Think Thrice Before Reranking: Multi-perspective Evidence and Reasoning Integration for Text Reranking

2026-09-17

AI Summary

为解决基于单一推理路径的文本重排序易出错问题，提出MERIT-Rank框架，通过多视角证据与推理整合提高重排序鲁棒性。

Abstract

Reasoning-based reranking with Large Language Models (LLMs) has shown promising improvements in text ranking. However, current methods predominantly rely on a single reasoning trajectory, resulting in rankings that are susceptible to reasoning errors and inherently constrained in modeling the multifaceted signals underlying document relevance. To resolve this dilemma, we propose MERIT-Rank(Multi-perspective Evidence and Reasoning Integration for Text Reranking), a framework that models complementary reasoning trajectories to improve reranking robustness. MERIT-Rank formulates a Multi-Trajectory Reasoning Space (MTRS) that evaluates query-document relevance from multiple perspectives and introduces a joint reranker that consolidates these reasoning paths into a unified ranking decision. We further develop Progressive Rank Policy Optimization (PRPO), a progressive training framework that stabilizes reasoning trajectories while continually improving ranking quality through staged optimization objectives. Experiments on both reasoning-intensive and traditional retrieval benchmarks show that MERIT-Rank consistently achieves superior performance over competitive baselines. The 4B model notably outperforms most 7B and even 32B rerankers on BRIGHT.

Problem

• Reranking
• Reasoning Trajectories
• Document Relevance
• Large Language Models
• Multi-perspective

Innovation

• Multi-perspective Evidence and Reasoning Integration
• Multi-Trajectory Reasoning Space
• Progressive Rank Policy Optimization