Author name cluster

Nan Sun

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6 papers

2 author rows

AAAI Conference 2026 Conference Paper

AMS-IO-Bench and AMS-IO-Agent: Benchmarking and Structured Reasoning for Analog and Mixed-Signal Integrated Circuit Input/Output Design

Zhishuai Zhang
Xintian Li
Shilong Liu
Aodong Zhang
Lu Jie
Nan Sun

In this paper, we propose AMS-IO-Agent, a domain-specialized LLM-based agent for structure-aware input/output (I/O) subsystem generation in analog and mixed-signal (AMS) integrated circuits (ICs). The central contribution of this work is a framework that connects natural language design intent with industrial-level AMS IC design deliverables. AMS-IO-Agent integrates two key capabilities: (1) a structured domain knowledge base that captures reusable constraints and design conventions; (2) design intent structuring, which converts ambiguous user intent into verifiable logic steps using JSON and Python as intermediate formats. We further introduce AMS-IO-Bench, a benchmark for wirebond-packaged AMS I/O ring automation. On this benchmark, AMS-IO-Agent achieves over 70% DRC+LVS pass rate and reduces design turnaround time from hours to minutes, outperforming the baseline LLM. Furthermore, an agent-generated I/O ring was fabricated and validated in a 28 nm CMOS tape-out, demonstrating the practical effectiveness of the approach in real AMS IC design flows. To our knowledge, this is the first reported human-agent collaborative AMS IC design in which an LLM-based agent completes a nontrivial subtask with outputs directly used in silicon.

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AAAI Conference 2026 Conference Paper

GlyphShield: Document Watermarking for the Physical World via Vector Typeface Synthesis

Nan Sun
Yuxing Lu
Han Fang
Hefei Ling
Sijing Xie
LuYu Yuan
Chengxin Zhao

Document protection has become a critical issue for preventing unauthorized copying, distribution, and tampering. Document encryption is a proven solution, but it is not resistant to attacks from the physical world such as screenshots, printing and photographing. A common document protection technique is font-based watermarking, which embeds imperceptible information by using sets of visually similar glyphs to encode traceable data. However, due to the non-differentiable rendering process of vector fonts, these methods often rely on time-consuming and laborious manual design. To address this challenge, we present GlyphShield, an innovative end-to-end vector font watermarking framework. We resolve the non-differentiability challenge by simulating differentiable rasterization through the computation of Signed Distance Field (SDF) for Bézier curves in vector fonts. Besides, to handle complex vector font structures, a novel dual-branch vector encoder is employed to ensure high-quality font synthesis. Extensive experiments demonstrate that our approach ensures more natural and smoother message embedding while ensuring robustness against noise attacks in diverse scenarios. Additionally, our framework demonstrates strong generalization across various font styles and languages.

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IROS Conference 2025 Conference Paper

AssistantX: An LLM-Powered Proactive Assistant in Collaborative Human-Populated Environments

Nan Sun
Bo Mao
Yongchang Li
Di Guo 0002
Huaping Liu 0001

Current service robots suffer from limited natural language communication abilities, heavy reliance on predefined commands, ongoing human intervention, and, most notably, a lack of proactive collaboration awareness in human-populated environments. This results in narrow applicability and low utility. In this paper, we introduce AssistantX, an LLM-powered proactive assistant designed for autonomous operation in real-world scenarios with high accuracy. AssistantX employs a multi-agent framework consisting of 4 specialized LLM agents, each dedicated to perception, planning, decision-making, and reflective review, facilitating advanced inference capabilities and comprehensive collaboration awareness, much like a human assistant by your side. We built a dataset of 210 real-world tasks to validate AssistantX, which includes instruction content and status information on whether relevant personnel are available. Extensive experiments were conducted in both text-based simulations and a real office environment over the course of a month and a half. Our experiments demonstrate the effectiveness of the proposed framework, showing that AssistantX can reactively respond to user instructions, actively adjust strategies to adapt to contingencies, and proactively seek assistance from humans to ensure successful task completion. More details and videos can be found at https://assistantx-agent.github.io/AssistantX/.

