1. AutoGUIWorld Generates 79,266 Interaction Samples Without Running Real Software, Lifting a Desktop Agent’s Score From 33.0% to 40.8% GUI agents need interaction trajectories—records of actions, resulting screen changes, and task progress—to learn how software responds and how to complete multi-step workflows.

2. Study Finds LLM Post-Training Often Sacrifices Repeated-Sampling Coverage, Proposes a “Sharpening Tax” to Measure the Loss Post-training can make a large language model more accurate on its first attempt, but a new study finds that the improvement often comes with a less obvious cost: given many attempts, the model may

3. Early-Layer 65,537-Parameter LoRA Raises Qwen3-8B Accuracy on 24-Line Reference Chains From 15.5% to 99% A small adapter inserted early in Qwen3-8B sharply improved its ability to follow long chains of references without producing a visible chain of thought.

In Brief

• OpenAI Rolls Out Dot Agent for Computer-Based Work OpenAI’s Dot agent can operate cloud and local computer apps, accept voice instructions, and perform tasks such as editing media or updating websites. The initial rollout targets top-tier subscribers, while hands-on testing found that security checks frequently required human intervention.

• OpenAI Safety Report Lead Resigns and Criticizes Company Culture David Robinson, who said he led safety reports for major OpenAI launches, resigned after three and a half years and argued that the company’s culture is “broken.” OpenAI said it is strengthening security, third-party evaluations, responsible-task training, and real-time monitoring.

• White House Rebrands AI as “Super Intelligence” President Donald Trump signed an executive order adopting the term “super intelligence” and convened major technology executives to sign an AI safety pledge that he called “morally binding.” Participants reportedly included leaders from Meta, Amazon, xAI, and Anthropic.

• Meta Open-Sources Tools for Building Muse Hardware Meta released code and SDKs that let developers connect its Muse AI agent to devices built with components such as ESP32 boards and Raspberry Pis. The company also made a waitlist available for 5,000 Muse Home Link units designed to control connected household equipment through community-built skills.

• AWS Stops Using NDAs in Data-Center Approval Talks AWS CEO Matt Garman said Amazon no longer uses nondisclosure agreements with government agencies involved in its data-center projects. The change comes amid local opposition and more than 100 proposed US data-center moratoriums, according to Garman.

• Stability AI Rebuilds Around Licensed Music Tools Stability AI is repositioning itself as an AI toolmaker for music professionals under investor Sean Parker and CEO Prem Akkaraju. The company raised $76 million from investors including Sony, Warner, and Universal, licensed their catalogs for training, and released three audio models plus music-editing software.

• Capcom Plans AI-Assisted Evolution of Its RE Engine Capcom programmer Satoshi Ishida outlined an incremental plan to integrate AI into development workflows and eventually turn the company’s RE Engine into an “AI-generation game engine.” Capcom has previously said it would use AI for production efficiency rather than AI-generated game assets.

• Circuit Breaker Labs Tests Chatbots for Psychological Harm Circuit Breaker Labs uses simulated users with different ages, cultures, languages, slang, and speech patterns to test whether AI systems respond safely across long conversations. The five-person startup has a working product for high-risk applications including AI coaching, journaling, and mental-health support, but has not named its customers.

• Microsoft Expands Nature-Based Data-Center Projects Microsoft plans to bring its “biomimicry” program—using native vegetation, habitat restoration, and landscape design around data centers—to more than 20 sites in the US and Germany. The company says it will apply the approach to all new US projects and a growing number elsewhere.

• GraphForge Builds Verifiable Training Workspaces from Real Files GraphForge creates agent-training tasks and evaluation rubrics from evidence graphs grounded in real workspace files. Fine-tuning Qwen3.6-27B on 2,169 generated trajectories improved reported results on GDPVal, Workspace-Bench-Lite, and SpreadsheetBench II.

• Distillation Study Questions the Default Preference for On-Policy Data A controlled study across Llama 3 and Qwen 2.5 models found that rollout policy was not consistently the main driver of distillation performance. Token-level KL direction more clearly affected performance and output coverage, while learning rate governed forgetting and update sparsity.

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