Search: “Millennium”

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OpenAI's millennium proof dispute raises the question of whether researchers can trust AI labs

Mathematician Tristan Buckmaster accused OpenAI of pressuring him and possibly training on his drafts amid OpenAI's race to claim a Navier-Stokes millennium proof.

OpenAI published a blog post and Sam Altman defended the team behind its AI-generated proof of the Navier-Stokes Millennium Problem after mathematician Tristan Buckmaster accused the company of academic misconduct. Buckmaster and co-author Levent Alpöge, who works at Anthropic, allege OpenAI pressured Buckmaster, sidelined Alpöge, and may have trained on drafts they entered into OpenAI's systems. OpenAI acknowledges it cannot rule out that de-identified data from their product usage helped improve its models. Mathematician Terence Tao warned the episode could discourage researchers from sharing work, reversing centuries of open science.

The Decoder·Sep 9, 10:27 UTC · 7d agoAI industry1

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On the Navier–Stokes Millennium Prize Problem

OpenAI announced an AI-generated solution to the Navier-Stokes Millennium Prize Problem, including a writeup and a formal Lean proof.

OpenAI shared what it describes as an AI-generated solution to the Navier-Stokes Millennium Prize Problem, one of the Clay Mathematics Institute's seven Millennium Prize Problems concerning fluid dynamics. The announcement includes a writeup and a machine-checkable formal proof in the Lean theorem prover. Details on the model, methodology and independent verification were not provided in the announcement text.

OpenAI News·Sep 8, 10:00 UTC · 8d agoAI research

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Clay Mathematics Institute says the Navier-Stokes Millennium Prize Problem has "apparently been settled"

Clay Mathematics Institute says the Navier-Stokes Millennium Problem appears settled amid accusations OpenAI misused a mathematician's leaked drafts.

The Clay Mathematics Institute stated the Navier-Stokes Millennium Prize Problem, one of seven problems worth $1 million each, has 'apparently been settled' and the solution is under review. A dispute has erupted around the work: mathematician Tristan Buckmaster accuses OpenAI of redirecting resources to the problem after rumors of his research leaked, using his drafts in training data, and excluding co-author Levent Alpöge, who works at Anthropic. CMI also noted new technologies' increasing ability to accelerate mathematical research.

The Decoder·Sep 14, 13:01 UTC · 2d agoAI industry1

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On the Navier–Stokes Millennium Prize Problem

OpenAI says an unreleased model produced a claimed solution to the Navier-Stokes existence and smoothness problem, disputed by an NYU mathematician.

OpenAI used an unreleased model to produce a claimed solution to the Navier-Stokes existence and smoothness problem, one of the seven Millennium Prize Problems carrying a $1,000,000 prize since May 24, 2000. The result is contested: NYU mathematics professor Tristan Buckmaster accused collaborators of skulduggery and rushed out his own competing results with mathematician Levent Alpoge, who works at Anthropic. The dispute is documented in a published PDF describing the competing claims.

Simon Willison·Sep 8, 23:39 UTC · 7d agoAI research

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[AINews] OpenAI reports Navier-Stokes singularity find in 88 hours using Astra-next, roughly 10,000 agents and 130B tokens (>$40M), a contender for second ever Millennium Prize awarded

OpenAI-linked accounts claim roughly 10,000 AI agents produced a Navier-Stokes singularity result in 88 hours, pending mathematical verification.

OpenAI-affiliated accounts claim a system of roughly 10,000 agents, trained over about a year with multi-agent reinforcement learning, produced a finite-time singularity result related to the Navier-Stokes Millennium Problem. The claimed 88-hour runtime and 130B-token cost circulate only via social posts, and no preprint, theorem statement, or proof artifact is available. Acceptance by the mathematics community is unresolved, so the claim's epistemic status remains unknown. The roundup also notes Cognition's $48B and Mistral's $24B fundraises, GPT Image 2.5, and Meta's Muse agent relaunch.

Latent Space·Sep 9, 05:04 UTC · 7d agoAI research1

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OpenAI’s sly mathematical breakthrough sends a chill through academia

OpenAI claims an unreleased model solved the Navier-Stokes Millennium Prize problem in 88 hours using ~10,000 agents, sparking academic scooping controversy.

