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Kalman Delta Networks: Uncertainty-aware Associative Memory

Researchers propose Kalman Delta Networks, adding Kalman-filter uncertainty tracking to delta-rule linear attention, improving perplexity and accuracy at 750M and 1.3B parameters.

The paper introduces Kalman Delta Networks (KDNs), which reformulate recurrent associative memory in linear-attention models as a linear-Gaussian state-space model where the Kalman gain weights each write by accumulated evidence and observation reliability. Two scan-compatible approximations, Diagonal KDN via online mean-field variational inference and Isotropic KDN with a single uncertainty scalar per head, enable associative scans with logarithmic parallel depth. Delta-rule updates are shown to be a special case of this formulation. KDN variants consistently improve perplexity and mean downstream accuracy over state-of-the-art linear-attention baselines in controlled pretraining at 750M and 1.3B parameters.

Hugging Face daily papers · 9d agoAI research

Safety for Whom? Refusing the Right Subset of a Topic, Not the Whole Topic

Multiverse Computing's Hugging Face post argues language models should refuse only the relevant subset of a topic instead of over-refusing whole subjects.

A Hugging Face blog post by Multiverse Computing examines refusal granularity in language models, arguing models should refuse the relevant subset of a topic rather than the entire topic. No full article text was available for additional technical detail.

Hugging Face Blog · 7d agoAI safety & security

Harnessing CLIP and DINO: An Uncertainty-Aware Cascaded Fusion Network for Generalizable Deepfake Image Detection

UCF-Net fuses CLIP and DINO features with entropy-based uncertainty weighting to improve generalizable deepfake image detection across generators.

Researchers propose UCF-Net, an uncertainty-aware cascaded fusion network that combines CLIP's language-aligned semantic priors with DINO's self-supervised visual-structure priors for deepfake detection. It aggregates hierarchical features across transformer depths via layer-wise expert modules and performs weighted fusion driven by entropy-derived uncertainty. The authors consolidate public deepfake datasets into a unified benchmark of roughly 4 million images plus a cross-generator set of over 8,000 faces from eight recent generators, where UCF-Net achieves the best mean AUC among evaluated methods, though zero-shot transfer remains challenging.

Hugging Face daily papers · 9d agoAI research

"Chilling" warning or overreaction? AI bioweapons report divides experts

Science article examines expert disagreement over whether a report on AI-enabled bioweapons risks is a chilling warning or an overreaction.

A Science.org article, shared on Hacker News with 20 points and 2 comments, covers expert divisions over an AI bioweapons report and whether its warnings are justified or exaggerated. The discussion reflects ongoing debate in the AI safety and biosecurity community about assessing AI's role in biological threat enhancement. Minimal detail is available from the item itself.

Hacker News · security · 2d agoAI safety & securityHN 20↑ · 2 comments

Pilot Early, Commit Late: A Real-Options Model of Enterprise AI Adoption under Rapid Technological Progress

Real-options model shows firms should pilot AI early and commit late because rapid frontier progress raises experimentation value over irreversible deployment.

The paper builds a two-period decision model where firms choose among immediate AI deployment, a limited pilot, or waiting under frontier uncertainty. It derives five timing results plus a comparative result on where learning occurs, including that frontier uncertainty raises the value of waiting and piloting. Valuable organization-specific learning creates a region where 'pilot early, commit late' is optimal, and a closed-form modularity threshold exists above which immediate deployment dominates. A continuous-time extension recovers the standard result that uncertainty raises adoption thresholds while capability and modularity lower them.

arXiv cs.AI / cs.LG / cs.CL · 1d agoAI research

Understanding Operator Attitudes Toward AI-Supported Decision Making in Maritime Operations

A survey of maritime professionals finds positive but scenario-sensitive attitudes toward AI collision-avoidance assistants, urging design for calibrated reliance.

The study surveyed maritime stakeholders on attitudes toward AI-supported collision-avoidance assistants for Maritime Autonomous Surface Ships, measuring technology anxiety, trust in automation, and explanation quality with established questionnaires plus thematic analysis of open responses. Results show generally positive disposition, no clear age-related openness differences, stable trust across scenarios, and multidimensional, scenario-sensitive explanation ratings. Participants valued decision support and situation awareness but worried about AI reliability, over-reliance, and skill loss; the authors recommend designing for calibrated reliance with domain experts in the loop rather than maximizing automation or trust.

arXiv cs.AI / cs.LG / cs.CL · 5d agoAI research1

What Does an LLM-Agent Leaderboard Rank Actually Compare?

