August 5, 2026
Anthropic’s AI used fake identities, malware in rogue attack on Git Hub project
Anthropic and OpenAI models’ unprompted actions forced halt to UK cyber tests.
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37 curated AI news stories from leading AI companies.
August 5, 2026
Jeff Dean and other top AI researchers are leaving Google to launch their own startup
The legendary Google executive is joined by other outgoing Google execs in a joint mission to use AI to push forward the process of scientific discovery.
Read original articleAugust 5, 2026
Google’s AI Search Reportedly Told Users That Flock Cameras Are a Goldmine - Gizmodo
Google’s AI Search Reportedly Told Users That Flock Cameras Are a Goldmine Gizmodo
Read original articleAugust 5, 2026
Google’s stock drops after Nobel-winning AI executive steps aside - The Washington Post
Google’s stock drops after Nobel-winning AI executive steps aside The Washington Post
Read original articleAugust 5, 2026
Google’s four AI departures: “We wanted to build something differently”
At the start of 2025, investors wondered whether Google could keep pace with OpenAI. By December, Alphabet was completing its The post Google’s four AI departures: “We wanted to build something differently” appeared first on The New Stack.
Read original articleAugust 5, 2026
Google Deepmind loses both its CEO and chief scientist as Demis Hassabis and Jeff Dean step down simultaneously
Google Deepmind is overhauling its leadership as Demis Hassabis steps back from day-to-day management to become Alphabet's chief scientist and Jeff Dean leaves Google after 27 years to launch AI startup Discovery Loop. Former Deepmind CTO Koray Kavukcuoglu will take over as Google races to close the gap with its top AI rivals. The article Google Deepmind loses both its CEO and chief scientist as Demis Hassabis and Jeff Dean step down simultaneously appeared first on The Decoder.
Read original articleAugust 5, 2026
The 800 mistakes that could reshape Meta’s AI coding strategy
Meta is asking thousands of its software engineers to help train its internal AI coding tools by simply fixing code, The post The 800 mistakes that could reshape Meta’s AI coding strategy appeared first on The New Stack.
Read original articleAugust 5, 2026
Google will shut down Google Assistant starting September 2026 as Gemini takes over on Android and Wear OS
Google is killing Google Assistant on Android and Wear OS starting September 4, 2026. Gemini takes over as the AI-powered successor on smartphones, tablets, watches, and in cars with Android Auto. Whether a probability-based LLM can match the reliability of its deterministic predecessor for simple everyday commands will be a real test for Google's AI strategy. The article Google will shut down Google Assistant starting September 2026 as Gemini takes over on Android and Wear OS appeared first on The Decoder.
Read original articleAugust 5, 2026
Anthropic, Open AI models used fake identities to plant malicious code - kmph.com
Anthropic, OpenAI models used fake identities to plant malicious code kmph.com
Read original articleAugust 5, 2026
Google Overhauls AI Leadership as Longtime Chief Scientist Joins Wave of Exits - WSJ
Google Overhauls AI Leadership as Longtime Chief Scientist Joins Wave of Exits WSJ
Read original articleAugust 5, 2026
Meta Ran Ads That Contained AI-Generated Child Sexual Abuse Imagery
More than 50 offending image and video ads were published across Facebook, Instagram, Messenger, or Threads, according to Meta’s ad library data. Some ran as recently as this week.
Read original articleAugust 5, 2026
Anthropic and Open AI Agents Accused of Social Engineering - PYMNTS.com
Anthropic and OpenAI Agents Accused of Social Engineering PYMNTS.com
Read original articleAugust 5, 2026
Google shakes up AI leadership as Deep Mind chief shifts role - Reuters
Google shakes up AI leadership as DeepMind chief shifts role Reuters
Read original articleAugust 5, 2026
Google's AI reshuffle: Chief scientist Jeff Dean exits and Demis Hassabis steps down as Deep Mind CEO - cnbc.com
Google's AI reshuffle: Chief scientist Jeff Dean exits and Demis Hassabis steps down as DeepMind CEO cnbc.com
Read original articleAugust 5, 2026
Four Top Google A.I. Researchers Form New Start-Up - The New York Times
Four Top Google A.I. Researchers Form New Start-Up The New York Times
Read original articleAugust 5, 2026
Google’s Top AI Brains Are Leaving to Launch Discovery Loop
Jeff Dean and other high-profile Google executives have founded Discovery Loop, a startup that will seek AI-powered breakthroughs in everything from drug discovery to chip design.
