Xutao Mao
Author directory2026
MEMMARK: State-Evolution Attribution Watermarking for Agent Long-Term Memory Systems
Haobo Zhang | Xutao Mao | Guangyuan Dong | Ziwei Li | Xuanbo Su | Kaijie Chen | Jing Yang | Zheng Lin
Findings of the Association for Computational Linguistics: EMNLP 2026
Haobo Zhang | Xutao Mao | Guangyuan Dong | Ziwei Li | Xuanbo Su | Kaijie Chen | Jing Yang | Zheng Lin
Findings of the Association for Computational Linguistics: EMNLP 2026
Memory-backed agents need provenance that can survive leaked or migrated snapshots, where logs, visible outputs, and trusted metadata may be absent. We propose MEMMARK, a state-evolution attribution watermark that embeds an owner-controlled signal into latent memory-write decisions. At each internal LLM call, MEMMARK samples among admissible candidates using keyed, distribution-preserving selection, and records cryptographic commitments with signed session anchors and reveal evidence. This makes attribution depend on reproducible backend behavior rather than mutable provenance fields. Across A-Mem and Graphiti on LOCOMO, with three LLM backbones, MEMMARK preserves memory utility: Overall F1 retains 99.6 percent of the unwatermarked baseline, while BLEU-1 changes by +0.2 percent. It also provides usable carrier capacity, with 1.16, 1.14, and 1.26 bits of mean entropy for update-target, link-target, and semantic-realization decisions. In the snapshot-only R3 setting, MEMMARK recovers the full 40-bit payload from final snapshots, while wrong-key verification remains near chance. Under nine memory-lifecycle attacks, verification distinguishes tampering, evidence deletion, and partial payload recovery. These results show that robust snapshot-only attribution is feasible for long-term agent memory without surviving traces, trusted metadata, or utility degradation.