Concept Tokens

Last Updated 20 May, 2026

by David Spuler, Ph.D.

What are Concept Tokens?

Concept tokens are an LLM inference optimization method that uses specific meta-tokens to represent individual concepts, rather than using tokens for words or part-words. These concept tokens can be used to improve accuracy of reasoning (because of less language ambiguity), and can also improve the efficiency of token processing, because there are fewer tokens for a given passage of text. The full use of concept tokens for the entire LLM is called a "concept model" or a Large Concept Model (LCM). It is also possible to use concept tokens in the interim steps of Chain-of-Thought reasoning, via reasoning tokens.

Concept Tokens: Book Excerpts and Blog Articles

Free online book excerpts with full text chapters online and free PDF downloads, and the Aussie AI blog, including related articles:

David Spuler, December 2025, Concepts, Chapter 10, "The Sweetest Lesson: Your Brain vs AI", https://www.aussieai.com/book/sweetest-lesson-10-concepts

Research on Concept Tokens

Research papers include:

LCM team, Loïc Barrault, Paul-Ambroise Duquenne, Maha Elbayad, Artyom Kozhevnikov, Belen Alastruey, Pierre Andrews, Mariano Coria, Guillaume Couairon, Marta R. Costa-jussà, David Dale, Hady Elsahar, Kevin Heffernan, João Maria Janeiro, Tuan Tran, Christophe Ropers, Eduardo Sánchez, Robin San Roman, Alexandre Mourachko, Safiyyah Saleem, Holger Schwenk, 15 Dec 2024 (v2), Large Concept Models: Language Modeling in a Sentence Representation Space, https://arxiv.org/abs/2412.08821 https://github.com/facebookresearch/large_concept_model (Model operates at the sentence concept level, using SONAR sentence embeddings.)
Dr. Ashish Bamania, Dec 2024, Meta’s Large Concept Models (LCMs) Are Here To Challenge And Redefine LLMs: A deep dive into ‘Large Concept Model’, a novel language processing architecture and evaluating its performance against state-of-the-art LLMs, https://levelup.gitconnected.com/metas-large-concept-models-lcms-are-here-to-challenge-and-redefine-llms-7f9778f88a87
Sachin Kumar, Sep 17, 2024, Hidden Chain-of-Thought decoding: faster and efficient CoT decoding to improve reasoning of LLMs, https://medium.com/@techsachin/hidden-chain-of-thought-decoding-faster-and-efficient-cot-decoding-to-improve-reasoning-of-llms-d95584bc9346 (Token reduction in CoT by compressing language tokens into an internal "hidden" concise token representation.)
Tianqiao Liu, Zui Chen, Zitao Liu, Mi Tian, Weiqi Luo, 13 Sep 2024, Expediting and Elevating Large Language Model Reasoning via Hidden Chain-of-Thought Decoding, https://arxiv.org/abs/2409.08561
Lance Eliot, Dec 18, 2024, Chain Of Continuous Thought Promises Mighty Boost For LLMs And Generative AI By Blowing Up The Fixation On Tokens, https://www.forbes.com/sites/lanceeliot/2024/12/18/chain-of-continuous-thought-promises-mighty-boost-for-llms-and-generative-ai-by-blowing-up-the-fixation-on-tokens/
Kyle Orland, 13 Dec 2024, Are LLMs capable of non-verbal reasoning? Processing in the "latent space" could help AI with tricky logical questions, https://arstechnica.com/ai/2024/12/are-llms-capable-of-non-verbal-reasoning/
Alex McFarland, December 16, 2024, Meta’s COCONUT: The AI Method That Thinks Without Language, https://www.unite.ai/metas-coconut-the-ai-method-that-thinks-without-language/
