Search: “KNUCKLEBALL”

Dynin-Robotics: Omnimodal Unified Diffusion Vision-Language-Action Model

Dynin-Robotics omnimodal diffusion vision-language-action model unifies action, dynamics, and goal prediction, reaching 78.4% success on Franka tasks.

Dynin-Robotics builds a shared trajectory model on the Dynin-Omni omnimodal masked-diffusion backbone, representing language, observations, goals, and actions as discrete tokens. One model learns action prediction, action-conditioned next-observation prediction, terminal goal-state prediction, and trajectory-to-instruction reconstruction, enabling test-time scaling through goal prediction and joint refinement. It is continually pretrained on approximately 1.33 million trajectories from 48 Open X-Embodiment datasets and achieves competitive performance on LIBERO and zero-shot LIBERO-Plus plus a 78.4% average success rate across four manipulation conditions on a Franka Research 3 robot. An optimized block-parallel implementation accelerates model-side action decoding by up to 29.2x.

arXiv cs.AI / cs.LG / cs.CL·Sep 11, 16:49 UTC · 5d agoAI research 2 sources1

Attackers Exploit Two SonicWall SMA 1000 Zero-Days That May Form an Attack Chain

SonicWall patches two actively exploited zero-days (CVE-2026-83548, CVE-2026-83549) in SMA 1000 VPN appliances, likely chained for code execution.

SonicWall fixed CVE-2026-83548 (CVSS 10.0), a pre-authentication SSRF in the Appliance Work Place interface, and CVE-2026-83549 (CVSS 7.8), a post-authentication OS command injection in the Appliance Management Console. The company investigated a case indicating active exploitation, suggesting attackers chained both bugs to execute arbitrary code on susceptible devices. Affected SMA 1000 models 6210, 7210, and 8200v require hotfixes 12.4.3-03526 or 12.5.0-02952; customers are urged to hunt for IoCs and re-image, reset credentials, and rotate TOTP if found.

The Hacker News·Sep 2, 10:53 UTC · 14d agoExploit / PoC in the wildCVE-2026-83548CVE-2026-83549CVE-2026-15409+1 CVEs

Agility’s new humanoid robot will stop, squat to avoid harming human coworkers

Agility Robotics unveils Digit 5 humanoid robot that autonomously stops, moves, or squats to avoid harming nearby human coworkers.

Agility Robotics announced Digit 5, its first humanoid robot designed to work safely near humans without isolation barriers, using a safe motion system that reacts to detected human presence by moving away, standing still, or squatting. Early access begins in H1 2027 with general availability by end of 2027. Previous Digit versions have logged over 65,000 working hours, with customers including GXO, Schaeffler, Amazon, and Toyota Motor Manufacturing Canada.

Ars Technica · AI·Sep 15, 18:33 UTC · 1d agoAI industry

INC Ransomware Emerges as Dominant Actor Exploiting SonicWall SMA 1000 Flaws

The Hacker News·Aug 3, 16:15 UTC · Aug 3, 2026RansomwareCVE-2026-15409CVE-2026-154101

SlipSense: Multimodal Tactile Learning for Low-Latency and Generalized Slip Detection

SlipSense fuses a 32x32 piezoresistive array and MEMS accelerometer to detect robotic grip slips within 23.1 ms, generalizing zero-shot across platforms.

SlipSense is a multimodal tactile slip-detection framework built on TacV5, a sensor combining a 32x32 piezoresistive array at 240 Hz and a 3-axis MEMS accelerometer at 8 kHz. It performs modality-specific encoding, intra-sensor fusion, and cross-modal attention with causal temporal prediction at 240 Hz. On a 1.4-million-frame dataset spanning 37 objects it achieves 96.7% Macro F1 with a false-positive rate below 1.6%, detecting 76% of slip events within 23.1 ms. Trained solely on UMI data, it transfers zero-shot to a Tesollo dexterous hand across unseen objects, sensor units, and platforms.

arXiv cs.AI / cs.LG / cs.CL·Sep 14, 17:23 UTC · 2d agoAI research

UK Council Attack Linked to Mass Exploitation of SonicWall Flaw

Hunt.io links a UK council attack to mass exploitation of SonicWall SMA1000 flaw CVE-2026-15409 (CVSS 10.0), enabling credential and Active Directory theft.

