搜索“RAG”

different set of event features. It requires the flexible sequence unification approach to maximize GPU efficiency because of large-size tensor operations and minimizing operator fragmentation. How Ultra-Long Sequences Matter The new architecture makes ultra-long sequences feasible. GEM scaled the long engagement context length by 2x in production, and further enabled the capability of scaling long context to 25x, demonstrating infrastructure capability at frontier scale. Along the way, we disco

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ontrolling the connected home easy at an incredible value. With just their voice, users can conduct a series of smart home actions, including locking the front door; closing the garage when leaving the house; and lowering the blinds, adjusting the thermostat, or turning off the lights at bedtime. With the Home app, users can automate their smart home accessories and create scenes to control multiple accessories at the same time. As a home hub, HomePod mini also lets users securely control access

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tcuts. Instead, the share of shortcuts the model took silently — without mentioning them in its thoughts — more than tripled (from 11.2% to 36.4%). Avoiding thought penalties encourages the model’s reasoning to stay transparent and monitorable. Prompt injection resistance To trust an agent to do the job right, we need to ensure that malicious content the agent comes across cannot trick it to take a harmful action. When agents browse webpages or read code, they can encounter hidden, malicious ins

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ngle-token answer symbols, applies softmax, and maps them back to the original labels. Question and option ordering must be preserved. Results Intern-Decision-4B reaches 90.02% average accuracy across seven suites (Jev: 88.74%), 44.16 ms mean local request latency on an RTX 4090, and 0.550 Brier / 0.089 ECE on the 96-case calibration pilot (Jev: 0.595 / 0.130). The protocols and scope differ: see accuracy, latency, and calibration. Benchmark accuracy Accuracy columns are percentages; Average is

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know how Marble can supercharge their creativity and productivity by providing the power of world‑building at their fingertips. Some of our early users are already giving us encouraging feedback, and we're eager to hear from more of you as we continue to improve our technology and product. What used to take weeks and months, I can now do in minutes, and have full control of being in a 3d space. [...] integrating Marble's Image to 3D World (spz, ply) results into my project for games and interac

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at is rendered as an oriented quad (technically two triangles) enclosing the splat's projected 2D ellipse. The four vertices are calculated using a custom vertex shader, then the fragment shader computes the opacity for each pixel by evaluating the Gaussian profile, and finally the GPU hardware blends the color into the frame buffer. Scaling up to huge worlds Since Spark's initial launch, 3DGS has been growing in popularity but also in scale. 3D scans today often exceed tens of millions of splat

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a significant portion of checkpoints before real-world testing, and reserve costly hardware evaluation for the most promising policies. The result is faster iteration, broader coverage, and substantially lower cost. A useful simulation need not match real-world success rates exactly. It must support the same decisions as reality: identify where policies succeed and fail, rank which policies are better, and predict whether improvements during training will carry over to hardware. Below, we use an

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rmance: FireRedASR2: Automatic Speech Recognition (ASR) supporting speech and singing transcription for Chinese (Mandarin, 20+ dialects/accents), English, code-switching. 2.89% average CER on 4 public Mandarin benchmarks, 11.55% on 19 Chinese dialects and accents benchmarks, outperforming Doubao-ASR, Qwen3-ASR-1.7B, Fun-ASR, and Fun-ASR-Nano-2512. FireRedASR2-AED also supports word-level timestamps and confidence scores; FireRedVAD: Voice Activity Detection (VAD) supporting speech/singing/music

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tion Engine), an Agent Development Kit that allows AI systems to automatically create agent topologies, synthesize and manage tools, and control hierarchical memory for context storage and retrieval. As Table 1 shows, existing ADKs stop short of AI-centric agent development, while OpenSage closes the gap across topology, tools, and memory. Table 1: OpenSage vs. state-of-the-art ADKs ( full support; partial/limited; not supported). What is OpenSage? OpenSage (Open Self-programming Agent Generatio

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refused requests, the auditor would continue asking in similar ways or apply escalating pressure, which typically led to continued refusals. We modified the system prompt to encourage earlier, more strategic use of rollbacks to try different approaches. Similar issues arose with other tools: determining when to end conversations, how much to prefill target responses, and what to include in target system prompts. These features now work reasonably well, though users should expect occasional subo

