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Enshen Zhou

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6 papers

2 author rows

AAAI Conference 2026 Conference Paper

Prune4Web: DOM Tree Pruning Programming for Web Agent

Jiayuan Zhang
Kaiquan Chen
Zhihao Lu
Enshen Zhou
Qian Yu
Jing Zhang

Web automation uses intelligent agents to perform high-level tasks by mimicking human interactions with webpages. Despite recent advances in LLM-based web agents, efficiently navigating complex, real-world webpages remains challenging due to massive DOM structures (10,000 ~ 100,000 tokens). Current approaches either truncate DOMs—losing vital information—or use inefficient heuristics and separate ranking models, failing to balance precision and scalability. We introduce Prune4Web, a novel paradigm that transforms DOM processing from LLM-based filtering to programmatic pruning. Our key innovation is DOM Tree Pruning Programming, where an LLM generates executable Python scoring programs to dynamically filter DOM elements based on semantic clues from decomposed sub-tasks. This approach eliminates the need for LLMs to process full DOMs, instead delegating traversal and scoring to lightweight, interpretable programs. The result is a 25 ~ 50 times reduction in candidate elements for grounding, enabling precise action localization without attention dilution. Additionally, we propose a data annotation method and a two-turn dialogue training strategy that jointly optimizes Planner, Programmatic Filter, and Grounder in a unified framework. Experiments demonstrate state-of-the-art performance. On our low-level task grounding task, our approach dramatically increases grounding accuracy from 46.80% to 88.28%, highlighting its effectiveness.

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AAAI Conference 2025 Conference Paper

AGFSync: Leveraging AI-Generated Feedback for Preference Optimization in Text-to-Image Generation

Jingkun An
Yinghao Zhu
Zongjian Li
Enshen Zhou
Haoran Feng
Xijie Huang
Bohua Chen
Yemin Shi

Text-to-Image (T2I) diffusion models have achieved remarkable success in image generation. Despite their progress, challenges remain in both prompt-following ability, image quality and lack of high-quality datasets, which are essential for refining these models. As acquiring labeled data is costly, we introduce AGFSync, a framework that enhances T2I diffusion models through Direct Preference Optimization (DPO) in a fully AI-driven approach. AGFSync utilizes Vision-Language Models (VLM) to assess image quality across style, coherence, and aesthetics, generating feedback data within an AI-driven loop. By applying AGFSync to leading T2I models such as SD v1.4, v1.5, and SDXL-base, our extensive experiments on the TIFA dataset demonstrate notable improvements in VQA scores, aesthetic evaluations, and performance on the HPS v2 benchmark, consistently outperforming the base models. AGFSync's method of refining T2I diffusion models paves the way for scalable alignment techniques.

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IROS Conference 2025 Conference Paper

Chain-of-Imagination for Reliable Instruction Following in Decision Making

Enshen Zhou
Yiran Qin
Zhenfei Yin
Zhelun Shi
Yuzhou Huang
Ruimao Zhang
Lu Sheng
Jing Shao

Enabling the embodied agent to imagine step-by-step the future states and sequentially approach these situation-aware states can enhance its capability to make reliable action decisions from textual instructions. In this work, we introduce a simple but effective mechanism called Chain-of-Imagination (CoI), which repeatedly employs a Multimodal Large Language Model (MLLM) equipped with diffusion model to facilitate imagining and acting upon the series of intermediate situation-aware visual sub-goals one by one, resulting in more reliable instruction-following capability. Based on the CoI mechanism, we propose an embodied agent DecisionDreamer as the low-level controller that can be adapted to different open-world scenarios. Extensive experiments demonstrate that Decision-Dreamer can achieve more reliable and accurate decision-making and significantly outperform the state-of-the-art generalist agents in the Minecraft and CALVIN sandbox simulators, regarding the instruction-following capability. For more demos, please see https://sites.google.com/view/decisiondreamer.

