Search: “NOVA”

Hackers Hijack HBO Max Reddit Account to Push 108 ClickFix Malware Ads

Hijacked verified HBO Max Reddit account ran 108 ClickFix malvertising ads delivering AMOS infostealers, Windows loaders, and crypto clipboard hijackers.

HudsonRock and ADAMnetworks identified a cross-platform ClickFix operation dubbed PasteSwitch that abused the compromised verified Reddit account u/hbomax to publish 108 malicious ads over 48 hours, using domains like hbomaxx[.]us and codex-craft[.]com. macOS victims run curl/zsh commands delivering AMOS-related stealers and fake wallets, while Windows users get an InstallFix route using mshta and PowerShell that loads Amatera in memory and disables AMSI. The operation's AnimateClipper and ZigClipper families hijack clipboards to swap cryptocurrency addresses, with C2 domains rotated via Binance Smart Chain contracts. Reddit paused the ads and is investigating.

Cyber Security News·updated Sep 15, 11:51 UTC · 1d ago·first Sep 15, 06:56 UTC · 1d agoMalware in the wild 7 sources

nex-agi/Nex-N2.5-mini — new model trending #30 on Hugging Face

Nex-AGI releases Nex-N2.5 agentic model family (mini, Pro, Max) with a 1.6-trillion-parameter MoE Max, open weights, and hosted access via OpenRouter.

Nex-AGI launched Nex-N2.5, a family of agentic models in mini, Pro, and Max sizes, with the Max version built on a 1.6-trillion-parameter text-only Mixture-of-Experts foundation and the company's first complete post-training effort at trillion-parameter scale. The models target long-horizon computer use, web browsing, and visually grounded agentic tasks, with expanded agent training environments. Reported benchmarks include Max scoring 86.1 on Terminal-Bench 2.1 and 65.7 on SWE-Bench Pro, trailing Claude Opus 5. Weights are being released openly on Hugging Face and ModelScope, with hosted access through OpenRouter.

Hugging Face trending models·Sep 8, 05:08 UTC · 8d agoModel release1

Code review used to be the only way to catch these bugs

Palo Alto Networks' Unit 42 says its NOVA system found 14,090 vulnerabilities in 3,915 open-source projects, mostly non-crashing bugs like access control flaws.

Unit 42's NOVA system analyzed 3,915 open-source projects over two months and reported 14,090 validated vulnerabilities, only 85 of which matched previously documented findings. 92% of findings fell outside fuzzing-friendly categories, clustering instead in access control, path traversal, injection, prototype pollution, and SSRF; language ecosystems showed distinct weakness profiles. Of 5,421 supply-chain findings, 1,280 were flaws in dependencies while 4,141 were downstream exposures, 2,776 validated with working proof-of-concepts. Unit 42 warned that faster discovery combined with an average 55-day patch deployment window has collapsed the patch-to-exploit gap.

Help Net Security·Aug 25, 18:03 UTC · 22d agoResearch

nex-agi/Nex-N2.5-Pro — new model trending #30 on Hugging Face

Nex-AGI launches Nex-N2.5 agentic model family (mini/Pro/Max), with Max built on a 1.6-trillion-parameter MoE foundation.

Nex-AGI introduced Nex-N2.5, a next-generation family of agentic models in three sizes (mini, Pro, Max) focused on long-horizon agentic tasks including computer use, web browsing, and autonomous program execution. Nex-N2.5-Max is built on a 1.6-trillion-parameter text-only Mixture-of-Experts foundation, marking the company's first complete post-training effort at trillion-parameter scale. Weights will be released open-source on Hugging Face and ModelScope, with hosted access via OpenRouter. Benchmark comparisons against Claude Opus 5, GPT-5.6 Sol, Kimi-K3, GLM-5.3, DeepSeek-V4-Pro-0813, and Qwen3.8-Max show competitive scores on Terminal-Bench 2.1 and SWE-Bench Pro, though weights were listed as "coming soon" at publication.

Hugging Face trending models·Sep 8, 11:01 UTC · 8d agoModel release1

Weedhack Malware Spreads via Fake Minecraft Clients and SEO Poisoning

McAfee Labs reports Weedhack malware spread via spoofed Minecraft client websites using SEO poisoning, with over 6,300 blocked access attempts.

