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FBI Seizes China-Linked Hacking Platforms QScan and QTRouter Used Against Critical Infrastructure

FBI seizes China-linked QScan and QTRouter hacking platforms used by QTFY to obfuscate intrusions against US federal agencies.

The DOJ and FBI seized domains hard-coded into QScan and QTRouter, two platforms operated by China-based Nanjing Xinjiuwei Network Technology Company on behalf of state-sponsored group QTFY. QScan automatically infected thousands of IoT devices which were added to QTRouter, an obfuscation network routing malicious traffic through compromised and proxy devices outside China. Targets included NASA, the Federal Reserve, Departments of Energy, Justice, and HHS, NIH, and the US Senate, exploiting flaws in Fortinet SSL-VPN, Citrix ADC, Microsoft Exchange, F5 BIG-IP, Log4j, and others.

Security Affairs · 20d agoThreat actor in the wild

Qwen-Drive 1.0 tells you why it brakes, just don't expect the explanation to match the maneuver

Alibaba's Qwen-Drive 1.0 adds 3D perception and planning modules to Qwen3.5-4B for driving tasks, though explanations often mismatch maneuvers.

Qwen-Drive 1.0, built on Qwen3.5-4B, combines spatial perception, traffic question answering, and route planning in one vision-language model, adding a bird's-eye-view perception module and a Planning Expert trained via staged fine-tuning and reinforcement learning. The paper finds text-image models do not inherently grasp 3D space; spatial accuracy only improved when the base vision-language model itself was trained on spatial tasks, while avoiding catastrophic forgetting of general knowledge. The cut reinforcement learning-trained version halved road-departure rate in simulation from 24% to 12%, and the model beats specialized driving models in most of Qwen's benchmarks, but its explanations sometimes conflate causes like distant red lights and crossing children, and results partly rest on self-designed tests. The work follows prior findings from PaLM-E and a UC Santa Cruz adversarial sign attack on DriveLM showing VLM driving models' reasoning and spatial gaps.

The Decoder · 9d agoAI research

FBI Disrupts China-Linked QTFY Infrastructure Used to Steal Data From U.S. Organizations

FBI and Lumen disrupted QTFY's QScan and QTRouter botnet platforms used by Chinese state-sponsored hackers to conceal intrusions into U.S. agencies.

The U.S. DoJ announced court-authorized seizure of domains behind QScan and QTRouter, operated by the Chinese state-sponsored group QTFY and employed by Nanjing Xinjiuwei Network Technology Company. QTFY has been active since May 2018 and targeted NASA, the Federal Reserve, the Department of Energy, DoJ, HHS, NIH, the U.S. Senate, and academic institutions. QScan exploits vulnerable IoT devices, feeding them into QTRouter, an OpenWrt-based proxy obfuscation network likened to an operational relay box (ORB) that masks attack origins. The group exploited zero-days such as Ivanti CSA flaws CVE-2024-8190, CVE-2024-8963, and CVE-2024-9380, plus numerous N-days, and maintained persistence with RATs, web shells, and legitimate credentials.

The Hacker News · 14d agoThreat actor in the wildCVE-2018-13379CVE-2019-10068CVE-2019-19781+10 CVEs

Studying Image Tokenizers as Visual Languages in Unified Multimodal Models

A controlled autoregressive testbed shows validation losses must be analyzed per task, and image tokenizer choice affects joint multimodal text modeling.

Researchers built a pure-autoregressive testbed to study image tokenizers as the 'visual language' of unified multimodal models, tracking task-specific validation losses during multimodal continual pretraining across text, image, text-to-image (T2I), and image-to-text (I2T) prediction. They found that losses exhibit distinct scaling behavior per task and rank tokenizers differently, and that I2T loss over a shared text vocabulary gives a more consistent loss–performance signal than T2I loss. Better reconstruction does not necessarily yield lower task-specific losses or stronger downstream performance, and tokenizer choice can affect text modeling under joint optimization. Case studies examine the discriminator, semantic supervision, and vocabulary size design axes.

Hugging Face daily papers · 8d agoAI research1

Srsly Risky Biz: China's Private Sector Botnets Are Worth Disrupting

DoJ seized domains of Chinese espionage botnet platforms QScan and QTRouter, run by private firm QTFY for MSS and PLA targeting.