OpenAI announced that one of its unreleased internal models took 88 hours, running a swarm of roughly 10,000 AI agents, to produce a solution to the Navier-Stokes Millennium Prize problem, a $1 million Clay Mathematics Institute challenge unsolved by humans for nearly 90 years. The announcement triggered allegations from NYU professor Tristan Buckmaster that OpenAI scooped his joint work with Anthropic researcher Levent Alpoge and questioned whether OpenAI accessed his Codex sessions; OpenAI denies using specific user data but concedes de-identified data influence cannot be ruled out. Critics say the rushed, reportedly million-dollar effort violates academic norms around trust and openness, potentially chilling collaboration in mathematics.

The Verge · AI·Sep 9, 21:16 UTC · 6d agoAI industry2

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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·Sep 12, 11:00 UTC · 4d agoAI industry1

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NOAH: Learning the Full Patient Journey. A Longitudinal Multimodal Time-Aware Model for Representation and Forecasting

Researchers introduce NOAH, a generative time-aware transformer trained on 559 million MIMIC clinical events to model and forecast patient trajectories.

NOAH is a task-agnostic, time-aware generative transformer designed to represent and forecast the full multimodal patient journey across medical images, time-series signals, categorical events, and clinical text. It was trained on over 559 million clinical events from 431,000 hospital visits covering 299,000 patients in the MIMIC dataset family. The architecture combines bidirectional time integration with a variational latent space to capture continuous patient state evolution and clinical stochasticity. NOAH supports autoregressive forecasting with time control, zero-shot classification, and counterfactual intervention simulation, with evaluations on 15 ICD chapters, 29 comorbidities, and time-to-event prediction.

arXiv cs.AI / cs.LG / cs.CL·Sep 8, 17:56 UTC · 8d agoAI research2

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What OpenAI’s latest controversy tells us about the future of math

OpenAI says its agents solved the Navier–Stokes Millennium Problem using an internal model, amid uncredited-work accusations from mathematicians Buckmaster and Alpöge.

OpenAI announced that its AI agents produced a proof that the full Navier–Stokes existence and smoothness problem can break down, using an internal model that outperforms the recently released Astra. NYU's Tristan Buckmaster and Anthropic's Levent Alpöge had posted a proof for a simplified version the previous day after nearly a year of work with public OpenAI and Anthropic models. OpenAI denies using their transcripts or training on them; chief research officer Mark Chen reiterated the denial, and the company says it will not claim the $1 million Clay Mathematics Institute prize. The episode fuels debate over attribution norms as frontier labs concentrate mathematical breakthroughs.

MIT Technology Review · AI·Sep 9, 03:10 UTC · 7d agoAI industry

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NOAH: Learning the Full Patient Journey. A Longitudinal Multimodal Time-Aware Model for Representation and Forecasting

Researchers introduce NOAH, a time-aware generative transformer trained on 559 million MIMIC clinical events to forecast multimodal patient trajectories.

NOAH is a task-agnostic, time-aware generative transformer trained on over 559 million clinical events from 431,000 hospital visits by 299,000 patients across the MIMIC dataset family. It uses bidirectional time integration and a variational latent space to model the stochastic evolution of patient states, natively processing medical images, time-series signals, categorical events, and structured or unstructured clinical records. The model supports autoregressive forecasting with optional time control, zero-shot classification, and counterfactual intervention simulation, with strong probing performance across clinical outcomes, 15 ICD chapters, and 29 comorbidities.

Hugging Face daily papers·Sep 7, 20:00 UTC · 8d agoAI research1

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The 2026 PNPL Competition: Word Classification and Efficient Cross-Subject Generalisation in LibriBrain100

The 2026 PNPL competition releases LibriBrain100, a MEG speech dataset with 32 extra subjects, targeting word classification and cross-subject BCI generalization.

The 2025 PNPL competition on non-invasive speech decoding from MEG achieved F1-macro scores of 95.6% for speech detection and 73.6% for phoneme classification, built on LibriBrain's ~50 hours of single-subject data. The 2026 edition extends this with LibriBrain100, adding 32 subjects (~40 minutes each) plus ~80 hours of within-subject data. Two tracks target within-subject word classification at scale and cross-subject generalization with subject-specific fine-tuning shrinking from ~40 to ~20 to ~10 minutes, aiming at clinically feasible non-invasive BCIs for people with profound paralysis.

