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

Researchers present TANGO, a whole-body vision-language-action model enabling humanoid robots to traverse cluttered spaces from language instructions.

TANGO predicts 29-DoF joint-space actions from egocentric RGB observations and natural-language instructions for whole-body humanoid navigation, going beyond 2D path planning. It is trained entirely in simulation using global path planning, kinematic whole-body motion generation, obstacle-aware motion editing, and RL-based tracking. The model reports state-of-the-art simulation performance and was deployed zero-shot on a Unitree G1 humanoid without any real-world navigation training data.

arXiv cs.AI / cs.LG / cs.CL · 8d agoAI research1

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

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

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

Hugging Face daily papers · 9d agoAI research1

Anthropic merges Claude chat and Cowork in one interface

Anthropic unified Claude chat, Cowork, and Artifacts into one auto-routing interface, adding Docs and Slides creation with PDF and PowerPoint export.

Anthropic merged the Claude chat and Cowork front-ends so one window routes requests automatically, ending customer confusion over which tab to use. The update adds Claude Docs and Slides with PDF and PowerPoint export, collaborative sections, and comments, and makes Claude Design, launched in April, available anywhere in Claude. It rolls out to Pro and Max plans on web, desktop, and mobile over coming weeks, with Free and Team tiers later, following a recent Cowork memory upgrade.

TechCrunch · AIupdated · 3h agofirst · 5h agoAI industry 5 sources

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

HarnessVLN: Unifying Training-Free Embodied Navigation through an Agent Harness

Researchers introduce HarnessVLN, a zero-shot training-free agent harness that sets new training-free SOTA on vision-language navigation benchmarks including R2R and HM3D.

HarnessVLN is a zero-shot, training-free framework for embodied vision-language navigation that coordinates perception, retrieval, grounding, navigation, recovery, and termination through a unified tool interface. It validates planner proposals against spatial evidence, geometric feasibility, and subgoal consistency, using hierarchical event memory and a persistent Spatiotemporal Graph that stores reusable spatial evidence and failure annotations. It reports success rates of 60.8% on R2R, 53.9% on RxR, 76.0% on HM3D-v2, and 59.3% on HM3D-OVON, surpassing prior training-free state of the art, with real-world humanoid deployment demonstrated.

Hugging Face daily papers · 3d agoAI research

Wire It, Run It, Deploy It: AI Workflows in Gradio

Hugging Face published a guide on wiring, running, and deploying AI workflows in the Gradio framework.

Hugging Face's blog post 'Wire It, Run It, Deploy It: AI Workflows in Gradio' is a tutorial on building AI workflows with Gradio. It covers wiring components, running applications, and deploying AI-powered apps. No security incident or vulnerability content is included.

Hugging Face Blog · 22d agoAI tools & infra

CMA-OT: Hierarchical Expert Supervision for Dance-to-Music Generation

CMA-OT aligns a music generator's latent features with hierarchical expert representations via curriculum learning and scale-aware optimal transport, improving dance-to-music quality.

CMA-OT introduces curriculum-guided multi-scale representation alignment with scale-aware optimal transport for dance-to-music generation. An external music expert provides hierarchical supervision over the generator's latent features, progressively transferring musical knowledge for stable representation learning. The optimal transport mechanism handles temporal mismatch and semantic variation across expert scales. Experiments on two datasets show state-of-the-art rhythmic synchronization, perceptual quality, and overall music generation.

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

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

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 · 7d agoAI research

The Evolution of the Agent Harness

Latent Space essay argues late-2025 agent gains came from models and harnesses maturing together, with harness logic absorbed into model weights.

The piece defines the agent harness as everything beyond model weights—tools, context, memory, guardrails—and charts its evolution from ReAct prompting (October 2022) through AutoGPT's premature autonomy, Cursor/Copilot's human-in-the-loop retreat, and Devin's roughly 15% success rate, to o1's capability overhang and Claude Code's February 2025 terminal agent with permission rules. It argues the Christmas 2025 jump cited by Transformer co-inventor Lukasz Kaiser reflected model and harness curves crossing, and that remaining harnesses will serve human attention rather than the model.

Latent Space · 25d agoAI tools & infra

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

Structural Jailbreaks Generalize but Do Not Compound: A cross-provider and multilingual study of Involuntary In-Context Learning

Researchers show IICL structural jailbreaks generalize to Google Gemini, lifting attack success to 80-100% on harm and financial benchmarks; non-English prompts attenuate it.

