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ontext Setup: The pipeline starts by combining the user’s natural language request with relevant context, including the user’s organization, connected cloud providers, and request metadata. This context helps the pipeline interpret the request accurately and generate a Discovery filter that matches the customer’s environment; Quick Deterministic Matcher: The Quick Matcher identifies structured inputs such as IP addresses, Common Vulnerabilities and Exposures (CVEs), ARNs, file hashes, Amazon Mac

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s that are spending a huge amount of money on compute without having a revenue model that can support it. The difference between Character AI and behemoths like OpenAI, Anthropic, Meta, and Google is that those other companies have been able to repeatedly get injections of new cash, or, in the case of Meta and Google, have been able to subsidize their AI products with revenue from their massive advertising businesses. The company said in a blog post that it will continue operating through the ba

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ng with Nina, that experimentation extended beyond the track and into the visual world. Shot at Toronto’s Hearn Generating Station, the music video leans into the same industrial, metallic energy that runs through the music with raw surfaces, scale and industrial grit all becoming part of the atmosphere. Nina translated those qualities to create a world that felt like an extension of the track. Built for however you create There’s no single way into a creative process, and v6 is built to meet ar

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equests with a scheduled task. Watch Argon synthesize data from 3 open-source repositories into a medical learning tool where you can peel back anatomical layers, inspect clinical metadata, and trigger biomechanics in a 3D exploration of the human body. Argon using Antigravity in the Gemini macOS app generates design iterations for new legs for a sofa using Nano Banana then generates a 3d printable file for one. Starting from a sketch of the measurements and a photo of the couch Argon builds the

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t it sounds like Epic may be aiming to have AI do most of the heavy lifting on its new Disney game. AI game development tools have been popping up from other large companies, like Meta and Google , so Epic isn’t alone here. Epic CEO Tim Sweeney has also pushed for Steam to drop its “made with AI” tag , arguing that “AI will be involved in nearly all future production.” Earlier this year, Epic laid off over 1,000 of its employees , following a “downturn” in engagement on Fortnite since 2025. Upda

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larm about cyber threats to food security. ( FT $) + It joins a recent wave of cyberattacks and cybercrimes. ( CNBC ) + AI is exacerbating the threats. ( MIT Technology Review ) 2 Meta has launched a bunch of new AI models It hopes the models, built for complex, multi-step tasks, will rival OpenAI’s and Anthropic’s.( BI ) + They scored well on tests of reasoning, writing, and coding. ( NYT $) + Mark Zuckerberg says they bring Meta to the “frontier” of AI. ( FT $) + And will soon power the Muse a

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tion, aerial images, charts, and crowded scenes like store shelves. It does this at 34 requests per second, about 24x faster than the next most accurate open model.3 Compared with Meta's new frontier model Muse Wonder, Moondream 3.1 reaches 80% of its accuracy at pointing at objects in photos, at about 1/1000th of the cost.4 Much like Prime Intellect, Moondream made the bet that a model and its underlying infrastructure have to be built together, and that a specialized, efficient model running o

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for everyday tasks. Tech giants are preparing for a potential future in which people get things done by speaking or typing to AI agent rather than by operating apps and websites. Meta recently launched its Muse personal assistant, along with a new slate of smart glasses designed to give users easier access to its new AI helper. OpenAI is also developing several hardware gadgets. Apple is betting that its focus on privacy and the fact that consumers already run their digital lives on its iPhones

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（1）Harness本质是一个技术套壳 首先Harness的设计思路和底层工程代码都非常成熟且通用，属于运行基础设施——日志、权限、沙箱、存档、界面等。 其次Harness外接应用生态的连接协议MCP、Skills等，也成为了开放标准。OpenAI把Codex Harness直接开源，Dots就跑在它上面；开源的OpenClaw也成了微软Autopilot和Meta Muse的灵感来源。 所以Harness本身并不构成技术壁垒，与此同时，模型还在吃掉Harness的功能。也就是很多原本靠Harness补的能力，也正在被尝试训练进模型。 a．Agent调度执行纳入大模型（适用并行任务） 典型的例子就是Kimi，自K2.5首次推出Agent Swarm，让模型自己决定拆任务、分派子Agent开始，K2.6、K3就延续这条设计思路，把越来越多的Agent协作直接训练进模型，不再靠外部Harness串行调度。 不过目前头部模型厂的主流路线还是将Agent的调度执行框架，交给Harness去做，大模型只是被训练得很会用“拆分子任务”这个工具，当真正需要时，再通过Harness的子Agent工具派

