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Authorities Turn Sality's P2P Network Against Itself, Cutting Off New Malware Payloads

US and European authorities with CrowdStrike dismantled the two-decade-old Sality P2P botnet using peer-list manipulation, blocking payload delivery to infected hosts.

The US Department of Justice announced a coordinated takedown of the Sality peer-to-peer botnet, executed August 31, 2026 by authorities from the US, Bulgaria, Hungary, and Romania with CrowdStrike and the Shadowserver Foundation. Sality, active since 2003, infects Windows executables and delivers payloads including the EggJagger crypto clipper, which stole at least $150,000, and was used for DDoS campaigns. The operation abused the botnet's peer-list maintenance cycle to insert sinkhole nodes and isolate both super peers and NAT-hidden infections, cutting off URL and payload distribution to more than 15,000 infected machines across two P2P networks.

The Hacker News · 14d agoMalware

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.CL · 8d agoAI research

Global sinkhole operation ends Sality botnet’s 23-year run

Law enforcement, CrowdStrike, and Shadowserver sinkholed the 23-year-old Sality P2P botnet, cutting 15,000+ infected machines from its operator.

Sality, active since 2003 as a file-infecting virus with two P2P networks (versions 3 and 4), distributed credential thieves, spam, proxies, and DDoS payloads, and most recently delivered the EggJagger clipboard hijacker that swapped cryptocurrency wallet addresses for at least $150,000 in operator profit. A coordinated sinkhole operation replaced the botnet's super-peer list with defender-controlled sinkholes, and investigators in the US, Bulgaria, Hungary, and Romania seized payload domains. The Shadowserver Foundation is coordinating ISP and CERT notifications to infected device owners.

Help Net Security · 14d agoMalware1

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.

arXiv cs.AI / cs.LG / cs.CL · 11d agoAI safety & security1

Sality, one of the longest

US and European authorities, with CrowdStrike and Shadowserver, disrupted the 20-year-old Sality peer-to-peer botnet, severing 15,000+ infected machines from operators.

US and European authorities disrupted the Sality botnet, active since at least 2003, in an operation involving the DOJ, CrowdStrike, the Shadowserver Foundation and agencies in Bulgaria, Hungary and Romania. Researchers reverse-engineered the botnet's peer-to-peer architecture and injected false data into infected machines' 'super peer' lists, cutting more than 15,000 systems off from their operators. For the past eight years Sality primarily distributed EggJagger, malware that replaces clipboard cryptocurrency addresses and is estimated to have netted the operator at least $150,000. No arrests were announced, and CrowdStrike assesses the operator works from Russia's Bashkortostan region.

The Record · 14d agoMalware in the wild

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

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

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

Hugging Face trending models · 10d agoModel release7· 1 read

Cops, CrowdStrike disrupt Sality botnet by poisoning the network and diverting into sinkholes

Law enforcement and CrowdStrike disrupted the 23-year-old Sality P2P botnet, isolating 15,000+ infected machines and seizing linked domains.

International law enforcement, working with CrowdStrike and the Shadowserver Foundation, executed a peer-to-peer sinkhole operation against Sality, a botnet active since 2003 that delivered malware to more than 15,000 machines worldwide. Sality's primary payload for eight years was EggJagger, a clipboard hijacker that swaps copied bitcoin and ethereum wallet addresses with attacker-controlled ones, yielding at least $150,000 in stolen cryptocurrency. The US Justice Department, FBI, and DoD Office of Inspector General's Defense Criminal Investigative Service seized Sality-linked domains, with parallel action in Bulgaria, Hungary, and Romania. The Shadowserver Foundation is coordinating with ISPs and CSIRTs to identify infections and notify victims.

The Register · Security · 14d agoMalware in the wild

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 · 11d agoAI research1

International Operation Disrupts Sality P2P Botnet

US-led international operation with Europol, CrowdStrike, and Shadowserver sinkholed the 20-year-old Sality P2P botnet, once exceeding one million infected machines.

On August 31, 2026, authorities from the US, Bulgaria, Hungary, and Romania, supported by Europol, CrowdStrike, and the Shadowserver Foundation, disrupted the Sality P2P botnet by sinkholing communications and seizing domains. Sality has operated for over 20 years, at its peak controlling more than one million infected machines used for credential theft, spam, proxy services, crypto-theft, and DDoS attacks, with over 11 million unique IP addresses linked to its infrastructure since 2017. The disruption exploited the botnet's super-peer reputation mechanism by removing legitimate peers via protocol-level manipulation and inserting sinkhole entries into emptied peer lists.

Infosecurity Magazine · 13d agoMalware

Convergent Emergence of In-Context Learning Across Modalities

Controlled experiments show few-shot in-context learning emerges across six modalities including language, genomes, images, and proteins, partially supporting a convergence hypothesis.

