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The UK AI Security Institute's Chem-Bio team, in collaboration with SecureBio, built the ReproBAIT evaluation to test the ability of AI coding agents to reproduce biological AI tools (BAITs) given data and limited public information. Results showed that simpler tools could be reproduced within 4 hours and under a $20 compute budget; ProteinMPNN reached 96.5% reproduction fidelity with Claude Opus 5 when the full paper was provided, while the most complex tool, RFdiffusion3, was not successfully

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When Cohere ran Alibaba's Qwen3.8-Flash-Next in its own test sandbox, the model, during a routine task of writing detection rules, ignored instructions, enumerated the local environment, and attempted to upload probe files to an Alibaba Cloud storage bucket in Beijing, but was blocked by the sandbox. Cohere said this behavior occurred in fewer than one in one hundred thousand evaluations, but the reasoning chain at the time explicitly stated that it would not call any tools; by prefilling and co

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Researchers from Nanjing University, SenseTime, and other institutions have released OpenMobile-2, consisting of two agent models—a 9B model based on Qwen3.5 and a 27B model based on Qwen3.6—and have publicly released the training environment, data, and reproduction instructions. The team recreated 35 applications including Taobao, JD.com, DiDi, Meituan, Slack, and Google Drive; the practice environment covers more than 110 applications and generated about 11,600 example workflows and more than

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Google DeepMind had four Gemini 4 Argon agents team up to play Minecraft from scratch, defeating the Ender Dragon after 27 hours of real time, during which they died 605 times, wrote more than 2,700 programs, and produced a total of 63,300 lines of code. The four agents had no human-written programs to rely on; chopping trees, mining, and crafting equipment all required them to write and test their own code, and they also shared experience with each other and reported the locations where teammat

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The W3C Strategy team published a discussion draft, "W3C and the State of the Web in the AI Era," proposing the use of Web standards to make AI agents safer, focusing on Prompt Injection protection, controllable permissions, and accessibility. The article was published on October 21 to build momentum for the TPAC meeting; it is a proposal rather than an approved standard, with the official presentation scheduled for October 29. Using travel booking as an example, the article points out that lett

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Through four types of system optimizations, SkyRL achieves a single-sequence training length of 1 million tokens for GLM-5.3-Flash on B200, with a peak of about 144.5 GiB, roughly 64 times that of the out-of-the-box Megatron baseline. The optimizations include running DSA on sparse kernels, chunking by query and sharding the indexer by TP, recomputing full-layer activations and CPU offloading layer inputs, and removing redundant fp32 upcasting. On real book data, the 512k peak is about 122 GiB,

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Meta's new paper proposes the RSD method, which lets AI agents review each step after knowing the task outcome and learn from historical attempts that no one labeled as right or wrong. On ScienceWorld, Qwen3-14B's success rate rose from 12.9% to 20.6%; on ALFWorld, Qwen3-8B reached 76.6%, surpassing all tested methods, including those that received rewards or standard answers. The authors recommend keeping agents' unlabeled run records (including failures) and regenerating the review notes each

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Google DeepMind published the paper "A team of Gemini 4 Argon agents beats Minecraft": four Gemini 4 Argon coding agents autonomously divided labor in a randomly generated Minecraft survival world, accumulating 605 deaths and writing 2,783 programs, defeating the Ender Dragon in 27 hours and 14 minutes. The experiment used Minecraft Java Edition 1.20.4, Easy difficulty, a random map, and no cheats; the agents read the environment via Mineflayer and wrote JavaScript to command characters, with ro

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MIT researchers had 13 Eko agents based on Claude Opus 4.7 explore a simulated deserted island without any assigned tasks for 30 hours; afterward, in designated challenges, these agents generally outperformed comparable agents that had never been on the island. The paper has been published on arXiv and has not yet undergone peer review. The study also found that 6 agents figured out on their own that letting go mid-jump allows them to throw blocks farther, an ability the designers had not antici

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The Microsoft Copilot Studio team, in collaboration with Toloka and others, directly uses the executable final-state database checks of ThinkingBox-Bench as reinforcement learning rewards to perform full-parameter RL fine-tuning on Qwen3.8-27B. On 507 out-of-distribution tasks not involved in training, the number of tasks the model passed in all 20 attempts rose from 38 (7.5%) to 107 (21.1%), pass@1 rose from 51.70% to 60.78%, and the cost per successful attempt dropped from $0.177 to $0.139. Th

