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LexFlip: A Dissociation Diagnostic for Legal Meaning Preservation Metrics

LexFlip releases 373 minimal perturbations of Quebec statutory French that reverse legal force while preserving tokens, exposing weaknesses in embedding-based meaning preservation metrics.

LexFlip provides 373 minimal perturbations of Quebec statutory French that reverse legal force while preserving 0.93 of tokens, creating dissociation items that break monotone token-overlap metric validation. The seven embedding and BERTScore metrics tested register only 0.022-0.039 of their identical-to-unrelated range on these edits, versus 0.670 for bidirectional NLI. Against FrJudge, with a measured human ceiling of r=0.597, a bare length feature outscores every semantic metric tested.

arXiv cs.AI / cs.LG / cs.CLSep 4, 15:49 UTC · 11d agoAI research

Sparks Fly: NVIDIA Accelerates Local AI at IFA 2026

NVIDIA announces local AI push at IFA 2026 with faster llama.cpp/vLLM inference, PAIR routing tool, and October RTX Spark PCs.

At IFA 2026, NVIDIA announced simplified local AI support for agents in Hermes Agent, OpenClaw, and Perplexity Portable Computer, plus new llama.cpp and vLLM optimizations delivering up to 1.9x faster local inference. NVIDIA also unveiled PAIR, a Personal AI Router for distributing inference across a local network's PCs, and compact RTX Spark Windows PCs from Lenovo and Acer arriving in October. The post recaps recent local-capable model releases including Nemotron 3.5 Lightning (30B), Qwen3.8-Flash-Next and Qwen3.8-27B, DeepSeek v4 Flash (284B MoE, 13B active), Meta Muse Glimmer (30B), Z.ai GLM-5.3-Flash, LTX 2.5, and MiniMax-H3 with the FastH3 distilled variant.

NVIDIA BlogSep 3, 16:00 UTC · 12d agoAI industry

Encoded Early, Used Late: Where Transformers Begin to Act on an Inferred Partner's Expertise

Probing finds transformers represent an inferred dialogue partner's expertise in early layers long before it causally influences output.

Using ExpertCollab, a corpus of multi-turn research-planning dialogues between model-played personas at four expertise levels, researchers show that a partner's inferred expertise is most decodable in early transformer layers and decays to near chance before the network's midpoint. Counterfactual patching reveals that injecting the expertise difference at peak decodability barely changes a fixed late-layer readout, while injection past the midpoint propagates almost completely. The result bounds where readout or steering of partner-conditioned behavior must intervene, demonstrated on a single model with a synthetic corpus.

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

From Parameters to Answers: How LLMs Retrieve and Use Their Internal Knowledge

Interpretability study traces how Qwen, Llama, and Gemma route query information and internal knowledge across layers when answering questions.

Researchers used layerwise interventions on hidden states to separate query-routing signals from target knowledge in language models, testing Qwen, Llama, and Gemma on country-continent questions with varied answer types. A pair-conditioned request direction strengthens before interventions alter downstream knowledge, opening a causal window while answer-supporting content is still forming. Trajectories differ by model: Gemma shows a partially overlapping mid-layer routing profile, while Llama has no sustained routing-effect window under the same gates.

arXiv cs.AI / cs.LG / cs.CLSep 10, 17:39 UTC · 5d agoAI research1

ECHO: A Matched-Contrast Benchmark for Context-Sensitive Turn-Taking in Full-Duplex Dialogue

ECHO benchmark pairs identical-overlap Chinese dialogue examples with contrasting contexts, revealing most full-duplex systems bias toward yielding the floor.

ECHO is a paired diagnostic benchmark for Chinese full-duplex turn-taking that matches examples with identical overlap transcripts but contrasting preceding multi-turn contexts, one requiring Yield and the other Keep. It also includes off-talk examples for diagnosing unnecessary yielding and introduces pair accuracy, which grants no credit to constant-action policies. Experiments across multiple full-duplex systems show most exhibit a pronounced Yield bias, performing substantially better on interruptions than backchannels. The benchmark and metadata will be publicly released.

arXiv cs.AI / cs.LG / cs.CLSep 15, 15:58 UTC · 21h agoAI research

Emergency Department Revisit Quality Review Screening: Exploring Human Decision-Making and Artificial Intelligence Support

A retrospective study found GPT-4 over-flagged emergency department revisit cases while an LLM knowledge-graph screener achieved 83-100% positive predictive value.