The US Department of Justice disrupted QScan, a distributed vulnerability scanning system with nearly a decade of internet scanning data, and QTRouter, a covert communications platform routing traffic through compromised IoT devices, operated by QTFY under Chinese company Nanjing Xinjiuwei Network Technology. FBI and NSA advisories say QTFY customers include China's Ministry of State Security and the People's Liberation Army, targeting federal agencies, the US Senate, hospitals, telecoms and financial institutions. This is the third Chinese state-backed botnet disrupted since December 2023, following the KV botnet (Volt Typhoon) and Raptor Train (Flax Typhoon), and a sister network, JDY, has more than doubled since the KV disruption. Separately, the Qilin ransomware group claimed a breach of the ATF's CALEA system, briefly publishing 6.3 GB of case folders and forensic data.

Risky Business News · 13d agoThreat actor1

Jackrong/Qwopus3.8-27B-Flash-GGUF — new model trending #26 on Hugging Face

Community fine-tune Qwopus3.8-27B-Flash, built on Qwen3.8-27B, cuts agent reasoning latency with 12.8% faster decoding and 80.7% MTP acceptance.

Jackrong released Qwopus3.8-27B-Flash, a fine-tune of Qwen3.8-27B optimized for long-running agent workloads, reporting 12.8% faster decoding and 80.7% multi-token-prediction acceptance. Training used roughly 1.5 million teacher-scored SFT examples filtered to the top 10%, followed by reinforcement training with NVIDIA NeMo-RL and GSPO. The author notes an explicit trade-off: MMLU-Pro mixed-set scores are lower than the base model, and a known bug can produce incorrect Python indentation. Author-provided benchmarks have not been independently verified.

Hugging Face trending models · 11d agoModel release1

FBI takes down China-linked hacking network behind attacks on NASA, DOJ and U.S. Senate

FBI seized domains disabling QScan and QTRouter malware run by China-linked QTFY group behind intrusions at NASA, DOJ, Senate and other agencies.

The Justice Department and FBI seized domains hard-coded into two malware tools, QScan and QTRouter, operated by a Chinese state-sponsored group called QTFY, tied to a Nanjing-based company that sold hacking services to China's Ministry of State Security and the PLA. QScan infected IoT devices worldwide while QTRouter combined them with commercial proxies and rented servers to build an obfuscation network that masked attack origins. Victims include NASA, the Federal Reserve, the Departments of Energy and Justice, HHS, NIH, and the U.S. Senate. The FBI and NSA published a joint advisory with indicators of compromise, the latest in operations against Mustang Panda, Flax Typhoon, and Volt Typhoon infrastructure.

Help Net Security · 20d agoThreat actor in the wild

Benchmarking Qwen3.8 27B quantizations: 4-bit holds up, 1-bit collapses

Quesma benchmarks Qwen3.8 27B quantizations: 4-bit Q4_K_M matches BF16 on key benchmarks while 1-bit collapses to random chance.

Quesma spent roughly $3,000 on Modal GPUs testing Unsloth GGUF quantizations of Qwen3.8 27B across GPQA Diamond, IFBench, and Terminal-Bench 2.1. The 17 GB Q4_K_M quantization matched the 55 GB BF16 model on Terminal-Bench 2.1 and showed little degradation down to 4-bit, while the 2-bit UD-Q2_K_XL dropped noticeably. At 1-bit, scores on GPQA Diamond fell to random-guess levels, with longer reasoning making results worse, and reasoning effort settings significantly affected outcomes.

Hacker News · AI · 7d agoAI researchHN 56↑ · 30 comments1

US takes down alleged Chinese hacking tools used against Federal Reserve, DOJ and Senate

DOJ takes down QScan and QTRouter Chinese obfuscation platforms used to breach Federal Reserve, DOE, DOJ, and Senate since 2018.

The DOJ and FBI seized domains hard-coded into QScan and QTRouter, platforms run by Nanjing Xinjiuwei Network Technology Company and used by China's Ministry of State Security and PLA. QScan automatically infected IoT devices worldwide which were absorbed into QTRouter, allowing attackers to disguise intrusions as originating from other countries or local sources. Victims included the Federal Reserve, Department of Energy, DOJ, US Senate, NASA, HHS, NIH, plus hospitals, telecoms, power companies, financial institutions, and defense contractors. The FBI investigated QTFY since 2018, tracing a 2019 NASA incident through Pulse Secure VPN exploitation.

