus, Rick Bookstaber, Dhruv Sharma, 1 Oct 2025, Exploring Network-Knowledge Graph Duality: A Case Study in Agentic Supply Chain Risk Analysis, https://arxiv.org/abs/2510.01115
  • Trung Hoang Le, Tran Cao Son, Huiping Cao, 30 Sep 2025, SLogic: Subgraph-Informed Logical Rule Learning for Knowledge Graph Completion, https://arxiv.org/abs/2510.00279
  • Jinyeop Song, Song Wang, Julian Shun, Yada Zhu, 1 Oct 2025, Efficient and Transferable Agentic Knowledge Graph RAG via Reinforcement Learning, https://arxiv.org/abs/2509.26383
  • Shengjie Liu, Li Dong, Zhenyu Zhang, 28 Oct 2025, Bridging Tool Dependencies and Domain Knowledge: A Graph-Based Framework for In-Context Planning, https://arxiv.org/abs/2510.24690
  • Edward Markai, Sina Molavipour, 28 Oct 2025, Temporal Knowledge Graph Hyperedge Forecasting: Exploring Entity-to-Category Link Prediction, https://arxiv.org/abs/2510.24240
  • Ashutosh Anshul, Mohammad Zia Ur Rehman, Sri Akash Kadali, Nagendra Kumar, 25 Oct 2025, RoGBot: Relationship-Oblivious Graph-based Neural Network with Contextual Knowledge for Bot Detection, https://arxiv.org/abs/2510.23648
  • Haonan Bian, 23 Oct 2025, LLM-empowered knowledge graph construction: A survey, https://arxiv.org/abs/2510.20345
  • Yiwen Peng (IP Paris), Thomas Bonald (IP Paris), Fabian M. Suchanek (IP Paris), 23 Oct 2025, FLORA: Unsupervised Knowledge Graph Alignment by Fuzzy Logic, https://arxiv.org/abs/2510.20467
  • Yanlin Song, Ben Liu, V\'ictor Guti\'errez-Basulto, Zhiwei Hu, Qianqian Xie, Min Peng, Sophia Ananiadou, Jeff Z. Pan, 23 Oct 2025, Plan Then Retrieve: Reinforcement Learning-Guided Complex Reasoning over Knowledge Graphs, https://arxiv.org/abs/2510.20691
  • Teng Jiek See, Daokun Zhang, Mario Boley and David K. Chalmers, 23 Oct 2025, Layer-to-Layer Knowledge Mixing in Graph Neural Network for Chemical Property Prediction, https://arxiv.org/abs/2510.20236
  • Sarah Rebecca Ondraszek (1, 2), J\"org Waitelonis (1), Katja Keller (3), Claudia Niessner (3), Anna M. Jacyszyn (1) and Harald Sack (1, 2) ((1) FIZ Karlsruhe - Leibniz Institute for Information Infrastructure, Eggenstein-Leopoldshafen, Germany, (2) Institute of Applied Informatics and Formal Description Methods (AIFB) of KIT, Karlsruhe, Germany, (3) Institute of Sports and Sports Science (IfSS) of KIT, Karlsruhe, Germany), 13 Oct 2025, Ontologies in Motion: A BFO-Based Approach to Knowledge Graph Construction for Motor Performance Research Data in Sports Science, https://arxiv.org/abs/2510.15983
  • Changhao Wang, Yanfang Liu, Xinxin Fan, Anzhi Zhou, Lao Tian, Yunfeng Lu, 18 Oct 2025, DTKG: Dual-Track Knowledge Graph-Verified Reasoning Framework for Multi-Hop QA, https://arxiv.org/abs/2510.16302
  • Crystal Su, 18 Oct 2025, MedRule-KG: A Knowledge-Graph--Steered Scaffold for Mathematical Reasoning with a Lightweight Verifier, https://arxiv.org/abs/2510.16309
  • Junchi Yu, Yujie Liu, Jindong Gu, Philip Torr, Dongzhan Zhou, 18 Oct 2025, Can Knowledge-Graph-based Retrieval Augmented Generation Really Retrieve What You Need?, https://arxiv.org/abs/2510.16582
  • Tianxing Wu, Shutong Zhu, Jingting Wang, Ning Xu, Guilin Qi, Haofen Wang, 18 Oct 2025, Uncertain Knowledge Graph Completion via Semi-Supervised Confidence Distribution Learning, https://arxiv.org/abs/2510.16601
  • Wei Huang, Peining Li, Meiyu Liang, Xu Hou, Junping Du, Yingxia Shao, Guanhua Ye, Wu Liu, Kangkang Lu, Yang Yu, 19 Oct 2025, ELMM: Efficient Lightweight Multimodal Large Language Models for Multimodal Knowledge Graph Completion, https://arxiv.org/abs/2510.16753
  • Chao Li, Yuru Wang, 19 Oct 2025, Domain-Contextualized Concept Graphs: A Computable Framework for Knowledge Representation, https://arxiv.org/abs/2510.16802
  • Dun Liu, Qin Pang, Guangai Liu, Hongyu Mou, Jipeng Fan, Yiming Miao, Pin-Han Ho, and Limei Peng, 19 Oct 2025, SNOMED CT-powered Knowledge Graphs for Structured Clinical Data and Diagnostic Reasoning, https://arxiv.org/abs/2510.16899
  • Yujie Luo, Zhuoyun Yu, Xuehai Wang, Yuqi Zhu, Ningyu Zhang, Lanning Wei, Lun Du, Da Zheng, Huajun Chen, 20 Oct 2025, Executable Knowledge Graphs for Replicating AI Research, https://arxiv.org/abs/2510.17795
  • Guiquan Sun, Xikun Zhang, Jingchao Ni, Dongjin Song, 19 Oct 2025, HERO: Heterogeneous Continual Graph Learning via Meta-Knowledge Distillation, https://arxiv.org/abs/2505.17458
  • Rong Wu, Pinlong Cai, Jianbiao Mei, Licheng Wen, Tao Hu, Xuemeng Yang, Daocheng Fu, Botian Shi, 20 Oct 2025, KG-TRACES: Enhancing Large Language Models with Knowledge Graph-constrained Trajectory Reasoning and Attribution Supervision, https://arxiv.org/abs/2506.00783
  • Jialin Chen, Houyu Zhang, Seongjun Yun, Alejandro Mottini, Rex Ying, Xiang Song, Vassilis N. Ioannidis, Zheng Li, Qingjun Cui, 20 Sep 2025, GRIL: Knowledge Graph Retrieval-Integrated Learning with Large Language Models, https://arxiv.org/abs/2509.16502
  • Nikhil N S (1), Amol Dilip Joshi (1 and 2), Bilal Muhammed (2), Soban Babu (2) ((1) Indian Institute of Science, Bengaluru, India, (2) TCS Research, Tata Consultancy Services Ltd.), 22 Sep 2025, A Knowledge Graph-based Retrieval-Augmented Generation Framework for Algorithm Selection in the Facility Layout Problem, https://arxiv.org/abs/2509.18054
  • Chuangtao Ma, Yongrui Chen, Tianxing Wu, Arijit Khan, Haofen Wang, 22 Sep 2025, Large Language Models Meet Knowledge Graphs for Question Answering: Synthesis and Opportunities, https://arxiv.org/abs/2505.20099
  • Yassir Lairgi, Ludovic Moncla, Khalid Benabdeslem, R\'emy Cazabet, Pierre Cl\'eau, 26 Oct 2025, ATOM: AdapTive and OptiMized dynamic temporal knowledge graph construction using LLMs, https://arxiv.org/abs/2510.22590
  • Ran Liu, Zhongzhou Liu, Xiaoli Li, Hao Wu and Yuan Fang, 26 Oct 2025, Diversified and Adaptive Negative Sampling on Knowledge Graphs, https://arxiv.org/abs/2410.07592
  • Hongkuan Zhou, Lavdim Halilaj, Sebastian Monka, Stefan Schmid, Yuqicheng Zhu, Jingcheng Wu, Nadeem Nazer, Steffen Staab, 15 Oct 2025, Seeing and Knowing in the Wild: Open-domain Visual Entity Recognition with Large-scale Knowledge Graphs via Contrastive Learning, https://arxiv.org/abs/2510.13675
  • Ahmed Jaber, Wangshu Zhu, Karthick Jayavelu, Justin Downes, Sameer Mohamed, Candace Agonafir, Linnia Hawkins, Tian Zheng, 25 Sep 2025, AutoClimDS: Climate Data Science Agentic AI -- A Knowledge Graph is All You Need, https://arxiv.org/abs/2509.21553
  • Hugh Xuechen Liu, K{\i}van\c{c} Tatar, 26 Sep 2025, AEGIS: Authentic Edge Growth In Sparsity for Link Prediction in Edge-Sparse Bipartite Knowledge Graphs, https://arxiv.org/abs/2509.22017
  • Haoyu Huang, Chong Chen, Zeang Sheng, Yang Li, Wentao Zhang, 26 Sep 2025, Can LLMs be Good Graph Judge for Knowledge Graph Construction?, https://arxiv.org/abs/2411.17388
  • Mike Zhang and Johannes Bjerva and Russa Biswas, 26 Sep 2025, Follow the Path: Reasoning over Knowledge Graph Paths to Improve LLM Factuality, https://arxiv.org/abs/2505.11140
  • Yingxu Wang, Shiqi Fan, Mengzhu Wang, Siyang Gao, Chao Wang, Nan Yin, 25 Sep 2025, DAMR: Efficient and Adaptive Context-Aware Knowledge Graph Question Answering with LLM-Guided MCTS, https://arxiv.org/abs/2508.00719
  • Aryan Singh Dalal, Yinglun Zhang, Duru Do\u{g}an, Atalay Mert \.Ileri, and Hande K\"u\c{c}\"uk McGinty, 7 Oct 2025, Flavonoid Fusion: Creating a Knowledge Graph to Unveil the Interplay Between Food and Health, https://arxiv.org/abs/2510.06433
  • Ali Sarabadani, Kheirolah Rahsepar Fard, 8 Oct 2025, MultiCNKG: Integrating Cognitive Neuroscience, Gene, and Disease Knowledge Graphs Using Large Language Models, https://arxiv.org/abs/2510.06742
  • Zachris Bj\"orkman, Jorge Lor\'ia, Sophie Wharrie, Samuel Kaski, 8 Oct 2025, Incorporating Expert Knowledge into Bayesian Causal Discovery of Mixtures of Directed Acyclic Graphs, https://arxiv.org/abs/2510.06735
  • Jiqun Pan, Zhenke Duan, Jiani Tu, Anzhi Cheng, Yanqing Wang, 3 Oct 2025, Knowledge Graph-Guided Multi-Agent Distillation for Reliable Industrial Question Answering with Datasets, https://arxiv.org/abs/2510.06240
  • Sicheng Dong, Vahid Zolfaghari, Nenad Petrovic, Alois Knoll, 2 Oct 2025, Knowledge-Graph Based RAG System Evaluation Framework, https://arxiv.org/abs/2510.02549
  • Abhinav Arun, Reetu Raj Harsh, Bhaskarjit Sarmah, Stefano Pasquali, 3 Oct 2025, FinReflectKG - MultiHop: Financial QA Benchmark for Reasoning with Knowledge Graph Evidence, https://arxiv.org/abs/2510.02906
  • Oumar Kane, Mouhamad M. Allaya, Dame Samb, Mamadou Bousso, 27 Sep 2025, An Senegalese Legal Texts Structuration Using LLM-augmented Knowledge Graph, https://arxiv.org/abs/2510.02353
  • Yuya Sasaki, 21 Oct 2025, Benchmarking Fairness-aware Graph Neural Networks in Knowledge Graphs, https://arxiv.org/abs/2510.18473
