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SAS: Simple Attention Sparsification via End-to-End Optimization of Context Ranking

SAS trains attention sparsification end-to-end with the language modeling loss, beating sparse attention baselines especially under tight context budgets.

Simple Attention Sparsification (SAS) injects the selector's continuous scores into attention logits in log form inside the softmax, letting gradients from the language modeling loss directly update the ranking of context units. The method uses normalized softmax gates calibrated against the current block and a memory-efficient Triton kernel integrated into FlashAttention-style computation. Across reasoning, long-context, and agentic tasks, SAS consistently outperforms trainable sparse attention baselines across budgets, with the largest gains under tight attention budgets.

Hugging Face daily papersupdated · 4d agofirst · 5d agoAI research 2 sources1

[AINews] DeepSeek v4.1-Flash: 763B-P8B-D16B novel causal Encoder–Decoder architecture with vision marks the Return of the Whale

DeepSeek released V4.1-Flash, an open-weight 763B-parameter model with a novel causal encoder-decoder architecture, 1M context, vision input, and MIT license.

DeepSeek launched V4.1-Flash, an open-weight MIT-licensed model using a novel causal encoder-decoder architecture with 763B total parameters and asymmetric active parameters: 8B for prefill and 16B for decode. It supports 1M-token context and text+image input, priced at $0.30 per 1M input and $1.20 per 1M output tokens with a 50% off-peak discount. Artificial Analysis scored it 40 on its Intelligence Index, above DeepSeek V4 Pro 0813, and Vals ranked it the #1 open-weight model ahead of Kimi K3. Baseten shipped day-0 support and Ollama began rolling it out to paid subscribers.

Latent Space · 4d agoModel release 7 sources1

NSA, CISA, FBI, DOE, and EPA Warn of Active AI

Five US agencies warn of active AI-assisted attacks on internet-exposed Siemens S7 PLCs across critical infrastructure, using disguised snap7 scripts for pre-positioning reconnaissance.

NSA, CISA, FBI, DOE, and EPA issued joint advisory CISA AA26-231A warning of an active hacking campaign against Siemens S7-series PLCs, from S7-200 through S7-1500 F-series, across US critical infrastructure. Threat actors use internet scanning services such as Censys and ZoomEye to locate exposed controllers, then perform read operations over S7comm on TCP port 102 using the legitimate snap7.dll and python-snap7 libraries, disguising AI-generated exploitation scripts as monitoring tools. The agencies assess the activity as pre-positioning ahead of possible process disruption, equipment damage, or safety incidents in Energy, Water, Critical Manufacturing, Chemical, Food and Agriculture, and Commercial Facilities sectors.

Security Affairs · 26d agoExploit / PoC in the wild

Zoom Annotation Flaws Could Let a Meeting Participant Hijack Another Attendee's Client

A Security researchers disclosed three Zoom annotation flaws enabling zero-click client hijacking; Zoom shipped fixes in June and July with no exploitation reported.

Researchers at A Security found three flaws in Zoom's annotation feature: CVE-2026-53413 (CVSS 8.3, buffer over-write), CVE-2026-53414 (CVSS 6.5, buffer over-read), and CVE-2026-53415 (CVSS 8.3, use-after-free). A crafted drawing object sent over the wrong message channel can overwrite adjacent memory and hijack another attendee's client with no user interaction. Fixes shipped in Zoom Workplace 7.1.5/7.0.6, VDI Client 7.0.11/6.6.16, and Zoom Rooms/Meeting SDK 7.1.0+ during June and July. No exploitation has been reported and the flaws are absent from CISA's Known Exploited Vulnerabilities catalog.

The Hacker News · Aug 11, 2026VulnerabilityCVE-2026-53413CVE-2026-53414CVE-2026-53415

US agencies warn of AI-powered attacks on Siemens industrial controllers

NSA, CISA, FBI, DOE, and EPA warn threat actors use AI-assisted scripts and snap7 libraries to access internet-exposed Siemens S7 PLCs in critical infrastructure sectors.