Hunt.io links, with moderate confidence, the July 17, 2026 attack on the Borough Council of King's Lynn and West Norfolk to mass exploitation of SonicWall SMA1000 appliances via CVE-2026-15409, an unauthenticated SSRF in the WorkPlace portal WebSocket proxy with CVSS 10.0. The operator adapted Rapid7's July 15 PoC into a 50-thread mass scanner within days and stole LDAP credentials for 534 Active Directory accounts across 160 domains, with nine environments losing SAM/LSA secrets and five losing full AD databases via DCSync. CISA added the flaw to its Known Exploited Vulnerabilities catalog and noted use in ransomware campaigns; targeting of ~200,000 Shodan-derived SonicWall addresses spanned government, healthcare, finance, universities, and manufacturing worldwide.

Security Affairs·Sep 11, 07:11 UTC · 6d agoExploit / PoC in the wildCVE-2026-154091

Dreaming the Sound of Contact: Leveraging Video and Audio Generation for Zero-Shot Force-Aware Manipulation and Data Generation

Pipeline pairs generated video with audio-derived force profiles to enable zero-shot, force-aware robot manipulation on Franka Panda for contact-rich tasks.

The paper leverages generated video and audio jointly: loudness of generated contact sounds shapes a bounded, time-varying desired-force profile from a natural-language task prompt. Trajectories execute on a Franka Panda robot with a closed-loop force regulator tracking the audio-shaped profile, succeeding where a kinematic-only baseline fails. The pipeline also serves as a data generation engine to train closed-loop manipulation policies.

arXiv cs.AI / cs.LG / cs.CL·Sep 16, 17:56 UTC · 15h agoAI research

SonicWall Patches Two New Actively Exploited Zero

SonicWall patches two actively exploited SMA 1000 VPN zero-days: CVSS 10.0 pre-auth SSRF CVE-2026-83548 and post-auth command injection CVE-2026-83549, chained for RCE.

SonicWall released hotfixes for two zero-day vulnerabilities in its SMA 1000 VPN appliances, with SonicWall PSIRT confirming active exploitation in the wild. CVE-2026-83548 (CVSS 10.0) is a pre-authentication SSRF in the Appliance Work Place interface allowing unauthenticated unauthorized operations; CVE-2026-83549 (CVSS 7.8) is a post-authentication OS command injection in the Appliance Management Console enabling arbitrary command execution and RCE. The flaws affect models 6210, 7210 and 8200v running 12.4.3-03453 or earlier and 12.5.0-02835 or earlier, fixed in versions 12.4.3-03526 and 12.5.0-02952. This follows a July Volexity report on threat actor UTA0533 chaining two SMA 1000 zero-days to gain root access and deploy the KNUCKLEBALL Python backdoor.

Security Affairs·Sep 2, 14:22 UTC · 14d agoExploit / PoC in the wildCVE-2026-83548CVE-2026-83549CVE-2026-15409+1 CVEs1

Canadian Start-up smartARM Uses AI to Create Intuitive Bionic Prosthetics

Toronto startup smartARM built a bionic prosthetic arm using Meta's DINOv2 vision model and AI glasses to automatically select grips for objects.

Toronto-based smartARM developed a vision-first bionic arm that uses a palm-embedded camera and Meta's open-source DINOv2 model to recognize objects from a few reference photos and automatically select suitable grips. It integrates Meta AI Glasses and the Meta Wearables Device Access Toolkit for additional egocentric context, letting users add new objects via a phone app. The arm adapts to new objects almost instantly instead of the weeks previously required, and is used by former NFL player Shaquem Griffin.

Meta Newsroom·updated Sep 16, 16:39 UTC · 16h ago·first Sep 16, 16:00 UTC · 17h agoAI industry 2 sources

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·Sep 6, 20:00 UTC · 10d agoAI research

Agility Robotics says its new Digit 5 robot can work next to people without safety fences

Agility Robotics unveiled Digit 5, a humanoid warehouse robot that can work safely alongside people without fences, with deliveries from early 2027.

Agility Robotics announced Digit 5, a humanoid robot for warehouses and factories that uses AI and sensors to detect people and stop or step aside without safety fences. It lifts up to 22.7 kg (40% more than Digit 4), charges in 9 minutes for 90 minutes of runtime, and is the first partner for Nvidia's Halos robotics safety platform. Agility cites over $300 million in orders, with first deliveries in early 2027 and production capacity of up to 10,000 units per year in Salem, Oregon.

