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cs.CL2026

Before You Continue: Evaluating Time-Sensitive Safety Awareness in Conversational Assistants

Peidi Dong, Yunze Xiao, Yu-Lin Tsai +5

Large language model (LLM) assistants increasingly help users with everyday activities that involve time-dependent constraints. However, users may not mention these constraints in…

cs.CL2026

IdeaScientist: Orchestrating Agents for Grounded Scientific Ideation

Jiarui Liu, Renjie Tao, Yiwei Liao +15

Despite rapid progress in automating scientific research, generating promising and well grounded research solutions remains a central challenge. We isolate research ideation as a s…

cs.CL2026

On the Behavioral Traits of LLM Agents

Haokai Zhao, Jie Gao, Yunze Xiao +5

Users increasingly describe different AI agents as distinct colleagues to work with. AI personality research aims to quantify such impressions by attributing human- like "traits" t…

cs.CL2026

Group Alignment-Induced Sycophancy: A Two-Sided Evaluation of Steerable Pluralistic Alignment

Haokai Zhao, Yunze Xiao, Weihao Xuan +3

Group alignment adapts a language model to a demographic group to produce responses that reflect the group's opinions, values, and preferences. Sycophancy, a well-documented by-pro…

cs.CL2026

MixSD: Mixed Contextual Self-Distillation for Knowledge Injection

Jiarui Liu, Lechen Zhang, Yongjin Yang +5

Supervised fine-tuning (SFT) is widely used to inject new knowledge into language models, but it often degrades pretrained capabilities such as reasoning and general-domain perform…

cs.CL2026

Is LLM's Moral Decision Compositional?

Weijia Zhang, Ruiqi Chen, Yunze Xiao +1

Moral evaluations of language models typically infer a model's values from its choices between single acts, yet in everyday life a person is judged on their history of good and bad…

cs.CL2026

The Chameleon's Limit: Investigating Persona Collapse and Homogenization in Large Language Models

Yunze Xiao, Vivienne J. Zhang, Chenghao Yang +3

Large language models (LLMs) have powered multi-agent simulations such as survey respondents or application users. However, LLMs lack the population-level diversity required by the…

cs.CL2026

Towards Valid Student Simulation with Large Language Models

Zhihao Yuan, Yunze Xiao, Ming Li +4

This paper presents a conceptual and methodological framework for large language model (LLM) based student simulation in educational settings. The authors identify a core failure m…