One Pass, Any Order: Position-Invariant Listwise Reranking for LLM-Based Recommendation

2026-04-30

AI Summary

This work addresses the sensitivity of large language models (LLMs) to the input order of candidate items in recommendation reranking, which leads to inconsistent ranking results. To resolve this issue, the authors propose InvariRank, a framework that enforces permutation invariance at the architectural level by employing a structured attention mask to block cross-attention among candidates and integrating Rotary Position Embeddings (RoPE) with shared positional encoding to eliminate positional bias. This design enables listwise reranking in a single forward pass while guaranteeing positional invariance—without relying on multi-permutation training strategies. InvariRank achieves competitive performance across multiple recommendation benchmarks and significantly enhances ranking stability under varying input permutations.

Abstract

Large language models (LLMs) are increasingly used for recommendation reranking, but their listwise predictions can depend on the order in which candidates are presented. This creates a mismatch between the set-based nature of recommendation and the sequence-based computation of decoder-only LLMs, where permuting an otherwise identical candidate set can change item scores and final rankings. Such order sensitivity makes LLM-based rerankers difficult to rely on, since rankings may reflect prompt serialization rather than user preference. We propose InvariRank, a permutation-invariant listwise reranking framework that addresses this dependence at the architectural level. InvariRank blocks cross-candidate attention with a structured attention mask and negates position-induced scoring changes through shared positional framing under Rotary Positional Embeddings (RoPE). Combined with a listwise learning-to-rank objective, the model scores all candidates in a single forward pass, avoiding permutation-based invariance training objectives that require multiple permutations of a candidate set. Experiments on recommendation benchmarks show that InvariRank maintains competitive ranking effectiveness while producing stable rankings across candidate permutations. The results suggest that architectural invariance is a practical route to reliable and efficient LLM-based recommendation reranking. The source code is at https://github.com/ejbito/InvariRank.

Problem

• order sensitivity
• position invariance
• listwise reranking
• LLM-based recommendation
• candidate permutation

Innovation

• permutation-invariant
• listwise reranking
• structured attention mask
• Rotary Positional Embeddings
• LLM-based recommendation