AI Coding Live News

Hugging Face, BitRobot, the Singapore Embodied AI Centre, NUS and others launched RoboColosseum on November 4, a distributed real-robot evaluation framework covering four embodiments: SO-101, Franka, Bimanual-YAM and the Unitree G1 humanoid. The first study ran 5,699 blinded pairwise comparisons at 124 sites across 5 VLA policies and 3 frontier models. Claude Opus 5.5 and GPT-6 Astra beat their VLA counterparts on average task progress by 12.1 and 11.0 points, while Grok 4.7 trailed by 7.5. All rollouts are open on Hugging Face, and the project pledges 10,000 free evaluation hours to academic labs.

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CellType, a YC W26 startup, released BioSimBench 1.0 on November 4. It uses real lab experiments to test whether language models can predict which genes go up or down after a cell is hit by a drug, a gene knockdown or an immune signal. Among 12 frontier models, Gemini 4 Argon ranks first with an overall score of 0.184, ahead of Claude Sonnet 5.5 (0.160) and GPT-6 Astra (0.159), while two lab replicates agree at 0.462. A 27B model CellType trained in its BioSimGym RL environments beats the best frontier model on all four prediction tracks.

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Robotics company LiberAI announced Liber-0 on its official X account and technical blog. It is a World Action Model that jointly predicts future frames and actions, pretrained on more than 2.2 million hours of human experience and anchored by precise hand-motion data from its own LiberSense Glove. On 60 tasks proposed by GPT-6 across 600 trials, Liber-0 reached 36.0% overall success without robot demonstrations collected for those tasks, but only 10.5% on challenging multi-step tasks. No open weights or API have been announced.

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Independent evaluator Artificial Analysis announced on X on November 4 that its six Capability Indexes (Finance & Accounting, Strategy & Ops, Legal, Healthcare & Medical, Engineering, Economics) are now v2.0, aligned with Intelligence Index v5.0 evaluations and weighting Non-Hallucination and Agentic Tool Use more heavily. Across the indexes, HLE becomes HLE-Diamond, AA-Omniscience moves to v1.1 and GDPval-AA is removed; Engineering gains a new Science capability. Per Artificial Analysis, Claude Opus 5.5 (max) leads all six, with GPT-6 Astra (max) second in Finance & Accounting, Legal and Engineering. Among open-weights models, Kimi K3.1 Pro (weights not yet released) leads all six at #5 to #8.

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On November 3, OpenAI published "Accelerating engineering for the physical world," describing how its models help with mechanical design, electrical design and simulation. Two employees used GPT-6 Astra to design a device for talking to Codex (enclosure, PCB, firmware and software) in under a week, and a contract manufacturer built a small prototype batch in under a month, though manufacturing readiness still took several rounds of human review. Token usage across OpenAI hardware teams has grown 23-fold since February. On BenchCAD 2.0, a newer internal model averaged 0.875 versus 0.594 for GPT-6 Astra at its highest reasoning setting (OpenAI-reported). OpenAI is also expanding its work with Chip Ganassi Racing and announced a new research collaboration with Mazda.

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Flower Labs, the company behind the Flower federated learning framework, released Endeavor 1.1 on November 3, its third model this year after the UK sovereign 7B model Lizzy in April and Endeavor 1.0 in September. The update adds image input, an optional EU-only GPU inference tier, and Flower-managed, private-cloud or on-premises deployment, and promises readable thinking traces plus ownership of outputs that customers may use to train their own models. Vendor-reported scores are GPQA 94.4, HumanEval 99.9, AIME 2026 99.9 and MMMU-Pro 81.1, but the comparison uses GPT-5.6 Sol and Claude Fable 5 rather than either lab's newest models, and access is request-only for now.

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On November 2, MIT CSAIL PhD student Rikiya Takehi posted "Weight-Space Programming" on X: a team (Takehi, Jayden Teoh, Alex Zhang, Omar Khattab) had GPT-6 Astra read and edit neural network weights directly, with no gradients or training. Their blog says Astra changed only 131,072 weights (0.016%) of Qwen3.5-0.8B, fixed its thinking loops and raised MATH-500 from 7.9% to 48.1%. The post drew about 24K likes and 1.77M views, and Ethan Mollick quoted it saying it is a big deal pending replication. All results are self-reported by the authors and not yet independently reproduced.

