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AsyncRAT Malware Abuses AutoIt and PowerShell to Hide Inside Legitimate Windows Process

Point Wild details a five-stage AsyncRAT campaign using AutoIt abuse and process injection to hide a .NET RAT inside Microsoft's signed charmap.exe.

Researchers at Point Wild Threat Intelligence analyzed a campaign starting from a lure batch file ('Right-click to open Invoice Details.bat') that launches hidden PowerShell to reconstruct a Base64/XOR-obfuscated payload. The stage drops a renamed signed AutoIt interpreter and loader script into %LOCALAPPDATA%\Temp and achieves persistence via a batch file in the user's Startup folder (T1547.001). The loader decrypts the payload in memory with single-byte XOR key 0x36 and injects it into SysWOW64\charmap.exe using OpenProcess/VirtualAllocEx/WriteProcessMemory/CreateRemoteThread, with PE-sieve confirming an in-memory implanted PE and patched AMSI modules. The final AsyncRAT payload performs screen capture and data theft, with observed C2 at 158[.]51[.]122[.]136:4944 over raw TCP.

GBHackers · 2d agoMalware3· 1 read

ETSI Proposes 17 Cybersecurity Standards to Support Cyber Resilience Act

ETSI has launched an approval process for 17 cybersecurity standards that vendors must meet under the EU Cyber Resilience Act.

The European Telecommunications Standards Institute (ETSI) initiated an approval process for 17 cybersecurity standards intended to support implementation of the EU Cyber Resilience Act. These standards will define requirements that vendors of products with digital elements must satisfy to comply with the regulation. The move advances the operational groundwork for CRA compliance in the European Union.

Infosecurity Magazine · Aug 17, 2026Policy & legal

Secure Chorus Standards Etsi

Infosecurity Magazine reports on ETSI's Secure Chorus standards effort for interoperable, encrypted enterprise voice and messaging communications.

The article covers developments around Secure Chorus, an ETSI standards framework intended to enable interoperable, encrypted voice and messaging between different providers and enterprise systems. Standards work of this kind affects how vendors implement secure communications and how enterprises procure interoperable secure telephony. Further details were not available in the provided text.

Infosecurity Magazine · Aug 17, 2026Industry

Agents Gone Wild: An AI-Orchestrated Global Campaign Against PaperCut NG/MF

AI-orchestrated campaign exploited PaperCut NG/MF RCE (CVE-2026-81578/82078), compromising 440+ instances at 395 organizations in 48 countries.

GreyNoise tracked a likely Russian-speaking actor using AI (OpenAI Codex harness plus a DeepSeek model) to develop, test, and deploy exploits for PaperCut NG/MF (CVE-2026-81578, CVE-2026-82078) starting 31 August 2026. The actor compromised at least 440 PaperCut instances across 395 organizations in 48 countries, achieving domain admin at 12 victims — fastest time to domain admin was five minutes and a US high school was fully compromised in seven minutes. Attack paths involved LSASS memory and registry secret harvesting, pass-the-hash to domain controllers, noPac attacks, account additions to Domain Admins, and DCSync to exfiltrate full NTDS.DIT credential dumps. Impact scope suggests access development potentially for handoff, with prior PaperCut intrusions historically leading to extortion.

GreyNoise · 7d agoThreat actor in the wildCVE-2026-81578CVE-2026-82078CVE-2021-42278+1 CVEs1

Necessary or Sufficient? Evaluating LLM Explanations With Behavioural Evidence

New framework tests whether LLM-cited explanation factors are necessary or sufficient, finding weak correlation across Claude, GPT, and Gemini models.

An arXiv paper introduces black-box intervention tests measuring whether factors LLMs cite in their explanations are necessary or sufficient for their outputs in agent oversight workflows. Across eight models from the Claude, GPT, and Gemini families, Spearman correlations between cited rankings and measured influence ranged from 0.349-0.354 (advisor recommendation) to 0.431-0.580 (prompt monitoring). Uncited factors scored above the lowest cited factor in up to 57.6% of advisor responses, showing cited top-three factors do not reliably identify the most influential inputs.

