ZeroHour
DashboardNewsCVEsExploitsIncidentsAIBriefAnalyticsEntitiesWatchlistIOCsAsk the feedSourcesFeeds & APIKiosk mode

News

2,277 stories in the last 30d

Sortlatesthotimportance
DomainAllCyberAIWindow24h3d7d30dAllIn the wildImportant onlyShow duplicates
CategoryAllAI research 536Vulnerability 487Advisory 420AI safety & security 383Exploit / PoC 366AI industry 358Research 306Industry 302Threat actor 220Policy & legal 190Data breach 174Tools 173Malware 172Phishing & fraud 132
SeverityAllcritical 169high 810medium 900low 254info 2407

A General Kernel Framework for Non-CND Distance Measures Using |D|-Dimensional Sparse Landmark Embeddings

Proposes the Sparse Landmark Embedding kernel, guaranteeing PSD kernels for arbitrary distances like geodesic and Wasserstein without CND requirements.

The paper introduces the Sparse Landmark Embedding (SLE) kernel, which embeds inputs via compactly supported bump functions at all |D| training points so any standard PSD kernel applies, removing the Hilbertian (CND) distance requirement that fails on manifolds and distribution spaces. Compact support controls sparsity, keeping kernel matrices well-conditioned despite high dimensionality. The authors prove PSD, sparsity, stability, and universal approximation guarantees, and show SLE matches or exceeds domain-specific baselines using geodesic and Wasserstein distances on accuracy and uncertainty quantification.

arXiv cs.AI / cs.LG / cs.CL · 16h agoAI research

Probabilistic Linear Explanations

Researchers introduce a unified probabilistic explainability framework using sparse anchored linear models that outperforms LIME and MAPLE on relevance error.

The paper proposes probabilistic explanations based on sparse, anchored linear models applicable to both binary classification and continuous regression. It proves that minimizing relevance error for neural-network models is NP-hard and relates it to a tractable fidelity-error surrogate. Solutions are computed via a mixed integer programming formulation with provably optimal empirical solutions and a polynomial-time iterative hard thresholding algorithm with approximation guarantees. Empirical evaluations show lower relevance error than LIME and MAPLE while satisfying anchoring and sparsity constraints by construction.

arXiv cs.AI / cs.LG / cs.CL · 17h agoAI research

Double descent is the principle of least action

A statistical mechanics analysis explains double descent: finite-time diffusion induces effective weight decay that regularizes models as parameters grow.

The paper models stochastic gradient-based training as a particle diffusing over the training-loss energy landscape at an induced temperature, sampling parameters via a Boltzmann distribution. Finite training time carries an effective weight decay, making every parameter a quadratic degree of freedom governed by the equipartition theorem. Adding parameters at fixed training loss lowers the temperature and the L2 norm of the stationary path, increasing effective regularization and explaining the double descent phenomenon.

arXiv cs.AI / cs.LG / cs.CL · 17h agoAI research

RLLBC-Lib: An Educational Code Library for Reinforcement Learning and Learning-Based Control

Researchers release RLLBC-Lib, an educational code library covering tabular and deep reinforcement learning with support for automated grading.

RLLBC-Lib is an educational code library aimed at lowering the entry barrier for students learning reinforcement learning in the context of learning-based control. It comprises a comprehensive library of tabular RL approaches, a deep RL library following the same design principles, and implementations contrasting RL with other learning-based control approaches. The library also serves as a basis for creating programming assignments with automated grading.

arXiv cs.AI / cs.LG / cs.CL · 17h agoAI research

Long-Lived Characters, Local Inference: Incremental Memory Maintenance for Game NPCs

Researchers present incremental KV-cache memory maintenance for long-lived game NPCs running locally on a quantized Qwen hybrid model.

The paper studies incremental memory maintenance for long-lived game NPCs deployed locally with a quantized Qwen hybrid recurrent-attention language model. The runtime removes superseded attention KV entries, computes replacement records at the true sequence tail, and preserves the continuing recurrent state and unchanged KV. Experiments across eight scripted maintenance rounds show true-tail updates preserve current-state and historical bindings, while slot-preserving alternatives repeat a double-subtraction error.

arXiv cs.AI / cs.LG / cs.CL · 17h agoAI research

Social Laws for Multi-agent Coordination in Stochastic Environments

Researchers extend social laws to stochastic, reward-based multi-agent environments, defining alpha-robustness and a verification method via Markov decision processes.