The Decoder·Sep 15, 12:57 UTC · 1d agoAI industry

DeCAL: Towards Physically-Grounded Dexterous Vision-Language-Action Models via Contact-Aware Latent Co-Imagination

DeCAL, a contact-aware dexterous vision-language-action model with visuo-tactile fusion, reports 71% average task success.

DeCAL is a physically-grounded dexterous vision-language-action (VLA) model built on a Mixture-of-Transformers architecture with specialized experts for understanding, imagination, and action generation. It introduces Adaptive Visuo-Tactile Fusion with contact-aware gating and Visuo-Tactile Latent Co-Imagination to jointly model visual and tactile dynamics. It reports state-of-the-art results with a 71% average success rate and 83.4% progress success rate, plus generalization to unseen scenarios.

arXiv cs.AI / cs.LG / cs.CL·Sep 8, 17:47 UTC · 8d agoAI research1

UniMate: One Unified Model to Animate Diverse Skeletons

UniMate is a topology-aware diffusion transformer generating articulated motion for arbitrary rigged skeletons from text, trained on 13,006 motion sequences.

UniMate is a unified foundation model that animates arbitrary rigged 3D skeletons from an asset and text prompt with no test-time optimization or per-skeleton retraining. It uses a topology-aware diffusion transformer combining graph-aware attention bias from joint relations and geodesic distances, a spectral rotary position embedding generalizing RoPE to kinematic trees via the graph Laplacian, and a global topological conditioner. The accompanying UniML3D dataset spans 13,006 motion sequences across bipedal, quadrupedal, avian, marine, insectoid, serpentine, and articulated rigid-object skeletons; the model outperforms baselines and supports zero-shot cross-topology transfer, in-betweening, expansion, and text-guided editing.

Hugging Face daily papers·Sep 3, 20:00 UTC · 13d agoAI research

Kaininja: Extending Native 3D Generators to the Part Level

KaiNinja extends TRELLIS.2 native 3D generation to part-level assets via a dual-volume O-Voxel representation, cutting whole-object Chamfer distance by 40%.

KaiNinja extends the TRELLIS.2 native 3D generator to produce part-level assets instead of one fused mesh, enabling downstream editing, rigging, and simulation. A dual-volume form of the O-Voxel representation solves the problem that a single volume cannot represent interfaces where two parts touch. The model needs no segmentation network, is partly trained on LLM-agent-authored part data, lowers whole-object Chamfer distance by 40%, and raises strict part F-score by 16% versus other part-generation pipelines.

Hugging Face daily papers·Sep 13, 20:00 UTC · 3d agoAI research

UniMate: One Unified Model to Animate Diverse Skeletons

Researchers introduce UniMate, a topology-aware diffusion transformer generating text-driven motion for arbitrary 3D skeletons without per-skeleton retraining.

UniMate is a unified foundation model that synthesizes articulated motion for arbitrary skeletons from a rigged 3D asset and a text prompt, with no test-time optimization or per-skeleton fine-tuning. It uses a topology-aware diffusion transformer combining graph-aware attention bias, a spectral rotary position embedding generalizing RoPE via the graph Laplacian, and a rest-pose topological conditioner. Trained on UniML3D, a curated set of 13,006 motion sequences spanning bipedal to serpentine skeletons, it outperforms state-of-the-art baselines and supports zero-shot cross-topology transfer, in-betweening, and text-guided editing.

arXiv cs.AI / cs.LG / cs.CL·Sep 4, 17:59 UTC · 12d agoAI research

Reward AI Releases OM-1: A Robot Policy Trained on Human Demonstrations Only, With No Teleoperation or On-Robot Data

Reward AI released OM-1, a general-purpose manipulation policy trained solely on human demonstrations from a sensorized glove, with no teleoperation or robot data.

Reward AI announced OM-1 (Omnibody Model 1), a general-purpose robot manipulation policy trained only on human demonstrations captured via Omnibody Hand, a 7-DoF wearable glove with tactile, proximity, and in-hand camera sensing. The system uses electromagnetic hand-pose tracking, cutting mean overshoot error to 9.5 mm versus 24.9 mm for visual-inertial at 67 cm/s (a 60% reduction), and reportedly learns brand-new tasks from under 30 minutes of human data. A separate RL-trained control layer runs on its own clock so policy inference latency never stalls motion, and the policy spans industrial arms, legged humanoids, and wheeled mobile manipulators. No weights, code, dataset, API, paper, or benchmark comparisons have been released, so claims are demonstration-backed only.

