The field of information retrieval is experiencing a significant shift in its approach to reranking, with a growing focus on efficient and effective methods for refining search results. Recent studies have highlighted the potential drawbacks of relying on explicit reasoning processes in large language models, with some findings suggesting that these processes can actually degrade performance in certain tasks. Instead, researchers are exploring alternative approaches, such as compact document representations and test-time reasoning, which have shown promising results in improving retrieval effectiveness. Notably, some papers have demonstrated that selective reasoning strategies can substantially recover lost performance, while others have found that non-reasoning based methods can outperform their reasoning-based counterparts.

Noteworthy papers include: When Thinking Fails: The Pitfalls of Reasoning for Instruction-Following in LLMs, which systematically exposes reasoning-induced failures in instruction-following and offers practical mitigation strategies. LLM-Based Compact Reranking with Document Features for Scientific Retrieval, which proposes a training-free, model-agnostic reranking framework for scientific retrieval that significantly improves reranking performance. Don't Overthink Passage Reranking: Is Reasoning Truly Necessary?, which challenges the assumption that reasoning is necessary for passage reranking, finding that non-reasoning based methods can be more effective.