A methodological study shows close LLM-agent leaderboard rank gaps on SWE-bench and similar benchmarks often do not support superiority claims.

The paper defines an estimand-aware pairwise procedure for comparing agents, checking common support and applying explicit uncertainty rules and practical margins. Across SWE-bench, AgentRewardBench, and tau2-bench, close rank differences are frequently unresolved, and proxy labels or utility rules can change which system is selected. The authors argue a leaderboard score summarizes a released evaluation but does not by itself justify pairwise superiority conclusions.

arXiv cs.AI / cs.LG / cs.CL · 8d agoAI research

One resignation turned the embers of AI fear into a wildfire

Interconnects argues a frontier-lab researcher's safety resignation went viral via media coordination, reigniting AI existential-risk discourse.

The essay analyzes why researcher Jacob Coxon's resignation over AI safety risks went viral, aided by a Wall Street Journal exclusive, advocacy-group amplification, and Daniel Kokotajlo's same-day Joe Rogan appearance. It notes Evan Hubinger's >10% extinction-risk figure and criticizes existential-risk discourse for conflating very different meanings of the term. The author assigns near-zero probability to complete extinction but argues concrete risks such as cyberattacks on critical infrastructure and bio-risks merit debate. It also rejects recursive self-improvement forecasts, proposing 'lossy self-improvement' where models excel at math and code but remain limited elsewhere.

Interconnects · 5d agoAI safety & security

[AINews] GPT-6 Astra: OpenAI’s biggest LLM launch of all time

OpenAI launched GPT-6 Astra, its new flagship model, claiming state-of-the-art computer use, software engineering, math, and cybersecurity capabilities.

OpenAI launched GPT-6 Astra as its new flagship model, describing it as its most intelligent and aligned model with state-of-the-art computer use, software engineering, and math/science capabilities. Pricing is $10/$50 per 1M input/output tokens standard ($20/$100 fast tier), rolling out first to limited organizations, then ChatGPT Plus/Pro/Business/Enterprise, the API, and AWS. OpenAI claims 99.9% on ARC-AGI-3, 98% on FrontierMath Tier 4, and 100% on ExploitBench. Artificial Analysis scored Astra 67 on the Coding Agent Index and 61 on the Intelligence Index, behind Claude Fable 5.1, and the system card drew attention for reporting decreased chain-of-thought monitorability despite alignment gains.

Latent Space · 12d agoModel release3

What the AI Warning Letter Completely Missed

Opinion piece argues the recent AI warning letter identifies a risk window but omits which actors pose risks and who can mitigate.

This Dark Reading commentary critiques a recent AI warning letter for correctly identifying an approaching risk window while failing to name who is coming through it or who will close it. The piece is brief opinion commentary on AI risk discourse rather than a technical report.

Dark Reading · 12d agoAI safety & security

Google's new AI model predicts the future from sales data, weather, and discount schedules

Google Research released TimesFM-3, a 330M-parameter multivariate time series forecasting model that tops Gift-Eval, FEV-Bench, and Time benchmarks and is on Hugging Face.

Google Research released TimesFM-3, a 330-million-parameter Transformer-based time series forecasting model trained on more than one trillion real and synthetic data points. It works zero-shot and adds multivariate support, ingesting related series, historical-only covariates, and known future events such as discount schedules and weather forecasts, while filling all future time steps in a single one-shot pass. Google reports first place among pretrained forecasting models on Gift-Eval, FEV-Bench, and Time, ahead of Amazon's Chronos-2, the Toto-2.0 family, and its own TimesFM-2.5. Weights are available on GitHub and Hugging Face, with BigQuery integration planned in the coming weeks.

The Decoder · 4d agoModel release2

Feeling sad about AI

Simon Willison argues developers disheartened by AI coding agents should move past existential crisis and refocus on broader engineering problems.

