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Researcher profile

Han Liu

Han Liu contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Artificial IntelligenceComputer VisionComputation and LanguageInformation RetrievalMachine LearningRobotics

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Published work

6 published item(s)

preprint2026arXiv

Any2Any 3D Diffusion Models with Knowledge Transfer: A Radiotherapy Planning Study

Voxel-wise dose prediction is a critical yet challenging task in practical radiotherapy (RT) planning, as bespoke models trained from scratch often struggle to generalize across diverse clinical settings. Meanwhile, generative models trained on billion-scale datasets from vision domains have achieved impressive performance. Herein, we propose DiffKT3D, a unified Any2Any 3D diffusion framework that leverages prior knowledge from pretrained video diffusion models for efficient and clinically meaningful dose prediction. To enable flexible conditioning across multiple clinical modalities (CT, anatomical structures, body, beam settings, etc.), we introduce an Any2Any conditional paradigm utilizing modality-specific embeddings without cross-attention overhead. Further, we design a novel reinforcement learning (RL) post-training mechanism guided by a clinically-informed Scorecard explicitly tailored to institutional treatment preferences. Compared with winner of GDP-HMM challenge, DiffKT3D sets a new state-of-the-art in dose prediction by reducing voxel-level MAE from 2.07 to 1.93. In addition, DiffKT3D achieves superior image quality and preference match. These results demonstrate that transferring diffusion priors via modality-aware conditioning and clinically aligned RL post-training can provide a robust and generalizable solution for RT planning across various clinical scenarios.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Efficient Paths and Dense Rewards: Probabilistic Flow Reasoning for Large Language Models

High-quality chain-of-thought has demonstrated strong potential for unlocking the reasoning capabilities of large language models. However, current paradigms typically treat the reasoning process as an indivisible sequence, lacking an intrinsic mechanism to quantify step-wise information gain. This granularity gap manifests in two limitations: inference inefficiency from redundant exploration without explicit guidance, and optimization difficulty due to sparse outcome supervision or costly external verifiers. In this work, we propose CoT-Flow, a framework that reconceptualizes discrete reasoning steps as a continuous probabilistic flow, quantifying the contribution of each step toward the ground-truth answer. Built on this formulation, CoT-Flow enables two complementary methodologies: flow-guided decoding, which employs a greedy flow-based decoding strategy to extract information-efficient reasoning paths, and flow-based reinforcement learning, which constructs a verifier-free dense reward function. Experiments on challenging benchmarks demonstrate that CoT-Flow achieves a superior balance between inference efficiency and reasoning performance.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

IntraStyler: Exemplar-based Style Synthesis for Cross-modality Domain Adaptation

Image-level domain alignment is the de facto approach for unsupervised domain adaptation, where unpaired image translation is used to minimize the domain gap. Prior studies mainly focus on the domain shift between the source and target domains, whereas the intra-domain variability remains under-explored. To address the latter, an effective strategy is to diversify the styles of the synthetic target domain data during image translation. However, previous methods typically require intra-domain variations to be pre-specified for style synthesis, which may be impractical. In this paper, we propose an exemplar-based style synthesis method named IntraStyler, which can capture diverse intra-domain styles without any prior knowledge. Specifically, IntraStyler uses an exemplar image to guide the style synthesis such that the output style matches the exemplar style. To extract the style-only features, we introduce a style encoder to learn styles discriminatively based on contrastive learning. We evaluate the proposed method on the largest public dataset for cross-modality domain adaptation, CrossMoDA 2023. Our experiments show the efficacy of our method in controllable style synthesis and the benefits of diverse synthetic data for downstream segmentation. Code is available at https://github.com/han-liu/IntraStyler.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Learning Semantic-Geometric Task Graph-Representations from Human Demonstrations

Learning structured task representations from human demonstrations is essential for understanding long-horizon manipulation behaviors, particularly in bimanual settings where action ordering, object involvement, and interaction geometry can vary significantly. A key challenge lies in jointly capturing the discrete semantic structure of tasks and the temporal evolution of object-centric geometric relations in a form that supports reasoning over task progression. In this work, we introduce a semantic-geometric task graph-representation that encodes object identities, inter-object relations, and their temporal geometric evolution from human demonstrations. Building on this formulation, we propose a learning framework that combines a Message Passing Neural Network (MPNN) encoder with a Transformer-based decoder, decoupling scene representation learning from action-conditioned reasoning about task progression. The encoder operates solely on temporal scene graphs to learn structured representations, while the decoder conditions on action-context to predict future action sequences, associated objects, and object motions over extended time horizons. Through extensive evaluation on human demonstration datasets, we show that semantic-geometric task graph-representations are particularly beneficial for tasks with high action and object variability, where simpler sequence-based models struggle to capture task progression. Finally, we demonstrate that task graph representations can be transferred to a physical bimanual robot and used for online action selection, highlighting their potential as reusable task abstractions for downstream decision-making in manipulation systems.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

MMGRec: Multimodal Generative Recommendation with Transformer Model

Multimodal recommendation aims to recommend user-preferred candidates based on her/his historically interacted items and associated multimodal information. Previous studies commonly employ an embed-and-retrieve paradigm: learning user and item representations in the same embedding space, then retrieving similar candidate items for a user via embedding inner product. However, this paradigm suffers from inference cost, interaction modeling, and false-negative issues. Toward this end, we propose a new MMGRec model to introduce a generative paradigm into multimodal recommendation. Specifically, we first devise a hierarchical quantization method Graph RQ-VAE to assign Rec-ID for each item from its multimodal and CF information. Consisting of a tuple of semantically meaningful tokens, Rec-ID serves as the unique identifier of each item. Afterward, we train a Transformer-based recommender to generate the Rec-IDs of user-preferred items based on historical interaction sequences. The generative paradigm is qualified since this model systematically predicts the tuple of tokens identifying the recommended item in an autoregressive manner. Moreover, a relation-aware self-attention mechanism is devised for the Transformer to handle non-sequential interaction sequences, which explores the element pairwise relation to replace absolute positional encoding. Extensive experiments evaluate MMGRec's effectiveness compared with state-of-the-art methods.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

UserLM-R1: Modeling Human Reasoning in User Language Models with Multi-Reward Reinforcement Learning

User simulators serve as the critical interactive environment for agent post-training, and an ideal user simulator generalizes across domains and proactively engages in negotiation by challenging or bargaining. However, current methods exhibit two issues. They rely on static and context-unaware profiles, necessitating extensive manual redesign for new scenarios, thus limiting generalizability. Moreover, they neglect human strategic thinking, leading to vulnerability to agent manipulation. To address these issues, we propose UserLM-R1, a novel user language model with reasoning capability. Specifically, we first construct comprehensive user profiles with both static roles and dynamic scenario-specific goals for adaptation to diverse scenarios. Then, we propose a goal-driven decision-making policy to generate high-quality rationales before producing responses, and further refine the reasoning and improve strategic capabilities with supervised fine-tuning and multi-reward reinforcement learning. Extensive experimental results demonstrate that UserLM-R1 outperforms competitive baselines, particularly on the more challenging adversarial set.

0 citations0 reviewsNo code linkedOpen access