Active Context Compression: Autonomous Memory Management in LLM Agents

TL;DR

This paper introduces Focus, an autonomous memory management system for LLM agents that selectively compresses interaction history to reduce computational costs while maintaining accuracy.

Contribution

It presents a novel agent-centric architecture inspired by biological strategies, enabling LLM agents to autonomously decide when to compress context for efficiency.

Findings

Achieved 22.7% token reduction without accuracy loss

Performed an average of 6 autonomous compressions per task

Token savings up to 57% on individual instances

Abstract

Large Language Model (LLM) agents struggle with long-horizon software engineering tasks due to "Context Bloat." As interaction history grows, computational costs explode, latency increases, and reasoning capabilities degrade due to distraction by irrelevant past errors. Existing solutions often rely on passive, external summarization mechanisms that the agent cannot control. This paper proposes Focus, an agent-centric architecture inspired by the biological exploration strategies of Physarum polycephalum (slime mold). The Focus Agent autonomously decides when to consolidate key learnings into a persistent "Knowledge" block and actively withdraws (prunes) the raw interaction history. Using an optimized scaffold matching industry best practices (persistent bash + string-replacement editor), we evaluated Focus on N=5 context-intensive instances from SWE-bench Lite using Claude Haiku 4.5. With aggressive prompting that encourages frequent compression, Focus achieves 22.7% token reduction (14.9M -> 11.5M tokens) while maintaining identical accuracy (3/5 = 60% for both agents). Focus performed 6.0 autonomous compressions per task on average, with token savings up to 57% on individual instances. We demonstrate that capable models can autonomously self-regulate their context when given appropriate tools and prompting, opening pathways for cost-aware agentic systems without sacrificing task performance.