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enyi Zhuang, Jinjie Gu; Don’t Just Fine-tune the Agent, Tune the Environment arxiv, 2025. paper Siyuan Lu, Zechuan Wang, Hongxuan Zhang, Qintong Wu, Leilei Gan, Chenyi Zhuang, etc Meta Learning Profile-Aware Maneuvering: A Dynamic Multi-Agent System for Robust GAIA Problem Solving by AWorld. arxiv, 2025. paper, code Zhitian Xie, Qintong Wu, Chengyue Yu, Chenyi Zhuang, Jinjie Gu; Recon-Act: A Self-Evolving Multi-Agent Browser-Use System via Web Reconnaissance, Tool Generation, and Task Execution.

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adapter. Responsibilities Start, wait for, inspect, measure, and delete sandboxes; Prepare local, OCI, Nydus, and S3-backed rootfs and mounts; Extract and inject OCI image startup metadata for in-sandbox runtimes; Allocate cgroups and pooled TAP or ephemeral veth endpoints, and configure iptables or eBPF for NAT; Discover, validate, and lease scheduler-selected accelerator devices Architecture API / CLI The public protobuf contract is api/runtime/v1/sandbox-api.proto. Runtime isolation, host req

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leaderboard sprints to surface emerging research directions 🤝 Contributing We welcome issues & PRs! Please check each project’s CONTRIBUTING.md and adhere to its license terms. 📄 Citation If you use any component of the ecosystem, please cite the corresponding paper listed in that project’s README. 🛡️ License This meta‑repository is released under Apache‑2.0. Individual projects may apply different licenses—see their respective READMEs. Maintained by the FlagEval team · Last updated: 2025‑04‑23

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peline 🤗 Model Zoo 🎲 General Visual Reasoning Tasks Then, your local directory should be like: Note: If you want to implement training process for our three main tasks, only three Meta-Json files -- 'A1-Spatial-Transformation-train-60k-cot.json', 'A2-Structure-Perception-train-4k5-cot.json' and 'A3-Visual-Counting-train-35k-cot.json' -- are needed respectively. Then, all the training-used json files can be constructed by these three Meta-Json files above. At this step, you should write the simpl

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L, and LeRobot formats to meet diverse training needs. Dataset ingest: author dataset yaml files (tasks, device models, scenes, actions, etc.), which are validated to produce info metadata for ingestion; Format conversion: write conversion configs, align pose files and check frame counts, then run the official conversion to produce standard LeRobot datasets (including parquet, mp4, and meta); Tech overview: More updates coming soon. DataForge DataForge is a toolkit for embodied data filtering an

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ion toolkit for the [RoboCOIN Dataset], this project is built upon the LeRobot repository. It maintains full compatibility with LeRobot’s data format while adding support for rich metadata—including subtasks, scene descriptions, and motion descriptions. RoboCOIN provides an end-to-end pipeline for dataset discovery, download, and standardized loading, along with model deployment capabilities across multiple robotic platforms. Key Features: Dataset Management: Seamless retrieval, downloading, and

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ith our provided data processing scripts. Step 2. Process Your Own Dataset Here, we use dataset/example raw data as an example. Step 3. Fine-Tune GRM with Your Own Dataset Add the meta-info of your own dataset to train/qwenvl/data/ init .py Modify the path of training script train/scripts/finetune grm.sh Launch the training script Before you launch training, make sure that you have installed the packages below: Then, run the script below: 📑 Citation If you find our work helpful, feel free to cit

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pendent, we’ve continued to invest in and refine our provenance tools. For general availability, our provenance safety tooling for our First-party (1P) products will include: C2PA metadata on all assets, providing verifiable origin through an industry standard; Visible moving watermark on videos downloaded from sora.com or the Sora app; Internal detection tools to help assess whether a certain video or audio was created by our products We recognize that there is not a single solution to provenan

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istry workflows, evaluating multimodal chemical structure understanding; structure-activity relationship (SAR); prediction of drug potency, toxicity, and absorption, distribution, metabolism, excretion (ADME); multiparameter lead-optimization decision-making, and retrosynthesis. Table 1 3.2 Genomics and quantitative biology To evaluate GPT-Rosalind-5.5 on genomics understanding and quantitative biology, we designed an agentic evaluation on long horizon, end-to-end genomics analysis tasks. This e

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e interpreted alongside remaining failures, evaluation awareness findings, and monitoring limitations; September 9, 2026: Naming and substance updates to the section on Verbalized Metagaming and Oversight Gaming. Verbalized Metagaming is when a model reasons in its Chain of Thought about how it will be graded, rewarded, or monitored, rather than only engaging the intended task. We revised this section to more prominently emphasize that we are currently measuring metagaming as verbalized in chain

