TrajsCogMap-Bench: Evaluating Cognitive Mapping and Spatial Memory from Egocentric Video Trajectories

Navigation requires more than remembering visited places: agents must integrate egocentric observations into spatial knowledge that supports subsequent queries and route planning. Evaluating this ability requires distinguishing local spatial understanding, integration across trajectories, and the retention of spatial information for future use. We introduce \textbf TrajsCogMap-Bench, an open-loop benchmark for evaluating cognitive mapping and spatial memory in multimodal large language models. Built from real-world egocentric videos in indoor environments rich in points of interest, the benchmark organizes spatial queries and route-prediction tasks across three levels: local motion understanding, single-trajectory integration, and multi-trajectory reasoning. The multi-trajectory setting probes spatial knowledge construction and use from multiple traversals of the same environment, covering different locations and viewpoints, including route inference beyond directly experienced paths. We report answer accuracy for all tasks except open-ended route navigation. For the latter, an executor model follows the generated instructions in a simulator grounded in the environment topology and egocentric observations, with performance measured by success rate (SR) and success weighted by path length (SPL). We provide complementary full-context and online-memory protocols: full-context evaluation tests joint integration of observations available at answering time, while online-memory evaluation tests the use of memory built incrementally from sequential observations without knowledge of future questions. Our evaluations reveal a gap between remembering landmarks and reasoning about their spatial relations to plan routes. Performance further declines when models rely on incrementally constructed memory, highlighting limitations in how current memory systems preserve spatial information for subsequent reasoning.