Self-Aware Reinforcement Learning
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SAAS: Self-Aware Reinforcement Learning for Over-Search Mitigation in Agentic Search
arXiv:2605.29796v2 Announce Type: replace Abstract: Agentic search enables LLMs to solve complex multi-hop questions through iterative reasoning and external search. Despite the effectiveness, these systems often suffer from a critical limitation in practice: agents fail to recognize their own knowledge boundaries, blindly triggering searches when internal knowledge suffices and failing to terminate search even when adequate evidence has been collected. The lack of self-awareness leads to...
When AI Says It Feels
Announce Type: new Abstract: Large language models (LLMs) are generally constrained from expressing feelings through human-preference alignment in post-training processes. This policy is designed using a top-down approach and may conflict with the goal of training models to exhibit human-like intelligence using human-generated texts. Here, we performed an experiment called Human-like Model eXpressions of Feeling (HMX-feel), in which LLMs were encouraged to express feelings, intentions, and...
Superintelligence: The Idea That Eats Smart People (2016)
This is the text version of a talk I gave on October 29, 2016, at Web Camp Zagreb [video] (45 mins) SuperintelligenceThe Idea That Eats Smart People | | | In 1945, as American physicists were preparing to test the atomic bomb, it occurred to someone to ask if such a test could set the atmosphere on fire. This was a legitimate concern.