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CVE-2026-56207: Apache Impala: SAML authentication bypass via forged bearer token

Apache Impala CVE-2026-56207 allows forged SAML bearer tokens on the hs2-http interface, letting attackers impersonate other users; fixed in 4.5.2.

Apache Impala versions 4.0.0 through 4.5.1 fail to verify the bearer token signature in the final step of SAML2 authentication for the hs2-http interface. An attacker can alter the username and act as another user. Users are recommended to upgrade to version 4.5.2, and Apache rated the issue critical. It was reported by Andrew Rukin of Arenadata.

oss-securitySep 8, 21:37 UTC · 8d agoVulnerability 3 sources CVE-2026-562071

Multi-Vector (Late Interaction) Embedding Models with Sentence Transformers

Hugging Face details building and using multi-vector late-interaction embedding models with Sentence Transformers for retrieval workloads.

Hugging Face published a guide on multi-vector, late-interaction embedding models (ColBERT-style) supported through Sentence Transformers. The post covers how practitioners can build and use these models for retrieval and RAG pipelines. It is a developer tooling and technique write-up, not a security advisory.

Hugging Face BlogAug 18, 00:00 UTC · Aug 18, 2026AI tools & infra1

CVE-2026-54048: Apache Impala: Avro Schema URL Server-Side Request Forgery

Apache Impala CVE-2026-54048 lets crafted Avro schema URLs trigger SSRF to internal endpoints, with responses potentially leaking via error messages.

A server-side request forgery in Apache Impala 2.0.0 through 4.5.1 on all platforms can be triggered via an Avro schema URL using an http or file:/// URI on a table. An attacker can cause Impala to send GET requests to internal endpoints it can access, and responses may be exposed through parsing error messages. Users are advised to upgrade to a fixed release.

oss-securitySep 8, 21:36 UTC · 8d agoVulnerability CVE-2026-540481

Rosetta at AlexandriaX-2026: LoRA-Adapted NileChat for Context-Aware Dialectal Arabic Dialogue Translation

Rosetta ranks 4th and 5th in AlexandriaX-2026 dialectal Arabic dialogue translation using a LoRA adapter on NileChat-3B, finding limited pretraining benefit.

The Rosetta system for the AlexandriaX-2026 shared task fine-tunes a LoRA adapter on NileChat-3B for context-aware English-to-dialectal Arabic dialogue translation. The adapter was additionally pretrained on MADAR and PADIC dialect corpora for the unconstrained track. It achieved spBLEU 26.10 (4th, constrained) and 25.09 (5th, unconstrained). External dialect pretraining improved only two of thirteen dialects while slightly degrading overall performance, indicating negative transfer.

arXiv cs.AI / cs.LG / cs.CLSep 9, 16:15 UTC · 7d agoAI research1

Wire It, Run It, Deploy It: AI Workflows in Gradio

Hugging Face published a guide on wiring, running, and deploying AI workflows in the Gradio framework.

Hugging Face's blog post 'Wire It, Run It, Deploy It: AI Workflows in Gradio' is a tutorial on building AI workflows with Gradio. It covers wiring components, running applications, and deploying AI-powered apps. No security incident or vulnerability content is included.

Hugging Face BlogAug 25, 00:00 UTC · 23d agoAI tools & infra

You Can't Prefer Emotions You Don't Sample: Intensity Undershoot in DPO-Tuned LLMs

Study quantifies DPO-tuned LLMs undershooting requested emotional intensity, tracing the gap to candidate-pool extremity rather than conditioning format.

Conditioning an instruction-tuned LLM on continuous valence-arousal targets yields gain of only 0.26 for valence and 0.13 for arousal on Llama-3.1-8B, far below faithful control of 1.0. The authors attribute undershoot to neutral-heavy preference corpora like EmoBank and candidate pools lacking extreme affect, leaving DPO without extreme exemplars. Uniform target coverage with a hotter candidate pool raises valence gain to 0.40 on Llama-3.1-8B and 0.44 on Qwen3-8B, with modest in-distribution cost; arousal gains remain unstable across seeds.

arXiv cs.AI / cs.LG / cs.CLSep 7, 17:47 UTC · 9d agoAI research

Muse, the band, lost its social media handles to Muse, Meta's new AI agent

Meta's new Muse AI agent took the @muse Instagram and X handles long used by the English rock band Muse.

