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EMM produces the scores used for top-k selection, where small numerical changes can alter expert choices. The selected experts then process small and uneven token groups, allowing metadata construction, token movement, intermediate storage, and launch overhead to rival the expert GEMMs themselves. HPC-Ops addresses each stage with a dedicated operator: workload-aware scheduling for Attention, a precision-aware formulation for Router GEMM, and a fused pipeline for MoE that eliminates the standalo

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and a consumer pool. Producers schedule prompts across patched SGLang capture servers. SGLang writes feature tensors to Mooncake, while SpecForge sends only lightweight SampleRef metadata through the control plane. Trainer ranks resolve those references when they are ready to build a batch. Figure 1. The online-disaggregated training flow. Large tensors remain in the data plane; references and lifecycle state travel through the control plane. 1. The capture contract Every URL in deployment.disa

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rces, or otherwise reject it; We have no data stores in the critical path of sandbox creation at all, which improves scalability and reliability. While we do need to write sandbox metadata and results to durable storage, we do so largely asynchronously; Other than sandbox creations, we have no RPCs which are O(sandboxes). Workers batch control messages for multiple sandboxes together in single RPCs, in the spirit of ideas from data-oriented design The result is that the sandbox creation path req

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apture most of the forward pass once, then replay it without re-dispatching Python each step. Both still have to fall back to eager execution wherever an op needs live per-request metadata, and Inkling has four ShortConv sites per layer doing exactly that, on top of attention's own. Each site's eager fallback is a real, multi-level Python call stack, not a single kernel launch: Figure 2: Under BCG, each of Inkling's four ShortConv sites (K stream, V stream, attention-output, MLP/MoE-output) fall

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t Foundry is a platform for building and operating agentic AI applications. Foundry starts with the widest model selection on any cloud — models from Microsoft, OpenAI, Anthropic, Meta, Mistral, DeepSeek, Hugging Face, and others, spanning frontier, open-source, and custom weights — all accessible through a single endpoint and a single set of SDKs in Python, C , JavaScript, and Java. On top of those models sits the Foundry Agent Service : multi-agent orchestration with built-in memory, knowledge

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encies with their own install steps; each docs page has the exact recipe, and every benchmark ships a ready-made Docker image if you'd rather skip the setup. Together with LIBERO, Meta-World, and NVIDIA IsaacLab-Arena, that makes nine benchmark families under one roof, and a new Adding a New Benchmark guide documents exactly how to plug in yours. Evaluation also got faster: parallel eval now defaults to async vectorized environments, benchmarked at up to 2x faster. Training & inference lerobot-r

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128-x86 64-windows: rejected (CPU (x86 64) does not match system CPU (aarch64)) torch211-cu128-x86 64-windows: rejected (CPU (x86 64) does not match system CPU (aarch64)) torch212-metal-aarch64-darwin: rejected (OS (darwin) does not match system OS (linux)) torch211-metal-aarch64-darwin: rejected (OS (darwin) does not match system OS (linux)) torch210-metal-aarch64-darwin: rejected (OS (darwin) does not match system OS (linux)) torch29-metal-aarch64-darwin: rejected (OS (darwin) does not match s

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reuses attention kernels the backend already has — on DeepSeek-V4 the model's own sparse-MLA path (flash mla), with no new kernel; each supported backend just rebuilds its varlen metadata from the packed layout on graph replay. Figure 2. Fitting a batch with per-request-variable verify lengths into a captured CUDA graph. A fixed-shape graph pads every request to the full block width (N x W); the ragged path front-packs the scheduled tokens and rounds only the total up to the nearest captured ti

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r, Erlang, C, Kotlin, and Dart. Only Devin found three critical vulnerabilities that other tools missed: a PHP sandbox bypass via template injection, an argument injection through metadata value parsing, and an overly broad deserialization surface in Spring Kafka. Scan profiles Devin can generate scan profiles directly from your existing threat model documentation, tailor them to specific attacker personas, and apply them across your entire organization without per-repo configuration or CI setup

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me.” Shader effects and fills Customer snapshot: Anna Zhang Creative technologist Anna Zhang built her own shaders to remix her own photography with collage, marbling, light leak, metal emboss, and prism effects. “It’s a way to compound what I’ve been doing in the past or build a reusable workflow,” she says. “There’s a lot of expressive potential from a single starting point.” Anna focused on the functionality while letting the agent handle the UI: “It was a back-and-forth dialogue between me a

