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Researchers at PhAI Labs have expanded Yann LeCun's JEPA architecture to work across seven fields, from robotics to biomedicine. The effort also produced a liver cancer treatment candidate that showed promise in lab tests, though the study doesn't establish whether it could become an actual therapy. The article Researchers stretch LeCun's JEPA AI into a universal world model that works from physics to biology appeared first on The Decoder .

Reka AI's Rho-1 is a 19-billion-parameter omni-model that processes and generates text, images, video, and robot control actions in a single neural network. Trained on 320 H100 GPUs in about three months, it uses a fraction of the compute today's top models need. Instead of routing tasks to specialized systems, Rho-1 runs all modalities as tokens in one shared context window. The article Reka AI's omni-model Rho-1 handles text, images, video, and robot control in a single model appeared first on The Decoder .

Self-improving AI agents tend to memorize their test tasks, so their gains shrink or disappear on new ones. RRSI, a new method from Google researchers, reins in this effect and lifts scores on unseen benchmarks by up to 4.7 points while using about 30 percent fewer tokens than an unregularized version. The article Google researchers find a way to keep self-improving AI agents from memorizing their tests appeared first on The Decoder .

NASA and IBM have released the Lunar Foundation Model, one of the first open-source AI models for lunar science. Trained on nearly 2 million tile bundles, mostly from 17 years of Lunar Reconnaissance Orbiter data, it cuts the error in predicting polar ice deposits. The article NASA and IBM's open source lunar model turns 17 years of orbiter data into a foundation for lunar science appeared first on The Decoder .