2 citations · 3 across the 30 of their papers we have counts for
33 papers
Gaussian Material Fields for Volumetric Multi-Energy CT Decomposition
Jian Lin, Jiancheng Fang, Hongming Shan +3
Volumetric material decomposition in multi-energy computed tomography requires a representation that organizes multiple three-dimensional material fields in a common spatial domain…
Hallucination Across the Reasoning Lifecycle: Interface Visibility, Causal Evidence, and Release Control in Large Reasoning Models
Zhe Yu, Mohan Li, Lei Yu +6
Reasoning errors can propagate into later decisions and memory. This survey synthesizes 312 papers and first-party reports on text-based reasoning hallucinations around three quest…
Readable Before Actionable: Causal Tracing of Indirect Prompt Injection
Zhe Yu, Wenpeng Xing, Xingxing Yang +1
Indirect prompt injection causes LLM agents to follow commands embedded in external data. A probe may distinguish instructions from data without identifying a state edit that chang…
TTS-Guard: Black-Box Ownership Verification of Text-to-Speech Models via Adaptive Adversarial Speaker-Pair Fingerprints
Xubin Yue, Zhenhua Xu, Zhebo Wang +5
The rapid maturation of zero-shot Text-to-Speech (TTS) models has turned high-quality voice cloning into a widely available capability, raising acute concerns over unauthorised rep…
FIDES: Faithful Inference via Deep Evidence Signals for Retrieval-Memory Conflict in RAG
Zhe Yu, Wenpeng Xing, Tiancheng Zhao +3
When retrieved evidence contradicts parametric memory, language models frequently ignore context and default to memorized priors -- a failure that undermines the core purpose of re…
TriLens: Per-Layer Logit-Lens Entropy for White-Box Hallucination Detection
Bohan Yang, Yijun Gong, Zhi Zhang +3
When a language model hallucinates, the final answer is wrong, but the mistake is not necessarily invisible inside the model. Different internal pathways may remain uncertain, disa…
Detecting Is Not Resolving: The Monitoring Control Gap in Retrieval Augmented LLMs
Zhe Yu, Wenpeng Xing, Chen Ye +4
Retrieval-augmented LLMs are deployed for tasks where evidence quality determines action safety, yet evaluation protocols assume that single-turn robustness predicts robustness whe…
Composition Collapse: Stable Factual Knowledge Does Not Imply Compositional Reasoning
Zhe Yu, Wenpeng Xing, Yunzhao Wei +4
Post-training is routinely evaluated through aggregate benchmark scores that treat multi-hop reasoning as a single capability -- as if a model that answers more questions correctly…
The Attribution Blind Spot: Detecting When Language Models Rely on Memory Rather Than Retrieved Context
Zhe Yu, Wenpeng Xing, Yunzhao Wei +4
Retrieval-augmented generation promises to ground language model outputs in external evidence, yet the field has no reliable way to verify whether retrieved context actually govern…
Cordon-MAS: Defending RAG against Knowledge Poisoning via Information-Flow Control
Zhe Yu, Wenpeng Xing, Gaolei Li +4
Retrieval-augmented generation (RAG) increasingly underpins high-stakes applications, yet remains vulnerable to Confundo-style poisoning where adversarially optimized documents man…
Silencing the Guardrails: Inference-Time Jailbreaking via Dynamic Contextual Representation Ablation
Wenpeng Xing, Moran Fang, Guangtai Wang +2
While Large Language Models (LLMs) have achieved remarkable performance, they remain vulnerable to jailbreak attacks that circumvent safety constraints. Existing strategies, rangin…
MO-RiskVAE: A Multi-Omics Variational Autoencoder for Survival Risk Modeling in Multiple MyelomaMO-RiskVAE
Zixuan Chen, Heng Zhang, YuPeng Qin +5
Multimodal variational autoencoders (VAEs) have emerged as a powerful framework for survival risk modeling in multiple myeloma by integrating heterogeneous omics and clinical data.…
