Artificial intelligence | MIT News | Massachusetts Institute of Technology
MIT highlights a training method that makes reasoning models better at expressing uncertainty without losing accuracy. For builders, that matters because calibrated confidence is a practical lever for reducing hallucinations and improving agent reliability.
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### Teaching AI models to say "I'm not sure" A new training method improves the reliability of AI confidence estimates without sacrificing performance, addressing a root cause of hallucination in reasoning models. April 22, 2026 Read full story → Headshots of Jacob Andreas and Brett McGuire ### Jacob Andreas and Brett McGuire named Edgerton Award winners The associate professors of EECS and chemistry, respectively, are honored for exceptional contributions to teaching, research, and service at…
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uncertaintyhallucinationreasoning-models
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