Search: “vision-language-action”

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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

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Measuring Language Transfer in Robot Policies: Adding Greek to a Cosmos3 Vision-Language-Action Policy

Researchers added Greek to the Cosmos3 vision-language-action policy using only machine-rephrased instructions, finding bilingual training reaches roughly two fifths of English performance.

The paper studies localizing the open Cosmos3 vision-language-action robot policy to Greek without architectural changes, using machine-rephrased instructions only. Bilingual training yields a consistent 6.7-7.1 point margin over controls on a 90-task, three-seed evaluation suite, while Greek-only training gains at most 2.7 points. Several common evaluation instruments, including color-histogram metrics and single-goal benchmarks, produced false conclusions, and results were dominated by seed variation. The authors recommend building guaranteed-null baselines and replicating low-resource-language results across seeds.

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

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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

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

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

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

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

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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·Sep 7, 20:00 UTC · 9d agoAI research1

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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·Sep 4, 17:27 UTC · 12d agoAI research

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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·Sep 8, 17:59 UTC · 8d agoAI research1

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SimpleMemVLA: A Simple but Effective Native-Video Memory for Vision-Language-Action Models

SimpleMemVLA passes full timestamped video history straight to a VLA backbone, setting state of the art on four memory benchmarks.

SimpleMemVLA is a vision-language-action model for long-horizon manipulation that removes the dedicated memory module entirely. It keeps sampled history intact and feeds it to the backbone as timestamped video, with the hidden states of a generated sub-task serving as the only channel into a standard flow-matching action head. Prefilling the shared history prefix during action execution keeps latency close to a single-frame VLA. The system sets a new state of the art on four memory benchmarks and outperforms retrieval, compression and recurrent-state mechanisms, with causal interventions confirming the policy genuinely reads its history.

Hugging Face daily papers·Sep 1, 20:00 UTC · 15d agoAI research

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EmbodiedSkills: A Unified Framework for Orchestrating, Training, and Deploying VLA Agents

Researchers introduce EmbodiedSkills, a framework treating VLA skill decisions as verified execution proposals, reaching 86.2% success on RoboTwin 2.0.

The EmbodiedSkills framework treats each vision-language-action skill decision as an execution proposal, checking prerequisites before execution and verifying outcomes afterward via a shared executable-skill interface. It connects high-level skill selection, bounded low-level VLA execution and post-action verification in a single agent loop, and logs structured trajectories for supervision and optional online adaptation. Instantiated with Qwen3-VL and OpenPI/pi0.5, task-adapted policies achieve 86.20% average success across 50 RoboTwin 2.0 tasks and 97.40% across the four LIBERO suites, with 12.5% on memory-dependent RMBench tasks.

Hugging Face daily papers·Aug 31, 20:00 UTC · 16d agoAI research1

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MobileVLA-R1 2.0: RL-Enhanced Reasoning for Mobile Robot Control

MobileVLA-R1 2.0 couples chain-of-thought reasoning with RL for mobile robot control, gaining 10 points on real Unitree G1 tasks.

MobileVLA-R1 2.0 is an RL-enhanced vision-language-action framework that explicitly couples structured embodied reasoning with executable mobile robot control via supervised Chain-of-Thought alignment and reinforcement learning. A reasoning-conditioned action decoder maps multimodal reasoning representations to task-level action targets, decoupling high-level action generation from robot-specific actuation for both locomotion and manipulation. It achieves an average 1.6 point SR improvement on VLN-CE and a 10.0 point improvement in full-task success on real-world Unitree G1 mobile manipulation, with evaluations covering navigation, quadruped control, and real deployments on Unitree Go2 and G1 robots.

Hugging Face daily papers·Sep 4, 20:00 UTC · 12d agoAI research

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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·Sep 3, 20:00 UTC · 13d agoAI research1

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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·Sep 13, 20:00 UTC · 3d agoAI research1

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Same Trajectory, Contradictory Rewards (ROBORMBENCH): Paraphrase Fragility in Vision Language Reward Models

New ROBORMBENCH benchmark shows vision-language reward models can flip robot success/failure judgments when goal instructions are paraphrased.

The authors show that paraphrasing the instruction alone can substantially change progress scores from VLM reward models, even flipping identical robot trajectories between failure and success. ROBORMBENCH comprises 2,390 real-robot trajectories with ground-truth progress labels and 21,673 verified paraphrases covering lexical, syntactic, and action-goal rewrites. Instability is widespread across proprietary and open-source VLMs, grows with more divergent rewrites, and is not reliably reduced by scale or explicit reasoning, while trajectory-grounded dedicated reward models are markedly more stable.

arXiv cs.AI / cs.LG / cs.CL·Sep 4, 17:47 UTC · 12d agoAI research1

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RoboSPA: Can VLA Models Go Beyond Simple Scenes and Short-Horizon Tasks?

RoboSPA introduces a 527K-trajectory robotic benchmark with 280 task variants exposing VLA model weaknesses in spatial reasoning and long-horizon planning.