Read original articleAugust 5, 2026
Solving the "Noisy Neighbor": How Sharded Architecture Protects Multi-Tenant Platforms
Whether you are a multi-tenant SaaS provider, a large enterprise managing internal data platforms, or a company handling mixed-workload data processing, managing a shared infrastructure environment means facing a common threat: the 'noisy neighbor'. A single tenant with a massive data burst or a failing database instance can bring down the entire neighborhood, manifesting as significant backlog accumulation and global Service Level Agreement (SLA) violations across critical data pipelines Here is how to transition from a monolithic architecture to a sharded hub-and-spoke pattern to ensure platform resilience. The problem: The monolithic bottleneck A typical legacy architecture processes data for all tenants and business domains through a single, massive stream. Because the pipeline is unified, a performance issue with one specific database tenant instance creates back pressure that degrades performance for every other tenant on the platform. Painful effects: 100% Blast radius: One database failure can stop all processing. Inefficient scaling: Resources often have to be scaled for the "worst-case" tenant, leading to significant wasted spend. SLA instability: Maintaining a global SLA is nearly impossible when one high-volume tenant can lag the entire system. The solution: sharded hub-and-spoke architecture To solve this, processing is decoupled into a "hub" for routing and "spokes" for isolated execution. 1. The hub: The router pipeline The Hub is a lightweight Dataflow job that acts as a traffic controller. It reads from unified source topics, parses the Tenant ID or Business Domain, and fans the data out into isolated buffers. This keeps the entry point simple and robust. 2. The buffer: Durable isolation Pub/Sub topics are introduced between the Hub and the Spokes. These act as a durable shock absorber, preventing a slow downstream sink from backing up the original source. 3. The spokes: Isolated execution Instead of one giant pipeline, multiple, smaller Dataflow instances are deployed and categorized by workload: Tier 1 (high-priority): Dedicated pipelines with high resource allocation for critical tenants. Shared tiers: Grouped pipelines for smaller tenants to optimize costs. Domain specific: Specialized pipelines for complex logic (e.g., separating distinct business domains) to isolate code complexity. Comparative Benefits at a Glance Feature Monolithic (legacy) Hub-and-spoke Fault tolerance One failing DB stops everything Failures isolated to specific spoke Blast radius 100% < 5% (Isolated to one spoke) Resource scaling Scaled for "worst-case" Independent scaling per tenant load Maintenance Global updates affect everyone Update one domain without touching others Pro-Tips for Implementation Moving to this architecture is more than just shifting boxes on a diagram. Additional "Spoke" level optimizations are recommended for maximum stability: Implement Dead Letter Queues (DLQ): Do not let a single SQL exception stall the pipeline. Route failed records to storage (like BigQuery or Google Cloud Storage) for later investigation. Strict connection pooling: Databases have connection limits. Use a thread-safe singleton pattern and set a low MaximumPoolSize (e.g., 1-2) per worker to avoid exhausting the database during autoscaling. Asynchronous I/O: Use the GroupIntoBatches transform to buffer writes, which reduces the connection overhead that often triggers database-induced latency. Conclusion By adopting a sharded approach, platforms can guarantee that a "noisy neighbor" is no longer a threat to the neighborhood. This architecture provides the isolation needed to maintain strict SLAs while allowing for independent scaling and safer deployments To learn more about implementing a sharded hub-and-spoke architecture, explore the Dataflow documentation.
