Interactive agents need fine-grained long-video understanding. Current Video-LLMs remain brittle when relevant evidence is subtle or sparse. Fixed frame budgets force rich streams into a few sampled frames and often discard decisive cues. We introduce Video* Bench, a benchmark with 2,212 real-world videos and ten tasks...
Rui-Zhe Chen, Zhi-Ting Fan, Yang Shi et al.· 1 citation
CDEG, a graph-based framework that learns reusable decision-critical evidence from historical diagnostic trajectories, is introduced, demonstrating that reliable long-horizon diagnosis requires moving beyond trajectory-level experience reuse toward evidence-level learning of the factors that truly shape clinical decisi...
Xi-Wei Dai, Zi-Jie Meng, Zhiting Fan et al.· 0 citations
This work presents MedUP, a Med-VLM that natively unifies perception and understanding within a shared token space, and curates UniMed-Train, a 1.84M-instance corpus spanning text-guided segmentation, region-grounded understanding, medical VQA and CoT-based segmentation, and introduces UniMed-Bench for unified evaluati...
Yuan Wang, Hualiang Wang, Yixin Chen et al.· 0 citations
ClinFusion sets a new state-of-the-art across a comprehensive suite of 2D and 3D multimodal medical benchmarks, outperforming leading open-source medical MLLMs and demonstrating multimodal capabilities better than powerful proprietary models such as GPT-5.2 and Gemini-3-Flash.
This work introduces LongMedBench, a real-world EHR-based benchmark for long-horizon clinical decision-making, and proposes an evaluation taxonomy with three suites: fact-based QA, temporal reasoning, and long-horizon decision-making.
