Tencent Study: VLM Agents Face Severe Safety Risks from Stale Spatial Memory
tencent · hf · 2026-08-06
Tencent conducted an empirical study on Visual Language Model (VLM) agents to examine how they reconcile confident but stale spatial memory with contradicting observations when environments change.
The research uncovers three key findings:
- Lack of Visual Grounding: Models that reliably flag stale entries in text mode see vision F1 scores plummet from 0.887 to 0.067 on identical image grids, with the weakest making confident decisions while ignoring the visual input.
- Safety Liability: In GPT-4o tests, an agent trusting raw memory dies more than twice as often as one with no memory at all.
- Filtering Limitations: While a transparent read-time filter reduces safety costs in text mode, it yields no consistent benefit when visual auditing is unreliable.
The paper frames spatial-memory staleness as a critical safety failure mode, isolating reliable visual grounding as a central open challenge for memory-augmented agents.
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