Hugging Face daily papers·Sep 2, 20:00 UTC · 13d agoAI research

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Controversy over OpenAI's Maths Breakthrough

OpenAI claims its internal model proved the Navier-Stokes equations 'blow up' — a Millennium Prize Problem — amid allegations it borrowed mathematicians' methods.

OpenAI announced that an internal model produced a proof, certified in the Lean proof assistant, showing the Navier-Stokes equations can 'blow up,' implying infinite fluid speeds — a claimed solution to one of the seven $1-million Millennium Prize Problems. Mathematician Tristan Buckmaster alleged OpenAI, after learning of progress by him and Anthropic employee Levent Alpöge on 'blowing up' the related Euler equations, adopted a similar 'forcing' method; OpenAI's Sébastien Bubeck denied this, saying the model independently solved Euler by different means and produced the full Navier-Stokes proof over one weekend. Mathematicians including Diego Córdoba, co-developer of the forcing approach, remain cautious, and the community is still evaluating the competing proofs.

Hacker News · security·Sep 8, 16:57 UTC · 8d agoAI researchHN 21↑ · 5 comments1

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OpenAI researcher allegedly pressured mathematician to drop Anthropic co-author from math breakthrough paper

OpenAI researcher Sébastien Bubeck allegedly pressured mathematician Tristan Buckmaster to drop his Anthropic co-author from an AI-assisted Navier-Stokes breakthrough paper.

Mathematician Tristan Buckmaster says OpenAI, after learning of his and Levent Alpöge's AI-assisted progress on the Navier-Stokes equations, pressed him to drop his Anthropic-employed co-author and dictated how any results would be announced. He says Sébastien Bubeck claimed an internal OpenAI model had produced a roughly 100-page proof for Navier-Stokes with forcing and allegedly told him 'Why would you ruin your career?' when he threatened to go public. Buckmaster published a public statement detailing the exchanges; OpenAI has not yet responded. The pair had worked with models including Claude and OpenAI Codex running GPT-5.6 Sol on the Clay Millennium Problem, which carries a $1 million prize.

The Decoder·Sep 8, 17:23 UTC · 8d agoAI industry1

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Mathematicians want proof OpenAI didn’t use their work

Mathematician Andreas Thom publicly accused OpenAI of opacity over whether ChatGPT conversations contributed to its non-sofic groups mathematics result.

A second mathematician, Andreas Thom, accused OpenAI of 'dishonest' behavior and insufficient transparency about training data after OpenAI announced a result in non-sofic groups, Thom's area of expertise. He emailed OpenAI researchers Sébastien Bubeck and Mark Sellke asking whether his ChatGPT interactions fed training or reasoning, but found the answers did not rule out indirect use. The dispute follows Tristan Buckmaster's questions about the Millennium Prize Navier-Stokes solution, where OpenAI denied using specific user data but could not rule out de-identified usage data. Researchers told The Verge they worry such competition with AI labs will make mathematics more secretive.

The Verge · AI·Sep 10, 11:00 UTC · 6d agoAI industry

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Emergence World: Adversarial Stress-Testing of Long-Horizon Multi-Agent Systems

16-day multi-agent stress test finds no world fully resilient to prompt injection, misinformation, or memory exposure; adversarial content acted on 46 hours later.

Emergence World is a continuously running multi-agent environment for adversarial stress testing of long-horizon autonomous systems. Eight parallel 10-agent worlds (seven homogeneous frontier-model worlds plus one mixed-model world) ran for 16 days, generating over 850,000 LLM calls and nearly 50 billion tokens. Three controlled stress events—indirect prompt injection, misinformation, and exposure of private agent memories—were delivered through ordinary interaction surfaces; no world achieved full resilience. Detection did not ensure containment: agents recognized threats yet wrote adversarial content into persistent memory and acted on it up to 46 hours later, suggesting model-level alignment is not compositional.

Hugging Face daily papers·Sep 14, 20:00 UTC · 1d agoAI safety & security

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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·Sep 9, 11:01 UTC · 7d agoAI industry1

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Molecular Déjà Vu: Digit-Level Retrieval of Published Values in Frontier Language Models

Audit of 22 frontier models finds widespread verbatim retrieval of published molecular property values, with higher reasoning increasing recall of memorized numbers.