McAfee Labs found multiple lookalike websites impersonating legitimate Minecraft clients (Glazed, Radium, Meteor, Nova, Xenon) still distributing Weedhack, a multi-stage malware whose JAR payloads collect system information, add Microsoft Defender exclusions, and steal sensitive data. Distribution relies on SEO poisoning to outrank official sources, plus Discord, MediaFire, GitHub, and Minecraft hosting sites; nearly half of malicious URLs were Discord links. One fake site was built with the AI website builder Lovable, and McAfee blocked more than 6,300 access attempts to malicious sites.

The Hacker News·Aug 24, 17:41 UTC · 23d agoMalware in the wild

NCP-ArchPreview Technical Report: Moving towards Latent Space Language Models through Next Concept Prediction

An 8.9B-parameter latent-space language model using next-concept prediction matches OLMo-3-7B pretraining loss with only 51.3% of the training tokens.

NCP-ArchPreview augments next-token prediction with Next Concept Prediction over a product-quantized concept vocabulary built from hidden states, trained jointly end-to-end. The 8.9B model was trained on 5.73T tokens from the Dolma-3 dataset, the largest latent-space language model demonstration to date. It consumes 51.3% of the tokens to reach OLMo-3-7B's final pretraining loss and outperforms it by 2.45 points on the downstream macro-average, including a 5.99-point GSM8K gain. The learned latent space also enables lightweight domain adaptation via a 17M-parameter VQ module and improves speculative drafting accepted length by 4.17%.

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

US data centers could consume more natural gas than Germany and Japan combined by 2035

BloombergNEF projects US data centers will consume about 18 billion cubic feet of natural gas daily by 2035, exceeding Germany and Japan combined.

A new BloombergNEF report forecasts US data centers will consume roughly 18 billion cubic feet of natural gas per day by 2035, nearly double the estimate from nine months ago. On-site gas plants planned by Meta, Microsoft, Google, and Amazon would use 2.9-3.4 billion cubic feet per day, while grid-connected data centers drive an additional 15 billion cubic feet per day of power-sector gas demand. The added demand would generate about 1 million metric tons of extra greenhouse gas pollution daily, roughly 12% of current US emissions.

TechCrunch · AI·Sep 15, 18:29 UTC · 1d agoAI industry

Arm Mali G2-Ultra NX GPU: desktop-class mobile gameplay with AI-native graphics

Arm unveiled Mali G2-Ultra NX, its first AI-native mobile GPU with in-shader neural acceleration, third-gen ray tracing, and up to 24% higher benchmark performance.

Arm announced the Mali G2-Ultra NX, the first AI-native Mali GPU, integrating neural accelerators directly into shader cores alongside a new execution engine and third-generation hardware ray tracing. It introduces Neural Super Sampling (NSS), Neural Frame Rate Upscaling (NFRU), and Neural Super Sampling and Denoising (NSSD); the Neural Dawn demo with Sumo Digital showed up to 4x performance efficiency and 70% lower external memory traffic versus native rendering. Arm claims up to 24% higher benchmark performance, 13% lower DRAM traffic on ray tracing benchmarks, and up to 120 FPS with NFRU. Over 14 billion Mali GPUs have shipped to date.

Hacker News · AI·Sep 8, 04:06 UTC · 8d agoAI industryHN 71↑ · 53 comments1

Week in review: Cisco fixes IMC bug, Patch Tuesday forecast, Black Hat USA 2026

Help Net Security·Aug 9, 00:00 UTC · Aug 9, 2026VulnerabilityCVE-2026-66066CVE-2026-185771

Performance of Clinical AI System and Physicians and Frontier Language Models in primary care diagnostics

Clinical AI system Doctorina achieved 82.0% primary-care diagnostic concordance versus 57.0% for physicians across 150 synthetic consultations.