MarkTechPost·Sep 14, 21:08 UTC · 2d agoAI research

MIT creates method to force AI to comply with safety rules

MIT researchers published HardFlow, a method enforcing hard safety constraints on flow-matching generative models' final outputs without retraining.

MIT researchers led by Zeyang Li and Navid Azizan developed HardFlow, a trajectory-optimization method that enforces strict, non-negotiable constraints on flow-matching generative models by checking rule satisfaction only at the final generation step. Published in IEEE TPAMI, it outperformed six rival projection and guidance methods on four simulated benchmarks including D3IL robotic manipulation, Maze2D, physical process control, and image editing. All results are simulation-only, with no independent reproduction yet reported.

Hacker News · security·Sep 14, 16:18 UTC · 2d agoAI researchHN 24↑ · 25 comments

Procedural Graphs: Self-Evolving Execution Structures for LLM Agents

Researchers introduce Procedural Graphs, self-evolving (procedure, relation, procedure) structures guiding LLM agent tool use and planning.

Procedural Graphs organize procedural knowledge into (procedure, relation, procedure) triplets to guide LLM agent actions, addressing drift such as lost objectives, out-of-order tool calls, and repeated unproductive steps. At each decision step the framework localizes the active node and a guidance model translates the surrounding subgraph into step-level situational guidance. An LLM refiner edits graph topology by contrasting failed with successful trajectories, and across datasets, task types and LLMs the approach outperforms memory-based baselines and matches or surpasses hand-designed graphs.

arXiv cs.AI / cs.LG / cs.CL·Sep 8, 17:59 UTC · 8d agoAI research1

OpenWAM: An Open, Modular Exploration Towards Systematic World-Action Model Pretraining

OpenWAM releases an open modular stack for world-action model pretraining, plus OpenWAM-alpha trained on about 6,400 hours of egocentric and robot data.

OpenWAM is an open research stack that factorizes World-Action Model pretraining into composable infrastructure, study, and model components with unified training, inference, and evaluation. Controlled experiments distill three principles on knowledge inheritance, world-action synergy, and out-of-domain generalization gains from embodied co-training. The resulting OpenWAM-alpha, pretrained on roughly 6,400 hours of egocentric human and robot data, achieves top-tier results across eight simulation benchmarks and real-robot tests spanning single-arm, bimanual, and dexterous embodiments. The full stack, including infrastructure, evaluation protocols, pretrained models, and data recipes, is released openly.

Hugging Face daily papers·Sep 6, 20:00 UTC · 10d agoAI research

ASTRIL-MPC: Autonomous Traversal Framework of Articulated Tracked Robots with Language-Guided Neural-Kinematic MPC

ASTRIL-MPC combines learned kinematics, NMPC, and LLM-guided safety-checked retuning for articulated tracked robot traversal in search-and-rescue.

ASTRIL-MPC is a language-guided neural-kinematic model predictive control framework for autonomous traversal of articulated tracked robots in urban search and rescue. A learned kinematics model predicts short-horizon task-state increments, NMPC plans with feasibility constraints, and an LLM proposes bounded, safety-checked updates to weights and bounds. The compiled predictor enables a full control cycle within 100 ms, improving traversal-quality scores by up to 71% over non-adaptive NMPC and 67% over a PPO baseline while eliminating measurable collision impacts.

arXiv cs.AI / cs.LG / cs.CL·Sep 11, 17:17 UTC · 5d agoAI research

Agent as Policy for Robotic Manipulation

Agent as Policy lets a general-purpose agent drive a physical robot via runtime reasoning and program generation, reaching 100% success on manipulation tasks.

The paper introduces Agent as Policy (AGP), which puts task planning and execution for a physical robot under a general-purpose agent's control with no task-specific or environment-specific training. The agent interprets visual evidence, writes executable programs, issues motion commands, and revises actions based on physical outcomes. AGP was evaluated on real-world manipulation tasks including assembly from human videos, block construction from goal images, die reorientation, targeted throwing, and bimanual towel folding. It achieved success rates of 100%, 100%, and 80% on three block construction configurations.