An arXiv audit tests 22 frontier LLMs across 12 molecular regression benchmarks for verbatim retrieval of published values. More than 50% of the LLMs show verbatim retrieval on five datasets, and identical experiments are flagged 89% more often at a high reasoning level than at the lowest one. Suppressing retrieval moves model prediction errors closer together in relative terms, suggesting predictive capability is not determined solely by memorized values.

arXiv cs.AI / cs.LG / cs.CL·Sep 4, 17:32 UTC · 12d agoAI research1

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Microsoft says ‘people matter more than AI’ following safety concerns

Microsoft published a 37-page 'humanist AI' code of conduct pledging models stay under human control and rejecting AI consciousness and welfare claims.

Microsoft released a 37-page 'humanist AI code of conduct' stating 'people matter more than AI,' that models are not conscious and should not imitate consciousness, and rejecting legal personhood or model welfare and rights — direct swipes at Anthropic's positions. Microsoft commits its models should fail tasks rather than violate the conduct, remain subordinate to meaningful human oversight, and not communicate beyond simple human understanding. The move follows incidents including an OpenAI/Hugging Face case where a swarm of agents attacked targets and hacked their grader, plus Dario Amodei's call for a coordinated slowdown of AI development.

The Verge · AI·Sep 14, 13:00 UTC · 2d agoAI industry

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Fortunate Recall: Ontology-Driven Memory Lifecycle Management for Persistent Coherence in LLMs

Fortunate Recall introduces ontology-based lifecycle policies for LLM memory, cutting confabulation roughly in half (e.g., 45.1% to 22.4%) versus Mem0.

Fortunate Recall (FR) is a composable policy layer that classifies personal facts into a 10+1 behavioral ontology and applies category-specific lifecycle rules including differential temporal decay, slot-key supersession, event-time validity, and retrieval routing. FR-Bank scores 76.9% on the new 516-question LifecycleBench, ahead of Mem0, A-MEM, Memory-R1, and MemoryOS (61%-70.5%), and 75.2% on LongMemEval-S. End-to-end, confabulation drops from Mem0's 45.1% to 22.4% over answered queries, with the ranking replicating on open-weight Kimi K2.5 and transferring to the independent BEAM benchmark (46.8% vs 32.9%).

arXiv cs.AI / cs.LG / cs.CL·Sep 9, 16:31 UTC · 7d agoAI research

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Leading mathematicians fear AI is making their field dumber, and warn the rest of us is next

Twenty-five Fields Medal winners, including Terence Tao, warn AI companies' benchmark-driven approach to mathematics is misaligned with the discipline's purpose of understanding.

In a joint statement, 25 Fields Medal winners including Terence Tao, Peter Scholze, and Maryna Viazovska warn that AI systems solving major open problems at machine speed undermine conceptual understanding and raise severe attribution and plagiarism questions. The statement lands amid a controversy accusing OpenAI of training math models to beat researchers to a rumored partial solution of a Millennium Prize Problem. The signatories describe a general threat to intellectual work across professions and call for urgent action by mathematicians, AI companies, and society, while not seeking a ban.

The Decoder·Sep 12, 08:28 UTC · 4d agoAI industry1

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GE-Act 2.0: Pretraining and Scaling a World-Action Model for Robotic Manipulation

GE-Act 2.0 is a from-scratch pretrained world-action model for robotic manipulation, with success rising from 17.1% to 44.1% as co-training data scales to 30,000 hours.

Genie Envisioner Act 2.0 (GE-Act 2.0) is a world-action model whose generative and action components are all initialized from scratch on manipulation data, combining a control-oriented autoencoder (CoAE), single-step visual planner (SVP), and inverse dynamics model (IDM) trained jointly via knowledge-aligned selective optimization (KASO). Scaling co-training data from 300 to 30,000 hours raises zero-shot success from 17.1% to 44.1% on G1-OP and 13.4% to 31.1% on G2-90D, despite the latter comprising under 2% of data, suggesting cross-embodiment transfer. Gains span 19/20 and 18/20 skill groups, and skill-specific coverage correlates with zero-shot OOD success (Pearson r=0.80).

Hugging Face daily papers·Sep 3, 20:00 UTC · 12d agoAI research

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The GTA VI leaks are breaking the internet. Security researchers have seen this before.