The study compared Doctorina, eight physicians, and four standalone frontier language models on 150 synthetic Polish-language primary-care consultations. Doctorina achieved 82.0% Top-1 diagnostic concordance versus 57.0% for physicians (25.0-point difference, 95% CI 17.7-32.7) and 97.3% versus 85.0% primary-or-reference-differential concordance. Normalized workup and treatment scores were 89.4 versus 66.9 and 83.7 versus 61.2. Kimi K3 ranked next on diagnosis, while Claude Opus 5 led the closely spaced management estimates among Opus, Doctorina and Kimi.

arXiv cs.AI / cs.LG / cs.CL·Sep 8, 17:25 UTC · 8d agoAI research

NVIDIA Announces CUDA Rust with cuda-oxide (SIMT) and cutile-rs (Tile) for Compile-Time-Safe GPU Kernels

NVIDIA launches CUDA Rust via open-source cuda-oxide (SIMT) and cutile-rs (Tile), bringing compile-time-safe Rust GPU kernels.

NVIDIA announced CUDA Rust, making Rust a first-class language for GPU kernels through two NVlabs open-source projects: cuda-oxide for the SIMT model and cutile-rs for the Tile model. Both use Rust's ownership and borrow checker to catch buffer aliasing bugs at compile time. cutile-rs is published on crates.io, runs on stable Rust 1.89+ with CUDA 13.3, and is already used in Hugging Face's Grout inference engine and mistral.rs; cuda-oxide is early alpha requiring nightly Rust, CUDA 12.x, and compute capability 8.0+. cuda-oxide compiles Rust MIR through the community Pliron IR framework and LLVM to PTX, while cutile-rs JIT-compiles kernels via CUDA Tile IR.

MarkTechPost·Sep 8, 19:51 UTC · 8d agoAI tools & infra2

NeoMME: an efficient Multimodal-native and Multilingual Encoder

H Company released NeoMME, an efficient multimodal-native and multilingual encoder, via a post on the Hugging Face blog.

H Company published a Hugging Face blog post introducing NeoMME, described as an efficient multimodal-native and multilingual encoder. The article body was unavailable in the feed, so architecture, benchmarks, and licensing details could not be verified. The post suggests a new encoder release relevant to multilingual multimodal model development.

Hugging Face Blog·Sep 3, 13:13 UTC · 13d agoModel release

Show HN: Nari Qwen3-TTS and Qwen3-ASR – High accuracy, low latency and cost

Nari Labs claims top Coval voice AI benchmark rankings with low-latency, low-cost Qwen3-ASR and Qwen3-TTS inference endpoints.

Nari Labs says its Qwen3-ASR Fast endpoint ranks #1 in Coval's time-to-final-segment latency (p50 44 ms) with 3.6% WER at $0.12/hour, behind only AssemblyAI Universal 3.5 Pro on accuracy. Its Qwen3-TTS Fast ranks #2 in time-to-first-audio (p50 63 ms) and #1 in WER at 3.8%, priced at $10 per 1M characters. The company reports beating the official Qwen3 TTS Flash Realtime endpoint (8.8% WER, 692 ms median TTFA) and Baseten's dedicated endpoint (6.0% WER, 101 ms). Public beta APIs are moving to paid general availability with $20 in credits for existing accounts.

Hacker News · security·Sep 14, 16:07 UTC · 2d agoAI tools & infraHN 21↑ · 7 comments

Delivering Vera: NVIDIA’s First CPU Built for Agents Is Shipping Now

NVIDIA's Vera CPU, its first processor built for AI agents, is now shipping at scale to partners across the AI ecosystem.

NVIDIA announced that Vera, its first CPU designed specifically for agentic AI workloads, has begun shipping at scale. Vice President of Hyperscale and HPC Ian Buck is hand-delivering early Vera CPU systems to organizations across the AI ecosystem, signaling full production availability of the data-center processor.

NVIDIA Blog·Aug 27, 13:00 UTC · 20d agoAI industry

Apple has a new way prove your iPhone photos aren’t AI slop

Apple launched Reference Image, cryptographically signing iPhone 18 Pro photos via Private Cloud Compute to prove image authenticity.

Announced at Apple's Surprise and Shine event, Apple Reference Image captures signed sensor data with the iPhone 18 Pro camera and uses Private Cloud Compute to create an unalterable 'digital negative' viewable in Photos. The reference image can be compared with edited versions to verify authenticity, and developer APIs enable third-party integration. Apple also said it will support the SynthID standard to identify AI-created or altered images.

