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19 stories in the last 7d

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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 · 16h agoAI research

Google Research Introduces Retrieve-for-Train (R4T): An RL-Compiled Diffusion Retriever for 12× to 20× Faster Query Fan-Out

Google Research introduced R4T, an RL-trained fan-out pipeline distilled into a 53.9M-parameter diffusion retriever achieving 12x-20x faster query fan-out.

Google Research introduced Retrieve-for-Train (R4T), which trains a fan-out language model with GRPO plus soft PPO regularization, then distills query fan-out into a 53.9M-parameter diffusion transformer that generates all retrieval embeddings in a single non-autoregressive pass. A three-term reward (groundedness 0.6, diversity 0.2 via Vendi Score, alignment 0.2) prevents paraphrastic collapse and reward hacking during training. On the Polyvore dataset, Gemma3-4B R4T-FOLM averaged 49.1 versus 40.9 for Best-of-N, and the diffusion retriever cut fan-out latency from 1.46s to 0.07s at batch size 8, a consistent 12x-20x speedup over autoregressive methods.

MarkTechPost · 4h agoAI research

Right Tool, Right Job: Native-Language Evaluation, Tokenizer Sensitivity, and Methodological Findings from a French-Only BabyLM

French BabyLM entry METRON-FR (125M GPT-2, 92.47M words) shows tokenizer artifacts dominate child-scale zero-shot evaluation; proposes standard diagnostics.

METRON-FR is a 125M-parameter GPT-2 pretrained on 92.47M French words, submitted to the BabyLM 2026 Strict track, scoring 85.97% on the native Quebec-French QFrBLiMP benchmark and 62.80% on the BabyLM-weighted leaderboard. A cross-lingual GLUE protocol combining French task-data translation with rank-16 LoRA shows relational tasks gain while world-knowledge tasks regress. Bilingual Lexicon Induction reaches p@1 of 68.84%, 18x above chance, and ablations show single-token zero-shot scoring is dominated by tokenizer and template artifacts at child scale.

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

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 · 2d agoAI research

Due to concerns about malicious applications, GPT2 will not be released (2019)

OpenAI's landmark 2019 GPT-2 post withheld the full 1.5B-parameter model over misuse concerns, releasing only a smaller variant and paper.

OpenAI announced GPT-2, a 1.5-billion-parameter transformer language model trained on 8 million web pages (40GB of text), achieving state-of-the-art zero-shot results including 70.70% on Winograd Schema and 63.24% on LAMBADA. Citing concerns about malicious applications such as scalable synthetic disinformation, OpenAI declined to release the trained model and instead published a smaller model and a technical paper as a 'responsible disclosure' experiment. The post, resurfaced on Hacker News in 2026, also documents failure modes like repetition and world-modeling errors, and discusses policy implications of controllable text generation.

Hacker News · AI · 3d agoAI safety & securityHN 44↑ · 19 comments

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

Evaluating Time-Series Foundation Models and Multimodal Dietary Context for CGM Forecasting

Study finds zero-shot time-series foundation models underperform on CGM forecasting; fine-tuned Chronos-Bolt cuts RMSE up to 18.4% and dietary context adds signal.

The paper evaluates time-series foundation models for continuous glucose monitoring forecasting across eight public datasets covering Type 1 diabetes, Type 2 diabetes, and non-diabetes populations. Under a unified protocol, zero-shot foundation models did not consistently outperform baselines like Elastic Net and PatchTST, but lightweight fine-tuning did, with fine-tuned Chronos-Bolt reducing RMSE by 6.5%-18.4% in the T1D cohort and 8.6%-18.2% in the non-diabetes/T2D cohort. A residual-based fusion framework adding dietary context from CGMacros reduced overall RMSE by about 3% and postprandial RMSE by about 15% versus CGM-only baselines.

arXiv cs.AI / cs.LG / cs.CL · 6d agoAI research

Bridging the Confidence Gap: Temperature Scaling for Calibrating Test-Time Prompt Tuning

CoTS temperature scaling cuts test-time prompt tuning's expected calibration error from 11.90% to 5.38% on ImageNet variants while raising accuracy.

The paper proposes CoTS, a post-hoc calibration method that applies temperature scaling to minimize the confidence gap between test-time-adapted and zero-shot predictions. A weak-strong ensemble variant, E-CoTS, further exploits multiple test-time augmentations to boost accuracy while maintaining calibration. E-CoTS reduces average expected calibration error from 11.90% to 5.38% on ImageNet variants while increasing accuracy from 60.74% to 62.95%.

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

Robot Visions: Breaking reCAPTCHA at Zero Cost and Zero Shot

Researchers defeat Google reCAPTCHA using free local models CLIP and OWLv2, achieving 92.6% per-session success at zero cost.

The paper taxonomizes Google reCAPTCHA challenges into Type A (independent tiles) and Type B (4x4 grid) and builds zero-shot, training-free solvers from open-source local models. CLIP solves 58% of Type A challenges and OWLv2 43.5% of Type B, while an end-to-end automated solver succeeds on 92.6% of 500 real-world sessions. The authors also show a non-technical adversary can solve challenges using natural-language instructions to a commodity AI assistant, collapsing the attacker skill floor and suggesting visual challenge CAPTCHAs have reached the end of their useful life.

arXiv cs.CR · 22h agoAI safety & security

Enhancing Accessibility of Medical Texts through Large Language Model-Driven Plain Language Adaptation

Study shows LLMs with Mixture-of-Agents and QLoRA finetuning effectively simplify medical texts into plain language while preserving content.