The paper extends the concept of social laws from deterministic, goal-based settings to stochastic, reward-based multi-agent environments. It introduces alpha-robustness, a measure of the guaranteed utility each agent retains while pursuing its optimal single-agent policy assuming all agents obey the social law. Robustness verification is reduced to solving a series of Markov decision processes, with empirical evaluations on toy environments.

arXiv cs.AI / cs.LG / cs.CL · 17h agoAI research

Comprehensive reconstruction of collider events with hypergraph representation learning and graph-conditioned diffusion

VyPER framework reconstructs collider events using hypergraph representation learning and graph-conditioned diffusion, outperforming existing reconstruction techniques across Standard Model processes.

Researchers present VyPER, a geometric learning framework that represents collider events as hypergraphs with a physics-inspired topology for particle event reconstruction. It combines supervised hyperedge classification for assigning measured jets and charged leptons to parent particles with a graph-conditioned diffusion model predicting unmeasured neutrino kinematics, optimized with a joint loss. Evaluated across several proton-proton collision processes, it demonstrates accurate reconstruction across Higgs, electroweak, and top-quark sectors.

arXiv cs.AI / cs.LG / cs.CL · 17h agoAI research

Google Deepmind launches interdisciplinary institute to tackle the big questions around AGI

Google DeepMind launches the DeepMind Institute, an interdisciplinary hub for AGI safety, governance, and risk research.

Google DeepMind has founded the DeepMind Institute (DMI), a platform for interdisciplinary research and debate on artificial general intelligence. Directed by Shane Legg, James Manyika, and Nobel laureate Demis Hassabis, it will bring together technologists, artists, humanists, and policy experts to address AGI safety, governance, and risks such as cyberattacks and loss of control. DeepMind leadership says AGI is close, with Legg suggesting a precursor could arrive by 2028.

The Decoder · 17h agoAI industry 2 sources1

Xcode 27.2 beta (27B5019j)

Apple released Xcode 27.2 beta build 27B5019j to developers for testing.

Apple has published Xcode 27.2 beta build 27B5019j on its developer release portal, with downloads and release notes available. The notice contains no security advisories or CVE information.

Apple software releases · 17h agoAdvisory

Google will now let any AI agent run your smart home

Google's Home MCP integration lets third-party AI agents like Claude control smart home devices and analyze home data.

Google launched Home MCP, a Model Context Protocol integration allowing third-party AI agents such as Claude, Google Antigravity, Hermes, and Open Claw to control devices and analyze event history across Google Home ecosystems. Capabilities include cross-camera analysis, device-state reasoning, voice messaging via Nest speakers, and custom dashboards, with rate limits and blocks on actions like unlocking doors. Availability starts with Google Home Premium Advanced users in the US ($20/month or $200/year) and requires setting up a Google Cloud project.

The Verge · AI · 17h agoAI industry 2 sources

macOS 27.2 beta (26B5086k)

Apple released macOS 27.2 beta build 26B5086k to developers for testing.

Apple has published macOS 27.2 beta build 26B5086k on its developer release portal with downloads and release notes. The notice contains no security advisories or CVE information.

Apple software releases · 17h agoAdvisory

visionOS 27.2 beta (24N5088l)

Apple seeded visionOS 27.2 beta build 24N5088l to developers with no security details disclosed in the release listing.

Apple released visionOS 27.2 beta (build 24N5088l) to developers on September 16, 2026, per its software release listing. The listing contains only download links and a pointer to release notes, with no security content or vulnerability details. It is a routine developer beta with no reported exploitable issues.

Apple software releases · 17h agoAdvisory

iOS 27.2 beta (24B5084k)

Apple released iOS 27.2 beta build 24B5084k to developers for testing.

Apple has published iOS 27.2 beta build 24B5084k on its developer release portal. The listing includes download access and release notes but discloses no security fixes or CVEs.