The study red-teams two Google Gemini models with Involuntary In-Context Learning (IICL), a structural jailbreak reframing harmful requests as the final cell of a data-labeling task. IICL lifts attack success from at most 6.7% to 80-90% on HarmBench and 97-100% on financial abuse (FinProof), an order of magnitude above prior results on OpenAI's GPT-5.4. Against a compounding hypothesis, forcing IICL output into Spanish, Hindi, or Arabic attenuates the attack in 11 of 12 conditions, attributed to a 'relevance curse' producing lower-quality harmful content in lower-resource languages. Findings replicate under an independent non-Google judge (Cohen's kappa 0.86 over 377 paired verdicts).

arXiv cs.CR · 8d agoAI safety & security

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

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

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

Hugging Face daily papers · 20d agoAI research1

Rosetta at AlexandriaX-2026: LoRA-Adapted NileChat for Context-Aware Dialectal Arabic Dialogue Translation

Rosetta ranks 4th and 5th in AlexandriaX-2026 dialectal Arabic dialogue translation using a LoRA adapter on NileChat-3B, finding limited pretraining benefit.

The Rosetta system for the AlexandriaX-2026 shared task fine-tunes a LoRA adapter on NileChat-3B for context-aware English-to-dialectal Arabic dialogue translation. The adapter was additionally pretrained on MADAR and PADIC dialect corpora for the unconstrained track. It achieved spBLEU 26.10 (4th, constrained) and 25.09 (5th, unconstrained). External dialect pretraining improved only two of thirteen dialects while slightly degrading overall performance, indicating negative transfer.

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

Puppeteer: Object-Grounded Posture-Aware Co-Speech Gesture Generation

Researchers present Puppeteer, a posture-aware, object-grounded diffusion model generating physically consistent co-speech gestures with temporal control.

Puppeteer decomposes long gestures into structured primitives encoded by a causal variational autoencoder into temporally ordered latent tokens. Conditional diffusion in the causal latent space conditions on speech signals, motion history, an initial posture reference, and object geometry to synthesize physically consistent gestures. The authors also introduce new evaluation metrics and release SceneGes, the first curated synthetic 3D dataset of embodied co-speech gestures with corresponding 3D objects.

Hugging Face daily papers · 17d agoAI research1

Training a coding model to paint watercolours with TRL and OpenEnv

Hugging Face tutorial trains a coding model with TRL and OpenEnv to paint watercolours through generated code.

A Hugging Face blog walkthrough uses the TRL reinforcement learning library and the OpenEnv environment framework to train a coding model. The target task is generating code that produces watercolour-style drawings, serving as a hands-on reinforcement learning training example. No article body was available in the feed, so specifics are limited to the title.

Hugging Face Blog · 13d agoAI tools & infra1

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

Rebuilding AUTOMATIC1111 with Gradio Workflow

Hugging Face demonstrates rebuilding the AUTOMATIC1111 Stable Diffusion web interface using its Gradio Workflow framework.

Hugging Face published a post showing how to rebuild the AUTOMATIC1111 Stable Diffusion WebUI experience with the Gradio Workflow framework. The article body was unavailable, so details beyond the title are limited, but the piece appears to be a tutorial on composing interactive AI interfaces with Gradio Workflow components.

Hugging Face Blog · 6d agoAI tools & infra

You Can't Prefer Emotions You Don't Sample: Intensity Undershoot in DPO-Tuned LLMs

Study quantifies DPO-tuned LLMs undershooting requested emotional intensity, tracing the gap to candidate-pool extremity rather than conditioning format.

Conditioning an instruction-tuned LLM on continuous valence-arousal targets yields gain of only 0.26 for valence and 0.13 for arousal on Llama-3.1-8B, far below faithful control of 1.0. The authors attribute undershoot to neutral-heavy preference corpora like EmoBank and candidate pools lacking extreme affect, leaving DPO without extreme exemplars. Uniform target coverage with a hotter candidate pool raises valence gain to 0.40 on Llama-3.1-8B and 0.44 on Qwen3-8B, with modest in-distribution cost; arousal gains remain unstable across seeds.

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

Inoculation Midtraining with Learned Neologisms

Inoculation Midtraining confines unsafe LLM behavior to a neologism-marked context, reducing misalignment after unsafe post-training but leaking under nearby contextual cues.