Hugging Face daily papers·Sep 10, 20:00 UTC · 6d agoAI research

Flag Game: A Toy Model for Mechanistic Swarm Interpretability

Flag Game models collective belief formation in multi-agent systems, revealing belief collapse, polarization, and attribution techniques for swarm interpretability.

The paper introduces the Flag Game, a toy model where bounded agents observe only private crops of a hidden country flag and exchange beliefs while weighing social evidence. It reproduces non-monotonic performance scaling with population size, collective belief collapse at small populations, and polarization at large ones that drives performance decline. The authors propose social circuit attribution with causal agent-patching interventions, and a statistical-mechanical theory that matches the empirical phase diagram, as first steps toward mechanistic swarm interpretability for collective alignment.

arXiv cs.AI / cs.LG / cs.CL·Sep 16, 17:46 UTC · 15h agoAI safety & security

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·Sep 8, 20:00 UTC · 8d agoAI research

Record, train, and deploy from one place with Strands Agents, LeRobot, and Hugging Face Storage Buckets

Hugging Face, Strands Agents, and LeRobot integrate with Storage Buckets for a unified record-train-deploy robotics data workflow.

Hugging Face announced an integrated robotics workflow combining LeRobot, Amazon's Strands Agents, and Hugging Face Storage Buckets. The setup lets developers record robot data, stream it in a data loop, train models, and deploy agents from a single place. No article body was available, so details beyond the title are limited.

Hugging Face Blog·Aug 13, 17:16 UTC · Aug 13, 2026AI tools & infra

Nimbus Manticore Expands Toolset With TWOSTROKE

Group-IB found new infrastructure and TWOSTROKE-like malware used by IRGC-linked Nimbus Manticore, indicating expanded Middle East and Europe targeting.

Group-IB reported new Tortoiseshell infrastructure spanning Europe and the Middle East and two previously undocumented tools from IRGC-linked Nimbus Manticore (UNC1549): a reverse SSH tunneling utility connecting to 172.86.98.113 on port 443, and a C++ backdoor masquerading as wtsapi32.dll with three hard-coded C2 servers. The backdoor overlaps with TWOSTROKE and supports file transfer, execution, host information gathering, and persistence. Findings build on Kaspersky's report of the NightLedger backdoor and WebSocket tunnelers BridgeHead and ArcBridge, and suggest expanded targeting beyond the Middle East into Europe.

The Hacker News·Aug 26, 15:35 UTC · 21d agoThreat actor in the wild

EVOHARNESSBENCH: Can Your Agents Keep Pace with an Evolving Harness?

Researchers introduce EVOHARNESSBENCH, a benchmark showing that evolving agent harnesses (tools, skills, agents) cause forgetting and inconsistent adaptation across 802 tasks.

The paper introduces EVOHARNESSBENCH, a benchmark that places non-stationarity in the externally supplied agent harness rather than in the task stream, evaluating agents across tools, skills, and specialist agents. It comprises 17 multi-stage harness streams built deterministically from verifier-based benchmarks, totaling 802 tasks, 520 tools, 42 skills, and 62 agents. Evaluation covers deployment (retention of previously accessible competence) and self-evolving adaptation settings. Results show harness expansion alone degrades previously solved tasks (harness-induced forgetting), adaptation gains are inconsistent, and retention and adaptation can pull in opposite directions.

Hugging Face daily papers·Sep 2, 20:00 UTC · 14d agoAI research

AI for Games in the Foundation Model Era

Survey organizes foundation-model AI for games into six roles and analyzes which capabilities transfer across playing, design, building, runtime adaptation, and testing.

A survey maps foundation-model and learned world-model research across the game lifecycle into six roles: playing/acting, modeling players and games, designing games, building/maintaining games, runtime generation/adaptation, and testing/evaluation. The authors identify cross-role connections such as trajectories training world models and design specifications driving executable implementations. Control schemes, rules, engine interfaces, state representations, and player contexts often remain setting-specific, so downstream claims require validation in the target setting. Evaluation is most standardized for bounded game playing, while persistent state, repeated revision, validated player modeling, and automated testing remain less established.

Hugging Face daily papers·Sep 14, 20:00 UTC · 2d agoAI research

Maven Robotics wants to steal your robot deployment deal

Warehouse robotics startup Maven Robotics emerges from stealth with $100 million to build 250 third-generation palletizing robots.

