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Siyu Wang

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7 papers

2 author rows

AAAI Conference 2026 Conference Paper

Organ-Aware Routing Mixture-of-Retrieval Augmented Generation for Fetal Ultrasound Reporting

Bin Pu
Siyu Wang
Rongbin Li
Xinpeng Ding
Lei Zhao
Chaoqi Chen
Shengli Li
Kenli Li

Fetal ultrasound screening is a uniquely complex diagnostic task involving the simultaneous assessment of multiple fetal organs—each with its own anatomical and clinical context—within a single examination. Automating report generation for such cases poses a significant challenge: unlike existing methods that focus on single-organ radiology tasks (e.g., chest X-rays), fetal ultrasound requires reasoning over a structured, multiple-to-multiple setting, i.e., multi-organ images corresponding to a multi-section report. In this paper, we introduce FetusR, the first large-scale dataset for multi-organ fetal ultrasound reporting, containing 15,594 real-world cases with rich organ-wise annotations. To address the intrinsic image-report alignment, we propose Organ-Aware Routing Mixture-of-Retrieval Augmented Generation (ORM-RAG) inspired by the Mixture-of-Experts paradigm. Our method decomposes the complex alignment problem into multiple one-to-one sub-retrieval tasks. Specifically, ORM-RAG integrates (1) an organ-aware mixture-of-retrieval module that partitions the retrieval space into organ-specific corpora for independent retrieval, and (2) a dynamic routing mechanism that selectively aggregates high-confidence organ-specific reports while filtering uncertain ones. Extensive experiments demonstrate that our method significantly outperforms state-of-the-art baselines across both textual similarity and clinical accuracy metrics. Our work opens a new direction for long-form, structured report generation in real-world, multi-organ medical imaging scenarios.

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AAAI Conference 2025 Conference Paper

CAD-GPT: Synthesising CAD Construction Sequence with Spatial Reasoning-Enhanced Multimodal LLMs

Siyu Wang
Cailian Chen
Xinyi Le
Qimin Xu
Lei Xu
Yanzhou Zhang
Jie Yang

Computer-aided design (CAD) significantly enhances the efficiency, accuracy, and innovation of design processes by enabling precise 2D and 3D modeling, extensive analysis, and optimization. Existing methods for creating CAD models rely on latent vectors or point clouds, which are difficult to obtain, and storage costs are substantial. Recent advances in Multimodal Large Language Models (MLLMs) have inspired researchers to use natural language instructions and images for CAD model construction. However, these models still struggle with inferring accurate 3D spatial location and orientation, leading to inaccuracies in determining the spatial 3D starting points and extrusion directions for constructing geometries. This work introduces CAD-GPT, a CAD synthesis method with spatial reasoning-enhanced MLLM that takes either a single image or a textual description as input. To achieve precise spatial inference, our approach introduces a 3D Modeling Spatial Mechanism. This method maps 3D spatial positions and 3D sketch plane rotation angles into a 1D linguistic feature space using a specialized spatial unfolding mechanism, while discretizing 2D sketch coordinates into an appropriate planar space to enable precise determination of spatial starting position, sketch orientation, and 2D sketch coordinate translations. Extensive experiments demonstrate that CAD-GPT consistently outperforms existing state-of-the-art methods in CAD model synthesis, both quantitatively and qualitatively.

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NeurIPS Conference 2025 Conference Paper

ReinAD: Towards Real-world Industrial Anomaly Detection with a Comprehensive Contrastive Dataset

Xu Wang
Jingyuan Zhuo
Zhiyuan You
Zhiyu Tan
Yikuan Yu
Siyu Wang
Xinyi Le

Recent years have witnessed significant advancements in industrial anomaly detection (IAD) thanks to existing anomaly detection datasets. However, the large performance gap between these benchmarks and real industrial practice reveals critical limitations in existing datasets. We argue that the mismatch between current datasets and real industrial scenarios becomes the primary barrier to practical IAD deployment. To this end, we propose ReinAD dataset, a comprehensive contrastive dataset towards Real-world industrial Anomaly Detection. Our dataset prioritizes three critical real-world requirements: 1) Contrast-based anomaly definition that is essential for industrial practice, 2) Fine-grained unaligned image pairs reflecting real inspections, and 3) Large-scale data from active production lines spanning multiple industrial categories. Based on our dataset, we introduce the ReinADNet. It takes both normal reference and test images as inputs, achieving anomaly detection through normal-anomaly comparison. To address the fine-grained and unaligned properties of real industrial scenes, our method integrates pyramidal similarity aggregation for comprehensive anomaly characterization and global-local feature fusion for spatial misalignment tolerance. Our method outperforms all baselines on the ReinAD dataset (e. g. , 64. 5% v. s. 59. 5% in 1-shot image-level AP) under all settings. Extensive experiments across several datasets demonstrate our dataset's challenging nature and our method's superior generalization. This work provides a solid foundation for practical industrial anomaly detection. Dataset and code are available at https: //tocmac. github. io/ReinAD.

