Large Language Models often exhibit dishonest behaviors–confidently hallucinating answers to questions beyond their knowledge boundary or hedging on queries they have mastered. Although existing mitigation methods have progressed to establishing the model knowledge boundary, this boundary is still typically treated as a coarse “known vs. unknown” distinction, leaving a finer-grained question underexplored: Does a failure reflect missing knowledge, or merely under-expressed capability? In this paper, we propose KCAPO, a framework that aligns the model’s output behavior with its latent capabilities. It estimates query-level solvability with a consistency-supervised hidden-state probe, then uses its mismatch with response confidence and factual behavior to refine the knowledge boundary into four capability–behavior cases. Based on this fine-grained characterization, we construct preference data to dynamically "Wake Up" dormant knowledge or "Suppress" blind guessing. Furthermore, we introduce an adaptive margin into the DPO objective, which prioritizes correcting responses with severe cognitive dissonance. Extensive experiments on short-form QA and refusal benchmarks show that KCAPO reduces hallucinations without compromising utility and improves selective abstention.

With the generative capabilities of large language models (LLMs) reshaping the information ecosystem, the concern with the sociological validity of claim detection benchmarks is increasing. Current claim detection benchmarks predominantly treat claims as static textual artifacts, overlooking the sociological etiology of how information naturally emerges and mutates. In this paper, we propose an evolutionary paradigm that models claims as socially evolving entities. In specific, we introduce a socially generative framework for synthetic claim generation, a multi-agent simulation grounded in the Open Claims Model. By decomposing claims into context, utterance, and proposition, our approach enables the precise simulation of unmitigated propagation to capture truth decay, and intervened propagation with multi-auditor oversight for targeted generation. Furthermore, we propose the background-user-perspective (BUP) framework, which reformulates check-worthiness as a condition-dependent probability rooted in social environment. Experiments on our datasets verify the data quality and reveal how network topology and user attributes systematically shape veracity drift.