1/6 Long-horizon agentic tasks are breaking our mental models. More tokens, bigger models, and best-of-N only go so far. Orchestrated approach to test-time-compute scaling is what long-horizon tasks need. Here’s what we learned using SWE-bench as a test case. Read the blog for
MACHINE LEARNING
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PyTorch version preferred over TensorFlow edition
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The one to the right is the PyTorch edition of the one to the left (which uses TensorFlow). Definitely would prefer that one.
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Hinton: AI Can Tackle Math Autonomously as a Closed System
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Le père de l'IA, Geoffrey Hinton, affirme que les mathématiques sont un système fermé, ce qui permet aux IA de les aborder comme un jeu.
— VISION IA (@vision_ia) 7 janvier 2026
Elles peuvent se poser leurs propres problèmes, tester des démonstrations et apprendre de ce qui fonctionne, sans dépendre d’exemples humains.… pic.twitter.com/TCciauPV22Le père de l'IA, Geoffrey Hinton, affirme que les mathématiques sont un système fermé, ce qui permet aux IA de les aborder comme un jeu. Elles peuvent se poser leurs propres problèmes, tester des démonstrations et apprendre de ce qui fonctionne, sans dépendre d’exemples humains.
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Native Parallel Reasoner: LLMs Learn Multi-Path Problem Solving
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What if an AI could truly think in parallel, not just fake it? Researchers at BIGAI introduce Native Parallel Reasoner (NPR), a new framework that teaches LLMs to break down and solve problems across multiple "thought" paths simultaneously, learning by trial and error. The
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LFM2.5: Next Generation On-Device AI from Liquid AI
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Introducing LFM2.5: The Next Generation of On-Device AI | Liquid AI https://
buff.ly/6x7JmPE
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Simulation Work Requires Understanding True Performance Gap
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I still think most work should be done in simulation, but it is crucial to calibrate your understanding of the true gap!
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Evaluating RL Performance: Challenges in Benchmarking Improvements
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Thanks — I always half expect someone to pop up say “we just cloned this other repo and ran it for a 50% performance improvement”. With RL it is very hard to know what “good performance” is.
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Tesla FSD significantly outperforms human drivers
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Tesla FSD is way better at driving than you are.
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Why Chasing New Models and More Data Often Fails
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Why chasing new models and more data often fails in AI training. pic.twitter.com/5hqmEYXSWi
— Satya Mallick (@LearnOpenCV) 7 janvier 2026Why chasing new models and more data often fails in AI training.
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Company behind low power AI chips in coolest CES products
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Great #ces2026 Report #7: The company inside the coolest AI products at CES.
— Robert Scoble (@Scobleizer) 6 janvier 2026
Makes low power AI chips inside a wide range of consumer products. Watches, rings, AI glasses, medical devices, all use their chips because they are best at running neural networks at very low power.… pic.twitter.com/BhaqWesHB6Great #ces2026 Report #7: The company inside the coolest AI products at CES. Makes low power AI chips inside a wide range of consumer products. Watches, rings, AI glasses, medical devices, all use their chips because they are best at running neural networks at very low power.