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Minghan Li

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7 papers

2 author rows

AAAI Conference 2026 Conference Paper

RFKG-CoT: Relation-Driven Adaptive Hop-count Selection and Few-Shot Path Guidance for Knowledge-Aware QA

Chao Zhang
Minghan Li
Tianrui Lv
Guodong Zhou

Large language models (LLMs) often generate hallucinations in knowledge-intensive QA due to parametric knowledge limitations. While existing methods like KG-CoT improve reliability by integrating knowledge graph (KG) paths, they suffer from rigid hop-count selection (solely question-driven) and underutilization of reasoning paths (lack of guidance). To address this, we propose RFKG-CoT: First, it replaces the rigid hop-count selector with a relation-driven adaptive hop-count selector that dynamically adjusts reasoning steps by activating KG relations (e.g., 1-hop for direct ''brother" relations, 2-hop for indirect ''father-son" chains), formalized via a relation mask. Second, it introduces a few-shot in-context learning path guidance mechanism with CoT (think) that constructs examples in a ''question-paths-answer" format to enhance LLMs' ability to understand reasoning paths. Experiments on four KGQA benchmarks show RFKG-CoT improves accuracy by up to 14.7 pp (Llama2-7B on WebQSP) over KG-CoT. Ablations confirm the hop-count selector and the path prompt are complementary, jointly transforming KG evidence into more faithful answers.

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NeurIPS Conference 2025 Conference Paper

DNAEdit: Direct Noise Alignment for Text-Guided Rectified Flow Editing

Chenxi Xie
Minghan Li
Shuai Li
Yuhui Wu
Qiaosi Yi
Lei Zhang

Leveraging the powerful generation capability of large-scale pretrained text-to-image models, training-free methods have demonstrated impressive image editing results. Conventional diffusion-based methods, as well as recent rectified flow (RF)-based methods, typically reverse synthesis trajectories by gradually adding noise to clean images, during which the noisy latent at the current timestep is used to approximate that at the next timesteps, introducing accumulated drift and degrading reconstruction accuracy. Considering the fact that in RF the noisy latent is estimated through direct interpolation between Gaussian noises and clean images at each timestep, we propose Direct Noise Alignment (DNA), which directly refines the desired Gaussian noise in the noise domain, significantly reducing the error accumulation in previous methods. Specifically, DNA estimates the velocity field of the interpolated noised latent at each timestep and adjusts the Gaussian noise by computing the difference between the predicted and expected velocity field. We validate the effectiveness of DNA and reveal its relationship with existing RF-based inversion methods. Additionally, we introduce a Mobile Velocity Guidance (MVG) to control the target prompt-guided generation process, balancing image background preservation and target object editability. DNA and MVG collectively constitute our proposed method, namely DNAEdit. Finally, we introduce DNA-Bench, a long-prompt benchmark, to evaluate the performance of advanced image editing models. Experimental results demonstrate that our DNAEdit achieves superior performance to state-of-the-art text-guided editing methods. Our code, model, and benchmark will be made publicly available.

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ICRA Conference 2025 Conference Paper

Na Vid-4D: Unleashing Spatial Intelligence in Egocentric RGB-D Videos for Vision-and-Language Navigation

Haoran Liu
Weikang Wan
Xiqian Yu
Minghan Li
Jiazhao Zhang
Bo Zhao 0015
Zhibo Chen 0001
Zhongyuan Wang 0006

Understanding and reasoning about the 4D space-time is crucial for Vision-and-Language Navigation (VLN). However, previous works lack in-depth exploration in this aspect, resulting in bottlenecked spatial perception and action precision of VLN agents. In this work, we introduce NaVid-4D, a Vision Language Model (VLM) based navigation agent taking the lead in explicitly showcasing the capabilities of spatial intelligence in the real world. Given natural language instructions, NaVid-4D requires only egocentric RGB-D video streams as observations to perform spatial understanding and reasoning for generating precise instruction-following robotic actions. NaVid-4D learns navigation policies using the data from simulation environments and is endowed with precise spatial understanding and reasoning capabilities using web data. Without the need to pre-train an RGB-D foundation model, we propose a method capable of directly injecting the depth features into the visual encoder of a VLM. We further compare the use of factually captured depth information with the monocularly estimated one and find NaVid-4D works well with both while using estimated depth offers greater gener-alization capability and better mitigates the sim-to-real gap. Extensive experiments demonstrate that NaVid-4D achieves state-of-the-art performance in simulation environment and makes impressive VLN performance with spatial intelligence happen in the real world.

