Duanchu Wang

Current vision-language models can *see* point cloud defects, but can't reliably *diagnose* them, highlighting a critical gap in grounded quality understanding.

LLMs can now perform traceable, multi-step ecological reasoning over complex forest environments by operating on ecological hypergraphs and invoking deterministic tools, achieving higher accuracy and faithfulness than single-step approaches.

Despite showing promise in reading raw height data, today's MLLMs often fail to translate geometric perception into reliable semantic reasoning about natural scenes, even performing worse than RGB-only models when both modalities are needed.