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August 03, 2026

23 curated AI news stories from leading AI companies.

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OpenAI

August 3, 2026

Influencers draw backlash for attending Open AI’s first luxury trip

OpenAI’s first-ever influencer brand trip is sparking online backlash as tensions over the use of AI continue.

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Nvidia

August 3, 2026

Sequoia’s Shaun Maguire leads $1 B round for nuclear startup Valar Atomics

Valar Atomics raised $1 billion at a $6 billion valuation after signing a development deal with Nvidia in June.

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Anthropic

August 3, 2026

Alibaba's new Qwen model is also taking your job, but this time it's great

Alibaba is marketing its new AI model Qwen 3.8 with a video that shows the AI working while a person enjoys their hobbies. It's a deliberate contrast to the job loss warnings from OpenAI and Anthropic. Of course, it's still just marketing. The article Alibaba's new Qwen model is also taking your job, but this time it's great appeared first on The Decoder.

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Google

August 3, 2026

Real-world mainframe modernization with AI: A safe, scalable path from mainframe to cloud

For too long, enterprises with legacy mainframe estates have been faced with a high-stakes dilemma: continue maintaining their mainframes, essentially kicking the modernization can down the road (they know they will need to deal with it eventually), or perform a dangerous "big bang" migration with many unknowns and risks. At Google Cloud, we propose an alternative: a modernization strategy that leverages the power of AI, agility of the cloud and allows for iterative and continuous modernization. This approach recognizes a fundamental truth: mainframe modernization isn’t a pure code-to-code conversion problem. Sure, modernizing a single, isolated and small application is relatively easy, especially with recent advancements with AI and large language models. The real challenge lies in modernizing at real-world scale without breaking the intricate web of dependencies and legacy data formats you find in a large global enterprise, all while ensuring functional equivalence. For example, some of these “real world” challenges include: Application logic is tightly fused directly to legacy and proprietary databases and record schemas. Non-relational formats that are inaccessible by AI Agents, such as VSAM, flat files, IMS hierarchical structures. Transaction monitors like CICS and IMS TM deliver highly integrated transaction management. A single transaction scenario can consist of millions of lines of code. Intricate sequential workflows with complex conditional step logic and dependencies. Internal/external boundaries utilizing proprietary protocols like CTG, IMS Connect, MQ, LU 6.2 Sockets. Deep operational lock-in with specialized proprietary mainframe utility suites. In other words, real-world modernization of mainframe applications is so much more than converting COBOL to Java. You also need to modernize the underlying data models, handle decades of obscured application dependencies and interfaces and modernize the underlying data stores. Most importantly, you need to validate and de-risk the modern code with actual production traffic before going live. Our approach combines the advanced reasoning and scale of our Gemini models for code understanding, with mainframe-specific modernization products to address real-world complexity and challenges. Our solutions span four core pillars: assessment, modernization, de-risking, and data migration. Let’s take a look at each of these. 1. Assessment: AI reverse-engineering of the legacy applications Our Mainframe Assessment Tool (MAT) reverse-engineers legacy codebases at massive scale to provide both explainability for the current legacy applications and sets the required foundation for modernization. MAT delivers deep insights into your mainframe environment in four key areas: Dependency visualization: Mapping relationships and interconnectivity between the different applications and data stores, such as DB2 databases or VSAM files. Automated business rule extraction (BRE): Translating complex mainframe application logic into both plain-language requirements and visual decision trees. Automated documentation: Generating comprehensive, up-to-date technical documentation directly from your production mainframe source code. Domain and business function discovery: Automatically identifying application boundaries, grouping applications into high-level business domains and visualizing the architecture for these domains including inputs, outputs, interfaces, and where processing occurs. MAT gives you the clean, verified logic requirements needed to understand your existing applications and business processes and to design a cloud-native future. By integrating these outputs directly into agentic modernization workflows through MCP, it equips your AI agents with the granular, application-specific context they need to guarantee high-accuracy code transformation, scale execution, and optimize for your own codebase. Google Mainframe Assessment Tool: Business rule extraction (BRE) from a legacy mainframe application Google Mainframe Assessment Tool: Showing reverse engineering of an application to generate an infographic visualizing inputs, outputs, processing and user interfaces 2. Modernization: code transformation Modernization requires strategic choices tailored to your desired business outcomes. There is rarely a single path that fits every use case. Our adaptable approach lets you select the exact depth of modernization your business