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TIST Journal 2025 Journal Article

Retentive Decision Transformer with Adaptive Masking for Reinforcement Learning-Based Recommendation Systems

Siyu Wang
Xiaocong Chen
Lina Yao

Reinforcement Learning-Based Recommender Systems (RLRS) have shown promise across a spectrum of applications, from e-commerce platforms to streaming services. Yet, they grapple with challenges, notably in crafting reward functions and harnessing large pre-existing datasets within the RL framework. Recent advancements in offline RLRS provide a solution for how to address these two challenges. However, existing methods mainly rely on the transformer architecture, which, as sequence lengths increase, can introduce challenges associated with computational resources and training costs. Additionally, the prevalent methods employ fixed-length input trajectories, restricting their capacity to capture evolving user preferences. In this study, we introduce a new offline RLRS method to deal with the above problems. We reinterpret the RLRS challenge by modeling sequential decision-making as an inference task, leveraging adaptive masking configurations. This adaptive approach selectively masks input tokens, transforming the recommendation task into an inference challenge based on varying token subsets, thereby enhancing the agent’s ability to infer across diverse trajectory lengths. Furthermore, we incorporate a multi-scale segmented retention mechanism that facilitates efficient modeling of long sequences, significantly enhancing computational efficiency. Our experimental analysis, conducted on both online simulator and offline datasets, clearly demonstrates the advantages of our proposed method.

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ICML Conference 2022 Conference Paper

Fast Lossless Neural Compression with Integer-Only Discrete Flows

Siyu Wang
Jianfei Chen 0001
Chongxuan Li
Jun Zhu 0001
Bo Zhang 0010

By applying entropy codecs with learned data distributions, neural compressors have significantly outperformed traditional codecs in terms of compression ratio. However, the high inference latency of neural networks hinders the deployment of neural compressors in practical applications. In this work, we propose Integer-only Discrete Flows (IODF) an efficient neural compressor with integer-only arithmetic. Our work is built upon integer discrete flows, which consists of invertible transformations between discrete random variables. We propose efficient invertible transformations with integer-only arithmetic based on 8-bit quantization. Our invertible transformation is equipped with learnable binary gates to remove redundant filters during inference. We deploy IODF with TensorRT on GPUs, achieving $10\times$ inference speedup compared to the fastest existing neural compressors, while retaining the high compression rates on ImageNet32 and ImageNet64.

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ICRA Conference 2021 Conference Paper

Robot Motion Control with Compressive Feedback

Congjian Li
Song Wang
Siyu Wang
Sheng Bi
Yisheng Guan
Ning Xi 0001

Robot motion control aims to generate control inputs for a robotic system to track a planned trajectory. Feedback provided by sensors plays an essential role in motion control by improving system performance when external disturbances and/or initial errors exist. However, feedback signals, such as images are often of a large size, which imposes a heavy computational burden on the system. In this paper, a new robot motion control scheme is proposed based on compressive feedback to improve feedback rate. The controller is designed in non-vector space using compressive feedback. As an application, visual servoing is formulated under the proposed framework by considering a feedback image as a set, instead of a traditional feature vector. Experiments are conducted to validate the proposed scheme.

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RLDM Conference 2017 Conference Abstract

What is the nature of decision noise in random exploration?

Siyu Wang
Robert Wilson

The explore-exploit tradeoff is a fundamental behavioral dilemma faced by all adaptive organ- isms. Should we explore new options in the hopes of finding a better meal, a better house or a better mate, or should we exploit the options we currently believe to be best? Striking the right balance between explo- ration and exploitation is hard computational problem and there is significant interest in how humans and other animals make explore-exploit decisions in practice. One particularly effective strategy for solving the explore-exploit dilemma is choice randomization [1]. In this strategy, the decision process is corrupted by noise meaning that high value “exploit” options are not always chosen and exploratory choices are some- times made by chance. In theory, such “random exploration”, can be surprisingly effective in explore-exploit problems and, if implemented correctly, can come close to optimal performance. Recent work suggests that humans actually use random exploration to solve simple explore-exploit problems [2]. Despite this progress a number of questions remain about the nature of random exploration as there are a number of ways in which seemingly stochastic choices could be generated. In one strategy, that we call the “external noise strategy”, participants could rely on stochasticity in the world and allow irrelevant features of the stimulus to drive choice. In another strategy called “internal noise strategy”, people could rely on stochastic processes within their own brains. In this work, we modified our recently published “Horizon Task” in such a way as to distinguish these two strategies. Using both a model-free and model-based analysis of human behavior we show that, while both types of noise are present in explore-exploit decisions, random exploration is domi- nated by internal noise. This suggests that random exploration depends on adaptive noise processes in the brain which are subject to (perhaps unconscious) cognitive control.

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