Maxime Peyrard, Martin Josifoski, Robert West, 21 Mar 2024, The Era of Semantic Decoding, https://arxiv.org/abs/2403.14562
Hussain Ahmad, Diksha Goel, 8 Jan 2025, The Future of AI: Exploring the Potential of Large Concept Models, https://arxiv.org/abs/2501.05487
Giuliano Liguori, Jan 2025, Large Concept Models (LCM): A New Frontier in AI Beyond Token-Level Language Models, https://www.linkedin.com/pulse/large-concept-models-lcm-new-frontier-ai-beyond-giuliano-liguori--dnj3f/
Hanyu Zhang, Xiting Wang, Chengao Li, Xiang Ao, Qing He, 10 Jan 2025, Controlling Large Language Models Through Concept Activation Vectors, https://arxiv.org/abs/2501.05764 (Training a vector used to control the model on certain attributes.)
Deqian Kong, Minglu Zhao, Dehong Xu, Bo Pang, Shu Wang, Edouardo Honig, Zhangzhang Si, Chuan Li, Jianwen Xie, Sirui Xie, Ying Nian Wu, 3 Feb 2025, Scalable Language Models with Posterior Inference of Latent Thought Vectors, https://arxiv.org/abs/2502.01567
Jonas Geiping, Sean McLeish, Neel Jain, John Kirchenbauer, Siddharth Singh, Brian R. Bartoldson, Bhavya Kailkhura, Abhinav Bhatele, Tom Goldstein, 7 Feb 2025, Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth Approach, https://arxiv.org/abs/2502.05171
DiJia Su, Hanlin Zhu, Yingchen Xu, Jiantao Jiao, Yuandong Tian, Qinqing Zheng, 5 Feb 2025. Token Assorted: Mixing Latent and Text Tokens for Improved Language Model Reasoning, https://arxiv.org/abs/2502.03275
Jihoon Tack, Jack Lanchantin, Jane Yu, Andrew Cohen, Ilia Kulikov, Janice Lan, Shibo Hao, Yuandong Tian, Jason Weston, Xian Li, 12 Feb 2025, LLM Pretraining with Continuous Concepts, https://arxiv.org/abs/2502.08524
Vishal Rajput, Feb 2025, Forget LLMs, It’s Time For Large Concept Models (LCMs), https://medium.com/aiguys/forget-llms-its-time-for-large-concept-models-lcms-05b75fe43185
Towards Practical Concept-Based Language Models: An Efficiency-Focused Implementation Vivek K. Tiwari, 2025, https://www.researchgate.net/profile/Vivek-Tiwari-41/publication/388753941_Towards_Practical_Concept-Based_Language_Models_An_Efficiency-Focused_Implementation/links/67a4bf86461fb56424cc6b62/Towards-Practical-Concept-Based-Language-Models-An-Efficiency-Focused-Implementation.pdf
Datacamp, Feb 21, 2025, Large Concept Models: A Guide With Examples: Learn what large concept models are, how they differ from LLMs, and how their architecture leads to improvements in language processing, https://www.datacamp.com/blog/large-concept-models
Mehul Gupta, Jan 5, 2025, Meta Large Concept Models (LCM): End of LLMs? What are LCMs and how is LCM different from LLMs, https://medium.com/data-science-in-your-pocket/meta-large-concept-models-lcm-end-of-llms-68cb0c5cd5cf
By AI Papers Academy, 3 January 2025, Large Concept Models (LCMs) by Meta: The Era of AI After LLMs? https://aipapersacademy.com/large-concept-models/
Andrea Viliotti, 20 Dec 2024, Large Concept Model (LCM): a new paradigm for large-scale semantic reasoning in AI, https://www.andreaviliotti.it/post/large-concept-model-lcm-a-new-paradigm-for-large-scale-semantic-reasoning-in-ai
Leadership in AI, January, 2025, Meta’s stunning LCM large concept models for artificial intelligence — they are thinking now! https://www.youtub e.com/watch?v=u Z3HCw8ApQ,
