CoEM: Empowering Long-Context Reasoning with Commit-on-Evidence Memory
Quick summary
arXiv:2609.36935v1 Announce Type: new Abstract: Long-context reasoning is essential for complex and long-horizon tasks, yet the performance of large language models (LLMs) degrades as context length increases. Recent approaches address this by processing input chunk by chunk while maintaining a bounded textual memory in model context. However, premature information compression can discard critical details essential for subsequent reasoning. In this paper, we introduce Commit-on-Evidence Memory (CoEM), which learns when to convert source evidence into compact memory facts. Specifically, under a
Key takeaways
- arXiv:2609.36935v1 Announce Type: new Abstract: Long-context reasoning is essential for complex and long-horizon tasks, yet the performance of large language models (LLMs) degrades as context length increases.
- Recent approaches address this by processing input chunk by chunk while maintaining a bounded textual memory in model context.
- However, premature information compression can discard critical details essential for subsequent reasoning.
Why it matters
“CoEM: Empowering Long-Context Reasoning with Commit-on-Evidence Memory” should be evaluated beyond branding and benchmark scores. Its practical importance will emerge in task accuracy, latency, unit cost, safety and integration with real workflows.
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