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12 Best Endpoint Privilege Management (EPM) Tools Compared (2026): Features & Pricing

A 2026 buyer's guide compares 12 endpoint privilege management tools, ranking CyberArk and BeyondTrust as enterprise leaders.

An editorial comparison evaluates 12 endpoint privilege management (EPM) tools on elevation control, manageability, and pricing model. The guide argues that standing local-admin rights fuel ransomware and lateral movement, making their removal a high-impact control that cyber insurers increasingly mandate. CyberArk and BeyondTrust are positioned as enterprise-depth leaders, with Delinea, Heimdal, and ManageEngine for the mid-market, and Admin By Request and CyberFOX AutoElevate for SMBs and MSPs. Pricing is generally per endpoint or per user, and the article is explicitly an assessment rather than a product release or incident report.

GBHackersupdated · 13h agofirst · 5d agoIndustry 14 sources

Meta is back with Muse Glimmer: local, agentic, multimodal, and open source

Meta releases Muse Glimmer, an open-source model built for local, agentic, multimodal use.

Meta has released Muse Glimmer, a new open-source model highlighted on the Hugging Face blog. The model is designed to run locally and supports agentic and multimodal workflows. Details on parameter count and benchmarks were not provided in the title; the release marks Meta's return to open model releases.

Hugging Face Blog · Aug 10, 2026Model release

Top 10 Best Application Control & Allowlisting Tools in 2026

Roundup ranks 2026 application allowlisting tools, scoring ThreatLocker 8.8/10, Airlock Digital 8.5/10 and free Microsoft WDAC among top default-deny options.

This is a scored buyer's guide to ten application control and allowlisting tools for 2026, with ThreatLocker ranked first (8.8/10), Airlock Digital second (8.5/10) and Microsoft's built-in WDAC/AppLocker third (7.2/10). It notes ransomware has renewed interest in default-deny execution controls and that CISA and other agencies list application control among the most effective yet under-deployed mitigations. The evaluation is research-based with no lab testing, weighting policy automation at 30% and operability at 25%.

Cyber Security News · 7d agoIndustry1

CodeTD: Topology of Attention Detects Hallucinations in Code LLMs

CodeTD detects hallucinations in code LLMs before execution by analyzing topological patterns of attention maps, outperforming recent baselines.

CodeTD applies topological data analysis (TDA) to code LLM attention maps to quantify prompt-generation mismatch as a pre-execution correctness signal. Experiments cover HumanEval, MBPP, BigCodeBench, and MultiPL-E across 5 programming languages and 10 code LLMs up to 34B parameters. The method outperforms recent baselines and transfers between coding benchmarks, helping catch code that fails the task or embeds security vulnerabilities.

arXiv cs.AI / cs.LG / cs.CL · 9d agoAI research1

Top 10 Best Endpoint Privilege Management (EPM) Tools in 2026

A 2026 scorecard ranks ten endpoint privilege management tools, led by BeyondTrust, ThreatLocker and Delinea for elevation, coverage and policy depth.

The article ranks ten endpoint privilege management (EPM) tools using weighted criteria covering elevation workflow, platform coverage, policy depth, time-to-value and value. BeyondTrust scored highest overall (8.4) for cross-platform breadth, with ThreatLocker (8.2), Delinea (8.1) and Admin By Request (8.0) highlighted for allowlisting integration, cloud administration and deployment speed respectively. It also notes that Netwrix acquired CoSoSys in 2024, which affects bundling when shortlisting both EPM and device control.

Cyber Security News · 6d agoIndustry

Show HN: LLM Attention Visualization

A developer released a browser-based tool that visualizes which past tokens influence each LLM output token using aggregated, value-weighted attention scores.

A Show HN project presents a React application built on Transformers.js that renders per-token attention influence by aggregating attention weights scaled by value-vector magnitudes across all attention heads and layers. To expose internal tensors, the author instrumented the ONNX computation graph, hosted a modified model on Hugging Face, and pre-generated prompts to avoid long model downloads in the browser. Demos with a 600-million-parameter model show how verbatim copying draws heavily on source tokens and how single outputs blend information from multiple phrases.

