3 citations · 4 across the 17 of their papers we have counts for
18 papers
FlexRouter: Learning Complementary Model Sets for Flexible LLM Routing
Wang Wei, Harry Yang, Tiankai Yang +6
Existing Large Language Model (LLM) routing methods score LLMs independently to select top- models. However, this ignores model correlations and enforces a rigid computational b…
Beyond Top- Skill Retrieval: Diversity-Aware Skill Routing for LLM Agents
Wang Wei, Tiankai Yang, Samyadeep Basu +7
Large language model (LLM) agents increasingly rely on external skills, but routing user requests over large skill registries is difficult because many skills are functionally redu…
HyperSkill: Self-Evolving LLM Agents via Hypergraph-Structured Skill Memory
Ruiyao Xu, Tiankai Yang, Wei-Chieh Huang
As agentic tasks grow in complexity, LLM agents increasingly rely on experiential memory to reuse procedural knowledge across tasks. Effective memory design must jointly address wh…
DOG-DPO:Dynamic Optimization in Geometry for Safety Alignment
Yi Nian, Tiankai Yang, Yudi Zhang +7
Safety alignment for large language models relies on preference data, but current pipelines often train on large, redundant datasets. Existing data selection methods typically scor…
CoAct: Co-Active LLM Preference Learning with Human-AI Synergy
Ruiyao Xu, Mihir Parmar, Tiankai Yang +3
Learning from preference-based feedback has become an effective approach for aligning LLMs across diverse tasks. However, high-quality human-annotated preference data remains expen…
Cat-DPO: Category-Adaptive Safety Alignment
Tiankai Yang, Yi Nian, Xinyuan Li +6
Aligning large language models with human preferences must balance two competing goals: responding helpfully to legitimate requests and reliably refusing harmful ones. Most prefere…
No Attacker Needed: Unintentional Cross-User Contamination in Shared-State LLM Agents
Tiankai Yang, Jiate Li, Yi Nian +5
LLM-based agents increasingly operate across repeated sessions, maintaining task states to ensure continuity. In many deployments, a single agent serves multiple users within a tea…
Learning to Route LLMs from Bandit Feedback: One Policy, Many Trade-offs
Wang Wei, Tiankai Yang, Hongjie Chen +4
Efficient use of large language models (LLMs) is critical for deployment at scale: without adaptive routing, systems either overpay for strong models or risk poor performance from…
M3OOD: Automatic Selection of Multimodal OOD Detectors
Yuehan Qin, Li Li, Defu Cao +3
Out-of-distribution (OOD) robustness is a critical challenge for modern machine learning systems, particularly as they increasingly operate in multimodal settings involving inputs…
A Personalized Conversational Benchmark: Towards Simulating Personalized Conversations
Li Li, Peilin Cai, Ryan A. Rossi +21
We present PersonaConvBench, a large-scale benchmark for evaluating personalized reasoning and generation in multi-turn conversations with large language models (LLMs). Unlike exis…
StealthRank: LLM Ranking Manipulation via Stealthy Prompt Optimization
Yiming Tang, Yi Fan, Chenxiao Yu +3
The integration of large language models (LLMs) into information retrieval systems introduces new attack surfaces, particularly for adversarial ranking manipulations. We present $\…
AD-AGENT: A Multi-agent Framework for End-to-end Anomaly Detection
Tiankai Yang, Junjun Liu, Wingchun Siu +6
Anomaly detection (AD) is essential in areas such as fraud detection, network monitoring, and scientific research. However, the diversity of data modalities and the increasing numb…
Efficient Model Selection for Time Series Forecasting via LLMs
Wang Wei, Tiankai Yang, Hongjie Chen +4
Model selection is a critical step in time series forecasting, traditionally requiring extensive performance evaluations across various datasets. Meta-learning approaches aim to au…
Treble Counterfactual VLMs: A Causal Approach to Hallucination
Shawn Li, Jiashu Qu, Yuxiao Zhou +3
Vision-Language Models (VLMs) have advanced multi-modal tasks like image captioning, visual question answering, and reasoning. However, they often generate hallucinated outputs inc…
PyOD 2: A Python Library for Outlier Detection with LLM-powered Model Selection
Sihan Chen, Zhuangzhuang Qian, Wingchun Siu +10
Outlier detection (OD), also known as anomaly detection, is a critical machine learning (ML) task with applications in fraud detection, network intrusion detection, clickstream ana…
AD-LLM: Benchmarking Large Language Models for Anomaly Detection
Tiankai Yang, Yi Nian, Shawn Li +9
Anomaly detection (AD) is an important machine learning task with many real-world uses, including fraud detection, medical diagnosis, and industrial monitoring. Within natural lang…
NLP-ADBench: NLP Anomaly Detection Benchmark
Yuangang Li, Jiaqi Li, Zhuo Xiao +4
Anomaly detection (AD) is an important machine learning task with applications in fraud detection, content moderation, and user behavior analysis. However, AD is relatively underst…
DPU: Dynamic Prototype Updating for Multimodal Out-of-Distribution Detection
Shawn Li, Huixian Gong, Hao Dong +3
Out-of-distribution (OOD) detection is essential for ensuring the robustness of machine learning models by identifying samples that deviate from the training distribution. While tr…