S3Mem: Structured Spatiotemporal Scene-Event Memory for Long-Horizon Interactive Question Answering
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Computer Science > Computation and Language
arXiv:2605.28831 (cs)
[Submitted on 10 Apr 2026]
Title:S3Mem: Structured Spatiotemporal Scene-Event Memory for Long-Horizon Interactive Question Answering
Authors:Encheng Su, Jinouwen Zhang, Jianyu Wu, Qiucheng Yu, Chen Tang, Pengze Li, Lintao Wang, Yizhou Wang, Xinzhu Ma, Shixiang Tang, Aoran Wang
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Abstract:Long-horizon interactive agents often accumulate large trajectory histories yet still fail to answer questions about earlier events reliably. We argue that the main bottleneck is not context length alone, but the trajectory-to-answer interface of long-term memory. When histories are stored as plain-text chunks and queried with standard retrieval-augmented generation (RAG), systems often retrieve locally relevant but chain-incomplete evidence, especially for spatial, temporal, repeated-event, and multi-hop state questions. We propose S3MEM, a structured scene-event episodic memory framework for long-horizon interactive question answering (QA). S3MEM writes trajectories into structured memory units, retrieves evidence through anchor-sensitive retrieval, and exposes a compact token-budget-aware evidence interface for answer-time inference. In this sense, S3MEM is a structured evidence harness that converts agent trajectories into query-aligned support. We evaluate S3MEM on two internal headline environments (Crafter, Jericho) and two out-of-family environments (SciWorld, ALFWorld). Under a shared frozen answer-time protocol, S3MEM consistently outperforms Vanilla RAG across all four environments, surpasses Graph-NoReader on Crafter, Jericho, and ALFWorld, and matches it on SciWorld while using dramatically fewer evidence tokens. Three adapted recent baselines -- A-MEM-inspired, MemoryOS-adapted, and LightMem-adapted -- improve over Vanilla RAG in several settings, but none matches S3MEM's overall accuracy-efficiency frontier. Overall, the evidence supports a bounded conclusion: under the current frozen answer-time protocol, structured writing and anchor-sensitive evidence routing provide a stronger accuracy-efficiency frontier for long-horizon interactive QA than more generic memory interfaces.
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2605.28831 [cs.CL] |
| (or arXiv:2605.28831v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2605.28831
arXiv-issued DOI via DataCite
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View a PDF of the paper titled S3Mem: Structured Spatiotemporal Scene-Event Memory for Long-Horizon Interactive Question Answering, by Encheng Su and 10 other authors
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