StreamMemBench：面向未来辅助的智能体记忆流式评测基准

View PDF HTML (experimental)

Abstract:A central role of personal-agent memory is to turn stored information and prior interactions into future-oriented assistance. In daily use, useful cues come from what the agent observes and how the user interacts with the agent, and the agent must carry them forward from the current request to similar future tasks. Existing memory benchmarks usually test dialogue recall or task improvement in isolation, leaving the trajectory from streaming observations to later assistance largely untested. We introduce StreamMemBench, a streaming benchmark that constructs a two-step task sequence around each evidence anchor from EgoLife egocentric streams. The initial task tests evidence use, while the follow-up task tests whether feedback and interaction experience are reused. Four metrics diagnose evidence recall, initial evidence use, feedback incorporation, and follow-up reuse. Experiments with eight memory systems across two backbones show that current systems often fail to use observed evidence or turn feedback into reliable follow-up behavior, even when evidence is stored or feedback is incorporated locally. StreamMemBench is publicly available at this https URL.

Comments:	Accepted to Findings of EMNLP 2026
Subjects:	Artificial Intelligence (cs.AI)
Cite as:	arXiv:2606.14571 [cs.AI]
	(or arXiv:2606.14571v3 [cs.AI] for this version)
	https://doi.org/10.48550/arXiv.2606.14571 arXiv-issued DOI via DataCite
Journal reference:	Findings of the Association for Computational Linguistics: EMNLP 2026, pages 20947-20960, Budapest, Hungary, October 24-29, 2026. Association for Computational Linguistics
Related DOI:	https://doi.org/10.18653/2026.findings-emnlp.992 DOI(s) linking to related resources

Submission history

From: Guanming Liu [view email]
[v1] Fri, 12 Jun 2026 15:48:43 UTC (773 KB)
[v2] Wed, 26 Aug 2026 13:12:16 UTC (776 KB)
[v3] Thu, 5 Nov 2026 08:50:47 UTC (773 KB)