Metacognitive Reuse: Turning Recurring LLM Reasoning Into Concise Behaviors
Fuente:
arXiv
Saved in:
| Main Authors: | Didolkar, Aniket, Ballas, Nicolas, Arora, Sanjeev, Goyal, Anirudh |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Metacognitive Capabilities of LLMs: An Exploration in Mathematical Problem Solving
by: Didolkar, Aniket, et al.
Published: (2024)
by: Didolkar, Aniket, et al.
Published: (2024)
Rethinking Thinking Tokens: LLMs as Improvement Operators
by: Madaan, Lovish, et al.
Published: (2025)
by: Madaan, Lovish, et al.
Published: (2025)
Learning Beyond Pattern Matching? Assaying Mathematical Understanding in LLMs
by: Guo, Siyuan, et al.
Published: (2024)
by: Guo, Siyuan, et al.
Published: (2024)
Masked Generative Priors Improve World Models Sequence Modelling Capabilities
by: Meo, Cristian, et al.
Published: (2024)
by: Meo, Cristian, et al.
Published: (2024)
Can Models Learn Skill Composition from Examples?
by: Zhao, Haoyu, et al.
Published: (2024)
by: Zhao, Haoyu, et al.
Published: (2024)
Keeping LLMs Aligned After Fine-tuning: The Crucial Role of Prompt Templates
by: Lyu, Kaifeng, et al.
Published: (2024)
by: Lyu, Kaifeng, et al.
Published: (2024)
Unlearning via Sparse Representations
by: Shah, Vedant, et al.
Published: (2023)
by: Shah, Vedant, et al.
Published: (2023)
Contextual Drag: How Errors in the Context Affect LLM Reasoning
by: Cheng, Yun, et al.
Published: (2026)
by: Cheng, Yun, et al.
Published: (2026)
Cog-Rethinker: Hierarchical Metacognitive Reinforcement Learning for LLM Reasoning
by: Sun, Zexu, et al.
Published: (2025)
by: Sun, Zexu, et al.
Published: (2025)
Instruct-SkillMix: A Powerful Pipeline for LLM Instruction Tuning
by: Kaur, Simran, et al.
Published: (2024)
by: Kaur, Simran, et al.
Published: (2024)
AI-Assisted Generation of Difficult Math Questions
by: Shah, Vedant, et al.
Published: (2024)
by: Shah, Vedant, et al.
Published: (2024)
CTRL-O: Language-Controllable Object-Centric Visual Representation Learning
by: Didolkar, Aniket, et al.
Published: (2025)
by: Didolkar, Aniket, et al.
Published: (2025)
ReasonBENCH: Benchmarking the (In)Stability of LLM Reasoning
by: Potamitis, Nearchos, et al.
Published: (2025)
by: Potamitis, Nearchos, et al.
Published: (2025)
Leveraging Knowledge Graphs and LLM Reasoning to Identify Operational Bottlenecks for Warehouse Planning Assistance
by: Parekh, Rishi, et al.
Published: (2025)
by: Parekh, Rishi, et al.
Published: (2025)
$α$-TCVAE: On the relationship between Disentanglement and Diversity
by: Meo, Cristian, et al.
Published: (2024)
by: Meo, Cristian, et al.
Published: (2024)
A Simple "Try Again" Can Elicit Multi-Turn LLM Reasoning
by: Liu, Licheng, et al.
Published: (2025)
by: Liu, Licheng, et al.
Published: (2025)
Retrieval-of-Thought: Efficient Reasoning via Reusing Thoughts
by: Ahmed, Ammar, et al.
Published: (2025)
by: Ahmed, Ammar, et al.
Published: (2025)
Skill-Targeted Adaptive Training
by: He, Yinghui, et al.
Published: (2025)
by: He, Yinghui, et al.
Published: (2025)
Provable unlearning in topic modeling and downstream tasks
by: Wei, Stanley, et al.
Published: (2024)
by: Wei, Stanley, et al.
Published: (2024)
Monte Carlo Tree Search Boosts Reasoning via Iterative Preference Learning
by: Xie, Yuxi, et al.
Published: (2024)
by: Xie, Yuxi, et al.
Published: (2024)
Steering Large Reasoning Models towards Concise Reasoning via Flow Matching
by: Li, Yawei, et al.
Published: (2026)
by: Li, Yawei, et al.
Published: (2026)
On the Impossibility of Retrain Equivalence in Machine Unlearning
by: Yu, Jiatong, et al.