needs, allowing you to apply different strategies to different mainframe workloads.To bridge the gap between theory and execution (aka AI to Applied AI), we partnered with our Mainframe Modernization Professional Services team to build specialized AI agents. These agents directly codify their proven methodologies and hands-on field experience into structured, agentic modernization workflows. With Google’s agentic mainframe modernization solution, you have two ways to modernize: Rewrite / Reimagine Choose this path for legacy applications where business logic innovation drives the highest strategic ROI. This pattern relies on the Mainframe Assessment Tool (MAT) to extract business rules together with our Mainframe Modernization Agents to handle forward-engineering. This agentic workflow analyzes and extracts complex business processes to translate legacy code into clear business specifications. Combined with Antigravity as the agentic harness, this solution provides a safe, AI-accelerated development pipeline with optional human-in-the-loop governance at every step: business rule extraction from the mainframe applications -> creating target application specifications -> creating target architecture design -> generate user stories and backlog -> create the agentic coding implementation plan and more. This allows engineering teams to decouple complex logic from implementation details and replaces the legacy mainframe "black box" with a transparent, easily maintainable, and highly evolvable cloud-native applications. Google Mainframe Modernization solutions working together to establish and agentic modernization workflow Deterministic modernization (like-to-like) This pattern is designed for use cases that require structural modernization while preserving exact, legacy application behavior. We use AI for direct code-to-code modernization of the internal application structures, using strict contract fidelity as our governing constraint. The modernized system must produce the identical business output as the legacy system for any given input, removing technical debt without altering external application interfaces. Matching the pattern to the workload What does modernizing with these two patterns look like in the real world? Imagine a customer in the financial services industry that applies a mixed approach across their estate. They could modernize stable, high-volume back-office batch jobs (like nightly statement processing) with the like-for-like path to reduce MIPS consumption with reduced risk and faster timelines. They might choose deterministic AI modernization for their core general ledger, modernizing the data structure to Google Cloud SQL for better analytics while preserving the regulatory compliance logic. Finally, for applications that are competitive differentiators, such as a customer-facing loan origination platform, they could deploy a rewrite-with-AI strategy, using Gemini to rewrite the application for real-time approvals, creating a true differentiator for the business. 3. The safety net: De-risk before going live To eliminate go-live risk, Google Cloud Dual Run processes real-world production workloads simultaneously across both your mainframe and your new Google Cloud environment. It automatically captures live mainframe transactions, runs them against your modern applications, and compares the outputs side-by-side (protocols, messages and changes to data). This continuous validation runs until you achieve complete logic and data equivalence, ensuring safety and zero operational disruption before you retire the legacy applications on the mainframe. Global enterprises are already using Dual Run to eliminate migration risk and even secure the strict regulatory approvals needed for modernization in certain industries. Think of Dual Run as your production-grade insurance policy for mainframe modernization success. 4. Data migration: Modernize siloed mainframe data Finally, with the Google Cloud Mainframe Connector, you can copy data off the mainframe and into various Google Cloud services such as BigQuery, Spanner, Cloud SQL, Cloud Storage, and others. Mainframe Connector handles the codebase and data-type conversions, and can easily be integrated into existing ETL processes to iteratively copy data off the mainframe and onto Google Cloud. Mainframe Connector lets you both offload processing from the mainframe to support both the augmentation modernization pattern as well as analytics/data-warehousing. Now you can unlock siloed mainframe data to create new business functions in the cloud, all while reducing MIPS usage. Combined, these four solutions demonstrate how targeted applied AI can help solve real-world modernization challenges for mainframe customers: understanding the existing business processes, modernizing the applications, de-risking before going live and modernizing the siloed data. Put us to the test Mainframe modernization shouldn't require a leap of faith. Put our AI-accelerated approach to the test through a targeted pilot program designed to move your enterprise from uncertainty to execution. Here is how we get started: Automated codebase assessment: Run a Mainframe Assessment Tool scan on a target application to map hidden dependencies, visualize domain architecture, and extract plain-language business rules directly from the legacy code. Agentic modernization workshop and pilot: Collaborate hands-on with Google Cloud experts and specialized partners to showcase the capabilities of how agentic workflows can be applied in the real-world to solve the mainframe modernization problem. Together we can pick one application to modernize and build the business case for modernization. Ready to get started? Contact us at mainframe@google.com.

