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Research1 мин

A Function-Space Approach to the Statistical Mechanics of Learning Dynamics

arXiv:2609.09589v1 Announce Type: new Abstract: Deep neural networks exhibit regular macroscopic behavior despite highly nonlinear dynamics in vast parameter spaces. We develop a statistical-mechanical description of learning directly in function space, treating parameter…

arXiv — cs.AI
Research1 мин

OpenDiscoveryTrace: Process Traces for Evaluating AI Scientist Workflows

arXiv:2609.09203v1 Announce Type: new Abstract: Existing benchmarks for autonomous AI scientists evaluate only final outputs---generated code, hypotheses, or papers---yet discard the reasoning process by which those outputs were obtained. This makes it impossible to audit…

arXiv — cs.AI
Research1 мин

Gradland: On Phenomenal Experience, Differentiated Across Many Dimensions

arXiv:2609.09306v1 Announce Type: new Abstract: This paper investigates the hypothesis that the first-order structure of physical interactions, i.e. gradients or Jacobians, characterizes the structure of phenomenal experience. It does so in an idealized world inhabited by neural…

arXiv — cs.AI
Research1 мин

Multi-Agent Agentic Graph Learning via Structural Signatures

arXiv:2609.09565v1 Announce Type: new Abstract: Agentic graph learning (AGL) has recently achieved promising results on graph reasoning tasks, where an agent powered by a large language model (LLM) sequentially samples the graph as evidence to support its final prediction.…

arXiv — cs.AI
Research1 мин

Seven Sources of Physical AI Capability Formation

arXiv:2609.09627v1 Announce Type: new Abstract: Capabilities relevant to Physical AI can arise from materially different formation histories, yet existing taxonomies organized by morphology, architecture, learning algorithm, task, or domain do not directly answer what gives rise…

arXiv — cs.AI
Research1 мин

Decision-Focused Active Learning for Scale-Aware Critical-Materials Recovery

arXiv:2609.09413v1 Announce Type: new Abstract: Choosing a recovery process for scale-up requires connecting laboratory results with product requirements, process costs, and scale effects. We analyze records from Pacific Northwest National Laboratory's Computer Intelligence for…

arXiv — cs.AI
Research1 мин

OpenAI’s sly mathematical breakthrough sends a chill through academia

Open AI CEO Sam Altman speaks during the G20 Innovation Ministerial. | (Photo by Matt RAMEY / AFP via Getty Images) OpenAI's announcement Tuesday that it has solved one of mathematics' legendary Millennium Prize problems should have been a moment of triumph. The result is both…

The Verge — AI
Research1 мин

AI just solved a million dollar math problem...

Watch how this moves. The air rolls into a ring. It stretches and breaks into smaller curls. There's more detail everywhere you look. It almost feels random, [music] but it's not. We can predict it. As scientists study things like this, we get [music] massive advancements in the…

YouTube — Matthew Berman
Research1 мин

Paul Christiano joins OpenAI Foundation Board

Paul Christiano joins the OpenAI Foundation Board and its Safety and Security Committee, bringing experience in AI alignment, safety, and standards.

OpenAI Blog