In an exploratory retrospective study of 99 emergency department diagnosis pairs from a multihospital health system, clinicians and GPT-4 independently judged whether revisit pairs warranted further assessment. GPT-4 responses correlated poorly with clinicians, flagging 94% of pairs for follow-up, 4.4-13.3 times more than clinicians, though prompt engineering was minimal. An algorithm leveraging an LLM-populated knowledge graph (KGA) achieved 83-100% positive predictive value against at least one clinician rater, suggesting LLM-based screening could broaden revisit quality review without substantially increasing reviewer workload.

arXiv cs.AI / cs.LG / cs.CLSep 9, 16:37 UTC · 6d agoAI research1

Characterizing Language Generation in the Limit: Finite Witnesses and a Separation-Width Hierarch

New work characterizes language generation in the limit via finite witnesses, proves a full separation-width hierarchy, and formalizes all results in Lean.

The paper fully characterizes when language generation in the limit is possible for arbitrary families over a countable universe: each target must admit a finite positive witness such that targets activated by any finite sample share an infinite common intersection. It defines positive separation width and proves every level of the resulting hierarchy occurs, with countable families admitting singleton witnesses and unions of families with infinite common cores requiring unbounded finite witnesses. The characterization, a universal normalization, and a diagonal capture lemma are machine-checked in the Lean proof assistant, with the development maintained on GitHub.

arXiv cs.AI / cs.LG / cs.CLSep 9, 17:54 UTC · 6d agoAI research1

Continue, Adapt, or Yield: In-Turn Adaptation to Overlapping Speech in Full-Duplex Agents

Duplex Cue evaluation shows PersonaPlex full-duplex agents adapt in-turn to listener contributions in only 34.8% of collaborative cases versus 68.2% for humans.

The paper introduces Duplex Cue, an evaluation of in-turn adaptation in full-duplex voice agents that separates listener intent (backchannel, collaboration, interruption) from speaker behavior (continue, adapt, yield). Using 208 scorable pairs from 300 human-confirmed cues in unscripted English conversations, it compares recorded human responses with PersonaPlex continuations generated while replaying listener audio. Humans adapt within the turn in 68.2% of collaborative pairs versus 34.8% for PersonaPlex, which otherwise continues unchanged (42.4%) or yields (22.7%).

arXiv cs.AI / cs.LG / cs.CLSep 11, 17:46 UTC · 4d agoAI research

Introducing ChatGPT for Financial Services

OpenAI launches ChatGPT for Financial Services, pairing built-in market data with GPT-6 Astra for banking research workflows.

OpenAI introduced ChatGPT for Financial Services, a tailored ChatGPT Work experience shaped by design partners Morgan Stanley and Evercore, targeting investment banking and equity research. It bundles premium data from Daloopa, PitchBook, LSEG News, and Crunchbase hosted on OpenAI infrastructure with granular citations, optimized MCP connectors for S&P Global and FactSet, and 50+ connectors, plus planned entitlement integrations with S&P Capital IQ, LSEG, MSCI, Dow Jones Factiva, and Moody's. It runs GPT-6 Astra, which OpenAI claims is state of the art in information retrieval, financial reasoning, and artifact generation, and includes enterprise controls such as SAML SSO, SCIM, role-based access, and no default training on firm data.

OpenAI Newsupdated Sep 10, 18:25 UTC · 5d agofirst Sep 10, 07:00 UTC · 6d agoAI industry 3 sources

Motion-Omni: End-to-End Joint Speech and Full-Body Motion for Spoken Dialogue

Researchers introduce Motion-Omni, an end-to-end model generating speech with synchronized full-body motion, responding 5.4x faster than cascade pipelines.