NSA, CISA, FBI, DOE, and EPA issued a joint advisory warning that threat actors combine open-source snap7.dll/python-snap7 libraries with AI-generated scripts to gain read/write access to Siemens S7-200 through S7-1500 PLCs over the S7comm protocol. Actors use Censys and ZoomEye to find internet-exposed devices and abuse default or weak credentials, with activity assessed as persistent reconnaissance and positioning for future write operations. Agencies urge device inventory, patching, removing PLCs from the internet, and hardening; the pattern resembles Iran-linked CyberAv3ngers targeting of Rockwell, Schneider, and Siemens PLCs.

Help Net Security · 27d agoAdvisory in the wild

Researchers found a way to hijack devices through Zoom screen sharing

Researchers used a public AI tool to find a Zoom flaw enabling device hijacking via screen sharing in under 20 prompts.

Security researchers discovered a serious vulnerability in Zoom that can be used to hijack devices through the screen-sharing feature. The flaw was reportedly found by a publicly available AI tool in fewer than 20 prompts, highlighting the role of agentic AI in vulnerability discovery. The article does not report active exploitation or assign a CVE in the provided text.

Ars Technica · Security · Aug 12, 2026Vulnerability

⚡ Weekly Recap: AI-Powered PLC Attacks, GitLab Attacks, Stripe Key Leaks and More

US agencies warn of AI-assisted attacks on exposed Siemens PLCs; the week also saw GitLab CVE-2026-19478 exploited and trojanized npm packages found.

The weekly recap leads with a US government warning that threat actors use AI-generated scripts and Censys/ZoomEye scanning to attack internet-exposed Siemens S7 PLCs in water, energy and manufacturing, calling it an active threat. Other stories include active exploitation of GitLab CVE-2026-19478 (CVSS 9.4, unauthenticated project rewriting), 14 trojanized npm packages delivering the RedC2 4.0 Linux backdoor, and the Zombie Card attack that revives expired Visa cards for contactless payment fraud. It also covers suspected Russian clusters UNC6293, UNC7005 and UNC5976 phishing campaigns, a faster Cloudflare Workers Spectre JWT leak, and a bespoke Cl0p JSP web shell deployed after exploiting PTC Windchill flaws.

The Hacker News · 19d agoThreat actor in the wildCVE-2026-194781

Zoom Patches “Zoomsday” Zero-Click Flaw Enabling Remote Code Execution

Zoom patched CVE-2026-53413, a zero-click annotation flaw dubbed "Zoomsday" allowing remote code execution on meeting participants' devices across all platforms.

Zoom patched four vulnerabilities, including CVE-2026-53413, a stack buffer overflow in CAnnoFormatBlock::Deserialize in the annotation protocol that allows zero-click remote code execution on another participant's device. A Security also found CVE-2026-53414, a buffer overread enabling denial-of-service crashes, and CVE-2026-53415, a use-after-free Zoom had already discovered internally. Updates shipped for Workplace 7.1.5 and 7.0.6, Rooms 7.1.5, and Meeting SDK 7.1.5 across all supported platforms.

Security Affairs · Aug 11, 2026VulnerabilityCVE-2026-53413CVE-2026-53414CVE-2026-53415

AI-Generated Exploit Scripts Target Siemens S7 PLCs in U.S. Critical Infrastructure

NSA, CISA, FBI, DOE and EPA warn that actors use AI-generated Python scripts to exploit internet-exposed Siemens S7 PLCs at U.S. critical infrastructure.

A joint advisory from NSA, CISA, FBI, DOE and EPA describes an active threat in which AI-generated exploit scripts disguised as legitimate monitoring tools target Siemens S7 Series PLCs (S7-200 through S7-1500, including F-series safety controllers). The actors use Censys and ZoomEye scanning to find internet-exposed, outdated PLCs and a custom Python script using snap7 libraries over the S7comm protocol for initial access, credential access and denial of service; no attribution was given. Targeted sectors include Critical Manufacturing, Energy, Water and Wastewater, Chemical, Food and Agriculture, and Commercial Facilities, with potential for process disruption and cascading impacts. A related Dream report details a near-autonomous AI-agent campaign (July 1-4, 2026, 12 waves) against Taiwan government entities, using Hermes and OpenClaw agents with eight parallel sub-agents to crack 85 accounts via password spraying and exfiltrate over 2,564 personnel records, SSO client secrets and database credentials.