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Claude

August 3, 2026

Could EDA AI Startups Be The New Claude Of Chip Design?

While EDA incumbents like Cadence and Synopsys integrate AI, three startups are vying for market adoption, which could also portend a new pricing model

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OpenAI

August 3, 2026

Congress’ favorite AI tool? Chat GPT

House spending records show OpenAI's ChatGPT dominates paid AI use on Capitol Hill, with congressional offices relying on the chatbot to draft memos, summarize legislation, and assist constituent communications.

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Nvidia

August 3, 2026

How to Run Isolated Tenant Kubernetes Clusters on Shared GPU Infrastructure

Running a dedicated Kubernetes cluster per team often results in more isolation than an organization requires. While one cluster can be successfully shared...

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Google

August 3, 2026

Cortex Framework v7 is GA: Build agentic workflows without disrupting SAP operations

Businesses want to quickly and safely deploy AI agents to drive revenue, mitigate risk, and optimize capital, all without disrupting mission-critical ERP systems. And to power AI agents, you need more than raw data: You need interoperable data products that act as a single, reusable source of truth, turning cryptic source system records into clear business terms. Today, we are announcing the general availability of Google Cloud Cortex Framework version 7. This release modernizes your data architecture for agent readiness, helping you quickly deploy, customize, and extend robust data products while simplifying orchestration and reducing the infrastructure overhead of traditional approaches. While ERP systems hold a wealth of foundational data, turning those transactional records into AI-ready data products is difficult, especially without slowing down core operations. Cortex Framework v7 solves this with purpose-built data product accelerators for SAP. Deployed directly in BigQuery and Knowledge Catalog, these data products feed Gemini Enterprise Agent Platform with accurate business context so your AI agents can execute with high fidelity. It also simplifies data orchestration using a modular, scalable deployment architecture powered by Dataform, an end-to-end experience that helps data teams build, version control, and orchestrate workflows in BigQuery. Read on for more details about what you’ll find in Cortex Framework v7, how it integrates with the recently released SAP Business Data Cloud Connect for BigQuery, what our customers are saying, and how to get started. What’s new in Cortex Framework v7 1. Deliver agent-ready data products Traditional business intelligence (BI) dashboards tell you what happened last quarter, but modern AI models and agents help you act in real time. To power this shift, Cortex Framework v7 packages your enterprise data into semantically rich data products that contain AI-friendly metadata. This enables your agents to reason, orchestrate, and execute high-impact workflows. By translating raw SAP tables into clear business terms, dynamically ingesting custom fields, and natively handling advanced logic (like SAP TCURX currency decimal shifts), the Framework maintains the high data fidelity required for large language model (LLM) interactions and enterprise analytics. Image showing Cortex Framework v7 deployed data products for SAP ERP inside BigQuery with built-in description metadata for field names and descriptions. These purpose-built data product accelerators for SAP ERP and SAP Business Data Cloud deploy directly in BigQuery. Once they’re deployed, you can trigger automatic registration in Knowledge Catalog to easily discover data and connect it with your AI agents. Image showing a Cortex Framework v7 data product registered in Knowledge Catalog with built-in metadata for descriptions and labels. To accelerate custom AI projects, this release also includes agent skills for an agentic data product builder that helps data and knowledge engineers use natural language to generate custom data products. Using the Framework's provided content, an AI agent can automatically handle the build process, including adapting to specific source data requirements and customizations. GIF showing Cortex Framework v7 delivered agentic data product builder skills in action using Google Antigravity with Gemini to automate the build and deployment of new data products using natural language prompts. With v7 you can also extend Cortex Framework to maintain a clean