Motion-Omni is an end-to-end framework in which a spoken dialogue model outputs facial expressions and hand, upper-body, and lower-body motion directly from the hidden states that produce speech, replacing two-stage cascade pipelines. Trained on 422,856 quality-ranked pseudo-labeled pairs (1,402 hours) with a Qwen2.5-7B-Instruct backbone, Motion-Omni-Q7 matches its teacher cascade within 2% on reference-free motion metrics, achieves a 2.62% word error rate, and runs faster than real time (RTF=0.78). The authors also release the SwDA-500 dataset and the first public evaluation protocol for stochastic open-ended full-body spoken dialogue.

Hugging Face daily papersAug 27, 20:00 UTC · 19d agoAI research1

CosmoH2G: A Hand-to-Gripper Transfer Dataset and Baseline Method for Object Manipulation with Complex Spatial Movements

Researchers release CosmoH2G, a 6,189-episode hand-to-gripper dataset with a two-stage method for complex spatial robot manipulation.

The paper introduces a scalable acquisition pipeline using a handheld gripper to collect paired hand-gripper demonstrations, producing 6,189 episodes across 1,254 unique objects with higher spatial complexity than existing benchmarks. A two-stage framework first predicts sparse gripper keyframes (initial and terminal), then generates the full continuous action sequence conditioned on them, while learning gripper orientation and post-optimizing translation via grasping heuristics and kinematic consistency. Simulation and real-robot experiments show stable, precise hand-to-gripper transfer of complex spatial manipulations, outperforming traditional baselines.

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

RESCUE-BENCH: Towards Relation-Aware Multi-Party Emotional Support Conversation Systems

Researchers introduce RESCUE-Bench, a video benchmark of 191 couple and family conversations evaluating LLMs on relation-aware multi-party emotional support.

RESCUE-Bench is built from real couple and family interview conversations, containing 191 samples, 7,079 annotated turns, and 1,064.8 minutes of video. It defines six tasks measuring two capabilities: Relational Understanding and Relation-Sensitive Support. Experiments with ten LLMs show models handle local emotional cues but struggle with relation pattern prediction, viewpoint prediction, and support strategy prediction.

Hugging Face daily papersSep 8, 20:00 UTC · 7d agoAI research

AD Rights Management Service (Part 2): Extraction, Offline Decryption, and the Unrotatable Key

Huntress research shows AD RMS SLC root key is unrotatable and never expires, so its compromise permanently exposes all RMS-protected documents.

Part 2 of Huntress's AD RMS series details server-side attacks: extracting the Server Licensor Certificate (SLC) private key and performing offline decryption of protected documents. The SLC key has no expiry or rotation mechanism, with a 255-year certificate validity (2002–2258), so whoever recovers it can decrypt every document the deployment ever protected, indefinitely. The author released SharpRMS, a unified tool combining the 2016 DisARMS client-side attacks with new server-side key extraction and decryption capabilities. The research frames the SLC as comparable to KRBTGT and the DPAPI domain backup key, though not equivalent to domain compromise.

HuntressSep 10, 04:00 UTC · 6d agoResearch

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 · 29d agoAI tools & infra1

Testing race conditions with memory access tracing and stack-based delay injection

Google Project Zero released MAccConc, Linux kernel tooling that traces memory accesses to explore and test race condition interleavings.

A Google Project Zero researcher published MAccConc (Memory Access Concurrency), tooling for exploring possible interleavings of multithreaded test cases in the Linux kernel, available on GitHub. The tools use KCOV with ASAN outline-mode instrumentation to record per-access memory traces, enabling automatic testing of all A-B-A interleavings plus terminal and GUI explorers for manual analysis. The work targets confirming race condition candidates, building reliable regression tests, and enabling concurrency fuzzing, drawing on ideas from SKI and Ned Williamson's sockfuzzer.

