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University of Science and Technology of China
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MAGE redefines memory management for long-horizon agents, achieving up to 20.4% higher task success rates while slashing token usage by over half.
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.
Get up to 4x faster video generation from diffusion transformers without sacrificing quality, thanks to a new clustering method that slashes attention costs.
