Reasoning models naturally compress context via thinking traces, with reward-constrained optimization yielding 17-23% gains over baselines on long-context QA at high compression ratios.
COMI: coarse-to-fine context compression via marginal information gain.CoRR, abs/2602.01719
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SeCo performs semantic-driven context compression for LLMs by anchoring on query-relevant semantic centers and applying consistency-weighted token merging, yielding better downstream performance, lower latency, and stronger out-of-domain robustness than position-based methods across 14 benchmarks.
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Thinking as Compression: Your Reasoning Model is Secretly a Context Compressor
Reasoning models naturally compress context via thinking traces, with reward-constrained optimization yielding 17-23% gains over baselines on long-context QA at high compression ratios.