🌘 Kimi-K2.7-Code, our latest coding model, is now released and open-sourced! 🔷 Improved coding & agent performance over K2.6: 21.8% on Kimi Code Bench v2, 11.0% on Program Bench, and 31.5% on MLS Bench Lite. 🔷 Reasoning efficiency: Less overthinking, with 30% lower reasoning-token usage compared to K2.6. 🔷 Long-horizon coding: Improved instruction following, higher end-to-end coding task success rates. ⚡️ 6x High-Speed Mode coming soon! 🔌 Available today via Kimi API and Kimi Code. 🔗 Kimi Code: kimi.com/code 🔗 API: platform.moonshot.ai

The GPT-4o moment for humanoids might finally be here. And yeah, sorry in advance for the rickroll. OMG runs a Unitree G1 off one brain that natively takes language, audio, and human motion. That "Never Gonna Give You Up" dance isn't the flex; one model fluent in every modality is. Here's the shift. Most humanoid policies are one-trick. Train per skill, hand-tune the rewards, repeat. The rest just replay a fixed motion you feed them. OMG instead works like a biological motor system. A "brain" that turns intent into future motion. A "cerebellum" that reactively runs it on the robot. The brain is one diffusion model. Language, audio, a reference pose, or any blend goes in. A robot-ready G1 trajectory comes out, live. New inputs attach through zero-init adapters. They start at zero, so the pretrained motion prior carries over intact instead of getting scrambled. That's how they bolted on VR teleoperation as a brand-new modality, reusing the same brain. And it behaves like a foundation model. Bigger backbone, cleaner motion. Finetune on 1% of new data, nearly match a model trained from scratch on 100%. Compose language audio at inference for combos never seen in training. 1000 hours of motion, all retargeted into one G1 body. One brain that scales. We keep racing to build stronger low-level controllers. OMG's bet is that the real bottleneck is the brain mapping human intent to motion. Congrats to @KnightNemo_, @li_yitang, @ShaotingZ38103 and whole team!

Flexible Locomotion Learning with Diffusion Model Predictive Control Excited to share that our paper has been accepted to #ICRA2026 @ieee_ras_icra! A diffusion-planning framework for flexible real-world quadruped locomotion. Instead of learning a fixed RL policy or relying on hand-crafted dynamics for MPC, we train a diffusion trajectory prior that jointly predicts future states and actions. Key Ideas: Diffusion-MPC: A diffusion planner unlocks flexible locomotion through test-time reward and constraint adaptation Interactive reward-weighted finetuning enables continual behavior refinement from online environment feedback Real-world deployment on Unitree Go2 with efficient and adaptive planning The same planner can adapt at test time to height changes, posture/joint constraints, balancing under external disturbances, energy-aware locomotion, and zero-shot outdoor walking on grass and slopes. 🌐Homepage: flexible-diffusion-mpc.githu… 📖Paper: arxiv.org/abs/2510.04234 🔗Code: github.com/hrh6666/Flexible-… This work is by @RunhanH, Haldun Balim, @hankyang94 , and @du_yilun. #ICRA2026 #Robotics #LeggedRobots #RobotLearning #DiffusionModels #MPC #MachineLearning

In the last couple of months, we have witnessed significant advances in Industry-scale World Models. Yet, for the broader community, the gap between reading about these models and deploying them remains disappointingly wide. Today we're releasing Nano World Models: a minimalist, batteries-included repo for advancing world model science. 🧵 (1/9)