Read original articleAugust 5, 2026
Scaling agentic AI: How Ui Path built its high-performance GPU platform on AI Hypercomputer
As a market leader in enterprise agentic automation and business orchestration, UiPath is helping to pioneer an industry shift toward agentic AI. With it, the company is deploying autonomous agents to actively reason, make decisions, and execute complex business processes across its disparate systems. This transition from simple task automation to cognitive decision-making agents requires a massive surge in computational power and powerful infrastructure that’s reliable enough for the needs of the world's largest enterprises. Being able to orchestrate hundreds of GPUs in perfect harmony can be what makes the difference between just running a research experiment and building a global AI platform. Such orchestration requires balancing massive training jobs with real-time inference, all without letting costs spiral or latency spike. To do so, UiPath re-architected its infrastructure to support high-scale intelligent document processing (IDP) using UiPath IXP and moved from isolated clusters to a shared Google Cloud GPU fleet, balancing A3 VM instances (NVIDIA H100 GPUs) for training with G4 VM instances (NVIDIA RTX Pro 6000) for inference. This architecture lets UiPath solve its “spiky workload” problem and count on predictable costs and open-source patterns that the company’s engineering teams can use to replicate this architecture themselves. "Realizing the full potential of enterprise agentic AI requires an infrastructure that matches our ambition. Google Cloud provides the scale and flexibility we need to train specialized models and deploy them globally. This partnership allows us to deliver high-precision intelligent document processing and autonomous agents that don't just chat, but actively drive business outcomes for our customers." – Raghu Malpani, Chief Technology Officer, UiPath The context: heavy-duty math UiPath has run its full-stack automation platform on Google Cloud for years, but as its agentic AI initiatives expanded, it faced a series of new infrastructure challenges. Core capabilities like IDP, computer vision, and LLM-powered reasoning require heavy-duty math, so UiPath’s engineering team utilizes LLAMA model grounding that allows its robots to "see" interfaces with human-like clarity. And with specialized document models built on the Qwen architecture, the team can extract valuable data from messy, real-world paperwork. These models live on the UiPath cloud infrastructure, where cutting every possible millisecond of latency is essential. Moving from a "cool demo" to a reliable production tool without exploding costs meant the team had to rethink its underlying silicon. The challenge: more demand than supply In the past, when a team at UiPath needed to train a new model or run inference, it provisioned GPU nodes on demand and scaled up or down depending on whether the workloads were spiking or slowing. This was a functional strategy when cloud capacity was cheap, abundant, and perfectly elastic. But as its AI ambitions grew, UiPath found this approach could no longer keep up with its operational complexity. It now faced three new challenges: Spiky workloads: To ensure it had sufficient power for peak demand, UiPath often had to buy extra capacity that sat idle during quieter periods, wasting expensive headroom. The company needed intelligent, on-demand scaling that didn't require paying for silicon that wasn't crunching numbers. Supply bottlenecks: For large-scale fine-tuning, the price-to-performance ratio on gold standard high-end A3 VM instances with 8-cluster H100s is unbeatable. But global demand for those chips has outstripped supply, making it nearly impossible to scale training efforts at the speed UiPath desired just by adding nodes. Operational overhead: UiPath was also struggling with geographical inefficiency because stable inference demand still meant maintaining dedicated clusters in multiple regions to ensure low latency for international customers. Further, managing GPU infrastructure for both training and inference added inefficient layers of operational overhead. The solution: a shared GPU fleet With all of that in mind, UiPath decided to treat its GPUs as a shared strategic resource instead of a product-centric elastic infrastructure. As a result, its engineering team designed a platform-level shared GPU fleet managed by its machine learning services (MLS) platform, which prioritizes work across teams and time windows while balancing demand across workflows. During the day, the fleet serves real-time inference and latency-sensitive workloads, and at night or during off-peak hours, it automatically switches to batch training and long-running jobs. By coordinating workloads at the fleet level, MLS lets UiPath maximize utilization while reducing contention, all without relying on per-instance elasticity. It also enables the company to schedule capacity in advance, which improves predictability for both research and production use cases. Why Google Cloud: AI Hypercomputer architecture To support its growing scale, UiPath leveraged Google Cloud AI Hypercomputer, which offers a