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Zhuan Shi

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#machine learning Preprint Sep 2026

LLM Persona Unlearning

Pre-training equips large language models (LLMs) with a broad repertoire of behavioral patterns associated with roles, styles, values, and goals. Post-training teaches conditional enactment and makes a helpful Assistant the default, but it does not erase alternative modes from the weights; explicit prompts can therefor...

Ke-Mou Li, Zhuan Shi, Qi-Zhou Wang et al. · 0 citations
Review Aug 2026

IP Protection in the Era of Visual Generative AI: A Survey

This survey presents a two-dimensional taxonomy for IP protection in visual generative models, which systematically review protection methods, align evaluation protocols with protection objectives, and discusses open challenges including proactive model-level safeguards, standardized evaluation, robustness against adap...

Zhuan Shi, Shun-Chang Liu, Alireza Dehghanpour Farashah et al. · 0 citations
#artificial intelligence Review Sep 2026

CABAL: Multi-Agent Simulacra for Tracing the Effects of Collusive Bidding in Peer Review

Alg is introduced, an end-to-end multi-agent simulacra framework for studying reviewer assignment integrity by holding the conference environment fixed and configuring LLM-driven reviewer agents with honest or collusive policies, and an affinity-guided collusive bidding strategy that uses mutual reviewer-paper affiniti...

Ji-Cheng Zhou, Ke-Mou Li, Kahim Wong et al. · 1 citation
Preprint Aug 2026

TEA: Text Encoder Alignment for Robust Concept Erasure in Text-to-Image Models

A lightweight Text Encoder Alignment framework that fine-tunes only the text encoder while keeping the generative backbone fully frozen, and achieves state-of-the-art erasure robustness against black-box and white-box adversarial attacks on Stable Diffusion v1.4, while preserving generation quality on benign prompts.

Alireza Dehghanpour Farashah, Zhuan Shi, Negar Rostamzadeh et al. · 0 citations

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