LatentAudit: Real-Time White-Box Faithfulness Monitoring for Retrieval-Augmented Generation with Verifiable Deployment
Zhe Yu, Wenpeng Xing, Meng Han
Retrieval-augmented generation (RAG) mitigates hallucination but does not eliminate it: a deployed system must still decide, at inference time, whether its answer is actually suppo…
From Retinal Evidence to Safe Decisions: RETINA-SAFE and ECRT for Hallucination Risk Triage in Medical LLMs
Zhe Yu, Wenpeng Xing, Meng Han
Hallucinations in medical large language models (LLMs) remain a safety-critical issue, particularly when available evidence is insufficient or conflicting. We study this problem in…
KinGuard: Hierarchical Kinship-Aware Fingerprinting to Defend Against Large Language Model Stealing
Zhenhua Xu, Xiaoning Tian, Wenjun Zeng +5
Protecting the intellectual property of large language models requires robust ownership verification. Conventional backdoor fingerprinting, however, is flawed by a stealth-robustne…
ICPO: Illocution-Calibrated Policy Optimization for Multi-Turn Conversation
Zhebo Wang, Xiaohu Mu, Zijie Zhou +4
Large Language Models (LLMs) in multi-turn conversations often suffer from a ``lost-in-conversation'' phenomenon, where they struggle to recover from early incorrect assumptions, p…
ForgetMark: Stealthy Fingerprint Embedding via Targeted Unlearning in Language Models
Zhenhua Xu, Haobo Zhang, Zhebo Wang +4
Existing invasive (backdoor) fingerprints suffer from high-perplexity triggers that are easily filtered, fixed response patterns exposed by heuristic detectors, and spurious activa…
DIAP: A Decentralized Agent Identity Protocol with Zero-Knowledge Proofs and a Hybrid P2P Stack
Yuanjie Liu, Wenpeng Xing, Ye Zhou +3
The absence of a fully decentralized, verifiable, and privacy-preserving communication protocol for autonomous agents remains a core challenge in decentralized computing. Existing…
EverTracer: Hunting Stolen Large Language Models via Stealthy and Robust Probabilistic Fingerprint
Zhenhua Xu, Meng Han, Wenpeng Xing
The proliferation of large language models (LLMs) has intensified concerns over model theft and license violations, necessitating robust and stealthy ownership verification. Existi…
Spectral Logit Sculpting: Adaptive Low-Rank Logit Transformation for Controlled Text Generation
Jin Li, Zhebo Wang, Tianliang Lu +3
Entropy-based inference methods have gained traction for improving the reliability of Large Language Models (LLMs). However, many existing approaches, such as entropy minimization…
PREE: Towards Harmless and Adaptive Fingerprint Editing in Large Language Models via Knowledge Prefix Enhancement
Xubin Yue, Zhenhua Xu, Wenpeng Xing +3
Addressing the intellectual property protection challenges in commercial deployment of large language models (LLMs), existing black-box fingerprinting techniques face dual challeng…
CoDe-NeRF: Neural Rendering via Dynamic Coefficient Decomposition
Wenpeng Xing, Jie Chen, Zaifeng Yang +5
Neural Radiance Fields (NeRF) have shown impressive performance in novel view synthesis, but challenges remain in rendering scenes with complex specular reflections and highlights.…
UW-3DGS: Underwater 3D Reconstruction with Physics-Aware Gaussian Splatting
Wenpeng Xing, Jie Chen, Zaifeng Yang +5
Underwater 3D scene reconstruction faces severe challenges from light absorption, scattering, and turbidity, which degrade geometry and color fidelity in traditional methods like N…
SproutBench: A Benchmark for Safe and Ethical Large Language Models for Youth
Wenpeng Xing, Lanyi Wei, Haixiao Hu +5
The rapid proliferation of large language models (LLMs) in applications targeting children and adolescents necessitates a fundamental reassessment of prevailing AI safety framework…