Meta's newly released AI agent Muse now uses the @muse social media handles that the English rock band Muse, which trademarked its name in 1999, had used on Instagram and X for years. The band's Instagram handle reportedly changed to @museband around June-July 2026, and Meta executives' launch posts about the agent mistakenly tagged the band's account. Meta has not explained the change, and the piece notes a pattern of Meta acquiring notable handles around major launches, including @Meta in 2021 and @threads in 2023.

Hacker News · AISep 9, 23:57 UTC · 7d agoAI industry HN 84↑ · 32 comments1

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.CLSep 10, 17:49 UTC · 6d agoAI research

Researchers Show How Meta's 'Pervert Glasses' Are Used to Harass Women

University of Sydney researchers detail how pickup artists use Meta Ray-Ban smart glasses to covertly film and harass women, then post the videos on Instagram.

Researchers Joanne Gray, Milica Stilinovic, Marcus Carter, and Ben Egliston analyzed 350 Instagram videos posted between September 2023 and March 2026 showing unsolicited approaches to women filmed with smart glasses. They found a clear correlation between covert filming and harassment severity, arguing ambient capture creates 'borderline' harassment that evades platform moderation mechanisms. Instagram head Adam Mosseri said the platform would remove harassing pickup-line content, though similar videos remain widespread a month later. Meta's safeguards, such as the recording light, were previously criticized as insufficient, and users have modded glasses to disable the light.

404 MediaAug 12, 14:05 UTC · Aug 12, 2026AI safety & security

[AINews] Hot Chips: OpenAI’s Jalapeño, Cerebras CS-5, Groq 3 LPX, Apple M6

OpenAI unveiled Jalapeno custom inference chip claiming 1.5-1.9x better perf-per-watt than NVIDIA GB200/GB300, deploying in-house by year-end.

At the 37th Hot Chips conference, OpenAI published first benchmark details for its custom Jalapeno inference chip, claiming 1.5-1.9x more work per watt, 1.7-3.6x lower end-to-end latency, and 2.1-4.1x higher interactive-workload performance versus NVIDIA GB200/GB300, with the 700W-rated part staying at or below 550W in tests. Deployment into OpenAI's own infrastructure begins by year-end, with Gen 2 deep in development and Gen 3 underway. OpenAI also said GPT-Astra and Codex helped write low-level kernels, reportedly 1.5-1.8x faster than human-expert code for selected attention and MoE blocks. Cerebras CS-5, Groq 3 LPX and Apple M6 were also featured at the conference.

Latent SpaceAug 27, 01:31 UTC · 21d agoAI industry

Rebuilding AUTOMATIC1111 with Gradio Workflow

Hugging Face demonstrates rebuilding the AUTOMATIC1111 Stable Diffusion web interface using its Gradio Workflow framework.

Hugging Face published a post showing how to rebuild the AUTOMATIC1111 Stable Diffusion WebUI experience with the Gradio Workflow framework. The article body was unavailable, so details beyond the title are limited, but the piece appears to be a tutorial on composing interactive AI interfaces with Gradio Workflow components.

Hugging Face BlogSep 10, 00:00 UTC · 7d agoAI tools & infra

Realtime-Venus: A full-duplex interaction system with asynchronous delegation

Realtime-Venus introduces two 9B full-duplex interaction models (Omni and Audio) that outperform Gemini 3.1 Live and GPT-4o on continuation metrics.

Realtime-Venus is a proactive full-duplex interaction system built on two separately trained 9B models: Realtime-Venus-Omni for audio-visual interaction and Realtime-Venus-Audio for spoken interaction. A dual-loop runtime lets foreground interaction continue while Realtime-Venus-Harness asynchronously executes background reasoning and tool tasks. Realtime-Venus-Omni leads on six of eight video benchmarks, including StreamingBench (70.2%), OVO-Bench (64.7%), and Daily-Omni (81.3%), while Realtime-Venus-Audio tops MMAU (78.0%) and MMAU-Pro (63.2%). On Full-Duplex-Bench v1.5, Realtime-Venus-Audio handles 75% of interruptions and exceeds Gemini 3.1 Live and GPT-4o on all three continuation metrics.

Hugging Face daily papersSep 11, 20:00 UTC · 5d agoAI research

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.CLSep 10, 17:43 UTC · 6d agoAI research

CARDEA: Auditable Reasoning Grounded in Spatial Evidence for End-to-End Coronary Angiography Interpretation

CARDEA, a vision-language model trained only on public data, matches cardiologists on coronary angiography complexity assessment while exposing auditable bounding-box evidence.