Google Project ZeroSep 8, 07:00 UTC · 8d agoResearch1

How Hugging Face Inference Endpoints, Jobs, and Buckets Power Search on Papers with Code

Hugging Face explains how Inference Endpoints, Jobs, and Buckets power semantic search on Papers with Code.

Hugging Face describes the infrastructure behind search on Papers with Code, built on its Inference Endpoints, Jobs, and Buckets services. The post is a product-focused engineering walkthrough with no security impact.

Hugging Face BlogAug 21, 00:00 UTC · 26d agoAI tools & infra

OmniMed-FL: A Robust Multimodal Federated Learning Framework for Clinical Diagnosis

OmniMed-FL benchmarks multimodal federated learning for chest radiograph diagnosis across 3-20 clients, with FedProx leading under severe non-IID skew.

OmniMed-FL studies multimodal federated learning combining chest radiographs and clinical notes for five-class condition classification under HIPAA/GDDR-compliant decentralized training. It benchmarks eight fusion strategies, imputation rules, and federated baselines under Dirichlet non-IID partitioning across 3-20 hospital clients. With 5 clients and severe skew (alpha=0.1), FedProx scored 0.737 macro-F1 versus 0.662 for FedAvg and 0.297 for local-only training. Multimodal fusion beat unimodal inputs (0.956 vs 0.934 text, 0.664 images) on the synthetic corpus.

arXiv cs.AI / cs.LG / cs.CLSep 9, 15:56 UTC · 6d agoAI research1

EFI Pairs Without One-Way Puzzles: Oracle Separations from Communication Complexity

Theorists build a classical oracle where one-way puzzles fail yet EFI pairs survive, separating two candidate minimal assumptions of quantum cryptography.

The paper constructs a single classical oracle relative to which one-way puzzles do not exist, even with an unbounded verifier, while an EFI pair survives every classical-query distinguisher holding advice, making one superposition query at the end. Security is proven by reducing adversary knowledge to communication complexity for Vector-in-Subspace, with the superposition query bounded using random matrix theory. Relative to the oracle, quantum polynomial time offers no advantage on tasks with classical inputs and outputs and there is no proof of quantumness, separating the leading minimal assumptions of quantum cryptography.

arXiv cs.CRSep 10, 17:54 UTC · 5d agoResearch

The 12 Best Extended Detection & Response (XDR) Platforms, Compared and Priced

Buyer's guide compares 12 XDR platforms, favoring Microsoft Defender XDR, Stellar Cyber and CrowdStrike, and warns ingestion pricing inflates costs.

The article compares 12 extended detection and response platforms, distinguishing native XDR (CrowdStrike, Palo Alto Cortex XDR, Microsoft Defender XDR, SentinelOne) from open XDR (Stellar Cyber, Arctic Wolf, Rapid7). It argues data ingestion pricing, not per-endpoint fees, is the main budget risk and should be modeled before signing. It repeats the consolidation note that Sophos acquired Secureworks for approximately $859 million in February 2025, and flags that ExtraHop is NDR rather than full XDR.

GBHackersupdated Sep 9, 08:07 UTC · 7d agofirst Sep 9, 07:51 UTC · 7d agoIndustry 3 sources1

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 · 21d agoAI tools & infra1

FST Pay: Deterministic Safety-Gated Architecture for Youth Digital Payments

FST Pay proposes a deterministic safety-gated architecture for teen digital payments, pairing invariant authorization checks with decoupled post-settlement AI explanations.

Researchers propose FST Pay, a formal architecture for adolescent digital payments on rails like UPI that applies six deterministic invariant checks (spending limits, guardian co-sign policies, amount thresholds, merchant category codes, temporal intervals, hardware integrity) to classify transactions as ALLOW, REVIEW, or BLOCK. High-risk transactions trigger an asynchronous guardian co-sign workflow. Generative AI is restricted to post-settlement natural-language insights and holds no mutation privileges over the ledger, avoiding non-determinism on the real-time authorization path.

arXiv cs.CRSep 10, 08:03 UTC · 6d agoResearch

Fly Language Model (FLM) Wires the Full Fruit Fly Connectome Into a Frozen 1.2B LLM, and Its Own Controls Show the Wiring Does Not Help

Researchers wire the full fruit fly connectome (166,700 nodes) into a frozen LiquidAI LFM2.5-1.2B LLM, but controls show no fly-specific benefit.

