Candidate-Conditioned Implicit Debate For Recommendation Agents

Recommendation agents can refine candidate lists through multi-role discussion, but shared summaries may obscure candidate-specific evidence, ranking changes may misalign states and items, and previous experience must be reused without interfering with the current interaction. We propose Candidate-Conditioned Implicit Debate (CCID), a recurrent framework that addresses these challenges through continuous candidate states, persistent candidate identities, and conditional reflection memory. A proposer initializes the candidates and their states; utility and risk roles assess candidate fit and the consequences of replacing the current leader; and an adjudicator updates both rankings and states for further assessment. Before discussion, relevant lessons from completed offline trajectories are retrieved from a frozen memory snapshot and remain fixed throughout the request. Experiments on four recommendation benchmarks show that CCID consistently outperforms single-round discussion. Ablations support the contributions of candidate correspondence, communication capacity, and reflection memory. Paired top-1 analysis shows that recurrent discussion corrects more errors than it introduces on all four datasets. Additional rounds improve recommendation quality with diminishing gains, while adaptive stopping achieves quality close to fixed four-round discussion with fewer model calls. Together, these results support iterative candidate-level refinement that preserves evidence, incorporates complementary assessments, and reuses applicable experience.