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CurateEvo transforms data curation from a static process into a dynamic, failure-driven evolution, significantly boosting performance and efficiency in LLM training.
Observation-Aligned supervision reveals that traditional chart-to-code training often leads to hallucinations, and aligning targets with identifiable quantities can dramatically improve model performance.
Even the best multimodal models struggle to reconstruct complex interactive dashboards, revealing a critical gap in current capabilities.
Language sensitivity in VLA models is a step-wise control problem, with certain task steps causing up to 50% performance degradation under non-English instructions.
Stop blindly throwing compute at agent harnesses: effective feedback, not raw tokens, dictates scaling laws.
Even the best large vision-language models struggle with multi-image reasoning, scoring only 50% on a new benchmark designed to challenge their capabilities.
Current multimodal agents are surprisingly bad at research workflows, struggling to integrate evidence across papers and figures in multi-turn settings.