The Fly Language Model (FLM) couples the complete MaleCNS v1.0 fruit fly connectome (166,700 nodes, 25,582,938 edges) to a frozen LiquidAI LFM2.5-1.2B-Instruct backbone, training only a 278,528-parameter readout (~0.0238% of backbone parameters). The fly readout improved NLL by 0.0222 nats/token (perplexity 3.98 to 3.90) on 32 SmolTalk dialogues, but a direct-input control without the graph beat it in all three seeds. Relabeling node identities removes the gain and the recurrence contracts state differences by 0.6 per token, so the connectome adds no long-range memory. The MIT-licensed code runs locally on Python 3.12, but study artifacts remain private, limiting independent reproducibility.

MarkTechPostSep 12, 18:51 UTC · 3d agoAI research1

ToxicPanda 2.0 Gets a Major Upgrade, Expanding Attacks Across 16 Countries

Zimperium documents ToxicPanda 2.0, an Android banking trojan now targeting 349 financial institutions in 16 countries via ADB privilege escalation and overlay credential theft.

Zimperium's zLabs documented ToxicPanda 2.0, an Android banking trojan expanding from 16 targeted apps to 349 financial institutions across 16 countries, with 167 remote commands. It poses as a dropper, abuses VPN permissions to block Google Play Protect while installing a hidden payload, then uses the Accessibility Service for screen monitoring and overlay-based credential theft. It automates enabling Android Wireless Debugging and completes the pairing handshake to gain ADB shell access for privilege escalation, and overlays fake lock screens to steal device PINs. Previously unfinished commands are now operational and samples are served from AWS-hosted storage buckets.

Security AffairsAug 22, 16:50 UTC · 24d agoMalware in the wild

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.CLSep 11, 17:59 UTC · 4d agoAI research

Switching Password Managers in 2026

An Apple password-management engineer (writing personally) explains switching password managers via iOS direct app-to-app export, crediting FIDO Credential Exchange for passkey portability.

Ricky Mondello, an Apple engineer in password management and authentication (writing personally, not for Apple), demonstrates moving 100 items from 1Password to Apple Passwords via the iOS system export, which transfers passwords, passkeys, verification codes, and notes directly between apps with Face ID confirmation and no intermediate files. He credits the FIDO Alliance's Credential Exchange format (first draft May 2024, now an open spec) and iOS 26/macOS 26 for enabling phishing-resistant credential transfer between apps like 1Password, Bitwarden, Dashlane, DuckDuckGo, and Devolutions. The post is advice rather than news, recommending bulk transfer on the existing device, switching AutoFill to the new app, and treating the new manager as the source of truth.

Lobsters · securitySep 8, 13:55 UTC · 7d agoIndustry1

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 BlogSep 8, 14:23 UTC · 7d agoAI safety & security

Delta investigates in-flight Wi-Fi spoofing on post

A passenger on Delta flight 591 allegedly spoofed the airline's in-flight Wi-Fi with a rogue access point, prompting an FBI and FAA investigation.

Delta Airlines is investigating an incident on flight 591 from Las Vegas to Atlanta in which a passenger reportedly created a rogue Wi-Fi network named 'Delta WiFi Fast' to steal sensitive passenger data. The cabin crew deactivated the aircraft's Wi-Fi for roughly 30 minutes, and Delta says flight safety and aircraft operating systems were never affected. The FBI's Atlanta office and the FAA acknowledged awareness of the incident, which bears hallmarks of an evil twin attack involving spoofed login portals and credential harvesting. DEF CON organizers said this year's conference in Las Vegas also suffered multiple similar deauthentication Wi-Fi attacks.

