Marin's 535b is now >80% through pretraining! Here’s loss vs. our scaling-ladder forecasts. The sharp drop comes with the 4K→16K context switch, so we're kind of cheating the forecast. (This was planned.) We'll re-eval at 4k too. https://t.co/YPREnmluJY https://t.co/hU9lhL63yx

GitHub has released Copilot Fusion in GitHub Copilot, which orchestrates between Anthropic and OpenAI models. It scores 2 points below Opus 5.5 in Claude Code at half the Cost per Task in the Coding Agent Index GitHub describes Copilot Fusion as a runtime orchestration system, which selects and coordinates models to optimize performance, cost, and latency for each task. Copilot Fusion chooses between a number of models from Anthropic and OpenAI, including Claude Opus 5.5, Claude Sonnet 5.5, GPT-6 Astra, and GPT-6 Sol. We benchmarked its max setting in GitHub Copilot CLI in the Artificial Analysis Coding Agent Index v2.0. Token costs are charged based on the underlying models used to complete a request. Key takeaways: ➤ Cost-efficient for near-frontier performance: Copilot Fusion in GitHub Copilot costs $5.59 per task, versus $11.42 for Claude Code with Opus 5.5 (max). Devin Fusion with Opus 5.5 (max) + GPT-6 Luna (high) scores 62 at $8.00 per task. ➤ Slight lead in Terminal-Bench 4.0: The Coding Agent Index v2.0 weights its three constituent benchmarks equally. Copilot Fusion in GitHub Copilot leads Terminal-Bench 4.0 with a score of 71. It ranks third on FrontierCode in the chart at 51, behind Claude Code with Opus 5.5 (max) at 56 and Devin Fusion with Opus 5.5 (max) + GPT-6 Luna (high) at 54. Its SWE-Atlas-QnA-AA score lags at 57, between GPT-6 Astra in Codex and Gemini 4 Argon in Antigravity CLI. ➤ Half the token use of Opus 5.5 in Claude Code: GitHub Copilot Fusion used 7.2M tokens per task in the Coding Agent Index, approximately half of the tokens used by Claude Opus 5.5 in Claude Code, and 30% less than Devin Fusion with Opus 5.5 (max) + GPT-6 Luna (high).

https://t.co/TcaAfe4JAR Great way to try out MMLU and get a sense for just what, exactly, we are using to evaluate LLMs! I doubt folks would knife fight for a percent on this benchmark if its contents were realized more broadly. https://t.co/TMPlaKRAnf

NVIDIA just released MotionBricks on Hugging Face A real-time generative motion model for humanoid robots and characters, reaching 15,000 FPS with 2ms latency across 350k+ motion clips. https://t.co/IIxgL3ZQgL

Big day for the @PyTorch ecosystem at #PyTorchConf2026 🚀 We’re officially open-sourcing TorchTPU—enabling native, hardware-accelerated PyTorch execution across Cloud TPUs and custom infrastructure. Here’s everything we launched to supercharge your training & inference workloads 🧵👇 https://t.co/1X5A5bhG0z

Together Post-Training is live Turn open models into custom models you own, with SFT or RL. You define the data, rewards, environments and the training loop itself. We run the hard infrastructure underneath it, tuned for quality and speed.

Local AI 202: Get Massive Speed-ups with Speculative Decoding https://t.co/3ywucgYbrD

Can a JEPA world model provably avoid contraction and merging of latent states over long rollouts? Unconstrained autoregressive predictors may contract the latent distribution. Our Symplectic Hamiltonian JEPA (LeHami) replaces the predictor with an action-conditioned Hamiltonian flow over a latent state (q, p), integrated with an exactly symplectic leapfrog step. Volume is preserved at every rollout step, independently of the learned parameters or action sequence. With @randall_balestriero @ylecun Paper: https://t.co/JqJ7rrOXmF Website: https://t.co/I5pmtHM9VB Code: https://t.co/D9EIFGS87U 1/6... 🧵👇

friendly reminder: this is on Monday 🤝 → learn about speculative decoding to speed-up inference → if you're a beginner and not interested, just use https://t.co/eZJZOpPFkl that does this for you automatically 🦙 https://t.co/97svGez4R3

Built for long-horizon agents. Monitor agents against your own criteria. Search and investigate patterns at scale. Benchmark improvements and regressions before rollout. https://t.co/EIFfrfR72q