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A*-Thought-V2 is presented, a geometric dynamics of LLM guided framework that models CoT as a hidden-state trajectory and replaces hard deletion with an explicit-implicit interleaved latent architecture that reflects broader soft targets that encourage richer step-level feature learning.
L DeLFE, a lightweight controlled detoxification approach that identifies specific toxic features across model layers and performs targeted interventions on them, and designs three feature-level strategies that adjust intervention timing and strength to reconstruct the target model’s original activations.
This work introduces a type-induced commitment-communication mechanism that grounds Bayesian Persuasion in natural language dialogue without pre-commitment, and implements two variants: Semi-Formal-Natural-Language (SFNL) and Fully-Natural-Language (FNL), evaluating them against strong baselines and human judges.
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