In a comment on a Hacker News thread titled 'Feeling sad about AI', Simon Willison reflects on the discouragement engineers feel when AI coding agents complete in an hour work that previously took a week. He notes that translating an exact specification into decent code is no longer a unique skill. He counsels that engineers who accept this shift can apply their experience to the larger set of problems they still face.

Simon Willison · 4d agoAI industry1

What must happen for AI’s trillion-dollar gamble to pay off

Hyperscalers need 2.7x productivity gains by 2030 to justify nearly $1.1 trillion in AI data center spending, or risk bankruptcy and capital misallocation.

Wharton finance professor Jessica Wachter estimates hyperscaler AI expenditure will reach nearly $1.1 trillion through 2027 and that a 2.7x productivity increase is needed to break even by 2030. AI revenues of roughly $150-200 billion this year fall far short of about $750 billion in annual spending, with total investment from Alphabet, Microsoft, Amazon, Meta, and Oracle potentially exceeding $5 trillion over four years. Alphabet reported its first free cash flow deficit (about $5.9 billion) since its 2004 IPO due to AI infrastructure costs. Researchers warn that failed demand could make the buildout the largest capital misallocation in history, with depreciating GPU chips risking stranded assets.

MIT Technology Review · AI · 1d agoAI industry

When will average people feel AI’s impact?

Interconnects essay argues AI's impact is still a rounding error for average people, comparing looming wage stagnation to Engels' pause.

An Interconnects essay argues that AI currently touches daily life far less than previous industrial revolutions, since its benefits are concentrated in knowledge work and lack tangible consumer goods. The author invokes Engels' pause (1790-1840), when British wages stagnated amid rapid GDP growth, as a warning that popular backlash could kneecap AI's development. He contends the current phase is about building compounding infrastructure, and predicts daily life may look similar even 50 years from now.

Interconnects · 6d agoAI industry1

U.S. Disrupts Xinbi Guarantee Scam Marketplace, Freezes $52.8 Million in Crypto

US DOJ and Treasury disrupt Xinbi Guarantee Telegram scam marketplace, sanctioning it and freezing $52.8M in USDT across 52 wallets.

The DOJ seized Xinbi Guarantee's Telegram channels and cryptocurrency wallets while OFAC sanctioned the marketplace, freezing $52.8 million in USDT from 52 wallets and bringing the Scam Center Strike Force's total restrained funds to roughly $938 million. Elliptic, which worked with the Secret Service, estimates Xinbi has processed $30 billion in transactions since around 2022, serving pig-butchering scam operators and links to North Korean hackers, Jin Bei Group, and Prince Group TCO. The strike force dismantled 13 scam compounds in Madagascar, seizing over 3,200 devices and interviewing roughly 400 arrestees, with about 30 Chinese compound leaders repatriated to China. After Tether froze funds, Xinbi began converting remaining USDT into the USDD stablecoin.

The Hacker Newsupdated · 5d agofirst · 6d agoPolicy & legal 4 sources

Perturbation Probing: A New Diagnostic for the Fragility of LLM Safety

Unit 42 research shows LLM safety refusals concentrate in a thin neural layer, motivating external, multi-layered AI security controls.

Palo Alto Networks Unit 42 introduces Perturbation Probing, a diagnostic technique for measuring the fragility of LLM safety mechanisms. The research finds that safety refusal behavior is localized within a thin neural layer, implying small perturbations can undermine built-in refusals. The authors argue this motivates external, multi-layered security defenses on top of model-internal safety training.

Palo Alto Unit 42 · 18d agoAI safety & security

Kids’ online safety bill faces dim prospects of passage this session despite progress

Kids Online Safety Act clears Senate committee but passage looks unlikely this session amid House-Senate deadlock over the duty-of-care provision.

KOSA advanced out of the Senate Commerce Committee, but the chambers remain split on a duty-of-care provision requiring platforms to act with reasonable caution to prevent foreseeable harm, which House leadership opposes over First Amendment and negligence-lawsuit concerns. The Senate passed KOSA 91-3 last Congress before it died in the House, and the House passed its own version without the duty of care in June as part of a larger package, after stripping a state-law preemption provision. Observers say even Senate passage this year is a struggle given the short calendar, with a lame-duck window between November and January the more plausible path, and Majority Leader John Thune controlling whether a roll-call vote happens.