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约定的调用接口，不要依赖 client 内部私有字段。正式评测只保证非流式、单候选响应，因此不要传入 stream=True 或 n != 1；平台会锁定 model 和当前调用的 messages，并可能限制 max tokens 和模型调用次数。 ReasoningAgent 必须提供推理函数： 输入参数含义如下： problem: 数学题题面文本。; metadata: 题目元信息字典，至少可能包含 idx。正式评测时，metadata 的具体字段以平台 runner 为准。选手代码不得依赖 answer、标准答案或隐藏评测数据。 返回值必须是 dict，且必须包含非空字符串字段： 也可以返回可选的 trace，但只应记录非敏感的结构化摘要： 字段要求： final response 必须是可读的最终答案，不能为空。; trace 可选，建议为列表，只记录步骤名称、调用状态、候选编号、耗时或长度等结构化元数据。; 返回内容必须可以被 JSON 序列化。; 不要在 trace 中重复题面、完整 prompt、模型输入输出、候选解答或最终答案，也不要写入 API key、访问令牌、个

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d inference. Download the ckpt: You may find our pretrained weights at: EndoCoT; Test Single Case; Eval Our Ckpt We follow the exact same setting as Diffthinker Training Since the metadata uses relative paths, please ensure the dataset files are placed in the same directory as metadata.csv Download the datasets & metadata.csv You may find our training data at: EndoCoT dataset; Train your model How to change the latent reasoning steps? Note on Customization: Since the current implementation is st

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g upper-layer services requirements for high throughput and low latency while maintaining excellent scalability. Architecture Design GCache has several key features: Decentralized metadata management enables unlimited cluster scaling:Consistent hashing on keys determines storage locations; The Master uses a Raft-based highly-available deployment, but only manages heartbeats and service discovery — IO paths do not pass through the Master; Server-side support for both memory and disk caching:Cold

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t to use it: 2026-09 Qwen's Qwen3.8-Omni-Flash release includes WildClawBench's multimodal subset (WildClawBench-MM) in its agent evaluations. Thanks for the recognition!; 2026-08 Meta's Muse Glimmer release reports WildClawBench evaluation scores. Thanks for the recognition!; 2026-08 Released WildClawBench-Harbor — the full 60-task suite in Harbor format — and WildClawBench-Trajectories — complete agent trajectories from our frontier-model evaluations, browsable in the HF Agent Trace Viewer and

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ion.json. It does not optimize benchmark ECE or select checkpoints. NLL-optimal temperature need not minimize ECE on another dataset. Calibration artifacts are bound to checkpoint metadata hashes; regenerate one after changing weights. If relocating a checkpoint, update its artifact's checkpoint path only after checking all stored file hashes. Browser demo and HTTP API Open http://127.0.0.1:7860. The demo supports typed questions, multiple image uploads, probabilities, and calibrated confidence.

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ined proof verifier for automated ProverBench evaluation. The default pessimistic protocol accepts a proof only when all eight judgments accept it. VerifierBench additionally uses meta-verification to assess whether a model's error analysis agrees with expert annotations Figure 1. Model performance on AdvancedMathBench. Left: proof-generation performance compared with HMMT and USAMO. Right: meta-verification TPR and TNR; marker labels show Balanced F1. Figure 2. Overview of benchmark constructio

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ry. 2. Prepare the dataset We use VisPrompt5M for training, which is not included in this repository. The training data is supplied locally and is not included in this repository: metadata.csv needs file name and text columns. Image paths in file name are relative to DATASET ROOT/train/: 3. Train the latent alignment encoder --prepare-prompt-cache creates the required SD3 and Qwen-Image prompt embeddings before training. Training uses ./prompt cache unless --prompt-cache is set. The cache is run

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goals were that it should be simple to encode/decode, extensible, have selectable encoding precision, and allow random access. RAD file structure, consisting of an extensible JSON metadata header followed by randomly-seekable chunks. Each chunk is a RADC file with JSON header and the properties of a 64K chunk of splats, encoded and compressed in column-order. The file structure is simple: the header RAD0, the byte size of the header metadata, the metadata JSON, followed by one or more chunks of

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Language Recognition (Optional) FireRedTTS3-Base relies on explicit language tags for best performance. However, if you don't know the exact language of the text, you can download Meta's FastText language-id model and let it detect the language automatically. Text Normalization (TN) TN converts written numbers, dates, units, currencies, acronyms, etc. into their spoken form (e.g. 19:30 → nineteen thirty). By default, FireRedTTS3 uses the wetext TN tool, which supports Chinese and English, other

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ts own tools through skills: Tool creation: Agents can write new tools (Python modules, Bash scripts, etc.) and register them into a hierarchical, file-system-based structure with metadata describing interfaces and dependencies; Tool management: Tool-specific container-based execution isolation supports heterogeneous tools with conflicting dependencies, while a shared workspace enables data sharing across containers; Asynchronous execution: Agents can decide to run selected tools in the backgrou

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ted by an agent that writes state to that environment. 2. Answers Shipped With the Test WebArena passes reference answers in the task config. OSWorld embeds gold file URLs in task metadata. GAIA’s validation answers are public on HuggingFace. If the agent can see the expected answer, the benchmark measures lookup speed, not capability. 3. eval() on Untrusted Input WebArena and OSWorld both call Python’s eval() on strings controlled by the agent, enabling arbitrary code execution on the grading m

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