DTInnovate

🔑 The key idea: reuse the pretrained 3D RoPE. Instead of learning where actions live, we place each action element in the video model's own positional space and rotate it exactly like the video tokens. Action–video relations then ride on relations learned from large-scale video pretraining. That's why UniAction follows actions precisely after training on just 10K clips per action type. 🧵 4/10

We're excited to introduce AW-0: Agentic World Action Models! 🤖 (0/8) The first World Action Model that plans in code, predicts the future, and controls the robot—all in one generative model. AW-0 jointly generates: 🧠 Code - explicit skill calls, object arguments, and control flow 🎥 Future video - predictive visual dynamics 🦾 Robot actions - continuous motor control All three are generated together through joint denoising, letting semantic plans directly shape both predicted futures and robot behavior, with: 🤝 Hybrid execution — generated programs can also run directly through perception and control APIs ⚡ Just 40 ms of additional inference latency over the base WAM on an RTX 5090 📚 Pretrained on ~3,000 hours of robot + human demonstrations One model for planning, prediction, and execution. 🌐 Project: agentic-wam.github.io/ 📄 Paper: arxiv.org/abs/2610.30136v1 💻 Code: github.com/ZhuoyangLiu2005/a… 🤗 Model: huggingface.co/miniFranka/ag… 🧵 How it works—and what we found ↓

SCOOP: Major U.S. banks are detecting unauthorized third-party AI agents inside customer accounts, including some attempting to take actions that appear to be independent of user instructions, sources told Axios. The findings have alarmed banking executives, who are racing to restrict access to their systems as AI moves at a velocity faster than financial institutions can respond, multiple industry and bank executives told Axios. Several banks are now preparing for worst-case and even “day after” scenarios, including how to rapidly secure or restore customer accounts if things go wrong, the people said. Several bank executives told Axios they're struggling to distinguish between human users and AI agents. One executive said banks also struggle to determine whether agents are authorized by customers or potentially operating without their permission, more akin to rogue agent behavior. Read my latest for Axios here: axios.com/2026/10/22/ai-agen…

Is 10²⁵ FLOPs Still the Frontier? A new Anthropic-written safety bill is circulating on Capitol Hill. Some of its ideas could be reasonable if limited to true frontier companies. As written, however, it will eventually sweep in most of Silicon Valley, damaging the startup ecosystem. The bill defines the “frontier” as any model trained on 10²⁵ FLOPs — the EU standard from 2023. Biden’s executive order, California’s SB 53, and New York’s RAISE Act used 10²⁶ (ten times more). 10²⁷ is where the frontier actually is today (e.g., GPT-6 Astra). Astra was reportedly trained on 100,000+ Grace Blackwell GPUs. A 10²⁵ model can be trained on as few as several hundred GB300 chips in three months. That is not frontier in 2026. The definition is also cumulative: it adds FLOPs from fine-tuning, reinforcement learning, or other material modifications. In practice, that captures almost all meaningful post-training — work that is becoming commonplace in the enterprise. The enterprise trend is toward thousands of smaller custom models built for specific workloads. These pose no frontier risk. Treating them as frontier systems would impose large compliance costs across corporate America for no safety gain. Dario has claimed that Anthropic designs rules to disadvantage frontier companies like his own while advantaging smaller competitors. This definition does the opposite. A stale 2023 cutoff favors incumbents who already clear the bar and can absorb the costs. It hobbles the open model ecosystem that threatens Anthropic’s pricing power. The frontier keeps moving. Any definition meant to track risk should move with it. Anthropic should clarify if they really believe 10²⁵ FLOPs represents the frontier.