# BlitzRank: Principled Zero-shot Ranking Agents with Tournament Graphs

arXiv: https://arxiv.org/abs/2602.05448 · HTML: https://mlsanity.com/paper/2602.05448/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Web, search, and research agents, one of 16 directions in LLM agents and tool use.
- Builds on 26 of its 42 references that are in the map.
- First posted on arXiv 5 Feb 2026.

## Placed in

- LLM agents and tool use / [Web, search, and research agents](https://mlsanity.com/agents/web-search-and-research-agents/)

## Builds on (most cited first, 26 of 26)

- [Lost in the Middle: How Language Models Use Long Contexts](https://mlsanity.com/paper/2307.03172/) (2023)
- [RULER: What's the Real Context Size of Your Long-Context Language Models?](https://mlsanity.com/paper/2404.06654/) (2024)
- [BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models](https://mlsanity.com/paper/2104.08663/) (2021)
- [Is ChatGPT Good at Search? Investigating Large Language Models as Re-Ranking Agents](https://mlsanity.com/paper/2304.09542/) (2023)
- [Large Language Models are Effective Text Rankers with Pairwise Ranking Prompting](https://mlsanity.com/paper/2306.17563/) (2023)
- [Overview of the TREC 2019 deep learning track](https://mlsanity.com/paper/2003.07820/) (2020)
- [Overview of the TREC 2020 deep learning track](https://mlsanity.com/paper/2102.07662/) (2021)
- [Improving Passage Retrieval with Zero-Shot Question Generation](https://mlsanity.com/paper/2204.07496/) (2022)
- [RankZephyr: Effective and Robust Zero-Shot Listwise Reranking is a Breeze!](https://mlsanity.com/paper/2312.02724/) (2023)
- [Beyond Yes and No: Improving Zero-Shot LLM Rankers via Scoring Fine-Grained Relevance Labels](https://mlsanity.com/paper/2310.14122/) (2023)
- [Zero-Shot Listwise Document Reranking with a Large Language Model](https://mlsanity.com/paper/2305.02156/) (2023)
- [A Setwise Approach for Effective and Highly Efficient Zero-shot Ranking with Large Language Models](https://mlsanity.com/paper/2310.09497/) (2023)
- [TourRank: Utilizing Large Language Models for Documents Ranking with a Tournament-Inspired Strategy](https://mlsanity.com/paper/2406.11678/) (2024)
- [Found in the Middle: Permutation Self-Consistency Improves Listwise Ranking in Large Language Models](https://mlsanity.com/paper/2310.07712/) (2023)
- [PAC Battling Bandits in the Plackett-Luce Model](https://mlsanity.com/paper/1808.04008/) (2018)
- [Overview of the TREC 2022 deep learning track](https://mlsanity.com/paper/2507.10865/) (2025)
- [How Deep is your Learning: the DL-HARD Annotated Deep Learning Dataset](https://mlsanity.com/paper/2105.07975/) (2021)
- [Overview of the TREC 2021 deep learning track](https://mlsanity.com/paper/2507.08191/) (2025)
- [LLM-RankFusion: Mitigating Intrinsic Inconsistency in LLM-based Ranking](https://mlsanity.com/paper/2406.00231/) (2024)
- [A Nearly Instance Optimal Algorithm for Top-k Ranking under the Multinomial Logit Model](https://mlsanity.com/paper/1707.08238/) (2017)
- [Overview of the TREC 2023 deep learning track](https://mlsanity.com/paper/2507.08890/) (2025)
- [Optimal Bounds for Noisy Sorting](https://mlsanity.com/paper/2302.12440/) (2023)
- [AcuRank: Uncertainty-Aware Adaptive Computation for Listwise Reranking](https://mlsanity.com/paper/2505.18512/) (2025)
- [ELSPR: Evaluator LLM Training Data Self-Purification on Non-Transitive Preferences via Tournament Graph Reconstruction](https://mlsanity.com/paper/2505.17691/) (2025)
- [REALM: Recursive Relevance Modeling for LLM-based Document Re-Ranking](https://mlsanity.com/paper/2508.18379/) (2025)
- [Query Complexity of Tournament Solutions](https://mlsanity.com/paper/1611.06189/) (2016)

## Abstract (arXiv)

Selecting the top m from n items via expensive k-wise comparisons is central to settings ranging from LLM-based document reranking to crowdsourced evaluation and tournament design. Existing methods either rely on heuristics that discard comparison information, or exploit it at prohibitive cost. We introduce a tournament graph framework that provides a principled foundation for k-wise ranking. Our key observation is that each k-item comparison reveals an induced tournament of binomk2 pairwise preferences; aggregating these into a global preference graph and computing its transitive closure yields many additional orderings without further oracle calls. We formalize when the current top-m output is certifiably determined and design a greedy query schedule that maximizes information gain towards identifying the top-m items. The framework also gracefully handles non-transitive preferences -- cycles induced by real-world oracles -- by collapsing them into equivalence classes that yield principled tiered rankings. Applied to LLM reranking across 14 benchmarks and 5 models, BlitzRank achieves Pareto dominance over existing approaches: matching or exceeding accuracy while requiring 25--40% fewer tokens than comparable methods; against pairwise reranking, it achieves near-identical quality with 7× fewer tokens. Code available at https://github.com/ContextualAI/BlitzRank.