The paper introduces Inoculation Midtraining, which teaches a base model during midtraining that unsafe behavior belongs to a context marked by a learned neologism token, then post-trains on unsafe data within that context. Across supervised fine-tuning and RL post-training regimes, the technique reduces misalignment while preserving transfer of benign properties like German or Shakespearean prose. However, it does not outperform standard Inoculation Prompting, is sensitive to training configuration, and produces a leaky boundary that nearby contextual cues can reactivate. The authors conclude it is not yet a load-bearing component of a developer safety framework.

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

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

Stellar Colosseum: A Many-Agent Harness for Long-Horizon Research in Mathematics and Theoretical Computer Science

Stellar Colosseum, a many-agent harness for long-horizon math and TCS research, solves open problems and reaches 71% on TCS-Bench with Gemini models.

Stellar Colosseum is a model-agnostic harness that allocates inference across long-horizon research in mathematics and theoretical computer science, using strategy exploration, a readiness gate, section-level decomposition, and verifier feedback routing. Integrated into Google Antigravity's Teamwork framework as the Long Proof pattern, it obtains new results on open problems from FOCS and JMLR papers using Gemini 3.1 Pro. On TCS-Bench it achieves 71.0% accuracy with Gemini 3.1 Pro and Gemini 3.7 Flash, and a Codeforces evaluation with Gemini 3.1 Pro solves 218 of 222 problems.

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

Update modules/auxiliary/scanner/http/elasticsearch_tika_xfa_xxe.rb

Rapid7 updated a Metasploit auxiliary scanner module that detects XML external entity injection in Elasticsearch via Apache Tika.

A commit in the Metasploit Framework updated modules/auxiliary/scanner/http/elasticsearch_tika_xfa_xxe.rb, an auxiliary scanner module. The module targets XML external entity (XXE) injection in Elasticsearch through Apache Tika, and was co-authored by jheysel-r7. The terse commit message contains no additional details, CVE references, or exploitation notes.

Metasploit Framework commits · 8d agoTools

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 · 6d agoAI research1

JarvisGUI: Towards Cross-Device GUI Agents with Dynamic Task Composition

JarvisGUI benchmark tests GUI agents on cross-device workflows across Android, Windows, and Ubuntu, revealing major gaps in state transfer and long-horizon reasoning.

JarvisGUI is a dynamic benchmark that formulates GUI tasks as input-output transformations under a lightweight type system, automatically composing multi-step cross-device workflows across Android, Windows, and Ubuntu virtual environments. Evaluation shows state-of-the-art open-source GUI agents struggle with state-transfer awareness, cross-platform contextual reasoning, and long-horizon dependency management, exposing a capability gap invisible to existing single-device benchmarks.

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

PhysBrain 1.5: From Vision-Language Models to Physical Foundation Models

PhysBrain 1.5, an 8B physical foundation model, sets open-source state of the art across 28 embodied understanding benchmarks.

The paper presents PhysBrain 1.5, a unified 8B model for understanding physical environments, generating actions, and predicting future states, built from a vision-language model with joint autoregressive next-token prediction over language, end-effector motion, and dense visual targets. Pre-training uses embodied supervision from human interaction videos, followed by supervised fine-tuning on human demonstrations, robot trajectories, and simulated experience. The model averages 72.5 across 28 embodied benchmarks, setting a new open-source state of the art and performing on par with proprietary GPT-6-Astra and Gemini 3.6 Flash, with best open-source results on 14 benchmarks.

Hugging Face daily papers · 3d agoAI research1

Fine-tuning a 350M Model for Better Structured Outputs in 100 GRPO Steps

Hugging Face guide fine-tunes a 350M-parameter model with 100 GRPO steps to improve structured output reliability.

A Hugging Face blog post demonstrates fine-tuning a 350M-parameter model using GRPO (Group Relative Policy Optimization) with TRL over 100 training steps. The stated goal is more reliable structured outputs from small language models. No article body was available, so details beyond the title are limited.

Hugging Face Blog · 13d agoAI tools & infra

Diffusion TV: Experiencing Diffusion Models through Tangible, Embodied Interaction

Researchers present Diffusion TV, a CRT-based installation where antenna manipulation lets audiences physically experience diffusion model denoising.

Diffusion TV is an interactive installation built around a modified CRT television where turning the antenna controls the clarity of AI-generated images and sounds, mirroring the denoising process of diffusion models. Three channels present AI-generated animals from the past, present, and future within a temporal and ecological narrative. The authors frame the work as an embodied, non-verbal alternative to explainable AI that highlights intermediate generative states rather than final outputs.

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