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Reusable skills give agents transferable procedural knowledge, making scalable acquisition essential for extending agents beyond prior experience. Existing methods face two limitations: trajectory-based synthesis requires interactions with specific environments, while document-derived skills may lack executable evidence and verification. Source code offers a complementary path: it requires no prior agent experience yet provides executable evidence for grounding abstractions. We present Code2Skill, a fully automated pipeline that transforms selected code units into implementation-anchored records of atomic operations, composite workflows, and recurring patterns, then verifies each record through source-body-blind reconstruction and source-aware comparison. Applied to 19,769 popular, actively maintained GitHub repositories, Code2Skill produces CodeSkillBank, a grounded bank of 1,006,822 accepted records with workflow, boundary, provenance, and source-evidence metadata. Across 72 protocol-matched evaluations covering nine model settings and eight benchmarks, models augmented with retrieved CodeSkillBank skills improve by 11.7% on average over matched baselines and outperform them in 57 cases. Under a unified downstream interface, Code2Skill also outperforms trajectory-derived skill banks on all seven shared benchmarks, showing that repository-derived skills can provide useful procedural knowledge before agents accumulate sufficient interaction experience. Skills synthesized from tested AI-generated code achieve a 93.50% pass rate, compared with 93.00% for human-written code, providing initial evidence that the pipeline can expand with the growing volume of AI-generated software. Overall, Code2Skill transforms procedural knowledge embedded in repositories into grounded, verifiable, and transferable agent skills.

Training capable coding agents via reinforcement learning (RL) requires diverse tasks with reliable verifiers. Open-source codebases offer a rich source of such tasks, while existing methods typically rely on development artifacts such as issues and commits, limiting the range of tasks that can be extracted. To better scale RL environments, we present CodeMidas, an agentic pipeline that turns implemented functionality in existing codebases into executable RL environments using source code as its only task-specific input. CodeMidas allocates agentic compute to every stage of environment construction: agents explore implemented functionality to formulate behavioral specifications, construct tests grounded in execution of the original code, and validate and filter candidate tasks through execution checks and repeated solution rollouts. The resulting dataset has 5,545 training tasks from 3,185 open-source codebases spanning 23 programming languages and 15 technical domains. Training MiMo-V2.5 on these tasks with GRPO improves performance on all five diverse benchmarks, covering issue repair (DeepSWE + 11.7%), whole-program construction (ProgramBench +17%), and terminal work (Terminal-Bench v2.1 +8.5%). Ablations show that increasing the number of high-quality training tasks improves performance. Trajectory analysis shows the RL-trained agent demonstrates better behaviors like increasing codebase exploration and more diverse self-verification. These results establish source code as a scalable foundation for constructing RL environments that improve coding agents across diverse software tasks.

Data agents aim to fulfill natural-language instructions over heterogeneous data, including tables, files, and databases. However, data agents face a challenging agent-data gap: heterogeneous data resides outside the agent, while the agent can access it (e.g., column names and file paths) only through generic tools. Existing approaches either let agents directly explore raw data sources or inject manually constructed semantic layers into prompts. However, neither scales well to large heterogeneous data sources nor adapts to different agent behaviors. In this paper, we introduce EvoOntology, a self-evolving ontology layer for data agents. EvoOntology encapsulates the ontology as an MCP server comprising a schema layer, a content layer, and a tool layer, enabling agents to actively query and interact with the ontology at runtime. To this end, we introduce a builder agent for autonomous ontology construction and a self-evolution loop that continuously refines the ontology through attribution-guided typed edits that are accepted only after a backbone-conditional paired evaluation. Experiments on three well-adopted data-agent benchmarks with four LLM backbones demonstrate that EvoOntology consistently outperforms strong baselines and existing semantic-layer approaches, effectively bridging the agent-data gap and enabling more effective interaction with heterogeneous data. Code: https://github.com/ruc-datalab/EvoOntology

