MarvisNav: Making Memory Visible on Route Choices for Zero-Shot Object Navigation

When searching for an object, people choose their next move by considering both likely target locations and places already explored. The current view can cue place-associated memories, bringing target relevance and prior exploration into the same spatial context. In many zero-shot object navigation (ZSON) methods, however, vision-language models (VLMs) infer promising search areas from egocentric images, but exploration history enters the decision through separate representations, e.g., as text or maps. This separation either requires an additional fusion step to align semantic evidence with exploration state, or leaves the correspondence between memory and route choices implicit for the VLM to recover. We instead make exploration memory visible directly on visual route choice. We propose MarvisNav, a ZSON framework that maintains a topological graph and projects candidate nodes together with their exploration states onto the egocentric perception as memory-bearing visual route choices. These topological states capture local exploration progress beyond binary visitation. By binding exploration state directly to each visual candidate, MarvisNav enables the VLM to jointly evaluate target relevance and exploration state and make the final high-level selection without a separate post-hoc fusion or reranking stage. Without policy training, MarvisNav achieves state-of-the-art performance on HM3D ($81.2\%$ SR and $42.5\%$ SPL), while competitive on MP3D. Moreover, it outperforms representative VLM-based methods with far fewer VLM calls (e.g., $7.5\%$ of WMNav). Real-robot experiments across diverse scenes further validate its practical deployability. Beyond MarvisNav, our study shows that memory representation shapes VLM decisions and ZSON performance, highlighting that effective memory use in ZSON depends not only on its availability, but also on how it is represented.