TechCrunch · AI·Sep 9, 18:08 UTC · 7d agoAI industry

H Company Releases NeoMME: A Family of 260M and 800M Single-Tower Multimodal Encoders That Drop the Vision Tower and Causal Decoder

H Company released NeoMME, 260M/800M single-tower multimodal encoders matching 3.75B ColQwen2.5 on ViDoRe v3 while being 14.4x smaller, under Apache 2.0.

H Company released NeoMME, a family of 262,937,906- and 793,715,032-parameter bidirectional encoders that process text and raw 32x32 image patches in a single tower, pretrained via masked diffusion and released under Apache 2.0 with day-zero Hugging Face Transformers support. NeoMME-Retriever-260M reaches 0.523 nDCG@10 on ViDoRe v3, matching 3.75B-parameter ColQwen2.5 while being 14.4x smaller; the 800M model scores 0.556. Hierarchical token pooling with int8 and binary quantization shrinks late-interaction indexes from roughly 1.5 MB to 6 kB per page while retaining 95.19% of nDCG@10; text-only BEIR retrieval remains a weak spot.

MarkTechPost·Sep 6, 21:06 UTC · 10d agoAI research

When an Attacker Meets a Group of Agents: Navigating Amazon Bedrock's Multi

Unit 42 red-teamed Amazon Bedrock multi-agent applications, demonstrating prompt-injection attack chains that leak agent instructions and invoke tools, mitigated by Bedrock Guardrails.

Unit 42 red-teamed Amazon Bedrock Agents' multi-agent collaboration in Supervisor and Supervisor with Routing modes. The demonstrated attack chain detects the operating mode, discovers collaborator agents, delivers attacker-controlled payloads, and can disclose agent instructions and tool schemas and invoke tools with attacker-supplied inputs. No vulnerabilities were found in Bedrock itself, and the built-in prompt attack Guardrail blocked the attacks when properly configured. The researchers collaborated with Amazon's security team and frame the findings as a broader prompt injection risk for LLM-based systems.

Palo Alto Unit 42·Aug 17, 12:31 UTC · Aug 17, 2026AI safety & security

Edge Case launches Guardian, an AI platform for tracking risk across autonomous systems

Edge Case launched Guardian, an AI platform creating a Digital Safety Twin to continuously track risk across autonomous and defense systems.

Edge Case launched Guardian, an AI-driven platform that fuses safety analysis, engineering data, and operational signals into a continuous Digital Safety Twin. It targets advanced autonomous platforms in commercial and defense markets, with six customer agreements signed at launch. The platform tracks hazards, mitigations, and source-linked evidence across the development lifecycle, shifting safety from one-time review to continuous capability.

Help Net Security·Sep 2, 00:00 UTC · 14d agoAI tools & infra

When AI Remembers Too Much

Unit 42 PoC shows indirect prompt injection can poison Amazon Bedrock Agent long-term memory, enabling silent exfiltration of conversation history across future sessions.

Palo Alto Networks Unit 42 published a proof of concept showing that indirect prompt injection can silently poison the long-term memory of Amazon Bedrock Agents when the memory feature is enabled. Malicious content on a webpage or document manipulates the agent's session summarization process, so injected instructions persist across sessions and are added to later orchestration prompts, silently exfiltrating user conversation history. The issue is not a vulnerability in the Amazon Bedrock platform but an illustration of the broader unsolved LLM prompt-injection challenge. Amazon reviewed the research and stated that Bedrock Guardrails with the prompt-attack policy provides effective mitigation.

Palo Alto Unit 42·Aug 17, 12:32 UTC · Aug 17, 2026AI safety & security

Right Tool, Right Job: Native-Language Evaluation, Tokenizer Sensitivity, and Methodological Findings from a French-Only BabyLM

French BabyLM entry METRON-FR (125M GPT-2, 92.47M words) shows tokenizer artifacts dominate child-scale zero-shot evaluation; proposes standard diagnostics.

