LongAttnComp: Cross-Family Context Compression for Long-Context Reasoning

• URL: http://arxiv.org/abs/2606.01336v1
• Date: Sun, 31 May 2026 16:40:36 GMT
• Title: LongAttnComp: Cross-Family Context Compression for Long-Context Reasoning
• Abstract summary: LongAttnComp is a long-context adaptation of AttnComp that fine-tunes a lightweight cross-attention scoring layer.<n>On InfiniteBench Code- Debug, LongAttnComp matches or exceeds full-context accuracy.<n>On LongBench v2, the two-stage recipe largely closes the Stage 1 gap on multi-document reasoning.
• Score: 2.1218260966433955
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As real-world applications increasingly require processing inputs of 100k+ tokens, the gap between context length and inference efficiency has become a critical bottleneck. Context compression offers a way to reduce prefill costs while preserving task accuracy. However, existing training-free attention-based methods leave substantial gaps in demanding long-context tasks such as code reasoning. We present LongAttnComp, a long-context adaptation of AttnComp that fine-tunes a lightweight cross-attention scoring layer and introduces tokenlevel chunking, a token-budget top-p algorithm, positional reordering, and a formatagnostic query parser. We further design a two-stage fine-tuning recipe for the compressor: Stage 1 builds a general retrieval foundation from NIAH-style data, and Stage 2 extends it with multi-hop and reasoning data for broader long-context task coverage. On InfiniteBench Code-Debug, LongAttnComp matches or exceeds full-context accuracy, substantially outperforms training-free baselines, and transfers across four target models from three families. On LongBench v2, the two-stage recipe largely closes the Stage 1 gap on multi-document reasoning while preserving Code-Debug performance.

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