One Pass, Any Order: Position-Invariant Listwise Reranking for LLM-Based Recommendation
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 c...
| Authors | Ethan Bito, Yongli Ren, Estrid He |
|---|---|
| Primary category | cs.IR |
| Published | 30 April 2026 |
| Last updated at source | 30 April 2026 |
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