CARDEA is a unified large vision-language model serving as the inference core of an end-to-end coronary angiography pipeline from multi-view videos to study-level diagnosis. It was trained on public datasets through visual alignment, self-distilled Chain-of-Box cold start, and reinforcement learning with verifiable rewards encouraging bounding-box reasoning. It reached 0.91 accuracy on dominance classification under domain shift and 0.90 on complexity assessment, comparable to two interventional cardiologists. RLVR raised zero-shot report generation vessel-severity macro-F1 from 0.513 to 0.686, while supervised imitation alone did not.

Hugging Face daily papersSep 6, 20:00 UTC · 10d agoAI research

Training and Finetuning Multi-Vector Embedding Models with Sentence Transformers

Hugging Face published a tutorial on training and finetuning multi-vector embedding models using the Sentence Transformers library.

Hugging Face's blog walks through training and finetuning multi-vector embedding models with Sentence Transformers. Multi-vector approaches store multiple vectors per document to support late-interaction retrieval. The post is a practical guide for developers building retrieval pipelines with the library.

Hugging Face BlogAug 26, 00:00 UTC · 22d agoAI tools & infra1

Omni Interaction Agent Technical Report

Researchers release Gander, an end-to-end omni interaction model with full-duplex streaming across video, speech, and text plus agentic capabilities.

Gander is an end-to-end model unifying omni perception, realtime interaction, and agentic capabilities in a single framework, accepting continuously streaming video, speech, and text. It uses a Cerebellum-Brain architecture where the Cerebellum handles realtime conversation and the Brain handles reasoning and agentic tasks, built on a streaming Thinker-Talker design with chunk-level token streams. Internal human evaluations report spoken dialogue on par with SOTA open source models and competitive omni interaction; the models, code, and data are released publicly.

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

Alissonerdx/Minimax-H3-ComfyUI — new model trending #30 on Hugging Face

Alissonerdx released a rank-64 LoRA sharpening videos from MiniMax H3 in ComfyUI via guide-latent conditioning, trending #30 on Hugging Face.

Alissonerdx published Minimax-H3-ComfyUI, a repository containing a rank-64 LoRA (minimax_h3_lms_v1.0_r64) that sharpens source videos while keeping them photorealistic. It targets the MiniMax H3 ref2va base model, runs with Comfy-Org's ComfyUI-ready weights, and uses the native MiniMaxH3AddGuide node for aligned guide-latent conditioning. The release ships with a ComfyUI workflow, eight before/after example videos, and an Apache 2.0 license.

Hugging Face trending modelsSep 6, 11:22 UTC · 10d agoModel release2

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

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

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

The DecoderSep 16, 15:19 UTC · 15h agoAI industry

Jalapeño’s first results show industry-leading speed and efficiency in AI inference

OpenAI's Jalapeño custom inference chip shows faster, more power-efficient inference with higher throughput and lower latency.

OpenAI announced first results for Jalapeño, its custom AI inference chip. The company claims industry-leading speed and efficiency, delivering higher throughput and lower latency for modern models compared with existing options. The chip targets more power-efficient serving of large models at scale.

OpenAI NewsAug 25, 07:00 UTC · 22d agoAI industry

Fine-tuning a 350M Model for Better Structured Outputs in 100 GRPO Steps

Hugging Face guide fine-tunes a 350M-parameter model with 100 GRPO steps to improve structured output reliability.

A Hugging Face blog post demonstrates fine-tuning a 350M-parameter model using GRPO (Group Relative Policy Optimization) with TRL over 100 training steps. The stated goal is more reliable structured outputs from small language models. No article body was available, so details beyond the title are limited.

Hugging Face BlogSep 3, 00:00 UTC · 14d agoAI tools & infra

Omni-Streaming Thinking

Omni-Streaming Thinking fixes premature cross-modal commitment in streaming omni-modal models via pending claims verified against modality-specific evidence, beating baselines by over 10%.

The paper identifies 'premature cross-modal commitment', where streaming models keep relaying early visual interpretations even after audio contradicts them. OST generates evidence-linked pending claims with future verification intervals, stores audio and visual evidence separately, and refutes claims when contradictory evidence appears. Built on a frozen Qwen3-Omni-30B-A3B-Instruct backbone with lightweight adaptation, it outperforms open baselines by more than 10% relative on five streaming and audio-visual benchmarks. On the new OST-DiagBench it reaches d-prime 2.95 versus at most 1.38 for open baselines, while reducing vision-induced auditory hallucinations.

Hugging Face daily papersSep 13, 20:00 UTC · 3d agoAI research1

Instagram’s AI detection is a mess (again)

Instagram is mislabeling ordinary edited photos as AI Content while some AI imagery goes unlabeled, repeating a 2024 detection failure.

