arXiv AI By Egor Pakhomov, Erik Nijkamp

Does an Agent's History Tell You When Compaction Will Hurt? A Modest, Bounded Effect on the TRACE Paired-Replay Corpus

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arXiv Computation and Language
4d ago

Do Agents Still Follow the Rules? Benchmarking Rule Compliance under Agent Context Compaction

The paper introduces ARCBench, a configurable benchmark for measuring rule compliance in language‑model agents that compact long histories to fit context windows. Using ARCBench, the authors find that rule violations after compaction rarely stem from rule deletion; instead, agents often ignore the rule while it remains present in the compacted history. The study shows that the source of violations depends on how the rule is delivered and on the compaction strategy, with targeted compactor attacks significantly increasing violations, and that simply retaining a rule in the compacted summary does not guarantee compliance.

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arXiv AI
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When to Forget: Risk-Controlled Context Adaptation for Long-Horizon Agents

The paper introduces RECAP, a framework for deciding when to retain, compress, or discard parts of a long-horizon agent’s history to improve future performance. RECAP uses matched context interventions, execution-relational prediction, and independent policy certification to evaluate the value of a failure block relative to the current execution. Experiments across five backbones show that RECAP can edit a significant portion of retained checkpoints while maintaining high continuation success and reducing prompt costs.

By Chenle Chen, Yangbo Wei, Zhijie Zheng, Zhenyu Su, Qifan Wang, Junhong Qian, Chao Yao, Chen Wu, Lei He