Lance Eliot, Jan 06, 2025, AI Is Breaking Free Of Token-Based LLMs By Upping The Ante To Large Concept Models That Devour Sentences And Adore Concepts, https://www.forbes.com/sites/lanceeliot/2025/01/06/ai-is-breaking-free-of-token-based-llms-by-upping-the-ante-to-large-concept-models-that-devour-sentences-and-adore-concepts/
Zen the innovator, Jan 5, 2025, Large Concept Models (LCMs), https://medium.com/@ThisIsMeIn360VR/large-concept-models-lcms-d59b86531ef6
Debabrata Pruseth, Jan 2025, LCMs: Large Concept Models – The Path to AGI ( Artificial General Intelligence) & The Future of AI Thinking, https://debabratapruseth.com/lcms-large-concept-models-the-path-to-agi-the-future-of-ai-thinking/
Asif Razzaq, December 15, 2024, Meta AI Proposes Large Concept Models (LCMs): A Semantic Leap Beyond Token-based Language Modeling, https://www.marktechpost.com/2024/12/15/meta-ai-proposes-large-concept-models-lcms-a-semantic-leap-beyond-token-based-language-modeling/
Aniket Hingane, Dec 27, 2024, Practical Advancements in AI: How Large Concept Models Are Redefining the Landscape of LLMs, https://medium.com/@learn-simplified/practical-advancements-in-ai-how-large-concept-models-are-redefining-the-landscape-of-llms-b0220296458b
Siddhant Rai and Vizuara AI, Dec 30, 2024, Large Concept models : Language Modeling in a Sentence Representation Space: Re-imagining the core principles behind representation generation in foundation model, https://vizuara.substack.com/p/large-concept-models-language-modeling?
J Liao, R Xie, S Li, X Wang, X Sun, Z Kang, X He, 2025, Multi-Grained Patch Training for Efficient LLM-based Recommendation, https://hexiangnan.github.io/papers/sigir25-PatchRec.pdf
Ignacio de Gregorio, June 2025, What If We Are All Wrong About AI? The contrarian bet by Meta, in plain English, https://medium.com/@ignacio.de.gregorio.noblejas/what-if-we-are-all-wrong-about-ai-f33a3c64055c
Tomek Korbak, Mikita Balesni, (and many more authors) July 2025, Chain of Thought Monitorability: A New and Fragile Opportunity for AI Safety, https://tomekkorbak.com/cot-monitorability-is-a-fragile-opportunity/cot_monitoring.pdf
Sebastian Raschka, Mar 8, 2025, Inference-Time Compute Scaling Methods to Improve Reasoning Models: Part 1: Inference-Time Compute Scaling Methods, https://sebastianraschka.com/blog/2025/state-of-llm-reasoning-and-inference-scaling.html
Chen Shani, Dan Jurafsky, Yann LeCun, Ravid Shwartz-Ziv, 30 Jun 2025 (v3), From Tokens to Thoughts: How LLMs and Humans Trade Compression for Meaning, https://arxiv.org/abs/2505.17117 (Humans organize information via "semantic compression".)
Shariar Kabir, Kevin Esterling, Yue Dong, 23 Apr 2025, Do Words Reflect Beliefs? Evaluating Belief Depth in Large Language Models, https://arxiv.org/abs/2504.17052
Sicheng Feng, Gongfan Fang, Xinyin Ma, Xinchao Wang, 15 Apr 2025, Efficient Reasoning Models: A Survey, https://arxiv.org/abs/2504.10903
David Spuler, December 2025, Concepts, Chapter 10, "The Sweetest Lesson", https://www.aussieai.com/book/sweetest-lesson-10-concepts
Hesam Hosseini, Ghazal Hosseini Mighan, Amirabbas Afzali, Sajjad Amini, Amir Houmansadr, 15 Nov 2024, ULTra: Unveiling Latent Token Interpretability in Transformer Based Understanding, https://arxiv.org/abs/2411.12589
Haoqiang Kang, Yizhe Zhang, Nikki Lijing Kuang, Nicklas Majamaki, Navdeep Jaitly, Yi-An Ma, Lianhui Qin, April 2026, LaDiR: Latent Diffusion Enhances LLMs for Text Reasoning, Apple Machine Learning Research, https://machinelearning.apple.com/research/ladir