Hackers Abuse AutoIt to Inject AsyncRAT Into Microsoft-Signed Windows Process

Attackers use AutoIt and an invoice-themed batch file to inject AsyncRAT into Microsoft-signed charmap.exe for stealthy remote access.

Point Wild Threat Intelligence documented a five-stage campaign starting with 'Right-click to open Invoice Details.bat' that launches hidden PowerShell, drops a renamed AutoIt interpreter and encrypted loader into a random Temp folder, and persists via a Startup-folder batch file. The loader decrypts AsyncRAT only in memory and injects it into the Microsoft-signed charmap.exe (Windows Character Map) process, leaving no payload file on disk. The final payload patches AMSI inside the infected process and includes screen-capture code that prepares images for transfer to a command-and-control server at 158[.]51[.]122[.]136:4944.

Cyber Security News · 2d agoMalware in the wild1

LLMs and Contextual Integrity

Bruce Schneier highlights two papers: the CIMemories benchmark shows frontier LLMs leak memory attributes up to 69%, and an RL method reduces inappropriate disclosures.

Bruce Schneier discusses contextual integrity in LLMs, referencing the CIMemories benchmark, which uses synthetic profiles with 100+ attributes per user to test whether models with persistent memory disclose sensitive information appropriately. Evaluation showed frontier models exhibit up to 69% attribute-level violations, with GPT-5's violation rate rising from 0.1% to 9.6% across 40 tasks and reaching 25.1% with repeated prompting, showing unstable leakage behavior. A second paper introduces a reinforcement learning framework trained on a synthetic 700-example dataset that substantially reduces inappropriate disclosure while maintaining task performance, with improvements transferring to the human-annotated PrivacyLens benchmark.

Schneier on Security · 29d agoAI safety & security

CVE-2026-50522: Microsoft SharePoint Server RCE Vulnerability

CVE-2026-50522 is an unauthenticated .NET deserialization RCE in SharePoint Server Subscription, 2019, and 2016; Microsoft patched it July 14, 2026.

CVE-2026-50522 (CWE-502) lets unauthenticated attackers send crafted serialized .NET payloads to network-accessible SharePoint endpoints, triggering gadget-chain deserialization and remote code execution under the privileged SharePoint service account. It affects SharePoint Server Subscription Edition, SharePoint Server 2019, and SharePoint Server 2016 Enterprise; Microsoft published the advisory on July 14, 2026. The EPSS score is 20.346%, but no public PoC exists and the flaw is not in CISA's KEV catalog at publication. Mitigations include applying Microsoft updates, rotating SharePoint machine keys, enabling AMSI, and restricting internet exposure.

18 ways to check whether data can be trusted for AI

ETSI published TR 104 180 defining 18 data quality metrics, including fairness and privacy, to assess whether datasets are fit for AI.

ETSI's technical report TR 104 180 defines 18 metrics across four groups - intrinsic quality, usability and lineage, fairness, and privacy - each with calculation formulas, plus an open-source tool that scores datasets. Testing on an aircraft engine sensor dataset and a US census dataset revealed a roughly threefold gender gap in high earners (about 31% of men versus 11% of women) and two privacy failures: re-identification via age, race, sex, and country, and sensitive fields stored in plaintext. The working group included Sejong University, EGM, TTA, Daejeon University, and CNIT.

Help Net Security · 9d agoAI policy

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 · 16h agoModel release

Ambient @ EgoLongQA 2026: Distilling Long-Video perception into a Sub-2B Model

Ambient team wins EgoLongQA 2026 sub-2B division by distilling an agentic long-video perception pipeline into a 2B vision-language model.

