FOCUS: Training-Free Decision-Preserving Context Compression for LLM Agents

Abstract:LLM agents accumulate interaction histories that grow linearly with task length, causing quadratic inference cost scaling and performance degradation from attention dilution. Existing context-compression methods learn what to discard offline: by contrastively optimizing guidelines, distilling compressors, or training compression policies. This incurs a substantial cost. Further, the compression policy is learned a priori and is not dynamically conditioned on the evolving test-time trajectories. In this paper we ask a complementary question: Which past interactions causally shape the agent's future decisions? We recast context compression as a causal decision preservation problem over discrete interaction units and introduce FOCUS, a training-free context compression framework that operates entirely at test time. Our method requires no offline data collection or fine-tuning, and is architecture-agnostic, attaching to any closed-API frontier model as a modular compression layer. We evaluate FOCUS on diverse agentic benchmarks including API and tool-calling, QA, web domain and multi-turn dialogue. Our method establishes new state of the art performance, cutting peak context by up to 48% and dependency by 73% while improving task success by up to 8.9 percentage points over uncompressed execution.

Preprint. Under Review

Artificial Intelligence (cs.AI); Computation and Language (cs.CL)

arXiv:2609.37590 [cs.AI]

(or arXiv:2609.37590v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2609.37590 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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