Active Forgetting for Multi-Turn LLM Agent

Multi-turn LLM agents rely on interaction histories to integrate evolving instructions, observations, and actions into subsequent decisions. However, retaining the full history can reinforce outdated assumptions in earlier model responses, while indiscriminate deletion can remove information essential for reasoning and tool execution. Existing approaches to history removal, compression, and memory management generally lack an explicit account of these dependencies, leaving unresolved what agents should retain and what they can safely forget. We introduce *ACTive Forgetting (ACTF)*, a training-free context selection mechanism that determines retention through explicit dependencies among interaction records. ACTF removes unreferenced assistant responses and superseded state while preserving current evidence and its required prerequisites, yielding a compact context satisfying the declared retention and dependency constraints. On the frozen Lost-in-Conversation suite, merged ACTF improves accuracy from 44.2% to 61.0%. A separate rendering audit reaches 61.7% while using 18% of full-history input tokens; controlled evidence-delivery and final-answer evaluations retain the gain. Complementary tool-use and state-update experiments show why selective retention matters: forgetting unsupported model outputs can improve reasoning, whereas preserving required interaction records avoids the failures caused by indiscriminate deletion. These findings highlight active forgetting as a promising approach to building more reliable and efficient LLM agents.