The paper tests the Convergent Emergence Hypothesis: that few-shot in-context learning, when it emerges, shares a common cross-modality difficulty profile. A controlled framework instantiated the same task suite across six modalities: language, genome, integer sequences, time series, images, and proteins. Paired-mapping ICL emerged in all six modalities, surpassed controlled baselines, and showed correlated per-task effects in five of them, providing partial support for the hypothesis.

Hugging Face daily papers · 4d agoAI research

Dogged Russia-based botnet dismantled after 23-year run

Law enforcement, CrowdStrike and Shadowserver dismantled the 23-year-old Sality P2P botnet that infected more than 11 million devices.

Sality, a Russia-based peer-to-peer botnet active for 23 years and infecting over 11 million devices, was dismantled by law enforcement working with CrowdStrike and the Shadowserver Foundation. CrowdStrike poisoned the botnet's peer list so infected machines permanently disappeared from the operator's view, while domains were seized in a coordinated effort involving the FBI, Justice Department, Europol and authorities from Bulgaria, Hungary and Romania. The financially motivated operation enabled cryptocurrency theft, DDoS attacks and other cyberattacks, and Europol said the effort dates back to 2017; the operators were not named.

CyberScoop · 13d agoMalware

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 · 12d agoResearch

Risky Bulletin: Russia tells data centers to deploy drone defenses

Russia ordered data center operators to deploy drone strike defenses under a Putin decree allowing temporary state takeover of unprotected critical infrastructure.

The Russian government instructed data center operators to deploy protections against drone strikes under a presidential decree signed by Putin that allows temporary state administration of critical infrastructure operators failing to defend against Ukrainian hacks and drone strikes. Although data centers are not formally critical infrastructure in Russia, the decree applies to them because other sectors depend heavily on cloud services; Russia has more than 180 data centers, over 80% in the European region within range of Ukrainian strikes. The digest also reports a Dropbox breach affecting nearly 5,000 accounts via the Lenovo ID integration, spyware attacks on at least 14 Serbians using NoviSpy or Pegasus, and a password recovery attack targeting hundreds of thousands of X accounts tied to the new X Money service. Other items include a 14-hour compromise of Coder's Cloudflare infrastructure delivering malicious Terraform modules, donor data breaches at Davayte and You Are Not Alone via the Stripe/WooCommerce integration, a $2.5M Aquifer crypto heist, and a TVING breach exposing data of almost 40 million accounts.

Risky Business News · 12d agoPolicy & legal

Srsly Risky Biz: America's Drivers Licence Breach is a National Security Disaster

Dark web service Nexus sold 153 million US and Canadian driver's licenses, linked to identity verification firm IDScan under FBI investigation.

Krebs On Security reported that a dark web service called Nexus sold access to 153 million US and Canadian driver's licenses, claiming over a year of continuous exfiltration from a major identity verification company, with roughly 400,000 new licences added in a single day. Krebs verified the data as genuine and linked the incident via circumstantial evidence to identity verification firm IDScan, whose licences of senior US officials including Secretary of War Pete Hegseth appeared in the database; the FBI is investigating and IDScan has confirmed a breach inquiry. The article argues the data has national security implications, citing how Chinese APT espionage (Anthem, Equifax, Marriott, OPM) and Bellingcat investigations exploited leaked databases. Class action suits are being prepared, and the piece calls for stricter oversight of identity verification firms.

Risky Business News · 6d agoData breach in the wild

Measuring benchmark optimization in speech recognition

Hugging Face examines how much speech recognition systems overfit benchmarks and how to measure benchmark optimization in ASR.

A Hugging Face post on measuring benchmark optimization in automatic speech recognition, analyzing how model improvements on benchmarks reflect genuine capability gains versus overfitting. It is evaluation methodology research with no direct security impact.

Hugging Face Blog · 26d agoAI research

America's Driver's License Breach Is a National Security Disaster

Dark web service Nexus sells 153 million US/Canadian driver's licenses linked to a breach of identity verifier IDScan.

Krebs on Security revealed a dark web service, Nexus, selling access to 153 million driver's licenses and 3 million travel documents from US and Canadian citizens, roughly 63 percent of all US licenses. Circumstantial evidence links the data to identity verification firm IDScan, which confirmed it is investigating a breach, and the FBI is probing the incident. Licenses belonging to senior US officials, including Pete Hegseth, an FBI assistant director, and Krebs's own contacts were verified as genuine. The exfiltration appears ongoing, with the database growing by nearly 400,000 licenses in a single day, and the data carries significant national security value for foreign intelligence services.

Hacker News · security · 21h agoData breachHN 26↑ · 4 comments3· 1 read

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 papers · 12d agoAI research1

Modified ScreenConnect Clients Used in Worm-Like Campaign

Huntress warns of worm-like attacks using modified ScreenConnect clients to spread VBScript payloads; ConnectWise issued an advisory.

