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SIGMA is proposed, a data generation and training pipeline enabling alignment self-improvement that generalizes to out-of-distribution settings and shows that a Model Spec balancing harmlessness and helpfulness, test-time reasoning for safety deliberation, and high-quality rubrics from SIGMA's task designer agent are c...
CLUE (Calibrated Latent Uncertainty Estimation), which combines self-consistent teacher calibration and distribution-based confidence probing to produce calibrated predictions, is introduced, which achieves equivalent or stronger performance than zero-shot baselines up to 32B parameters across all modalities.
This work presents AutoMat, a benchmark for evaluating LLM-based agents'ability to reproduce claims from computational materials science, and curates a set of claims from real materials science papers to test whether coding agents can recover and execute the end-to-end workflow needed to support (or undermine) such cla...
The Information Abundance Paradox is supported and it is suggested that scaling toward near-infinite context is not simply a matter of supplying more data, even when high-quality long-context data is abundant.
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