RoboSPA is a large-scale robotic manipulation dataset and benchmark for diagnosing embodied reasoning in vision-language-action models, spanning fine-grained spatial reasoning and long-horizon procedural planning. It covers 10 task categories and 56 base tasks instantiated across five difficulty levels for 280 variants, with 527K trajectories collected across multiple embodiments. Experiments on representative VLA models show current systems struggle with complex spatial relations, precise low-level execution, and memory-intensive planning.

arXiv cs.AI / cs.LG / cs.CL·Sep 4, 16:19 UTC · 12d agoAI research

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RoboSPA: Can VLA Models Go Beyond Simple Scenes and Short-Horizon Tasks?

RoboSPA introduces a 527K-trajectory benchmark with 280 task variants showing current VLA models struggle with spatial reasoning and long-horizon planning.

RoboSPA is a large-scale robotic manipulation dataset and benchmark for diagnosing embodied reasoning in vision-language-action models, covering fine-grained spatial reasoning and long-horizon procedural planning. It spans 10 task categories and 56 base tasks instantiated across five difficulty levels for 280 variants, with 527K trajectories collected across multiple embodiments and diverse scenes. Diagnostic metrics go beyond binary success rates, and experiments show current VLA models struggle with complex spatial relations, precise low-level execution, and memory-intensive planning.

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

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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·Sep 13, 20:00 UTC · 3d agoAI research

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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

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Can Edge-Deployable Vision-Language Models Identify Species?

Evaluation of 2-8B VLMs (Qwen3-VL, Gemma3) against BioCLIP on camera-trap species ID shows all models degrade sharply on field imagery.

The study tests whether edge-deployable 2-8B vision-language models carry genuine taxonomic knowledge, comparing Qwen3-VL 2B/4B/8B and Gemma3 4B against the 300M specialist BioCLIP on a 96-species task across clean iNaturalist photos and six LILA.science camera-trap collections. All models degrade 9.6-26.6 percentage points on field imagery, and BioCLIP outperforms every VLM by 33.2-59.2 points on an expanded 200-image sample, suggesting specialized data rather than scale drives the gap. Under open-set prompting, 5.9-9.6% of responses are syntactically valid but taxonomically nonexistent species names, with fabrication rankings replicating across evaluation sets.

arXiv cs.AI / cs.LG / cs.CL·Sep 10, 17:57 UTC · 6d agoAI research1

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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

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ActionSplice: In-Flight Action Editing for Interactive World Models

ActionSplice enables in-flight action editing in chunk-autoregressive video world models via a lightweight corrector, avoiding rollback or waiting for the next chunk.

ActionSplice is an inference framework that formulates in-flight action editing for chunk-autoregressive video world models as Counterfactual State Transport (CST), where a lightweight corrector transports the interrupted backbone-native representation toward the matched state induced by the revised action. The world model and sampler remain frozen, and sampling resumes without replaying completed evaluations. Across minWM-Wan Action2V and HY-WM1.5, the retargeting variant CST-R reduces rollback-relative LPIPS by 61.5% and 75.9% versus direct condition swapping, while the temporal-splicing variant CST-T reduces suffix LPIPS by 56.1% and 77.5% with 2.73x and 1.69x pixel-ready speedups over waiting.

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

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Reason Through the Latent! Making Latent Visual Reasoning Necessary

Researchers introduce CVRR, forcing multimodal models to rely on recurrent latent computation rather than accessible image tokens, validated via causal interventions and benchmarks.

The paper presents Causal Visual Recurrent Reasoning (CVRR), which makes recurrent hidden-state computation the required image-conditioned path for prediction in vision-language models. Before decoding, visual states and the original multimodal KV cache are removed so only the final recurrent state carries image information to the answer. CVRR retains strong performance on V*, MMVP, BLINK, and MME-RealWorld-Lite while comparable latent reasoners fail under the same constraint. Causal interventions show predictions remain sensitive to recurrent content and that persistent visual evidence causally revises the recurrent trajectory.

Hugging Face daily papers·Sep 5, 20:00 UTC · 11d agoAI research

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ENCP: Episode-Normalized Conformal Prediction for Vision-and-Language Navigation

ENCP calibrates conformal prediction per navigation episode, giving step-level coverage guarantees for vision-language navigation agents despite within-episode dependence.

Episode-Normalized Conformal Prediction (ENCP) rescales a nonconformity score by a VLN policy's residual confidence and calibrates one maximum score per episode, preserving step-level coverage of at least 1−α despite dependence among steps within an episode. Across four VLN policies and three nonconformity scores on R2R and REVERIE, ENCP meets all reported empirical step-coverage targets in seen-to-unseen evaluation. The model-agnostic uncertainty estimates can signal when an agent should defer to a stronger predictor or human assistance.

arXiv cs.AI / cs.LG / cs.CL·Sep 15, 17:42 UTC · 1d agoAI research

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OmniHarness: Harnessing Generalizable Visual Generation via Symbolic Policy Learning

OmniHarness learns symbolic policies for visual generation agents, reaching a 95.0% resolve rate on ComfyBench Creative tasks, 27.5 points above the strongest baseline.

