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Yupeng Hu

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11 papers

1 author row

AAAI Conference 2026 Conference Paper

Circuit-Think: A Multimodal Reasoning Framework for Automated Circuit-to-Netlist Translation with Trajectory-Guided Reinforcement Learning

Yuqi Jiang
Yupeng Hu
Jinyuan Deng
Xiaotian Qiu
Yucheng Cui
Xuyang He
Ruidong Li
Qi Sun

Vision Language Models (VLMs) have shown strong performance in multimodal understanding, offering promise for the circuit-to-netlist translation task. However, the diverse component symbols and complex connections in circuit images challenge VLMs in understanding physical layouts and reasoning for electrical connection logic. To address these, we propose Circuit-Think, the first multimodal reasoning framework for the automated circuit-to-netlist translation task, which employs a Trajectory-Guided Reinforcement Learning (TGRL) paradigm for structured logical reasoning on circuit images. Circuit-Think initializes reasoning capabilities through supervised fine-tuning (SFT) on image-netlist pairs, then optimizes reasoning trajectories and netlist generation decisions using TGRL. Firstly, TGRL introduces a step-by-step reasoning paradigm, which guides the model with stepwise reward functions to simulate the human cognitive trajectory of ``identifying ports, recognizing devices, and inferring connections''. Secondly, we customize a multi-level reward that maps reasoning and answers into graph structures and node sets, jointly optimizing logical consistency and netlist accuracy via graph similarity and set matching. Thirdly, TGRL contains a reflective learning mechanism for low-scoring samples, which corrects the reasoning trajectory through reference answers as hints, avoiding local optima caused by sparse reward signals or erroneous reasoning paths. Moreover, we construct a circuit image-netlist reasoning dataset with 3,100 samples, offering step-by-step annotations for converting circuit images to netlists. Extensive experiments demonstrate that Circuit-Think achieves SOTA netlist accuracy and significantly improves the accuracy of downstream tasks.

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AAAI Conference 2026 Conference Paper

D2MoRA: Diversity-Regulated Asymmetric MoE-LoRA Decomposition for Efficient Multi-Task Adaptation

Jianhui Zuo
Xuemeng Song
Haokun Wen
Meng Liu
Yupeng Hu
Jiuru Wang
Liqiang Nie

Low-Rank Adaptation (LoRA) has emerged as a powerful parameter-efficient fine-tuning method for adapting large language models to downstream tasks. Recent studies have leveraged Mixture-of-Experts (MoE) mechanism to effectively integrate multiple LoRA modules, facilitating efficient parameter adaptation for multi-task scenarios. It has been shown that fostering knowledge sharing across LoRA experts can greatly enhance parameter adaptation efficiency. However, the existing approach for LoRA expert knowledge sharing still faces two key limitations: constrained functional specialization and induced expert homogenization. To address these issues, we propose a novel diversity-regulated asymmetric MoE-LoRA decomposition framework, which achieves flexible knowledge sharing through asymmetric expert decomposition and guarantees expert diversity with a dual orthogonality regularization. Extensive experiments on eight public benchmarks, spanning both multi-task and single-task settings, demonstrate the superiority of our approach over existing methods.

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AAAI Conference 2026 Conference Paper

Decompose and Conquer: Compositional Reasoning for Zero-Shot Temporal Action Localization

Haoyu Tang
Tianyuan Liang
Han Jiang
Xuesong Liu
Qinghai Zheng
Yupeng Hu

Current Zero-Shot Temporal Action Localization (ZSTAL) methods, whether training-based or training-free ones, still predominantly rely on a single, unified query to localize an entire action. This unified representation is fundamentally ill-suited for complex real-world activities, as it fails to capture their internal compositional structure and adapt to dynamic, multi-stage variations across videos. To address this, we regard ZSTAL as a compositional reasoning task and introduce CASCADE, a Context-Aware Staged Action DEcomposition framework. Inspired by the human cognitive process of perceiving context, decomposing events, and reconstructing instances, CASCADE follows a training-free pipeline. It first perceives the video's context by leveraging a Multimodal Large Language Model (MLLM) to both filter out irrelevant actions and then generate a rich, video-specific caption for each action present in the video. An LLM then decomposes this caption into multiple, temporally ordered stages, which serve as fine-grained queries to guide the MLLM in estimating frame-level confidence scores. Recognizing that this decomposition can fragment a single action, a novel hierarchical merging logic then reconstructs complete instances by intelligently fusing these preliminary temporal segments based on their semantic progression and coherence. Extensive experiments and ablation studies on THUMOS14 and ActivityNet-1.3 show that CASCADE not only sets a new state-of-the-art among training-free methods but, most notably, significantly outperforms all prior training-based approaches on ActivityNet-1.3.