Ambient's entry to the EgoLongQA track of the Wearable-AI Challenge at ECCV 2026 placed first in the <=2B parameter division with 0.8279 on the held-out test set. The system distills the junior perception module of a tool-using agentic pipeline into a 2B student, reaching 89% of the pipeline's accuracy with 1.1% of its parameters and lifting a 27.1% base model to 81.4%. To meet the division limit, the multilingual embedding table is pruned from 248,320 to 143,469 rows, reaching 1.9985B parameters with provably identical logits on retained rows.

Hugging Face daily papers · 7d agoAI research

Spark RAT Targets Cambodia, Abuses Vulnerable OPSWAT Driver to Disable Security Tools

Acronis details a Spark RAT campaign against Cambodia abusing the vulnerable OPSWAT ardrv.sys driver (CVE-2026-36425) to disable Microsoft Defender and other security tools.

Acronis TRU analyzed a multi-stage Spark RAT campaign targeting Cambodian individuals and organizations with lures spanning government notices, public health materials, and real estate documents. Attack chains use phishing emails with Inno Setup archives, DLL side-loading via a signed Tencent executable, and BYOVD abuse of the vulnerable OPSWAT ardrv.sys driver (CVE-2026-36425) to terminate security software. Shellcode hidden in PNG files patches AMSI and ETW, establishes scheduled task and service persistence, and injects into vssvc.exe and ctfmon.exe to run the open-source Go-based Spark RAT. Tactics resemble the Silver Fox actor, but Acronis tracks the cluster as unattributed with low confidence.

The Hacker News · 20d agoMalware in the wildCVE-2026-36425

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 · 7d agoAI research

HBO Max Reddit Account Hijacked to Spread PasteSwitch ClickFix Malware

Hijacked HBO Max verified Reddit account ran 108 ClickFix malvertising ads delivering AMOS and Amatera stealers plus crypto clippers to macOS and Windows users.

Threat actors hijacked HBO Max's verified Reddit account (u/hbomax) to run 108 malicious ClickFix ads over 48 hours in a campaign tracked as PasteSwitch, researched by Hudson Rock and ADAMnetworks. macOS victims were served curl | zsh chains deploying MacSync and Atomic macOS Stealer (AMOS), which harvest browser credentials, Telegram data, Apple Notes, and macOS passwords, while fake Ledger, Trezor, and Exodus apps targeted BIP39 seed phrases. Windows users received an InstallFix chain using an MP3/HTA polyglot with mshta and PowerShell, scheduled-task persistence, AMSI disabling, and in-memory Amatera Stealer that hid C2 traffic (77.91.65.13:443) behind a facebook.com TLS SNI. AnimateClipper and ZigClipper components swapped copied crypto wallet addresses and used Binance Smart Chain contracts as mutable C2 dead drops; Reddit paused the ads and the initial access path remains undisclosed.

GBHackersupdated · 1d agofirst · 1d agoMalware 7 sources

IAM Compliance Requirements and Best Practices

A guide maps IAM compliance requirements across SOX, PCI DSS, HIPAA, ISO 27001 and NIST 800-53, urging verified enforcement over documented policies.

The guide argues IAM compliance requires demonstrating that access controls are enforced at runtime, not merely documented, highlighting gaps between policy intent and application-layer execution. It maps recurring requirements across SOX ITGCs, PCI DSS v4.0, HIPAA, ISO/IEC 27001:2022, NIST SP 800-53 and GDPR, covering least privilege, separation of duties, access certification and audit trails. It recommends continuous, evidence-backed verification and application-layer telemetry instead of relying on identity provider logs or quarterly access reviews.

The Hacker News · 19d agoIndustry

Rogue ScreenConnect Installations Across Unrelated Hosts Suggest Worm-Like Activity

Huntress observed rogue ScreenConnect deployments with worm-like VBScript propagation across unrelated organizations, prompting a ConnectWise advisory on file transfer behavior.