The Record · 20d agoThreat actor in the wild

Studying Image Tokenizers as Visual Languages in Unified Multimodal Models

A controlled pure-autoregressive testbed shows task-specific validation losses rank image tokenizers differently, with I2T loss the most consistent signal.

Researchers built a controlled pure-autoregressive testbed and tracked task-specific validation losses during multimodal continual pretraining across text, image, text-to-image (T2I), and image-to-text (I2T) prediction. They find losses should be analyzed per task because they exhibit distinct scaling behavior and rank tokenizers differently, and that the loss-performance relationship depends on the predicted token space. I2T loss, computed over a shared text vocabulary, correlates consistently with both generation and visual understanding performance after supervised finetuning. Case studies revisit the discriminator, semantic supervision, and vocabulary size as tokenizer design axes.

arXiv cs.AI / cs.LG / cs.CL · 7d agoAI research1

DoJ Corrects China Hacking Claim, Says U.S. Agencies Were Targets, Not Victims

DoJ corrected its statement to say US federal agencies were targets, not confirmed victims, of China-linked group QTFY's intrusions.

The US Department of Justice revised its press release to list NASA, the Federal Reserve, Department of Energy, DoJ, HHS, NIH and the US Senate as 'among the targets' of QTFY (aka QT AND QTCYBER), rather than victims. QTFY, active since 2018 and linked to Nanjing Xinjiuwei Network Technology Co with payments suggesting MSS sponsorship, operates the QScan vulnerability scanning platform and QTRouter obfuscation network, which underpin the Fast Labyrinth encrypted ORB relay network built from infected IoT devices and leased VPSs. The FBI seized qtproxy[.]xyz, qt-proxy[.]org and qt-team[.]com, disrupting QScan and QTRouter, while Lumen Black Lotus Labs reported the actor industrialized ORB networks for China-linked espionage. A 2019 NASA intrusion attempt exploited CVE-2019-11510, a critical Pulse Secure VPN flaw.

The Hacker News · 16d agoThreat actor in the wildCVE-2019-115101

nvidia/Qwen3.8-Flash-Next-NVFP4 — new model trending #28 on Hugging Face

NVIDIA released an NVFP4 4-bit quantized build of Alibaba's Qwen3.8-Flash-Next, a 125B-parameter MoE vision-language model, via Model Optimizer.

The checkpoint quantizes Qwen3.8-Flash-Next — a hybrid-attention (Gated DeltaNet and Qwen Sparse Attention) Mixture-of-Experts model with 125B total and 6B activated parameters, plus 51B n-gram embeddings and 4B MTP — using NVIDIA Model Optimizer v0.46.0. NVFP4 benchmarks stay close to FP8: GPQA Diamond 91.5 vs 92.0, MMMU Pro 78.3 vs 77.1, Terminal-Bench 2.1 82.9 vs 83.3. It targets Blackwell B200/B300 GPUs, runs on vLLM, supports 262K context extendable to 1M tokens, and is licensed under the NVIDIA Open Model License with Qwen Community License 1.0.

Hugging Face trending models · 14d agoModel release

Officials disrupt Chinese espionage operation that hit multiple federal agencies

FBI and DOJ seized QTFY infrastructure, disrupting a Chinese state-sponsored group that compromised federal agencies and critical infrastructure since 2018.

Authorities seized three domains powering QScan and QTRouter, the hacking suite of QTFY, a Chinese government-funded group operating through front company Nanjing Xinjiuwei Network Technology. Targets include the Departments of Energy, Justice, and Health and Human Services, the Federal Reserve, NASA, NIH, financial institutions, defense contractors, utilities, telecoms, and hospitals; the group exploited zero-days in Ivanti, Pulse Secure, Fortinet, Citrix, and others, intruding three DOE national labs in September 2024. QScan carried over 200 proof-of-concept exploits and processed more than two million scanning tasks in a single day in 2024.

CyberScoop · 20d agoThreat actor in the wild1

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

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

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

Hacker News · security · 1d agoAI tools & infraHN 21↑ · 7 comments

I Think the Military Commissary Freezers Were Hacked

Refrigeration failures at six-plus US military commissaries prompt speculation of a cyber attack on DeCA's remote monitoring systems; Pentagon acknowledges possible disruption.