separation between Cortex Framework-delivered content and your custom models. This helps you apply the latest code updates without impacting your custom work. 2. Build faster with modular deployments Dataform powers the modernized data pipelines in Cortex Framework v7, offering version-controlled SQL and native dependency management. When you select a packaged data product for deployment, Cortex Framework retrieves and processes only the required tables. Dependency resolution automatically handles order-of-operations and generates a dependency graph for efficient processing. You can also add custom fields or logic without breaking standard models. Image showing Cortex Framework v7 data products in Dataform with automated dependency graph showing data asset and table dependencies as well as built-in metadata like natural language tags. This modular approach extends natively to complex, multi-system SAP environments. Using built-in logic differentiation, Dataform compiles and deploys Cortex Framework data products for SAP ECC, SAP S/4HANA, and SAP BDC source systems in parallel, while dynamic schema discovery automatically ingests and processes custom SAP fields such as Z-fields. Together, these capabilities help you bring in data from multiple systems simultaneously, reducing the need to manually refactor pipelines. 3. Scale cost-effectively with flexible processing Cortex Framework now defaults to BigQuery incremental loading and non-destructive schema updates, processing only new or modified data. This approach minimizes compute time and significantly lowers operational expenses. Because orchestration relies on Dataform’s serverless, BigQuery-native execution, data processing scales without additional infrastructure overhead. You can also use a bring-your-own (BYO) change data capture (CDC) approach. You can use this approach to bypass built-in CDC data processing and connect your existing CDC-processed pipelines directly. Interoperability with SAP Business Data Cloud Following the recent general availability of SAP Business Data Cloud Connect for BigQuery, Cortex Framework v7 now natively supports SAP BDC standard and custom data products. By combining Cortex Framework-delivered data products with SAP BDC data products, you can uncover new business opportunities. To accelerate this, v7 includes solution samples across both SAP BDC and SAP ERP data to help you quickly answer a wide range of critical questions such as assessing the health of your sales pipeline or identifying procurement inefficiencies. Image showing Cortex Framework v7 consumption data product solution samples deployed on SAP BDC sourced data products via SAP Business Data Cloud Connect for BigQuery integration. Co-innovating with industry leaders We’ve worked closely with our Professional Services Organization and industry leaders to test these new capabilities. By serving as our lighthouse customers, these organizations provided valuable feedback to help us shape a framework built for enterprise-scale agility and AI readiness. “As an existing Cortex Framework customer, we are excited about the capabilities introduced with the latest v7 release. Bridging complex SAP ECC and S/4 environments with Google Cloud’s AI capabilities requires a trusted data foundation with accurate business context. Based on the capabilities announced for Cortex Framework v7, including the agentic data product builder, we see significant potential to accelerate and scale the creation of enterprise data products for analytics and agentic AI use cases. The direction of the platform is particularly promising for organizations looking to make SAP data more accessible, semantically meaningful, and ready to support future reporting and AI initiatives.” - Dr Jwan Ibrahim, Director, Data and Analytics, SLB Get started Cortex Framework v7 changes how enterprises operationalize their most critical data for AI agents. By combining serverless execution, AI-ready semantics, and native SAP integration, Google Cloud helps you turn complex enterprise data into a strategic asset. To get started, check out the following resources: Execute a deployment: Try a Cortex Framework demo deployment (with a predefined sample dataset) to explore your first v7 data products today. Learn more: Read the comprehensive Cortex Framework v7 technical documentation for deep dives into production deployment steps, configuration, extensibility, source system integration, and packaged data products. Talk to an expert: Contact your Google Cloud account team to learn how Cortex Framework v7 can accelerate your specific data-to-AI journey.