The Hacker News · 26d agoExploit / PoC in the wild1

OracleZoom: On-Policy Self-Distillation Inspired Reference-Constrained Recursive Image Super Resolution

OracleZoom enables recursive extreme-scale image super-resolution via reference-constrained on-policy distillation, reducing hallucinations at deep zoom scales.

OracleZoom tackles recursive super-resolution, where repeated feeding of predictions back into the same model leaves deeper-scale outputs unsupervised as required source resolution grows geometrically. The framework trains on its own trajectory while carrying the last ground-truth evidence beyond the supervision boundary, combining direct and cross-scale supervision, a no-reference quality objective, a KL-constrained pretrained latent prior, and EMA consistency. Across seven datasets it achieves state-of-the-art SR quality across zoom scales, averaging 0.713 CLIPIQA with larger gains at deeper scales and significantly reduced hallucinations. Code, data, and models are publicly released.

Hugging Face daily papers · 10d agoAI research

Meta Patents AI Glasses to Use Facial Recognition to Identify People, Make Highlight Reels of Your Dinner Party

Meta filed a patent for AI glasses that identify people via facial recognition and auto-generate highlight reels of events like dinner parties.

A newly published Meta patent describes smartglasses that use facial recognition to detect people in frame, capture video clips of their actions, and compile highlight reels. The filing signals Meta's continued, controversial push to combine facial recognition with its AI glasses line. The patent was published Thursday and offers granular detail on Meta's product plans, though it does not confirm a shipping feature.

404 Media · Aug 14, 2026AI industry

AlayaVista: Streaming World Modeling from Panoramic States to Perspective Video

AlayaVista is a camera-controllable streaming video world model that decouples panoramic scene evolution from perspective synthesis, trained on a 1,318-hour 4K dataset.

AlayaVista builds a 360-degree scene prior from a single perspective image, evolves it as a camera-conditioned panoramic latent state, and maps it to perspective video via a latent viewport renderer plus a perspective refiner. Chunk-autoregressive generation and few-step distillation enable efficient streaming. The authors introduce MUGEN, a real-world panoramic video dataset with 1,318 hours of at-least-4K video and rich semantic and geometric annotations.

Hugging Face daily papers · 3d agoAI research

Iris-mini and Iris-pro are the strongest open-weight search agents in their class

Chinese lab AllSpark releases Iris-mini (35B) and Iris-pro (397B) open-weight search agents claiming best-in-class results on BrowseComp and other research benchmarks.

AllSpark's paper introduces Iris-mini (35B parameters, built on Qwen3.6-35B-A3B) and Iris-pro (397B parameters, built on Qwen3.5-397B-A17B), both with 256,000-token context windows. Iris-pro scores 88.6 on BrowseComp, 85.1 on BrowseComp-ZH, 92.9 on DeepSearchQA, and 56.4 on Humanity's Last Exam; Iris-mini reaches 82.2, 84.8, 86.9, and 52.3 respectively. Training tasks are reverse-engineered from web link structure, filtered by a judge model, and refined via alternating SFT and reinforcement learning ('SFT-RL climbing') against live web search. Weights are available on Hugging Face, and the Iris Harness with agent loop, tools, and all four benchmarks is on GitHub.

The Decoder · 3d agoModel release1

Breaking the Vision-Action Shortcut: Latent Interface Training for Generalizable Robotics Foundation Models

Latent Interface Training improves robot foundation model generalization by constraining visual conditioning, boosting LIBERO-Plus success up to 10.7 points.

The paper identifies vision-action shortcuts where robot policies exploit task-irrelevant visual cues that fail under distribution shift. Latent Interface Training (LIT) first trains an action expert conditioned on language, robot state, and terminal SE(3) end-effector poses without images, then constrains visual input through a pose-supervised latent interface. Across four VLA and world-action architectures (Pi0.5, MolmoAct2, FAST-WAM, ImageWAM), LIT improves LIBERO-Plus success by 3.87-10.70 percentage points. Real-world tests show 13.30-16.70 percentage-point gains under unseen cameras, lighting, and distractors.