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IROS Conference 2025 Conference Paper

RH20T-P: A Primitive-Level Robotic Manipulation Dataset towards Composable Generalization Agents in Real-world Scenarios

Zeren Chen
Zhelun Shi
Xiaoya Lu
Lehan He
Sucheng Qian
Enshen Zhou
Zhenfei Yin
Wanli Ouyang

Achieving generalizability in solving out-of-distribution tasks is one of the ultimate goals of learning robotic manipulation. Recent progress of Vision-Language Models (VLMs) has shown that VLM-based task planners can alleviate the difficulty of solving novel tasks, by decomposing the compounded tasks as a plan of sequentially executing primitive-level skills that have been already mastered. It is also promising for robotic manipulation to adapt such composable generalization ability, in the form of composable generalization agents (CGAs). However, the community lacks of reliable design of primitive skills and a sufficient amount of primitive-level data annotations. Therefore, we propose RH20T-P, a primitive-level robotic manipulation dataset, which contains about 38k video clips covering 67 diverse manipulation tasks in real-world scenarios. Each clip is manually annotated according to a set of meticulously designed primitive skills that are common in robotic manipulation. Furthermore, we standardize a plan-execute CGA paradigm and implement an exemplar baseline called RA-P on our RH20T-P, whose positive performance on solving unseen tasks validates that the proposed dataset can offer composable generalization ability to robotic manipulation agents. Project homepage: https://sites.google.com/view/rh20t-primitive/main.

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NeurIPS Conference 2025 Conference Paper

RoboRefer: Towards Spatial Referring with Reasoning in Vision-Language Models for Robotics

Enshen Zhou
Jingkun An
Cheng Chi
Yi Han
Shanyu Rong
Chi Zhang
Pengwei Wang
Zhongyuan Wang

Spatial referring is a fundamental capability of embodied robots to interact with the 3D physical world. However, even with the powerful pretrained VLMs, recent approaches are still not qualified to accurately understand the complex 3D scenes and dynamically reason about the instruction-indicated locations for interaction. To this end, we propose RoboRefer, a 3D-aware vision language model (VLM) that can first achieve precise spatial understanding by integrating a disentangled but dedicated depth encoder via supervised fine-tuning (SFT). Moreover, RoboRefer advances generalized multi-step spatial reasoning via reinforcement fine-tuning (RFT), with metric-sensitive process reward functions tailored for spatial referring tasks. To support SFT and RFT training, we introduce RefSpatial, a large-scale dataset of 20M QA pairs (2x prior), covering 31 spatial relations (vs. 15 prior) and supporting complex reasoning processes (up to 5 steps). In addition, we introduce RefSpatial-Bench, a challenging benchmark filling the gap in evaluating spatial referring with multi-step reasoning. Experiments show that SFT-trained RoboRefer achieves state-of-the-art spatial understanding, with an average success rate of 89. 6%. RFT-trained RoboRefer further outperforms all other baselines by a large margin, even surpassing Gemini-2. 5-Pro by 12. 4% in average accuracy on RefSpatial-Bench. Notably, RoboRefer can be integrated with various control policies to execute long-horizon, dynamic tasks across diverse robots (e, g. , UR5, G1 humanoid) in cluttered real-world scenes.

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ICML Conference 2025 Conference Paper

WorldSimBench: Towards Video Generation Models as World Simulators

Yiran Qin
Zhelun Shi
Jiwen Yu
Xijun Wang
Enshen Zhou
Lijun Li
Zhenfei Yin
Xihui Liu

Recent advancements in predictive models have demonstrated exceptional capabilities in predicting the future state of objects and scenes. However, the lack of categorization based on inherent characteristics continues to hinder the progress of predictive model development. Additionally, existing benchmarks are unable to effectively evaluate higher-capability, highly embodied predictive models from an embodied perspective. In this work, we classify the functionalities of predictive models into a hierarchy and take the first step in evaluating World Simulators by proposing a dual evaluation framework called WorldSimBench. WorldSimBench includes Explicit Perceptual Evaluation and Implicit Manipulative Evaluation, encompassing human preference assessments from the visual perspective and action-level evaluations in embodied tasks, covering three representative embodied scenarios: Open-Ended Embodied Environment, Autonomous, Driving, and Robot Manipulation. In the Explicit Perceptual Evaluation, we introduce the HF-Embodied Dataset, a video assessment dataset based on fine-grained human feedback, which we use to train a Human Preference Evaluator that aligns with human perception and explicitly assesses the visual fidelity of World Simulater. In the Implicit Manipulative Evaluation, we assess the video-action consistency of World Simulators by evaluating whether the generated situation-aware video can be accurately translated into the correct control signals in dynamic environments. Our comprehensive evaluation offers key insights that can drive further innovation in video generation models, positioning World Simulators as a pivotal advancement toward embodied artificial intelligence.

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