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Unifying public and private data: Scale knowledge graphs with Data Commons on Spanner

To make informed decisions, businesses often need to connect their internal data with public reference data, to create a knowledge graph that connects real-world things and their relationships. However, bridging data from public and private worlds has traditionally been complex. Today, we are streamlining these connections with the general availability of Data Commons on Spanner Graph and the preview of the new Data Commons Platform to unify your private knowledge with knowledge graphs from public datasets. The overarching Data Commons project supports Google’s mission to organize the world's information and make it universally accessible and useful. Data Commons unifies fragmented public datasets from over 100 authoritative providers, including the United Nations, World Bank, US Census Bureau, Eurostat, WHO, and NOAA, with over 400 billion data points structured using standardized Schema.org definitions. Data Commons provides data exploration tools, MCP tools, and cloud-based APIs to access and integrate the clean datasets. Data Commons integrates public information across multiple domains, including agriculture, demographics, economy, environment, and health. This standardized approach unlocks powerful use cases, for instance, letting you analyze national GDP trends, map regional smoke pollution levels, track local health equity, or demographic distributions over time, all using data that has already been preprocessed and normalized for you. Data Commons knowledge graph dimensions Dimension Size Technical description Statistical observations 400+ billion Individual metric data points Graph edges 2.6+ billion Relationships Knowledge graph nodes 1.7+ billion Standardized entities Data sources 100+ providers Authoritative institutions Data Commons makes meaningful quantities of public administrative data available to users on readily consumable cloud-based infrastructure. A modern infrastructure powered by Spanner Graph When we first built Data Commons, our goal was to aggregate massive, disparate public datasets using the tools available at the time. The platform relied on Bigtable as a caching layer, which was an effective strategy for handling large-scale lookups in the absence of native graph database technology. Today, we have transitioned our architecture to a native graph model with Spanner Graph, which brings the convenience of a SQL-like interface and graph expressiveness to Spanner, with its high availability, horizontal scale-out, multi-region transactional consistency, and native ISO/IEC 39075 Graph Query Language (GQL) support. By adopting a multi-entity Spanner Graph schema, we represent entities as nodes and their domain links as dynamic graph edges, allowing us to move away from pre-computed cache structures and perform complex relationship queries directly within the database using GQL. This architecture also simplifies our pipelines by removing the need for complex, pre-computed indices that require costly in-memory rebuilds and multiple snapshots. Spanner Graph enables incremental updates to specific datasets without refreshing the entire database, while stale reads maintain consistent data snapshots during ingestion. Key benefits by moving to Spanner Graph Unified storage and incremental updates: By utilizing Spanner Graph’s multi-entity schema, the platform replaces complex caches with a model that supports incremental data imports, allowing for targeted updates to specific datasets. Dynamic graph traversals via GraphRAG: The system executes multi-hop queries such as navigating hierarchies like continent → country → state → county → city on the fly. This removes reliance on static caches and enables GraphRAG workflows, where the database maps natural language queries directly to structured path-matching traversals. Consistent data snapshots: Leveraging Spanner TrueTime and stale reads, the platform provides you with a version-consistent snapshot of data, maintaining integrity across distributed nodes following batch ingestion cycles. Operational analytics at scale: Spanner’s columnar engine efficiently scans massive time-series datasets by reading only the necessary fields, while BigQuery federation that leverages Spanner’s Data Boost technology performs complex aggregations via EXTERNAL_QUERY in an isolated environment, helping isolate production traffic. Bridging systems with SDMX 3.0 interoperability To facilitate the use of complex statistical data, Data Commons adopts a lean implementation of Statistical Data and Metadata eXchange (SDMX) technical standard. As an ISO specification, SDMX provides a consistent approach for describing and exchanging statistical data along with descriptive statistical meta-information. In this Data Commons Platform update we added support for the SDMX technical standard version 3.0, providing out-of-the-box integration with third-party tools like Tableau, Flourish, and Observable for multi-dimensional datasets. This is made possible using the API standard SDMX-JSON and SDMX-CSV 2.0 formats across two high-value endpoints: The availability API: A programmatic discovery mechanism to identify existing dimensions, variables, and date ranges without reading raw values. The data API: Retrieves actual observations and metadata, using named parameters to help prevent code from breaking when dimensions are added. Transforming private instances of Data Commons Platform For organizations that want to build private instances of the Data Commons Platform, this new modern architecture resolves legacy scaling limits and simplifies data schematization. Developers can instantiate a private instance of the Data Commons Platform leveraging the same scalable technology that powers Google’s Data Commons instance. As a private instance, users retain full control of their own data and have the ability to limit access, while enabling natural language queries to blend results from their private data with Google’s public data that is hosted on the Google Data Commons instance. By federating across our public knowledge graph and a private knowledge graph containing your own data, you can light up exciting new use cases, while maintaining data isolation and ensuring no data duplication. For instance, a retail enterprise can combine public data such as national GDP trends, regional demographic breakdowns, and employment statistics, with their own enterprise data, including sales histories, store performance metrics, and supply chain logistics. This allows analysts to contrast public macroeconomic indicators against their own company transactions to optimize merchandise distribution and identify untapped markets. Example of a natural language query combining statistical data from the Directorate General of Commercial Intelligence and Statistics (DGCIS) stored in a Data Commons Platform private instance with World Development Indicators from the World Bank stored in the Google Data Commons public instance. A user is querying a Data Agent for average annual temperature trends in the country. The agent retrieves information from Data Commons, explaining that while historical data is available, it provides projected temperature changes, climate drivers, and CMIP6 climate model scenarios (SSPs), with options to export the generated report. A user asks the Data Agent to compare the Worker Population Ratio (WPR) of rural versus urban males in a country. Fetching data from Data Commons, the agent defines WPR—the percentage of workers relative to the total population—and outlines the available demographic variables to analyze and compare both groups. Get started today Explore Data Commons: Visit datacommons.org to query global statistical knowledge. Explore Spanner Graph's use cases and setup guide for your knowledge graphs. Deploy Data Commons Platform: contact support@datacommons.org to request preview access and to review the developer tools.