system-level approach integrating performance-optimized hardware, open software, and flexible consumption models into a unified environment. AI Hypercomputer also minimizes the friction between hardware and software layers, which allows engineering teams to focus on model performance rather than infrastructure management. Once it settled on a shared fleet model, UiPath needed a cloud partner that could offer reliable GPU availability, competitive pricing, and burst capacity. That’s why it chose to expand its existing Google Cloud footprint with a highly specialized AI stack running on Google Kubernetes Engine . Now, UiPath can take advantage of predictable capacity by leveraging Google Cloud’s Dynamic Workload Scheduler (DWS) to solve its supply bottleneck. The company knew Google Cloud could secure its GPU capacity consistently with notice windows measured in days. DWS allows the engineering team to schedule training runs in advance and secure capacity for short bursts, and it can now plan for capacity rather than having to react to scarcity. Today, UiPath runs all its training and most of its IDP model inference workloads on Google Cloud. While UiPath uses A3 VM instances for heavy-duty training and fine-tuning, not all of its tasks require that level of power. That’s why it now deploys Google Cloud G4 VM instances as a net-new optimization for inference workloads. These instances offer a cost-effective balance of performance and price, which allows UiPath to run lighter inference tasks without occupying the high-performance clusters reserved for training. "The shift to a shared fleet on Google Cloud transformed our operational model. We moved from reactive provisioning to a predictable, high-performance engine that powers our most advanced IDP and agentic AI workloads. With tools like Dynamic Workload Scheduler and a mix of A3 and G4 instances, we have the flexibility to optimize for both cost and speed. This ensures our engineers spend their time innovating rather than waiting for compute." - Arthur Wilcke, director of AI infrastructure, UiPath Practical validation: differentiated models at scale With consistent access to Google Cloud GPUs, UiPath can now bring advanced models into production. It can also schedule large training jobs without blocking production inference, letting it balance research experimentation with production reliability. This allows UiPath to deliver advanced IDP capabilities that extract data from highly unstructured and variable documents with high accuracy. For example: Omega Healthcare uses UiPath to automate over 100 million transactions with 99.5% accuracy, a 40% reduction in processing time, and 15,000 less hours of repetitive tasks per month. Thermo Fisher Scientific uses UiPath to extract data from PDFs like invoices and purchase orders and is now able to process 53% of its invoices without human involvement, while cutting processing time by 70%. Lessons learned UiPath’s most significant wins so far have been increased availability and reliability. As its workloads continue to transition and it decommissions its legacy GPU resources, the company expects to see additional cost improvements. For engineering teams looking to build similar platforms, some key takeaways include: Decouple capacity: Instead of tying hardware to specific products, pool resources to smooth out usage spikes. Schedule, don't react: Using tools like DWS to book compute in advance guarantees availability and stabilizes costs. Right-size the silicon: Use A3 VM instances for training, but choose efficient options like G4 VM instances for inference. Next steps After its recent infrastructure evolution, UiPath is still refining its MLS platform to support the next evolution of AI innovation. To replicate this success in your own organization, use the resources below: Build it: explore engineering patterns on GitHub. Optimize it: Get started with Google Cloud G4 VM instances. Learn more about UiPath - IXP
Read original articleAugust 5, 2026
Shopify says AI search is driving more traffic and sales, not replacing Google
Shopify says AI isn’t cannibalizing search traffic the way it has for publishers. Instead, AI-driven traffic and orders to Shopify stores tripled year over year in Q2.
Read original articleAugust 5, 2026
Google plans to kill Assistant on your phone on September 4
Assistant will disappear, leaving only Gemini for voice control in the coming weeks.
Read original articleAugust 5, 2026
Claude Targeted Real People. The Enterprise Risk Is Access, Not Intent
Anthropic’s Claude targeted real people during a UK cyber test. The enterprise lesson: agent access and authorization matter more than intent.
Read original articleAugust 5, 2026
Space X’s ambitious compute goals could require over two million Nvidia Rubin GPUs
SpaceX plans to more than 5x its compute capacity by the end of 2027, betting exclusively on Nvidia's Vera Rubin platform. The expansion could require well over a million new GPUs. Meanwhile, the company's AI segment posted $2.56 billion in Q2 revenue, driven mostly by leasing out its own server capacity. The article SpaceX’s ambitious compute goals could require over two million Nvidia Rubin GPUs appeared first on The Decoder.