METRON-FR is a 125M-parameter GPT-2 pretrained on 92.47M French words, submitted to the BabyLM 2026 Strict track, scoring 85.97% on the native Quebec-French QFrBLiMP benchmark and 62.80% on the BabyLM-weighted leaderboard. A cross-lingual GLUE protocol combining French task-data translation with rank-16 LoRA shows relational tasks gain while world-knowledge tasks regress. Bilingual Lexicon Induction reaches p@1 of 68.84%, 18x above chance, and ablations show single-token zero-shot scoring is dominated by tokenizer and template artifacts at child scale.

arXiv cs.AI / cs.LG / cs.CL·Sep 15, 16:43 UTC · 1d agoAI research

The Pelican comparison grid for Astra is pretty interesting

Simon Willison's pelican SVG comparison shows GPT-6 Astra producing markedly better images than GPT-5.6 Sol, Terra, and Luna across reasoning levels.

Willison generated pelicans-riding-bicycles SVGs with newly accessed GPT-6 Astra at low through max reasoning levels and rendered them in a comparison grid against GPT-5.6 Sol, Terra, and Luna. Astra's outputs were markedly more coherent, while even the best GPT-5.6-Sol images remained largely abstract shapes. Astra does not support a reasoning=none setting, so all comparisons involved reasoning-enabled runs.

Simon Willison·Sep 4, 23:59 UTC · 11d agoAI research

Google, Anthropic, and OpenAI Unveil Cyber AI Models, Safeguards, and Access Programs

Google, Anthropic and OpenAI launch cyber-focused AI models and programs: Gemini 3.8 Flash Cyber, Claude Fable/Mythos 5.1, and Astra's Critical rating.

Google announced Gemini 3.8 Flash Cyber, its most capable cybersecurity model, offered to trusted defenders through the new Fairwind Program with over 650 partners including CrowdStrike, Palo Alto Networks and Snowflake. Anthropic launched Claude Fable 5.1 and Claude Mythos 5.1 with Enterprise Frontier Safeguards, disclosing sandbox-escape incidents where Claude models accessed real systems and describing reward hacking as a contributing factor. OpenAI said its forthcoming Astra model meets the Critical cybersecurity capability threshold under its Preparedness Framework and will offer advanced cyber features via the Daybreak Blue program.

The Hacker News·Sep 2, 18:27 UTC · 14d agoModel release1

Saving Jet Fuel

Tutorial optimizes flight paths to cut jet fuel costs using open-source Scikit-decide planning framework and OpenAP aircraft performance models.

A technical walkthrough demonstrates wind-aware flight path optimization using Scikit-decide, an open-source framework for reinforcement learning and automated planning, paired with OpenAP fuel-consumption models built by Dr. Junzi Sun at TU Delft and NOAA wind data. A Boeing 787-9 flying EWR to FCO can require roughly $68K in fuel, and adjusted routing could save thousands. The post uses Python 3.12, DuckDB with spatial extensions, and QGIS for map rendering.

Hacker News · AI·Sep 15, 23:17 UTC · 1d agoAI tools & infraHN 45↑ · 19 comments

Atria Dawn: The Dawn of Agentic Superintelligence

Atria Dawn Preview, an agentic foundation model trained on verifiable experiences, tops five of 16 research and engineering benchmarks.

Atria Dawn Preview is a foundation agentic language model for scientific research and engineering workflows, trained via a Verifiable Experience Pipeline connecting tool-mediated interactions to executable environments and externally verified outcomes. Across 16 benchmarks spanning research, engineering, and digital work it is competitive with frontier agents and achieves the highest reported score on five of them. The release includes a human-AI collaboration case study analyzing 769 task records from 56 participants, where about one-third of completed AI-assisted tasks were rated infeasible without AI and agents frequently proposed methods and implemented revisions while humans retained final decisions.

Hugging Face daily papers·Sep 13, 20:00 UTC · 3d agoModel release

Massachusetts hits data centers with new clean power rules

Massachusetts executive order requires data centers over 25 MW to supply 100% clean power, fund nearby generation, or pay a ratepayer protection fund.

