Author name cluster

Jiaxin Guo

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8 papers

2 author rows

JBHI Journal 2026 Journal Article

A 3D Edge-Attention Denoising Diffusion Network for Prostate Segmentation in Puncture Biopsy

Haomin Kuang
Jiaxin Guo
Kai Xu
Yun-Hui Liu

Prostate cancer is the second most common cancer in men, and transrectal ultrasound (TRUS) guided biopsy is the standard method to diagnose prostate cancer. Accurate prostate segmentation in TRUS images is crucial for precise biopsy. Manual segmentation is laborious, while automated segmentation faces significant challenges due to the low signal-to-noise ratio, blurred boundaries, and presence of noise and artifacts. To address these issues, this paper proposes a 3D edge-attention denoising diffusion network, aiming to achieve high accuracy and generalizability for prostate segmentation in TRUS-guided biopsy. The proposed network incorporates an edge attention denoising U-Net (EAD U-Net) to extract and utilize desired edge information in TRUS images, improving the segmentation accuracy in challenging regions of the prostate. To reduce uncertainty and enhance network accuracy, we incorporate a Kalman fusion module, which utilizes the Kalman filter and all estimations from the EAD U-Net in reverse process to obtain the optimal segmentation estimation. The proposed network was evaluated using 1834 3D ultrasound images from two open-source datasets. Comparative experiments with existing methods demonstrate that our method surpasses state-of-the-art techniques, proving its effectiveness in prostate segmentation from TRUS images. The proposed method achieved an average Dice similarity coefficient of 92. 92% and 94. 0%, and the 95th percentile of Hausdorff distance of 1. 07 mm and 0. 77 mm on two datasets, demonstrating the potential to facilitate accurate MRI-TRUS fusion guided prostate biopsy.

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AAAI Conference 2026 Conference Paper

Mitigating Error Accumulation in Knowledge Editing for Multi-Hop Question Answering

Jiaxin Guo
Hao Sun
Wenhao Zhang
Xuanbo Fan
Yan Zhang

Knowledge editing (KE) has emerged as an effective approach for updating factual information in large language models (LLMs) without the need for full retraining. Most of the existing methods for addressing the "ripple effect" in KE adopt a chain-structured reasoning process, making them vulnerable to error accumulation from early incorrect steps. Moreover, their conflict detection mechanisms are often susceptible to the LLM's inherent confirmation bias, further undermining the reliability of the editing process. To overcome these challenges, we propose Tree of Editing (ToE), a tree-structured, retrieval-enhanced knowledge editing framework designed to support robust reasoning under factual updates. ToE expands reasoning paths using a breadth-first strategy combined with score-guided beam search, enabling diverse and error-tolerant inference. Besides, we introduce an observer to objectively update knowledge, avoiding the bias caused by LLMs' over-confidence. Experimental results on two benchmarks, namely MQuAKE-CF (targeting ripple-aware editing) and DUNE (free-form editing), demonstrate that ToE framework significantly outperforms existing methods.

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AAAI Conference 2026 Conference Paper

SegMem-RAG: Adaptive Memory for Retrieval-Augmented Generation in Open-Ended Knowledge Environments

Xuanbo Fan
Tianqi Zhao
Yi Cheng
Chi Xiu
Jiaxin Guo
Boci Peng
Bingjing Xu
Jessica Zhang

Retrieval-Augmented Generation (RAG) improves the factual accuracy of large language models by grounding responses in external content. However, most RAG systems assume access to static and well-organized corpora with fixed retrieval logic. In practice, real-world sources are heterogeneous and unlabeled, including user-uploaded documents, manuals, and datasets. Effective access in such settings requires adaptive and self-directed retrieval behavior. We present SegMem‑RAG, a memory-augmented RAG framework that learns to route queries across multiple unlabeled corpora based on experience. It incrementally updates a structured memory and uses self-reflection to guide retrieval over time without supervision. Experimental results demonstrate that SegMem‑RAG significantly outperforms recent baselines in generation quality on multi-corpus QA tasks.

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NeurIPS Conference 2025 Conference Paper

AdaSPEC: Selective Knowledge Distillation for Efficient Speculative Decoders

Yuezhou Hu
Jiaxin Guo
Xinyu Feng
Tuo Zhao

Speculative Decoding (SD) accelerates large language model inference by employing a small draft model to generate predictions, which are then verified by a larger target model. The effectiveness of SD hinges on the alignment between these models, which is typically enhanced by Knowledge Distillation (KD). However, conventional KD methods aim to minimize the KL divergence between the draft and target models across all tokens, a goal that is misaligned with the true objective of SD, which is to maximize token acceptance rate. Therefore, draft models often struggle to fully assimilate the target model's knowledge due to capacity constraints, leading to suboptimal performance. To address this challenge, we propose AdaSPEC, a novel method that incorporates selective token filtering into the KD process. AdaSPEC utilizes a reference model to identify and filter out difficult-to-fit tokens, enabling the distillation of a draft model that better aligns with the target model on simpler tokens. This approach improves the overall token acceptance rate without compromising generation quality. We evaluate AdaSPEC across diverse tasks, including arithmetic reasoning, instruction-following, coding, and summarization, using model configurations of 31M/1. 4B and 350M/2. 7B parameters. Our results demonstrate that AdaSPEC consistently outperforms the state-of-the-art DistillSpec method, achieving higher acceptance rates across all tasks (up to 15\%). The code is publicly available at \url{https: //github. com/yuezhouhu/adaspec}.