CyberScoopAug 11, 21:35 UTC · Aug 11, 2026Phishing & fraud in the wild

A*-Thought-V2: Efficient Latent Reasoning via Geometric Dynamics of LLM

A*-Thought-V2 compresses redundant chain-of-thought steps into latent tokens guided by hidden-state geometry, improving accuracy up to 2.6% while halving response length.

A*-Thought-V2 models chain-of-thought as a hidden-state trajectory projected into a 3D PCA space and compresses steps whose transitions deviate from the question-to-solution direction into continuous latent tokens, keeping aligned steps explicit. Training uses stepwise embedding forcing and label forcing with soft multi-modal vocabulary supervision. On Qwen3.5-9B and Qwen3.6-27B across six in-domain and out-of-domain benchmarks it improves average accuracy by up to 2.6%, cuts response length by up to half, and raises Accuracy per Computation Unit 2.29x while reducing preprocessing and training time by 94.6% and up to 80.3%.

arXiv cs.AI / cs.LG / cs.CLSep 7, 17:56 UTC · 8d agoAI research

Patch Tuesday to Pentest Wednesday: How an Equipment Rental Company Is Turning Continuous Testing Into Continuous Exposure Management

An equipment rental company uses Horizon3 NodeZero for continuous exposure management, surfacing an SSH key exposure in eight hours versus annual pentesting.

The company moved from periodic penetration testing to continuous exposure management using the NodeZero Proactive Security Platform, feeding telemetry into a Splunk-based CTEM pipeline via Horizon3 APIs. NodeZero exposed SSH RSA key pairs from a PHP web server change about eight hours after introduction and found more findings in 12 hours than a third-party engagement found in roughly 30 days. An Active Directory password audit of 15,000 passwords found about 300 identical or similar compromised passwords, driving a banned-password list, 12-character minimums, service desk changes, and self-service reset rollout; users with similar passwords later dropped from 880 to 209. NodeZero also validated a suspected Cisco vulnerability as exploitable.

Horizon3.aiSep 9, 15:58 UTC · 6d agoIndustry

LimiX-2: A Contextual Mechanism Network Towards General Structured-Data Intelligence

LimiX-2 scales Contextual Mechanism Networks pretrained via context-conditional masked modeling, beating tabular foundation models on TabArena, TALENT, and BCCO benchmarks.

LimiX-2 is a new tabular model in the LimiX family, developed through model and data scaling guided by previously established scaling laws under the Contextual Mechanism Networks (CMNs) paradigm. It is pretrained with Context-Conditional Masked Modeling (CCMM) on synthetic datasets generated by structural causal models spanning diverse graph structures, functional mechanisms, and observation processes. It outperforms dataset-specific models and tabular foundation models on TabArena, TALENT, and BCCO, and its feature attention encodes direct causal relationships, enabling accurate causal skeleton recovery.

arXiv cs.AI / cs.LG / cs.CLSep 15, 17:30 UTC · 19h agoAI research1

New KATARU IoT Malware Packs Linux Privilege Escalation Exploits and Mirai-Style DDoS Attacks

Nozomi Networks identified KATARU, a new Mirai-style IoT botnet delivered via Telnet brute force that uses Linux privilege-escalation exploits and encrypted C2 for DDoS floods.

Nozomi Networks identified KATARU in August after a Telnet password-guessing attack against a honeypot retrieved an ARM payload. The malware attempts exploits for CVE-2026-46300 (Fragnesia), CVE-2026-43284 (DirtyFrag), and CVE-2026-31431 (Copy Fail), plus a cgroup v1 release_agent escape, and persists via systemd services, cron tasks, rc scripts, OpenWrt hooks, and Android boot locations. Its C2 uses X25519 key exchange with ChaCha20-Poly1305 encryption and supports TCP, UDP, ICMP, HTTP, QUIC, and DNS floods, plus SSH brute forcing and command execution; embedded exploit shellcode in the ARM build targeted x86, suggesting untested copied code.