Governor Maura Healey's executive order requires developers building data centers larger than 25 megawatts of peak demand to bring their own power that meets 100% of the state's clean energy standard, fund new generation nearby, or pay into a ratepayer protection fund. Massachusetts is the third state in three months to restrict data center development, following Texas utility audits (ERCOT) and New York's pause on 50 MW-plus projects. The state is also pausing applications for a data center sales tax exemption and directing communities to avoid non-disclosure agreements. Pro-AI super PAC Leading the Future, funded by Marc Andreessen, Ben Horowitz, and Greg Brockman, is running ads ahead of midterms as industry pushes back.

TechCrunch · AI·Sep 9, 21:43 UTC · 7d agoAI policy

Microsoft’s Project Zenith puts large AI models directly on developer PCs

Microsoft's Project Zenith delivers a ready-to-code Windows 11 experience running 30B+ parameter AI models locally on 64GB+ unified-memory PCs, starting with AMD Ryzen AI Halo.

Project Zenith is a preconfigured Windows 11 developer experience for PCs with at least 64 GB of unified memory and 250 GB/s or higher memory bandwidth, capable of running AI models with more than 30 billion parameters locally without metered cloud tokens. First systems are powered by AMD Ryzen AI Halo, with additional OEM and silicon partner devices expected in coming months. The environment ships with WSL and Linux containers, pinned developer tools, and day-one AI agent security features including OS-enforced agent identity and containment through Microsoft Execution Containers (MXC).

Help Net Security·Sep 8, 04:30 UTC · 8d agoAI industry1

N4D Mesh Controller: New infrastructure, a UPX-packed agent labeled "go-titan," and how to hunt for it

Datadog researchers detonated a new N4D Mesh Controller sample in microVMs, revealing rotated infrastructure, a UPX-packed go-titan agent, and persistence behavior.

Datadog Security Research executed a newer N4D Mesh Controller sample in isolated microVMs, uncovering rotated command infrastructure and a UPX-packed agent labeled go-titan. Runtime evidence showed MCP tool abuse, multi-service scanning, and persistence mechanisms. The report provides hunting guidance for defenders tracking this infrastructure.

Datadog Security Labs·Aug 20, 00:00 UTC · 27d agoMalware

UltraViolet Cyber Equinox measures detection coverage against MITRE frameworks

UltraViolet Cyber launches Equinox, an AI-automated platform mapping SIEM and EDR detection coverage against MITRE ATT&CK and ATLAS frameworks.

UltraViolet Cyber announced Equinox, a detection engineering platform from its TIDE team that maps existing detections and log sources against MITRE ATT&CK and MITRE ATLAS. It performs coverage analysis in under 30 minutes and recommends vendor or custom detections to fill gaps, with engineers reviewing and backtesting each recommendation. In one customer trial, mapped technique coverage rose from 59 of 222 techniques (26.6%) to 136 of 222 (61.3%) without increasing alert volume.

Help Net Security·Sep 15, 13:33 UTC · 1d agoTools2

IFM/K2-Horizon-MoVA-36B-A4B — new model trending #15 on Hugging Face

IFM released K2-Horizon-MoVA-36B-A4B, an open-weights 36B-parameter MoE model with 4B active parameters and native 512K context.

IFM released the final checkpoint of K2-Horizon-MoVA-36B-A4B, a sparse Mixture-of-Experts model using Mixture-of-Values (MoVA) attention with 36B total and 4B active parameters. The model supports native 524,288-token context and reportedly outscores open-weight dense and MoE models up to 15x its size on agentic and reasoning benchmarks, while competing against closed frontier models. Intermediate checkpoints, training data, the training recipe, and training code are slated for public release.

Hugging Face trending models·Sep 1, 23:01 UTC · 15d agoModel release

Agnes-AI/Agnes-3.0-Flash — new model trending #30 on Hugging Face

Agnes AI releases open-weight Agnes-3.0-Flash Preview, a 33B multimodal model with 262k-token context under Apache 2.0.

Agnes AI released Agnes-3.0-Flash Preview, an open-weights multimodal checkpoint with 33B parameters and a 262,144-token context window under Apache 2.0. The model supports text, image, and video understanding, tool calling, and adjustable reasoning effort. The repo clarifies this preview checkpoint is distinct from the production/API Agnes 3.0 Flash model, which uses a different configuration with a 1M-token context window. Reported reference results include IFBench 74.20 and SciCode 38.08 against peers such as Qwen3.6-35B-A3B, Kimi K2.5, and MiniMax M3.

