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Reflection in search agents can be transformed into a powerful memory-control policy, leading to superior performance in complex reasoning tasks.
Dynamic rubrics that evolve with the policy can significantly enhance reinforcement learning performance, even without external supervision.
ScaffoldAgent's utility-guided approach ensures that outlines evolve dynamically, leading to superior long-form report generation and improved factual accuracy.
The $\ell_2$ norm of hidden states serves as a powerful indicator of reasoning intensity in LLMs, enabling new techniques that boost reasoning performance without extra training.
LLMs can reason far better on clinical records when demonstrations are selected using a graph-guided approach that combines patient data with LLM-estimated information gain.