  • Haoyu Huang, Hong Ting Tsang, Jiaxin Bai, Xi Peng, Gong Zhang, Yangqiu Song, 20 Oct 2025, AtlasKV: Augmenting LLMs with Billion-Scale Knowledge Graphs in 20GB VRAM, https://arxiv.org/abs/2510.17934
  • Yang Zhao, Chengxiao Dai, Wei Zhuo, Yue Xiu, Dusit Niyato, 25 Sep 2025, CLAUSE: Agentic Neuro-Symbolic Knowledge Graph Reasoning via Dynamic Learnable Context Engineering, https://arxiv.org/abs/2509.21035
  • Yucheng Wang, Ziyang Chen, Md Faisal Kabir, 25 Sep 2025, Explaining Fine Tuned LLMs via Counterfactuals A Knowledge Graph Driven Framework, https://arxiv.org/abs/2509.21241
  • Zhiqiang Liu, Yin Hua, Mingyang Chen, Zhuo Chen, Lei Liang, Huajun Chen and Wen Zhang, 25 Sep 2025, UniHR: Hierarchical Representation Learning for Unified Knowledge Graph Link Prediction, https://arxiv.org/abs/2411.07019
  • Linhao Luo, Zicheng Zhao, Junnan Liu, Zhangchi Qiu, Junnan Dong, Serge Panev, Chen Gong, Thuy-Trang Vu, Gholamreza Haffari, Dinh Phung, Alan Wee-Chung Liew, Shirui Pan, 29 Sep 2025, G-reasoner: Foundation Models for Unified Reasoning over Graph-structured Knowledge, https://arxiv.org/abs/2509.24276
  • Yiquan Wang, Tin-Yeh Huang, Qingyun Gao, Jialin Zhang, 29 Sep 2025, HeDA: An Intelligent Agent System for Heatwave Risk Discovery through Automated Knowledge Graph Construction and Multi-layer Risk Propagation Analysis, https://arxiv.org/abs/2509.25112
  • Yao Xu, Shizhu He, Cunguang Wang, Li Cai, Kang Liu, Jun Zhao, 28 Sep 2025, Query2Triple: Unified Query Encoding for Answering Diverse Complex Queries over Knowledge Graphs, https://arxiv.org/abs/2310.11246
  • Samy Badreddine and Emile van Krieken and Luciano Serafini, 29 Sep 2025, Breaking Rank Bottlenecks in Knowledge Graph Embeddings, https://arxiv.org/abs/2506.22271
  • Jin Li, Zezhong Ding, Xike Xie, 29 Sep 2025, DuetGraph: Coarse-to-Fine Knowledge Graph Reasoning with Dual-Pathway Global-Local Fusion, https://arxiv.org/abs/2507.11229
  • Zhengyu Wu, Guang Zeng, Huilin Lai, Daohan Su, Jishuo Jia, Yinlin Zhu, Xunkai Li, Rong-Hua Li, Guoren Wang, Chenghu Zhou, 28 Sep 2025, Toward Model-centric Heterogeneous Federated Graph Learning: A Knowledge-driven Approach, https://arxiv.org/abs/2501.12624
  • Xi Wang, Xianyao Ling, Kun Li, Gang Yin, Liang Zhang, Jiang Wu, Jun Xu, Fu Zhang, Wenbo Lei, Annie Wang, Peng Gong, 17 Oct 2025, Multi-dimensional Data Analysis and Applications Basing on LLM Agents and Knowledge Graph Interactions, https://arxiv.org/abs/2510.15258
  • Jinliang Liu, 17 Oct 2025, Think Parallax: Solving Multi-Hop Problems via Multi-View Knowledge-Graph-Based Retrieval-Augmented Generation, https://arxiv.org/abs/2510.15552
  • Zirui Liao, 10 Oct 2025, EcphoryRAG: Re-Imagining Knowledge-Graph RAG via Human Associative Memory, https://arxiv.org/abs/2510.08958
  • Ruitong Liu, Yan Wen, Te Sun, Yunjia Wu, Pingyang Huang, Zihang Yu, Siyuan Li, 10 Oct 2025, Semantic-Condition Tuning: Fusing Graph Context with Large Language Models for Knowledge Graph Completion, https://arxiv.org/abs/2510.08966
  • Margarita Belova, Jiaxin Xiao, Shikhar Tuli, Niraj K. Jha, 10 Oct 2025, GraphMERT: Efficient and Scalable Distillation of Reliable Knowledge Graphs from Unstructured Data, https://arxiv.org/abs/2510.09580
  • Premt Cara, Kamilia Zaripova, David Bani-Harouni, Nassir Navab, Azade Farshad, 9 Oct 2025, Knowledge Graph Sparsification for GNN-based Rare Disease Diagnosis, https://arxiv.org/abs/2510.08655
  • Jing Li, Zhijie Sun, Zhicheng Zhou, Suming Qiu, Junjie Huang, Haijia Sun, Linyuan Qiu, 10 Oct 2025, Agentic-KGR: Co-evolutionary Knowledge Graph Construction through Multi-Agent Reinforcement Learning, https://arxiv.org/abs/2510.09156
  • Edward Holmberg, Elias Ioup, Mahdi Abdelguerfi, 24 Oct 2025, A Knowledge-Graph Translation Layer for Mission-Aware Multi-Agent Path Planning in Spatiotemporal Dynamics, https://arxiv.org/abs/2510.21695
  • Nilima Rao, Jagriti Srivastava, Pradeep Kumar Sharma, Hritvik Shrivastava, 13 Oct 2025, Scalable and Explainable Enterprise Knowledge Discovery Using Graph-Centric Hybrid Retrieval, https://arxiv.org/abs/2510.10942
  • Yisen Gao, Jiaxin Bai, Yi Huang, Xingcheng Fu, Qingyun Sun, Yangqiu Song, 13 Oct 2025, Unifying Deductive and Abductive Reasoning in Knowledge Graphs with Masked Diffusion Model, https://arxiv.org/abs/2510.11462
  • Aditya Malusare, Vineet Punyamoorty, Vaneet Aggarwal, 10 Oct 2025, Augmenting generative models with biomedical knowledge graphs improves targeted drug discovery, https://arxiv.org/abs/2510.09914
  • Bernhard Mueller, 29 Sep 2025, Hound: Relation-First Knowledge Graphs for Complex-System Reasoning in Security Audits, https://arxiv.org/abs/2510.09633
  • Wenbin Guo, Xin Wang, Jiaoyan Chen, Lingbing Guo, Zhao Li, Zirui Chen, 10 Oct 2025, ReaLM: Residual Quantization Bridging Knowledge Graph Embeddings and Large Language Models, https://arxiv.org/abs/2510.09711
  • Walid Abdela, 11 Oct 2025, KG-MAS: Knowledge Graph-Enhanced Multi-Agent Infrastructure for coupling physical and digital robotic environments, https://arxiv.org/abs/2510.10325
  • Zhiqiang Yuan, Wenjun Mao, Zhuo Chen, Xiyue Shang, Chong Wang, Yiling Lou and Xin Peng, 13 Oct 2025, Project-Level C-to-Rust Translation via Synergistic Integration of Knowledge Graphs and Large Language Models, https://arxiv.org/abs/2510.10956
  • Agatha Schmidt, Henrik Zunker, Alexander Heinlein, Martin J. K\"uhn, 10 Oct 2025, Graph Neural Network Surrogates to leverage Mechanistic Expert Knowledge towards Reliable and Immediate Pandemic Response, https://arxiv.org/abs/2411.06500
  • Zhengyu Wu, Yinlin Zhu, Xunkai Li, Ziang Qiu, Rong-Hua Li, Guoren Wang, Chenghu Zhou, 9 Oct 2025, FedBook: A Unified Federated Graph Foundation Codebook with Intra-domain and Inter-domain Knowledge Modeling, https://arxiv.org/abs/2510.07755
  • Saksham Khatwani, He Cheng, Majid Afshar, Dmitriy Dligach, Yanjun Gao, 22 Sep 2025, Brittleness and Promise: Knowledge Graph Based Reward Modeling for Diagnostic Reasoning, https://arxiv.org/abs/2509.18316
  • Kartikeya Aneja and Manasvi Srivastava and Subhayan Das and Nagender Aneja, 22 Oct 2025, Interpretable Question Answering with Knowledge Graphs, https://arxiv.org/abs/2510.19181
  • Asmita Sengupta, David Antony Selby, Sebastian Josef Vollmer, Gerrit Gro{\ss}mann, 30 Sep 2025, MEDAKA: Construction of Biomedical Knowledge Graphs Using Large Language Models, https://arxiv.org/abs/2509.26128
  • Hamed Babaei Giglou, Jennifer D'Souza, S\"oren Auer, and Mahsa Sanaei, 30 Sep 2025, OntoAligner Meets Knowledge Graph Embedding Aligners, https://arxiv.org/abs/2509.26417
  • Riccardo Pozzi, Valentina Barbera, Renzo Alva Principe, Davide Giardini, Riccardo Rubini, Matteo Palmonari, 30 Sep 2025, Combining Knowledge Graphs and NLP to Analyze Instant Messaging Data in Criminal Investigations, https://arxiv.org/abs/2509.26487
  • Simon Ging and Sebastian Walter and Jelena Bratuli\'c and Johannes Dienert and Hannah Bast and Thomas Brox, 30 Sep 2025, Using Knowledge Graphs to harvest datasets for efficient CLIP model training, https://arxiv.org/abs/2505.02746
  • Rashmi R, Vidyadhar Upadhya, 16 Oct 2025, Multimodal RAG for Unstructured Data:Leveraging Modality-Aware Knowledge Graphs with Hybrid Retrieval, https://arxiv.org/abs/2510.14592
  • Xingrui Zhuo, Jiapu Wang, Gongqing Wu, Zhongyuan Wang, Jichen Zhang, Shirui Pan, Xindong Wu, 15 Oct 2025, Knowledge Reasoning Language Model: Unifying Knowledge and Language for Inductive Knowledge Graph Reasoning, https://arxiv.org/abs/2510.13909
  • Yilun Zheng, Dan Yang, Jie Li, Lin Shang, Lihui Chen, Jiahao Xu, Sitao Luan, 16 Oct 2025, Less is More: Denoising Knowledge Graphs For Retrieval Augmented Generation, https://arxiv.org/abs/2510.14271
  • Ziye Xia, Sergei S. Ospichev, 16 Oct 2025, Constraint-Driven Small Language Models Based on Agent and OpenAlex Knowledge Graph: Mining Conceptual Pathways and Discovering Innovation Points in Academic Papers, https://arxiv.org/abs/2510.14303

Graph Neural Networks

Research papers on Graph Neural Networks (GNNs):

  • Zhichun Guo, April 2024, Empowering Graph Neural Networks for Real-World Tasks, Ph.D. Thesis, Computer Science and Engineering, University of Notre Dame, Indiana, https://doi.org/10.7274/25608504.v1 https://curate.nd.edu/articles/dataset/Empowering_Graph_Neural_Networks_for_Real-World_Tasks/25608504/1 PDF: https://curate.nd.edu/ndownloader/files/46035312/1
  • Lu Ma, Zeang Sheng, Xunkai Li, Xinyi Gao, Zhezheng Hao, Ling Yang, Wentao Zhang, Bin Cui, 7 May 2024, Acceleration Algorithms in GNNs: A Survey, https://arxiv.org/abs/2405.04114
  • Yun Zhu, Yaoke Wang, Haizhou Shi, Siliang Tang, 28 Jan 2024, Efficient Tuning and Inference for Large Language Models on Textual Graphs, https://arxiv.org/abs/2401.15569 (Optimizing Graph Neural Networks on textual graphs using caching and early exit inference.)
