https://x.com/i/article/2104259869402599424

In my latest PhD paper, we declare WAR on sensor-maxxing. For the first time, push-resilient humanoid walking, just with joint encoders! No IMU, no F/T sensors. 🥁 Introducing Blind Dexterity 🧵 👇 w/ @OKaidanov @liu_puze @Jan_R_Peters at @DFKI @ias_tudarmstadt

To address long-horizon robotic manipulation tasks, we propose X-Planner, an embodied long-horizon task planner: It decomposes high-level natural language instructions into event-level subtasks, and can also output continuous implicit Chain-of-Thought (CoT). It directly interfaces with downstream VLA or WAM models to operate robots. GitHub: https://github.com/X-Square-Robot/Xplanner Checkpoint: https://huggingface.co/x-square-robot/X-Planner-9B-0916 Benchmark: https://huggingface.co/datasets/x-square-robot/xplanner-benchmark

Claude has discovered a previously unknown enzyme system hidden in the DNA of bacteriophages. Beside the enzyme’s gene sits a long array of repeating DNA—a structure that looks somewhat similar to CRISPR. We don’t yet understand what this system does, but only a handful of known systems share its features, and all of them are able to cut, copy, and paste DNA. Historically, the discovery of such programmable systems has helped revolutionize medicine. CRISPR, for instance, is now the foundation of genetic medicines. But it will take much more work to learn what this system does, and whether it can be put to similar use. Read more: https://www.anthropic.com/news/claude-discovers-novel-enzyme-system

Introducing VC-Attention: fast and accurate low-bit attention without retraining. On MiniMax-H3, VC-Attention speeds up attention by 1.6× on B200 and 1.5× on B300 over FlashAttention-4, with better fidelity than SageAttention2. It also works with existing sparse attention methods. Two key innovations: • V-Smooth reduces value quantization error. • ExpCast-FP8 speeds up softmax. Nunchux Attention, our proprietary extension, pushes the speedup to 1.9× on B200 and 1.8× on B300. Blog: http://www.nunchux.ai/blog/attention-is-the-video-bottleneck Technical Report: http://arxiv.org/pdf/2609.15810 Joint work by researchers at MIT, CMU, UC Berkeley, Stanford, and NVIDIA.