Hacker News · AI · 8d agoAI tools & infraHN 41↑ · 9 comments

Apple Updates Everything, (Mon, Sep 14th)

Apple patched a record 261 vulnerabilities across iOS 27, macOS Golden Gate 27, watchOS 27, and other platforms, with none flagged as exploited.

Apple's annual OS update shipped iOS/iPadOS 27, macOS Golden Gate 27, tvOS 27, watchOS 27, and visionOS 27 plus bug-fix releases for the 26 and 15 branches, fixing a record 261 vulnerabilities. Notable issues include multiple kernel flaws allowing root privilege escalation (CVE-2026-43689, CVE-2026-43691, CVE-2026-43698, CVE-2026-43786), remote code execution in CUPS (CVE-2026-43692), kernel memory corruption via malicious NFS servers (CVE-2026-43686, CVE-2026-43687), and WebKit memory corruption from crafted web content (CVE-2026-43715). No vulnerabilities are labeled as actively exploited, and Apple does not assign per-CVE severities. Users report iOS 26.7 being downloaded when iOS 27 is intended, and tools like Little Snitch and BlockBlock need updates before upgrading to macOS 27.

SANS Internet Storm Center · 2d agoVulnerabilityCVE-2022-3437CVE-2026-20683CVE-2026-28899+27 CVEs

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

The Apple Security Update Review for September 2026

Apple's September 2026 updates patch 45+ flaws, including a 9.8 Screen Sharing authentication bypass (CVE-2026-65400) already listed in CISA's KEV.

ZDI's review of Apple's September 2026 security updates catalogs dozens of CVEs across macOS, iOS, iPadOS, watchOS and other platforms. CVE-2026-65400 (CVSS 9.8) lets a network attacker authenticate to Screen Sharing Server without valid credentials and is flagged as KEV, while CVE-2026-65414 (CVSS 9.8) enables remote code execution via Bluetooth. The set also includes 8.8-rated memory corruption flaws in WebKit, WebRTC, CUPS, ImageIO and the kernel, plus sandbox escapes, privilege escalations to root, and arbitrary code execution via crafted files.

Zero Day Initiative Blog · 3h agoVulnerability in the wildCVE-2026-65400CVE-2026-65414CVE-2026-43692+27 CVEs

Building a Production Greek-English Speech Recognizer

Engineering report details Sophea, a production Greek-English ASR reaching 4.26% WER on public English sets via ROVER ensemble and data-pipeline calibration.

Across 23 training iterations, two architectures, and nine production gates, no single data composition passed all gates; a three-model ROVER ensemble reached 9 of 9 gates and cut overlapping-speech WER from 53.35% to 37.87%. Calibrating an audio-quality filter against in-domain anchors reduced discarded scored Greek audio from 98.7% to 10.6%, and a pre-registered ablation traced a hallucination defect to one training-data package. The sophea/asr-k1 preview arbiter lists 4.26% average WER on eight public English test sets and 25.88% WER on live Greek noisy traffic; no weights or training data are released.

Hugging Face daily papers · 6d agoAI research

Apple Patches 200 Vulnerabilities With New iOS 27, macOS Golden Gate 27 Releases

Apple patched a record 200+ vulnerabilities in iOS 27 and macOS Golden Gate 27, including 20 kernel flaws; none exploited in the wild.

Apple's iOS 27 and iPadOS 27 releases fix roughly 126 security flaws, 20 of them in the kernel, while macOS Golden Gate 27 addresses 210 vulnerabilities, about 100 shared with the mobile release. macOS Tahoe 26.7 patches 153 unique CVEs, including 26 kernel defects that could cause memory corruption, privilege escalation, system termination, and information leaks. Notable fixes include CVE-2026-64752, a CoreMedia memory corruption flaw allowing iPhone compromise via a malicious image, and CVE-2022-3437, a heap buffer overflow in Heimdal Samba enabling denial-of-service. Apple states none of the patched flaws are known to be exploited in the wild.

