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01 ·Agents·★ MEMBER·PAPER·10 min read Paper Walkthrough — UrbanGround: Where MLLM Agents Break Down on a Real Street Drop an MLLM agent into a real-scale replica of Hong Kong built from territory-wide 3D geospatial data. Visual recognition clears 90%, orientation sits near 40%, long-range navigation is close to 0%. A walkthrough of the benchmark that measures the gap between seeing and moving. 02 ·Agents·★ MEMBER·PAPER·12 min read Paper Walkthrough: Embodied-Navigator (TAMP-Nav) — Let the VLM Just Point, and Navigation Gets Both Faster and Better Instead of asking a VLM for 3D coordinates, have it point at a 2D pixel; think and store memory only at key nodes; align it all with two-level GRPO. A ground-up walkthrough of the design that hits 66.2% SR on R2R-CE at 16.58s per task using only 90k training trajectories.