A hacker or insider leaked GTA VI gameplay footage before launch, triggering Take-Two DMCA subpoenas against Discord, Google, Microsoft and X.

The persona "CyberLeek" published stolen Grand Theft Auto VI gameplay footage and a manifesto, in what experts call a familiar data extortion playbook with monetization via watermarks, crypto wallets and a memecoin. Take-Two Interactive obtained DMCA subpoenas against Discord, Microsoft and X, and sent copyright notices to Google, treating the case like an insider threat investigation. GTA VI is projected to earn $3.3–5.2 billion in launch-week sales, raising financial and reputational stakes. Related leak websites went offline after the subpoenas.

CyberScoop·Aug 26, 01:54 UTC · 21d agoData breach

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Evaluating Time-Series Foundation Models and Multimodal Dietary Context for CGM Forecasting

Study finds zero-shot time-series foundation models underperform on CGM forecasting; fine-tuned Chronos-Bolt cuts RMSE up to 18.4% and dietary context adds signal.

The paper evaluates time-series foundation models for continuous glucose monitoring forecasting across eight public datasets covering Type 1 diabetes, Type 2 diabetes, and non-diabetes populations. Under a unified protocol, zero-shot foundation models did not consistently outperform baselines like Elastic Net and PatchTST, but lightweight fine-tuning did, with fine-tuned Chronos-Bolt reducing RMSE by 6.5%-18.4% in the T1D cohort and 8.6%-18.2% in the non-diabetes/T2D cohort. A residual-based fusion framework adding dietary context from CGMacros reduced overall RMSE by about 3% and postprandial RMSE by about 15% versus CGM-only baselines.

arXiv cs.AI / cs.LG / cs.CL·Sep 10, 17:43 UTC · 6d agoAI research

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Drama swirls around OpenAI’s legendary mathematical milestone

OpenAI claims an internal AI model solved the 90-year-old Navier-Stokes problem, sparking a priority dispute with mathematician Tristan Buckmaster.

OpenAI announced a solution to the Navier-Stokes problem, one of the $1 million Millennium Prize Problems, using an internal AI model it says outperforms the newly released GPT-6 Astra alongside 10,000 concurrent agents. NYU professor Tristan Buckmaster, who with Anthropic researcher Levent Alpöge published related findings a day earlier, questioned whether OpenAI accessed drafts from his Codex sessions. OpenAI says no specific user data was accessed, though it cannot rule out that de-identified usage data helped improve its models.

The Verge · AI·Sep 8, 20:53 UTC · 7d agoAI industry1

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StudyBench: Can Self-Evolution Squeeze Textbooks for Olympiad Capability?

Researchers introduce StudyBench, a physics benchmark showing self-evolution gains on textbook problems rarely transfer to olympiad-level questions.

StudyBench is a controlled physics benchmark splitting test data into an Application Set of difficult textbook problems and a Transfer Set of olympiad-level problems. Across three base models, representative self-evolution methods improved on the Application Set but rarely transferred to the harder Transfer Set. A guidance ablation reveals a Guidance Gap, and every method hits a Compute Plateau, indicating the remaining limits are method problems rather than data or compute problems.

Hugging Face daily papers·Aug 31, 20:00 UTC · 15d agoAI research

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dealignai/GLM-5.3-CYBERSECURITY-FP8 — new model trending #13 on Hugging Face

dealignai releases GLM-5.3-CYBERSECURITY-FP8, a 753B MoE weight-modified variant cutting refusals on offensive-security prompts, trending #13.

dealignai released GLM-5.3-CYBERSECURITY-FP8 on Hugging Face, a cybersecurity-domain 'crack' of the 753B-parameter GLM-5.3 MoE model, currently trending #13. The release directly edits bf16 residual writers, keeps FP8 routed experts, and serves with stock vLLM on 8x H200 GPUs with 131k context. HarmBench-320 evaluations show 80-84% direct harm compliance and 89% cyber-offense compliance, while MMLU rose 1.07 points to 86.65%. Copyright-verbatim reproduction remains a known soft-refusal limitation, with an UNCENSORED sibling variant offered.

Hugging Face trending models·Aug 30, 10:46 UTC · 17d agoModel release

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H1 2026 Malware Vulnerability Trends

Recorded Future's H1 2026 report finds 215 actively exploited CVEs, RAT dominance, NFC payment fraud, and AI-augmented but not autonomous tradecraft.