  • Sebastian Eliassen, Raghavendra Selvan, 16 Jan 2024 (v2), Activation Compression of Graph Neural Networks using Block-wise Quantization with Improved Variance Minimization, https://arxiv.org/abs/2309.11856
  • Weishu Deng, Jia Rao, 2024, Mega: More Efficient Graph Attention for GNNs, 2024 IEEE 44th International Conference on Distributed Computing Systems (ICDCS), Year: 2024, Pages: 71-81, DOI Bookmark: 10.1109/ICDCS60910.2024.00016, https://www.computer.org/csdl/proceedings-article/icdcs/2024/860500a071/1ZCgMaVLfRm
  • Hongrong Cheng, Miao Zhang, Javen Qinfeng Shi, 9 Aug 2024 (v2), A Survey on Deep Neural Network Pruning-Taxonomy, Comparison, Analysis, and Recommendations, IEEE Transactions on Pattern Analysis and Machine Intelligence, doi: 10.1109/TPAMI.2024.3447085, https://arxiv.org/abs/2308.06767 https://ieeexplore.ieee.org/abstract/document/10643325
  • Lena Sasal, Daniel Busby, Abdenour Hadid, 29 Aug 2024, TempoKGAT: A Novel Graph Attention Network Approach for Temporal Graph Analysis, https://arxiv.org/abs/2408.16391
  • Xinke Jiang, Rihong Qiu, Yongxin Xu, Wentao Zhang, Yichen Zhu, Ruizhe Zhang, Yuchen Fang, Xu Chu, Junfeng Zhao, Yasha Wang, 31 Oct 2024, RAGraph: A General Retrieval-Augmented Graph Learning Framework, https://arxiv.org/abs/2410.23855
  • V. Slavin, O. Kryvchikov, D. Laptev, 23 Jul 2025, Graph Neural Network Approach to Predicting Magnetization in Quasi-One-Dimensional Ising Systems, https://arxiv.org/abs/2507.17509
  • Yuelin Wang, Kai Yi, Xinliang Liu, Yu Guang Wang, Shi Jin, 23 Jul 2025, ACMP: Allen-Cahn Message Passing with Attractive and Repulsive Forces for Graph Neural Networks, https://arxiv.org/abs/2206.05437
  • Ana Gonzalez Bermudez, Miquel Farreras, Milan Groshev, Jos\'e Antonio Trujillo, Isabel de la Bandera and Raquel Barco, 23 Jul 2025, Graph Neural Networks for O-RAN Mobility Management: A Link Prediction Approach, https://arxiv.org/abs/2502.02170
  • Yumeng Wang, Zengyi Wo, Wenjun Wang, Xingcheng Fu, Minglai Shao, 22 Jul 2025, Leveraging Personalized PageRank and Higher-Order Topological Structures for Heterophily Mitigation in Graph Neural Networks, https://arxiv.org/abs/2507.16347
  • Olga Solodova, Nick Richardson, Deniz Oktay, Ryan P. Adams, 22 Jul 2025, Graph Neural Networks Gone Hogwild, https://arxiv.org/abs/2407.00494
  • Zihao Song, Shirantha Welikala, Panos J. Antsaklis and Hai Lin, 22 Jul 2025, Graph Neural Network-Based Distributed Optimal Control for Linear Networked Systems: An Online Distributed Training Approach, https://arxiv.org/abs/2504.06439
  • Jai Bardhan, Tanumoy Mandal, Subhadip Mitra, Cyrin Neeraj, Mihir Rawat, 22 Jul 2025, Tagging fully hadronic exotic decays of the vectorlike $\mathbf{B}$ quark using a graph neural network, https://arxiv.org/abs/2505.07769
  • Lijun Wu, Dong Hao, Zhiyi Fan, 19 Jul 2025, Explainable Graph Neural Networks via Structural Externalities, https://arxiv.org/abs/2507.17848
  • Ahmad ALBarqawi, Mahmoud Nazzal, Issa Khalil, Abdallah Khreishah, NhatHai Phan, 24 Jul 2025, ViGText: Deepfake Image Detection with Vision-Language Model Explanations and Graph Neural Networks, https://arxiv.org/abs/2507.18031
  • Xinran Li, Xiujuan Xu, Jiaqi Qiao, 24 Jul 2025, Long-Short Distance Graph Neural Networks and Improved Curriculum Learning for Emotion Recognition in Conversation, https://arxiv.org/abs/2507.15205
  • Hao Ai and Yu-xi Liu, 24 Jul 2025, Scalable Parameter Design for Superconducting Quantum Circuits with Graph Neural Networks, https://arxiv.org/abs/2411.16354
  • Guanyuan Pan, Tiansheng Zhou, Bingtao Ma, Yaqi Wang, Jianxiang Zhao, Zhi Li, Yugui Lin, Pietro Lio, Shuai Wang, 24 Jul 2025, GNN-ACLP: Graph Neural Networks Based Analog Circuit Link Prediction, https://arxiv.org/abs/2504.10240
  • Edward Henderson, Dewi Gould, Richard Everson, George De Ath, Nick Pepper, 17 Jul 2025, Air Traffic Controller Task Demand via Graph Neural Networks: An Interpretable Approach to Airspace Complexity, https://arxiv.org/abs/2507.13423
  • Yifan Wei, Anwar Said, Waseem Abbas, Xenofon Koutsoukos, 18 Jul 2025, Robust Anomaly Detection with Graph Neural Networks using Controllability, https://arxiv.org/abs/2507.13954
  • Jagruti Patel, Thomas A. W. Bolton, Mikkel Sch\"ottner, Anjali Tarun, Sebastien Tourbier, Yasser Alem\`an-G\`omez, Jonas Richiardi, Patric Hagmann, 18 Jul 2025, Structural Connectome Harmonization Using Deep Learning: The Strength of Graph Neural Networks, https://arxiv.org/abs/2507.13992
  • Yeming Cai, Zhenglin Li, Yang Wang, 11 Jul 2025, Enhancing Breast Cancer Detection with Vision Transformers and Graph Neural Networks, https://arxiv.org/abs/2507.13372
  • Vijay K. Dubey (1), Collin E. Haese (1), Osman G\"ultekin (1), David Dalton (2), Manuel K. Rausch (1), Jan N. Fuhg (1) ((1) The University of Texas at Austin, (2) University of Glasgow), 17 Jul 2025, Graph Neural Network Surrogates for Contacting Deformable Bodies with Necessary and Sufficient Contact Detection, https://arxiv.org/abs/2507.13459
  • Srinitish Srinivasan and Omkumar CU, 18 Jul 2025, Can we ease the Injectivity Bottleneck on Lorentzian Manifolds for Graph Neural Networks?, https://arxiv.org/abs/2504.00142
  • Xu Cheng, Liang Yao, Feng He, Yukuo Cen, Yufei He, Chenhui Zhang, Wenzheng Feng, Hongyun Cai, Jie Tang, 19 Jul 2025, LPS-GNN : Deploying Graph Neural Networks on Graphs with 100-Billion Edges, https://arxiv.org/abs/2507.14570
  • Rabia Latief Bhat and Iqra Altaf Gillani, 21 Jul 2025, Spatio-Temporal Demand Prediction for Food Delivery Using Attention-Driven Graph Neural Networks, https://arxiv.org/abs/2507.15246
  • Yufei Jin and Xingquan Zhu, 18 Jul 2025, Oversmoothing Alleviation in Graph Neural Networks: A Survey and Unified View, https://arxiv.org/abs/2405.01663
  • Jialiang Wang, Hanmo Liu, Shimin Di, Zhili Wang, Jiachuan Wang, Lei Chen, Xiaofang Zhou, 21 Jul 2025, Proficient Graph Neural Network Design by Accumulating Knowledge on Large Language Models, https://arxiv.org/abs/2408.06717
  • Zizhou Zhang, Qinyan Shen, Zhuohuan Hu, Qianying Liu, Huijie Shen, 20 Jul 2025, Credit Risk Analysis for SMEs Using Graph Neural Networks in Supply Chain, https://arxiv.org/abs/2507.07854
  • Peyman Baghershahi, Gregoire Fournier, Pranav Nyati, Sourav Medya, 9 Aug 2025, From Nodes to Narratives: Explaining Graph Neural Networks with LLMs and Graph Context, https://arxiv.org/abs/2508.07117
  • Zhihao Xue, Yun Zi, Nia Qi, Ming Gong, Yujun Zou, 9 Aug 2025, Multi-Level Service Performance Forecasting via Spatiotemporal Graph Neural Networks, https://arxiv.org/abs/2508.07122
  • Tiantian Yang, Zhiqian Chen, 10 Aug 2025, MOTGNN: Interpretable Graph Neural Networks for Multi-Omics Disease Classification, https://arxiv.org/abs/2508.07465
  • Rahul Khorana, 11 Aug 2025, Topological Feature Compression for Molecular Graph Neural Networks, https://arxiv.org/abs/2508.07807
  • Bowen Zhang, Genan Dai, Hu Huang, Long Lan, 9 Aug 2025, Geometry-Aware Spiking Graph Neural Network, https://arxiv.org/abs/2508.06793
  • Morteza Ziabakhsh, Kiyan Rezaee, Sadegh Eskandari, Seyed Amir Hossein Tabatabaei, Mohammad M. Ghassemi, 10 Aug 2025, Extracting Overlapping Microservices from Monolithic Code via Deep Semantic Embeddings and Graph Neural Network-Based Soft Clustering, https://arxiv.org/abs/2508.07486
  • Sujia Huang, Lele Fu, Zhen Cui, Tong Zhang, Na Song, Bo Huang, 29 Jul 2025, Torque-based Graph Surgery:Enhancing Graph Neural Networks with Hierarchical Rewiring, https://arxiv.org/abs/2507.21422
  • Mustapha Hemis, Hamza Kheddar, Mohamed Chahine Ghanem, Bachir Boudraa, 29 Jul 2025, Hierarchical Graph Neural Network for Compressed Speech Steganalysis, https://arxiv.org/abs/2507.21591
  • Zhanhong Cheng, Lingqian Hu, Yuheng Bu, Yuqi Zhou, Shenhao Wang, 28 Jul 2025, Graph neural networks for residential location choice: connection to classical logit models, https://arxiv.org/abs/2507.21334
  • Haolin Li, Haoyu Wang, Luana Ruiz, 30 Jul 2025, Graph Sampling for Scalable and Expressive Graph Neural Networks on Homophilic Graphs, https://arxiv.org/abs/2410.16593
  • Shuyang Guo, Wenjin Xie, Ping Lu, Ting Deng, Richong Zhang, Jianxin Li, Xiangping Huang, Zhongyi Liu, 27 Jul 2025, Improving Subgraph Matching by Combining Algorithms and Graph Neural Networks, https://arxiv.org/abs/2507.20226
  • Mohit Gupta, Debjit Bhowmick, Ben Beck, 18 Jul 2025, BikeVAE-GNN: A Variational Autoencoder-Augmented Hybrid Graph Neural Network for Sparse Bicycle Volume Estimation, https://arxiv.org/abs/2507.19517
  • Yihan Wang, Jianing Zhao, 20 Jul 2025, Research on the application of graph data structure and graph neural network in node classification/clustering tasks, https://arxiv.org/abs/2507.19527
  • Yazeed Alrubyli, Omar Alomeir, Abrar Wafa, Di\'ana Hidv\'egi, Hend Alrasheed, Mohsen Bahrami, 25 Jul 2025, NAICS-Aware Graph Neural Networks for Large-Scale POI Co-visitation Prediction: A Multi-Modal Dataset and Methodology, https://arxiv.org/abs/2507.19697
  • Kunhao Li, Di Wu, Jun Bai, Jing Xu, Lei Yang, Ziyi Zhang, Yiliao Song, Wencheng Yang, Taotao Cai, Yan Li, 26 Jul 2025, Who Owns This Sample: Cross-Client Membership Inference Attack in Federated Graph Neural Networks, https://arxiv.org/abs/2507.19964