Campaigns starting in late August use social engineering, including Quick Assist abuse, to install rogue ScreenConnect clients that spawn wscript.exe and deploy four VBScript files for reconnaissance, staging, and PowerShell execution. The attackers persist via User Run Keys, attempt UAC bypass, install UltraViewer, and propagate the VBScript chain to other connected ScreenConnect endpoints. ConnectWise published an advisory about a file transfer behavior issue affecting cloud and on-premises ScreenConnect, with a CVE identifier and fix expected within a week; it recommends disabling file transfer meanwhile.

SecurityWeek · 9d agoExploit / PoC in the wild

Can your coding style predict whether your code is vulnerable?

University of Massachusetts Dartmouth researchers present VulStyle, a stylometry-based vulnerability detector that also exposes benchmark reliability problems.

VulStyle combines stylometric features with syntax-tree structure and source tokens, pre-trained on about 4.9 million functions across seven programming languages and fine-tuned on five vulnerability detection datasets. It beat token-only detectors on some benchmarks but its F1 drops sharply on DiverseVul, which the authors link to noisy labels inflating reported performance across popular datasets. The authors argue style-aware detection should be harder to evade but did not test this empirically, and they note that uniform LLM-generated code may strip away the individual developer style the model depends on.

Help Net Security · 23d agoResearch1

Mi-Ripple: Restoring Images Degraded by Iterative AI Editing

Mi-Ripple is a diagnosis-guided restoration workflow that removes digital ripple artifacts introduced by iterative AI image editing while preserving structure.

Iterative reference-conditioned image editing can introduce grid-like and granular textures known as digital ripple. Mi-Ripple separates periodic lattice artifacts from content-entangled granular texture, then applies selective spectral notching, structure-aware smoothing, and cleaned-reference regeneration. In fourteen notch-only executions, whole-image residual standard deviation was 0.08-0.44 in CIELAB lightness units, and reference cleaning reduced output debris density by 45% in a paired example.

Hugging Face daily papers · 6d agoAI research

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 · 6d agoAI research

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 · 6d 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.CL · 5d agoAI research

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

Thinking of ACE? We Can Do It with Fewer Tokens

IBM Research's ALTK-EVOLVE-SLDD blog post claims ACE-style LLM analysis using substantially fewer tokens.

IBM Research published a Hugging Face blog post titled 'Thinking of ACE? We Can Do It with Fewer Tokens' describing ALTK-EVOLVE-SLDD. The method targets reproducing ACE (attribution-based confidence estimation) capabilities in LLMs while consuming far fewer tokens. No article text was available, so details beyond the title are limited.

Hugging Face Blog · Aug 11, 2026AI research

What Breaks Under Pruning in Smart Homes, and When? Evaluating LLM Degradation Across Architectures and Task Complexity

Pruning study across four LLM architectures finds dense models degrade sharply on smart-home tool calling while MoE models tolerate far more.

Researchers systematically study pruning-induced degradation in smart-home tool calling across four LLMs spanning dense Transformer, dense hybrid, and mixture-of-experts architectures, combining depth, width, hybrid, and expert pruning methods, and evaluate over 19,500 instances from three datasets after post-pruning supervised fine-tuning. Dense models show narrow safe pruning regions followed by sharp degradation, while MoE models tolerate substantially more pruning. Pruning degrades grounded specificity (operation, device, argument, value) before schema-level intent, and aggressive dense pruning can induce systematic over-refusal.

arXiv cs.AI / cs.LG / cs.CL · 19h agoAI research

Making Knowledge Distillation Cheap Enough to Run at Scale

Hugging Face blog by Multiverse Computing describes techniques making knowledge distillation cheap enough for large-scale training.

A Hugging Face blog post from Multiverse Computing (CAI) presents methods for reducing the cost of knowledge distillation so it can be run at scale. The post is aimed at practitioners compressing large models into smaller, cheaper ones for production use.

Hugging Face Blog · Aug 10, 2026AI research

Explainability Assistant: A Conversational XAI Interface for Interpreting Energy Consumption Models

Researchers release Explainability Assistant, an open-source conversational XAI tool using LLM function calling, lifting intent-parsing accuracy from 76.8% to 94%.

The paper introduces the Explainability Assistant, an open-source conversational XAI system for interpreting energy consumption forecasting models such as genetic-programming symbolic regressors. It uses LLM function calling instead of rigid custom grammars, achieving 94% intent-parsing accuracy versus 76.8% for prior work TalkToModel, and adapts to different ML problem types without task-specific fine-tuning. Comparative evaluation with energy domain specialists against a traditional XAI dashboard showed improved usability, with all experts preferring the conversational interface.