GPT-Live enables continuous voice interaction with AI, using a turnless speech model and low-latency architecture for faster, more natural conversations.

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MPP-GNN: Subject-Adaptive Community Detection for f MRI-Based Alzheimer's Disease Classification

arXiv:2607.28681v1 Announce Type: new Abstract: Functional magnetic resonance imaging (fMRI) is a widely used technique for studying the brain. Recent methods that utilize graph neural networks (GNNs) for analysis of brain functional connectivity have shown great potential for the classification of brain disorders, such as Alzheimer's disease (AD). However, these methods often assume a preset number of functional modules across all subjects, which overlooks inter-subject variability. In addition, the discovered modules are rarely used to directly guide the learned connectivity patterns. Here, to address these issues, we propose a Meta Probabilistic Pooling GNN (MPP-GNN). We frame the model's task as a coupled, bilevel optimization that performs adaptive graph partitioning hierarchically to discover subject-specific modules and then uses the discovered brain modules as an explicit prior to guide edge refinement and representation learning. We validate MPP-GNN on two public datasets for AD classification, achieving the highest AUC in comparison to established baselines for both datasets. Furthermore, our analysis demonstrates that MPP-GNN shows significant alignment with the canonical functional-network organization defined by the Yeo brain atlas and reveals a network-level dedifferentiation pattern for AD.

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OpenAI

August 3, 2026

Mitigating Class-Tail Undercoverage in Medical Vision-Language Models under Clinical Shift

