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.

Argumentative essay generation (AEG) is a complex task that requires advanced semantic understanding, logical reasoning, and organized integration of perspectives. Despite showing a promising performance, current efforts often overlook the dynamical and hierarchical nature of structural argumentative planning, and struggle with flexible rhetorical expression, leading to limited argument divergence and rhetorical optimization. Inspired by human debate behavior and Bitzer’s rhetorical situation theory, we propose a debate-driven rhetorical framework for argumentative writing. The uniqueness lies in three aspects: (1) dynamic assesses the divergence of viewpoints and progressively reveals the hierarchical outline of arguments based on a depth-then-breadth paradigm, improving the perspective divergence within argumentation; (2) simulates human debate through iterative defender-attacker interactions, improving the logical coherence of arguments; (3) incorporates Bitzer’s rhetorical situation theory to flexibly select appropriate rhetorical techniques, enabling the rhetorical expression. Experiments on four benchmarks validate that our approach significantly improves logical depth, argumentative diversity, and rhetorical persuasiveness over existing state-of-the-art models.