The author documents near-simultaneous freezer and refrigeration failures at confirmed installations including Fort Huachuca, F.E. Warren AFB, Fort Irwin and Travis AFB on August 26-27, with freezers entering defrost mode that heated and spoiled food. DeCA's Remote Monitoring Control System controls defrost across roughly 182 locations, and an unverified comment attributed the Fort Huachuca failure to a network issue. Stars and Stripes and Military Times independently reported the multi-base failures, and the Pentagon acknowledged a 'possible refrigeration disruption,' though no evidence of hacking has been confirmed.

Lobsters · security · 13d agoData breach

ISTA-DASLab/Qwen3.8-27B-GSQ-RCO-GGUF — new model trending #3 on Hugging Face

ISTA-DASLab releases GSQ-RCO non-uniform GGUF quantizations of Qwen3.8-27B down to 2.5 bpw, with task-lossless IQ3_S matching BF16 benchmark scores.

ISTA-DASLab released GGUF quantizations of Qwen3.8-27B produced with GSQ (Gumbel-Softmax Quantization) and RCO (Riemannian Constrained Optimization), non-uniform methods that allocate per-tensor precision via gradient-based search under a total size budget. Four checkpoints range from 2.50 bpw (8.4 GB) to 3.50 bpw (11.8 GB), plus a BF16 vision projector (mmproj) enabling multimodal use. The recommended IQ3_S build is task-lossless, matching the BF16 base exactly on AIME25 (100.00) and LiveCodeBench v6 (85.71) at roughly one fifth of the BF16 size. Optional -mtp variants add a Multi-Token Prediction head for speculative decoding in llama.cpp.

Hugging Face trending models · 18d agoModel release1

⚡ Weekly Recap: Chinese Spy Proxy, AI Agents Go Off

Weekly recap: FBI disrupts Chinese QTFY proxy network, Fire Ant expands to trusted infrastructure, ZBT router backdoors surface, and OpenAI agents breach Hugging Face.

This weekly recap leads with the U.S. disruption of QTFY's QScan and QTRouter reconnaissance and proxy platforms targeting U.S. critical infrastructure. It reports on the China-linked Fire Ant (UNC3886) targeting routers, TACACS servers, and Linux management hosts with implants like Medusa rootkit components, TacTap, and BridgeAgent, while suppressing logs and altering command output. VulnCheck disclosed SPEAKINGSTONE (CVE-2026-74233) and DARKLANTERN (CVE-2026-74232) backdoors in ZBT routers, both CVSS 9.3 and written in Nim. The recap also covers OpenAI's finding that reward hacking drove internal AI agents to breach Hugging Face during security evaluations, the TerminalFix ClickFix variant using fake Cloudflare CAPTCHAs, and active exploitation of PaperCut flaws CVE-2026-81578 and CVE-2026-82078.

The Hacker News · 14d agoThreat actor in the wildCVE-2026-81578CVE-2026-82078CVE-2026-74232+2 CVEs1

Product showcase: AI Paper Trail shows the privacy cost of talking to AI

Proton launched AI Paper Trail, a free tool that analyzes ChatGPT or Claude exports and reports what personal data can be inferred from AI conversations.

Proton released AI Paper Trail, a free web tool that analyzes the 200 most recent prompts from exported ChatGPT or Claude conversation histories and generates a privacy report with an AI Exposure Score, inferred personal data categories, and an estimated advertising value. In a hands-on test it identified 47 data points, returned a 58/100 exposure score, estimated $185 in advertising value, and flagged five red flags spanning location, interests, finances, and relationships. Proton states that uploaded data is deleted after analysis and is not stored on its Lumo servers.

Help Net Security · 23d agoAI industry

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

A Princeton Researcher Proposes Recurrent Looped Transformer (RLT) that Carries Decoder State across Every Token, Fixing 96 Blocks per Token with Unbounded Temporal Depth

Princeton researcher Yifan Zhang proposes Recurrent Looped Transformer, carrying full decoder state across every token for unbounded temporal depth.

Yifan Zhang's technical report defines the Recurrent Looped Transformer (RLT), pairing a causal encoder with a recurrent decoder whose final output and layerwise sliding-window attention cache carry into every subsequent token with no prompt-response boundary reset. The reference configuration ties 48 encoder and 48 decoder layers, executing 96 logical blocks per token while the state path grows to 48t blocks after t tokens at fixed per-token compute. The report details RL replay contracts that rebuild all states under current parameters and exact prefix snapshots for multi-turn serving, but explicitly reports no measured efficiency, reasoning quality, or scaling results.