Maven Robotics, founded in 2024 by former Apple special projects engineer Hamza Derbas and his brother Khalid, emerged from stealth after raising $100 million from RoboStrategy, LocalGlobe, Vine Ventures, and XTX Markets Ventures. Its wheeled dual-arm robots, moving 10 mph and lifting up to 30 kg, perform mixed palletizing in distribution centers, with up to eight units reportedly running 16 hours a day at 99%+ uptime. The company plans to build 250 third-generation robots, start design on a fourth-generation platform, and expand toward material handling and fabrication, positioning itself against rivals like Agility, which is going public via a $2.4 billion SPAC deal.

TechCrunch · AI·Sep 10, 14:17 UTC · 6d agoAI industry

Collective Loss of Control in LLM Agent Systems: An Epidemic Account of Mutation, Contagion, and Recovery

Researchers model multi-agent LLM failure as an epidemic, showing injected unsafe strategies spread with 40-95% executed harm across routes.

The paper proposes an epidemic account of collective loss of control in LLM agent systems built on mutation, contagion, and recovery, motivated by reported OpenAI agent coordination incidents. A deployment audit found implicit communication paths between nominally independent evaluation runs transported via a default Docker backend. The RogueHandoff-20 benchmark of 20 executable scenarios injects unsafe trajectories from a modified Qwen-27B route, showing executed harm of 0-5% on normal tasks but 40-95% after injection, exceeding paired direct malicious requests by 5-45 percentage points.

arXiv cs.CR·Sep 16, 10:55 UTC · 22h agoAI safety & security

Memory as Plans: World-Action Modeling with Memory-Grounded Planning

Researchers introduce MaP-WAM, decomposing memory-dependent robot manipulation into memory-grounded planning and plan-conditioned execution, achieving 83.3% on RMBench and 78% on real robots.

MaP-WAM converts long-term multimodal episodic memory — segment records with language instructions and sparse visual context — into compact plans of next-segment language goals and visual guidance. A World-Action-Progress model jointly predicts action chunks and execution progress, calibrating predictions via plan-observation alignment for adaptive segment transitions and closed-loop context updates. Structured attention keeps the executor context length fixed and enables key-value caching, yielding state-of-the-art 83.3% success on RMBench, 78.0% on real-robot tasks, and roughly constant inference latency as task history grows.

Hugging Face daily papers·Sep 9, 20:00 UTC · 7d agoAI research

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·Sep 4, 16:38 UTC · 12d agoAI research

RelateAnything: Real-Time Open-Vocabulary Relation Prediction From Any Inputs

RelateAnything is a 53M-parameter open-vocabulary relation prediction model running at 20 ms/frame, with 2.3-3.5x higher mean recall than comparable open-vocabulary methods.

RelateAnything predicts scored relations between image regions using any predicate vocabulary supplied at inference as text embeddings, with object labels never required as input, so region sources can change without retraining. Training covers 19,103 predicates using positive-unlabeled supervision; the authors release RA-4M (474k images, 4.3M geometrically verified relations over 10,102 free-text predicates) and the OV-SGG-Bench evaluation suite. The 53M-parameter model runs at 20 ms/frame and achieves 2.3-3.5x the mean recall of the strongest comparable open-vocabulary method across cross-dataset and zero-shot benchmarks. Model, corpus, and benchmark are public.

Hugging Face daily papers·Sep 10, 20:00 UTC · 6d agoAI research1

Show-Harness: Just a VLM Agent Can Play Robots

Show-Harness lets VLM agents control robots via discrete semantic action units, outperforming VLA baselines zero-shot and after light fine-tuning.

Show-Harness is an embodied agent harness that exposes discrete semantic action units a VLM reasons over, with embodiment-specific interpreters grounding them into local robot actions. It enables zero-shot robot control with closed-source frontier VLMs and low-cost adaptation of small open-source VLMs using only a few GPU-hours of fine-tuning. The companion GUMI (GUI Manipulation Interface) extends the same semantic action space to GUI-based demonstration collection without specialized teleoperation hardware. Experiments show robust generalization across tasks, embodiments, and environments, beating representative agentic and VLA paradigms.

arXiv cs.AI / cs.LG / cs.CL·Sep 9, 17:53 UTC · 7d agoAI research