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Jinghan Li

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3 papers

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ICML Conference 2025 Conference Paper

Closed-Loop Long-Horizon Robotic Planning via Equilibrium Sequence Modeling

Jinghan Li
Zhicheng Sun 0001
Yadong Mu

In the endeavor to make autonomous robots take actions, task planning is a major challenge that requires translating high-level task descriptions to long-horizon action sequences. Despite recent advances in language model agents, they remain prone to planning errors and limited in their ability to plan ahead. To address these limitations in robotic planning, we advocate a self-refining scheme that iteratively refines a draft plan until an equilibrium is reached. Remarkably, this process can be optimized end-to-end from an analytical perspective without the need to curate additional verifiers or reward models, allowing us to train self-refining planners in a simple supervised learning fashion. Meanwhile, a nested equilibrium sequence modeling procedure is devised for efficient closed-loop planning that incorporates useful feedback from the environment (or an internal world model). Our method is evaluated on the VirtualHome-Env benchmark, showing advanced performance with improved scaling w. r. t. inference-time computation. Code is available at https: //github. com/anonymous-icml-2025/equilibrium-planner.

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ICML Conference 2025 Conference Paper

DAMA: Data- and Model-aware Alignment of Multi-modal LLMs

Jinda Lu
Junkang Wu
Jinghan Li
Xiaojun Jia
Shuo Wang 0008
Yifan Zhang 0004
Junfeng Fang
Xiang Wang 0010

Direct Preference Optimization (DPO) has shown effectiveness in aligning multi-modal large language models (MLLM) with human preferences. However, existing methods exhibit an imbalanced responsiveness to the data of varying hardness, tending to overfit on the easy-to-distinguish data while underfitting on the hard-to-distinguish data. In this paper, we propose Data- and Model-aware DPO (DAMA) to dynamically adjust the optimization process from two key aspects: (1) a data-aware strategy that incorporates data hardness, and (2) a model-aware strategy that integrates real-time model responses. By combining the two strategies, DAMA enables the model to effectively adapt to data with varying levels of hardness. Extensive experiments on five benchmarks demonstrate that DAMA not only significantly enhances the trustworthiness, but also improves the effectiveness over general tasks. For instance, on the Object HalBench, our DAMA-7B reduces response-level and mentioned-level hallucination by 90. 0% and 95. 3%, respectively, surpassing the performance of GPT-4V.

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ICLR Conference 2025 Conference Paper

DiffGAD: A Diffusion-based Unsupervised Graph Anomaly Detector

Jinghan Li
Yuan Gao
Jinda Lu
Junfeng Fang
Congcong Wen
Hui Lin
Xiang Wang 0010

Graph Anomaly Detection (GAD) is crucial for identifying abnormal entities within networks, garnering significant attention across various fields. Traditional unsupervised methods, which decode encoded latent representations of unlabeled data with a reconstruction focus, often fail to capture critical discriminative content, leading to suboptimal anomaly detection. To address these challenges, we present a Diffusion-based Graph Anomaly Detector (DiffGAD). At the heart of DiffGAD is a novel latent space learning paradigm, meticulously designed to enhance the model's proficiency by guiding it with discriminative content. This innovative approach leverages diffusion sampling to infuse the latent space with discriminative content and introduces a content-preservation mechanism that retains valuable information across different scales, significantly improving the model’s adeptness at identifying anomalies with limited time and space complexity. Our comprehensive evaluation of DiffGAD, conducted on six real-world and large-scale datasets with various metrics, demonstrated its exceptional performance. Our code is available at https://github.com/fortunato-all/DiffGAD

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