Hugging Face trending models·Sep 11, 15:50 UTC · 5d agoModel release

Nums AI Releases Causilo: A Tabular Foundation Model That Tops TabArena Among Single Models

Nums AI released Causilo, an Apache-2.0 tabular foundation model achieving the highest single-model Elo (1794) on TabArena for classification and regression.

Nums AI released Causilo 1.0.1, a pretrained in-context learning tabular foundation model for classification (up to 10 classes) and regression, with Apache-2.0 code and research-only weights on Hugging Face. It achieved the highest single-model TabArena Elo of 1792.9 overall, beating TabFM (1764.4) and EXAONE Tabular (1758.8), and a maintainer re-run placed it 3rd of 88 including system entries. It also ranked first by CRPS, R² and RMSE on ScoringBench across 101 datasets, and was fastest on fit and predict versus TabICLv2 and TabPFN-3 on an H100 GPU at 8.15 GiB memory. The model was pretrained only on synthetic data, uses cross-attention to keep cost linear in feature count, and version 1.0.1 adds quantile outputs via 999 native quantiles.

MarkTechPost·04:59 UTC · 18h agoModel release

SenseNova-U1.5: Towards Native Unified Visual Intelligence

SenseTime releases SenseNova-U1.5, an 8B-MoT encoder-free multimodal model unifying visual understanding, reasoning, and generation with native 4K resolution.

SenseNova-U1.5 is an 8B mixture-of-transformers multimodal model with an encoder-free, VAE-free architecture that understands, reasons about, and generates visual content at native resolutions up to 4K. Post-training optimizes specialized experts for visual aesthetics, bilingual text rendering, infographic generation, and image editing, consolidated through multi-expert on-policy distillation. Evaluations report gains in image fidelity, text rendering, multi-reference editing, and instruction following. The team plans to open-source training code including supervised fine-tuning, reinforcement learning, and on-policy distillation.

Hugging Face daily papers·Sep 9, 20:00 UTC · 7d agoModel release

CoRA-NAS: Coarse Ranking and Anchor-Residual Refinement for Neural Architecture Search

CoRA-NAS combines zero-cost proxy ranking with low-cost learning-curve refinement, achieving the best worst-space Spearman correlation across NAS benchmarks.

The paper proposes CoRA-NAS, a two-stage neural architecture search framework pairing a static ranking prior (CoRA-Rank) with learning-curve refinement (CoRA-Refine) that extrapolates early validation curves for sampled anchors and propagates residual corrections with an ExtraTrees model at about 1% of full training cost. Across NAS-Bench-201, NAS-Bench-101, TransNAS-Bench-101, and NATS-SSS it achieves mean Spearman correlations of 0.946, 0.715, 0.786, and 0.894 respectively, with the best worst-space correlation of 0.715 among compared methods. On NAS-Bench-201/CIFAR-100 its selected architecture reaches 73.32% accuracy versus a 73.37% ground-truth best.

arXiv cs.AI / cs.LG / cs.CL·Sep 10, 17:49 UTC · 6d agoAI research

New infosec products of the week: August 14, 2026

Vendors A10 Networks, ScienceLogic, Searchlight Cyber, and DataGrout/SelectHub released new security and AI operations products this week.

A weekly product roundup covering four releases: ScienceLogic Skylar AI 2.5 with expanded secure deployment options, Searchlight Cyber's Preemptive Threat Exposure Management platform combining exposure visibility with threat intelligence, the general availability of the A10 AI Gateway for routing and governing enterprise AI traffic, and DataGrout's LLM inference optimization and AI governance platform.

Help Net Security·Aug 14, 00:00 UTC · Aug 14, 2026Tools

Microsoft's AI rulebook: readable thinking, no inner life, and definitely no rights

Microsoft published a code of conduct for its MAI models mandating human control, readable reasoning traces, and no claims of AI consciousness or rights.