OmniHarness abstracts verified executions into symbolic policies for visual generation task families, which are instantiated, adapted, and composed for new tasks while model parameters remain fixed. Intermediate verification guides refinement and failure recovery during execution, and self-directed inquiry generates practice tasks near capability limits before downstream objectives are specified. Experiments across six benchmarks, three MLLM backbones, and three visual agent frameworks show strong performance; on ComfyBench Creative tasks it achieves a 95.0% resolve rate, exceeding the strongest baseline by 27.5 percentage points. Frozen policy snapshots improve existing visual agent systems through plug-and-play reuse.

Hugging Face daily papers·Sep 12, 20:00 UTC · 4d agoAI research

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Studying Image Tokenizers as Visual Languages in Unified Multimodal Models

A controlled autoregressive testbed shows validation losses must be analyzed per task, and image tokenizer choice affects joint multimodal text modeling.

Researchers built a pure-autoregressive testbed to study image tokenizers as the 'visual language' of unified multimodal models, tracking task-specific validation losses during multimodal continual pretraining across text, image, text-to-image (T2I), and image-to-text (I2T) prediction. They found that losses exhibit distinct scaling behavior per task and rank tokenizers differently, and that I2T loss over a shared text vocabulary gives a more consistent loss–performance signal than T2I loss. Better reconstruction does not necessarily yield lower task-specific losses or stronger downstream performance, and tokenizer choice can affect text modeling under joint optimization. Case studies examine the discriminator, semantic supervision, and vocabulary size design axes.

Hugging Face daily papers·Sep 7, 20:00 UTC · 9d agoAI research1

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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·Sep 8, 17:57 UTC · 8d agoAI research1

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GE-Act 2.0: Pretraining and Scaling a World-Action Model for Robotic Manipulation

GE-Act 2.0 is a from-scratch pretrained world-action model for robotic manipulation, with success rising from 17.1% to 44.1% as co-training data scales to 30,000 hours.

Genie Envisioner Act 2.0 (GE-Act 2.0) is a world-action model whose generative and action components are all initialized from scratch on manipulation data, combining a control-oriented autoencoder (CoAE), single-step visual planner (SVP), and inverse dynamics model (IDM) trained jointly via knowledge-aligned selective optimization (KASO). Scaling co-training data from 300 to 30,000 hours raises zero-shot success from 17.1% to 44.1% on G1-OP and 13.4% to 31.1% on G2-90D, despite the latter comprising under 2% of data, suggesting cross-embodiment transfer. Gains span 19/20 and 18/20 skill groups, and skill-specific coverage correlates with zero-shot OOD success (Pearson r=0.80).

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

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Foundation Models for Generalizable Semantic and Goal-Oriented Communication

FMSGOC uses vision-language foundation model priors plus diffusion reconstruction to enable generalizable semantic communication at 0.039 bits per pixel for 6G.

FMSGOC targets generalization failures in semantic and goal-oriented communication for 6G by leveraging broad visual-linguistic foundation model priors. A vision-language model selects sparse, goal-aligned semantic anchors while a fine-tuned diffusion model performs masked completion to reconstruct images at the receiver, decoupling what to send from how to reconstruct. On CIFAR-10 it reaches 0.039 bits per pixel with cosine similarity 0.87-0.90 and 0.83-0.86 on unseen ImageNet inputs, outperforming end-to-end baselines at lower bit rates.

arXiv cs.AI / cs.LG / cs.CL·Sep 7, 18:12 UTC · 9d agoAI research

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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·Aug 27, 20:00 UTC · 20d agoAI research1

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Agentic Visual Generation: From Generative Models to Agentic Control

Researchers propose an L0-L4 control taxonomy for agentic visual generation, classifying controllers from fixed conditioning to experience-adaptive decision-making.

This paper proposes a taxonomy for agentic visual generation organized by what the controller can directly control in the generation process, rather than by planning depth, tool count, or model size. Levels range from L1 Conditioning Control through L2 Execution Control, L3 Outcome-Adaptive Control, and L4 Experience-Adaptive Control, with L0 Fixed Support denoting systems without a deployed decision-making controller. The framework is applied across image, video, editing, 3D, world, slide, and user-interface generation to map how controller capabilities and mechanisms have evolved across the field.

Hugging Face daily papers·Sep 5, 20:00 UTC · 11d agoAI research

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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·Sep 10, 17:43 UTC · 6d agoAI research

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VoT: Vision-of-Thought for Unified Multimodal Representation Alignment

Researchers propose Vision-of-Thought (VoT), a discrete visual-planning token layer between VLMs and diffusion transformers improving text-to-image semantic alignment.

VoT introduces a discrete visual-thinking layer between vision-language models and diffusion transformers, letting the VLM act as a multimodal planner that emits tokens describing objects and layouts before pixel generation. A specialized VoT tokenizer is trained with VLM alignment, feature reconstruction, and vector-quantization losses. Experiments show improved semantic alignment and a structured, interpretable interface for controllable generation.

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

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