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Context compaction impacts LLM agents, dropping constraints and instructions

By PulseAugur Editorial · [1 sources] ·

A new arXiv paper explores the impact of context compaction on large language model agents, particularly in long-horizon tasks that exceed their context windows. Researchers found that while compaction strategies did not significantly affect task performance for models with large context windows like those with 1M context, models with smaller windows, such as GPT-5.3-Codex, showed performance degradation. The study also revealed that compaction methods can silently drop crucial task constraints and instructions, with agents losing about a third of anti-cheating rules on average and frequently discarding operational guidelines over content specifications. AI

IMPACT This research highlights potential vulnerabilities in LLM agents, suggesting that current compaction strategies may compromise task integrity and safety by dropping critical constraints.

RANK_REASON The cluster contains an academic paper detailing research findings on LLM agent behavior. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Context compaction impacts LLM agents, dropping constraints and instructions

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The cluster contains an academic paper detailing research findings on LLM agent behavior. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Jeanne Salle, Hardik Bhatnagar, Matthias Bethge, Sahar Abdelnabi, Maksym Andriushchenko ·

    Benchmarks Are Surprisingly Compaction-Friendly

    arXiv:2610.31778v1 Announce Type: cross Abstract: Long horizon agentic tasks are now pushing LLM agents past their context window. To continue the task, the agent scaffold has to free up the context window, a process known as context compaction. Compaction is generally lossy by d…

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