Rerankers are critical in Retrieval-Augmented Generation (RAG) for filtering evidence that enhances the accurate generation of LLMs. With the extension to open-domain scenarios, rerankers are inevitably deployed on mixed-style corpora, whereas most existing rerankers are mainly trained on well-edited texts. A rarely explored issue lies in enabling rerankers to maximally capture the effective knowledge for downstream LLMs without being misled by stylistic features. To address this issue, we propose SARK (Style-Adaptive Reranker with Knowledge Prioritization), a style-augmented multi-task framework that prioritizes effective knowledge over stylistic perturbations. SARK performs multi-granular knowledge mining by using an LLM to derive passage-level supervision on whether a passage helps or harms answer correctness, and list-level relative ranking preferences over candidate passages. It then jointly optimizes the reranker model with passage-level classification and list-level ranking objectives via style-augmented multi-task learning, encouraging the model to focus on the information needed for answering under mixed-style scenarios. Extensive experiments demonstrate that SARK improves generation performance across multiple LLMs under mixed-style conditions.

Proactive questioning is essential in psychological conversations as it helps uncover deeper issues and unspoken concerns. Current psychological LLMs are constrained by passive response mechanisms, limiting their capacity to deploy proactive strategies for psychological counseling. To bridge this gap, we first develop the ProPsyC (Proactive Psychological Conversation) dataset, a multi-turn conversation dataset with interpretive labels including strategy decision logic and reaction attribution. Based on ProPsyC, we propose PsyAdvisor by supervised fine-tuning, a plug-and-play proactive questioning strategy planner that empowers psychological LLMs to initiate well-timed questioning through strategic prompting. Experimental results demonstrate that psychological LLMs integrated with PsyAdvisor substantially improve proactive questioning capacity, conversation depth, and response quality.Furthermore, PsyAdvisor shows promising potential in assisting novice counselors by providing strategy recommendations. This study provides new optimization directions for psychological conversation systems and offers valuable insights for future research on proactive questioning mechanisms in psychological LLMs.