arXiv:2607.28696v1 Announce Type: new Abstract: Medical vision-language models (VLMs) can retain high observed marginal coverage after clinical shift while substantially under-covering an individual disease class. The affected class varies with acquisition protocol and backbone geometry, so source prevalence does not reliably reveal the failure. Existing localized and tail-aware conformal methods respectively adapt to test neighborhoods and source-frequency tails, leaving held-out class-wise coverage failure unmodeled. We introduce Class-Tail Adaptive Localized Conformal Deferral (CALCoDe), a post-hoc reliability layer for frozen medical VLMs. Cross-fitted validation predictions identify classes at risk of undercoverage, and a disjoint calibration split estimates their class-conditional tail thresholds. CALCoDe combines each protected threshold with a localized conformal threshold using a one-sided maximum. The resulting set contains every label admitted by the localized rule, with additional protection confined to validation-identified classes. An independently calibrated support audit defers cases with insufficient inlier support. Under exchangeability among accepted examples within each protected class, CALCoDe provides finite-sample coverage at the prespecified guard level and contains the corresponding localized conformal sets; coverage on shifted external cohorts is evaluated empirically. Among standard conformal baselines and recent VLM-specific conformal methods evaluated across two dermatology shifts (HAM10000 to ISIC 2019 and HAM10000 to PAD-UFES-20) and four frozen VLM backbones (BiomedCLIP, OpenAI CLIP ViT-B/32, PubMedCLIP ViT-B/32, and MedSigLIP-448), CALCoDe is the only approach whose observed marginal and worst-class accepted coverage both reach 0.95 in all eight settings. On HAM10000 to ISIC 2019, its average worst-class accepted coverage is 0.970, compared with 0.926 for sTACP and 0.864 for LCP-VLM.

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Hypergradient-based Bilevel Reinforcement Learning with Improved Sample Complexity

arXiv:2607.28849v1 Announce Type: new Abstract: Bilevel reinforcement learning (RL) is an important framework within the literature of RL that can be used to formalize various categories of problems, such as meta-learning, hierarchical task decomposition, and reinforcement learning from human feedback (RL-HF). Most of the bilevel RL algorithms are either not scalable because of using hypergradient with Hessian, or they suffer from high sample complexity because of using penalty-based approximation methods. In this work, we propose a hypergradient-based bilevel RL algorithm using the optimality of the Boltzmann policy for the entropy regularized discounted RL objective function. Our proposed algorithm is Hessian-free and obtains an iteration complexity of $O(\epsilon^{-1})$ and state-of-the-art sample complexity of $\tilde{O}(\epsilon^{-2})$ under mild regularity conditions. Further, in our convergence analysis, we are able to remove the assumption of the Polyak-Lojasiewicz (PL) condition on the outer-level objective function present in the prior state-of-the-art sample complexity work.

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Claude

August 3, 2026

Can AI Evaluate AI Scientists? A Benchmarking Study of Autonomous Research Generation Systems Using Automated Multi-Model Review

arXiv:2607.28631v1 Announce Type: new Abstract: AI Scientist systems capable of autonomous research have the potential to significantly accelerate scientific discovery. However, evaluating and comparing the quality of AI-generated papers remains an open challenge. We propose and implement a rigorous benchmarking protocol using an automated peer-review system that harnesses frontier large language models to assess scientific papers across four core dimensions: originality, scientific rigor, clarity, and significance. We evaluate four leading AI Scientist frameworks: \textit{Sakana AI (v1 & v2)}, \textit{CycleResearcher}, and \textit{Data-to-Paper}. Each framework was run on a consistent set of 15 research proposals published by a commercial autonomous AI scientist company (FARS), generating 60 papers that we evaluate alongside 15 FARS benchmark papers. Using three independent LLM reviewers (GPT-5.4, Gemini, and Claude), we find that FARS benchmark papers significantly outperform all competing frameworks, achieving mean scores of 2.14--2.47 on a 1--5 scale compared to 1.00--1.87 for other systems. Notably, FARS scores are more than 2$\times$ higher than the next-best systems on Gemini and Claude evaluations. We find strong agreement among Gemini and Claude ($\rho$ = 0.907, $p < 0.001$), and both correlate extremely strongly with the synthesis score ($\rho$ = 0.961, $p < 0.001$), validating the reliability of automated evaluation. However, GPT-5.4 exhibits weaker agreement ($\rho \approx 0.32$), suggesting it evaluates papers using different criteria. These results establish the first quantitative benchmark for AI Scientist systems and demonstrate that multi-model LLM evaluation provides a scalable, consistent framework for assessing autonomous research quality.

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