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AAAI Conference 2026 Conference Paper

HABIT: Chrono-Synergia Robust Progressive Learning Framework for Composed Image Retrieval

Zixu Li
Yupeng Hu
Zhiwei Chen
Shiqi Zhang
Qinlei Huang
Zhiheng Fu
Yinwei Wei

Composed Image Retrieval (CIR) is a flexible image retrieval paradigm that enables users to accurately locate the target image through a multimodal query composed of a reference image and modification text. Although this task has demonstrated promising applications in personalized search and recommendation systems, it encounters a severe challenge in practical scenarios known as the Noise Triplet Correspondence (NTC) problem. This issue primarily arises from the high cost and subjectivity involved in annotating triplet data. To address this problem, we identify two central challenges: the precise estimation of composed semantic discrepancy and the insufficient progressive adaptation to modification discrepancy. To tackle these challenges, we propose a cHrono-synergiA roBust progressIve learning framework for composed image reTrieval (HABIT), which consists of two core modules. First, the Mutual Knowledge Estimation Module quantifies sample cleanliness by calculating the Transition Rate of mutual information between the composed feature and the target image, thereby effectively identifying clean samples that align with the intended modification semantics. Second, the Dual-consistency Progressive Learning Module introduces a collaborative mechanism between the historical and current models, simulating human habit formation to retain good habits and calibrate bad habits, ultimately enabling robust learning under the presence of NTC. Extensive experiments conducted on two standard CIR datasets demonstrate that HABIT significantly outperforms most methods under various noise ratios, exhibiting superior robustness and retrieval performance.

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AAAI Conference 2026 Conference Paper

INTENT: Invariance and Discrimination-aware Noise Mitigation for Robust Composed Image Retrieval

Zhiwei Chen
Yupeng Hu
Zhiheng Fu
Zixu Li
Jiale Huang
Qinlei Huang
Yinwei Wei

Composed Image Retrieval (CIR) is a challenging image retrieval paradigm that enables to retrieve target images based on multimodal queries consisting of reference images and modification texts. Although substantial progress has been made in recent years, existing methods assume that all samples are correctly matched. However, in real-world scenarios, due to high triplet annotation costs, CIR datasets inevitably contain annotation errors, resulting in incorrectly matched triplets. To address this issue, the problem of Noisy Triplet Correspondence (NTC) has attracted growing attention. We argue that noise in CIR can be categorized into two types: cross-modal correspondence noise and modality-inherent noise. The former arises from mismatches across modalities, whereas the latter originates from intra-modal background interference or visual factors irrelevant to the coarse-grained modification annotations. However, modality-inherent noise is often overlooked, and research on cross-modal correspondence noise remains nascent. To tackle above issues, we propose the Invariance and discrimiNaTion-awarE Noise neTwork (INTENT), comprising two components: Visual Invariant Composition and Bi-Objective Discriminative Learning, specifically designed to handle the two-aspect noise. The former applies causal intervention on the visual side via Fast Fourier Transform (FFT) to generate intervened composed features, enforcing visual invariance and enabling the model to ignore modality-inherent noise during composition. The latter adopts collaborative optimization with both positive and negative samples, and constructs a scalable decision boundary that dynamically adjusts decisions based on the loyalty degree, enabling robust correspondence discrimination. Extensive experiments on two widely used benchmark datasets demonstrate the superiority and robustness of INTENT.