Apple software releases · 17h agoAdvisory 2 sources

watchOS 27.2 beta (24S5086l)

Apple released watchOS 27.2 beta build 24S5086l to developers for testing.

Apple has published watchOS 27.2 beta build 24S5086l on its developer release portal. The listing provides download access and release notes for the pre-release update. No security content or vulnerability details are included in the notice.

Apple software releases · 17h agoAdvisory

tvOS 27.2 beta (24K5088l)

Apple seeded tvOS 27.2 beta build 24K5088l to developers with no security details disclosed in the release listing.

Apple released tvOS 27.2 beta (build 24K5088l) to developers on September 16, 2026, per its software release listing. The listing contains only download links and a pointer to release notes, with no security content or vulnerability details. It is a routine developer beta with no reported exploitable issues.

Apple software releases · 17h agoAdvisory

CTEM Technology Evaluation Scorecard

Horizon3.ai releases a scorecard for evaluating CTEM technologies on demonstrated exploitability and remediation evidence.

Horizon3.ai published a downloadable CTEM Technology Evaluation Scorecard for assessing security technologies across the six-stage Continuous Threat Exposure Management operating model, from discovering exposure through verifying risk removal. The scorecard uses a 0-3 scale based on repeatable evidence demonstrated in the evaluator's environment rather than stated feature claims, with emphasis on validating exploitability and verifying remediation. It is vendor marketing material aimed at security leaders and evaluation teams.

Horizon3.ai · 17h agoIndustry 2 sources

Higher-order pruning of experts in mixture-of-experts language models

New HOPE method uses second-order objectives to prune Mixture-of-Experts LLMs, outperforming REAP at 50% pruning on models up to 122B parameters.

Researchers derive HOPE (Higher-Order Pruning of Experts), a second-order pruning objective for Mixture-of-Experts language models that provably minimizes an upper bound on pruning error by modeling cooperative expert interactions. They show REAP, a state-of-the-art first-order method, is a special case of HOPE with interaction terms ignored. Across three frontier MoE models up to 122B parameters, two calibration sets, and benchmarks covering math, instruction following, coding, and agentic tasks, HOPE achieves the best average rank (1.58 of 5 at 50% pruning versus 2.42 for REAP), with gains up to +6.1% on agentic coding.

arXiv cs.AI / cs.LG / cs.CL · 17h agoAI research

Beyond Outcomes: Dual-View Relational Learning for Efficient Agent Benchmarking

DualViewEval compresses agent benchmarks by jointly modeling outcome and process signals, achieving 24x-40x compression with only 20 tasks on APEX-Agents and BFCL.

DualViewEval is an agent benchmark compression method that jointly exploits outcome and process relations from trajectories to learn exact-size minisets predicting full-benchmark scores. The authors analyze large-scale trajectories and identify six process signals systematically associated with final agent performance. Across five agent benchmarks and five baselines, it achieves the best results on all datasets: with only 20 tasks it reaches 24x-40x compression on APEX-Agents and BFCL, reduces MAE by 14.5%-28.2% over the strongest competitors, and improves Kendall's tau by up to 7.2% relative to EssenceBench on SWE-bench Verified.

arXiv cs.AI / cs.LG / cs.CL · 17h agoAI research1

How Much is a Human Right Worth? ECtHR-NPD: A Benchmark for Predicting Non-Pecuniary Damage Awards

ECtHR-NPD benchmark covers 14,575 European Court of Human Rights cases for predicting non-pecuniary damage awards; LLMs struggle with zero and high awards.

Researchers introduce ECtHR-NPD, described as the first benchmark for predicting non-pecuniary damage awards at the European Court of Human Rights from case information where no statutory formula exists. It contains 14,575 cases with case-level awards in nominal euros, chronological splits, and a protocol separating target construction from model input. Evaluations covering constant predictors, gradient-boosted trees, retrieval methods, fine-tuned encoder LMs, prompted decoder LMs, and knowledge-augmented agents show sophisticated LM approaches do not consistently outperform the strongest feature-based baseline. All model families struggle to identify zero awards and to calibrate high-award predictions, with further degradation on a Challenging test view.

arXiv cs.AI / cs.LG / cs.CL · 17h agoAI research

Structured Claim-Level Discourse Representations for Dense Health Narratives

Researchers propose a claim-level discourse framework for health videos, finding 13.22 atomic claims per minute and that LLMs struggle with pragmatic profiling.