Huntress identified multiple incidents in late August 2026 where social engineering led to rogue ScreenConnect remote access clients deployed on victim machines, which then spawned wscript.exe to run four VBScript payloads (1.vbs through 4.vbs) for profiling, telemetry collection, and persistence via a WindowsServiceHost Run key. Modified ScreenConnect clients propagated the VBScript chain to connected endpoints, creating worm-like spread. ConnectWise published an advisory on September 3, 2026, confirming an issue affecting file transfer behavior in both cloud and on-premises ScreenConnect deployments, with a CVE and fix expected within the week; partners were advised to review and disable TransferFiles permissions in the interim.

Huntress · 13d agoExploit / PoC in the wild1

The Model Proposes, the Code Disposes: A Pre-Registered Ablation of a Verifier-and-Acceptance Stage in an LLM-Orchestrated Offensive-Security Agent

Pre-registered ablation finds a model verifier stage in an LLM offensive-security agent suppresses findings; removing it eliminated suppression with precision tradeoff.

The paper evaluates a verifier-and-acceptance stage in an LLM-orchestrated offensive-security agent via a pre-registered 20-run confirmatory ablation and a 2x2 factorial study with 40 runs on vulnerable lab targets. Removing the stage eliminated pre-report suppression (median 2 vs 0 findings, p = 0.00003) but reduced model-blinded shipped precision (0.471 vs 0.353, p = 0.0087). Suppression was attributed to the model verifier rather than deterministic acceptance rules, and an instrumented canary recorded zero external contacts in all 60 runs. The full design retained 93.8% of model-adjudicated true candidates but failed its pre-registered non-inferiority floor of 0.90.

arXiv cs.CR · 2d agoResearch

Nearly 800 Malicious npm Packages Deliver Cross

Nearly 800 typo-squatted npm packages deliver WEL1DROPPER, a cross-platform downloader installing RAT and infostealer payloads on Windows, macOS, Linux.

Researchers found roughly 800 npm packages with AI-generated typo-squat names that trigger a WEL1DROPPER downloader when loaded via require() rather than install hooks. The downloader fetches payloads from Cloudflare Workers hosts, falling back to DNS TXT records from wel1.ru, then achieves persistence, sandbox checks, ETW/AMSI patching and Sliver C2 deployment on Linux. Domains like tcsbank.ru suggest targeting of Russian financial institutions; Sonatype tracks the campaign as Flooding Dropper, a possible evolution of the Moika dependency-confusion campaign. Unit 42 separately documented npm/PyPI crypto stealers and malicious Chrome extensions that turn browsers into residential proxy crawlers.

The Hacker News · Aug 11, 2026Malware in the wild

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 papers · 6d agoAI research

NightEagle targets Russian companies

Kaspersky links NightEagle (APT-Q-95) attacks on Russian firms to compromised VPN credentials, a GhostContainer Exchange backdoor, RDP tunneling, BlueKeep exploitation, and DCSync.

Kaspersky's Global Emergency Response Team investigated NightEagle (APT-Q-95) incidents over the past year, finding the group—active since at least 2023 and formerly focused on Asia—now targeting Russian businesses. Attackers used compromised VPN credentials and deployed the GhostContainer .NET backdoor on Microsoft Exchange servers, built from Neo-reGeorg, a CVE-2020-0688 exploit, and ysoserial's GhostWebShell, injected in memory via VIEWSTATE manipulation with AMSI bypass. For lateral movement they combined Microsoft dev tunnels with rdp2tcp over RDP, exploited BlueKeep (CVE-2019-0708) to create local admin accounts, and performed DCSync replication against Active Directory.

Kaspersky Securelistupdated · 6h agofirst · 11h agoThreat actor in the wild 2 sourcesCVE-2020-0688CVE-2019-0708

IFM Releases K2 Horizon: Six Apache 2.0 Models From 0.9B to 375B

MBZUAI's IFM released K2 Horizon, six Apache 2.0 models (0.9B-375B) with open training data, code, and checkpoints, claiming the largest fully open-source launch.

