DUAL-MEM: Cross-Task Memory Formation and Within-Task Active Memory Control for Tool-Using LLM Agents

Tool-using large language model (LLM) agents can encode past interaction experiences into procedural memory. However, memory retrieval alone does not determine how retrieved memory should be adapted during execution. When execution stalls, the agent must decide whether to revise the current strategy or replace the retrieved procedure. We propose DUAL-MEM, a dual lifecycle framework that separates Strategy adaptation within a task from procedural memory evolution across tasks. During execution, an event-driven Controller decides whether to regenerate the Strategy while preserving the current Card or to retrieve a different Card when the procedure no longer provides suitable guidance. After each task, observed usage outcomes update Card history, while successful unmatched trajecto- ries can contribute new procedural memory for later tasks. Across four interactive benchmarks, DUAL-MEM improves average success rate from 46.30% to 49.30% over the strongest baseline. On ALFWorld, success improves by 2.98%–3.73% across three different model backbones, while average online input tokens are reduced by 4.10% to 4.50%. Over a 120-task continual stream, fixed probe success increases from 68.91% to 73.88%. Execution traces indicate a clear timescale separation between the two levels. Active Cards have an RMST@40 2.27 times that of Strategies, suggesting that Strategy revision often occurs without replacing the underlying procedural memory.