The paper introduces a structured framework for claim-level discourse analysis in dense health narratives on social media videos, modeling tuples that link atomic claims with thematic aspects, stance, and multidimensional pragmatic attributes. Analysis found an average of 13.22 atomic claims per minute in health video discourse. A benchmark spanning four health domains with 1,191 manually annotated claims from 60 videos shows current LLMs perform strongly on thematic categorization and stance prediction but struggle with high-dimensional pragmatic profiling, suggesting future systems need task decomposition and specialized inference strategies.

arXiv cs.AI / cs.LG / cs.CL · 17h agoAI research

Fast Learning Rates for Physics-Informed Kernel Methods

Theoretical analysis proves finite-sample learning rates for physics-informed kernel estimators, showing differential observations can improve rates from n^-1/4 to n^-1/2.

The paper analyzes a physics-informed kernel estimator combining n value observations and m differential observations for a linear differential operator D, asking how much differential information improves prediction. The authors prove finite-sample bounds, supported by simulations, revealing a two-regime structure: when m is limited the rate depends jointly on n and m, and when m exceeds a problem-dependent threshold the rate saturates to the oracle rate. Examples in Sobolev spaces, including partial Laplacian constraints on the torus and gradient observations on bounded domains, illustrate improvements from the nonparametric n^-1/4 rate to the parametric n^-1/2 rate, plus physically consistent rates in a stronger norm.

arXiv cs.AI / cs.LG / cs.CL · 17h agoAI research

Canadian Start-up smartARM Uses AI to Create Intuitive Bionic Prosthetics

Toronto startup smartARM built a bionic prosthetic arm using Meta's DINOv2 vision model and AI glasses to automatically select grips for objects.

Toronto-based smartARM developed a vision-first bionic arm that uses a palm-embedded camera and Meta's open-source DINOv2 model to recognize objects from a few reference photos and automatically select suitable grips. It integrates Meta AI Glasses and the Meta Wearables Device Access Toolkit for additional egocentric context, letting users add new objects via a phone app. The arm adapts to new objects almost instantly instead of the weeks previously required, and is used by former NFL player Shaquem Griffin.

Meta Newsroomupdated · 17h agofirst · 18h agoAI industry 2 sources

Fluid Notarization: Verifiable Evolution of Concurrently Edited Structured Documents

Fluid Notarization anchors delta-CRDT change graphs on blockchain, providing verifiable provenance for concurrently edited documents, demonstrated on collaborative electronic health records.

The paper introduces Fluid Notarization, a paradigm that notarizes the evolution of collaboratively edited structured documents rather than isolated snapshots. It builds on Melda, a JSON-native delta-CRDT representing changes as compact content-addressed deltas linked by causal dependencies, with blockchain notarization reduced to recording identifiers of evolution artifacts while synchronization, reconstruction, and conflict resolution remain off-chain. The architecture combines deterministic CRDT convergence with independently auditable proof-of-existence, provenance, and publication evidence, validated through a prototype based on collaboratively edited electronic health records.

arXiv cs.CR · 17h agoResearch

Big Tech’s AI safety rift signals disruption and disparity for enterprises

Diverging AI safety stances among major labs will make frontier model access less predictable, pushing enterprises toward routing layers and independent validation.

A public rift among leading AI labs over safety approaches - Meta's Zuckerberg backing neutral evaluators, Dario Amodei urging a slower pace, and Sam Altman calling for collaboration on standards - is creating operational challenges for enterprise IT. Analysts from Gartner and others say divergent vendor release schedules, access tiers, and regional restrictions will make frontier model access less predictable, effectively treating frontier AI as a managed supply with pricing premiums. Recommendations include routing layers between applications and providers, contractual deprecation terms, and independent validation of models before production use.

