When Memory Lies: An Empirical Study of Spatial Memory Staleness in VLM Agents
Memory-augmented VLM agents act on persistent spatial knowledge, yet that knowledge silently goes stale as the environment changes. We ask what happens when an agent must reconcile a confident memory claim with a contradicting observation, and whether current models can catch the conflict before it becomes a safety-relevant mistake. Using a dynamic FrozenLake testbed, we pair a staleness-detection task with a downstream navigation task across three closed-source models and three open-weight VLMs under both text and image inputs (1,800 detection runs, and 12,000 text-mode navigation episodes over four LLM navigators at a shared 50-seed scale). Three findings emerge. First, text solvability...
| Authors | Yushi Sun, Yanjie Zhang |
|---|---|
| Primary category | cs.CL |
| Published | 5 August 2026 |
| Last updated at source | 5 August 2026 |
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