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IROS Conference 2025 Conference Paper

Design and Kinematics for the Cystoscope of a Transurethral Continuum Surgical Robotic System

Haomin Kuang
Jiaxin Guo
Wei Chen 0068
Kai Xu 0001
Yun-Hui Liu 0001

To achieve en bloc resection of bladder tumor and the anterior tumor resection in transurethral resection of bladder tumor (TURBT), a cystoscope transurethral continuum robotic system has been proposed. A continuum cystoscope in the system needs to bend more than 180° and its base has translation, axial rotation, and tilt degrees of freedom to achieve full bladder accessibility. Under the constant-curvature assumption, the analytical solution of inverse kinematics already exists for multi-segment continuum robots with variable segment lengths and continuum robots with two inextensible segments. However, there is a lack of analytical inverse kinematics solution for continuum robots with features of the continuum cystoscope. Therefore, this paper proposes a novel and efficient inverse kinematics solving algorithm for the continuum cystoscope used in TURBT. The proposed method simplifies the inverse kinematics problem by constructing a robot plane coordinate and uses geometric relationships to derive a non-linear constraint equation containing only one intermediate variable. By solving this non-linear equation, the solution to the entire inverse kinematics problem is obtained. Additionally, based on this inverse kinematics algorithm, the length of the continuum segment is designed to ensure full bladder accessibility. In the comparative experiments with the Jacobian-based method, which involves 12500 target poses, the proposed method solves 100% of the inverse kinematics problems with a much greater computational efficiency.

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NeurIPS Conference 2025 Conference Paper

Reward Reasoning Models

Jiaxin Guo
Zewen Chi
Li Dong
Qingxiu Dong
Xun Wu
Shaohan Huang
Furu Wei

Reward models play a critical role in guiding large language models toward outputs that align with human expectations. However, an open challenge remains in effectively utilizing test-time compute to enhance reward model performance. In this work, we introduce Reward Reasoning Models (RRMs), which are specifically designed to execute a deliberate reasoning process before generating final rewards. Through chain-of-thought reasoning, RRMs leverage additional test-time compute for complex queries where appropriate rewards are not immediately apparent. To develop RRMs, we implement a reinforcement learning framework that fosters self-evolved reward reasoning capabilities without requiring explicit reasoning traces as training data. Experimental results demonstrate that RRMs achieve superior performance on reward modeling benchmarks across diverse domains. Notably, we show that RRMs can adaptively exploit test-time compute to further improve reward accuracy. The pretrained models are available at https: //huggingface. co/Reward-Reasoning.

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ICRA Conference 2024 Conference Paper

Ada-Tracker: Soft Tissue Tracking via Inter-Frame and Adaptive-template Matching

Jiaxin Guo
Jiangliu Wang
Zhaoshuo Li
Tongyu Jia
Qi Dou 0001
Yun-Hui Liu 0001

Soft tissue tracking is crucial for computer-assisted interventions. Existing approaches mainly rely on extracting discriminative features from the template and videos to recover corresponding matches. However, it is difficult to adopt these techniques in surgical scenes, where tissues are changing in shape and appearance throughout the surgery. To address this problem, we exploit optical flow to naturally capture the pixel-wise tissue deformations and adaptively correct the tracked template. Specifically, we first implement an inter-frame matching mechanism to extract a coarse region of interest based on optical flow from consecutive frames. To accommodate appearance change and alleviate drift, we then propose an adaptive-template matching method, which updates the tracked template based on the reliability of the estimates. Our approach, Ada-Tracker, enjoys both short-term dynamics modeling by capturing local deformations and long-term dynamics modeling by introducing global temporal compensation. We evaluate our approach on the public SurgT benchmark, which is generated from Hamlyn, SCARED, and Kidney boundary datasets. The experimental results show that Ada-Tracker achieves superior accuracy and performs more robustly against prior works. Code is available at https://github.com/wrld/Ada-Tracker.

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EAAI Journal 2023 Journal Article

Hybrid VMD-CNN-GRU-based model for short-term forecasting of wind power considering spatio-temporal features

Zeni Zhao
Sining Yun
Lingyun Jia
Jiaxin Guo
Yao Meng
Ning He
Xuejuan Li
Jiarong Shi

Accurate and reliable short-term forecasting of wind power is vital for balancing energy and integrating wind power into a grid. A novel hybrid deep learning model is designed in this study to increase the prediction accuracy of short-term wind power forecasting on a wind farm in Jiang County, Shanxi, China. The proposed hybrid deep learning model comprises variable mode decomposition (VMD), convolutional neural network (CNN), and gated recurrent unit (GRU). VMD substantially reduces the volatility of wind speed sequences. CNN automatically extracts complex spatial features from wind power data, and GRU can directly extract temporal features from historical input data. The forecasting accuracy of the combined VMD-CNN-GRU model is higher than that of any single model for wind power. The study used data obtained in 15 min intervals from the wind farm to determine the effectiveness of the proposed model against other advanced models. Compared with the other deep learning models, VMD-CNN-GRU is the best at short-term forecasting, with an RMSE of 1. 5651, MAE of 0. 8161, MAPE of 11. 62%, and R2 of 0. 9964. This method is valuable for practical applications and can be used to maintain safe wind farm operations in the future.

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