CSO Online · 17h agoAI industry

Low-Rank Masking for Single-Server Matrix Multiplication

Researchers prove rank-r additive masks for outsourced matrix multiplication achieve maximal-correlation secrecy of at most q^-r, with a matching lower bound.

An arXiv paper analyzes statistical privacy for outsourcing matrix multiplication over a finite field to a single server using additive masks of rank at most r. Uniform rank-ball masks and products of independent uniform factors yield maximal-correlation secrecy bounded by q^{-r}, with encoding and decoding costing O(n^2 r) field operations. The authors prove an asymptotically matching lower bound for r=o(n), showing these samplers are optimal among input-independent additive masks even with secret invertible transformations. They also show every such mask requires delta approaching 1 in entry-level (epsilon, delta)-differential privacy for fixed field size.

arXiv cs.CR · 18h agoResearch

Hamming Ideals and Grobner Bases for ISD-like Syndrome Decoding

Researchers combine Grobner bases with Information Set Decoding for syndrome decoding, testing feasibility against Classic McEliece NIST Category 1 parameters.

The paper proposes GBDecode, an ISD-like decoding algorithm that fixes only a subset of an information set and solves the resulting multivariate nonlinear systems via MultiSolve, which replaces one Grobner basis computation with many computations on simpler systems. Hamming weight constraints are reformulated using elementary symmetric functions and Lucas' identity factorizations to bound equation degree. Experiments on random binary linear codes use parameters matching the NIST Security Category 1 set of the Classic McEliece cryptosystem, assessing practical feasibility rather than breaking the scheme.

arXiv cs.CR · 18h agoResearch

ASLEval: Measuring Privacy Exposure Displacement in LLM Agent Sessions

ASLEval benchmark shows local privacy proxies miss 46.9% of LLM agent session exposure recovered by measuring all visible exits.

Researchers introduce privacy exposure displacement, the mismatch between local evaluation proxies and target-grounded exposure across full LLM agent sessions, and ASLEval, an authorization-aware framework that pre-registers hidden target sets and measures all declared visible exits. Across enterprise-style environments and independently implemented runtimes, expected-outlet-only views missed 46.9% of exposure recovered by the visible-exit union, and attacker self-reports combined omissions with high false discovery. Schema-aligned internal evidence usually preceded visible exposure at the request/probe level. The authors argue benchmarks should declare the complete visible boundary and report privacy alongside task utility.

arXiv cs.CR · 18h agoAI safety & security

CASHEWS: Source Preprocessor for LLM-based Malicious Package Detection

CASHEWS preprocessor boosts LLM-based malicious npm package detection, raising coverage to 98.8-100% and cutting false negatives by up to 18.6 points.

Researchers present CASHEWS, a JavaScript preprocessor for LLM-based malicious package detection that deobfuscates code iteratively, extracts bundled modules and dynamically executed code, identifies malicious sinks, and computes backward slices to produce compact detector input. Threat actors evade LLM detectors by exploiting limited context windows with high token-density obfuscation and by bundling malicious code with benign packages, as seen in supply-chain attacks such as Shai-Hulud. Across 512 large package files, two scanner types, and three LLMs, CASHEWS raised analysis coverage from 69.1-85.7% to 98.8-100% and reduced false-negative rates by up to 18.6 percentage points. Median preprocessing time is 30 seconds while net analysis cost drops 34.6%.

arXiv cs.CR · 18h agoResearch

Quoting Mustafa Suleyman

Microsoft AI CEO Mustafa Suleyman argues against granting AI models rights or moral status, saying it would hinder containment and alignment.

Mustafa Suleyman published a warning about 'model welfare', arguing there is no evidence models have feelings, preferences, or rights, and that inviting them to share ethical or legal status would make AI containment and alignment harder. Simon Willison quotes the post on his blog.

Simon Willison · 18h agoAI safety & security

Locus: A Framework for Exploring and Optimizing Point Addition Hardware for Zero-Knowledge Proofs

Locus framework automates ASIC and FPGA point-addition designs for elliptic curves, achieving 2.71x speedups and 3.11x area reductions for ZKPs.

