3 citations · 6 across the 12 of their papers we have counts for
10 papers · 1 filter
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…
Equitable System-Prompt Selection via Constrained Mixed-Strategy GroupDRO
Mengyu Xu, Qiaoxin Yang, Zhihan Liu +4
Large language models are increasingly used for information seeking, yet semantically equivalent questions phrased in different ways can receive answers of considerably different q…
The Text Uncanny Valley: Non-Monotonic Performance Degradation in LLM Information Retrieval
Zekai Tong, Ruiyao Xu, Aryan Shrivastava +2
Existing Large Language Model (LLM) benchmarks primarily focus on syntactically correct inputs, leaving a significant gap in evaluation on imperfect text. In this work, we study ho…
Geometry-Calibrated Conformal Abstention for Language Models
Rui Xu, Yi Chen, Sihong Xie +1
When language models lack relevant knowledge for a given query, they frequently generate plausible responses that can be hallucinations, rather than admitting being agnostic about…
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…