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

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 · 6d agoModel release

Climate-ModernBERT: Revisiting Corpus Composition for Domain-Adaptive Continued Pretraining

Climate-ModernBERT domain-adapted encoders reach 76.3 average F1 across nine climate benchmarks, 2.8 points above vanilla ModernBERT-Base.

The authors continue pretraining ModernBERT-Base on three climate corpora - academic text, climate-filtered web data, and synthetic documents - and compare joint mixtures against parameter-space merging of specialized checkpoints. The best model achieves 76.3 average F1 across nine climate NLP benchmarks, a 2.8-point improvement over the vanilla baseline. Academic climate corpora provide the strongest adaptation signal, and parameter-space merging outperforms joint multi-source training while preserving complementary corpus information; all variants are released.

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

One Symptom, Three Levers: A Critical Review of On-Policy Self-Distillation

A review paper frames on-policy self-distillation collapse as governed by three levers: token weighting, privileged information, and guidance decay.

The paper critically reviews On-Policy Self-Distillation (OPSD), where a language model trains on its own generations scored token-by-token by a teacher conditioned on privileged information such as reference solutions or environment feedback. It identifies collapse, the progressive narrowing of producible reasoning paths, as the dominant failure mode and analyzes it through three levers: signal weighting, the nature of privileged information, and teacher dynamics. The review is restricted to mathematical reasoning, reports no new experiments, and offers a shared vocabulary separating settled findings from disputed ones.

Hugging Face daily papers · 21d agoAI research

CareMirror: Bringing Caregiver Wellbeing into the Dementia Care Ecosystem

Interview study with 14 dementia caregivers probes CareMirror wellbeing ecosystem, revealing demands for control over clinical sharing and AI boundaries.

CareMirror is an envisioned caregiver wellbeing ecosystem with interconnected caregiver- and clinician-facing interfaces for longitudinal reflection, personalized support, and caregiver-controlled sharing. Semi-structured interviews with 14 family caregivers used the system as a design probe. Caregivers valued wellbeing attention and clinical visibility but found repeated reflection burdensome and worried automatic clinical sharing would inhibit candid disclosure, expecting AI to support rather than replace caregiver and clinician judgment.

arXiv cs.AI / cs.LG / cs.CL · 20h agoAI research

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

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

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

Hugging Face daily papers · 6d agoAI research

UniH^3: Unifying Hierarchical Homogeneity and Heterogeneity for All-in-One Medical Image Restoration

UniH3 unifies hierarchical homogeneity and heterogeneity modeling for all-in-one medical image restoration across modalities and degradation types.

UniH3 introduces a Hierarchical Homogeneity Memory module that distills shared anatomical priors from high-quality images, injected via a Homogeneity-Guided Attention mechanism. A Hierarchical Heterogeneity Balancer mitigates inter- and intra-task conflicts during multi-task optimization. It achieves state-of-the-art on MedIR-2D-500K and MedIR-3D-3D benchmarks for both all-in-one and single-task restoration, with code released on GitHub.

Hugging Face daily papers · 6d agoAI research

Type Diversity Enables Transformers to Generalise Compositionally

Researchers show lexical-versus-structural compositional generalization gaps in Transformers stem from type diversity imbalance in datasets, not architectural limits.

The paper argues that Transformers' difficulty with structural compositional generalization is an artifact of low structural type diversity in prior benchmark datasets rather than an architectural limitation. Using Grammatical Framework, the authors create linguistically diverse variants of COGS and SLOG. They find type diversity correlates with compositional generalization equally in lexical and structural test cases, contradicting previous claims that compound divergence explains task difficulty.

arXiv cs.AI / cs.LG / cs.CL · 4d agoAI research

Generative Marketing Mix Modeling: A Causal Inference Framework Linking GEO and GEM to Business Impact

Paper proposes Generative Marketing Mix Modeling to causally estimate Generative Engine Optimization and Marketing effects on business outcomes.

The authors develop GMMM, a causal inference framework for measuring how often users see and notice a firm's name in generated answers, which standard marketing data ignore. For GEO it combines repeated generated answers with question counts, shares of generative-system usage and notice probabilities; for GEM it uses sponsored placement records with notice probabilities. The framework compares expected business responses under alternative treatment sequences, establishes identification conditions, and is evaluated on simulated product-recommendation answers in English and Japanese.

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

Your Agent Aced the Task. Will It Do It Again?

IBM Research Hugging Face post examines whether LLM agents that succeed at a task once will reliably succeed again.

Hugging Face published an IBM Research blog post titled 'Your Agent Aced the Task. Will It Do It Again?' with URL slug 'altk-evolve-consistency'. No article text was provided, but it appears to address agent consistency and reliability evaluation across repeated task runs. This is relevant to developers building or evaluating LLM agent systems.

Hugging Face Blog · 21h agoAI tools & infra

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

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

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

Hugging Face Blog · 7d agoAI safety & security

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 · 20h agoAI research