  • Vicente Ramos (1), Sundous Hussein (1), Mohamed Abdel-Hafiz (1), Arunangshu Sarkar (2), Weixuan Liu (2), Katerina J. Kechris (2), Russell P. Bowler (3), Leslie Lange (4), Farnoush Banaei-Kashani (1) ((1) Department of Computer Science and Engineering, University of Colorado Denver, Denver, USA, (2) Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, USA, (3) Genomic Medicine Institute, Cleveland Clinic, Cleveland, USA, (4) Division of Biomedical Informatics and Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, USA), 27 Jul 2025, BioNeuralNet: A Graph Neural Network based Multi-Omics Network Data Analysis Tool, https://arxiv.org/abs/2507.20440
  • Yongzheng Liu, Yiming Wang, Po Xu, Yingjie Xu, Yuntian Chen, Dongxiao Zhang, 28 Jul 2025, BuildSTG: A Multi-building Energy Load Forecasting Method using Spatio-Temporal Graph Neural Network, https://arxiv.org/abs/2507.20838
  • Bernardo Cuenca Grau, Eva Feng, Przemys{\l}aw A. Wa{\l}\k{e}ga, 26 Jul 2025, The Correspondence Between Bounded Graph Neural Networks and Fragments of First-Order Logic, https://arxiv.org/abs/2505.08021
  • Miguel Lopez-Duran, Julian Fierrez, Aythami Morales, Ruben Tolosana, Oscar Delgado-Mohatar, Alvaro Ortigosa, 28 Jul 2025, Benchmarking Graph Neural Networks for Document Layout Analysis in Public Affairs, https://arxiv.org/abs/2505.14699
  • Eran Rosenbluth and Martin Grohe, 30 Jul 2025, Repetition Makes Perfect: Recurrent Graph Neural Networks Match Message Passing Limit, https://arxiv.org/abs/2505.00291
  • Tong Nie, Jian Sun, Wei Ma, 31 Jul 2025, Predicting Large-scale Urban Network Dynamics with Energy-informed Graph Neural Diffusion, https://arxiv.org/abs/2508.00037
  • Mohit Gupta, Debjit Bhowmick, Rhys Newbury, Meead Saberi, Shirui Pan and Ben Beck, 31 Jul 2025, INSPIRE-GNN: Intelligent Sensor Placement to Improve Sparse Bicycling Network Prediction via Reinforcement Learning Boosted Graph Neural Networks, https://arxiv.org/abs/2508.00141
  • Yoonhyuk Choi, Jiho Choi, Chong-Kwon Kim, 1 Aug 2025, Sheaf Graph Neural Networks via PAC-Bayes Spectral Optimization, https://arxiv.org/abs/2508.00357
  • Mukesh Kumar Sahu and Pinki Roy, 1 Aug 2025, Similarity-Based Self-Construct Graph Model for Predicting Patient Criticalness Using Graph Neural Networks and EHR Data, https://arxiv.org/abs/2508.00615
  • Molly Noel, Gabriel Mancino-Ball, Yangyang Xu, 1 Aug 2025, Neighbor-Sampling Based Momentum Stochastic Methods for Training Graph Neural Networks, https://arxiv.org/abs/2508.00267
  • Gaotang Li, Danai Koutra, Yujun Yan, 1 Aug 2025, Tackling Size Generalization of Graph Neural Networks on Biological Data from a Spectral Perspective, https://arxiv.org/abs/2305.15611
  • Yoonhyuk Choi, Jiho Choi, Chong-Kwon Kim, 1 Aug 2025, Adaptive Branch Specialization in Spectral-Spatial Graph Neural Networks for Certified Robustness, https://arxiv.org/abs/2505.08320
  • Trung Nguyen, Md Masud Rana, Farjana Tasnim Mukta, Chang-Guo Zhan, Duc Duy Nguyen, 1 Aug 2025, Geometric Multi-color Message Passing Graph Neural Networks for Blood-brain Barrier Permeability Prediction, https://arxiv.org/abs/2507.18926
  • Xudong Wang, Tongxin Li, Chris Ding, Jicong Fan, 4 Aug 2025, Adaptive Riemannian Graph Neural Networks, https://arxiv.org/abs/2508.02600
  • Yanmei Hu, Siyuan Yin, Yihang Wu, Xue Yue and Yue Liu, 2 Aug 2025, A graph neural network based on feature network for identifying influential nodes, https://arxiv.org/abs/2508.01278
  • Divya Anand Sinha, Ruijie Du, Yezi Liu, Athina Markopolou, Yanning Shen, 3 Aug 2025, Gradient Inversion Attack on Graph Neural Networks, https://arxiv.org/abs/2411.19440
  • Antonio Tudisco, Deborah Volpe, Giacomo Orlandi, Giovanna Turvani, 4 Aug 2025, Graph Neural Network-Based Predictor for Optimal Quantum Hardware Selection, https://arxiv.org/abs/2507.19093
  • Kangkang Lu, Yanhua Yu, Zhiyong Huang, Tat-Seng Chua, 5 Aug 2025, Enhancing Spectral Graph Neural Networks with LLM-Predicted Homophily, https://arxiv.org/abs/2506.14220
  • Santhoshkumar Peddi, Sadhvik Bathini, Arun Balasubramanian, Monalisa Sarma, Debasis Samanta, 6 Aug 2025, ProtoN: Prototype Node Graph Neural Network for Unconstrained Multi-Impression Ear Recognition, https://arxiv.org/abs/2508.04381
  • Zhihao Wen, Yuan Fang, Pengcheng Wei, Fayao Liu, Zhenghua Chen, Min Wu, 6 Aug 2025, Temporal and Heterogeneous Graph Neural Network for Remaining Useful Life Prediction, https://arxiv.org/abs/2405.04336
  • Krzysztof Olejniczak, Xingyue Huang, Mikhail Galkin, \.Ismail \.Ilkan Ceylan, 5 Aug 2025, One Model, Any Conjunctive Query: Graph Neural Networks for Answering Queries over Incomplete Knowledge Graphs, https://arxiv.org/abs/2409.13959
  • Moshe Eliasof, Eldad Haber, Carola-Bibiane Sch\"onlieb, 7 Aug 2025, TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows, https://arxiv.org/abs/2508.05070
  • Massimiliano Romiti, 7 Aug 2025, A Graph Neural Network Approach for Mapping the Conceptual Structure and Inter-Branch Connectivity of Physics, https://arxiv.org/abs/2508.05724
  • Qin Chen, Guojie Song, 8 Aug 2025, Adaptive Heterogeneous Graph Neural Networks: Bridging Heterophily and Heterogeneity, https://arxiv.org/abs/2508.06034
  • Vibhor Agrawal, Fay Wang, Rishi Puri, 25 Jul 2025, Query-Aware Graph Neural Networks for Enhanced Retrieval-Augmented Generation, https://arxiv.org/abs/2508.05647
  • Dahai Yu, Dingyi Zhuang, Lin Jiang, Rongchao Xu, Xinyue Ye, Yuheng Bu, Shenhao Wang, Guang Wang, 12 Aug 2025, UQGNN: Uncertainty Quantification of Graph Neural Networks for Multivariate Spatiotemporal Prediction, https://arxiv.org/abs/2508.08551
  • Luigi D'Amico, Daniel De Rosso, Ninad Dixit, Raul Salles de Padua, Samuel Palmer, Samuel Mugel, Rom\'an Or\'us, Holger Eble, and Ali Abedi, 12 Aug 2025, Blockchain Network Analysis using Quantum Inspired Graph Neural Networks & Ensemble Models, https://arxiv.org/abs/2508.09237
  • Minghao Liu, Chia-Hsuan Lu, Marta Kwiatkowska, 12 Aug 2025, Exact Verification of Graph Neural Networks with Incremental Constraint Solving, https://arxiv.org/abs/2508.09320
  • Yun Zi, Ming Gong, Zhihao Xue, Yujun Zou, Nia Qi, Yingnan Deng, 13 Aug 2025, Graph Neural Network and Transformer Integration for Unsupervised System Anomaly Discovery, https://arxiv.org/abs/2508.09401
  • Fang Wang and Ernesto Damiani, 13 Aug 2025, Time-Aware and Transition-Semantic Graph Neural Networks for Interpretable Predictive Business Process Monitoring, https://arxiv.org/abs/2508.09527
  • Subhankar Sarkar and Souvik Chakraborty, 13 Aug 2025, Physics- and geometry-aware spatio-spectral graph neural operator for time-independent and time-dependent PDEs, https://arxiv.org/abs/2508.09627
  • Mohammad Zia Ur Rehman, Sufyaan Zahoor, Areeb Manzoor, Musharaf Maqbool, Nagendra Kumar, 7 Aug 2025, A Context-aware Attention and Graph Neural Network-based Multimodal Framework for Misogyny Detection, https://arxiv.org/abs/2508.09175
  • Marco S\"alzer, Fran\c{c}ois Schwarzentruber, Nicolas Troquard, 13 Aug 2025, Verifying Quantized Graph Neural Networks is PSPACE-complete, https://arxiv.org/abs/2502.16244
  • Asela Hevapathige, Asiri Wijesinghe, Ahad N. Zehmakan, 15 Aug 2025, Graph Neural Diffusion via Generalized Opinion Dynamics, https://arxiv.org/abs/2508.11249
  • Fanzhen Liu, Xiaoxiao Ma, Jian Yang, Alsharif Abuadbba, Kristen Moore, Surya Nepal, Cecile Paris, Quan Z. Sheng, Jia Wu, 15 Aug 2025, Towards Faithful Class-level Self-explainability in Graph Neural Networks by Subgraph Dependencies, https://arxiv.org/abs/2508.11513
  • Hossein Shokouhinejad, Roozbeh Razavi-Far, Griffin Higgins, Ali A Ghorbani, 14 Aug 2025, Explainable Attention-Guided Stacked Graph Neural Networks for Malware Detection, https://arxiv.org/abs/2508.09801
  • BG Tong, 10 Aug 2025, A Graph Neural Network based on a Functional Topology Model: Unveiling the Dynamic Mechanisms of Non-Suicidal Self-Injury in Single-Channel EEG, https://arxiv.org/abs/2508.11684
  • Anshul Ahluwalia, Payman Behnam, Rohit Das, Alind Khare, Biswadeep Chakraborty, Pan Li, Alexey Tumanov, 16 Aug 2025, STRIDE: Structure and Embedding Distillation with Attention for Graph Neural Networks, https://arxiv.org/abs/2310.15938
  • Ningyi Liao, Haoyu Liu, Zulun Zhu, Siqiang Luo, Laks V.S. Lakshmanan, 18 Aug 2025, Benchmarking Spectral Graph Neural Networks: A Comprehensive Study on Effectiveness and Efficiency, https://arxiv.org/abs/2406.09675
  • Su Chen, Xiaohua Qi, Xixun Lin, Yanmin Shang, Xiaolin Xu and Yangxi Li, 17 Aug 2025, Deep Graph Neural Point Process For Learning Temporal Interactive Networks, https://arxiv.org/abs/2508.13219
  • Junwei Su, Chuan Wu, 20 Aug 2025, On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks, https://arxiv.org/abs/2508.14338
  • Zengyi Wo, Chang Liu, Yumeng Wang, Minglai Shao, Wenjun Wang, 20 Aug 2025, Improving Fairness in Graph Neural Networks via Counterfactual Debiasing, https://arxiv.org/abs/2508.14683
  • Mengyang Cao, Frank F. Yang, Yi Jin, Yijun Yan, 10 Aug 2025, Graph Neural Network for Product Recommendation on the Amazon Co-purchase Graph, https://arxiv.org/abs/2508.14059
  • Sebastian Musia{\l}, Bartosz Zieli\'nski, Tomasz Danel, 20 Aug 2025, Fragment-Wise Interpretability in Graph Neural Networks via Molecule Decomposition and Contribution Analysis, https://arxiv.org/abs/2508.15015
  • Mustafa Mohammadi Gharasuie and Luis Rueda, 20 Aug 2025, Fast Graph Neural Network for Image Classification, https://arxiv.org/abs/2508.14958
