Arcee's new, open source Trinity-Large-Thinking is the rare, powerful U.S.-made AI model that enterprises can download and customize - VentureBeat
Arcee's new, open source Trinity-Large-Thinking is the rare, powerful U.S.-made AI model that enterprises can download and customize VentureBeat
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A Tale of Two Rigours
A familiarity with the pre-rigor/post-rigor ontology might be helpful for reading this post. University math is often sold to students as imbuing in them the spirit of rigor and respect for iron-clad truth. The value in a real analysis course comes not from the specific results that it teaches — those are largely known to scientifically literate students by the time they take it. Instead, they are asked to relearn all those things from first principles; in so doing, they strip themselves of bad habits they previously learned and are inducted into the skeptical culture of the mathematician. Pedagogical and exam materials usually support this goal, putting emphasis on proof-writing, careful argumentation and attention to detail. This incentivises the student to cultivate an invaluable attitu
Does GPT-2 Have a Fear Direction?
Anthropic dropped a paper this morning showing that Claude Sonnet 4.5 has steerable emotion representations. Actual directions in activation space that, when injected, shift the model's behavior in predictable ways. They found a non-monotonic anger flip: push the steering vector hard enough and the model will flip to something qualitatively different than anger. The paper only covered their very large, heavily instruction tuned model. This paper is a write-up on the same same experiment at a tiny scale. The Setup: I generated 40 situational prompt pairs to extract a fewer direction via difference-in-means. No emotional words for the prompts and the contrast is entirely situational. Ex: standing at the edge of a rooftop versus standing at the edge of a meadow, alone in a parking garage at m
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Does GPT-2 Have a Fear Direction?
Anthropic dropped a paper this morning showing that Claude Sonnet 4.5 has steerable emotion representations. Actual directions in activation space that, when injected, shift the model's behavior in predictable ways. They found a non-monotonic anger flip: push the steering vector hard enough and the model will flip to something qualitatively different than anger. The paper only covered their very large, heavily instruction tuned model. This paper is a write-up on the same same experiment at a tiny scale. The Setup: I generated 40 situational prompt pairs to extract a fewer direction via difference-in-means. No emotional words for the prompts and the contrast is entirely situational. Ex: standing at the edge of a rooftop versus standing at the edge of a meadow, alone in a parking garage at m
Google DeepMind s Research Lets an LLM Rewrite Its Own Game Theory Algorithms — And It Outperformed the Experts
Designing algorithms for Multi-Agent Reinforcement Learning (MARL) in imperfect-information games — scenarios where players act sequentially and cannot see each other s private information, like poker — has historically relied on manual iteration. Researchers identify weighting schemes, discounting rules, and equilibrium solvers through intuition and trial-and-error. Google DeepMind researchers proposes AlphaEvolve, an LLM-powered evolutionary coding agent [ ] The post Google DeepMind s Research Lets an LLM Rewrite Its Own Game Theory Algorithms — And It Outperformed the Experts appeared first on MarkTechPost .
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