MIT tests AI reasoning with 121 unfamiliar games
A Nature study from MIT, Cambridge, Princeton and collaborators suggests humans reason about new games with fast, shallow, goal-directed simulation rather than deep search.
4 verified stories covering Reasoning models, product updates and industry developments.
A Nature study from MIT, Cambridge, Princeton and collaborators suggests humans reason about new games with fast, shallow, goal-directed simulation rather than deep search.
An internal general reasoning model found an algebraic-number-theory construction that external mathematicians said is journal-worthy.
Arcee AI, a 26-person startup, spent $20 million and 33 days training Trinity-Large-Thinking, a 40-billion-parameter open-source reasoning model that tops agent benchmarks and rivals proprietary models.
A study published in Nature Communications shows that large reasoning models can autonomously attack other AI models, achieving a 97.14% success rate in bypassing safety guardrails across 25,200 tests.