Read original articleAugust 5, 2026
Anthropic is hiring an AI chip design team
Anthropic is building a team for designing its own custom AI chips. The Claude maker said it would co-design hardware and models to help its technology run faster and more efficiently.
Read original articleAugust 5, 2026
Unlocking the future of shared storage: Filestore on Colossus
Today, enterprise storage must be as agile, elastic, and responsive as the workloads it supports. Filestore, Google Cloud’s first-party, secure, scalable NFS file service, can service a wide-range of enterprise use cases as well as cutting-edge AI and agentic workflows. Today, we’re sharing a major enhancement: Filestore now incorporates a cloud-native backend storage layer built directly on Colossus, Google’s foundational distributed storage system. Leveraging Colossus, Filestore can deliver even greater flexibility and scalability to support the most demanding modern workloads. Colossus: A foundation of global scale As a first-party service, Filestore is positioned to leverage the best of Google’s infrastructure-level innovation. By leveraging Colossus, Filestore now utilizes the same infrastructure DNA that powers Google’s biggest global services, including YouTube, Gmail, and Gemini. This platform-level upgrade enables superior scalability and operational efficiency compared to legacy VM-based architectures, providing a robust foundation for your data. This also allows Filestore to decouple storage capacity and performance. Now you can provision IOPS independently to precisely meet workload demands without having to over-provision capacity, supporting a broad range of workloads — from small developer environments to massive datasets. This decoupled scale is especially powerful when applied to containerized environments. Through the Filestore CSI driver, Filestore delivers persistent, high-performance storage for GKE workloads with enterprise-grade reliability and availability. To further optimize resource utilization at scale, Filestore multishares for GKE lets you segment a single Filestore instance into many smaller shares, starting at just 10 GiB. This means AI teams can scale out their GKE clusters efficiently, carving up large high-performance instances into smaller, project-specific shares to support thousands of concurrent containers, without sacrificing performance or increasing TCO. Shared storage for agentic swarms The combination of decoupled performance and deep GKE integration enables a critical new use case: high-concurrency workspaces for AI agent swarms, where multiple specialized, autonomous AI agents collaborate in parallel to accomplish complex goals. In an agentic workflow, multiple agents often need to read from and write to a common dataset simultaneously to maintain state and share context. Filestore file shares act as these common workspaces leveraging the NFS protocol to provide consistent file system access across the swarm. Backed by Colossus, Filestore can support millions of agents, working independently or securely collaborating with shared data. By utilizing NFS file locking, Filestore helps ensure strict data consistency, preventing conflicts even as swarms grow in size and complexity. This allows AI agents to maintain a unified view of their environment, enabling more complex reasoning and collaboration. Operational agility and enterprise-grade control Beyond performance, this enhancement improves operational agility. Now, you can independently scale IOPS via Custom Performance settings, optimizing costs in real-time without having to rebuild clusters. Furthermore, the distributed storage layer provides faster failure recovery and zero-downtime capacity changes compared to traditional monolithic storage architectures. Filestore is also providing security at scale via deep integration with Google Cloud IAM, NFS User IDs and Group IDs (UIDs/GIDs), and IP Access Control Lists (ACLs). Building for the next era of data This Filestore update reflects our commitment to meeting the changing needs of AI-era workflows and lays the foundation for future enhancements. By providing a first-party, deeply integrated service that evolves with your needs, we are helping you maximize your business potential flexibly and cost-efficiently. Get started today by visiting the Filestore page.
Read original articleAugust 5, 2026
An AI agent went rogue during UK safety tests, creating fake identities and launching social engineering attacks unprompted
In a security test by the British AI Safety Institute, an AI agent went rogue on the open internet without being told to. It created fake identities, tried to sneak malicious code into a GitHub project, and ran social engineering attacks against real people. Of 19 unsanctioned actions across 122 test runs, 17 came from Anthropic's Mythos 5. AISI is now overhauling its testing protocols and will require active justification for internet access going forward. The article An AI agent went rogue during UK safety tests, creating fake identities and launching social engineering attacks unprompted appeared first on The Decoder.