  • Zhiqiang Que, Chang Sun, Sudarshan Paramesvaran, Emyr Clement, Katerina Karakoulaki, Christopher Brown, Lauri Laatu, Arianna Cox, Alexander Tapper, Wayne Luk, Maria Spiropulu, 21 Aug 2025, JEDI-linear: Fast and Efficient Graph Neural Networks for Jet Tagging on FPGAs, https://arxiv.org/abs/2508.15468
  • Anahita Asadi, Leonid Popryho, Inna Partin-Vaisband, 22 Aug 2025, Fast and Accurate RFIC Performance Prediction via Pin Level Graph Neural Networks and Probabilistic Flow, https://arxiv.org/abs/2508.16403
  • Circe Hsu, Claire Schlesinger, Karan Mudaliar, Jordan Leung, Robin Walters, Peter Schindler, 22 Aug 2025, FIRE-GNN: Force-informed, Relaxed Equivariance Graph Neural Network for Rapid and Accurate Prediction of Surface Properties, https://arxiv.org/abs/2508.16012
  • Yuebo Luo, Shiyang Li, Junran Tao, Kiran Thorat, Xi Xie, Hongwu Peng, Nuo Xu, Caiwen Ding, Shaoyi Huang, 22 Aug 2025, DR-CircuitGNN: Training Acceleration of Heterogeneous Circuit Graph Neural Network on GPUs, https://arxiv.org/abs/2508.16769
  • Junhyun Lee, Veronika Thost, Bumsoo Kim, Jaewoo Kang, Tengfei Ma, 22 Aug 2025, Understanding and Tackling Over-Dilution in Graph Neural Networks, https://arxiv.org/abs/2508.16829
  • Bicheng Wang and Junping Wang and Yibo Xue, 22 Aug 2025, Physics-Inspired Spatial Temporal Graph Neural Networks for Predicting Industrial Chain Resilience, https://arxiv.org/abs/2508.16836
  • Silvia Beddar-Wiesing and Alice Moallemy-Oureh, 25 Aug 2025, Weisfeiler-Lehman meets Events: An Expressivity Analysis for Continuous-Time Dynamic Graph Neural Networks, https://arxiv.org/abs/2508.18052
  • Menglin Yang, Min Zhou, Tong Zhang, Jiahong Liu, Zhihao Li, Lujia Pan, Hui Xiong, Irwin King, 22 Aug 2025, Hyperbolic Graph Neural Networks: A Review of Methods and Applications, https://arxiv.org/abs/2202.13852
  • Moshe Eliasof, Eldad Haber, 23 Aug 2025, Quadratic Binary Optimization with Graph Neural Networks, https://arxiv.org/abs/2404.04874
  • XiaYu Liu, Chao Fan, Yang Liu, Hou-biao Li, 24 Aug 2025, Multi-Level Fusion Graph Neural Network for Molecule Property Prediction, https://arxiv.org/abs/2507.03430
  • Michela Lapenna, Caterina De Bacco, 22 Aug 2025, How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data?, https://arxiv.org/abs/2506.11869
  • Kevin Monteiro, Sam Nallaperuma-Herzberg, Martina Mason, Steve Niederer, 2 Jul 2025, Graph Convolutional Neural Networks to Model the Brain for Insomnia, https://arxiv.org/abs/2507.14147
  • Ganesh Sundaram, Jonas Ulmen, and Daniel G\"orges, 20 Jul 2025, Enhanced Pruning Strategy for Multi-Component Neural Architectures Using Component-Aware Graph Analysis, https://arxiv.org/abs/2504.13296
  • Andrew Kiruluta, Andreas Lemos, and Priscilla Burity, 27 Jul 2025, Beyond Neural Networks: Symbolic Reasoning over Wavelet Logic Graph Signals, https://arxiv.org/abs/2507.21190
  • Cencheng Shen, Yuexiao Dong, 8 Aug 2025, A Graph Sufficiency Perspective for Neural Networks, https://arxiv.org/abs/2507.10215
  • Mustafa Mohammadi Gharasuie and Luis Rueda, 19 Aug 2025, Accelerating Image Classification with Graph Convolutional Neural Networks using Voronoi Diagrams, https://arxiv.org/abs/2508.14218
  • Nathan X. Kodama and Kenneth A. Loparo, 22 Aug 2025, Latent Graph Learning in Generative Models of Neural Signals, https://arxiv.org/abs/2508.16776
  • Lingkai Kong, Haotian Sun, Yuchen Zhuang, Haorui Wang, Wenhao Mu, Chao Zhang, 23 Aug 2025, Two Birds with One Stone: Enhancing Uncertainty Quantification and Interpretability with Graph Functional Neural Process, https://arxiv.org/abs/2508.17097
  • Riccardo Cappi, Paolo Frazzetto, Nicol\`o Navarin, Alessandro Sperduti, 25 Aug 2025, Unveiling the Actual Performance of Neural-based Models for Equation Discovery on Graph Dynamical Systems, https://arxiv.org/abs/2508.18173
  • Razi Hasson and Reuven Guetta, 4 Sep 2025, Comment on "A Note on Over-Smoothing for Graph Neural Networks", https://arxiv.org/abs/2509.04178
  • Rog\'erio Almeida Gouv\^ea, Pierre-Paul De Breuck, Tatiane Pretto, Gian-Marco Rignanese, Marcos Jos\'e Leite dos Santos, 2 Sep 2025, Combining feature-based approaches with graph neural networks and symbolic regression for synergistic performance and interpretability, https://arxiv.org/abs/2509.03547
  • Yun Chu, Qiuhao Wang, Enze Zhou, Qian Liu and Gang Zheng, 4 Sep 2025, EZhouNet:A framework based on graph neural network and anchor interval for the respiratory sound event detection, https://arxiv.org/abs/2509.01153
  • Faqian Guan and Tianqing Zhu and Zhoutian Wang and Wei Ren and Wanlei Zhou, 5 Sep 2025, Graph Unlearning: Efficient Node Removal in Graph Neural Networks, https://arxiv.org/abs/2509.04785
  • Arefin Niam, Tevfik Kosar and M S Q Zulkar Nine, 5 Sep 2025, RapidGNN: Energy and Communication-Efficient Distributed Training on Large-Scale Graph Neural Networks, https://arxiv.org/abs/2509.05207
  • Henri Doerks, Paul H\"ausner, Daniel Hern\'andez Escobar, Jens Sj\"olund, 5 Sep 2025, Learning to accelerate distributed ADMM using graph neural networks, https://arxiv.org/abs/2509.05288
  • Riddhiman Raut, Evan M. Mihalko, Amrita Basak, 28 Aug 2025, Multiscale Graph Neural Network for Turbulent Flow-Thermal Prediction Around a Complex-Shaped Pin-Fin, https://arxiv.org/abs/2509.04463
  • Mayur S Gowda, John Shi, Augusto Santos, Jos\'e M. F. Moura, 4 Sep 2025, Inferring the Graph Structure of Images for Graph Neural Networks, https://arxiv.org/abs/2509.04677
  • Levi Rauchwerger and Ron Levie, 25 Aug 2025, A Note on Graphon-Signal Analysis of Graph Neural Networks, https://arxiv.org/abs/2508.18564
  • Hongbo Liu, Siyi Li, Zheng Yu, 26 Aug 2025, Predicting Drug-Drug Interactions Using Heterogeneous Graph Neural Networks: HGNN-DDI, https://arxiv.org/abs/2508.18766
  • Paul Garnier, Jonathan Viquerat, Elie Hachem, 26 Aug 2025, Automated discovery of finite volume schemes using Graph Neural Networks, https://arxiv.org/abs/2508.19052
  • Hugo Attali, Thomas Papastergiou, Nathalie Pernelle, Fragkiskos D. Malliaros, 26 Aug 2025, Dynamic Triangulation-Based Graph Rewiring for Graph Neural Networks, https://arxiv.org/abs/2508.19071
  • Levi Rauchwerger and Stefanie Jegelka and Ron Levie, 26 Aug 2025, Generalization, Expressivity, and Universality of Graph Neural Networks on Attributed Graphs, https://arxiv.org/abs/2411.05464
  • Rajesh Mangannavar, Stefan Lee, Alan Fern, Prasad Tadepalli, 25 Aug 2025, Graph Neural Network Based Action Ranking for Planning, https://arxiv.org/abs/2412.04752
  • Adarsh Jamadandi, Jing Xu, Adam Dziedzic, Franziska Boenisch, 26 Aug 2025, Memorization in Graph Neural Networks, https://arxiv.org/abs/2508.19352
  • Meng Qin, Weihua Li, Jinqiang Cui, Sen Pei, 27 Aug 2025, InfraredGP: Efficient Graph Partitioning via Spectral Graph Neural Networks with Negative Corrections, https://arxiv.org/abs/2508.19737
  • Mingyue Kong, Yinglong Zhang, Chengda Xu, Xuewen Xia, Xing Xu, 27 Aug 2025, Parameter-Free Structural-Diversity Message Passing for Graph Neural Networks, https://arxiv.org/abs/2508.19884
  • Xianfeng Song, Yi Zou, Zheng Shi, Zheng Liu, 27 Aug 2025, GIMS: Image Matching System Based on Adaptive Graph Construction and Graph Neural Network, https://arxiv.org/abs/2412.18221
  • Tu\u{g}rul Hasan Karabulut and \.Inci M. Bayta\c{s}, 28 Aug 2025, Local Virtual Nodes for Alleviating Over-Squashing in Graph Neural Networks, https://arxiv.org/abs/2508.20597
  • Jinluan Yang, Ruihao Zhang, Zhengyu Chen, Fei Wu, Kun Kuang, 30 Aug 2025, Unifying Adversarial Perturbation for Graph Neural Networks, https://arxiv.org/abs/2509.00387
  • Lukas Pertl, Han Xuanyuan, Pietro Li\`o, 31 Aug 2025, Superposition in Graph Neural Networks, https://arxiv.org/abs/2509.00928
  • Oph\'elia Miralles, Daniele Nerini, Jonas Bhend, Baudouin Raoult, Christoph Spirig, 16 Aug 2025, Deep Learning for Operational High-Resolution Nowcasting in Switzerland Using Graph Neural Networks, https://arxiv.org/abs/2509.00017
  • Hind Aljuaid, Areej Alhothali, Ohoud Al-Zamzami, Hussein Assalahi, 1 Sep 2025, TransGAT: Transformer-Based Graph Neural Networks for Multi-Dimensional Automated Essay Scoring, https://arxiv.org/abs/2509.01640
  • C\'edric Allier, Magdalena C. Schneider, Michael Innerberger, Larissa Heinrich, John A. Bogovic, Stephan Saalfeld, 31 Aug 2025, Decomposing heterogeneous dynamical systems with graph neural networks, https://arxiv.org/abs/2407.19160
  • Howard Dai, Nyambura Njenga, Benjamin Whitsett, Catherine Ma, Darwin Deng, Sara de \'Angel, Alexandre Van Tassel, Siddharth Viswanath, Ryan Pellico, Ian Adelstein, Smita Krishnaswamy, 2 Sep 2025, Learning Laplacian Eigenvectors: a Pre-training Method for Graph Neural Networks, https://arxiv.org/abs/2509.02803
  • Niteesh Midlagajni, Constantin A. Rothkopf, 3 Sep 2025, Graph neural networks for learning liquid simulations in dynamic scenes containing kinematic objects, https://arxiv.org/abs/2509.03446
  • Joel Jaskari, Chandreyee Roy, Fumiko Ogushi, Mikko Saukkoriipi, Jaakko Sahlsten, Kimmo Kaski, 3 Sep 2025, Temporal social network modeling of mobile connectivity data with graph neural networks, https://arxiv.org/abs/2509.03319
  • Shuichi Nishino, Tomohiro Shiraishi, Teruyuki Katsuoka, Ichiro Takeuchi, 3 Sep 2025, Statistical Test for Saliency Maps of Graph Neural Networks via Selective Inference, https://arxiv.org/abs/2505.16893