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rol (Figure 15) and mostly consistent across the nine CoT instruction types (Figure 16). Qualitatively, while GPT-5.5 frequently thought about the instruction at length, including meta-discussion of whether to obey constraints on the analysis channel expressed in the prompt, GPT-5.6 Sol more often either follows the constraint directly or fails in a much terser way. We are investigating the causes of increased CoT controllability of GPT-5.6 Sol. Update on June 27th: we updated some plots in the

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rol (Figure 15) and mostly consistent across the nine CoT instruction types (Figure 16). Qualitatively, while GPT-5.5 frequently thought about the instruction at length, including meta-discussion of whether to obey constraints on the analysis channel expressed in the prompt, GPT-5.6 Sol more often either follows the constraint directly or fails in a much terser way. We are investigating the causes of increased CoT controllability of GPT-5.6 Sol. Metagaming is when a model reasons about how it wi

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Provenance We have continued to prioritize enhancing our provenance tools. For ChatGPT Images 2.5, our expanded provenance safety tooling includes: A continued commitment to C2PA metadata, an industry-standard framework that enables automated disclosure of provenance information, through the C2PA Conformance Program; To make provenance more resilient, we are taking a multi-layered approach and incorporating watermarking through Google DeepMind’s SynthID⁠ through ChatGPT⁠, Codex, and the OpenAI

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Provenance We have continued to prioritize enhancing our provenance tools. For ChatGPT Images 2.0, our expanded provenance safety tooling includes: A continued commitment to C2PA metadata, an industry-standard framework that enables automated disclosure of provenance information, through the C2PA Conformance Program; Integrating an imperceptible, robust, and content-specific watermark alongside internal tooling to help assess whether a certain image was created by our products We recognize that

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DALL·E and Sora, we have continued to prioritize enhancing our provenance tools. For general availability of 4o image generation, our provenance safety tooling will include: C2PA metadata on all assets (verifiable origin, industry standard); Internal tooling to help assess whether a certain image is created by our products We recognize that there is no single solution to provenance, but are committed to improving the provenance ecosystem, continuing to collaborate on this issue across industry

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rs express thoughts of harming themselves, such as directing them to professional helplines.) At least one AI company is thinking about monetizing its users’ mortality. Last year, Meta was granted a patent for an AI system that could keep a user account active well after death. In the words of the patent, “the language model may be used for simulating the user when the user is absent from the social networking system, for example, when the user takes a long break or if the user is deceased”. It’

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such as scheduling, splitting bills, gifting, and sharing content. “Now, we will use AI technology to build the next 10 years of connections,” she said. Kanana Space is similar to Meta's AI agent, Muse. Swiping the KakaoTalk screen with two fingers brings up Ryan, Kakao's AI character. Users can ask Ryan to send messages, manage schedules, search the web, make reservations, and process payments. They can also engage in everyday conversations with the assistant. Kakao plans to develop Kanana Spac

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gers. “What can we reasonably survive without breaking the bank?” Temperature is one of the easiest problems to handle. Materials science has come a long way, and there are myriad metals, ceramics, plastics, compounds, and alloys that can resist wild temperature swings. Radiators keep things cool; heaters keep batteries from dying prematurely. Astronauts survive just fine too, with their fancy spacesuits—but that technology has proved itself only during brief excursions. It’s not yet clear how l

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rguing that their platforms are safe for teens, but quite a few parents aren’t convinced. They, along with a growing number of regulators, have increasingly accused companies like Meta of designing products that are addictive to younger users. AI firm OpenAI, thrust into the public consciousness much more recently than Meta, has had to defend against dozens of lawsuits alleging chatbot ChatGPT lacks safety guardrails. Now, workers at the world’s leading AI firms have warned that their employers

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窗口数值不校验：字符串数字直接流入 LimitInfo → 客户端 NaN% 进度条 | 逐字段 toNumber 强转 | | 5 | neo 部分失败不可见：subscription 挂、wallet 成功 → 行显示"正常"但窗口消失 | 失败分支 console.warn 记录 | | 6 | 测试硬编码绝对路径：公开仓库换机即挂 | import.meta.url 相对路径；real-mount 无本机 DSH 时自动跳过 | | 7 | Host 核心逻辑零测试 | 新增 test/monitor.mjs（去重/排序/warn 阈值/error+stale 行/并发去重/hidden+disabled） | | 8 | 死代码：limitLine() 及只被它用的 windowUnit 文案、generic-http 的 void sub、永不执行的 noneFetcher.fetch | 全部删除 | | 9 | remote-client.ts 行号漂移 | 与源码真实行号同步 | | 10 | snapshot() 只拷贝 balances 不拷 limits | 两

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