The paper introduces Time‑Frequency Geometric Cross‑Attention (TFGCA), a module that enhances vision‑language‑action models by decomposing action chunks into time‑frequency tokens using a learnable wavelet transform. TFGCA fuses dot‑product similarity with wedge‑product magnitude to better capture both frequency‑based smooth trends and cross‑phase orthogonal motion structures. When added to a pretrained VLA model, it yields significant performance gains across in‑distribution and out‑of‑distribution benchmarks, including a 28.5‑point improvement under RoboTwin domain randomization and an 11.67‑point increase on real‑robot AgiBot A2 tasks.
arXiv:2607. 09818v1 Announce Type: cross Abstract: Vision-language-action (VLA) models aim to understand natural-language instructions and visual observations, and to generate and execute corresponding actions as embodied agents.
arXiv:2610.09940v1 Announce Type: cross Abstract: Joint-embedding predictive architectures (JEPAs) predict masked or future observations in representation space, offering a natural source of predicti...
The paper introduces Concertina, an Embodied Vision‑Language Model (eVLM) that integrates manipulation and cognition within a single diffusion VLM backbone, eliminating the need for separate action‑generation modules. By encoding continuous trajectory representations as latent action tokens and reconstructing them with a corrupt‑and‑reconstruct operator, the model achieves strong performance on LIBERO, LIBERO‑Plus, and CALVIN ABC‑D benchmarks. The authors demonstrate that cross‑time trajectory coupling naturally emerges in the shared backbone’s middle and late layers, as confirmed by their Action Interface Audit.
arXiv:2609.37165v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models remain brittle under visual distribution shifts, often relying on spurious correlations tied to domain-specific f...
arXiv:2605.29858v3 Announce Type: replace Abstract: Temporal action localization (TAL) requires recognizing the target event and localizing its start and end times precisely in untrimmed videos. Rece...
UniWALM is a unified world-action-language model that jointly learns video, action, and language in a single shared representation, using a mixture-of-transformers backbone with diffusion heads that co-denoise across modalities. By generating language as part of the same pass as action, the model closes a language‑as‑memory loop without an external planner, and introduces Flow Matching with Correction to maintain reliable action streams. Experiments show UniWALM outperforms baselines on compositional pick‑and‑place tasks and memory‑dependent benchmarks, and dramatically improves success on a real‑world Find‑the‑Item task when using generated textual memory.
LoopVLA introduces a recurrent Vision‑Language‑Action architecture that learns to refine multimodal representations, predict actions, and estimate when further refinement is unnecessary. By iteratively applying a shared Transformer block and producing a sufficiency score at each step, it decouples refinement from fixed layer indices and aligns confidence scores with action quality through a self‑supervised objective. Experiments on LIBERO, LIBERO‑Plus, and VLA‑Arena demonstrate that LoopVLA reduces model parameters by 45% and boosts inference throughput up to 1.7× while matching or surpassing strong baselines in task success.
The paper argues that diffusion-based action policies can use a frozen, observation‑free backbone as a reusable trajectory prior, with task adaptation handled entirely by the conditioning pathway. By pretraining a general action head on forward‑kinematics data and then freezing it, the authors show that a single backbone can match or outperform normally trained models on MimicGen and LIBERO. Their experiments reveal that a small 5 M‑parameter MLP backbone can rival large U‑Net and transformer backbones, indicating that action backbones are often over‑parameterized and that image‑style architectures may not be the best fit for low‑dimensional action generation.
MT‑WAM enhances the Fast‑WAM framework by adding complementary supervision for future 2‑D point trajectories and visual features while keeping the original training objectives. A lightweight dual‑stream branch and structured attention mask isolate motion‑specific processing, and motion‑stream tokens provide additional dynamics cues to the action expert. During inference, MT‑WAM skips future‑video prediction, using cached video and motion information to achieve higher success rates on LIBERO, LIBERO‑Plus, RoboTwin 2.0 Clean2Rand, and several real‑world tasks.
VETO (Video Efficient Token Optimization for Vision Language Models) is a plug‑in that reduces the quadratic cost of visual tokens in long‑video inference by applying dual‑axis compression: an intra‑frame compressor merges semantically similar tokens within each frame, and an inter‑frame compressor merges temporally redundant frames. By first compressing spatial dimensions, VETO lowers the cost of subsequent global temporal matching, surpassing single‑axis methods and achieving up to 45% faster inference on models such as LLaVA‑OneVision‑7B while maintaining or improving accuracy. The approach is universally applicable across LLaVA‑OneVision, InternVL‑2.5, and LongVA, preserving or enhancing zero‑shot accuracy even under extreme token budgets.
arXiv:2605. 21854v2 Announce Type: replace-cross Abstract: Vision-Language-Action (VLA) models have rapidly converged on a small set of architectural patterns: discrete-token autoregression (e.
The paper introduces CF‑Prune, a training‑free method that prunes future‑frame tokens in pretrained World Action Models (WAMs) to reduce inference latency. By using instruction and action attention during the initial denoising pass, the framework identifies task‑relevant tokens, while an entropy‑based phase identification keeps dense computation in precision phases to avoid performance loss. Experiments on RoboLab, RoboCasa, and real‑world manipulation tasks show consistent speedups—up to 1.33× at 50% pruning—while maintaining task success rates within 1 percentage point of the baseline.