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AAAI Conference 2026 Conference Paper

ReTrack: Evidence-Driven Dual-Stream Directional Anchor Calibration Network for Composed Video Retrieval

Zixu Li
Yupeng Hu
Zhiwei Chen
Qinlei Huang
Guozhi Qiu
Zhiheng Fu
Meng Liu

With the rapid growth of video data, Composed Video Retrieval (CVR) has emerged as a novel paradigm in video retrieval and is receiving increasing attention from researchers. Unlike unimodal video retrieval methods, the CVR task takes a multi-modal query consisting of a reference video and a piece of modification text as input. The modification text conveys the user's intended alterations to the reference video. Based on this input, the model aims to retrieve the most relevant target video. In the CVR task, there exists a substantial discrepancy in information density between video and text modalities. Traditional composition methods tend to bias the composed feature toward the reference video, which leads to suboptimal retrieval performance. This limitation is significant due to the presence of three core challenges: (1) modal contribution entanglement, (2) explicit optimization of composed features, and (3) retrieval uncertainty. To address these challenges, we propose the evidence-dRivEn dual-sTream diRectionAl anChor calibration networK (ReTrack). ReTrack is the first CVR framework that improves multi-modal query understanding by calibrating directional bias in composed features. It consists of three key modules: Semantic Contribution Disentanglement, Composition Geometry Calibration, and Reliable Evidence-driven Alignment. Specifically, ReTrack estimates the semantic contribution of each modality to calibrate the directional bias of the composed feature. It then uses the calibrated directional anchors to compute bidirectional evidence that drives reliable composed-to-target similarity estimation. Moreover, ReTrack exhibits strong generalization to the Composed Image Retrieval (CIR) task, achieving SOTA performance across three benchmark datasets in both CVR and CIR scenarios.

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AAAI Conference 2026 Conference Paper

When Eyes and Ears Disagree: Can MLLMs Discern Audio-Visual Confusion?

Qilang Ye
Wei Zeng
Meng Liu
Jie Zhang
Yupeng Hu
Zitong Yu
Yu Zhou

Can Multimodal Large Language Models (MLLMs) discern confused objects that are visually present but audio-absent? To study this, we introduce a new benchmark, AV-ConfuseBench, which simulates an “Audio-Visual Confusion” scene by modifying the corresponding sound of an object in the video, e.g., mute the sounding object and ask MLLMs “Is there a/an {muted-object} sound”. Experimental results reveal that MLLMs, such as Qwen2.5-Omni and Gemini 2.5, struggle to discriminate non-existent audio due to visually dominated reasoning. Motivated by this observation, we introduce RL-CoMM, a Reinforcement Learning-based Collaborative Multi-MLLM that is built upon the Qwen2.5-Omni foundation. RL-CoMM includes two stages: 1) To alleviate visually dominated ambiguities, we introduce an external model, a Large Audio Language Model (LALM), as the reference model to generate audio-only reasoning. Then, we design a Step-wise Reasoning Reward function that enables MLLMs to self-improve audio-visual reasoning with the audio-only reference. 2) To ensure an accurate answer prediction, we introduce Answer-centered Confidence Optimization to reduce the uncertainty of potential heterogeneous reasoning differences. Extensive experiments on audio-visual question answering and audio-visual hallucination show that RL-CoMM improves accuracy by 10~30% over the baseline model with limited training data.

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AAAI Conference 2025 Conference Paper

Content-aware Balanced Spectrum Encoding in Masked Modeling for Time Series Classification

Yudong Han
Haocong Wang
Yupeng Hu
Yongshun Gong
Xuemeng Song
Weili Guan

Due to the superior ability of global dependency, transformer and its variants have become the primary choice in Masked Time-series Modeling (MTM) towards time-series classification task. In this paper, we experimentally analyze that existing transformer-based MTM methods encounter with two under-explored issues when dealing with time series data: (1) they encode features by performing long-dependency ensemble averaging, which easily results in rank collapse and feature homogenization as the layer goes deeper; (2) they exhibit distinct priorities in fitting different frequency components contained in the time-series, inevitably leading to spectrum energy imbalance of encoded feature. To tackle these issues, we propose an auxiliary content-aware balanced decoder (CBD) to optimize the encoding quality in the spectrum space within masked modeling scheme. Specifically, the CBD iterates on a series of fundamental blocks, and thanks to two tailored units, each block could progressively refine the masked representation via adjusting the interaction pattern based on local content variations of time-series and learning to recalibrate the energy distribution across different frequency components. Moreover, dual-constraint loss is devised to enhance the mutual optimization of vanilla decoder and our CBD. Extensive experimental results on ten time-series classification datasets show that our method nearly surpasses a bunch of baselines. Meanwhile, a series of explanatory results are showcased to sufficiently demystify the behaviors of our method.

