S3Mem: Structured Spatiotemporal Scene-Event Memory for Long-Horizon Interactive Question Answering
arXiv:2605.28831v1 Announce Type: cross 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 st
The paper addresses a critical limitation in current AI agents concerning long-term memory and reliable information retrieval, which is a significant bottleneck for advancing autonomous systems.
Improving long-horizon interactive question answering and memory for AI agents is crucial for their reliability and capability in complex, real-world tasks, enabling more sophisticated automation.
The proposed S3Mem system offers a structured approach to memory that could significantly enhance AI agent performance in understanding and interacting with dynamic environments over extended periods.
- · AI agent developers
- · Robotics
- · Generative AI
- · Autonomous systems
- · Systems reliant on simple RAG
- · Developers of brittle AI solutions
More robust and reliable AI agents capable of sustained, complex interactions.
Acceleration of white-collar task automation and more sophisticated human-AI collaboration.
Enhanced AI capabilities leading to new economic models and changes in labor markets, driven by more capable autonomous agents.
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Read at arXiv cs.AI