The Verge documents weeks of erroneous AI Content labels on Instagram, including images edited only with Canva's Background Remover or an iPhone Photos app, while some generative images escape tagging. Canva said some of its assistive AI tools were being tagged as generative and claims the issue is fixed, though users still report tagging. Meta scans IPTC and C2PA metadata and uses signals like Google's SynthID, but remains vague about detection criteria; one tester found only Meta AI-created or edited images reliably triggered labels, and an image-poisoned photo was tagged. A similar mislabeling wave hit Instagram in 2024.

The Verge · AISep 4, 12:00 UTC · 12d agoAI industry

[AINews] Muse Spark 1.3 matches GPT-5.6-Sol, confirming Meta Superintelligence as the newest Frontier Lab, >90% discount for training

Meta's Muse Spark 1.3 reportedly ranks as the world's #3 model, matching frontier models from OpenAI and Anthropic with planned open weights.

The Latent Space AI News roundup leads with Muse Spark 1.3, promised in Zuckerberg's letter, which ranks #3 worldwide per AAII, is slated for open weights, and uses a pricing model over 90% cheaper when users opt in to training. The issue also covers the rumored Gemini 3.8 Flash launch and analysis arguing OpenAI's rumored looped-transformer 'Astra' architecture is a modest tweak rather than a breakthrough. Additional coverage includes ByteDance Seed's HarnessDev harness-evaluation benchmark, a retrieval-invoked actual-use evaluation method, Stanford's revamped agent engineering curricula, and Photon 2.1 adding TTS models and NVIDIA B200 support.

Latent SpaceSep 3, 04:38 UTC · 14d agoModel release1

Meta bets on AI agent Muse to catch up in AI race

Meta launched Muse, a free personal AI agent in the US that autonomously shops, emails, and plans trips, powered by its in-house Muse Spark model.

Meta unveiled Muse, a consumer personal AI agent that handles tasks like online shopping, emailing, and trip planning, continues working in the background, and requests approval for purchases. It launches in the US on iOS, Android, and muse.ai, is free for most users with unspecified paid tiers, and runs on Meta's in-house Muse Spark model. Muse executes on a cloud virtual machine with a Sentinel agent policing outbound actions, and Meta promises an encrypted confidential VM later this year plus 1Password, Shop Pay, and Stripe Link support. The launch is positioned as the centerpiece of Meta's effort to catch up with OpenAI, Anthropic, and Google.

The Verge · AISep 8, 19:00 UTC · 8d agoAI industry 3 sources1

Sure, Meta’s AI Muse works, but it sure creeps me out

Hands-on review finds Meta's Muse AI agent completes shopping and email tasks but surfaces personal Instagram API data beyond user-visible ad-topic settings.

Meta launched Muse, its first agentic AI productivity assistant, which performs tasks like shopping, email management, trip planning, media generation, and creating webpages or documents via a cloud-based virtual computer. The Verge's hands-on found it successfully deleted thousands of promotional emails and completed an Amazon purchase, but it also revealed detailed personal interests inferred from Instagram and Facebook account API data that is not visible in the apps' ad-topic settings. Meta says Muse only exchanges data needed for third-party integrations and does not share information with advertisers; the reviewer frames privacy unease as the main adoption hurdle.

The Verge · AIupdated Sep 12, 20:00 UTC · 4d agofirst Sep 10, 15:00 UTC · 6d agoAI industry 11 sources

Technical Manual for a Toolkit for Measuring Contextual Individuation in Transformer Language Models

An open methodology toolkit measures whether transformer language models contextualize fixed word forms across domains using bridge forms and layer-wise silhouette analysis.

The manual documents an open toolkit built around 'bridge forms' - identical written words recurring across two or more subject domains with a different sense in each - to test whether transformer language models individuate word occurrences by context beyond the embedding layer. It covers declarative specification of bridge forms, Wikipedia corpus acquisition, occurrence localization, layer-wise representation extraction, domain-pairwise silhouette measurement, and visualization, justifying each choice against failure modes such as sense contamination and subword-tokenization misalignment. It is a methodological and implementation reference and reports no empirical results.

arXiv cs.AI / cs.LG / cs.CLSep 4, 16:38 UTC · 12d agoAI research

Suno releases its first AI music model made with record industry help

Suno released its v6 music model family (v6, v6-wild, v6-mini), the first trained with licensed data from Warner Music Group, BMG, and Believe.

