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New method predicts the success of LLMs on untried tasks with high accuracy
A team from the Universitat Politècnica de València, part of the Valencian University Research Institute for Artificial Intelligence (VRAIN) and ValgrAI, has participated in the development of ADeLe, a new methodology that offers precise explanations and predictions regarding whether large language models (LLMs) will succeed or fail at specific new tasks they have not yet performed. Furthermore, this methodology identifies exactly the limits of any given model's reasoning capacity.
Moonlake: Causal World Models should be Multimodal, Interactive, and Efficient — with Chris Manning and Fan-yun Sun
We cap out our World Models coverage with one of the most exciting new approaches - long running, multiplayer, interactive world models built with agents bootstrapped from game engines!
Arcee AI: Trinity Large Thinking (arcee-ai/trinity-large-thinking)
Trinity Large Thinking is a powerful open source reasoning model from the team at Arcee AI. It shows strong performance in PinchBench, agentic workloads, and reasoning tasks. It is free in open claw for the first five days. Launch video: https://youtu.be/Gc82AXLa0Rg?si=4RLn6WBz33qT--B7
AI benchmarks are broken. Here’s what we need instead.
For decades, artificial intelligence has been evaluated through the question of whether machines outperform humans. From chess to advanced math, from coding to essay writing, the performance of AI models and applications is tested against that of individual humans completing tasks. This framing is seductive: An AI vs. human comparison on isolated problems with clear…
There are more AI health tools than ever—but how well do they work?
Earlier this month, Microsoft launched Copilot Health, a new space within its Copilot app where users will be able to connect their medical records and ask specific questions about their health. A couple of days earlier, Amazon had announced that Health AI, an LLM-based tool previously restricted to members of its One Medical service, would…
AI benchmark helps robots plan and complete their chores in the real world
No matter how sophisticated they are, robots can often be indecisive and struggle with multi-step chores in the real world. For example, if you tell a robot to tidy a messy room, it might understand the goal but not know where to grab each object. It could even end up inventing steps. To address these common mistakes, Microsoft and a group of academics have developed an AI benchmark system to improve the accuracy of robot planning. The details of their work are published in a paper on the arXiv preprint server.
[AINews] H100 prices are melting *UP*
a quiet day lets us report an important GPU trend
[AINews] Everything is CLI
a quiet day lets us reflect on the growing trend of CLIs for ~everything~ agents
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