Hugging Face daily papers · 5d agoAI research

RelightFormer: Feed-forward Generative Transformer for Multiview Object Relighting

RelightFormer is a feed-forward generative transformer for photorealistic single- and multi-view object relighting, trained on a 90K-object dataset.

Researchers introduce RelightFormer, a feed-forward generative transformer adapted from a video foundation model that performs direct image relighting without explicit intrinsic property estimation. The architecture injects target environment maps via a latent illumination module with cross-attention and uses permutation-invariant positional encodings for unordered multi-view inputs. Training relies on the newly constructed Laval Objaverse Dataset (LOD) with 90K objects and 39K unique illuminations, and the model shows state-of-the-art quality with strong zero-shot generalization across single-view, multi-view, and novel-view relighting.

Hugging Face daily papers · 9d agoAI research

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

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

TransNormal-2: Geometry-Grounded Rectified Flow with Edge-Aware Decoding for Precise Normal Estimation

TransNormal-2 improves monocular surface-normal estimation by fixing VAE edge degradation with geometry-aware losses and refinement, matching MoGe-2 with 1.4% of annotations.

TransNormal-2 is a FLUX.2-based rectified-flow framework for monocular surface-normal estimation with single-step deterministic inference. The authors quantify that VAE 8x spatial compression introduces 1.3-8.5 degrees of mean angular error even on ground-truth normals, with edge error up to 2.8x the global error. The method adds geometry-aware pixel-space losses and an RGB-guided Geometric Refinement Module to correct boundary-localized decoding errors. It matches or exceeds MoGe-2 on all eight reported metrics using only 1.4% as many task-specific annotations, and cuts transparent-object MAE by 4.2 degrees on ClearGrasp and 3.1 degrees on ClearPose.

Hugging Face daily papers · 10d 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

deepseek-ai/DeepSeek-V4-Flash-Vision-Exp — new model trending #10 on Hugging Face

DeepSeek released DeepSeek-V4-Flash-Vision-Exp, its first experimental multimodal model, with large multimodal agent benchmark gains over V4-Flash-0731.

DeepSeek AI published DeepSeek-V4-Flash-Vision-Exp, an experimental multimodal model built on the DeepSeek-V4-Flash architecture with added visual modules and continued training. It scores 83.9 on Terminal Bench 2.1 and 36.5 on ApexBench Pass@1 versus 26.2 for DeepSeek-V4-Flash-0731, while remaining comparable to Opus-4.8 on several benchmarks. The MIT-licensed repository ships a tokenizer, OpenAI-style and TXT prompt encoding, and a minimal PyTorch inference implementation, with vLLM and SGLang deployment recipes.

Hugging Face trending models · 16d agoModel release1

What Matters, When? Diagnosing and Improving Conditional Visual Grounding in Visuomotor Imitation Policies

Researchers diagnose conditional visual grounding failures in visuomotor imitation policies and show targeted interventions substantially improve distractor robustness.

The paper studies why ACT-based visuomotor imitation policies fail when visually similar distractor objects or receptacles are introduced, finding sensitivity depends on both distractor type and manipulation stage. Interventions including distractor augmentation, phase-dependent attention regularization, and appearance-based visual prompting improve target selection while preserving spatial control information, with gains in simulation and on a physical UR3e. The same failure pattern is confirmed in a pretrained vision-language-action policy on a state-conditioned medical instrument-handling task.

arXiv cs.AI / cs.LG / cs.CL · 11d agoAI research

Axis Robotics Releases AXIS: A Browser-Based Data Engine With 207 Robot Manipulation Tasks and 50,129 Trajectories

Axis Robotics and academic partners released AXIS, a browser-based teleoperation system yielding 207 manipulation tasks and 50,129 trajectories that lifts pi0.5 to 88.8 on LIBERO-Plus.

A team from Axis Robotics, UC Berkeley, Georgia Tech, and NTU introduced AXIS, a browser-based data engine where contributors teleoperate a simulated Franka Research 3 in a MuJoCo WebAssembly frontend while GPU backends handle task generation, training, and evaluation. The released snapshot holds 207 tasks, 50,129 episodes, and 60K+ task or scene variants from more than 70,000 community contributors. Continual pretraining of pi0.5 on AXIS data raises LIBERO-Plus performance from 83.9 to 88.8, versus 57.5 for a volume-matched RoboCasa365 control; the 2.36 TB dataset is gated for non-commercial academic use.

