arXiv Artificial Intelligence

ConvMem: Convolutional Memory for Long-Context Reasoning

1 hour ago 11.09.2026 07:00 👁 1 views
ConvMem: Convolutional Memory for Long-Context Reasoning

Quick summary

arXiv:2609.10441v1 Announce Type: new Abstract: While Large Language Models (LLMs) have demonstrated impressive capabilities, they often struggle with extremely long contexts due to fixed context limits. To address this, sequential approaches like MemAgent extend the effective context by reading text in segments and iteratively updating a fixed-size memory. However, this sequential paradigm suffers from high latency and requires costly reinforcement learning (RL) training, which can lead to overfitting on specific datasets. To overcome these limitations, we propose ConvMem, a training-free, hi

Key takeaways

  • arXiv:2609.10441v1 Announce Type: new Abstract: While Large Language Models (LLMs) have demonstrated impressive capabilities, they often struggle with extremely long contexts due to fixed context limits.
  • To address this, sequential approaches like MemAgent extend the effective context by reading text in segments and iteratively updating a fixed-size memory.
  • However, this sequential paradigm suffers from high latency and requires costly reinforcement learning (RL) training, which can lead to overfitting on specific datasets.

Why it matters

“ConvMem: Convolutional Memory for Long-Context Reasoning” 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.

Kaynak sitede devamını oku: arXiv Artificial Intelligence ↗