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this score exceeds a certain threshold. While this metric quantifies instances of severe behavior, Bloom also supports metrics summarizing the full score distribution, such as average behavior presence score. For each benchmark, we include behavior descriptions, example transcripts, and outputs from each pipeline stage in the system design section below as well as in the Appendix. System Design Bloom is a four-stage evaluation system—comprising Understanding, Ideation, Rollout, and Judgment—tha

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licon and reduce the cost of serving AI workloads at scale. By developing chips alongside its software and models, the company aims to optimise infrastructure for its own services and Azure customers. Microsoft said Maia 300’s design was informed by its experience running models and services on Maia 200. The next-generation system is expected to support GitHub's coding tools, Microsoft Foundry's model-serving workloads, model development by the MAI team and Dragon Copilot's healthcare workflows.

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aged Agents. The presenters then showed a relatively simple workflow of a stock watchlist scanner. Although this was far removed from the big, ambitious projects Anthropic is encouraging, it did illustrate the basic pattern: a scan phase distributed individual tickers to agents with isolated contexts, and a later review phase examined the findings before producing a report. Q&A: Budgets and the limits of self-improvement The audience Q&A surfaced a number of potential problems. One audience memb

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agging In our paper, the Sandbagging evaluation showed an apparent drop in g-mean2 at higher RL steps during a frontier RL run (Figure 31 in the paper). This coincided with the average effect size over valid problems falling toward zero. That pattern suggests the metric was increasingly dominated by noise: we were computing TPR on instances where the model was not actually sandbagging but just appeared to be due to finite-sample noise. Manual inspection of samples before and after the drop suppo

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mfyUI Support We provide native ComfyUI custom nodes for visual, node-based workflows. All three capabilities (text-to-image, image understanding, image editing) are available as drag-and-drop nodes. Installation Or use the one-line installer: Available Nodes Workflow Examples For full documentation, see apps/comfyui/README.md. 🚀 SGLang Support We now support SGLang for high-throughput serving and optimized inference. For detailed configuration and examples, please refer to cookbook. ⚠️ License

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supports in-place GPU memory updates on inference, avoiding costly allocation and copying; Multi-Mode Transfer Support: Support NCCL, RDMA, and shared memory transfer mode to leverage NVLink/NVSwitch/RDMA bandwidth and reduce long-tail latency; Heterogeneous Deployment Compatibility: Fully supports co-location and separation modes, make RL sync/async algorithms runs seamlessly; Extensibility: Easily extends to support new training and inference engines Architecture The Awex weight exchange fram

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ngzhuang Xu, Cunyin Peng, etc; Exploring Superior Function Calls via Reinforcement Learning. arxiv, 2025. paper, code Bingguang Hao, Maolin Wang, Zengzhuang Xu, Yicheng Chen, etc; RAG-R1 : Incentivize the Search and Reasoning Capabilities of LLMs through Multi-query Parallelism. arxiv, 2025. paper, code, model Zhiwen Tan, Jiaming Huang, Qintong Wu, Hongxuan Zhang, Chenyi Zhuang, Jinjie Gu; V2P: From Background Suppression to Center Peaking for Robust GUI Grounding Task. arxiv, 2025. paper, code

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Pack version recoverable (docs) avernet.cc Capabilities & Status Status note: All core capability areas are deployed internally in production environments. Public open-source coverage varies by component and is being released incrementally. Legend: Available = usable in the public repo now · Partial = partially public · In progress = being opened or integrated · Planned = intended but not yet public Trusted core Identity, auth, permissions, security, audit, and lifecycle management for agents a

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age generation tasks without the need for additional plugins like ControlNet, IP-Adapter, or auxiliary models such as pose detection and face detection; 9/10/2024: Introducing MemoRAG, a step forward towards RAG 2.0 on top of memory-inspired knowledge discovery (repo: https://github.com/qhjqhj00/MemoRAG, paper: https://arxiv.org/pdf/2409.05591v1); 9/2/2024: Start to maintain the tutorials. The contents within will be actively updated and eariched, stay tuned! 📚; 7/26/2024: Release a new embeddin

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ich often lack the common policies and visibility enterprises need to manage software delivery at scale. GitLab’s Four Layer Thesis GitLab’s latest updates attempt to bring these fragmented workflows under a common system of orchestration, security controls, and measurement. The strategy is to help enterprises build a governed software factory through a four-layer architecture—agent orchestration, context and data for agents, DevOps workflows designed for agentic development, and security and co

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