Computer-use agents (CUAs) have advanced along two separate lines: graphical interaction and software development through code and the command line. Real digital work requires both, interleaved rather than stacked end to end. We study hybrid CUAs that autonomously decide when to explore an interface, implement software, and run and visually verify their artifacts. We introduce RecreationWorld, a five-platform framework built around recreation: given a running reference, an agent must discover its behavior and build a faithful implementation with no prescribed workflow. RecreationWorld provides reproducible environments on Ubuntu, macOS, Windows, Android, and Web, plus a unified harness with native GUI control and coding tools. The running reference serves as an oracle for hidden behavioral tests, providing execution-grounded rewards. We scale trajectory generation with high-quality open-source applications. Models trained on these trajectories improve across five out-of-distribution coding and hybrid computer-use benchmarks and more frequently verify their rendered outputs, providing evidence of transfer beyond recreation. For held-out evaluation, we introduce RecreationBench, comprising 250 diverse tasks across domains and platforms. Reference-grounded programmatic and visual assertions cover action-conditioned outcomes at multiple interaction depths; each is validated on the reference and by human reviewers before the suite is frozen for automatic scoring. GPT-6 Astra leads at 58.1% overall, but passes all programmatic tests on just 2.8% of tasks. Agents reproduce static interface structure more reliably than interactions and computed outputs, while generated applications remain smaller and more monolithic than their references. We release the benchmark, environments, and test suites.

We define OmniVChat (Omni Video Chat) as the task of native audio-visual dialogue between a user and an omni model. In OmniVChat, omni models directly and simultaneously receive audio and video from a user and return text. The user's query is embedded in the audio and video, without a separate text question, external captioning, or speech recognition. Direct audio-visual input reduces external latency and computation while preserving perceptual cues. However, research on OmniVChat faces two constraints: data availability and evaluation. Recordings of people using their own devices are scarce. Furthermore, a good reply often needs to account for the user's surroundings, facial expressions, and nearby objects, and such responses can be expressed in many different ways, making keyword matching unreliable for evaluating reply quality. Recent progress in agent systems and video generation makes generation for comprehension viable, which means using synthesized dialogues for training and evaluation. Therefore, we present OmniVChat-Studio, a multi-agent data engine for synthesizing single- and multi-turn audio-visual dialogues. We use synthesized dialogues to build OmniVChat-Bench, an evaluation benchmark that evaluates omni models' basic dialogue abilities across five ability categories. We also present OmniVChat-RL, a reinforcement learning reward design that jointly targets reply correctness, efficiency, and style in OmniVChat. Training Qwen3-Omni-Instruct with OmniVChat-RL on synthesized dialogues improves its performance on both OmniVChat-Bench and the human-recorded OmniVChat-Bench-Human. These gains validate the reward design and show transfer to real-world dialogues in training and evaluation.

Professional design requires any-color control: the ability to specify an object's target color with any 24-bit hex value for image generation and editing. Prior work has explored color generation, editing, and colorization, but often relies on dedicated color representations or specialized inference procedures. Advances in large language models offer a simpler starting point: even compact models can associate hex values with color semantics. We present Paint-Anything, which learns a shared hex-prompt interface for generation and editing through object-level color supervision. We develop a data pipeline that constructs Paint-500K from real images through object grounding, perceptual color labeling, and editing-pair synthesis. Since shadows make real-image labels only approximate colors, we complement this supervision with pure-color anchors whose pixels exactly match their paired hex values. These anchors are used only at high-noise timesteps, leaving low-noise training to natural images. We further introduce Any Color Benchmark (ACBench), comprising ACBench-T2I and ACBench-Edit, to measure object-level hex color fidelity across both tasks. On FLUX.2-4B, Paint-Anything improves ACBench-T2I and ACBench-Edit scores by 85.3% and 28.3%, respectively, relative to the base model, with ablations supporting the training recipe. It also achieves the highest average CompColor score among the compared methods.