SecurityWeek · 1d agoVulnerabilityCVE-2022-3437CVE-2026-647521· 1 read

FLAT: Resampling Image and Text into 1D Flexible-Length Aligned Transmodal Tokens for Retrieval and Generation

FLAT jointly trains a multimodal encoder with text-to-image and image-to-text decoders, producing flexible-length tokens that hit 83.1 GenEval on T2I after fine-tuning.

FLAT (Flexible-Length Aligned Transmodal representations) is a pre-training framework that jointly optimizes a shared multimodal encoder with T2I and I2T decoders, combining contrastive alignment with bidirectional cross-modal generative objectives. It maps visual and textual inputs into a unified continuous 1D sequence space and uses nested dropout over prefix-K tokens for dynamic output lengths. A single pre-training stage supports cross-modal retrieval and generation (71.1 GenEval), with task-specific fine-tuning reaching 83.1 GenEval on T2I, 40.5 BLEU-4 and 138.6 CIDEr on MS-COCO captioning, and strong Recall@5 on MS-COCO and Flickr30K.

Hugging Face daily papers · 2d agoAI research1

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 · 13d agoModel release

Technical Manual for a Toolkit for Measuring Contextual Individuation in Transformer Language Models

An open methodology toolkit measures whether transformer language models contextualize fixed word forms across domains using bridge forms and layer-wise silhouette analysis.

The manual documents an open toolkit built around 'bridge forms' - identical written words recurring across two or more subject domains with a different sense in each - to test whether transformer language models individuate word occurrences by context beyond the embedding layer. It covers declarative specification of bridge forms, Wikipedia corpus acquisition, occurrence localization, layer-wise representation extraction, domain-pairwise silhouette measurement, and visualization, justifying each choice against failure modes such as sense contamination and subword-tokenization misalignment. It is a methodological and implementation reference and reports no empirical results.

arXiv cs.AI / cs.LG / cs.CL · 12d agoAI research

H Company Releases NeoMME: A Family of 260M and 800M Single-Tower Multimodal Encoders That Drop the Vision Tower and Causal Decoder

H Company released NeoMME, 260M/800M single-tower multimodal encoders matching 3.75B ColQwen2.5 on ViDoRe v3 while being 14.4x smaller, under Apache 2.0.

H Company released NeoMME, a family of 262,937,906- and 793,715,032-parameter bidirectional encoders that process text and raw 32x32 image patches in a single tower, pretrained via masked diffusion and released under Apache 2.0 with day-zero Hugging Face Transformers support. NeoMME-Retriever-260M reaches 0.523 nDCG@10 on ViDoRe v3, matching 3.75B-parameter ColQwen2.5 while being 14.4x smaller; the 800M model scores 0.556. Hierarchical token pooling with int8 and binary quantization shrinks late-interaction indexes from roughly 1.5 MB to 6 kB per page while retaining 95.19% of nDCG@10; text-only BEIR retrieval remains a weak spot.

MarkTechPost · 10d agoAI research

The 2026 PNPL Competition: Word Classification and Efficient Cross-Subject Generalisation in LibriBrain100

The 2026 PNPL competition releases LibriBrain100, a MEG speech dataset with 32 extra subjects, targeting word classification and cross-subject BCI generalization.

The 2025 PNPL competition on non-invasive speech decoding from MEG achieved F1-macro scores of 95.6% for speech detection and 73.6% for phoneme classification, built on LibriBrain's ~50 hours of single-subject data. The 2026 edition extends this with LibriBrain100, adding 32 subjects (~40 minutes each) plus ~80 hours of within-subject data. Two tracks target within-subject word classification at scale and cross-subject generalization with subject-specific fine-tuning shrinking from ~40 to ~20 to ~10 minutes, aiming at clinically feasible non-invasive BCIs for people with profound paralysis.

Hugging Face daily papers · 14d agoAI research

Molecular Déjà Vu: Digit-Level Retrieval of Published Values in Frontier Language Models

Audit of 22 frontier models finds widespread verbatim retrieval of published molecular property values, with higher reasoning increasing recall of memorized numbers.

