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ConvMem: Convolutional Memory for Long-Context Reasoning

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TL;DR - ConvMem is a training-free framework for long-context LLM reasoning that hierarchically summarizes text segments using a parallel, convolution-inspired process. It reduces sequential reasoning depth from linear to logarithmic while outperforming training-free baselines on two multi-hop QA benchmarks.

  • Treats a query-prompted LLM as a convolutional kernel that summarizes context segments in a hierarchical tree.
  • Uses configurable strides and skip connections to preserve and propagate evidence while limiting error accumulation.
  • Applies multiple kernels to split complex queries into distinct semantic reasoning channels.
  • Enables parallel processing across context segments and reasoning threads without costly reinforcement learning.

Sources (1)

ConvMem: Convolutional Memory for Long-Context Reasoning

arXiv cs.AI Hongming Zhang, Zhaozhen Gu, Fengshuo Bai, Ming Hao, Qingyang Zhang, Yuanyuan Wang, Shiyang Tang, Yanna Wang, Bo Xu 2026-09-09 arXiv:2609.10441
Public signals Semantic Scholar citations 0 · Semantic Scholar influential citations 0
Providers: Hugging Face · N/A OpenAlex · N/A Publisher · N/A Semantic Scholar · Citations 0 · Influential citations 0 X · N/A Fetched 2026-09-25 14:21:32.734416 UTC

TL;DR - ConvMem is a training-free framework for long-context LLM reasoning that hierarchically summarizes text segments using a parallel, convolution-inspired process. It reduces sequential reasoning depth from linear to logarithmic while outperforming training-free baselines on two multi-hop QA benchmarks.

  • Treats a query-prompted LLM as a convolutional kernel that summarizes context segments in a hierarchical tree.
  • Uses configurable strides and skip connections to preserve and propagate evidence while limiting error accumulation.
  • Applies multiple kernels to split complex queries into distinct semantic reasoning channels.
  • Enables parallel processing across context segments and reasoning threads without costly reinforcement learning.
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