Preference-Aware Neural Reranking with Collaborative Large Language Model Agents
Keywords:
neural reranking, large language models, collaborative agents, preference modeling, information retrieval, socio-technical systems, governance, fairness, robustness, sustainabilityAbstract
Neural reranking has become a central mechanism for improving search, recommendation, and retrieval-augmented generation systems by reordering candidate items according to learned relevance signals. Recent advances in large language models have expanded reranking from single-pass scoring toward multi-agent collaboration, where different agents assume specialized roles such as query interpretation, evidence assessment, preference elicitation, and final arbitration. This paper examines preference-aware neural reranking through the lens of collaborative large language model agents. It argues that the next generation of reranking systems should not be understood merely as model architectures but as socio-technical infrastructures that coordinate user preferences, institutional constraints, computational resources, and governance obligations. The analysis develops a system-level framework covering architectural decomposition, agent coordination, preference representation, fairness, robustness, deployment sustainability, and policy implications. It considers how preference-aware reranking can improve personalization and contextual relevance while introducing new risks related to manipulation, opacity, feedback loops, and uneven access. The paper also discusses evaluation challenges, including the need for heterogeneous benchmarks, auditable logs, model documentation, and lifecycle monitoring. The central claim is that collaborative large language model agents can make reranking more adaptive and pluralistic, but only if their coordination protocols, preference models, and deployment environments are designed with explicit attention to accountability, resource asymmetry, and long-term maintainability. The paper concludes that preference-aware reranking should be treated as a governed infrastructure rather than a narrow ranking optimization problem.
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