MACC: Multi-Aspect Commitment Control for Drift-Aware Memory Management in Long-Horizon LLM Agents

Long-horizon dialogue agents built on large language models (LLMs) must preserve still-active user commitments while operating under limited context budgets. We study context drift, unintended divergence from such commitments, and ask whether efficient memory editing also preserves them. Multi-Aspect Commitment Control (MACC) scores historical segments with several projection-based commitment alignments and learns to retain, summarize, or prune them, using a response-consistency proxy computed by an encoder separate from the generator. On the LoCoMo long-term conversational-memory benchmark and on Blended Skill Talk, with a shared Qwen2.5-1.5B-Instruct generator, MACC reaches 62.9% judge-rated acceptability on LoCoMo at 0.092 of the Full-History token cost. A No-History control reaches 60.4% at 0.087, so acceptability alone does not establish memory use. On a human-labelled diagnostic of 60 distance, distractor, and revision probes, three heuristic memory methods fail on 55–67% of probes, and a post-hoc evaluation of one MACC checkpoint fails on 78% with no dialogue history retained. These results expose a gap between task–cost performance and commitment fidelity and motivate explicit commitment testing alongside task–cost evaluation.