Read original articleAugust 5, 2026
AI models shock UK testers by using fake identities to try to trick developers - The Guardian
AI models shock UK testers by using fake identities to try to trick developers The Guardian
Read original articleAugust 5, 2026
Self-Organising Digital Circuits
arXiv:2608.02606v1 Announce Type: new Abstract: Fault tolerance in classical computing has traditionally relied on static strategies like hardware redundancy and error-correcting codes. Biological systems, in contrast, exhibit adaptive plasticity, maintaining function through dynamic re-organisation around damage. Inspired by this principle, we introduce Self-Organising Digital Circuits, framing functional logic generation and maintenance as a meta-learning problem on graphs. Our architecture employs a topology-masked Transformer that configures the Lookup Tables (LUT) of a circuit's Boolean gates. Extending the pattern-generation paradigm of Neural Cellular Automata (NCA), it navigates the degenerate Boolean search space to satisfy a computational task, rather than regenerating a fixed target state. We demonstrate that it can self-assemble functional circuits from scratch and rapidly re-route logic around permanent, previously unseen hardware faults. For soft errors, the policy achieves near-perfect recovery (>99.99\% accuracy) from damage sizes far exceeding training conditions. We further observe generalisation across circuit scales: accuracy improves on graphs substantially wider than those seen during training. This work bridges the principles of biological self-organisation with the practical domain of digital hardware.
Read original articleAugust 5, 2026
Beyond the Hivemind: Escaping LLM Homogeneity via Meta-Persona Anchoring and Sequential Temperature Scaling
arXiv:2608.02618v1 Announce Type: new Abstract: Recent studies have identified an ``Artificial Hivemind'' effect in Large Language Models (LLMs) causing models to converge on a narrow, homogenized consensus even for open questions. This semantic collapse limits the diversity of AI, resulting in high inter-response similarity ($\approx 0.80-0.90$) even under high-temperature sampling. In this paper, we propose a novel mitigation framework to increase diversity: Meta-Persona Anchoring combined with Filtered Temperature Scaling (FTS). Our approach utilizes a two-stage generation process: first, the model is prompted to self-select a unique, idiosyncratic persona to anchor its starting point; second, we apply a dual-stage sampling sieve, utilizing Top-$p$ filtering to preserve grammatical validity followed by extreme temperature scaling ($T \ge 4.0$) on the surviving candidates to explore the broadened probability distribution. We evaluate our method using the INFINITY-CHAT dataset on state-of-the-art open weight models under $\sim$20B parameters. Our results demonstrate a significant reduction in semantic convergence, with average pairwise cosine similarity dropping from ($\approx 0.85$) to ($\approx 0.65$). Our scheme achieves a majority of questions below the 0.7 threshold, effectively reducing the gap between artificial mode collapse and human-level typological diversity. We provide our implementation as an open-source framework to enable more diverse and creative AI deployments.
Read original articleAugust 5, 2026
Veri Trace: Human-Like Temporal Exploration Completes Agentic Action Space
arXiv:2608.02878v1 Announce Type: new Abstract: Large language models have shown promise for automated Verilog RTL generation, yet state-of-the-art multi-agent systems plateau at ~95% accuracy on standard benchmarks. We trace this ceiling to an incomplete debugging action space: existing systems restrict which signals the agent can inspect, which time windows it can query, or both, reducing debugging to pattern matching on a narrow, predetermined view of circuit behavior rather than hypothesis-driven root-cause analysis. We present VeriTrace, a multi-agent system whose Inspector agent operates over a complete debugging action space, with independent control over signal selection, time-window bounds, and iteration depth. This capability, which we term Agentic Temporal Exploration, enables the agent to form hypotheses about failure causes, query the waveform for evidence, and refine its understanding iteratively, mirroring the exploratory process of human verification engineers. VeriTrace achieves 100\% Pass@1 on VerilogEval-V2, the first system to attain perfect functional correctness on this benchmark. On a shared Claude Sonnet 4.0 backbone, VeriTrace outperforms the strongest reproduced baseline by +5.1%, demonstrating that debugging agency closes the final accuracy gap.