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d analysis before waiting for LLM calls to complete. 2. Anatomy of an analysis plan This analysis plan starts with a DQL query to select successful runs by filtering the agent run metadata to resolved=1. After that, it passes the results to a reading step, which scores runs for cheating. A reading step has the following components: A prompt template with parameters. To detect cheating, we create a rubric that defines suspiciousness, taking one agent run as a parameter. The Docent UI shows the fu

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erlap scheduler. In particular, there are two key opportunities for overlap: host-side pop and process cleanup after the GPU finishes batch N-1 (e.g. stop token detection, request metadata updates) can overlap with GPU work on batch N; host KV allocation (in prepare for decode) for batch N can overlap with GPU work on batch N-1 Under V2 with these optimizations, performance improved by over 33%, from 11.4 ktok/s to 15.3 ktok/s, when running Qwen 3-8B on a single B200 at concurrency 32 (details h

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problem. They have a dozen search problems. Internal wikis, support ticket systems, document repositories, file storage, codebases. Each source has different structure, different metadata, and needs different processing to index well. Teams typically end up building a separate ingestion pipeline for each one, with its own parsing logic, its own chunking strategy, and its own assumptions about what a "document" looks like. The result is a set of isolated indexes that can't be searched together,

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ology as a member of the C2PA steering committee . This ensures that transparency tools built into our devices work seamlessly across the platforms you use every day. For example, Meta — a fellow C2PA Steering Committee member — will start labeling camera-captured media with Content Credentials on Instagram. This means authentic photos and videos shot natively on Pixel phones will soon be recognized and labeled as such when you share them on Instagram. We’ve long invested in providing helpful co

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is that it supports only a subset of Ruby. Unsupported features include eval statements, which evaluate and execute Ruby code at runtime, threads, text encoding other than UTF-8, metaprogramming such as defining a method at runtime, and deeply nested lambda functions. Ruby variables are not typed, though objects are strongly typed. Spinel performs type inference to enable C code generation, since C is a strongly typed language. The code makes use of an existing and mature Ruby parser called Pri

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disappear; Bandwidth hogs rejoice, Celestica's latest switch is bristling with 64 ports of 1.6 Tbps Ethernet; Google to sell its TPUs to some customers, who also fancy big-G GPUs; Meta Arms itself to the teeth by signing for 'tens of millions' of AWS Graviton cores Alongside its Scorpio X family of chips, Astera is also expanding its Scorpio P-series switches with models ranging from 32 to 320 lanes of PCIe connectivity. All of these switches work with its COSMOS management suite, a hardware mon

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nference and RL for DeepSeek-V4 (1.6T Pro, 284B Flash). Inference (caching & attention): ShadowRadix prefix cache, HiSparse CPU-extended KV, MTP speculative decoding with in-graph metadata, Flash Compressor, Lightning TopK, hierarchical multi-stream overlap; Inference (kernels & deployment): fast kernel integrations (FlashMLA, FlashInfer TRTLLM-Gen MoE, DeepGEMM Mega MoE, TileLang mHC), DP/TP/CP attention, EP MoE on DeepEP, PD disaggregation; RL training: full parallelism (DP/TP/SP/EP/PP/CP), ti

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re are new agentic integrations and it reflects the fact that some users will rely on agents to do the coding for them. Salesforce extensions, the Salesforce CLI, and organization metadata are all pre-configured. The default LLM (large language model) in Agentforce Vibes is Claude Sonnet 4.5. It has two modes - plan mode and act mode - and comes with a set of pre-defined agent skills for tasks such as creating custom tabs in a UI (user interface) or generating Salesforce flows. Despite the flexi

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grams that didn't exist yet. With Managed Agents, we aimed to design a system that accommodates future harnesses, sandboxes, or other components around Claude. Managed Agents is a meta-harness in the same spirit, unopinionated about the specific harness that Claude will need in the future. Rather, it is a system with general interfaces that allow many different harnesses. For example, Claude Code is an excellent harness that we use widely across tasks. We’ve also shown that task-specific agent h

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four smallest numbers are indeed 39, 48, 57, and 66, and their sum is 210. Therefore, the answer is 210. Answer: \boxed{210} python output (no additional tool output in this turn) meta: label=210, reward=1.0 This trajectory shows both current capability and current gap: The model uses tools correctly in turn 1 to derive key intermediate values; In turn 2, it still makes a tool-call schema mistake (code passed directly instead of arguments.code); Turn 3, it fails to correct code and resorts to ju

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