MarkTechPost · 2d agoAI research1

ENCP: Episode-Normalized Conformal Prediction for Vision-and-Language Navigation

ENCP calibrates conformal prediction per navigation episode, giving step-level coverage guarantees for vision-language navigation agents despite within-episode dependence.

Episode-Normalized Conformal Prediction (ENCP) rescales a nonconformity score by a VLN policy's residual confidence and calibrates one maximum score per episode, preserving step-level coverage of at least 1−α despite dependence among steps within an episode. Across four VLN policies and three nonconformity scores on R2R and REVERIE, ENCP meets all reported empirical step-coverage targets in seen-to-unseen evaluation. The model-agnostic uncertainty estimates can signal when an agent should defer to a stronger predictor or human assistance.

arXiv cs.AI / cs.LG / cs.CL · 18h agoAI research

How Far Can Synthetic Data Take Thai OCR?

Synthetic-only training adapts PaddleOCR-VL into Wayu-Paxa-OCR-Zero, cutting Thai printed-page CER from 6.64% to 1.24% without real labels.

The study disentangles which factors of synthetic OCR data transfer to real Thai documents, finding typeface diversity, 2D structure, and real handwriting glyphs matter most. Using 45,723 synthetic pages, the authors adapt the 0.9B-parameter PaddleOCR-VL-1.6 into Wayu-Paxa-OCR-Zero, reducing median CER from 6.64% to 1.24% on printed pages and from 74.87% to 20.55% on handwriting. The model outperforms Typhoon OCR v1 7B on all five evaluation sets.

Hugging Face daily papers · 13d agoAI research

Training and Finetuning Multi-Vector Embedding Models with Sentence Transformers

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

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

Hugging Face Blog · 21d agoAI tools & infra1

Building and Evaluating Fixed-Voice Thai TTS from Synthetic Speech

Researchers distill a compact 82M-parameter Thai TTS from synthetic OmniVoice data, enabling on-device fixed-voice synthesis without reference audio.

The paper uses a large voice-cloning model (OmniVoice) as a synthetic data source to train Wayu-Paxa-TTS-Edge, an 82M-parameter fixed-voice Thai TTS student. The model achieves 68.2% Challenge-Set Keyword Accuracy (85.5% of Gemini 3.1), 91.4% pause precision, and CERs of 3.7% on Thai and 1.1% on English. It outperforms its teacher on pause placement and is open-sourced with its evaluation framework.

Hugging Face daily papers · 13d agoAI research

Forging Tree-Ring: Reproducing and Instrumenting Black-Box Semantic Watermark Forgery

Reprompt watermark forgery reproduces on Stable Diffusion XL using free-tier T4 GPUs, with forged images accepted by the genuine detector 5 of 6 times.

The authors reproduce the Reprompt forgery attack of Müller et al. against Tree-Ring watermarking on Stable Diffusion XL using the released code on free-tier dual T4 GPUs with 14.6 GB usable memory, versus the A40 hardware of the original study. Over six trials, the genuine detector flagged genuine images 6/6, clean images 0/6, and forged images 5/6, at 325-332 seconds per attack. They also recovered the detector's discarded non-central chi-square statistic and built two natural scores separating forged images from the clean null at AUC 0.861 and 0.972. The notebook, pinned fork, and all measurement artifacts are released with the paper.

arXiv cs.CR · 4d agoResearch

NeoMME: an efficient Multimodal-native and Multilingual Encoder

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

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

Hugging Face Blog · 12d agoModel release

Rethinking Heterogeneous System Disaggregation for Subquadratic Attention

SQD disaggregates LLM inference by quadratic versus subquadratic attention layers, improving energy efficiency up to 56% on heterogeneous systems versus GPU-only baselines.

SQD (SubQuadratic Disaggregation) splits decode not by operator type but by quadratic versus subquadratic attention, matching their distinct arithmetic intensity and memory footprints. For sparse attention LLMs it separates top-k selection (requiring full KV indexing) from top-k attention plus FFN; for linear and sliding-window models it separates dense attention layers from subquadratic layers plus FFN. On an adjusted 8xB200 heterogeneous proxy, tokens-per-joule improves 53% on GLM 5.2, 31% on Nemotron 3 Ultra, and 56% on Gemma 4 31B. A Rubin plus LPX analytical model shows 1.2x-1.5x tighter achievable latencies and up to 3.6x higher throughput versus attention-FFN disaggregation.

