AutoDavis is introduced, a first-of-its-kind automatic and dynamic evaluation protocol that enables on-demand benchmarking of LVLMs across specific capability dimensions and shows effectiveness and reliability, offering a new paradigm for dynamic benchmarking of multimodal intelligence.
Han Bao, Yue Huang, Yan-Bo Wang et al.· Proceedings of the 32nd ACM...· 0 citations
KITE (Knowledge-boundary Instruction Tuning via Exploration), a two-stage framework that combines failure-guided data generation with boundary-aware uncertainty curation, is proposed, showing that KITE yields more stable improvement than strong synthetic-data baselines.
MemoHarness is introduced, an adaptive harness optimization framework that learns from its own executions and improves over the fixed harnesses it is compared against and shows selective transfer to unseen suites and base models.
Yue Huang, Wenjie Wang, Han Bao et al.· arXiv.org· 4 citations
AutoDavis is introduced, a first-of-its-kind automatic and dynamic evaluation protocol that enables on-demand benchmarking of LVLMs across specific capability dimensions and shows effectiveness and reliability, offering a new paradigm for dynamic benchmarking of multimodal intelligence.
Han Bao, Yue Huang, Yanbo Wang et al.· Proceedings of the 32nd ACM...· 0 citations
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