Reference-to-video (R2V) generation is evolving toward increasingly general and versatile reference control, giving rise to the emerging paradigm of omni R2V generation. However, existing benchmarks fall short of these emerging capabilities: their test cases cover limited reference types and compositions, and their evaluation protocols largely assess holistic reference consistency, overlooking whether reference factors are properly preserved, disentangled, and routed. Meanwhile, the high cost of constructing omni R2V training data makes suitable training resources scarce. To address these gaps, we introduce OmniVBench and the Omni-R2V Dataset for evaluating and training omni R2V models. OmniVBench expands R2V evaluation across broader reference types, fine-grained control tasks, and richer reference compositions, covering 7 task families and 18 fine-grained tasks spanning content, motion, style, structure, narrative, and multi-reference settings. We introduce factor-grounded evaluation with 12,172 case-specific checklist items, assessing whether intended reference factors are faithfully preserved, correctly disentangled and bound to their targets, and properly realized according to the instruction. We further introduce the Omni-R2V Dataset, bringing industrial-grade training resources for diverse R2V tasks to the broader research community. Drawing primarily on a large-scale corpus of professional video footage, it comprises 340K processed training samples spanning diverse reference types and multi-reference compositions. We develop task-specific pipelines for reference-target pair construction, offering a practical and scalable recipe for omni R2V data construction. Extensive evaluation of advanced open- and closed-source R2V models reveals clear performance gaps across task families and evaluation dimensions on OmniVBench, highlighting remaining limitations of current R2V models.

Business intelligence (BI) is a cornerstone of enterprise decision-making and is widely used by enterprise users in software such as Power BI and Tableau. In traditional BI workflows, users need to prepare data by (1) identifying relevant tables, (2) performing data transformations, and (3) building join relationships, before they can (4) answer their business questions. These steps can be complex and time-consuming, making BI challenging. Given the strong capabilities of large language models (LLMs) in working with data, we study their ability to answer BI questions end-to-end, without requiring users to manually perform the tedious preparation steps. To do this, we harvest a large collection of real-world BI projects from public sources, and manually extract pairs of (questions, ground-truth answers) from real user dashboards. The resulting benchmark, BI-Bench, is the first benchmark to systematically study LLMs' ability on end-to-end BI. We find that even frontier LLMs perform poorly on BI-Bench, with less than 50% accuracy. To address their limitations, we design a tool-augmented BI-Agent that decomposes BI workflows into subtasks on structured data, such as search, join, and transform, and orchestrates specialized data management methods across BI stages. Furthermore, we develop a post-training framework that synthesizes training trajectories from real BI projects, enabling BI-Agent to be further post-trained using both supervised fine-tuning (SFT) and reinforcement learning (RL). BI-Agent achieves substantial accuracy gains of up to 40 percentage points with vanilla LLMs, and post-trained BI-Agent yields gains of up to 30 points. Our results highlight the importance of combining tool-augmented reasoning with domain-specific post-training in complex BI workflows, and point to promising directions for future research.

Recent Omni-Modal Generative Models (Omni-Models) have advanced content generation toward unified modeling of text, images, video, and audio. MiniMax-H3 exemplifies this transition by combining multimodal context understanding with joint audio-visual generation in a shared latent framework. Its unified architecture raises a fundamental question: Can multimodal alignment improve the model's world reasoning, and what new evaluation paradigms do omni-modal inputs enable? To investigate this question, this work introduces a comprehensive evaluation framework organized around four complementary dimensions of physical world reasoning. Unlike existing evaluation frameworks for video generation and world models, which are often constrained by limited input modalities and evaluation settings where prompts closely match the target video content, our evaluation is specifically designed to exploit the multimodal inputs of Omni-Model. We construct a diverse set of novel tasks that require models to integrate complementary information across modalities. Specifically, we consider four scenarios, including implicit prompts paired with multiple frames, audio-image, prefix-videos, and audio-video inputs. Every single modality provides only partial evidence about the underlying event, requiring the model to jointly reason over the complementary semantic cues to infer latent event states and future dynamics. Across 517 evaluation instances, MiniMax-H3 achieves an overall success rate of 41.97%. Video-based Decision Reasoning yields the highest success rate at 56.00%, while Audio-based Disambiguation Reasoning is the weakest, reaching only 27.40%. These results indicate that effective multimodal integration remains key to fully exploiting the benefits of diverse input modalities. The project is available at https://github.com/gulucaptain/MiniMax-H3-Reason.

