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20242026
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cs.CL2026

ArcANE: Do Role-Playing Language Agents Stay in Character at the Right Time?

Woojung Song, Nalim Kim, Sangjun Song +3

Role-playing language agents (RPLAs) should play characters whose values and behavior evolve as the story progresses, not maintain a fixed persona. Existing benchmarks measure fact…

cs.CL2026

Bridging the Knowledge-Prediction Gap in LLMs on Multiple-Choice Questions

Yoonah Park, Haesung Pyun, Yohan Jo

While large language models (LLMs) perform strongly on diverse tasks, their trustworthiness is limited by erratic behavior that is unfaithful to their internal knowledge. In partic…

cs.CL2026

Dual Mechanisms of Value Expression: Intrinsic vs. Prompted Values in Large Language Models

Jongwook Han, Jongwon Lim, Injin Kong +1

Large language models can express values in two main ways: (1) intrinsic expression, reflecting the model's inherent values learned during training, and (2) prompted expression, el…

cs.CL2026

Human Psychometric Questionnaires Mischaracterize LLM Behavior

Woojung Song, Dongmin Choi, Yoonah Park +3

We examine whether human psychometric questionnaires can serve as reliable tools for characterizing and predicting LLM behavior in everyday user interactions. We analyze eight open…

cs.CL2026

Psychometric Item Validation Using Virtual Respondents with Trait-Response Mediators

Sungjib Lim, Woojung Song, Eun-Ju Lee +1

As psychometric surveys are increasingly used to assess the traits of large language models (LLMs), the need for scalable survey item generation suited for LLMs has also grown. A c…

cs.CL2026

Where Should Diffusion Enter a Language Model? Geometry-Guided Hidden-State Replacement

Injin Kong, Hyoungjoon Lee, Yohan Jo

Continuous diffusion language models lag behind autoregressive transformers, partly because diffusion is applied in spaces poorly suited to language denoising and token recovery. W…