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KEEP-Bench: Evaluating Evidence Preservation in Context Compression

Ziqing Man, Di Wu, Jia-Chen Gu, Junyi Zhang, Kai‐Wei Chang, Nanyun Peng · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.26827 · cited by 0 works (OpenAlex) · open-access PDF

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Las evaluaciones existentes se basan en las métricas de QA, pero un lector todavía puede responder correctamente a partir de una compresión poco fiel, por lo que estas métricas subestiman dicha pérdida. Para hacer frente a esta brecha, introducimos el KEEP-Bench, un índice de diagnóstico que evalúa directamente la fidelidad de la compresión con 4,102 casos en tres configuraciones desafiantes que requieren la conservación de evidencias para resoluciones de referencia plausibles, cualificaciones explícitas y perspectivas minoritarias. Los experimentos muestran que incluso con una advertencia personalizada, los modelos más fuertes siguen siendo poco fiables: a pesar de que las ventanas de diagnóstico de contexto superan los tokens 100K e insertan sólo los tokens 3.1K en promedio, el mejor modelo de

Machine translation (Meta M2M100); the English original follows.

Abstract

Context compression improves long-context efficiency but may introduce faithfulness issues through an information bottleneck. Existing evaluations rely on QA metrics, but a reader can still answer correctly from an unfaithful compression, so these metrics underestimate such loss. To bridge this gap, we introduce KEEP-Bench, a diagnostic benchmark that directly assesses compression faithfulness with 4,102 cases across three challenging settings that require keeping evidence for plausible reference resolutions, explicit qualifiers, and minority perspectives. Experiments show that even with tailored prompting, the strongest models remain unreliable: despite context windows exceeding 100K tokens and inputs averaging only 3.1K tokens, the best open-weight model (31B) preserves all required evidence in only 66.8% of cases, and the best proprietary model in only 61.6%. Performance also varies sharply across evidence structures, so conclusions from limited structures may be biased. We further introduce a metric, Evidence Preservation Return (EPR), to relate evidence recall to inference FLOPs, and find that it peaks at moderate scales (4B-8B), where recall remains low. Additional output budget mainly benefits already strong models and widens capability gaps, yet the stronger preservation of these models comes at a steep computational cost. These findings highlight the importance for future work to investigate compressors with strong information preservation under a small computation budget, rather than relying on a large output budget or model scaling.

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Topics: Computer science, Context (archaeology), Inference, Benchmark (surveying), Compression (physics), Data compression

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