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Researchers extend LeCun's JEPA into a general-purpose world model spanning physics to biology

PhAI Labs, together with Stanford, Oxford, and Princeton, proposed JEPA-Anything, which splits JEPA's single prediction into multiple specialized prediction modules and lets each module differentiate into distinct roles during training, thereby covering seven domains—including physics, robotics, weather, single-cell, and clinical—with a single architecture. In a simplified Pong environment, prediction error dropped 35%, and on unseen intervention combinations it dropped 13%; on the Burgers equation benchmark the error was nearly halved, but after 50 steps the advantage shrank to about 3%; in simulated walking-robot planning, it won two of three environments. The team also screened biological data to identify a candidate liver cancer regimen combining IL-18 with CD73 blockade, and tested it in organoids, patient tissue, and mice; the paper also notes that this does not yet show it can become a therapy, and the code and models have been made public.

Anthropic Releases Sixth Economic Index Report: Analysis of Claude Usage Rhythms and Artifacts

Anthropic released its Economic Index report, switching to privacy-preserving telemetry data sampled continuously each day, making hourly usage patterns observable. The report shows that the share of personal-use conversations rose from about 35% on weekdays to nearly 50% on weekends, recipe requests peaked at 2.3 times at 6 p.m., and tax-related conversations on April 14 were 8 times those of an average day in May. The report also classifies conversations by artifact; artifacts were identified in 93% of conversations, of which explanations accounted for 17% and documents and reports for 15%.

Anthropic Project Fetch Phase 2: Opus 4.7 Operates Robot Dog Independently, Faster Than Human Teams

Anthropic published the Phase 2 experiment of Project Fetch: Claude Opus 4.7, running Claude Code without human assistance, completed the tasks that human teams did last year about 20 times faster than the fastest human team. On every task completed by at least one human team, Opus 4.7 was at least 10 times faster; on tasks completed by all four human teams, it was on average more than 37 times faster than teams without Claude and more than 18 times faster than teams with Claude, while generating nearly ten times less code than Team Claude. The model still fails at the closed-loop control step of precisely pushing the beach ball; Anthropic says this is not a solved robotics problem, and that the improvement comes from general capability scaling rather than robot-specific training.