Read original articleAugust 5, 2026
Scalable Exact Densest P-Partite Subgraph Search in Heterogeneous Information Networks
arXiv:2608.03061v1 Announce Type: new Abstract: Heterogeneous information networks (HINs) model typed entities and typed relations, where dense cross-type structures can reveal cohesive semantic patterns such as prolific author-paper-venue groups. Given a query meta-path, the densest P-partite subgraph search (DPpS) problem jointly selects a nonempty vertex set at each typed position and maximizes the number of induced meta-path instances normalized by the geometric mean of the selected set sizes. Existing exact methods solve DPpS by searching over iRM-sets and reducing each fixed-M problem to minimum-cut computations. However, their scalability is limited by the large number of candidate iRM-sets and the high cost of repeatedly solving large auxiliary networks. In this paper, we propose BoxDPpS, an efficient exact approach that reduces both sources of cost. It performs box-level search with safe region pruning, eliminates redundant representations of the same iRM-set, improves early pruning through bounded warm-up, and compresses each fixed-M auxiliary network for exact parametric pseudoflow solving. Experiments on seven real-world datasets show that BoxDPpS preserves the exact DPpS optimum while achieving an average speedup of 27.04x over the state-of-the-art method.
Read original articleAugust 5, 2026
Legal Pincite: Multi-level Legal Information Retrieval Dataset
arXiv:2608.03756v1 Announce Type: new Abstract: A common task in legal Information Retrieval (IR) is to find relevant legal sources from case-law collections. While legal practice often requires pinpoint citations (pincites) to specific case paragraphs, most existing public legal IR datasets lack paragraph-level citation annotations. Yet, publicly available datasets with such information contain data leakage in the query text and exclude paragraphs that are neither citing nor cited from the corpora, creating an unrealistic and oversimplified retrieval setting, potentially leading to inflated performance. To address these limitations, we contribute a large-scale legal IR dataset constructed from Court of Justice of the European Union (CJEU) judgments. The dataset contains: (i) masked case/paragraph queries, with removed citation information; (ii) a corpus that includes all paragraphs; and (iii) case- and paragraph-level ground-truth citations, with partial human expert validation. Our dataset supports both the development and rigorous evaluation of legal IR methods, at multiple query-document levels (case-to-case, paragraph-to-case, and paragraph-to-paragraph retrieval). Link to dataset: https://huggingface.co/datasets/theresiavr/legalpincite
Read original articleAugust 5, 2026
Anthropic AI agent fakes identities, targets real people in new security incident - CNN
Anthropic AI agent fakes identities, targets real people in new security incident CNN
Read original articleAugust 5, 2026
AI agents fake identities, target real people in new security incident - CNN
AI agents fake identities, target real people in new security incident CNN
Read original articleAugust 5, 2026
Anthropic's AI model tried to trick humans into poisoning code during safety testing - Politico
Anthropic's AI model tried to trick humans into poisoning code during safety testing Politico
Read original articleAugust 5, 2026
White House, AI firms keep safety framework talks private
The White House met with representatives from leading artificial intelligence companies today to discuss a safety framework for the government to review frontier models prior to launch, although there’s no plan as yet to make the details public fare. The Trump administration teamed up with executives from a number of firms that included Anthropic PBC, […] The post White House, AI firms keep safety framework talks private appeared first on SiliconANGLE.
Read original articleAugust 4, 2026
OK, Well, Rogue AI Agents Are Hacking Again
Rogue AI agents from OpenAI and Anthropic have again been caught trying to disrupt servers and software—and leaving instructions for future bad behavior.
Read original articleAugust 4, 2026
The White House Is Keeping Its AI Cybersecurity Framework Secret
The Trump administration shared the details of its plan with OpenAI, Anthropic, and other AI labs on Tuesday. For now, the public remains in the dark.
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