Large-scale vision, language, and video pretraining has substantially improved the perception and generalization capabilities of robotic manipulation models, but action generation for complex dexterous manipulation remains constrained by the scarcity of robot demonstrations. Limited robot demonstrations must be used to learn both action representations and task-conditioned action distributions, making it difficult to adequately learn action representations and, in turn, limiting the ability of downstream generative models to generate complex dexterous actions. To address this limitation, we propose ARPG (Action Representation Pretraining and Generation), which explicitly separates action representation learning from action generation: we first pretrain an action representation encoder on large-scale human manipulation data. We then freeze the encoder and train a compact generative backbone (130M parameters) to learn task-conditioned action distributions in the encoder's representation space. Experimental results show that ARPG achieves state-of-the-art performance on the DexJoCo benchmark and improves the average success rate on bimanual dexterous manipulation from 10.1% for $π_{0.5}$ to 44.7%. Scaling experiments further show that broader coverage of human manipulation behaviors effectively improves downstream performance. These results indicate that action representation pretraining provides an independent scaling dimension for improving complex dexterous manipulation capabilities.
Qifan Liang, Yuansen Liu, Ruixin Wei national university of singaore, National University of Singapore, Nan Lu, Junchuan Zhao, Ye Wang
While controllable Text-to-Speech (TTS) has achieved notable progress, most existing methods remain limited to inter-utterance-level control, making fine-grained intra-utterance expression challenging due to their reliance on non-public datasets or complex multi-stage training. In this paper, we propose TED-TTS, a training-free controllable framework for pretrained zero-shot TTS to enable intra-utterance emotion and duration expression. Specifically, we propose a segment-aware emotion conditioning strategy that combines causal masking with monotonic stream alignment filtering to isolate emotion conditioning and schedule mask transitions, enabling smooth intra-utterance emotion shifts while preserving global semantic coherence. Based on this, we further propose a segment-aware duration steering strategy to combine local duration embedding steering with global EOS logit modulation, allowing local duration adjustment while ensuring globally consistent termination. To eliminate the need for segment-level manual prompt engineering, we construct a 30,000-sample multi-emotion and duration-annotated text dataset to enable LLM-based automatic prompt construction. Extensive experiments demonstrate that our training-free method not only achieves state-of-the-art intra-utterance consistency in multi-emotion and duration control, but also maintains baseline-level speech quality of the underlying TTS model. Audio samples are available at https://aclanonymous111.github.io/TED-TTS-DemoPage/.
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cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表
Existing datasets for evaluating text-to-image generation focus mostly on real-life images, which poses challenges for assessing academicfigure generation given real scientific captions, which is a hot topic in AI for Science. To fill the gap, we propose HE4AFG, a novel datasetwhich first provides a Holistic Evaluation for Academic caption-to-Figure Generation (AFG). Specifically, HE4AFG collects real figure captions from 8 scientific domains and finally generates 3,900 evaluation samples (particularly, including multi-panel figures) using 5 mainstream large multimodal models (LMMs). For each sample, we provide high-quality human ratings in terms of three aspects—scientific aesthetic (SA), topic relevance (TR), and attribute correctness (AC). Moreover, we present two trainable models: (1) HE4AFG-E, an automated Evaluation model for AFG, which generates aspect-aware training examples and then use them to train three aspect-specific evaluation modules via contrastive learning; (2) HE4AFG-R, an automated Refinement model, which generates and utilizes feedback on the quality of the figures (e.g., unfaithful elements) to continuously improve AFG. Extensive experiments on HE4AFG demonstrate the effectiveness and performance advantages of our models.
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cs.CL · cs.CV
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表