1. Court Upholds Anthropic Blacklisting as Company Sends AI to a Psychiatrist A panel of Trump-appointed judges denied Anthropic's emergency motion to stay the federal government's technology blacklisting order this week.

2. Linux Kernel Maintainers Write the First Official Rules for AI-Assisted Code A new file in the Linux kernel's Documentation directory runs just 60 lines. Its core rule: AI agents must not sign off on code. Only humans can.

3. OpenAI Rotates macOS Signing Certificates After Axios Supply Chain Attack A compromised npm package forced OpenAI to rotate its macOS code signing certificates — the same week the company is marketing HIPAA-compliant ChatGPT to hospitals.

In Brief

• AI2 Releases MolmoWeb, a Fully Open Web-Browsing Agent With Training Data AI2 published MolmoWeb, an autonomous web agent built entirely on open models and open training data. The release includes MolmoWebMix, a large-scale dataset of web navigation demonstrations, along with full training recipes. Prior web agents with comparable performance relied on proprietary models and undisclosed data.

• DMax Speeds Up Diffusion Language Models With Aggressive Parallel Decoding Researchers introduced DMax, a decoding method for diffusion-based language models that replaces binary mask-to-token transitions with progressive self-refinement. A new training strategy called On-Policy Uniform Training reduces error accumulation during parallel token generation. The approach enables higher decoding parallelism without degrading output quality.

• OpenVLThinkerV2 Tackles Cross-Task Reward Variance in Multimodal RL Training A new open-source multimodal reasoning model addresses two bottlenecks in applying group relative policy optimization across diverse visual tasks: wildly different reward distributions between task types, and the tension between fine-grained perception and multi-step reasoning. The model handles multiple visual domains within a single generalist architecture.

• Researchers Propose "Meta-Cognitive" Fix for AI Agents That Over-Use Tools A paper identifies a failure pattern in multimodal agents: reflexive tool invocation even when the answer is visible in the image. The proposed method teaches models to judge when internal knowledge suffices and when external tool calls are actually needed. The fix reduced unnecessary tool calls while maintaining accuracy on queries that genuinely require external lookups.

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