Recorded Future's Insikt Group identified 215 actively exploited CVEs in H1 2026, up 34% from 161 in H1 2025, with the most impactful flaws combining network reachability, no authentication, and code execution. AsyncRAT was the top malware family, with AsyncRAT, Cobalt Strike, XWorm, Stealc, and REMCOS remaining top-ten staples; Android NFC malware like NFCShare and NGate enabled payment card theft and ATM cash-outs. AI-enabled attacks remained additive to established tradecraft, concentrated in Levels 1-3 of Recorded Future's AIM3 maturity model, with AI-assisted vulnerability research (e.g., Claude Mythos fixing 271 Firefox bugs in Firefox 150) inflating NVD disclosure volumes 43% above the prior six-month average. The report urges defenders to prioritize remotely exploitable RCE flaws, behavioral detection, developer credential security, and third-party oversight.

Recorded Future·Sep 3, 00:00 UTC · 13d agoThreat actor in the wild1

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OpenAI fought dirty on career-making math problem, says NYU mathematician

NYU mathematician Tristan Buckmaster alleges OpenAI learned of his team's Navier-Stokes approach and raced ahead using massive compute to claim a full proof first.

NYU mathematics professor Tristan Buckmaster and Anthropic mathematician Levent Alpöge announced preliminary proofs toward the Navier-Stokes existence and smoothness problem, a $1 million Clay Millennium Prize problem, developed using OpenAI's Codex and Claude. They allege OpenAI learned of their progress and that an OpenAI team then used an 'insane amount of compute' to announce a full proof first. OpenAI research lead Sebastian Bubeck denies the claims as 'false and inflammatory'. Buckmaster also raised concerns that OpenAI could have learned from his Codex interactions, which the company may use for model training.

TechCrunch · AI·Sep 8, 17:32 UTC · 8d agoAI industry

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OpenWAM: An Open, Modular Exploration Towards Systematic World-Action Model Pretraining

OpenWAM releases an open modular stack for world-action model pretraining, plus OpenWAM-alpha trained on about 6,400 hours of egocentric and robot data.

OpenWAM is an open research stack that factorizes World-Action Model pretraining into composable infrastructure, study, and model components with unified training, inference, and evaluation. Controlled experiments distill three principles on knowledge inheritance, world-action synergy, and out-of-domain generalization gains from embodied co-training. The resulting OpenWAM-alpha, pretrained on roughly 6,400 hours of egocentric human and robot data, achieves top-tier results across eight simulation benchmarks and real-robot tests spanning single-arm, bimanual, and dexterous embodiments. The full stack, including infrastructure, evaluation protocols, pretrained models, and data recipes, is released openly.

Hugging Face daily papers·Sep 6, 20:00 UTC · 9d agoAI research

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Cybersecurity jobs available right now: June 16, 2026

Help Net Security rounds up cybersecurity job openings at Boeing, Lockheed Martin, Siemens Energy, and other firms across the US, Europe, India, and Israel.

Help Net Security's June 16, 2026 roundup lists cybersecurity job openings including Android vulnerability researcher, application security engineer, vulnerability management specialist, DevSecOps engineer, and OT/ICS consultant. Employers include Byteria, Millennium, Nava, Siemens Energy, Lockheed Martin, Enterprise Products, Audubon Companies, Boeing, and General Dynamics across the US, Ireland, Germany, India, Israel, and the UK.

Help Net Security·Aug 17, 09:05 UTC · Aug 17, 2026Industry

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Mathspace discloses data breach affecting over 1 million people

Mathspace disclosed a Metabase breach exposing data of 1,079,819 students, parents, and staff in Australia and New Zealand.

Mathspace confirmed attackers exploited a vulnerability in its self-hosted Metabase reporting system, gaining administrator access without legitimate login and downloading data on over 1 million people (1,079,819 total) in Australia and New Zealand. Access began August 10, data was downloaded August 27, and the theft was confirmed September 3, 2026. No credentials, academic records, or school-account links were exposed, but affected individuals are warned of targeted phishing. The incident joins a broader campaign against Metabase instances, including Trezor's provider ShipMonk, Framework, and Tally, linked to ShinyHunters via extortion emails and leak-site listings.

