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Yixiang Chen

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4 papers

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AAAI Conference 2026 Conference Paper

SGMHand: Structure-Guided Modulation for Structure-Aware Hand Inpainting

Chuancheng Shi
Shiming Guo
Ke Shui
Yixiang Chen
Fei Shen

Diffusion-based generative models have demonstrated remarkable capabilities in image synthesis, yet realistic hand generation remains a persistent challenge due to complex articulations, self-occlusion, and the lack of explicit structural guidance. To address these issues, we present SGMHand, a novel structure-guided hand inpainting framework that explicitly injects topological priors to enhance structural fidelity and spatial precision. Specifically, we present a structure-guided modulation (SGM) module that synergistically combines structure spatial attention with global feature calibration, enabling fine-grained geometric control over the generative process. Then, we devise a keypoint-aware (KA) loss that enforces topological coherence by aligning attention activations with structures, thereby bridging the gap between high-level semantics and low-level geometry. By jointly optimizing over structural constraints in both representation and learning objectives, SGMHand achieves semantically consistent and geometrically plausible hand synthesis, even under severe occlusion. Extensive experiments demonstrate the effectiveness and strong generalization ability of SGMHand across various foundation models, significantly enhancing the quality and realism of human image synthesis in diverse scenarios.

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NeurIPS Conference 2025 Conference Paper

BridgeVLA: Input-Output Alignment for Efficient 3D Manipulation Learning with Vision-Language Models

Peiyan Li
Yixiang Chen
Hongtao Wu
Xiao Ma
Xiangnan Wu
Yan Huang
Liang Wang
Tao Kong

Recently, leveraging pre-trained vision-language models (VLMs) for building vision-language-action (VLA) models has emerged as a promising approach to effective robot manipulation learning. However, only few methods incorporate 3D signals into VLMs for action prediction, and they do not fully leverage the spatial structure inherent in 3D data, leading to low data efficiency. In this paper, we introduce a new paradigm for constructing 3D VLAs. Specifically, we first pre-train the VLM backbone to take 2D images as input and produce 2D heatmaps as output. Using this pre-trained VLM as the backbone, we then fine-tune the entire VLA model while maintaining alignment between inputs and outputs by: (1) projecting raw point cloud inputs into multi-view images, and (2) predicting heatmaps before generating the final action. Extensive experiments show that the resulting model, BridgeVLA, can learn 3D manipulation both efficiently and effectively. BridgeVLA outperforms state-of-the-art baselines across three simulation benchmarks. In RLBench, it improves the average success rate from 81. 4\% to 88. 2\%. In COLOSSEUM, it demonstrates significantly better performance in challenging generalization settings, boosting the average success rate from 56. 7\% to 64. 0\%. In GemBench, it surpasses all the comparing baseline methods in terms of average success rate. In real-robot experiments, BridgeVLA outperforms a state-of-the-art baseline method by 32\% on average. It generalizes robustly in multiple out-of-distribution settings, including visual disturbances and unseen instructions. Remarkably, it is able to achieve a success rate of 95. 4\% on 10+ tasks with only 3 trajectories per task, while other VLA methods such as $\pi_{0}$ fail completely. Project Website: https: //bridgevla. github. io/.

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TMLR Journal 2025 Journal Article

The Curse of CoT: On the Limitations of Chain-of-Thought in In-Context Learning

Tianshi Zheng
Yixiang Chen
Chengxi Li
Chunyang Li
Qing Zong
Haochen Shi
Baixuan Xu
Yangqiu Song

Chain-of-Thought (CoT) prompting has been widely recognized for its ability to enhance reasoning capabilities in large language models (LLMs). However, our study reveals a surprising contradiction to this prevailing perspective within the fundamental domain of pattern-based in-context learning (ICL). Through extensive experiments involving 16 state-of-the-art LLMs and nine diverse pattern-based ICL datasets, we demonstrate that CoT and its reasoning variants consistently underperform direct answering across varying model scales and benchmark complexities. To systematically investigate this unexpected phenomenon, we designed extensive experiments to validate several hypothetical explanations. Our analysis uncovers a fundamental hybrid mechanism of explicit-implicit reasoning driving CoT’s performance in pattern-based ICL: while explicit reasoning falters due to LLMs’ struggles to infer underlying patterns from demonstrations, implicit reasoning—disrupted by the increased contextual distance of CoT rationales—often compensates, delivering correct answers despite flawed rationales. This hybrid mechanism explains CoT’s relative underperformance, as noise from weak explicit inference undermines the process, even as implicit mechanisms partially salvage outcomes. Notably, even long-CoT reasoning models, which excel in abstract and symbolic reasoning, fail to fully overcome these limitations despite higher computational costs. Our findings challenge existing assumptions regarding the universal efficacy of CoT, yielding novel insights into its limitations and guiding future research toward more nuanced and effective reasoning methodologies for LLMs.

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I&C Journal 2023 Journal Article

On divergence-sensitive weak probabilistic bisimilarity

Kangli He
Hengyang Wu
Yixiang Chen

Weak probabilistic bisimilarity is a well-established notion to equate concurrent probabilistic systems that behave observably equivalent. This notion can be pivotal in the model checking of large probabilistic systems, because the considered model can be replaced by an equivalent, but possibly much smaller one prior to model checking. The conventional work has thus far considered weak probabilistic bisimilarity while ignoring the divergent behavior (interpreted as infinite internal computations). However, we argue that divergence can have a remarkable influence on the equivalence of two concurrent probabilistic systems. We thus explore divergence-sensitive refinements of weak probabilistic bisimilarity. We work in the setting of probabilistic automata, and study the consistent feasible verification method for the notion of divergence-sensitivity that discriminates presence and absence of divergence in bisimilar states. We furthermore present a novel polynomial-time algorithm to compute divergence-sensitive bisimilarity. It intertwines partition-refinement and inductive verification steps in a highly non-trivial manner.

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