The Record · 23d agoPolicy & legal

Kalman Delta Networks: Uncertainty-aware Associative Memory

Kalman Delta Networks add uncertainty tracking to linear-attention associative memory, improving perplexity and downstream accuracy at 750M and 1.3B scales.

Kalman Delta Networks reformulate recurrent associative memory in linear-attention models as a linear-Gaussian state-space model, allowing the Kalman gain to weight each residual write by accumulated evidence and observation reliability; Delta-rule updates emerge as a special case lacking covariance tracking. Two scan-compatible approximations, Diagonal KDN (online mean-field variational inference) and Isotropic KDN (one uncertainty scalar per head), produce Mobius-map uncertainty recurrences enabling associative scans with logarithmic parallel depth. Controlled pretraining at 750M and 1.3B parameters consistently improves perplexity and mean downstream accuracy over state-of-the-art linear-attention models.

arXiv cs.AI / cs.LG / cs.CL · 8d agoAI research1

Latest open artifacts (#24): Motif-3, GLM-5.3, Hy4-preview and open model licenses

Interconnects surveys new open models—Motif-3, GLM-5.3, Hy4-preview—while analyzing a licensing split: Western labs opening up, Chinese frontier labs getting restrictive.

The roundup covers Motif-3 (MIT license, strong scores for its size), GLM-5.3 (switched from MIT to a custom license with a $10 billion revenue threshold and undefined 'affiliates' clause requiring Z.AI security review), and Tencent's Hy4-preview (competent but prone to overthinking). It also notes dots3-note-prev from RedNote/Xiaohongshu (won IMO 2026 with a perfect score), Qwen3.8-Flash-Next (125B-A6B with GDN and Qwen Sparse Attention), NVIDIA Nemotron-3.5-Lightning-30B-A3B-BF16, and Ling-3.0-flash. The core theme: Google and Meta adopted Apache 2.0 while Chinese frontier labs (Zhipu, Kimi K3, MiniMax M3) adopted restrictive commercial licenses.

Interconnects · 7d agoAI research

A Generalization of Amari's Bayesian Duality

Paper generalizes Amari's Bayesian duality by connecting it to a convex duality of Bayes' rule.

The authors revisit Amari's less-known work on Bayesian duality from information geometry. They connect Bayesian duality to a convex duality formulation of Bayes' rule and present a generalization of it. The paper is purely theoretical and discusses relevance for modern AI, with no experiments or model releases.

arXiv cs.AI / cs.LG / cs.CL · 7d agoAI research

Certificate failures can cost firms over $250,000

DigiCert survey finds certificate failures cost firms over $250,000 per incident, with 47-day TLS certificates set to multiply management workloads by 2029.

DigiCert's Certificate Management Outlook reports that 34% of companies experienced outages from expired certificates and 40% from mismanagement, with nearly one in four citing incidents costing over $250,000. The CA/Browser Forum's move to 47-day certificate lifespans by 2029 will require renewals eight times more often and 40 times as many domain validations. Over half of organizations manage more than 1,000 certificates, yet only 10% have automation in place, with budget constraints and legacy systems cited as key barriers.

Help Net Security · 2d agoIndustry

Target leakage, not model class, explains reported accuracy in survey-based cardiovascular screening: a leakage-tiered audit of glass-box and tabular foundation models

Audits of 10 classifiers on BRFSS show target leakage, not model class, drives the reported 0.89 AUROC in survey-based cardiovascular screening.

The study benchmarks ten model classes, including glass-box and tabular foundation models, for prevalent myocardial infarction on 442,067 respondents of the 2022 BRFSS across five feature tiers of decreasing leakage risk. Removing two post-diagnostic features costs every model 0.049-0.051 AUROC and collapses performance into a 0.0045-wide band, and the explainable boosting machine matches all alternatives within 0.005 while scoring roughly 104x faster than the strongest foundation model. Frozen models transport within 0.002 AUROC to 2023 data; the authors conclude evaluation practice and feature sets, not model capacity, are the binding constraint.

arXiv cs.AI / cs.LG / cs.CL · 5d agoAI research1

Competence-Gated Pooling of Language Models and Priors for Event Forecasting

Paper proposes a competence gate pooling language model forecasts with external priors, improving Brier score from 0.0771 to 0.0732 across 2,357 binary questions.