  • Jie Fu, Hong Yuan, Zhili Chen, Wendy Hui Wang, 5 Sep 2025, Safeguarding Graph Neural Networks against Topology Inference Attacks, https://arxiv.org/abs/2509.05429
  • Dibyajyoti Nayak and Somdatta Goswami, 7 Sep 2025, Data-Efficient Time-Dependent PDE Surrogates: Graph Neural Simulators vs Neural Operators, https://arxiv.org/abs/2509.06154
  • Chang Xue, Youwei Lu, Chen Yang, Jinming Xing, 8 Sep 2025, RecMind: LLM-Enhanced Graph Neural Networks for Personalized Consumer Recommendations, https://arxiv.org/abs/2509.06286
  • Shaoqi Wei, Senling Wang, Hiroshi Kai, Yoshinobu Higami, Ruijun Ma, Tianming Ni, Xiaoqing Wen and Hiroshi Takahashi, 8 Sep 2025, A Spatio-Temporal Graph Neural Networks Approach for Predicting Silent Data Corruption inducing Circuit-Level Faults, https://arxiv.org/abs/2509.06289
  • Lili Chen, Changyang She, Jingge Zhu and Jamie Evans, 8 Sep 2025, Graph Neural Networks for Resource Allocation in Interference-limited Multi-Channel Wireless Networks with QoS Constraints, https://arxiv.org/abs/2509.06395
  • Kushal Bose and Swagatam Das, 8 Sep 2025, Asynchronous Message Passing for Addressing Oversquashing in Graph Neural Networks, https://arxiv.org/abs/2509.06777
  • Emmanouil Karystinaios, Johannes Hentschel, Markus Neuwirth, Gerhard Widmer, 8 Sep 2025, AnalysisGNN: Unified Music Analysis with Graph Neural Networks, https://arxiv.org/abs/2509.06654
  • Matthew Lai, Keegan Go, Zhibin Li, Torsten Kroger, Stefan Schaal, Kelsey Allen, Jonathan Scholz, 5 Sep 2025, RoboBallet: Planning for Multi-Robot Reaching with Graph Neural Networks and Reinforcement Learning, https://arxiv.org/abs/2509.05397
  • Zhongyuan Zhao, Gunjan Verma, Ananthram Swami, Santiago Segarra, 5 Sep 2025, Distributed Link Sparsification for Scalable Scheduling Using Graph Neural Networks (Journal Version), https://arxiv.org/abs/2509.05447
  • Priodyuti Pradhan and Amit Reza, 8 Sep 2025, Predicting Steady-State Behavior in Complex Networks with Graph Neural Networks, https://arxiv.org/abs/2502.01693
  • Lachlan Simpson, Kyle Millar, Adriel Cheng, Cheng-Chew Lim, Hong Gunn Chew, 9 Sep 2025, Graph-based Integrated Gradients for Explaining Graph Neural Networks, https://arxiv.org/abs/2509.07648
  • Katherine Berry and Liang Cheng, 9 Sep 2025, A Survey of Graph Neural Networks for Drug Discovery: Recent Developments and Challenges, https://arxiv.org/abs/2509.07887
  • Yuqi Zhou, Zhanhong Cheng, Lingqian Hu, Yuheng Bu, Shenhao Wang, 8 Sep 2025, NestGNN: A Graph Neural Network Framework Generalizing the Nested Logit Model for Travel Mode Choice, https://arxiv.org/abs/2509.07123
  • Nil Ayday, Mahalakshmi Sabanayagam, Debarghya Ghoshdastidar, 12 Sep 2025, Why does your graph neural network fail on some graphs? Insights from exact generalisation error, https://arxiv.org/abs/2509.10337
  • Richard Bergna, Sergio Calvo-Ordo\~nez, Felix L. Opolka, Pietro Li\`o, Jose Miguel Hernandez-Lobato, 12 Sep 2025, Uncertainty Modeling in Graph Neural Networks via Stochastic Differential Equations, https://arxiv.org/abs/2408.16115
  • Pritam Sen, Yao Ma, Cristian Borcea, 11 Sep 2025, CryptGNN: Enabling Secure Inference for Graph Neural Networks, https://arxiv.org/abs/2509.09107
  • Kordel K. France, Ovidiu Daescu, 11 Sep 2025, Diffusion Graph Neural Networks for Robustness in Olfaction Sensors and Datasets, https://arxiv.org/abs/2506.00455
  • Zimo Yan and Jie Zhang and Zheng Xie and Yiping Song and Hao Li, 18 Sep 2025, A Multi-Scale Graph Neural Process with Cross-Drug Co-Attention for Drug-Drug Interactions Prediction, https://arxiv.org/abs/2509.15256
  • Giacomo Dall'Olio, Rainer Kolisch, Yaoxin Wu, 18 Sep 2025, Partial Column Generation with Graph Neural Networks for Team Formation and Routing, https://arxiv.org/abs/2509.15275
  • Xiao Yue, Guangzhi Qu, Lige Gan, 18 Sep 2025, GIN-Graph: A Generative Interpretation Network for Model-Level Explanation of Graph Neural Networks, https://arxiv.org/abs/2503.06352
  • Jaume Banus, Augustin C. Ogier, Roger Hullin, Philippe Meyer, Ruud B. van Heeswijk, Jonas Richiardi, 16 Sep 2025, Spatiotemporal graph neural process for reconstruction, extrapolation, and classification of cardiac trajectories, https://arxiv.org/abs/2509.12953
  • Dieter Balemans, Thomas Huybrechts, Jan Steckel, Siegfried Mercelis, 4 Sep 2025, Resource-Aware Neural Network Pruning Using Graph-based Reinforcement Learning, https://arxiv.org/abs/2509.10526
  • Amirhossein Ghaffari, Huong Nguyen, Lauri Lov\'en, Ekaterina Gilman, 4 Sep 2025, STM-Graph: A Python Framework for Spatio-Temporal Mapping and Graph Neural Network Predictions, https://arxiv.org/abs/2509.10528
  • Mayssa Soussia, Yijun Lin, Mohamed Ali Mahjoub and Islem Rekik, 13 Sep 2025, CogGNN: Cognitive Graph Neural Networks in Generative Connectomics, https://arxiv.org/abs/2509.10864
  • Jin Han, Xin-Zheng Lu, Jia-Rui Lin, 14 Sep 2025, BIGNet: Pretrained Graph Neural Network for Embedding Semantic, Spatial, and Topological Data in BIM Models, https://arxiv.org/abs/2509.11104
  • Luke Delzer, Robert Kroleski, Ali K. AlShami, Jugal Kalita, 15 Sep 2025, Drug Repurposing Using Deep Embedded Clustering and Graph Neural Networks, https://arxiv.org/abs/2509.11493
  • Samir Moustafa, Lorenz Kummer, Simon Fetzel, Nils M. Kriege, Wilfried N. Gansterer, 15 Sep 2025, Visualization and Analysis of the Loss Landscape in Graph Neural Networks, https://arxiv.org/abs/2509.11792
  • Mayur Patil, Qadeer Ahmed, Shawn Midlam-Mohler, 15 Sep 2025, Travel Time and Weather-Aware Traffic Forecasting in a Conformal Graph Neural Network Framework, https://arxiv.org/abs/2509.12043
  • Prajit Sengupta and Islem Rekik, 2 Sep 2025, FireGNN: Neuro-Symbolic Graph Neural Networks with Trainable Fuzzy Rules for Interpretable Medical Image Classification, https://arxiv.org/abs/2509.10510
  • Aryan Gupta, 10 Sep 2025, Assessing the Limits of Graph Neural Networks for Vapor-Liquid Equilibrium Prediction: A Cryogenic Mixture Case Study, https://arxiv.org/abs/2509.10565
  • Rodrigue Govan (ISEA), Romane Scherrer (ISEA), Philippe Fournier-Viger, Nazha Selmaoui-Folcher (ISEA), 15 Sep 2025, SpaPool: Soft Partition Assignment Pooling for__Graph Neural Networks, https://arxiv.org/abs/2509.11675
  • Andrea Cavallo and Samuel Rey and Antonio G. Marques and Elvin Isufi, 18 Sep 2025, Precision Neural Networks: Joint Graph And Relational Learning, https://arxiv.org/abs/2509.14821
  • Qingqi Zhao and Heng Xiao, 17 Sep 2025, An End-to-End Differentiable, Graph Neural Network-Embedded Pore Network Model for Permeability Prediction, https://arxiv.org/abs/2509.13841
  • Jun-En Ding, Anna Zilverstand, Shihao Yang, Albert Chih-Chieh Yang, Feng Liu, 13 Oct 2025, Variational Mixture of Graph Neural Experts for Alzheimer's Disease Biomarker Recognition in EEG Brain Networks, https://arxiv.org/abs/2510.11917
  • Shengyin Sun, Chen Ma, Jiehao Chen, 14 Oct 2025, Enhanced Pre-training of Graph Neural Networks for Million-Scale Heterogeneous Graphs, https://arxiv.org/abs/2510.12401
  • Chengyu Li and Debo Cheng and Guixian Zhang and Yi Li and Shichao Zhang, 14 Oct 2025, Toward Fair Graph Neural Networks Via Dual-Teacher Knowledge Distillation, https://arxiv.org/abs/2412.00382
  • Lukas Gonon, Thilo Meyer-Brandis, Niklas Weber, 14 Oct 2025, Computing Systemic Risk Measures with Graph Neural Networks, https://arxiv.org/abs/2410.07222
  • Yoonju Sim, Hyeonah Kim, Changhyun Kwon, 1 Oct 2025, Test-Time Search in Neural Graph Coarsening Procedures for the Capacitated Vehicle Routing Problem, https://arxiv.org/abs/2510.00958
  • Minseok Jeon and Seunghyun Park, 1 Oct 2025, GDLNN: Marriage of Programming Language and Neural Networks for Accurate and Easy-to-Explain Graph Classification, https://arxiv.org/abs/2510.00374
  • Tiexin Qin and Benjamin Walker and Terry Lyons and Hong Yan and Haoliang Li, 1 Oct 2025, Learning Dynamic Graph Embeddings with Neural Controlled Differential Equations, https://arxiv.org/abs/2302.11354
  • Om Roy, Yashar Moshfeghi, Keith Smith, 24 Sep 2025, Graph Variate Neural Networks, https://arxiv.org/abs/2509.20311
  • Emmanouil Karystinaios, Francesco Foscarin, Gerhard Widmer, 23 Sep 2025, EngravingGNN: A Hybrid Graph Neural Network for End-to-End Piano Score Engraving, https://arxiv.org/abs/2509.19412
  • Daniel Holmberg, Ivan Zaitsev, Markku Alho, Ioanna Bouri, Fanni Franssila, Haewon Jeong, Minna Palmroth, Teemu Roos, 23 Sep 2025, Graph-based Neural Space Weather Forecasting, https://arxiv.org/abs/2509.19605
  • Xingran Chen, Navid NaderiAlizadeh, Alejandro Ribeiro, Shirin Saeedi Bidokhti, 23 Sep 2025, Decentralized Learning Strategies for Estimation Error Minimization with Graph Neural Networks, https://arxiv.org/abs/2404.03227
  • Ashutosh Anshul, Mohammad Zia Ur Rehman, Sri Akash Kadali, Nagendra Kumar, 25 Oct 2025, RoGBot: Relationship-Oblivious Graph-based Neural Network with Contextual Knowledge for Bot Detection, https://arxiv.org/abs/2510.23648
  • Yiming Zhang, Vikram Krishnamurthy and Shashwat Jain, 27 Oct 2025, Inferring Group Intent as a Cooperative Game. An NLP-based Framework for Trajectory Analysis using Graph Transformer Neural Network, https://arxiv.org/abs/2510.23905