An arXiv audit tests 22 frontier LLMs across 12 molecular regression benchmarks for verbatim retrieval of published values. More than 50% of the LLMs show verbatim retrieval on five datasets, and identical experiments are flagged 89% more often at a high reasoning level than at the lowest one. Suppressing retrieval moves model prediction errors closer together in relative terms, suggesting predictive capability is not determined solely by memorized values.

arXiv cs.AI / cs.LG / cs.CL · 12d agoAI research1

Fortunate Recall: Ontology-Driven Memory Lifecycle Management for Persistent Coherence in LLMs

Fortunate Recall introduces ontology-based lifecycle policies for LLM memory, cutting confabulation roughly in half (e.g., 45.1% to 22.4%) versus Mem0.

Fortunate Recall (FR) is a composable policy layer that classifies personal facts into a 10+1 behavioral ontology and applies category-specific lifecycle rules including differential temporal decay, slot-key supersession, event-time validity, and retrieval routing. FR-Bank scores 76.9% on the new 516-question LifecycleBench, ahead of Mem0, A-MEM, Memory-R1, and MemoryOS (61%-70.5%), and 75.2% on LongMemEval-S. End-to-end, confabulation drops from Mem0's 45.1% to 22.4% over answered queries, with the ranking replicating on open-weight Kimi K2.5 and transferring to the independent BEAM benchmark (46.8% vs 32.9%).

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

ModaLens: Measuring Image Sensitivity in Report-Conditioned Medical VLMs

ModaLens image-swap audit shows report availability cuts MedGemma-27B image sensitivity on MIMIC-CXR from 20.94% to 4.26% answer changes.

ModaLens is a paired image-swap audit measuring how report availability affects image sensitivity in report-conditioned medical VLMs. On MedGemma-27B across 3,199 paired MIMIC-CXR cases from 293 patients (14 questions per case), generated answers changed on 4.26% of image-swap trials with the report versus 20.94% without it, a 16.7-point paired difference (95% CI 15.6-17.7). The original prompt with a lowercase first-token readout gave 4.70% versus 17.07%, and the direction replicated in two further model lineages. Labels derived from reports limit conclusions about visual correctness; code, prompts, and run records are publicly released.

Hugging Face daily papers · 3d agoAI research

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 · 13d agoAI research1

VDiff-Bench: A Challenging Benchmark for Fine-Grained Image Difference Identification

VDiff-Bench, a 1,756-question benchmark, shows multimodal LLMs struggle with fine-grained image-difference identification, scoring as low as 8.7% on low-level changes.

VDiff-Bench is a multiple-choice benchmark of 1,756 four-way questions over image pairs covering 10 change categories including position, motion, color, texture, OCR/text and illumination, with curated hard negatives. Evaluation of 11 state-of-the-art open- and closed-source MLLMs shows fine-grained visual comparison remains brittle: 7-8B-scale open-source models score 52.5-70.6% on semantic changes but only 8.7-33.3% on low-level changes like noise and texture. Notably, Grok 4.3 shows a sharp performance drop on noise and texture differences, falling behind large open-source models like Kimi K2.5 and K3.

Hugging Face daily papers · 12d 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 · 20d agoAI tools & infra1

HypoEvolve: Genetic Algorithms Enable Multi-Agent LLMs to Discover Scientific Hypotheses

HypoEvolve couples a generational genetic algorithm with specialized LLM agents to generate drug-repurposing hypotheses, beating six baselines on DepMap selectivity (0.171 vs 0.115).

HypoEvolve coordinates specialized LLM agents through a generational genetic algorithm in which scientific judgments and new proposals reshape a hypothesis population. Evaluation centers on drug repurposing, linking mechanistic explanations to target-level biological claims assessed via external measures adapted from DepMap and Open Targets. Across 34 cancer types, HypoEvolve scores highest against six baselines on both measures, with DepMap selectivity of 0.171 versus 0.115 for the strongest baseline, and gains generalize to held-out cancer types.

arXiv cs.AI / cs.LG / cs.CL · 2d agoAI research1

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.CL · 6d agoAI research1

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 · 12d agoAI research

K-Bench: a clinically calibrated benchmark for evaluating large language models in high-risk mental health conversations

Clinician-calibrated K-Bench evaluates 125 LLM configurations on 200 high-risk mental health vignettes, exposing wide variation in suicide and violence risk handling.