We study length inflation in on-policy distillation (OPD), where student responses can become excessively long and even exhaust the generation budget. We identify termination-token mismatch between base students and post-trained teachers as an important source of this behavior. Across Qwen3, Llama, and Gemma, the two models can place their stopping probability on different EOS tokens, even when their declared stopping sets are identical. This mismatch can suppress the student's preferred termination action without reliably transferring the teacher-preferred alternative. We show that aligning the decoding stopping set alone is insufficient, while treating functionally equivalent EOS tokens as a shared semantic stopping action substantially mitigates mismatch-induced length inflation across all three model families. To further understand how termination behavior evolves over training, we study OPD across different K2-Horizon training stages. This stage-wise analysis shows that termination preferences can shift substantially during training, while also revealing a distinct length inflation late in the OPD run that persists beyond termination alignment. Together, these results identify termination mismatch as an important, but not exhaustive, source of OPD length dynamics. We release an implementation incorporating the proposed termination-handling corrections.

LLM assistants are widely used for daily social advice, yet evaluating their social reasoning in such consultation settings remains challenging since (i) it requires setups where the assistant learns about social situations from subjective user narratives, and (ii) social properties, such as others' intentions, typically lack verifiable ground truth. To address these challenges, we introduce Fuse, a multi-agent simulation framework for studying user-mediated social reasoning. In Fuse, a target agent with a hidden motive interacts with other agents including one representing the user, who then consults the evaluated assistant to infer the target's motive, providing verifiable ground truth by construction. Simulation faithfulness is validated through a human study with 24k annotations. We apply Fuse to 12 LLMs and demonstrate its analytical utility by systematically isolating key factors, showing that (i) user mediation compounds the inherent difficulty of social reasoning; (ii) LLMs exhibit systematic sensitivity to biased user framing; (iii) models can require more details than humans need to reach a correct prediction; and (iv) longer conversations do not always improve performance despite providing opportunities for clarifying questions. We open-source Fuse and a dataset with 21k examples.

Platform abuse campaigns conceal redirection instructions with emojis, homophones, character decomposition, and redundant symbols, then route users through disguised links to services associated with pornography, fraud, gambling, or illicit transactions. Existing benchmarks evaluate obfuscated text and risky webpages separately, obscuring how target recovery affects downstream evidence acquisition. We introduce RiskChainBench, pairing 3,600 synthetic token-text restoration inputs from 600 source sessions with 600 corresponding human-labeled local web environments. A model first restores the message, operational intent, and destination; the same underlying model then acts as a VLM-driven web agent that investigates the correctly associated website and produces a frozen, evidence-cited risk report without message-side semantics or domain-reputation cues. We score restoration and correct-routing web investigation separately and compose them offline by applying the frozen primary-entry prediction as a gate to the same Task 2 result. Human labels determine task correctness, while a fixed multimodal evidence judge assesses faithfulness, sufficiency, completeness, and consistency. Across ten models, Entry Top-1 ranges from 35.2% to 95.2% and web decision accuracy from 26.3% to 62.8%; the leading systems differ across entry recovery, full reconstruction, website decisions, and fine-grained typing. Execution failures account for 31.9% of web runs, whereas post-decision type errors account for only 0.9%, identifying stable exploration and risk judgment as the principal bottlenecks. We release the benchmark, protocol, and resettable local sandbox.