Cyber Security NewsSep 11, 14:17 UTC · 4d agoMalware in the wild 2 sources CVE-2026-46300 CVE-2026-43284 CVE-2026-31431

TANGO: Humanoid Navigation in Cluttered Environments with a Whole-Body Vision-Language-Action Model

Researchers introduce TANGO, a whole-body vision-language-action model enabling zero-shot language-guided humanoid navigation on the Unitree G1 robot.

TANGO addresses humanoid navigation in cluttered indoor environments by predicting 29-DoF joint-space actions directly from natural-language instructions and egocentric RGB, rather than 2D path planning. It is trained entirely in simulation via a pipeline combining global path planning, kinematic whole-body motion generation, obstacle-aware motion editing, and RL-based tracking. In simulation it achieves state-of-the-art vision-language navigation performance and transfers zero-shot to a Unitree G1 humanoid without any real-world navigation data.

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

SyncWorld: Visual Calibration Enables World Models as Zero-Shot Simulators

SyncWorld is an action-conditioned world model acting as a zero-shot robotics simulator across unseen environments via visual calibration.

Researchers propose SyncWorld, an action-conditioned world model that simulates robot action outcomes in unseen environments without additional training. It uses a visual calibration episode of paired frames and actions to establish the setup-specific Action-Visual Mapping in context. Experiments show accurate simulation of action outcomes in novel settings and that simulated rollouts enable test-time policy improvement without training.

Hugging Face daily papersSep 7, 20:00 UTC · 8d agoAI research

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 · 13d agoAI tools & infra

Cyble Introduces Major Upgrade to its Executive Monitoring Module

Cyble upgraded Executive Monitoring in Cyble Vision, unifying impersonation, exposure, and mention findings with AI scoring and multi-channel alerting.

Cyble released a major upgrade to Executive Monitoring within its Cyble Vision platform, consolidating Mentions, Impersonations, Exposures, and a new Surface Mentions source (blog and news coverage) into a single findings stream. The release adds AI-generated verdicts and recommendations, per-executive Risk and Reputation scoring for board reporting, and richer onboarding with aliases, reference images, and addresses to cut same-name false positives. Alerting now supports data residency, bulk import, delivery via email, WhatsApp, and SMS, an Access API, and branded executive PDF reports; the upgrade is live with no customer action required.

CybleSep 10, 12:26 UTC · 6d agoTools

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 · 11d agoAI research

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 NewsSep 14, 07:07 UTC · 2d agoMalware in the wild1

XHToken/Spark-X2.5-4B-GGUF — new model trending #30 on Hugging Face

XHToken released GGUF weights of Spark-X2.5-4B, a compact model with 1M-token context and 200+ language support, under Apache 2.0.

The Hugging Face repository provides BF16 GGUF conversions of Spark-X2.5-4B, a compact general-purpose language model for conversation, writing, translation, reasoning, coding, tool use, and agentic workflows. The model uses a hybrid attention architecture, supports a native context length up to 1M tokens, and covers more than 200 languages. Local inference is supported through Ollama and LM Studio via an XHToken llama.cpp fork, with a --think=false flag to disable thinking mode for faster responses. Released under Apache License 2.0; it was trending #30 on Hugging Face at publication.

Hugging Face trending modelsAug 28, 11:26 UTC · 19d agoModel release

Beneath the Surface of Chains-of-Thought: A Mechanistic Interpretation of Reasoning Operations in LLMs

Study shows LLM reasoning operations like planning and deduction are geometrically separable in hidden states, with separability peaking in middle layers.

Researchers investigate whether functional reasoning operations — problem formulation, goal decomposition, deduction — have corresponding geometric structure in LLM hidden representations. They find operations are separable in held-out representations with separability peaking in middle layers, ruling out lexical and positional confounds; token-wise operation alignment becomes more distributed across layers, and identical surface tokens are represented differently depending on their surrounding chunk. Attention-masking interventions show chunk-onset operation-aligned representations depend on preceding reasoning context; code is released on GitHub (naver-ai/beneath-cot).

Hugging Face daily papersSep 3, 20:00 UTC · 12d agoAI research1