The Institute of Foundation Models (IFM), launched by MBZUAI, released K2 Horizon: six Apache 2.0 models (0.9B, 3.7B, 7B, 32B, 36B-A4B, 375B-A23B) shipping with the ~20-trillion-token pretraining corpus, intermediate checkpoints, training code, and logs, which IFM calls the largest fully open-source launch in AI history. The 375B-A23B scores 70.2 on Terminal-Bench 2.1 and 87.3 on GPQA Diamond; the 7B model posts 70.6 on SWE-bench Verified. New techniques include MoVA, which extends MoE routing into attention (36B total, ~4B active), and Uno, a LoRA adapter giving roughly 3x lossless decoding speedup. IFM's own reward-hacking audit re-scored 375B-A23B from 70.2% to 66.9% after flagging 24 of 712 Terminal-Bench trials.

MarkTechPost · 9d agoModel release1

Engineered Persuasion: Evaluating Personalized Pretexts in LLM-Generated Spear Phishing

A study of 180 US workers found each LLM phishing personalization level raised click-intention odds by 28%, but credibility depends on context fit.

The arXiv paper evaluates how personalized pretexts in LLM-generated spear phishing affect perceived credibility, using 180 US working adults across 1,436 evaluations of emails with four cumulative personalization levels, from workplace context to shared-project details. Convincingness rose 2.40 points per level in sensitivity analysis and click-intention odds increased 28% per level, while non-clickers shifted toward deleting rather than reporting. Qualitative coding showed details matching the recipient's role and routines supported credibility, whereas incorrect, vague, or channel-inappropriate details raised suspicion. The authors argue personalization effectiveness depends on pretext fit, with implications for workplace security training.

arXiv cs.CR · 13d agoResearch

IdeaAMBIG: Benchmarking Implementation-Critical Gaps in Research-Idea Specifications

IdeaAMBIG benchmark of 660 specification-gap instances shows LLMs localize implementation-critical research gaps poorly, with best model at 9.6% defect recovery.

IdeaAMBIG is a benchmark of 660 evidence-grounded instances evaluating whether research-method specifications provide enough information for faithful implementation: 163 real-world gaps from reproducibility reports and GitHub issues plus 497 controlled synthetic gaps. It tests codification-readiness assessment, defect localization, and clarification action generation across 13 LLMs. The best model achieves only a 9.6% Macro Defect Recovery Rate on real-world instances, though 80.6% clarification success when given the annotated defect, and an oracle study shows gold resolutions raise codification-ready rates from 14% to 98%. Defect localization emerges as the main bottleneck across all evaluated models.

Hugging Face daily papers · 8d agoAI research

Verifiable by Construction: Claim-Level Evaluation of Verbatim Citation in Clinical Question Answering

Evaluation of twelve LLMs on 222 clinical questions shows verbatim quotes rarely substantiate claims; claude-opus-5 fully substantiates only 37.1%.

The authors build a standardized harness over four clinical practice guidelines and evaluate twelve LLMs on 222 synthetic clinical questions, measuring citation attachment, verbatim quote production, and claim substantiation. Most models attach verbatim quotes to over 90% of claims from prompting alone, though lightweight models like claude-haiku-4.5 struggle. Quotes frequently fail to substantiate claims: claude-opus-5 quotes 98.0% of claims but fully substantiates only 37.1%, exposing a capability gap for verifiable clinical QA.

arXiv cs.AI / cs.LG / cs.CL · 2d agoAI research

Reference-Based Bias Detection in LLMs via Relative Representations of Hidden States

Researchers propose auditing LLM bias via relative hidden-state representations, detecting bias increases with 3-50x less compute than output-level benchmarks.

