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Autonomous ML engineering agents can achieve frontier-model performance by learning from long experimental trajectories using outcome-guided supervision and windowed reinforcement learning.
Heterogeneous LLMs can share internal factual states without fine-tuning by exchanging word indices mapped to receiver-local vector directions.
Aesthetic code generation is unlocked by using a VLM judge to translate visual design principles from a reference image into actionable rewards for code models.
MathForm-8B not only outperforms specialized autoformalizers but also sets a new standard for verified mathematical formalization with an impressive 88.06% pass rate on syntax checks.