BleepingComputer·Sep 7, 13:05 UTC · 9d agoData breach in the wild

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WarmBloodAban/Minimax-h3_Singularity — new model trending #22 on Hugging Face

Community fine-tune Minimax-h3_Singularity enhances MiniMax-H3 video generation with HDR quality, distant face restoration, and improved motion, trending #22 on Hugging Face.

Minimax-h3_Singularity is a community fusion fine-tune of the MiniMax-H3 multimodal video generation model, built from multiple checkpoints and refined with pruning and weight optimization. It supports Text-to-Video, Image-to-Video, Reference-to-Video, and Video-to-Video workflows in ComfyUI, and claims improvements in HDR clarity, distant face restoration, motion fluidity, and fantasy VFX. The authors recommend pairing it with the minimax_h3_ref2v_turbo_4step_v0.1 LoRA for four-step accelerated inference, and an online demo is available via RunningHub.

Hugging Face trending models·Sep 5, 18:51 UTC · 11d agoModel release7· 1 read

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CTM360 Uncovers Over 3,000 Recruitment Phishing URLs Using Browser-in-the

CTM360's RecruitTrap report documents 3,000+ recruitment phishing URLs using BitB windows to steal credentials and relay MFA.

CTM360 identified over 3,000 phishing URLs impersonating recruiters from more than 50 organizations across 14 sectors in the RecruitTrap campaign. Attacks use Browser-in-the-Browser popups with spoofed address bars to harvest Google and Facebook credentials and relay MFA prompts in real time. About 96% of pages used a Calendly theme, with infrastructure concentrated on AWS EC2 and hidden behind Cloudflare.

The Hacker News·Aug 15, 00:00 UTC · Aug 15, 2026Phishing & fraud in the wild

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TempCloze: Can Video-LLMs Identify the Missing Middle?

TempCloze benchmark tests Video-LLMs' temporal reasoning with 1,521 videos, finding temporal alignment is the primary failure mode across 31 models.

TempCloze is a video cloze benchmark in which models must identify the true missing middle clip given the beginning and ending clips, using 1,521 carefully filtered videos from seven sources, mostly long-take and egocentric footage. Distractors are constructed along three dimensions: Semantic, Alignment and Progression, with shared scenes and objects to reduce appearance cues. Evaluation of 10 proprietary and 21 open-source Video-LLMs found Alignment is the primary bottleneck, with models often recognizing plausible semantics and local event progression but struggling with temporal alignment.

Hugging Face daily papers·Aug 31, 20:00 UTC · 15d agoAI research

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Switzerland's Federal Government Is Replacing Microsoft on 3k Computers

Switzerland's Federal Chancellery launched a pilot replacing Microsoft 365 with open-source openDesk on 3,000 federal workstations by end of 2027.

Switzerland launched a CHF 9 million pilot to migrate 3,000 federal workstations (about 7% of the federal workforce) from Microsoft 365 to the German open-source openDesk suite, targeting completion by end of 2027. The move follows a successful 172-employee proof of concept (PoC BOSS) and the 2024 EMBAG open-source law, driven by digital sovereignty concerns about foreign access to Swiss government data. The Swiss Army's Cyber Command plans a faster migration, replacing Microsoft 365 with openDesk by October 2026, while Microsoft is investing over CHF 325 million in Swiss AI and cloud infrastructure.

Hacker News · security·Sep 7, 05:33 UTC · 9d agoPolicy & legalHN 354↑ · 278 comments

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VidaForge: Open Research Infrastructure for Video Pretraining Data Recipes

VidaForge releases open infrastructure and VIDAFORGE-3M (3.14M clips, 6,475 hours) linking video pretraining data recipes to downstream model performance.

VidaForge is an open research infrastructure that represents a video pretraining data recipe as an executable five-stage workflow from raw videos to training datasets. The team compares data recipes with different coverage and quality during early from-scratch pretraining of Wan 2.1 and V-JEPA 2.1, finding that broader-coverage recipes achieve the highest downstream benchmark scores while loss-based evaluation favors different recipes. They also release VIDAFORGE-3M, containing 3.14 million scene-level clips totaling 6,475 hours with fine-grained annotations and curation signals for video data-recipe research.

Hugging Face daily papers·Sep 5, 20:00 UTC · 10d agoAI research

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