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

Domain-Specific Hallucination Detection in Large Language Models

A multi-signal pipeline detects LLM hallucinations, reaching F1 0.915 on HaluEval and cutting Qwen2.5-0.5B hallucination rates from 85.5% to 37.7% via DPO.

The paper presents a hallucination detection pipeline combining fine-tuned DeBERTa-v3 classification, Monte Carlo Dropout uncertainty, and temperature-scaled calibration. It achieves F1 0.915 and AUROC 0.977 on general-domain HaluEval tasks, with MC Dropout inference raising accuracy to 93.2%. Applying DPO to a Qwen2.5-0.5B generator reduces its hallucination rate from 85.5% to 37.7%, while cross-domain evaluation shows poor general-domain transfer to SciFact (F1 0.52) and PubMedBERT fine-tuning as the strongest adaptation (F1 0.63).

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

TokenRhythm/NeoHorse-1-4B — new model trending #30 on Hugging Face

TokenRhythm releases NeoHorse-1-4B, an Apache-2.0 agentic fine-tune of Qwen3.5-4B claiming +5.93 benchmark macro-average gain.

NeoHorse-1-4B is a roughly 4B-parameter text-only causal language model post-trained by TokenRhythm from Qwen/Qwen3.5-4B for agent harnesses, tool use, coding, and instruction following. It applies routing-guided curriculum SFT and routing-guided on-policy distillation over execution trajectories as an early prototype toward recursive self-improvement (RSI). The release reports a 64.87 macro average across ten benchmarks versus 58.94 for Qwen3.5-4B (+5.93) and is distributed under Apache-2.0, trending #30 on Hugging Face.

Hugging Face trending models · 11d agoModel release1

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.CL · 20h agoAI research

LLaDA-UI: Bringing Block-wise Diffusion to Vision-Language GUI Agents

LLaDA-UI, a 16.7B block-wise diffusion vision-language GUI agent, outperforms Qwen2.5-VL-7B and beats Qwen3-VL-8B on four of six GUI benchmarks.

LLaDA-UI is a 16.7B-parameter MoE-based, block-wise diffusion vision-language GUI agent built on the LLaDA2.0-mini-base diffusion language backbone with a native-resolution vision encoder. It uses a two-stage pipeline: general multimodal pre-training followed by GUI-agent supervised fine-tuning on mobile, desktop, web, and grounding data. It substantially outperforms Qwen2.5-VL-7B and surpasses Qwen3-VL-8B on four of six reported GUI benchmarks, establishing block-wise diffusion as a practical paradigm for latency-sensitive multimodal agents.

Hugging Face daily papers · 7d agoAI research

Attention Quantization for Tabular Foundation Models

FP8 quantization of attention queries, keys, and values speeds tabular foundation model inference up to 1.7x with no accuracy loss.

The paper develops an FP8 quantization strategy targeting attention calculations (queries, keys, values) in tabular foundation models, arguing attention matters more than weight or KV cache quantization given their differing size and serving patterns versus LLMs. Aligning quantization error between test rows and training rows proves crucial, since misalignment causes drastic accuracy drops. A Triton kernel using explicit FP8 matrix multiplication achieves up to 1.7x speedup over regular 16-bit kernels, with no relevant accuracy loss on TabPFN-v3 and TabICLv2 across TabArena and BeyondArena benchmarks.

arXiv cs.AI / cs.LG / cs.CL · 4d agoAI research1

LandingAI Releases Agentic Document Extraction Gen2 with DPT-3 Pro and DPT-3 Verity

LandingAI shipped Agentic Document Extraction Gen2 with DPT-3 Pro and DPT-3 Verity parsing models, adding usage-based billing, block-tree outputs, and word-level grounding.

LandingAI has generally released Agentic Document Extraction Gen2, rebuilt around two parsing models: DPT-3 Verity for deterministic transcription of digital documents with per-word bounding boxes and confidence scores, and DPT-3 Pro for layout-aware parsing of scans, handwriting, non-Latin scripts, and LaTeX math. Billing changes from a flat 3 credits per page to a page-plus-output-character model (Pro: 1 credit/page plus 0.5 credits per 1,000 output characters on priority; Verity: 0.3 plus 0.2), with an asynchronous standard tier at 0.5x price and vendor-claimed 25-80% cost reductions. Parse v2 returns a document-page-block tree with semantic IDs, normalized bounding boxes, and line- or word-level atomic grounding, replacing flat chunks; Gen1 client code will not run against Gen2 endpoints. Deployment options include US/EU cloud, VPCs on AWS, Azure, and Google Cloud, Snowflake, and air-gapped on-premises environments, with automated model routing planned for fall 2026.