K-Bench is a clinician-calibrated, protected benchmark evaluating 125 model configurations from 33 base models across 14 providers on 200 multi-turn vignettes covering suicide, self-harm, domestic violence, substance misuse and no-risk presentations. A frozen GPT-4o judge achieved 94.2% exact agreement with clinician consensus across 6,751 eligible comparisons from 151 clinician-rated transcripts. Leading models combined supportive conversation with combined-risk scores above 95, while risk exploration varied substantially among weaker configurations; therapeutic prompting helped weaker models and elevated reasoning produced no average improvement. A continuously updated public leaderboard is hosted at k-bench.ai with protected test materials.

arXiv cs.AI / cs.LG / cs.CL · 2d agoAI safety & security

CanvasAnneal: Curriculum Reinforcement Learning for Diffusion Language Models

CanvasAnneal injects teacher reasoning traces into diffusion canvases during curriculum RL, improving diffusion LLMs on MATH500, Countdown, and Tau2.

CanvasAnneal is a curriculum-guided reinforcement learning framework for diffusion language models that addresses exploration bottlenecks in standard RL. It warm-starts exploration by injecting teacher-generated reasoning traces into the initial diffusion canvas, then gradually removes this guidance so the model generates reasoning trajectories independently. Across mathematical reasoning and tool-use benchmarks, it improves over standard diffu-GRPO on MATH500, Countdown, and Tau2 and accelerates reward improvement, though gains are task-dependent.

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

ReactHuman: A Physics-Grounded Benchmark for Human-Like Reactive Decision-Making in Embodied Multimodal LLMs

ReactHuman benchmark tests whether multimodal LLMs react safely to sudden household hazards; seven evaluated models mishandle roughly one hazard in three.

ReactHuman is the first physics-grounded benchmark for human-like reactive decision-making, placing a multimodal LLM as the brain of a simulated humanoid facing 17 event families of sudden household hazards across over 1,000 bit-for-bit reproducible scenes with annotation-free ground truth from 240 Hz rigid-body simulation, including adversarial objects whose appearance contradicts their physics. A five-metric suite scores each reaction along reasonable, safe, and physically grounded axes, and every committed plan is physically executed. Seven representative MLLMs mishandle roughly one hazard in three, act from fixed dispositions rather than the observed scene, trust appearance over motion, and miss interception points at meter scale; none of these failures shrink with model scale.

Hugging Face daily papers · 8d 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 · 11d agoModel release7· 1 read

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 · 8d agoAI research1

CareMirror: Bringing Caregiver Wellbeing into the Dementia Care Ecosystem

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

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

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

[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 4 sources1

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 · 8d agoAI research

Rewiring Democracy Series on The Renovator

Schneier and Sanders link a four-part Rewiring Democracy essay series covering civic AI in Japan, Switzerland, Brazil and Scotland.

Bruce Schneier and Nathan E. Sanders announced links to their Rewiring Democracy essay series on The Renovator. The four parts cover Japan's digital democracy party Team Mirai, Switzerland's public AI model Apertus, Brazil's Open Knowledge civic technologists, and civic AI in Scotland. The post is a brief index without technical details.

Schneier on Security · 15d agoAI policy

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

Gradium Launches Voice Design: Write a Prompt, Get a Brand New Synthetic Voice in Seconds

Gradium, a Kyutai spinout, launched Voice Design, generating custom synthetic voices from text descriptions in seconds across five languages.

Gradium, a Paris-based voice AI company spun out of Kyutai, launched Voice Design, which generates new synthetic voices from 1-500 character text descriptions in seconds without needing reference audio or speaker consent. The feature is live in the Gradium API and Studio, free on every plan including the free tier, and kept voices run on the standard streaming TTS endpoint at the same latency as catalog voices. Vendor-run blind pairwise listening tests across 7,627 comparisons report a 72.6% win rate, 13.6 points ahead of ElevenLabs at 59.0%, placing first in all five tested languages, with the largest margins on regional accents such as Quebecois French (97%).

MarkTechPost · 7d agoAI industry