EONav: Support-Centric Memory and Active Search for Everyday Object Agentic Navigation

Everyday object navigation spans space and time: small targets may require close inspection of plausible supporting surfaces, while portable objects can relocate between searches. An agent must therefore decide where to inspect, how to use potentially stale sightings, and which new observations to trust. We introduce an Everyday Object Navigation benchmark with 560 static episodes and 600 ordered dynamic episodes across 35 scenes, jointly evaluating fine-grained discovery and repeated search under relocation. We further propose EONav, a training-free, VLM-centered framework that connects memory, planning, and active verification through relatively stable supports. Its memory agent retains multiple verified, timestamped target–support observations; their historical frequencies provide an empirical support-level prior, with recency as an additional cue. A VLM planning harness combines this history with current observations and inspection feedback to select supports to revisit, falling back to support-guided exploration when history is unavailable or exhausted. Active verification obtains closer or alternative views, redirecting search after rejections and admitting only confirmed observations to memory. EONav achieves 54.46% success rate (SR) and 26.58% success weighted by path length (SPL) in static search, and 61.50% SR and 40.14% SPL in dynamic search, outperforming the evaluated baselines. Temporal memory raises dynamic SR from 23.26% to 61.50%, while EONav uses 23.1% fewer tokens per episode than STRIVE. We also demonstrate real-robot object search and will release the code and benchmark.