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S3Mem proposes a structured spatiotemporal scene-event memory framework for long-horizon interactive question answering, using anchor-sensitive retrieval and token-budget-aware evidence interface to outperform standard RAG in multiple environments.
Proposes Structure-Aware RAG (SA-RAG), which uses tables as an intermediate structured representation to reduce noise in retrieval-augmented generation for conversational agents, with quality-aware metadata generation and two table generation methods, outperforming existing baselines on noisy real-world datasets.
This paper introduces Group of Skills (GoSkills), a retrieval method that organizes atomic skills into role-labeled execution contexts to improve agent performance within limited context budgets.