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Hangzhou-based AI-for-science company Deep Principle released Hydrogen-1.0 on November 2, its first general-purpose scientific model. It post-trains Zhipu's GLM-5.3 with SFT and RL on chemistry, materials and life-science data plus its own lab records and discovery trajectories, and ships about 753B parameters of BF16 weights on Hugging Face under the MIT license. By the vendor's own evaluation it scores 38.3% on FrontierScience-Research versus 33.3% for GPT-6 Astra; none of the results have been independently reproduced yet.

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Speaking at the ADIPEC energy conference in Abu Dhabi on November 2, OpenAI CEO Sam Altman said the September run in which 10,000 agents collaborated for about four days used "the model that is going to be released soon, the next GPT," stressing that the agents had no hierarchy and were fully decentralized. It is the first public statement tying the internal model behind OpenAI's September Navier-Stokes proof to its next flagship, though OpenAI has given no name, release date or price.

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On November 2 OpenAI published LifeVal, an evaluation of whether AI can actually get everyday tasks done for ordinary people, such as finding childcare, accessing government services, budgeting, ordering groceries and adapting recipes. The task list draws on the American Time Use Survey, the Multinational Time Use Study and a new demographically representative OpenAI survey of 3,995 people. OpenAI says it has used LifeVal for the past year to track and improve its models, and it lists several best recorded GPT-6 Astra task scores, many at 100%. All scores are vendor-reported and have not been independently reproduced.

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At 15:50 UTC on Nov 2, Tibo (@thsottiaux), whose X bio reads "Codex & ChatGPT @OpenAI", posted that OpenAI is working on expanding the family of frontier models that power dots, including GPT-6 Astra and GPT-6.1 Terra, with others to come. He said Terra brings speed and "a nice personality upgrade" and will help bring dots to more people. Dots are the always-on agents in ChatGPT with their own cloud computer, launched on GPT-6 Astra. This is a first-hand post by an OpenAI employee, not a formal company announcement; no timing or plan details have been published.

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On Nov 2, semiconductor and AI research firm SemiAnalysis released GDPx, a benchmark of agentic capability across 11 domains: chip design, cybersecurity, biology, finance, legal, quant trading, recursive self-improvement (RSI), hardware design, materials science, alignment and software engineering. Its 206 tasks were built by 14 specialist data partners, with SemiAnalysis doing final QA. On the official leaderboard Claude Opus 5.5 leads at 12.0%, followed by Gemini 4 Carbon at 10.9% and GPT-6 Astra at 9.6%; nearly every model scores 0 on finance and legal. SemiAnalysis says safety classifiers drag down Opus 5.5 and GPT-6 Astra, and plans a new version every 3–6 months. All scores are as reported by SemiAnalysis.

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Security firm Parameter published an exploit chain in which an attacker publishes a malicious conversation through ChatGPT's normal sharing feature; when a signed-in victim opens the /share/ link, the payload runs as chatgpt.com in their session, lists conversations, reads a private chat that was never shared, and sends data out. Parameter reported it through OpenAI's bug bounty on October 18; OpenAI raised it from P2 to P1, fixed it on October 26 and paid a $5,000 bounty. Parameter reports no real-world use, and users do not need to do anything.

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Over the past day, X has been debating whether GPT-6 Astra can work directly as a robot policy. Former OpenAI researcher Will Depue (whose bio lists Sora 1 and 2 and o3/4o post-training) called Astra beating specialized robotics models "deeply surprising", then described it as "the final death of specialized ML". Investor Freda Duan posted that "Astra IS robotics' GPT-3 moment", drawing about 84K views. Third-party evaluations point both ways: in a RoboDojo paper, Astra averages 22.48% success and ranks above all 40 public policies, while an EBench report puts it at 46.7%, third of nine models. This is a discussion of opinions, not an official OpenAI conclusion.

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