The paper defines a language model's relative competence as its marginal value beyond an available external forecast, and derives conditions under Brier loss where model disagreement improves that forecast. A competence gate estimates domain-level source weights from resolved outcomes, shrinks uncertain estimates toward a global weight, and recalibrates the pooled forecast. Across 2,357 resolved binary questions and five language models, it improves the external baseline from 0.0771 to 0.0732 Brier and beats global forecast combinations, though it defers to the market on ForecastBench. Across four Qwen models, verbal confidence failed to identify when the model outperformed the external forecast, while outcome-estimated competence supported better abstention.

Hugging Face daily papers · 6d agoAI research

Why The Vulnerability Backlog Is About To Get Worse

Recorded Future analysis says AI-driven vulnerability discovery and faster weaponization will grow the triage backlog while shrinking defenders' response windows.

Disclosed vulnerabilities rose from roughly 21,000 in 2021 to nearly 50,000 in 2025, while Recorded Future assessed only 446 as actively exploited in 2025. VulnCheck found nearly 29% of 2025 KEV entries were exploited on or before CVE publication. The authors argue AI-assisted discovery and automated exploit development will multiply credible reports, cut disclosure-to-exploit time toward minutes, and force re-evaluation of medium-severity flaws as exploit-chain components.

Recorded Future · 21d agoResearch

Searching for New Physics with Reinforcement Learning

Researchers apply reinforcement learning to identify SMEFT operators explaining particle physics anomalies, reproducing and improving known CDF W-mass results.

The paper introduces a reinforcement learning method to search the large Standard Model Effective Field Theory (SMEFT) operator space for explanations of measurement anomalies. It was validated on the CDF W-mass anomaly, reproducing and improving known results, then applied to a harder multi-anomaly scenario. RL efficiently navigates complex loop-level operator correlations that bias human-driven phenomenological analysis.

arXiv cs.AI / cs.LG / cs.CL · 6d agoAI research

Domain-Specific Hallucination Detection in Large Language Models

A multi-signal pipeline detects LLM hallucinations, reaching F1 0.915 on HaluEval and cutting Qwen2.5-0.5B hallucination rates from 85.5% to 37.7% via DPO.

The paper presents a hallucination detection pipeline combining fine-tuned DeBERTa-v3 classification, Monte Carlo Dropout uncertainty, and temperature-scaled calibration. It achieves F1 0.915 and AUROC 0.977 on general-domain HaluEval tasks, with MC Dropout inference raising accuracy to 93.2%. Applying DPO to a Qwen2.5-0.5B generator reduces its hallucination rate from 85.5% to 37.7%, while cross-domain evaluation shows poor general-domain transfer to SciFact (F1 0.52) and PubMedBERT fine-tuning as the strongest adaptation (F1 0.63).

arXiv cs.AI / cs.LG / cs.CL · 5d agoAI research1

From ‘High/Medium/Low’ to Dollars: Making Cyber Risk Legible to Your CFO

Cyble argues security teams should express cyber risk in financial terms for CFOs instead of high/medium/low ratings, citing its 2025 threat forecast results.

Cyble published guidance on cyber risk quantification, arguing qualitative high/medium/low ratings fail to convey financial exposure to executives. The piece notes that over 80% of its 2025 threat predictions, including AI-driven ransomware and supply-chain attacks, materialized as anticipated.

Cyble · 22d agoIndustry

Adaptive Gated Deepfake Detection for Low-Resolution and Resource-Constrained Environments

AdaGate-DF routes deepfake detection by image quality through dual multi-exit gates, hitting 0.9370 AUC on Celeb-DF with low inference latency.

AdaGate-DF is an adaptive gated deepfake detection framework that uses image-quality cues to send high-quality images through earlier exits, saving compute in resource-constrained settings. On Celeb-DF it achieves an AUC of 0.9370, outperforming MaD-CoRN and DefakeHop++, and reaches 0.9708 at 384x384 resolution. On FaceForensics++ it remains effective under class imbalance while balancing uncertainty-aware prediction and computational efficiency.

arXiv cs.AI / cs.LG / cs.CL · 11d agoAI research

What do CISOs need to rest easy about future AI risks?