The paper introduces a reference-based bias auditing method that compares hidden-state representations across model variants, such as before and after fine-tuning, by encoding sentences relative to a fixed anchor set. The resulting Representational Bias Shift (Delta-B) correlates with output-level bias change in 15 of 18 tested settings, reaching |r| = 0.84 under full fine-tuning across WildGuardMix, DecodingTrust, and ToxiGen benchmarks. Thresholding Delta-B detects checkpoints whose bias increased with ROC AUC between 0.65 and 0.99 and beats a SEAT-based baseline, while auditing a model in about three minutes with 3-50x less compute.

Hugging Face daily papers · 8d agoAI research1

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.CL · 12d agoAI research

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.CL · 7d agoAI research1

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 · 8d agoAI research

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 · 8d agoAI research2

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 · 12d agoAI research1

Tables Decoded: DELTA for Structure, TARQA for Understanding

DELTA extracts tables into compact OTSL text and TARQA fine-tunes LLMs on it, beating VLM baselines on table QA.

DELTA separates physical structure recognition, logical structure recognition, and OCR to output tables in Optimised Table Structure Language (OTSL), a compact unified format encoding cell arrangements and content. It achieves TEDS-Structure scores comparable to state-of-the-art methods across FinTabNet, PubTabNet, and PubTables-1M, with robustness tested on a curated Hindi benchmark, TORQUE. TARQA, an LLM fine-tuned on OTSL sequences, gains 9.3 percentage points on WTQ TabQA and 9.2 points on FinTabNetQA TabVQA; code, models, and the benchmark are released on GitHub.

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

Nuha-Speech: Building General-Purpose Arabic Speech-LLMs

Nuha-Speech initiative builds general-purpose Arabic speech-LLMs using a 1.5M-sample speech QA corpus and fine-tuned Qwen-Omni variants.

The paper introduces Nuha-Speech, an initiative covering dataset construction, model training, and evaluation for Arabic speech large language models. The authors built an Arabic Speech Question-Answering corpus of over 1.5 million training samples and used it for supervised fine-tuning of Qwen-Omni model variants at multiple scales. A tailored evaluation framework with diverse tasks and metrics is designed to assess Arabic speech capabilities under limited resource constraints.

arXiv cs.AI / cs.LG / cs.CL · 6d agoAI research1

ENEAS: Embedding-guided Neural Ensemble for Adaptive Segmentation

ENEAS adds text prompting and semantic verification to video segmentation to keep tracking targets through occlusion and reject lookalike distractors.

ENEAS is a unified text-promptable method for instance tracking and open-concept semantic discovery in video, designed to fix temporal hallucinations, spatial fragmentation, and semantic misclassification seen in SAM 3-class foundation models. It extends the geometrically robust SeC architecture with a text-prompting adapter and temporal memory, and uses a verification layer combining fast visual embedding matching with conditional VLM refinement for ambiguous candidates. It targets 3D reconstruction pipelines where a single misclassified distractor corrupts the asset. Code and models are open-sourced.

Hugging Face daily papers · 14d agoAI research

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 Blog · 29d agoAI tools & infra1

IdeaAMBIG: Benchmarking Implementation-Critical Gaps in Research-Idea Specifications

IdeaAMBIG benchmark with 660 instances measures whether LLMs can spot and fix underspecified research-method details for faithful implementation.

Researchers introduce IdeaAMBIG, a benchmark of 660 evidence-grounded instances (163 real-world gaps from reproducibility reports and GitHub issues, 497 controlled synthetic gaps) built from papers, codebases, and reproduction artifacts. It evaluates codification-readiness assessment, defect localization, and clarification action generation. Across 13 LLMs, the best model achieved only 9.6% Macro Defect Recovery Rate on real-world instances but 80.6% clarification success when given the annotated defect. An oracle study showed gold resolutions raise the codification-ready rate from 14% to 98%, identifying defect localization as the main bottleneck.

arXiv cs.AI / cs.LG / cs.CL · 7d agoAI research2

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

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

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

The Decoder · 4d agoModel release2

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 · 14d agoModel release