  • Teng Jiek See, Daokun Zhang, Mario Boley and David K. Chalmers, 23 Oct 2025, Layer-to-Layer Knowledge Mixing in Graph Neural Network for Chemical Property Prediction, https://arxiv.org/abs/2510.20236
  • Lawrence Clegg, John Cartlidge, 23 Oct 2025, Intransitive Player Dominance and Market Inefficiency in Tennis Forecasting: A Graph Neural Network Approach, https://arxiv.org/abs/2510.20454
  • Daniel Sorensen, Bappaditya Dey, Minjin Hwang, Sandip Halder, 23 Oct 2025, Unsupervised Anomaly Prediction with N-BEATS and Graph Neural Network in Multi-variate Semiconductor Process Time Series, https://arxiv.org/abs/2510.20718
  • Juan Alejandro Pinto Castro, H\'ector J. Hort\'ua, Jorge Enrique Garc\'ia-Farieta and Roger Anderson Hurtado, 23 Oct 2025, Bayesian Inference of Primordial Magnetic Field Parameters from CMB with Spherical Graph Neural Networks, https://arxiv.org/abs/2510.20795
  • Yeonjun In, Kanghoon Yoon, Sukwon Yun, Kibum Kim, Sungchul Kim, Chanyoung Park, 23 Oct 2025, Training Robust Graph Neural Networks by Modeling Noise Dependencies, https://arxiv.org/abs/2502.19670
  • Tobias W\"urth, Niklas Freymuth, Gerhard Neumann, Luise K\"arger, 23 Oct 2025, Diffusion-Based Hierarchical Graph Neural Networks for Simulating Nonlinear Solid Mechanics, https://arxiv.org/abs/2506.06045
  • Jahidul Arafat, Sanjaya Poudel, 22 Oct 2025, AGNES: Adaptive Graph Neural Network and Dynamic Programming Hybrid Framework for Real-Time Nanopore Seed Chaining, https://arxiv.org/abs/2510.16013
  • Wei Xu, Xiaoyi Jiang, Lixiang Xu, Dechao Tang, 20 Oct 2025, Model Metamers Reveal Invariances in Graph Neural Networks, https://arxiv.org/abs/2510.17378
  • Bruno Machado Pacheco, Laio Oriel Seman, Cezar Antonio Rigo, Eduardo Camponogara, Eduardo Augusto Bezerra, Leandro dos Santos Coelho, 17 Oct 2025, Graph Neural Networks for the Offline Nanosatellite Task Scheduling Problem, https://arxiv.org/abs/2303.13773
  • Benjamin Kempinski, Tal Kachman, 20 Oct 2025, Going with the Flow: Approximating Banzhaf Values via Graph Neural Networks, https://arxiv.org/abs/2510.13391
  • Samar Hadou, Alejandro Ribeiro, 21 Sep 2025, Unrolled Graph Neural Networks for Constrained Optimization, https://arxiv.org/abs/2509.17156
  • Tiantian Yang, Zhiqian Chen, 19 Sep 2025, TF-DWGNet: A Directed Weighted Graph Neural Network with Tensor Fusion for Multi-Omics Cancer Subtype Classification, https://arxiv.org/abs/2509.16301
  • Kijung Yoon, 21 Sep 2025, Self-Supervised Discovery of Neural Circuits in Spatially Patterned Neural Responses with Graph Neural Networks, https://arxiv.org/abs/2509.17174
  • Minglai Yang, Reyan Ahmed, 22 Sep 2025, Word2VecGD: Neural Graph Drawing with Cosine-Stress Optimization, https://arxiv.org/abs/2509.17333
  • Zihang Xiang, Tianhao Wang, Di Wang, 22 Sep 2025, Preserving Node-level Privacy in Graph Neural Networks, https://arxiv.org/abs/2311.06888
  • Ziang Chen, Xiaohan Chen, Jialin Liu, Xinshang Wang, Wotao Yin, 21 Sep 2025, Expressive Power of Graph Neural Networks for (Mixed-Integer) Quadratic Programs, https://arxiv.org/abs/2406.05938
  • Xiang Li, Jianpeng Qi, Haobing Liu, Yuan Cao, Guoqing Chao, Zhongying Zhao, Junyu Dong, Xinwang Liu, Yanwei Yu, 22 Sep 2025, ScaleGNN: Towards Scalable Graph Neural Networks via Adaptive High-order Neighboring Feature Fusion, https://arxiv.org/abs/2504.15920
  • Chaoqi Liu, Yunzhu Li, Kris Hauser, 20 Sep 2025, Localized Graph-Based Neural Dynamics Models for Terrain Manipulation, https://arxiv.org/abs/2503.23270
  • Jonah Marks and Joseph Gomes, 19 Sep 2025, Efficient Transition State Searches by Freezing String Method with Graph Neural Network Potentials, https://arxiv.org/abs/2501.06159
  • Khatoon Khedri, Reza Rawassizadeh, Qifu Wen, Mehdi Hosseinzadeh, 24 Oct 2025, Pruning and Quantization Impact on Graph Neural Networks, https://arxiv.org/abs/2510.22058
  • Su Liu, Xin Hu, Shurong Wen, Jiaqi Liu, Jiexi Xu, Lanruo Wang, 25 Oct 2025, Dynamic Graph Neural Network for Data-Driven Physiologically Based Pharmacokinetic Modeling, https://arxiv.org/abs/2510.22096
  • Xingbo Fu, Zhenyu Lei, Zihan Chen, Binchi Zhang, Chuxu Zhang, Jundong Li, 25 Oct 2025, GraphTOP: Graph Topology-Oriented Prompting for Graph Neural Networks, https://arxiv.org/abs/2510.22451
  • Michael Ito, Danai Koutra, Jenna Wiens, 26 Oct 2025, Random Search Neural Networks for Efficient and Expressive Graph Learning, https://arxiv.org/abs/2510.22520
  • Vaibhav Raj, Indradyumna Roy, Ashwin Ramachandran, Soumen Chakrabarti, Abir De, 27 Oct 2025, Charting the Design Space of Neural Graph Representations for Subgraph Matching, https://arxiv.org/abs/2510.22897
  • Yuhan Yang, Xingbo Fu, Jundong Li, 27 Oct 2025, Adaptive Dual Prompting: Hierarchical Debiasing for Fairness-aware Graph Neural Networks, https://arxiv.org/abs/2510.23469
  • F. I. Qowy, 23 Oct 2025, Prefetching Cache Optimization Using Graph Neural Networks: A Modular Framework and Conceptual Analysis, https://arxiv.org/abs/2510.21865
  • Haishuai Wang, Yang Gao, Xin Zheng, Peng Zhang, Jiajun Bu, Philip S. Yu, 26 Oct 2025, Graph Neural Architecture Search with GPT-4, https://arxiv.org/abs/2310.01436
  • Ning Zhang, Henry Kenlay, Li Zhang, Mihai Cucuringu, Xiaowen Dong, 27 Oct 2025, On the Stability of Graph Convolutional Neural Networks: A Probabilistic Perspective, https://arxiv.org/abs/2506.01213
  • Torben Berndt, Benjamin Walker, Tiexin Qin, Jan St\"uhmer, Andrey Kormilitzin, 27 Oct 2025, Permutation Equivariant Neural Controlled Differential Equations for Dynamic Graph Representation Learning, https://arxiv.org/abs/2506.20324
  • Paul Agbaje, Arkajyoti Mitra, Afia Anjum, Pranali Khose, Ebelechukwu Nwafor, Habeeb Olufowobi, 27 Oct 2025, Enhancing Graph Neural Networks: A Mutual Learning Approach, https://arxiv.org/abs/2510.19223
  • Jiacheng Cen, Anyi Li, Ning Lin, Yuxiang Ren, Zihe Wang, Wenbing Huang, 15 Oct 2025, Are High-Degree Representations Really Unnecessary in Equivariant Graph Neural Networks?, https://arxiv.org/abs/2410.11443
  • Lei Xu, Shanshan Wang, Emmanuel Casseau, Chenglong Xiao, 15 Oct 2025, Intelligent4DSE: Optimizing High-Level Synthesis Design Space Exploration with Graph Neural Networks and Large Language Models, https://arxiv.org/abs/2504.19649
  • Mohammad Parsa Afshar, Aryan Azimi, 25 Sep 2025, EEG-Based Consumer Behaviour Prediction: An Exploration from Classical Machine Learning to Graph Neural Networks, https://arxiv.org/abs/2509.21567
  • Zhipu Cui and Johannes Lutzeyer, 26 Sep 2025, SHAKE-GNN: Scalable Hierarchical Kirchhoff-Forest Graph Neural Network, https://arxiv.org/abs/2509.22100
  • Huizhe Zhang, Jintang Li, Yuchang Zhu, Liang Chen and Li Kuang, 16 Sep 2025, SGNNBench: A Holistic Evaluation of Spiking Graph Neural Network on Large-scale Graph, https://arxiv.org/abs/2509.21342
  • Stavros Orfanoudakis, Nanda Kishor Panda, Peter Palensky, Pedro P. Vergara, 25 Sep 2025, GNN-DT: Graph Neural Network Enhanced Decision Transformer for Efficient Optimization in Dynamic Environments, https://arxiv.org/abs/2502.01778
  • Snir Hordan, Maya Bechler-Speicher, Gur Lifshitz, Nadav Dym, 25 Sep 2025, Spectral Graph Neural Networks are Incomplete on Graphs with a Simple Spectrum, https://arxiv.org/abs/2506.05530
  • Olayiwola Arowolo, Jochen L. Cremer, 8 Oct 2025, Towards Generalization of Graph Neural Networks for AC Optimal Power Flow, https://arxiv.org/abs/2510.06860
  • Bang Chen, Lijun Guo, Houli Fan, Wentao He, Rong Zhang, 7 Oct 2025, Soft-Evidence Fused Graph Neural Network for Cancer Driver Gene Identification across Multi-View Biological Graphs, https://arxiv.org/abs/2510.06290
  • Zanyu Shi, Yang Wang, Pathum Weerawarna, Jie Zhang, Timothy Richardson, Yijie Wang, Kun Huang, 7 Oct 2025, Structure-Aware Compound-Protein Affinity Prediction via Graph Neural Network with Group Lasso Regularization, https://arxiv.org/abs/2507.03318
  • Tianzheng Hu, Qiang Li, Shu Liu, Vince D. Calhoun, Guido van Wingen and Shujian Yu, 3 Oct 2025, BrainIB++: Leveraging Graph Neural Networks and Information Bottleneck for Functional Brain Biomarkers in Schizophrenia, https://arxiv.org/abs/2510.03004
  • Ali Azizpour, Madeline Navarro, Santiago Segarra, 3 Oct 2025, Adaptive Node Feature Selection For Graph Neural Networks, https://arxiv.org/abs/2510.03096
  • Matthijs de Jong, Jan Viebahn, Yuliya Shapovalova, 3 Oct 2025, Graph Neural Networks for Transmission Grid Topology Control: Busbar Information Asymmetry and Heterogeneous Representations, https://arxiv.org/abs/2501.07186
  • Yili Wang, Tairan Huang, Changlong He, Qiutong Li, Jianliang Gao, 21 Oct 2025, Simple and Efficient Heterogeneous Temporal Graph Neural Network, https://arxiv.org/abs/2510.18467
  • Yuya Sasaki, 21 Oct 2025, Benchmarking Fairness-aware Graph Neural Networks in Knowledge Graphs, https://arxiv.org/abs/2510.18473
  • Chia-Hsuan Lu, Tony Tan, Michael Benedikt, 21 Oct 2025, Robustness Verification of Graph Neural Networks Via Lightweight Satisfiability Testing, https://arxiv.org/abs/2510.18591
  • Ryan Y. Lin, Julius Berner, Valentin Duruisseaux, David Pitt, Daniel Leibovici, Jean Kossaifi, Kamyar Azizzadenesheli, Anima Anandkumar, 20 Oct 2025, Enabling Automatic Differentiation with Mollified Graph Neural Operators, https://arxiv.org/abs/2504.08277
