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

> Source: <https://blitzrank.ai/>
> Published: 2026-11-02 17:46:33+00:00

[Paper](https://arxiv.org/abs/2602.05448)

[Code](https://github.com/ContextualAI/BlitzRank)

⚡ BlitzRank uses tournament graphs to extract maximal information from each LLM call—a principled framework achieving **Pareto optimality** across 14 benchmarks and 5 LLMs with **25–40% fewer queries**.
        

## Accurate & Efficient

## Algorithm Visualization

## Abstract

            Large language models have emerged as powerful zero-shot rerankers for retrieval-augmented generation,
            offering strong generalization without task-specific training. However, existing LLM reranking methods
            either rely on heuristics that fail to fully exploit the information revealed by each ranking decision
            or are inefficient when they do. We introduce a tournament graph framework that provides a principled
            foundation for k-wise reranking. Our key observation is that each k-document comparison reveals a
            complete tournament of *k choose 2* pairwise preferences. These tournaments are aggregated into
            a global preference graph, whose transitive closure yields many additional orderings without further
            model invocations. We formalize when a candidate's rank is certifiably determined and design a query
            schedule that greedily maximizes information gain towards identifying the top-m items. Our framework
            also gracefully handles non-transitive preferences—cycles induced by LLM judgments—by
            collapsing them into equivalence classes that yield principled tiered rankings. Empirically, across
            14 benchmarks and 5 LLMs, our approach achieves Pareto dominance over existing methods: matching or
            exceeding accuracy while requiring 25–40% fewer tokens than comparable approaches, and
            7× fewer than pairwise methods at near-identical quality.
          

## Quick Start

```
uv pip install blitzrank
python
from blitzrank import BlitzRank

ranker = BlitzRank(model="openai/gpt-4.1")

query = "capital of France"
docs = ["Berlin is in Germany", "Paris is in France", "Tokyo is in Japan"]

indices = ranker(query, docs, topk=2)  # -> [1, 0]
top_docs = [docs[i] for i in indices]
```

## Citation

```
@article{blitzrank2026,
  title={BlitzRank: Principled Zero-shot Ranking Agents with Tournament Graphs},
  author={Agrawal, Sheshansh and Nguyen, Thien Hang and Kiela, Douwe},
  journal={arXiv preprint arXiv:2602.05448},
  year={2026}
}
```