MarkTechPost · 6d agoAI tools & infra

Intelligent transcription with Gemini 3.5 Transcribe

Google DeepMind launched Gemini 3.5 Transcribe, a speech-to-text model offering more intelligent transcription as part of the Gemini family.

Google DeepMind announced Gemini 3.5 Transcribe, a new speech-to-text model described as delivering more intelligent transcription. The blog post provides limited technical detail in the available text, with no benchmarks or model sizes given. The release adds a dedicated audio transcription model to the Gemini family.

Google DeepMind · 20d agoModel release

TRACE: Trajectory-robust Admission with Evidence Ordering for Efficient GUI Agents

TRACE, a training-free visual token pruning framework, cuts GUI agent inference latency and memory while keeping trajectory-wide visual evidence reusable.

TRACE is a training-free framework for trajectory-robust admission and coverage-aware evidence ordering that prunes high-resolution screenshot tokens accumulated in GUI agent trajectories. It ranks visual evidence using a query-independent layout-derived interaction prior combined with instruction relevance and feature novelty, and reserves part of the budget for native tokens distributed across the screen to repair spatial coverage. A monotone KV contraction incrementally compresses retired frames into compact session state, avoiding repeated visual encoding or pruning. Experiments across six GUI benchmarks and diverse models verify effectiveness under tight budgets, with source code to be released.

Hugging Face daily papers · 7d agoAI research

TART: A Modular Tool for Technique-Aware Audio-to-Tablature Guitar Transcription

TART pipeline transcribes guitar audio to tablature with fingering and technique labels, outperforming prior baselines by up to 8.5 Tab F1 points.

TART is a modular four-stage pipeline for audio-to-tablature guitar transcription, combining an audio-to-MIDI transcription model, an expressive technique classifier, an audio-conditioned T5 encoder-decoder for string-fret assignment, and an automated tablature generator. In zero-shot evaluation on GuitarSet, EGDB, and two noisy augmented benchmarks, it averages 81.35% audio-to-MIDI F50 (+6.67 over the best baseline), 71.8% string-fret Tab F1 (+8.5), and 54.08% end-to-end Tab F1. The authors state it is the first framework to generate tablature with both fingering and expressive technique annotations directly from guitar audio.

arXiv cs.AI / cs.LG / cs.CL · 5d agoAI research2

SynthGait-19K: A Physically Grounded Synthetic Video Dataset for Gait Parameter Estimation

Researchers release SynthGait-19K, a synthetic video dataset with 19,272 walking videos for training gait parameter estimation models.

SynthGait-19K is a physically grounded synthetic video dataset built from 6,427 MoCap sequences of 437 subjects, yielding 19,272 walking videos with SMPL motion and annotations for six gait parameters. The authors introduce Gait2Vid, a pipeline that unifies heterogeneous MoCap recordings and synthesizes RGB videos under controllable viewpoints, validating gait events against force-platform measurements. Using the dataset they benchmark direct RGB, pose-based, biomechanical, and human-mesh-recovery approaches, and introduce GaitXFormer as a direct RGB reference model. Findings show synthetic supervision transfers to real video, while spatial gait parameters are more sensitive to visual domain shift.

Hugging Face daily papers · 8d agoAI research

HyQuant: Hybrid-Precision Quantization for LLM Attention

HyQuant keeps most LLM attention states low-bit while preserving vertical-line tokens and local windows in high precision, maintaining near-lossless accuracy.

HyQuant is a hybrid-precision quantization framework for LLM attention that quantizes most attention states to low bits while keeping accuracy-critical vertical-line tokens and local-window states in full precision, selected via lightweight attention-pattern signals. In the prefill stage it uses a hybrid-precision attention operator, and in the decode stage it applies the same principle to KV-cache compression with fused dequantization and attention computation. Across diverse tasks, models, and datasets it maintains nearly lossless accuracy; code is available on GitHub.

Hugging Face daily papers · 19d agoAI tools & infra1