@inproceedings{chu-etal-2026-faithful,
title = "Faithful Persona Steering under Incongruity via Dual-Stream Refinement",
author = "Chu, Yu-An and
Pong, Jen-Ren and
Yeh, Chia-Yao and
Chiang, Meng-Fen",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.1205/",
pages = "24078--24095",
ISBN = "979-8-89176-395-1",
abstract = "Standard LLM personalization typically frames identity as a static retrieval task, overlooking the inherent incongruity of human personas, where stable traits coexist with atypical, context-specific stances. Existing methods struggle to reconcile these dimensions: prompting succumbs to context drift over long sequences, while fine-tuning often suppresses idiosyncratic ``quirks'' in favor of generic distributional patterns. To bridge this gap, we present QuirkyMind, a framework that disentangles identity definition from its expression. First, Traits Anchoring constructs a dual-stream latent state, fusing a sentence-level summary for semantic stability with a token-level sequence for generative control. This state is stabilized via In-Context Narrative Refinement using an alternating objective: a discriminative InfoNCE loss anchors the persona in representation space to prevent drift, while a generative cross-entropy loss ensures faithful verbalization. Finally, Persona Steered Generalization transfers the refined state to downstream tasks via parameter-efficient adapters. Empirical evaluations on Persona-Steered QA and Narrative Inference demonstrate that QuirkyMind mitigates drift, consolidating persona knowledge without erasing authentic incongruities."
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<abstract>Standard LLM personalization typically frames identity as a static retrieval task, overlooking the inherent incongruity of human personas, where stable traits coexist with atypical, context-specific stances. Existing methods struggle to reconcile these dimensions: prompting succumbs to context drift over long sequences, while fine-tuning often suppresses idiosyncratic “quirks” in favor of generic distributional patterns. To bridge this gap, we present QuirkyMind, a framework that disentangles identity definition from its expression. First, Traits Anchoring constructs a dual-stream latent state, fusing a sentence-level summary for semantic stability with a token-level sequence for generative control. This state is stabilized via In-Context Narrative Refinement using an alternating objective: a discriminative InfoNCE loss anchors the persona in representation space to prevent drift, while a generative cross-entropy loss ensures faithful verbalization. Finally, Persona Steered Generalization transfers the refined state to downstream tasks via parameter-efficient adapters. Empirical evaluations on Persona-Steered QA and Narrative Inference demonstrate that QuirkyMind mitigates drift, consolidating persona knowledge without erasing authentic incongruities.</abstract>
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%0 Conference Proceedings
%T Faithful Persona Steering under Incongruity via Dual-Stream Refinement
%A Chu, Yu-An
%A Pong, Jen-Ren
%A Yeh, Chia-Yao
%A Chiang, Meng-Fen
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F chu-etal-2026-faithful
%X Standard LLM personalization typically frames identity as a static retrieval task, overlooking the inherent incongruity of human personas, where stable traits coexist with atypical, context-specific stances. Existing methods struggle to reconcile these dimensions: prompting succumbs to context drift over long sequences, while fine-tuning often suppresses idiosyncratic “quirks” in favor of generic distributional patterns. To bridge this gap, we present QuirkyMind, a framework that disentangles identity definition from its expression. First, Traits Anchoring constructs a dual-stream latent state, fusing a sentence-level summary for semantic stability with a token-level sequence for generative control. This state is stabilized via In-Context Narrative Refinement using an alternating objective: a discriminative InfoNCE loss anchors the persona in representation space to prevent drift, while a generative cross-entropy loss ensures faithful verbalization. Finally, Persona Steered Generalization transfers the refined state to downstream tasks via parameter-efficient adapters. Empirical evaluations on Persona-Steered QA and Narrative Inference demonstrate that QuirkyMind mitigates drift, consolidating persona knowledge without erasing authentic incongruities.
%U https://aclanthology.org/2026.findings-acl.1205/
%P 24078-24095
Markdown (Informal)
[Faithful Persona Steering under Incongruity via Dual-Stream Refinement](https://aclanthology.org/2026.findings-acl.1205/) (Chu et al., Findings 2026)
ACL