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The model family shows gains in held-out scientific-code repair and across selected general-purpose benchmarks in code, reasoning, and knowledge, providing evidence of positive transfer from scientific experience to broader capabilities.
Existing benchmarks miss the mark on faithfulness, but a new dependency-aware checklist reveals the true performance gaps in T2I models.
Rethinking supervised fine-tuning as target distribution design reveals that optimizing token likelihood may overlook richer model knowledge, leading to significant performance gains.
Imagine AI scientists that not only reason but also autonomously conduct experiments in the real world – that's the promise of Intelligent Science Laboratories.