Suno's v6 comes in three variants: v6, the more unpredictable v6-wild, and resource-light v6-mini offered free to all users. The model was trained from the ground up on a new dataset including licensed content from Warner Music Group, BMG, and Believe, plus user data, though it is unclear if all dubiously obtained content was excluded. v6 shows dramatically improved genre fidelity, adds plain-language chat editing of individual song elements, multi-element mashups, and prompts based on images, video, or audio. The Verge notes it still cannot produce intentional imperfections like off-key vocals, and v6 starts rolling out now with older models eventually retired.

The Verge · AISep 9, 21:42 UTC · 7d agoModel release

Knowing What Not to Answer: Selective Non-Compliance in Vision-Language Models

Researchers introduce KoNA, a benchmark exposing vision-language models' failures at selective non-compliance, plus fine-tuning that improves refusal and abstention accuracy.

KoNA is a benchmark for evaluating selective non-compliance in vision-language models across five categories: False Premise, Visual Inaccessibility, Universal Unknown, Task Feasibility and Safety. It tests both query-level and component-level non-compliance using paired single and compound queries, and evaluations across diverse VLMs show models often fail to refuse, correct or abstain appropriately, with failures worsening on compound queries. Fine-tuning VLMs on KoNA examples substantially improves non-compliance accuracy while largely maintaining performance on fully answerable tasks.

Hugging Face daily papersSep 3, 20:00 UTC · 13d agoAI research1

Building a Production Greek-English Speech Recognizer

Engineering report details Sophea, a production Greek-English ASR reaching 4.26% WER on public English sets via ROVER ensemble and data-pipeline calibration.

Across 23 training iterations, two architectures, and nine production gates, no single data composition passed all gates; a three-model ROVER ensemble reached 9 of 9 gates and cut overlapping-speech WER from 53.35% to 37.87%. Calibrating an audio-quality filter against in-domain anchors reduced discarded scored Greek audio from 98.7% to 10.6%, and a pre-registered ablation traced a hallucination defect to one training-data package. The sophea/asr-k1 preview arbiter lists 4.26% average WER on eight public English test sets and 25.88% WER on live Greek noisy traffic; no weights or training data are released.

Hugging Face daily papersSep 10, 20:00 UTC · 6d agoAI research

Anthropic Adds Plugin Evals to Claude Code: 6 Grader Types, a No-Plugin Baseline, and a CI Gate for Skills

Anthropic ships a plugin evals workflow for Claude Code with six grader types, a no-plugin baseline arm, and a CI gate via threshold and cost flags.

Anthropic published a plugin evals workflow for Claude Code, exposed via the "claude plugin eval" command on v2.1.269+. Six grader types exist: regex, tool_used, tool_order, and file_exists are free transcript checks, while llm and baseline invoke a billed judge model. Every case runs with and without the plugin, and the delta (Δ) isolates the plugin's contribution; a Δ near zero with a failing tool_used:Skill grader indicates the skill never triggers. CI gating uses --threshold 0.8, --max-cost-usd, --trust-plugin, and --no-publish flags, with results written to a report.html under evals/results/.

MarkTechPostSep 11, 21:05 UTC · 5d agoAI tools & infra2

The Frontier AEO Tracker: What Astra Chooses (and every other frontier model, and what you can do about it)

Latent Space launches an AEO tracker scoring 7 frontier models' product recommendations across 161 categories, revealing generational bias flips.

Latent Space built a tracker measuring Answer Engine Optimization by running 6 prompt variations across 7 frontier models with search enabled over 161 product categories, scoring first choices, alternatives, mentions, and anti-recommendations. It found 28 categories with a universally dominant primary choice and observed soft biases, such as models favoring their own lab's coding agents. Analysis of Anthropic's Sol→Astra and Opus→Fable generations showed newer models consulting fewer sources and being less likely to change answers when questions are paraphrased.

Latent SpaceSep 7, 21:32 UTC · 9d agoAI research

Vidu S2: Real-Time Interactive, Editable, and Spatial Video Generation

Vidu S2 delivers real-time 720p interactive avatar generation plus real-time video editing with style, clothing, character, and background replacement.

Vidu S2 comprises Vidu S2-Avatar, a real-time interactive digital-character model, and Vidu S2-Editing, a real-time video editing model. S2-Avatar supports real-time 720p generation, dynamic references updatable at any moment, and stronger instruction following such as dancing, while S2-Editing performs real-time style rendering, clothing replacement, character replacement, and background replacement. The work also explores real-time spatial video generation for both models, reports outperforming all baselines, and offers a playable online demo at vidu.com.

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