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AAAI Conference 2025 Conference Paper

END^2: Robust Dual-Decoder Watermarking Framework Against Non-Differentiable Distortions

Nan Sun
Han Fang
Yuxing Lu
Chengxin Zhao
Hefei Ling

DNN-based watermarking methods have rapidly advanced, with the ``Encoder-Noise Layer-Decoder'' (END) framework being the most widely used. To ensure end-to-end training, the noise layer in the framework must be differentiable. However, real-world distortions are often non-differentiable, leading to challenges in end-to-end training. Existing solutions only treat the distortion perturbation as additive noise, which does not fully integrate the effect of distortion in training. To better incorporate non-differentiable distortions into training, we propose a novel dual-decoder architecture (END^2). Unlike conventional END architecture, our method employs two structurally identical decoders: the Teacher Decoder, processing pure watermarked images, and the Student Decoder, handling distortion-perturbed images. The gradient is backpropagated only through the Teacher Decoder branch to optimize the encoder thus bypassing the problem of non-differentiability. To ensure resistance to arbitrary distortions, we enforce alignment of the two decoders' feature representations by maximizing the cosine similarity between their intermediate vectors on a hypersphere. Extensive experiments demonstrate that our scheme outperforms state-of-the-art algorithms under various non-differentiable distortions. Moreover, even without the differentiability constraint, our method surpasses baselines with a differentiable noise layer. Our approach is effective and easily implementable across all END architectures, enhancing practicality and generalizability.

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NeurIPS Conference 2025 Conference Paper

Ultra-high Resolution Watermarking Framework Resistant to Extreme Cropping and Scaling

Nan Sun
LuYu Yuan
Han Fang
Yuxing Lu
Hefei Ling
Sijing Xie
Chengxin Zhao

Recent developments in DNN-based image watermarking techniques have achieved impressive results in protecting digital content. However, most existing methods are constrained to low-resolution images as they need to encode the entire image, leading to prohibitive memory and computational costs when applied to high-resolution images. Moreover, they lack robustness to distortions prevalent in large-image transmission, such as extreme scaling and random cropping. To address these issues, we propose a novel watermarking method based on implicit neural representations (INRs). Leveraging the properties of INRs, our method employs resolution-independent coordinate sampling mechanism to generate watermarks pixel-wise, achieving ultra-high resolution watermark generation with fixed and limited memory and computational resources. This design ensures strong robustness in watermark extraction, even under extreme cropping and scaling distortions. Additionally, we introduce a hierarchical multi-scale coordinate embedding and a low-rank watermark injection strategy to ensure high-quality watermark generation and robust decoding. Experimental results demonstrate that our method significantly outperforms existing schemes in terms of both robustness and computational efficiency while preserving high image quality. Our approach achieves an accuracy greater than 98\% in watermark extraction with only 0. 4\% of the image area in 2K images. These results highlight the effectiveness of our method, making it a promising solution for large-scale and high-resolution image watermarking applications.

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IJCAI Conference 2024 Conference Paper

Enhancing Multimodal Knowledge Graph Representation Learning through Triple Contrastive Learning

Yuxing Lu
Weichen Zhao
Nan Sun
Jinzhuo Wang

Multimodal knowledge graphs incorporate multimodal information rather than pure symbols, which significantly enhance the representation of knowledge graphs and their capacity to understand the world. Despite these advancements, existing multimodal fusion techniques still face significant challenges in representing modalities and fully integrating the diverse attributes of entities, particularly when dealing with more than one modality. To address this issue, this article proposes a Knowledge Graph Multimodal Representation Learning (KG-MRI) method. This method utilizes foundation models to represent different modalities and incorporates a triple contrastive learning model and a dual-phase training strategy to effectively fuse the different modalities with knowledge graph embeddings. We conducted comprehensive comparisons with several different knowledge graph embedding methods to validate the effectiveness of our KG-MRI model. Furthermore validation on a real-world Non-Alcohol Fatty Liver Disease (NAFLD) cohort demonstrated that the vector representations learned through our methodology possess enhanced representational capabilities, showing promise for broader applications in complex multimodal environments.

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