Maureen Farrell / New York Times : Sources: SoftBank's SB Energy has delayed its IPO, originally planned for this month, as investors question the company's sought-after valuation of $50B+ —  Several companies tied to the data center industry have delayed their initial public offerings amid the increasing public backlash to these energy guzzling facilities.

The Information : Source: before the Hugging Face incident, OpenAI was negotiating a legally binding deal with Anthropic for the companies to stress-test each other's models —  OpenAI is rethinking a range of safety strategies as it responds to fears from employees and others about the dangers its AI poses.

Maria Curi / Axios : Ahead of Sam Altman's UN address, OpenAI urges the US to lead an effort to develop global safety and security standards for building frontier systems —  OpenAI on Monday released international AI safety standards as world leaders, namely the U.S. and China, weigh how to mitigate risk.

Qwen : Alibaba releases Qwen-Image-2.1, a 7B open-weight model it says outperforms most closed-source models, with native transparency and up to ten reference images —  · QwenTeam丨Translations:.体中文  —  We are excited to open-source Qwen-Image-2.1 …

Josipa Majic Predin / Forbes : Vercel, Cloudflare, and others quickly add Jev, as it makes AI tool selection much faster and cheaper; TypeSafe: Jev matches GPT-5.6 and Sonnet 5 workflow evals —  Most of what an AI agent asks a frontier model to do is not writing.  It is choosing.  Which tool to call next, whether to retry, whether a command is safe to run.

Mark Gurman / Bloomberg : A closer look at the upcoming Siri AI-powered home hub, a key pillar of Apple's strategy for the home; sources: Apple started cutting Fitness+ staff —  Also: What happened with the iPhone Duo Pencil and layoffs at Fitness+.  —  Apple's new “intelligent personal hub” strategy is about much more than the iPhone.

Ann Cao / South China Morning Post : Alibaba's Damo Academy open sources RADAR, a medical vision-language model it says can read CT scans and identify ~150 abdominal conditions, including cancers —  Tested on nearly 40,000 real-world exams, the model outperformed most radiologists, according to a new study published in Science

María Paula Mijares Torres / Bloomberg : Trump says he will appoint an AI czar and form an “AI Force”, in a Truth Social post that rejects AI safety concerns as a “hoax” —  President Donald Trump said he would name an artificial intelligence czar as he continued to push tech companies to race ahead with development despite growing fears safety.

Axios : Sources: the USPTO and US Copyright Office were surprised by the DOJ's brief supporting OpenAI and Microsoft in their dispute with the New York Times —  The Department of Justice's statement of interest supporting OpenAI and Microsoft in the New York Times' copyright infringement lawsuit …

Wall Street Journal : Sources: Anthropic plans to stage its IPO in November, later than many investors expected; the decision was made before ex-Anthropic researcher Jacob Coxon left —  Investors had expected the Claude maker to debut in October; AI industry grappling with calls to slowdown pace of development

New York Times : Sources: Anthropic expects to generate $100B+ in annualized revenue this year, up from $65B as of July, as it moves ahead with its IPO amid the AI safety debate —  The company is expected to generate $100 billion in annualized revenue this year, even as chief executive Dario Amodei calls …

Lauren Feiner / The Verge : Virginia Gov. Abigail Spanberger signs an EO to slow down data center approvals, create an AI task force to evaluate workforce displacement, and more —  Gov. Abigail Spanberger (D) signed an executive order addressing data canters and AI risks. … Virginia Gov. Abigail Spanberger (D) …

