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Looped language models (LoopLMs) increase computational depth through parameter sharing, offering a path to scale inference computation without adding parameters. However, it remains unclear when additional recurrence is beneficial and how architectural choices affect its effectiveness. Through controlled experiments,...
This work establishes an end-to-end AI-driven paradigm for understanding and exploring complex ionic systems, demonstrating the promise of chemically specialized foundation models as a generalizable strategy for scientific discovery.
The results support OPD as preparation for RL and favor forward KL when OPD is followed by RL in the authors' comparison, which highlights the importance of both objective choice and the states receiving supervision for subsequent RL.
Research Attention Prediction (RAP), a rolling benchmark covering 278 AI/ML fields and 1,390 episodes, is introduced, a rolling benchmark covering 278 AI/ML fields and 1,390 episodes for large language models to track shifts in research attention.
Controlled fine-tuning, distillation, and agentic consistency-checking support the same conclusions, and the SciTrue team's participation in both subtasks of the NTCIR-19 SciClaimEval task is described.
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