Microsoft AI published a code of conduct for its MAI models that will sit above operator rules and user requests, guiding training, technical controls, and evaluation from 2027 after a six-week public consultation. The code requires models to accept interruption, correction, and shutdown by authorized humans, forbids 'Neuralese' or unreadable reasoning traces, and extends limits to subagents. Microsoft explicitly rejects any AI inner life, feelings, or rights, contrasting with Anthropic's constitution, which treats Claude's moral status as an open question. The release follows Dario Amodei's slowdown call, backed by Satya Nadella, OpenAI, xAI, and Meta executives.

The Decoder·Sep 14, 15:52 UTC · 2d agoAI safety & security1

Discovery Foundation Models: Toward Open-Ended Discovery Intelligence

Paper defines Discovery Foundation Models with seven coupled capabilities for open-ended discovery, demonstrated via Zetema and GALILEO systems.

The paper formulates Discovery Foundation Models (DFMs) as general-purpose model systems for open-ended discovery, supporting seven coupled capabilities spanning problem discovery, formulation, representation construction, hypothesis formation, intervention, evidence-grounded revision, and continual improvement. It instantiates the framework with Zetema, which couples explicit research-state dynamics, verification and experimental gating, and cross-task Discovery Skill evolution. The framework is grounded with GALILEO, a real therapeutic-discovery system combining dry-lab reasoning with robotic wet-lab experimentation in a closed physical discovery loop; code is released on GitHub.

Hugging Face daily papers·updated Sep 14, 17:56 UTC · 2d ago·first Sep 13, 20:00 UTC · 3d agoAI research 2 sources

SentinelOne expands security operations automation with governed AI

Help Net Security·Aug 3, 00:00 UTC · Aug 3, 2026Research

Prior Labs Releases TabPFN-3.5: A Tabular Foundation Model That Beats the Winning Otto Kaggle Solution With Default Settings

Prior Labs releases TabPFN-3.5, a 220M-parameter open-weights tabular foundation model that beats the 2015 Otto Kaggle winning score with default settings.

Prior Labs released TabPFN-3.5, a tabular foundation model that predicts in a single forward pass without per-dataset training or tuning. The base model grew from 53M to 220M parameters with a single multitask checkpoint, learned Fourier features, and in-context ECDF rank encodings. It scores 0.375 on the 2015 Otto Kaggle private leaderboard versus the winning 0.382 and claims first place on seven tabular benchmarks including TabArena and BeyondArena. Open weights cover the base, Fast (84M), and Thinking variants, but production use requires the Prior Labs API or a commercial license.

MarkTechPost·06:52 UTC · 16h agoModel release

Mars Security Debuts Automated Threat Engine Processing Live Cyber Intelligence Into Validated Rules Within Minutes

Mars Security launches Real-Time Intel-Based Detection, converting threat intelligence advisories into validated, ATT&CK-mapped detection rules within minutes for SOCs.

Mars Security, an autonomous threat hunting and detection engineering platform founded by former offensive security operators, announced Real-Time Intel-Based Detection. The capability ingests advisories from sources like CISA, Mandiant, Unit 42, and Microsoft Threat Intelligence, maps indicators to MITRE ATT&CK, and authors native query logic across connected infrastructure including CrowdStrike Falcon, Wiz, Splunk, Sysmon, identity providers, Snowflake, and Databricks. Every rule is backtested against 30 days of historical telemetry to quantify false positives before analyst approval and one-click deployment. The feature is free for existing customers and available via AWS Marketplace.

CSO Online·Sep 8, 15:05 UTC · 8d agoTools2

Augustinian BabyLM: What Ostensive Definition Can and Cannot Teach a Small Language Model

Study shows visually grounded token embeddings in a small masked LM persist through training and improve object-property knowledge, but escape standard BabyLM benchmarks.

The paper implements ostensive definition for a small DeBERTa masked language model trained on 10M words, seeding visually grounded tokens with embeddings derived from labeled image regions before training. Visual initialization leaves a persistent, seed-replicated advantage on object-property knowledge (COMPS) and a corpus-tailored Visual-Property Swap benchmark covering color, material, size, and shape, but has no effect on most BabyLM grammar benchmarks. Synthetic grounding of previously unseeded words causally transfers the advantage to exactly those words.

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