8 citations · 10 across the 12 of their papers we have counts for
12 papers
SPK: Eliciting Structured Prior Knowledge for Interpretable Out-of-Distribution Detection in Real-Time Object Detection
Changshun Wu, Weicheng He, Xiaowei Huang +1
Object detectors often produce over-confident predictions for objects outside their training categories, leading to so-called out-of-distribution (OoD) hallucinations. Existing app…
Position: A Three-Layer Probabilistic Assume-Guarantee Architecture Is Structurally Required for Safe LLM Agent Deployment
S. Bensalem, Y. Dong, M. Franzle +6
This position paper argues that enforcing LLM agent safety within a single abstraction layer is not merely suboptimal but categorically insufficient for deployed LLM agents -- a st…
Safety-Constrained Reinforcement Learning with Post-Training Reachability Verification for Robot Navigation
Qisong He, Xinmiao Huang, Jinwei Hu +4
Safe navigation for mobile robots demands policies that remain reliable under the high-consequence perception uncertainty of cluttered environments. Yet most existing safe reinforc…
PrefixGuard: Online Failure Warning and Trace-Grounded Diagnosis for LLM Agents
Xinmiao Huang, Jinwei Hu, Rajarshi Roy +4
Large language model (LLM) agents execute multi-step tasks, but terminal verdicts arrive too late for intervention. Agent traces mix messages, tool calls, and feedback, making it d…
What Matters to an LLM? Behavioral and Computational Evidences from Summarization
Yongxin Zhou, Changshun Wu, Philippe Mulhem +2
Large Language Models (LLMs) are now state-of-the-art at summarization, yet the internal notion of importance that drives their information selections remains hidden. We propose to…
Randomized Smoothing Meets Vision-Language Models
Emmanouil Seferis, Changshun Wu, Stefanos Kollias +2
Randomized smoothing (RS) is one of the prominent techniques to ensure the correctness of machine learning models, where point-wise robustness certificates can be derived analytica…
LoRA-BAM: Input Filtering for Fine-tuned LLMs via Boxed Abstraction Monitors over LoRA Layers
Changshun Wu, Tianyi Duan, Saddek Bensalem +1
Fine-tuning large language models (LLMs) improves performance on domain-specific tasks but can lead to overfitting, making them unreliable on out-of-distribution (OoD) queries. We…
Revisiting Out-of-Distribution Detection in Real-time Object Detection: From Benchmark Pitfalls to a New Mitigation Paradigm
Changshun Wu, Weicheng He, Chih-Hong Cheng +2
Out-of-distribution (OoD) inputs pose a persistent challenge to deep learning models, often triggering overconfident predictions on non-target objects. While prior work has primari…
Safeguarding Large Language Models: A Survey
Yi Dong, Ronghui Mu, Yanghao Zhang +9
In the burgeoning field of Large Language Models (LLMs), developing a robust safety mechanism, colloquially known as "safeguards" or "guardrails", has become imperative to ensure t…
Runtime Monitoring and Enforcement of Conditional Fairness in Generative AIs
Chih-Hong Cheng, Changshun Wu, Xingyu Zhao +2
The deployment of generative AI (GenAI) models raises significant fairness concerns, addressed in this paper through novel characterization and enforcement techniques specific to G…
BAM: Box Abstraction Monitors for Real-time OoD Detection in Object Detection
Changshun Wu, Weicheng He, Chih-Hong Cheng +2
Out-of-distribution (OoD) detection techniques for deep neural networks (DNNs) become crucial thanks to their filtering of abnormal inputs, especially when DNNs are used in safety-…
A Digital Twin prototype for traffic sign recognition of a learning-enabled autonomous vehicle
Mohamed AbdElSalam, Loai Ali, Saddek Bensalem +6
In this paper, we present a novel digital twin prototype for a learning-enabled self-driving vehicle. The primary objective of this digital twin is to perform traffic sign recognit…