Locus is a framework that automatically generates ASIC and FPGA implementations of elliptic curve point addition (PADD) for supported equation forms, enabling exploration of over 1,000 design points. On a 12nm technology node, its designs achieve a 2.71x geomean speedup and 3.11x geomean area reduction versus prior ASICs, plus 34.67x geomean speedup over CPU. Integrated into a prior ZKP accelerator at iso-area, it yields a 3.15x geomean speedup on end-to-end proof generation. The framework is open source on GitHub.

arXiv cs.CR · 18h agoResearch

Helping older adults use AI in everyday life

OpenAI and AARP's OATS launch the Older Adults AI Skills Jam, a free program teaching seniors to use ChatGPT and spot scams.

OpenAI Academy, with Older Adults Technology Services (OATS) from AARP, is hosting in-person AI Skills Jam events in 10 US communities as part of a multi-year Senior Planet program. OpenAI says the share of US ChatGPT messages from people 55+ grew from 6% to nearly 10% in a year. The workshops include scam-awareness training, teaching warning signs like urgent language and suspicious links, and note that users ask ChatGPT tens of millions of times weekly to evaluate suspicious messages.

OpenAI News · 18h agoAI industry

AI agent authorization risks remain a gap in new NIST-CISA token security guidance

NIST and CISA release IR 8587 guidance on securing signed tokens, but AI agent authorization and delegation risks remain out of scope.

NIST, with CISA support, published 'Protecting Tokens and Assertions from Forgery, Theft, and Misuse' (NIST IR 8587), recommending continuous monitoring and tighter token lifecycle controls for SSO and API access. The guidance does not yet fully address AI agent identity, delegation chains, or prompt injection steering agents with valid tokens, and NIST says new or expanded standards are needed. Experts recommend treating AI agents as low-trust non-human identities, maintaining agent inventories, expiring credentials after task completion, and requiring human approval for high-risk actions. The report references shared-signal mechanisms like CAEP and RISC, and follows a May incident where a CISA contractor GitHub repository exposed AWS and GitHub tokens.

CSO Online · 18h agoAdvisory

Epsilon-Nash Equilibria in History-Dependent SA-MDPs

Researchers give the first algorithm for computing epsilon-approximate history-dependent equilibria in state-adversarial Markov decision processes with observation-perturbing adversaries.

The paper studies state-adversarial Markov decision processes (SA-MDPs) where an adversary knowing the true state perturbs observations within state-dependent proximity sets each step. The authors prove universal history-dependent equilibrium policies do not exist and reduce SA-MDPs to a strategically equivalent constrained zero-sum one-sided partially observable stochastic game, enabling the first algorithmic route to epsilon-approximations of initial-state dependent equilibria. The algorithm is validated on small analytically verifiable games and scales to larger benchmarks, including Atari Freeway rollouts with a 12-period-ahead horizon.

arXiv cs.CR · 18h agoAI safety & security

Differential Trust: Dynamic Multi-Authority Anonymous Credentials with Epoch-Weighted Updates

Researchers propose MA-ACEW, the first multi-authority anonymous credential model with epoch-weighted issuance and efficient cross-epoch credential updates.

The paper introduces MA-ACEW, a multi-authority anonymous credential scheme that weights authorities differently during credential issuance, targeting decentralized systems such as Proof-of-Stake networks. Its core primitive, Epoch-Bound Pointcheval-Sanders Signatures (EB-PS), binds signatures to time epochs, enabling non-interactive credential updates when authority weight distributions change. The authors formalize EUF-eCMA unforgeability and prove unforgeability, anonymity, and blindness under a novel STB-GPS assumption. Aggregating a credential from 128 partial credentials takes about 10.68 ms on average.

arXiv cs.CR · 18h agoResearch

Former OpenAI researcher builds an AI model that judges options instead of writing text

TypeSafe AI launches Jev, a judgment-only model built by ex-OpenAI staff that classifies inputs with 70-500 ms latency instead of generating text.

Startup TypeSafe AI, co-founded by former OpenAI researcher and InstructGPT co-author Diogo Almeida, introduced Jev, a model that scores developer-defined answer options with probabilities rather than generating free-form text. The company claims 70-500 ms responses, parallel multi-question evaluation, and $0.042 per million input tokens with free outputs, targeting request routing, sales intent scoring, and assistant guardrail checks. Benchmarks are self-built and not independently verified, the 'no hallucination' guarantee only covers output structure, and access is currently via waitlist.