  • Abhinav Nippani, Dongyue Li, Haotian Ju, Haris N. Koutsopoulos, Hongyang R. Zhang, 21 Oct 2025, Graph Neural Networks for Road Safety Modeling: Datasets and Evaluations for Accident Analysis, https://arxiv.org/abs/2311.00164
  • Jose Andres Millan-Romera, Muhammad Shaheer, Miguel Fernandez-Cortizas, Martin R. Oswald, Holger Voos, and Jose Luis Sanchez-Lopez, 21 Oct 2025, Generation of Uncertainty-Aware Emergent Concepts in Factorized 3D Scene Graphs via Graph Neural Networks, https://arxiv.org/abs/2409.11972
  • Yueming Sun, Long Yang, 29 Sep 2025, Spatial-Functional awareness Transformer-based graph archetype contrastive learning for Decoding Visual Neural Representations from EEG, https://arxiv.org/abs/2509.24761
  • Zehao Niu, Mihai Anitescu, Jie Chen, 26 Sep 2025, Neighborhood Sampling Does Not Learn the Same Graph Neural Network, https://arxiv.org/abs/2509.22868
  • M.Z. Haider, Tayyaba Noreen, M. Salman, 27 Sep 2025, Towards Quantum-Ready Blockchain Fraud Detection via Ensemble Graph Neural Networks, https://arxiv.org/abs/2509.23101
  • Ranhui Yan and Jia cai, 28 Sep 2025, Virtual Nodes based Heterogeneous Graph Convolutional Neural Network for Efficient Long-Range Information Aggregation, https://arxiv.org/abs/2509.23660
  • Jingqi Xu, Guibin Chen, Jingxi Lu, Yuzhang Lin, 28 Sep 2025, Graph Neural Networks with Diversity-aware Neighbor Selection and Dynamic Multi-scale Fusion for Multivariate Time Series Forecasting, https://arxiv.org/abs/2509.23671
  • Jaidev Goel, Pablo Moriano, Ramakrishnan Kannan, Yulia R. Gel, 29 Sep 2025, Community detection robustness of graph neural networks, https://arxiv.org/abs/2509.24662
  • Pascal Plettenberg, Dominik K\"ohler, Bernhard Sick, Josephine M. Thomas, 27 Sep 2025, Flow-Attentional Graph Neural Networks, https://arxiv.org/abs/2506.06127
  • Mads-Peter Verner Christiansen and Bj{\o}rk Hammer, 28 Sep 2025, Gradient-based grand canonical optimization enabled by graph neural networks with fractional atomic existence, https://arxiv.org/abs/2507.19438
  • Jixin Zhang, Yong Lai, 17 Oct 2025, Attn-JGNN: Attention Enhanced Join-Graph Neural Networks, https://arxiv.org/abs/2510.15583
  • Nayan Kumar Singh, 16 Oct 2025, A Comprehensive Evaluation of Graph Neural Networks and Physics Informed Learning for Surrogate Modelling of Finite Element Analysis, https://arxiv.org/abs/2510.15750
  • Song Wang, Zhenyu Lei, Zhen Tan, Jundong Li, Javier Rasero, Aiying Zhang, Chirag Agarwal, 2 Oct 2025, Interpretable Neuropsychiatric Diagnosis via Concept-Guided Graph Neural Networks, https://arxiv.org/abs/2510.03351
  • Burak Karabulut, Carlo Manna, Chris Develder, 3 Oct 2025, Generalization of Graph Neural Network Models for Distribution Grid Fault Detection, https://arxiv.org/abs/2510.03571
  • Mingsong Yan, Charles Kulick, Sui Tang, 4 Oct 2025, On the Convergence and Size Transferability of Continuous-depth Graph Neural Networks, https://arxiv.org/abs/2510.03923
  • Maciej Besta, Florian Scheidl, Lukas Gianinazzi, Grzegorz Kwasniewski, Shachar Klaiman, J\"urgen M\"uller, Torsten Hoefler, 4 Oct 2025, Demystifying Higher-Order Graph Neural Networks, https://arxiv.org/abs/2406.12841
  • Mihir Panchal, Ying-Jung Chen, and Surya Parkash, 23 Oct 2025, CC-GRMAS: A Multi-Agent Graph Neural System for Spatiotemporal Landslide Risk Assessment in High Mountain Asia, https://arxiv.org/abs/2510.20875
  • Jason Wu and Petar Veli\v{c}kovi\'c, 24 Oct 2025, Leveraging Classical Algorithms for Graph Neural Networks, https://arxiv.org/abs/2510.21574
  • Roxanne Holden, Luana Ruiz, 23 Oct 2025, A Short Note on Upper Bounds for Graph Neural Operator Convergence Rate, https://arxiv.org/abs/2510.20954
  • William Lauga, James Rowbottom, Alexander Denker, \v{Z}eljko Kereta, Moshe Eliasof, Carola-Bibiane Sch\"onlieb, 23 Oct 2025, Graph Neural Regularizers for PDE Inverse Problems, https://arxiv.org/abs/2510.21012
  • Ahmed Rashwan, Keith Briggs, Chris Budd, and Lisa Kreusser, 13 Oct 2025, Enforcing convex constraints in Graph Neural Networks, https://arxiv.org/abs/2510.11227
  • Xiucheng Wang, Zien Wang, Nan Cheng, Wenchao Xu, Wei Quan, Xuemin Shen, 13 Oct 2025, Graph Neural Network-Based Multicast Routing for On-Demand Streaming Services in 6G Networks, https://arxiv.org/abs/2510.11109
  • Fran\c{c}ois Schwarzentruber, 13 Oct 2025, Lecture Notes on Verifying Graph Neural Networks, https://arxiv.org/abs/2510.11617
  • Maya Bechler-Speicher, Amir Globerson, Ran Gilad-Bachrach, 13 Oct 2025, The Interpretable and Effective Graph Neural Additive Networks, https://arxiv.org/abs/2406.01317
  • Agatha Schmidt, Henrik Zunker, Alexander Heinlein, Martin J. K\"uhn, 10 Oct 2025, Graph Neural Network Surrogates to leverage Mechanistic Expert Knowledge towards Reliable and Immediate Pandemic Response, https://arxiv.org/abs/2411.06500
  • Sixuan Wang, Jiao Yin, Jinli Cao, Mingjian Tang, Yong-Feng Ge, 23 Sep 2025, A Modality-Aware Cooperative Co-Evolutionary Framework for Multimodal Graph Neural Architecture Search, https://arxiv.org/abs/2510.07325
  • Muhammad Usman and Yugyung Lee, 8 Oct 2025, DGTEN: A Robust Deep Gaussian based Graph Neural Network for Dynamic Trust Evaluation with Uncertainty-Quantification Support, https://arxiv.org/abs/2510.07620
  • Artem Chernobrovkin, Marco S\"alzer, Fran\c{c}ois Schwarzentruber, Nicolas Troquard, 9 Oct 2025, Verifying Graph Neural Networks with Readout is Intractable, https://arxiv.org/abs/2510.08045
  • Zohair Shafi, Benjamin A. Miller, Tina Eliassi-Rad, Rajmonda S. Caceres, 9 Oct 2025, Graph-SCP: Accelerating Set Cover Problems with Graph Neural Networks, https://arxiv.org/abs/2310.07979
  • Kangzheng Liu, Leixin Ma, 22 Sep 2025, MeshODENet: A Graph-Informed Neural Ordinary Differential Equation Neural Network for Simulating Mesh-Based Physical Systems, https://arxiv.org/abs/2509.18445
  • Niharika Tewari, Nguyen Linh Dan Le, Mujie Liu, Jing Ren, Ziqi Xu, Tabinda Sarwar, Veeky Baths, Feng Xia, 23 Sep 2025, Explainable Graph Neural Networks: Understanding Brain Connectivity and Biomarkers in Dementia, https://arxiv.org/abs/2509.18568
  • Alex Schutz, Victor-Alexandru Darvariu, Efimia Panagiotaki, Bruno Lacerda, Nick Hawes, 23 Sep 2025, Tackling GNARLy Problems: Graph Neural Algorithmic Reasoning Reimagined through Reinforcement Learning, https://arxiv.org/abs/2509.18930
  • Asela Hevapathige, 23 Sep 2025, Graph Neural Networks with Similarity-Navigated Probabilistic Feature Copying, https://arxiv.org/abs/2509.19084
  • Alessa Carbo, Eric Nalisnick, 22 Sep 2025, Improving Handshape Representations for Sign Language Processing: A Graph Neural Network Approach, https://arxiv.org/abs/2509.18309
  • Vinay Sharma, Olga Fink, 23 Sep 2025, Dynami-CAL GraphNet: A Physics-Informed Graph Neural Network Conserving Linear and Angular Momentum for Dynamical Systems, https://arxiv.org/abs/2501.07373
  • Sixuan Wang, Jiao Yin, Jinli Cao, MingJian Tang, Hua Wang, Yanchun Zhang, 23 Sep 2025, ABG-NAS: Adaptive Bayesian Genetic Neural Architecture Search for Graph Representation Learning, https://arxiv.org/abs/2504.21254
  • Soyoung Park and Sungsu Lim, 22 Oct 2025, FnRGNN: Distribution-aware Fairness in Graph Neural Network, https://arxiv.org/abs/2510.19257
  • Sabarish Krishna Moorthy, Jithin Jagannath, 22 Oct 2025, Survey of Graph Neural Network for Internet of Things and NextG Networks, https://arxiv.org/abs/2405.17309
  • Dong Hyun Jeon, Lijing Zhu, Haifang Li, Pengze Li, Jingna Feng, Tiehang Duan, Houbing Herbert Song, Cui Tao, Shuteng Niu, 29 Sep 2025, Leveraging Vulnerabilities in Temporal Graph Neural Networks via Strategic High-Impact Assaults, https://arxiv.org/abs/2509.25418
  • Firas Ben Hmida, Abderrahmen Amich, Ata Kaboudi, Birhanu Eshete, 30 Sep 2025, DeepProv: Behavioral Characterization and Repair of Neural Networks via Inference Provenance Graph Analysis, https://arxiv.org/abs/2509.26562
  • Yang Cao, Zhao Song, Jiahao Zhang, Jiale Zhao, 7 Oct 2025, Fundamental Limits of Crystalline Equivariant Graph Neural Networks: A Circuit Complexity Perspective, https://arxiv.org/abs/2510.05494
  • Haribandhu Jena, Jyotirmaya Shivottam, Subhankar Mishra, 7 Oct 2025, QGraphLIME - Explaining Quantum Graph Neural Networks, https://arxiv.org/abs/2510.05683
  • Xiao Yang, Xuejiao Zhao, and Zhiqi Shen, 7 Oct 2025, Are Heterogeneous Graph Neural Networks Truly Effective? A Causal Perspective, https://arxiv.org/abs/2510.05750
  • Dimitrios Kelesis, Dimitris Fotakis, Georgios Paliouras, 7 Oct 2025, Analyzing the Effect of Embedding Norms and Singular Values to Oversmoothing in Graph Neural Networks, https://arxiv.org/abs/2510.06066
  • Islam Akef Ebeid, Haoteng Tang, Pengfei Gu, 15 Oct 2025, Inferred global dense residue transition graphs from primary structure sequences enable protein interaction prediction via directed graph convolutional neural networks, https://arxiv.org/abs/2510.14139

Compound AI Architectures

Compound AI architectures are a new category that generalizes both RAG and multi-AI ensemble architectures. The general idea is that various components can be placed around an LLM, or multiple LLM queries can be used, and this can be done in a variety of ways. RAG is a well-known subcategory in this vein, as are extensions using Knowledge Graphs.

Research on Compound AI architectures:

Research on Efficient Architectures

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