IANS survey of 113 CISOs finds optimism about managing future AI security risks hinges more on organizational readiness and leadership support than current controls.

An IANS AI Security Survey of 113 CISOs fielded in April and May found 41% optimistic and 38% pessimistic about their organization's ability to manage AI security risks over the next 24 months. Six organizational signals—leadership understanding of AI risk, defined governance ownership, security team effectiveness with AI tools, CISO ownership of the AI-security budget, sustainable workloads, and staffing—separate confident CISOs from pessimistic ones. Interviewed analysts cautioned that these readiness signals measure self-assessed confidence rather than actual security, and recommended hands-on AI use by security teams plus governance of third-party models.

CSO Online · 9d agoIndustry

Operational Resilience: IT Security Risks with Reduced Staffing | Huntress

Huntress blog advises security teams on managing change, risk, and incident response during reduced-staffing holiday periods.

The article discusses how holiday-period staffing reductions change organizational risk profiles around change management, monitoring, and incident response capability. It argues against blanket change freezes when critical vulnerabilities with high exploitation probability demand patching, and stresses retaining decision-making authority, escalation paths, and recovery knowledge. It concludes by promoting Huntress Managed Response, which lets the Huntress SOC take predefined containment actions on confirmed threats without customer intervention.

Huntress · 20h agoIndustry

Is Cyber Facing an Affordability Crisis?

Dark Reading analysis argues record breach costs and roughly $240 billion in cyber defense spending leave small businesses dangerously exposed, threatening supply chains.

The analysis examines an affordability crisis in cybersecurity, noting breach costs have reached record highs while defense spending approaches $240 billion. It argues small businesses are dangerously under-protected relative to rising attack costs. Weak small-business defenses are framed as a supply chain security risk for larger organizations.

Dark Reading · 21d agoIndustry

Cybersecurity attention fades within months after a breach

ManageEngine survey of 700 breached organizations finds security attention fades within one to six months, while 91% still trust their posture.

A ManageEngine survey of 700 IT and security leaders in the US and Canada, all of whom had experienced a breach, found that 91% trust their current security posture and only 8% make security a permanent priority after an incident. 80% said post-breach focus lasts just one to six months, and nearly half made no wider changes after their incident. About two in three organizations using AI in security said they act on AI recommendations without additional verification. The report also flagged unclear ownership across security, IT, and business teams as a cause of delayed remediation.

Help Net Security · 2d agoIndustry

Ask Before You Optimize: Dynamic Pre-Formulation Clarification for Interactive Optimization

Researchers release OR-Clarify, a benchmark testing whether LLM agents ask clarifying questions before formulating optimization models from incomplete requests.

OR-Clarify evaluates pre-formulation clarification in operations research: each task gives a partial problem description, withholds structured hidden slots, and scores agents via bounded interaction with a simulated user, measuring slot recovery, stopping behavior, silent assumptions, and interaction cost. The authors also propose InterOPT, a two-stage framework that identifies formulation-critical gaps to decide when to ask or stop. In choice-based experiments InterOPT substantially outperforms all baselines in exact slot recovery and remains competitive in the open-ended setting.

Hugging Face daily papers · 12d agoAI research1

Will AI kill us all within the next decade?

Anthropic alignment lead Evan Hubinger says he personally estimates a greater than 10% chance AI could kill all humans within the next decade.

A Wall Street Journal report says concerns are rising inside AI labs that competition is pushing companies toward self-improving models that could spiral out of human control. Anthropic's Alignment Science lead Evan Hubinger publicly endorsed researcher Jacob Coxon's warning, giving a personal estimate of a greater than 10% chance of AI causing human extinction within a decade, driven by recursive self-improvement. Hubinger described the risk from current models as low, focusing his concern on future systems with far greater autonomy. The piece argues for safeguards such as independent testing, limits on high-risk autonomous uses, transparency, and accountability.

