Role of LLMs in Human-AI Interaction (Keynote @ Cogsima 2026) | Subbarao Kambhampati
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Learning to Reason with LLMs
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berkeley.edu
CS Prof explains why LLMs can’t generate new scientific ideas. Bcz LLMs learn a structured “map”, Bayesian manifold, of known data and work well within it, but fail outside it. But true discovery means creating new maps, which LLMs cannot do. | Md Ismail Sojal
50:48
CS Prof explains why LLMs can’t generate new scientific ideas. Bcz LLMs learn a structured “map”, Bayesian manifold, of known data and work well within it, but fail outside it. But true discovery means creating new maps, which LLMs cannot do. | Md Ismail Sojal
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FacebookMd Ismail Sojal
We are finally beginning to understand how LLMs work: No, they don't simply predict word after word
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Researchers upend AI status quo by eliminating matrix multiplication in LLMs
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LLMs’ “simulated reasoning” abilities are a “brittle mirage,” researchers find
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How do you make LLMs reason both faster and smarter? This new paper shows that just shrinking the context window during RL fine-tuning slashes reasoning length by 25%—but it also destabilizes accuracy, a confound most prior work missed.Enter Step-level Advantage Selection (SAS): a lightweight tweak that only reinforces the most confident steps in a chain-of-thought and shields good reasoning from being punished when rollouts get truncated. No extra model, just clever credit assignment.On 8 reaso
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