CoEM: Empowering Long-Context Reasoning with Commit-on-Evidence Memory

arXiv:2609.36935 · cs.AI, cs.CL · Submitted 2026-09-29 · Read on arXiv

cs.AI, cs.CL

Submitted: 2026-09-29

Updated: 2026-09-30

Code: https://github.com/benmagnifico/CoEM

Terminology

• Verifier — 89× in this paper · explained in 2 episodes
• Verification — 50× in this paper · explained in 2 episodes
• Calibration — 14× in this paper · explained in 7 episodes
• Benchmark — 8× in this paper · explained in 2 episodes
• Ablation — 7× in this paper · explained in 1 episode
• Provenance — 5× in this paper · explained in 4 episodes
• Regularization — 5× in this paper · explained in 3 episodes
• Reinforcement Learning — 5× in this paper · explained in 4 episodes
• Commit-on-Evidence Memory — 4× in this paper
• KL Regularization — 4× in this paper · explained in 2 episodes
• Trace — 4× in this paper · explained in 2 episodes
• Transformer — 4× in this paper · explained in 2 episodes
• Adaptation — 3× in this paper · explained in 1 episode
• Credit assignment — 2× in this paper · explained in 3 episodes
• Fine-Tuning — 2× in this paper · explained in 5 episodes
• Information Gain — 2× in this paper · explained in 2 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• Accuracy — 1× in this paper · explained in 1 episode
• Actor — 1× in this paper · explained in 3 episodes
• Adam — 1× in this paper · explained in 2 episodes
• Commentary — 1× in this paper · explained in 2 episodes
• Continual Learning — 1× in this paper · explained in 2 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Federated Learning — 1× in this paper · explained in 15 episodes
• Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes
• Pruning — 1× in this paper · explained in 4 episodes
• Tokenization — 1× in this paper · explained in 2 episodes

Sources

• InfoMem: Training Long-Context Memory Agents with Answer-Conditioned Information Gain
• RULER: What's the Real Context Size of Your Long-Context Language Models?
• A Comprehensive Survey on Long Context Language Modeling
• RoBERTa: A Robustly Optimized BERT Pretraining Approach
• Proximal Policy Optimization Algorithms
• When to Memorize and When to Stop: Gated Recurrent Memory for Long-Context Reasoning
• Look Back to Reason Forward: Revisitable Memory for Long-Context LLM Agents
• Memory-Efficient Federated Fine-Tuning of Large Language Models via Layer Pruning
• MemAgent: Reshaping Long-Context LLM with Multi-Conv RL-based Memory Agent

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