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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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hmark rules require four test runs with standard deviation reported for any record claim. The reported record doesn't meet that bar. Without the selection method, Odyssey-3 Pro averaged 63.37 points across four runs. Both results appear on the official leaderboard but were submitted by Odyssey itself. On the WorldMark benchmark , Odyssey-3 ranks first in three of four categories based on its own evaluation : First-Person Stylized (77.2), Third-Person Real (79.0), and Third-Person Stylized (76.3)

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Fragmented data, disconnected tools, and unclear experimental records can get in the way of scientific work: the data may be in one place, the analysis in another, and the record of how a result was produced somewhere else. Following the evidence means keeping those pieces connected as you decide what to investigate next. That’s why we built Rosalind Workbench – to bring that work into one environment. It provides life science users with a central environment to leverage their favorite scientifi

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ore sign-in and an order change made afterward are part of the same visit. The customer doesn’t have to start over, and the company sees the whole journey instead of disconnected fragments; Getting the job done. The company decides which channels to offer, based on what works best for its customers:Its website: the agent browses the company’s regular pages, and the company applies the customer’s permissions on each one; Its APIs: the agent connects through interfaces built on standards such as O

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sible to guide the model via text prompts (for example, to specify a style), the canvas offers a 2D interface for expressing creative intent with familiar tools like painting and dragging. These actions correspond to manipulating the model's internal activations at specific spatial regions of the image being generated. Here are a few examples of the app in action: This application complements Goodfire's work across other model architectures and modalities, such as natural language reasoning mode

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ions you supply. It also returns per-option probabilities and a confidence field; score: rates input against ordered levels, indexed from 0. The score is a probability-weighted average of level indices OpenAI’s severity example makes the math concrete. Level probabilities of 0.1, 0.7 and 0.2 yield a score of 1.1. That value sits between ‘Workaround available’ and ‘Fully blocked’. When should you use Structured Outputs instead? OpenAI draws a clear line. Use Decisions for probabilities, choices o

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with these goals in mind: Highlight Workflows independence from llama index : You can use Workflows to write the orchestration logic of any Python and Typescript application; Encourage contributions and experimentation: Workflows can easily be adopted in environments where llama index wasn't previously used; Support a wider range of use cases, from agentic systems to task orchestration, with a limited set of dependencies What’s New in version 1.0 The v1.0 release marks the first standalone versi

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Follow us on 2 When we launched LlamaIndex as an open-source project, our focus was on standardizing the core primitives of RAG: chunking, embedding, indexing, and retrieval. For basic question-answering workloads, that foundational blueprint worked perfectly. But enterprise agents have completely outgrown it. Traditional RAG treats data access as a static, one-shot preprocessing step. It pulls a handful of context fragments, bundles them into a prompt window, and blindly hopes for the best. Aut

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amaIndex, Claude Code, Cursor, etc.) by loading extracted Parquet files and cell metadata into your agent's context What's Next During the beta period, we're focused on performance optimization, enhanced accuracy, additional output formats, and future API’s that build on the region and table extraction to provide more end-to-end experiences. We encourage users to try the API, provide feedback on extraction quality, and help us prioritize features for the full release! Let us know what you think:

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while dynamically allocating LLM inference traffic for maximum efficiency; Across a diverse set of realistic and challenging environments, adaptive experimentation reduced the average time to correctly identify the best LLM variants (prompts, models, etc.) by 37% compared to simple A/B testing Developing LLM applications involves numerous design decisions: which model to use, how to structure prompts, what temperature and sampling parameters to set, etc. While offline evaluations and synthetic

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rastructure providers validate their stacks before users encounter issues. Continuous Benchmarking: We will maintain a public leaderboard of vendor results. This transparency encourages vendors to prioritize accuracy. Testing Cost Estimation We completed full evaluation workflow validation on Two NVIDIA H20 8-GPU servers, with sequential execution taking approximately 15 hours. To improve evaluation efficiency, scripts have been optimized for long-running inference scenarios, including streaming

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ompt with high reference, the first token latency is cut from 13s to just 500ms. Lowering Costs Even without any optimization, historical data shows that users save over 50% on average. The service has no additional fees beyond the $0.014 per million tokens for cache hits, and storage usage for the cache is free. Security Concerns The cache system is designed with robust security strategy. Each user's cache is isolated and logically invisible to others, ensuring data privacy and security. Unused

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ness users get saved BigQuery reports that rerun without token costs. Three services ground the answers. Knowledge Catalog maps business definitions once for every agent. Smart Storage enriches unstructured objects in place; Google says 90% of enterprise data is unstructured. Borderless Lakehouse queries Amazon S3 and Azure Data Lake with no variable egress fees. How is the agent governed? Google frames governance as 4 questions: who, what it may do, what it did, and what it must never touch. Id

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agents can perform tasks across days, sessions, and shifting responsibilities; they have dedicated identities, and are given their own @agents.company.com emails and persistent storage. However, Google says, they only have access to specifically-defined data and channels. Enterprise-dictated security, governance, and cost controls are built-in. “You give it objectives, not instructions,” Google Cloud CEO Thomas Kurian said in his keynote. “Work now starts in the prompt window.” The agent runs in

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kebase lets you branch an entire database in under a second, regardless of its size. It uses copy-on-write, so branches share the parent branch data but only consume additional storage as they diverge. In addition, they can scale-to-zero, meaning idle branches incur no compute cost. This is important as you run several agents at the same time. Each branch is fully isolated, allowing an agent to apply migrations, run tests, and retire the branch when they’re done. In this article, I'll show you w

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ies are continually improving. “We know that the cyberattackers are especially getting more sophisticated, so [enterprises] could use more sophisticated tools themselves,” said Chirag Shah, a professor at the University of Washington Information School. Moreover, Chinese vendor Z.ai’s open-weight models, GLM-5.2 and GLM-5.3, are viewed as direct competitors to Mythos. Therefore, with more bad actors having access to open-weight models, such as those from Z.ai, they are more likely to be dangerou

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loits can now be developed in minutes , projects that can find and address vulnerabilities faster can better secure their software against attackers who are racing to find and leverage these same weaknesses. We will continue to manually disclose human-verified vulnerability reports via our existing coordinated vulnerability disclosure (CVD) process , especially for projects without the resourcing to triage reports themselves. But we are now making available an optional fast-track for those who w

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hich brings frontier Claude models, on-site engineers, and our threat research to those trusted providers. Its founding partners are Accenture, Booz Allen, CrowdStrike, Deloitte, Dragos, Hitachi, Insane Cyber, Nozomi Networks, Palo Alto Networks, PwC, and Rockwell Automation. These organizations represent the providers that operators rely on to secure these systems: the consulting and advanced technology companies that run security programs, the security companies that guard the business and ind

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