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AAAI Conference 2025 Conference Paper

ENCODER: Entity Mining and Modification Relation Binding for Composed Image Retrieval

Zixu Li
Zhiwei Chen
Haokun Wen
Zhiheng Fu
Yupeng Hu
Weili Guan

The objective of Composed Image Retrieval (CIR) is to identify a target image that meets the requirement based on a multimodal query (including the reference image and the modification text) provided by the user. Despite the notable success of existing approaches, they fail to adequately address the modification relation between visual entities and modification actions. This limitation is non-trivial due to three challenges: 1) irrelevant factor perturbation, 2) vague semantic boundaries, and 3) implicit modification relations. To address the above challenges, we propose an Entity miNing and modifiCation relatiOn binDing nEtwoRk (ENCODER), which has been designed to mine visual entities and modification actions, and then bind modification relations. Among the various components of the proposed ENCODER, we have initially designed the Latent Factor Filter (LFF) module to filter visual and textual latent factors related to modification semantics based on a threshold gating mechanism. Secondly, we propose Entity-Action Binding (EAB), which comprises modality-shared Learnable Relation Queries (LRQ) that are capable of mining visual entities and modification actions, as well as learning implicit modification relations for entity-action binding. Finally, the Multi-scale Composition module is introduced to achieve multi-scale feature composition, with guidance provided by entity-action binding. Extensive experiments on four benchmark datasets demonstrate the superiority of our proposed method.

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IJCAI Conference 2024 Conference Paper

Breaking Barriers of System Heterogeneity: Straggler-Tolerant Multimodal Federated Learning via Knowledge Distillation

Jinqian Chen
Haoyu Tang
Junhao Cheng
Ming Yan
Ji Zhang
Mingzhu Xu
Yupeng Hu
Liqiang Nie

Internet of Things (IoT) devices possess valuable yet private multimodal data, calling for a decentralized machine learning scheme. Though several multimodal federated learning (MFL) methods have been proposed, most of them merely overlook the system heterogeneity across IoT devices, resulting in the inadaptability to real world applications. Aiming at this, we conduct theoretical analysis and exploration experiments on straggler impacts and uncover the fact that stragglers caused by system heterogeneity are fatal to MFL, resulting in catastrophic time overhead. Motivated by this, we propose a novel Multimodal Federated Learning with Accelerated Knowledge Distillation (MFL-AKD) framework, which is the first attempt to integrate knowledge distillation to combat stragglers in complex multimodal federated scenarios. Concretely, given the pretrained large-scale vision-language models deployed in the central server, we apply a fast knowledge transfer mechanism to conduct early training of local models with part of the local data. The early-trained model is then enhanced through the distillation of the pretrained large model and further trained on the remaining data. Extensive experiments on two datasets for video moment retrieval and two datasets for image-text retrieval demonstrate that our method achieves superior results with high straggler robustness.

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AAAI Conference 2024 Conference Paper

Exploiting the Social-Like Prior in Transformer for Visual Reasoning

Yudong Han
Yupeng Hu
Xuemeng Song
Haoyu Tang
Mingzhu Xu
Liqiang Nie

Benefiting from instrumental global dependency modeling of self-attention (SA), transformer-based approaches have become the pivotal choices for numerous downstream visual reasoning tasks, such as visual question answering (VQA) and referring expression comprehension (REC). However, some studies have recently suggested that SA tends to suffer from rank collapse thereby inevitably leads to representation degradation as the transformer layer goes deeper. Inspired by social network theory, we attempt to make an analogy between social behavior and regional information interaction in SA, and harness two crucial notions of structural hole and degree centrality in social network to explore the possible optimization towards SA learning, which naturally deduces two plug-and-play social-like modules. Based on structural hole, the former module allows to make information interaction in SA more structured, which effectively avoids redundant information aggregation and global feature homogenization for better rank remedy, followed by latter module to comprehensively characterize and refine the representation discrimination via considering degree centrality of regions and transitivity of relations. Without bells and whistles, our model outperforms a bunch of baselines by a noticeable margin when considering our social-like prior on five benchmarks in VQA and REC tasks, and a series of explanatory results are showcased to sufficiently reveal the social-like behaviors in SA.

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