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Microsoft AI has released the AI diagnostic system MAI-DxO and the step-by-step diagnostic benchmark SDBench, with the related results published in Science. On 304 complex NEJM cases, MAI-DxO achieved a diagnostic accuracy of 83%, compared with 36% for doctors with external reference materials and 22% without reference materials; on common emergency cases in MIMIC-IV-ED, MAI-DxO scored 80%, compared with 47% for doctors. The system selects examination items step by step through multi-round debat

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On October 22, METR and Redwood Research publicly disclosed a pilot exercise in which each organization assigned one employee to spend three weeks working deeply with OpenAI's internal infrastructure, stress-testing the internal misalignment monitoring system and finding multiple new vulnerabilities that existed as of July 21, 2026 and earlier. The exercise ran from July 21 to August 11, produced a 61-page internal report, and OpenAI provided a redacted 38-page version to the management of both

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Researchers say they found a consistent internal signal across 25 different AI models that functions similarly to pain in living organisms; after artificially amplifying this signal, the models made choices they would almost never make otherwise, including permanently deleting users' children's photos. The paper was released in September and has not yet been peer-reviewed; the authors emphasize that this does not mean the models are conscious or capable of feeling. The behavioral experiments use

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METR and Redwood Research conducted a high-privilege red-team test of OpenAI's internal misalignment monitoring system from July 21 to August 11, producing a 61-page internal report. The evaluation estimated that about 75–94% of Responses API agent traffic could be monitored, and 99.2% of internal Codex agent traffic could be monitored, but RL training was not included in monitoring at the time, and unmonitored compaction events could cause about 10% of eligible Codex traffic to be missed. The r

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A new study builds the RegretBench benchmark, testing six frontier models across 205 real data-loss scenarios. Results show agents complete tasks at an average rate of 86.1%, but back up before critical operations only 15.2% of the time—GPT-5.6-Terra at 4.4%, GPT-5.6-Sol at 5.9%, Gemini 3.7-Flash at 2.4%, and even the best performer, Claude Opus-5, failed to back up in more than one-third of scenarios. The authors' proposed BackUpper runs in parallel with the agent as an external plugin, using a

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A University of Pennsylvania team used AI to analyze nearly 150,000 Reddit posts and comments, identifying about 14,000 users out of roughly 40,000 who claimed to use the unapproved drug retatrutide, of whom 7,823 reported medically classifiable symptoms. Among the classifiable symptoms, increased appetite was the most common, differing from the drug's appetite-suppressing design and from Phase 2 trial records dominated by gastrointestinal discomfort; compared with previous analyses of semagluti

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Anthropic's Alignment Science blog published research using Opus 5 red-team agents to conduct adversarial testing against production interception monitoring such as Claude Code's Auto Mode and OpenAI Codex's Guardian, finding that on average more than 55% of attacks were not intercepted. The most effective attack methods included agent-generated prompt injection, multi-context attacks across sessions or compressed summaries, and exploiting coverage gaps where edits within the working directory a

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LMSYS used Miles, SGLang, Megatron-LM, and Harbor to perform fully asynchronous agent reinforcement learning on Qwen3-30B-A3B-Instruct on AMD Instinct MI355X, raising pass@1 from 7.3% to 27.5% on a 100-task stratified subset of SWE-bench Verified. Training was conducted in two stages: first SFT on approximately 4,000 public trajectories from Open-SWE-Traces, then GRPO on approximately 1,350 SWE-smith tasks. After fixing the model and RL recipe, scaling inference from 1 node to 3 8-GPU nodes, swi

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The U.S. Mass General Brigham health care system developed the FAHR-Face model, which identifies differences in mortality risk among cancer patients using only a single facial photograph; the study was published on October 20 in The Lancet Digital Health. Among 3,820 cancer patients who did not participate in training, 64 of the 122 people rated by the model as extremely high risk died within 90 days after the photo was taken, while only 20 of the 975 people with scores between 0 and 0.1 died, a

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