The paper evaluates Plain Language Adaptation (PLA) using GPT-4o-mini, Gemini-1.5-pro, and LLaMA in zero-shot and few-shot settings. It compares prompting strategies, QLoRA finetuning across models, and integrates Mixture-of-Agents (MoA) techniques for robustness. Results demonstrate LLM-driven PLA makes healthcare texts more comprehensible while retaining essential content.

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

[AINews] AEF-1 standard emerges for Third Party Evaluators, as Xai, OpenAI, and Anthropic all cosign

xAI, OpenAI, and Anthropic cosign the AEF-1 third-party evaluation standard while Dario Amodei proposes embedded evaluators for safety verification.

The AI Evaluator Forum published AEF-1, a baseline standard for independent third-party AI evaluations covering access, conflicts of interest, funding relationships, recusal, and transparency, cosigned by xAI, OpenAI, and Anthropic. Dario Amodei wrote a rare personal blogpost proposing embedded evaluators such as METR with desks, badges, company laptops, and internal-risk-team-level access to verify safety commitments, plus democratic and global coordination frameworks. The roundup also covers the pacing debate: Bilal Chughtai left Google DeepMind arguing progress may outrun alignment, while critics including Aidan Gomez and Cohere push back against slowdowns and lab gatekeeping. Additional items include Cline Desktop's launch with open-weight model support.

Latent Space · 2d agoAI safety & security

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

The Router Within: Eliciting Native Skill Routing from a Frozen LLM

Gavel reads skill-routing signals from a frozen LLM's forward passes with two linear maps, beating retrieval pipelines by up to 21.9 points.

Gavel (Glance And Verdict) shows a frozen agent LLM already contains skill-routing signals in its forward passes, read out via two trained linear maps without loading skill text into context. A glance step scores the full library using mid-layer states and per-skill banks built in one forward pass; a verdict step fuses the model's own likelihood and yes/no judgment as a product of experts. Trained once, it transfers zero-shot to three public benchmarks and SkillTraj (372 simulated agent trajectories); on Qwen3-32B it beats progressive disclosure and retrieve-and-rerank pipelines adding 1.2B-16B external parameters by up to 13.4 points (21.9 mid-rollout).

arXiv cs.AI / cs.LG / cs.CL · 2d agoAI research 2 sources1

Google's new AI model predicts the future from sales data, weather, and discount schedules

Google Research released TimesFM-3, a 330M-parameter multivariate time series forecasting model that tops Gift-Eval, FEV-Bench, and Time benchmarks and is on Hugging Face.

Google Research released TimesFM-3, a 330-million-parameter Transformer-based time series forecasting model trained on more than one trillion real and synthetic data points. It works zero-shot and adds multivariate support, ingesting related series, historical-only covariates, and known future events such as discount schedules and weather forecasts, while filling all future time steps in a single one-shot pass. Google reports first place among pretrained forecasting models on Gift-Eval, FEV-Bench, and Time, ahead of Amazon's Chronos-2, the Toto-2.0 family, and its own TimesFM-2.5. Weights are available on GitHub and Hugging Face, with BigQuery integration planned in the coming weeks.

The Decoder · 5d agoModel release2

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

Dynin-Robotics unifies action, goal, and dynamics prediction in one omnimodal masked-diffusion VLA model, reaching 78.4% success on Franka Research 3 manipulation tasks.

Built on the Dynin-Omni masked-diffusion backbone, the model represents language, observations, goals, and actions as discrete tokens and is continually pretrained on roughly 1.33 million trajectories from 48 Open X-Embodiment datasets. The shared trajectory interface enables test-time scaling via goal prediction, action-candidate evaluation, and joint action/future-state refinement. It achieves competitive results on LIBERO and zero-shot LIBERO-Plus, 78.4% average success across four Franka Research 3 conditions, and up to 29.2x faster model-side action decoding from a block-parallel implementation.

Hugging Face daily papersupdated · 5d agofirst · 6d agoAI research 2 sources1

StepAudio 3 Gen Technical Report

StepAudio 3 Gen unifies TTS, voice design, music, and sound effects via discrete autoregressive modeling over RVQ tokens.

StepAudio 3 Gen is a general-purpose audio generation model covering zero-shot TTS, voice design, vocal generation, sound effects, music, vibe speech, and mixed audio in one framework. It uses discrete autoregressive modeling over residual vector quantization (RVQ) tokens rather than the diffusion Transformer paradigm, with a StepAudio Tokenizer representing audio at 12.5 Hz in a shared 16x2048 residual code space. Key design principles include interference-aware progressive pretraining, an RVQ Adaptor for multi-codebook acoustic representations, and shared discrete autoregressive modeling. The model reports state-of-the-art performance on TTS and voice design while retaining strong generation across speech, vocals, sound effects, and music.

Hugging Face daily papers · 6d 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 · 6d agoAI research1

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

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

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

arXiv cs.AI / cs.LG / cs.CL · 6d agoAI research2

Augustinian BabyLM: What Ostensive Definition Can and Cannot Teach a Small Language Model

Study shows visually grounded token embeddings in a small masked LM persist through training and improve object-property knowledge, but escape standard BabyLM benchmarks.

The paper implements ostensive definition for a small DeBERTa masked language model trained on 10M words, seeding visually grounded tokens with embeddings derived from labeled image regions before training. Visual initialization leaves a persistent, seed-replicated advantage on object-property knowledge (COMPS) and a corpus-tailored Visual-Property Swap benchmark covering color, material, size, and shape, but has no effect on most BabyLM grammar benchmarks. Synthetic grounding of previously unseeded words causally transfers the advantage to exactly those words.

arXiv cs.AI / cs.LG / cs.CL · 6d agoAI research