Decoupled Decision-Stage Awareness for Conversational Recommendation with Large Language Model Agents in Information Analysis

Information analysis recommendation differs from conversational recommender systems (CRS) because relevance changes with the decision phase. The same event may support observation, interpretation, option selection, or action feedback, yet most large language model (LLM)-agent CRS represent dialogue state as intent and preference. This study examines whether explicit decision-stage awareness improves recommendation and whether it can be added independently of the LLM backbone. We propose Stage-Aware Conversational Recommender System (SA-CRS), a plug-in layer guided by the Observe–Orient–Decide–Act cycle. It decouples stage detection from LLM reasoning and uses detected stages to guide dialogue strategy and candidate re-ranking. We evaluate SA-CRS on an information analysis recommendation dataset from event-structured reports, using multi-turn simulated dialogues and four LLM backbones. Oracle stage injection improves Hit@5 by 3.0 percentage points (pp), showing that decision stage provides a signal beyond topic matching. With a prompt-based detector, SA-CRS improves Hit@5 by 9.0 pp on a strong backbone; with an independent Bidirectional Encoder Representations from Transformers (BERT) detector and probabilistic re-ranking, gains range from 6.5 to 15.5 pp. Negative controls with uniform or random stage signals fail to reproduce the improvements and may reduce efficiency. The results suggest that, within the evaluated single-domain information-analysis setting, decoupled decision-stage awareness is practical for decision-intensive CRS.