MarkTechPost · 8d agoAI research

FreeFlow: A Bias-free Hierarchical Transformer for Optical Flow Estimation

FreeFlow is a bias-free hierarchical transformer achieving state-of-the-art optical flow results on Sintel, KITTI-2015, and Spring benchmarks.

FreeFlow replaces task-specific inductive biases like correlation volumes and iterative warping with a single feed-forward encoder-decoder combining window, shifted-window, and reduced-resolution global attention. It reaches 0.68/1.48 EPE on Sintel Clean/Final, 3.23 Fl-all on KITTI-2015, and 3.192 1px on Spring. The architecture scales consistently from small to large variants and remains memory efficient at 1080p inference.

Hugging Face daily papers · 6d agoAI research

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

One Editor, Many Edits: A Unified Training-Free Framework for Diverse Video Editing

Researchers introduce EditVid, a training-free video editing framework scoring 78.16 FiVE-Acc versus 58.95 for the strongest comparable baseline.

EditVid is a unified training-free framework for diverse instruction-guided and subject-guided video editing. It combines sparse causal memory for local coherence, correspondence-based post-attention token injection for long-range identity preservation, and soft latent blending for edit locality. On the FiVE benchmark it reaches 78.16 FiVE-Acc against 58.95 for the strongest evaluated training-free baseline, with competitive results on IVEBench. A user study showed 51.8% overall preference for EditVid over 7 competing methods.

Hugging Face daily papers · 13d agoAI research

Multi-Grid Post-Training for Long-Form Multi-Shot Video Generation

MovieGrid arranges long videos on spatial grids during post-training, generating 6.05x more shots than temporal packing with state-of-the-art cross-shot consistency.

MovieGrid is a multi-grid post-training paradigm that decomposes long videos into temporally ordered chunks arranged on a spatial grid for joint modeling, enabling cross-chunk information exchange. The authors build the Multi-Grid Long Video (MGLV) dataset from 1,000 long-form videos, producing 54K grid videos paired with character-aware story prompts. Under the same token budget, MovieGrid generates 6.05x more shots than Temporal Packing in a 1,616-frame video. It achieves state-of-the-art intra-shot consistency of 0.9131 versus 0.8086 for HoloCine and inter-shot consistency of 0.5914 versus 0.5384 for StoryMem.

Hugging Face daily papers · 10d agoAI research

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 papers · 9d agoAI research

3D Point Splatting for mmWave Radar Novel View Synthesis

Researchers propose 3DPS, a differentiable point renderer for mmWave radar novel view synthesis that outperforms optical-NVS baselines by 1.7x-5.2x.

The paper introduces 3D Point Splatting (3DPS), the first differentiable point renderer for radar, derived from the solid-angle form of the radar equation with ITU-R P.2040 material models and complex phasor splatting. On six outdoor ColoRadar scenes it reaches 0.587 mean Pearson correlation on held-out range-azimuth images, between 1.7x and 5.2x the RadarSplat, Radar Fields, and DART baselines. The same optimized scene produces ADC, complex range profile, and RA outputs via standard FFT pipelines, and training takes about 3 minutes per scene on an RTX 4090.

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

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

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

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 · 12d agoModel release1

ChatGPT Images 2.5: Faster, more precise, but not the same for everyone

OpenAI released GPT-Image-2.5 (Flare and Sunburst variants), cutting image generation latency up to 50% and improving multi-round edit consistency.

OpenAI launched GPT-Image-2.5 in two API variants: Flare, the faster default with higher quality than GPT-Image-2 at up to 50% lower latency, and Sunburst, built for precise multi-round edits. Both cost $8 per million input and $30 per million output tokens, with new xhigh and max quality tiers; a max-tier 1024x1024 image runs roughly $0.21. Testing found edit consistency strong in ChatGPT Work but inconsistent in Chat, and OpenAI has not documented how ChatGPT routes users between the models.

The Decoder · 7d agoModel release