Aussie AI

LLM Hooks

Last Updated 8 August, 2025

by David Spuler, Ph.D.

What are LLM Hooks?

LLM hooks are integrations of tools that perform processing on prompt inputs. The use of hooks is a special case of Tool-Augmented Language Models (TALM). The idea with pre-processing hooks is that the tools can augment the input prompts with extra information that the LLM can use, which is similar to RAG-based retrieval, but is based on non-LLM tools that perform dynamic computation.

Research on LLM Hooks

Research papers on LLM hook integrations to dynamic tools:

Damien de Mijolla, Wen Yang, Philippa Duckett, Christopher Frye, Mark Worrall, 8 Dec 2024, Language hooks: a modular framework for augmenting LLM reasoning that decouples tool usage from the model and its prompt, https://arxiv.org/abs/2412.05967
Muhayy Ud Din, Jan Rosell, Waseem Akram, Isiah Zaplana, Maximo A Roa, Lakmal Seneviratne, Irfan Hussain, 10 Dec 2024, Ontology-driven Prompt Tuning for LLM-based Task and Motion Planning, https://arxiv.org/abs/2412.07493 https://muhayyuddin.github.io/llm-tamp/ (Detecting objects in the prompt text and then using a RALM algorithm to query an ontology database.)
Florian Dietz, Dietrich Klakow, 1 Jan 2025, IGC: Integrating a Gated Calculator into an LLM to Solve Arithmetic Tasks Reliably and Efficiently, https://arxiv.org/abs/2501.00684
Julian Perry, Surasakdi Siripong, Thanakorn Phonchai, 15 Jan 2025, Dynamic Knowledge Integration for Enhanced Vision-Language Reasoning, https://arxiv.org/abs/2501.08597 (Augment training data dynamically by retrieving extra information.)
Zhibin Gou, Zhihong Shao, Yeyun Gong, Yelong Shen, Yujiu Yang, Minlie Huang, Nan Duan, Weizhu Chen, 21 Feb 2024 (v4), ToRA: A Tool-Integrated Reasoning Agent for Mathematical Problem Solving, https://arxiv.org/abs/2309.17452
Liu, Z., Zheng, Y., Yin, Z. et al. ArithmeticGPT: empowering small-size large language models with advanced arithmetic skills. Mach Learn 114, 24 (2025). https://doi.org/10.1007/s10994-024-06681-1 https://link.springer.com/article/10.1007/s10994-024-06681-1 https://github.com/ai4ed/ArithmeticGPT (Integrate a calculator into the processing.)
Yifan Yu, Yu Gan, Lily Tasi, Nikhil Sarda, Jiaming Shen, Yanqi Zhou, Arvind Krishnamurthy, Fan Lai, Henry M. Levy, David Culler, 22 Jan 2025, EchoLM: Accelerating LLM Serving with Real-time Knowledge Distillation, https://arxiv.org/abs/2501.12689 (Using a semantic cache to prepend previously computed answers from similar queries as promopt examples, to improve results from a smaller LLM's final result.)
Jianfeng Pan, Senyou Deng, Shaomang Huang, 4 Feb 2025, CoAT: Chain-of-Associated-Thoughts Framework for Enhancing Large Language Models Reasoning, https://arxiv.org/abs/2502.02390 (Integrating results from an "associative memory" in CoT reasoning paths at inference time.)
Ling Yang, Zhaochen Yu, Bin Cui, Mengdi Wang, 10 Feb 2025, ReasonFlux: Hierarchical LLM Reasoning via Scaling Thought Templates, https://arxiv.org/abs/2502.06772 https://github.com/Gen-Verse/ReasonFlux (RALM-like retrieval of reasoning prompt templates at inference time.)
Sam Lin, Wenyue Hua, Lingyao Li, Zhenting Wang, Yongfeng Zhang, 17 Feb 2025. ADO: Automatic Data Optimization for Inputs in LLM Prompts, https://arxiv.org/pdf/2502.11436 (Reformulating the input context such as by semantical marking of relevant content or formatting changes.)
Andrew Neeser, Kaylen Latimer, Aadyant Khatri, Chris Latimer, Naren Ramakrishnan, 16 Feb 2025, QuOTE: Question-Oriented Text Embeddings, https://arxiv.org/abs/2502.10976 (Augmenting RAG chunks with additional information, such as questions the chunk might answer.)
Wendi Cui, Jiaxin Zhang, Zhuohang Li, Hao Sun, Damien Lopez, Kamalika Das, Bradley A. Malin, Sricharan Kumar, 26 Feb 2025, Automatic Prompt Optimization via Heuristic Search: A Survey, https://arxiv.org/abs/2502.18746 (Survey of auto prompting, from basic LLM enhancements to some methods quite similar to RALM and TALM.)
Leixian Shen, Haotian Li, Yifang Wang, Xing Xie, Huamin Qu, 4 Mar 2025, Prompting Generative AI with Interaction-Augmented Instructions, https://arxiv.org/abs/2503.02874
Anthropic, July 2025, Hooks, https://docs.anthropic.com/en/docs/claude-code/hooks
Brown Ebouky, Andrea Bartezzaghi, Mattia Rigotti, 13 Jun 2025, Eliciting Reasoning in Language Models with Cognitive Tools, https://arxiv.org/abs/2506.12115
Can Yang, Bernardo Pereira Nunes, and Sergio Rodríguez Méndez. 2025. LLM as Auto-Prompt Engineer: Automated NER Prompt Optimisation. In Companion Proceedings of the ACM on Web Conference 2025 (WWW '25). Association for Computing Machinery, New York, NY, USA, 2574–2578. https://doi.org/10.1145/3701716.3717818 https://dl.acm.org/doi/abs/10.1145/3701716.3717818 https://dl.acm.org/doi/pdf/10.1145/3701716.3717818
Saurabh Srivastava, Ziyu Yao, 10 Apr 2025, Revisiting Prompt Optimization with Large Reasoning Models-A Case Study on Event Extraction, https://arxiv.org/abs/2504.07357?
Al Hauna, A. D., Yunus, A. P. ., Fukui, M. ., & Siti Khomsah. (2025). Enhancing LLM Efficiency: A Literature Review of Emerging Prompt Optimization Strategies. International Journal on Robotics, Automation and Sciences, 7(1), 72–83. https://doi.org/10.33093/ijoras.2025.7.1.9 https://mmupress.com/index.php/ijoras/article/view/1311