Published: (2025)
by: Yu, Jiatong, et al.
Published: (2025)
Self-Training Elicits Concise Reasoning in Large Language Models
by: Munkhbat, Tergel, et al.
Published: (2025)
by: Munkhbat, Tergel, et al.
Published: (2025)
Graph-Memoized Reasoning: Foundations Structured Workflow Reuse in Intelligent Systems
by: Singh, Yash Raj
Published: (2025)
by: Singh, Yash Raj
Published: (2025)
Discovering environments with XRM
by: Pezeshki, Mohammad, et al.
Published: (2023)
by: Pezeshki, Mohammad, et al.
Published: (2023)
Not All Turns Are Equally Hard: Adaptive Thinking Budgets For Efficient Multi-Turn Reasoning
by: Jali, Neharika, et al.
Published: (2026)
by: Jali, Neharika, et al.
Published: (2026)
When Errors Can Be Beneficial: A Categorization of Imperfect Rewards for Policy Gradient
by: Shang, Shuning, et al.
Published: (2026)
by: Shang, Shuning, et al.
Published: (2026)
CARE: Turning LLMs Into Causal Reasoning Expert
by: Dong, Juncheng, et al.
Published: (2025)
by: Dong, Juncheng, et al.
Published: (2025)
Evidence for Limited Metacognition in LLMs
by: Ackerman, Christopher
Published: (2025)
by: Ackerman, Christopher
Published: (2025)
RelayCaching: Accelerating LLM Collaboration via Decoding KV Cache Reuse
by: Geng, Yingsheng, et al.
Published: (2026)
by: Geng, Yingsheng, et al.
Published: (2026)
Didactic to Constructive: Turning Expert Solutions into Learnable Reasoning
by: Mendes, Ethan, et al.
Published: (2026)
by: Mendes, Ethan, et al.
Published: (2026)
Why is Your Language Model a Poor Implicit Reward Model?
by: Razin, Noam, et al.
Published: (2025)
by: Razin, Noam, et al.
Published: (2025)
RecurFormer: Not All Transformer Heads Need Self-Attention
by: Yan, Ruiqing, et al.
Published: (2024)
by: Yan, Ruiqing, et al.
Published: (2024)
When Continual Learning Moves to Memory: A Study of Experience Reuse in LLM Agents
by: Hu, Qisheng, et al.
Published: (2026)
by: Hu, Qisheng, et al.
Published: (2026)
Unrealized Expectations: Comparing AI Methods vs Classical Algorithms for Maximum Independent Set
by: Wu, Yikai, et al.
Published: (2025)
by: Wu, Yikai, et al.
Published: (2025)
Automating Deception: Scalable Multi-Turn LLM Jailbreaks
by: Kumarappan, Adarsh, et al.
Published: (2025)
by: Kumarappan, Adarsh, et al.
Published: (2025)
HAPO: Training Language Models to Reason Concisely via History-Aware Policy Optimization
by: Huang, Chengyu, et al.
Published: (2025)
by: Huang, Chengyu, et al.
Published: (2025)
Zero-Shot Object-Centric Representation Learning
by: Didolkar, Aniket, et al.
Published: (2024)
by: Didolkar, Aniket, et al.
Published: (2024)
Gaussian Embeddings: How JEPAs Secretly Learn Your Data Density
by: Balestriero, Randall, et al.
Published: (2025)
by: Balestriero, Randall, et al.
Published: (2025)
ConciseRL: Conciseness-Guided Reinforcement Learning for Efficient Reasoning Models
by: Dumitru, Razvan-Gabriel, et al.
Published: (2025)
by: Dumitru, Razvan-Gabriel, et al.
Published: (2025)
Similar Items
-
Metacognitive Capabilities of LLMs: An Exploration in Mathematical Problem Solving
by: Didolkar, Aniket, et al.
Published: (2024) -
Rethinking Thinking Tokens: LLMs as Improvement Operators
by: Madaan, Lovish, et al.
Published: (2025) -
Learning Beyond Pattern Matching? Assaying Mathematical Understanding in LLMs
by: Guo, Siyuan, et al.
Published: (2024) -
Masked Generative Priors Improve World Models Sequence Modelling Capabilities
by: Meo, Cristian, et al.
Published: (2024) -
Can Models Learn Skill Composition from Examples?
by: Zhao, Haoyu, et al.
Published: (2024)