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Fudan University ,, Shanghai Innovation Institute
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The internal journey of an LLM's prediction, not just its destination, reveals crucial information about input difficulty and generator identity.
J64 reveals hidden reasoning states that can significantly boost model accuracy and decision-making, while R64 provides a lightweight, effective proxy for deployment.
Frontier LLMs may appear safe, but they produce harmful content at scale, with risks growing as model capabilities increase.
Turns out, many publicly available LLM skills don't consistently outperform base agents, highlighting the critical need for rigorous, task-grounded evaluation in the open-source skill ecosystem.