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NeurIPS Conference 2025 Conference Paper

SplitFlow: Flow Decomposition for Inversion-Free Text-to-Image Editing

Sung-Hoon Yoon
Minghan Li
Gaspard Beaudouin
Congcong Wen
Muhammad Rafay Azhar
Mengyu Wang

Rectified flow models have become a $\textit{de facto}$ standard in image generation due to their stable sampling trajectories and high-fidelity outputs. Despite their strong generative capabilities, they face critical limitations in image editing tasks: inaccurate inversion processes for mapping real images back into the latent space, and gradient entanglement issues during editing often result in outputs that do not faithfully reflect the target prompt. Recent efforts have attempted to directly map source and target distributions via ODE-based approaches without inversion; however, these methods still yield suboptimal editing quality. In this work, we propose a flow decomposition-and-aggregation framework built upon an inversion-free formulation to address these limitations. Specifically, we semantically decompose the target prompt into multiple sub-prompts, compute an independent flow for each, and aggregate them to form a unified editing trajectory. While we empirically observe that decomposing the original flow enhances diversity in the target space, generating semantically aligned outputs still requires consistent guidance toward the full target prompt. To this end, we design a projection and soft-aggregation mechanism for flow, inspired by gradient conflict resolution in multi-task learning. This approach adaptively weights the sub-target velocity fields, suppressing semantic redundancy while emphasizing distinct directions, thereby preserving both diversity and consistency in the final edited output. Experimental results demonstrate that our method outperforms existing zero-shot editing approaches in terms of semantic fidelity and attribute disentanglement. The code is available at https: //github. com/Harvard-AI-and-Robotics-Lab/SplitFlow.

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NeurIPS Conference 2024 Conference Paper

Human-Aware Vision-and-Language Navigation: Bridging Simulation to Reality with Dynamic Human Interactions

Heng Li
Minghan Li
Zhi-Qi Cheng
Yifei Dong
Yuxuan Zhou
Jun-Yan He
Qi Dai
Teruko Mitamura

Vision-and-Language Navigation (VLN) aims to develop embodied agents that navigate based on human instructions. However, current VLN frameworks often rely on static environments and optimal expert supervision, limiting their real-world applicability. To address this, we introduce Human-Aware Vision-and-Language Navigation (HA-VLN), extending traditional VLN by incorporating dynamic human activities and relaxing key assumptions. We propose the Human-Aware 3D (HA3D) simulator, which combines dynamic human activities with the Matterport3D dataset, and the Human-Aware Room-to-Room (HA-R2R) dataset, extending R2R with human activity descriptions. To tackle HA-VLN challenges, we present the Expert-Supervised Cross-Modal (VLN-CM) and Non-Expert-Supervised Decision Transformer (VLN-DT) agents, utilizing cross-modal fusion and diverse training strategies for effective navigation in dynamic human environments. A comprehensive evaluation, including metrics considering human activities, and systematic analysis of HA-VLN's unique challenges, underscores the need for further research to enhance HA-VLN agents' real-world robustness and adaptability. Ultimately, this work provides benchmarks and insights for future research on embodied AI and Sim2Real transfer, paving the way for more realistic and applicable VLN systems in human-populated environments.

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NeurIPS Conference 2024 Conference Paper

Nearest Neighbor Speculative Decoding for LLM Generation and Attribution

Minghan Li
Xilun Chen
Ari Holtzman
Beidi Chen
Jimmy Lin
Wen-tau Yih
Xi V. Lin

Large language models (LLMs) often hallucinate and lack the ability to provide attribution for their generations. Semi-parametric LMs, such as kNN-LM, approach these limitations by refining the output of an LM for a given prompt using its nearest neighbor matches in a non-parametric data store. However, these models often exhibit slow inference speeds and produce non-fluent texts. In this paper, we introduce Nearest Neighbor Speculative Decoding (NEST), a novel semi-parametric language modeling approach that is capable of incorporating real-world text spans of arbitrary length into the LM generations and providing attribution to their sources. NEST performs token-level retrieval at each inference step to compute a semi-parametric mixture distribution and identify promising span continuations in a corpus. It then uses an approximate speculative decoding procedure that accepts a prefix of the retrieved span or generates a new token. NEST significantly enhances the generation quality and attribution rate of the base LM across a variety of knowledge-intensive tasks, surpassing the conventional kNN-LM method and performing competitively with in-context retrieval augmentation. In addition, NEST substantially improves the generation speed, achieving a 1. 8x speedup in inference time when applied to Llama-2-Chat 70B. Code will be released at https: //github. com/facebookresearch/NEST/tree/main.

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AAAI Conference 2022 Conference Paper

Listwise Learning to Rank Based on Approximate Rank Indicators

Thibaut Thonet
Yagmur Gizem Cinar
Eric Gaussier
Minghan Li
Jean-Michel Renders

We study here a way to approximate information retrieval metrics through a softmax-based approximation of the rank indicator function. Indeed, this latter function is a key component in the design of information retrieval metrics, as well as in the design of the ranking and sorting functions. Obtaining a good approximation for it thus opens the door to differentiable approximations of many evaluation measures that can in turn be used in neural end-to-end approaches. We first prove theoretically that the approximations proposed are of good quality, prior to validate them experimentally on both learning to rank and text-based information retrieval tasks.

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