Hacktron 安全团队组合利用 OpenAI 的 SSO（单点登录）配置错误以及其社区论坛使用的 Discourse 软件 libheif 软件包堆缓冲区溢出漏洞，成功控制了多名 OpenAI 员工的 ChatGPT 账户。利用这些账户安全研究人员能访问 OpenAI 内部代码库，以及其他关联服务。他们向 OpenAI 和 Discourse 报告了 bug，从 OpenAI 获得了 6500 美元的赏金。安全研究人员利用了 OpenAI 竞争对手 Anthropic 的 Claude AI 工具去辅助发现 bug，以及实现远程代码执行 RCE。他们一开始使用的是较旧的模型 Opus 4.8，之后使用了新发布的 Opus 5.5。整个 AI 辅助 bug 发现和辅助攻击消耗的 token 支出不到 3000 美元，AI 智能体花费了数天时间，而研究人员投入的人工时间仅仅数小时，凸显了 AI 时代黑客攻击成本的低廉。

Mozilla 发表《State of Open Source AI》报告，称美国科技公司的前沿 AI 模型性能仅领先中国公司最优秀的开放权重模型 4.4 个月。由于前沿模型价格更昂贵，很多公司都将开放权重模型用于处理日常工作，将前沿模型用于处理特定工作负荷。美国闭源前沿模型优势主要在于专家级专业工作、高强度检索以及长上下文。 配送公司 DoorDash 将月之暗面的 Kimi 模型用于处理日常工作，将 Anthropic 的闭源模型 Fable 用于更复杂的任务。前沿模型完成一项任务的成本经常达到了最先进开放权重模型的五倍。

美国空军部长 Troy Meink 周一在 Air and Space Forces Association 的《Air, Space & Cyber Conference》会议上首次公开宣传，美国已在轨道上部署了太空控制武器。他没有披露太空武器的任何细节，但披露其存在这一事实，凸显了五角大楼在轨道战思维上的重大转变——直到几年前，还没有任何美国军方高级官员愿意公开讨论这一话题。如今军方官员已开始公开讨论轨道作战、卫星防御性反制措施，以及一种旨在瘫痪敌方在轨卫星的新型陆基武器。Meink 也未说明这些太空武器是何时部署的。

安全工程领域有一种名为隐晦式安全（Security through obscurity）的设计方式，即只要网络和系统的架构以及任何漏洞或弱点保密或不为人知，它们就是安全的。但在 AI 辅助 bug 发现的时代，这种设计方式过时了。软件供应商和独立研究人员正利用 AI 智能体在各种产品和开源代码中搜寻漏洞——其中一些漏洞极其隐蔽且存在已久。这导致安全漏洞披露和补丁发布数量创下历史新高，同时也给项目维护者带来了巨大的积压事项。微软上周二释出例行安全更新，修复了 974 个 CVE 漏洞。趋势科技的 Dustin Child 指出，微软和 Adob​​e 所修复漏洞涉及的组件多年来基本无人问津，如 Telnet 客户端、Windows RNDIS、NFS Portmapper 和 Link Layer Topology Discovery。与此同时，攻击者也在利用 AI 对补丁进行逆向工程，在数小时内开发出相应的漏洞利用方法。在 AI 时代，攻击者无需成为某个领域的专家就能针对关键网络和设施发动破坏性网络攻击。

在 AI 辅助发现 bug 的时代，主要操作系统每个月修复的 bug 数都在飙升。微软周二释出了九月例行安全更新，总共修复了 974 个 bug，数量为史上最多，今年的安全补丁数已超过 2600。最新补丁包括了 2 个正被利用的提权 0day 漏洞 CVE-2026-81963 和 CVE-2026-85880，113 个 bug 被列为高危，438 个提权 bug，19 个安全功能绕过 bug，258 个远程代码执行 bug，173 个信息泄露 bug，56 个拒绝服务 bug，16 个欺骗 bug。比较严重的 bug 包括存在于 Windows Server 2012 以上版本 和 Windows 10 的 DNS 漏洞 CVE-2026-69730，未经身份验证的攻击者只需向受影响系统发送特制数据包即可利用该漏洞；另一个是 Windows Shell 程代码执行 bug CVE-2026-69829，威胁评分 9.8.10，极其容易利用。