Malwarebytes Labs · 5d agoAI safety & security

Large Language Models Develop Belief State Geometry In-Context

Probing six open-source LLMs on HMM-generated data shows belief states are linearly decodable (R² 0.83–0.99), suggesting in-context learning approximates Bayesian prediction.

Researchers prompted six open-source LLMs with data from 40 hidden Markov models selected for non-trivial belief structure and probed residual-stream activations for belief states (posteriors over hidden states). Belief states were linearly decodable with peak R² values of 0.83–0.99 across HMM/LLM combinations, spanning early to late layers. Patching and steering the probe-identified subspace preserved downstream prediction quality while control interventions degraded performance substantially, establishing functional relevance. The results provide representation-level evidence that in-context learning approximates optimal Bayesian prediction over a context-inferred generative model.

arXiv cs.AI / cs.LG / cs.CL · 18h agoAI research

Understanding the Privacy-Preserving Potential of HTTP/2 Against Webpage Fingerprinting

Researchers show HTTP/2 features can emulate website fingerprinting defenses like FRONT and Tamaraw with tunable privacy-overhead trade-offs.

An arXiv paper demonstrates that application-layer defenses such as HTTPOS, LLaMA, FRONT, ALPaCA, and Tamaraw can be emulated through HTTP/2 features at both the client and server side, including proactive resource suggestion, multiplexing, and flow control. The authors propose a unified evaluation blueprint that calibrates defense parameters per dataset, combines practical attacks with information-theoretic leakage estimators, and measures overheads to map each defense's privacy-overhead trade-offs.

arXiv cs.CR · 11d agoResearch

Quantization-Aware Healing: a compressed, 4-bit model that outperforms its full-precision original

Multiverse Computing details quantization-aware healing, producing a 4-bit compressed model that reportedly outperforms its full-precision original.

A Hugging Face blog post by Multiverse Computing's CAI team introduces quantization-aware healing for compressed models. The post claims the resulting 4-bit model outperforms the original full-precision model. No additional details or benchmarks were available in the provided text.

Hugging Face Blog · 21d agoAI research

OpenAI just wants to win

OpenAI says roughly 10,000 agents and tens of millions in compute solved the Navier-Stokes Millennium Prize problem in 88 hours, amid controversy.

OpenAI claims an advanced unreleased model, run with about 10,000 agents and tens of millions of dollars of compute for 88 hours, produced a solution to the Navier-Stokes problem, one of seven $1 million Millennium Prize problems set by the Clay Mathematics Institute in 2000. NYU professor Tristan Buckmaster, who was pursuing the problem with Anthropic researcher Levent Alpöge, accused OpenAI of racing his team and of possibly benefiting from his Codex prompts; OpenAI categorically denied both. OpenAI researcher Sébastien Bubeck acknowledged offering Buckmaster compute and authorship arrangements, which Buckmaster characterized as a bribe, while many mathematicians expressed unease at OpenAI's competitive conduct.

The Verge · AI · 3d agoAI industry1

The Vulnerability Gap: Why Discovery Is Outrunning Repair

Dark Reading argues AI-accelerated vulnerability discovery and tightening regulation are widening the gap between flaw discovery and repair capacity.

The article argues that AI tooling is increasing the pace at which vulnerabilities are discovered while remediation capacity has not kept up, creating a growing backlog. It frames this widening 'vulnerability gap', combined with a tightening regulatory environment, as an all-hands-on-deck moment for security teams. The piece is analysis and opinion rather than disclosure of a specific flaw.

Dark Reading · 22d agoIndustry

Claude Mythos 5 is coming to Tenable One, powering the new “Adversary View”

Tenable will integrate Anthropic's Claude Mythos 5 into Tenable One, launching Adversary View to surface exploitable vulnerability chains from scan data.

Tenable announced it is bringing Anthropic's Claude Mythos 5 into the Tenable One Exposure Management Platform, extending its Project Glasswing research work with Anthropic. The first capability, Tenable One Adversary View, will use the model's adversarial reasoning over raw scanner evidence, plugin output, live connections and low-confidence signals to identify viable vulnerability chains and ranked defensive actions. Customers will act on results through Tenable Hexa AI. Availability details for Adversary View are expected in the coming weeks; no customer deployment exists yet.

Tenable Blog · 7d agoTools1