Lost in the Hallway: Benchmarking Persistent Spatial Memory Across Revisits

Persistent spatial understanding is essential for embodied agents to operate reliably in environments they repeatedly encounter. It requires recognizing revisited places despite viewpoint changes, distinguishing them from similar-looking rooms, and updating memories as environments change. Yet existing evaluations often leave physical place identity across separated visits implicit. We introduce ReSpaceBench, a benchmark comprising 3,244 questions across 590 synthetic and real-home scenes, organized into 9 tasks spanning room association, persistent state tracking, and downstream reasoning. Controlled walkthroughs define visits by physical room entry and deliberately include same-type room distractors and cross-session object changes. Our evaluation of 32 models and 3 agentic video-understanding frameworks reveals substantial limitations: the strongest evaluated zero-shot model achieves 50.5% overall accuracy and 52.2% on a 450-question subset, compared with 90.1% for human annotators. The evaluated agentic frameworks offer no overall improvement over direct inference with the same base model. Analysis of agent execution traces reveals evidence omissions and incorrect cross-visit room associations as major failure modes, suggesting that iterative evidence seeking alone does not ensure consistent spatial memory. Further analyses expose a gap between recognizing familiar rooms and identifying the correct visited room, while annotation-guided sampling highlights the importance of preserving intervening visits and transitions. We also provide RS-30K, a separate training dataset from synthetic sources with no template, scene, route, or video overlap with the benchmark. Fine-tuning Qwen3-VL-8B raises overall accuracy from 35.5% to 60.4%, with the largest gains in association and improvements extending to real-life scenes. ReSpaceBench and RS-30K provide resources for diagnosing and improving persistent spatial memory across revisits.