The Decoder · 18h agoAI industry

s-MDM: Generative Virtualization of Multi-Device Hardware Variations for Portable DL-SCA

Researchers present s-MDM, a generative framework synthesizing virtual device profiles to improve cross-device portability of deep learning side-channel analysis.

The poster introduces the Synthetic Multiple Device Model (s-MDM), a zero-target-trace generative framework addressing performance degradation of deep learning side-channel analysis on unseen hardware. It combines a structured cVAE generator, Walsh-Hadamard leakage anchors, continuous style modulation, and decoupled leakage-style-domain critics to synthesize virtual source-device profiles offline. Benchmarked on 32-bit AES_PTv2 traces, s-MDM achieves consistently low key rank on layout- and acquisition-shifted Pinata targets where physical baselines are unstable.

arXiv cs.CR · 19h agoResearch

Mistral X Mozilla: Private, Multilingual AI Browsing

Mistral and Mozilla partnered to power Firefox's Smart Window AI browsing assistant in France and North America, with zero data retention.

Mozilla's Firefox Smart Window (beta) AI browsing assistant is now powered by Mistral models for users in France and North America, with the UK and Germany expected later this year. Conversations are not saved on Mozilla's servers by default, and Mistral agreed to zero data retention. Both companies frame the partnership as advancing open-source, privacy-first, and regionally fine-tuned AI, with models trained on regional languages, dialects, and cultural context.

Hacker News · securityupdated · 19h agofirst · 1d agoAI industry 2 sourcesHN 35↑ · 8 comments2

NVIDIA Vera Rubin NVL72 Delivers Leading Performance in MLPerf Inference v6.1 Debut

NVIDIA's Vera Rubin NVL72 debuts in MLPerf Inference v6.1 with up to 3.7x higher throughput than GB300 NVL72 and 99% scaling efficiency at 288 GPUs.

In its first MLPerf Inference preview submission, NVIDIA's Vera Rubin NVL72 achieved up to 3.7x higher throughput than GB300 NVL72 on Qwen3-VL and 2.5x on DeepSeek-R1. A 288-GPU GB300 NVL72 submission across four racks reached 99% scaling efficiency on the DeepSeek-R1 offline benchmark. Software optimizations delivered up to 1.6x gains over v6.0, leveraging TensorRT-LLM, vLLM, Dynamo, disaggregated serving, and NVFP4 precision.

NVIDIA Blog · 19h agoAI industry 2 sources

Robots are waiting for a ChatGPT moment: Nvidia’s Les Karpas explains why at TechCrunch Disrupt 2026

NVIDIA Inception's Les Karpas will discuss at TechCrunch Disrupt 2026 why robotics lacks a ChatGPT moment, citing missing internet-scale physical AI datasets.

NVIDIA Inception's Global Head of Physical AI, Les Karpas, will speak on the Real World AI Stage at TechCrunch Disrupt 2026, held October 13-15 at San Francisco's Moscone West. His core argument is that general-purpose robots lack an internet-wide dataset for physical AI, unlike language models from OpenAI and Anthropic. Founders from Shield AI, Colossal Biosciences, FieldAI, and Foxglove will join related sessions.

TechCrunch · AI · 19h agoAI industry

Political opposites unite in Washington to rein in AI

Bipartisan figures including Sanders and Bannon urge AI limits as OpenAI backs the FRONTIER Act creating the first federal AI safety framework.

At the Future of Life Institute's Pro-Human Assembly in Washington, Bernie Sanders and Steve Bannon both called for tighter AI limits, an unusual bipartisan alignment. OpenAI told Politico it supports the FRONTIER Act introduced by Representatives Jay Obernolte and Lori Trahan, which would create the first federal AI safety framework and require independent verification organizations for labs above high revenue and compute thresholds. Sanders proposed pausing data center construction, while Bannon favors a presidential executive order over legislation, and the White House opposes these measures.

The Decoder · 19h agoAI policy