Literature Survey: Re-ranking Methods
======================================

This section contains detailed surveys and analyses of individual papers related to 
Stage 2 re-ranking methods, including cross-encoders, late interaction models, and 
LLM-based rerankers.

.. toctree::
   :maxdepth: 2
   :caption: Papers:

   muvera-multi-vector-retrieval
   zelo-hard-negative

Overview
--------

The papers in this section provide in-depth technical analysis of key contributions 
to the re-ranking literature. Each survey includes:

* **Problem formulation** and mathematical foundations
* **Algorithmic innovations** with theoretical guarantees
* **Empirical results** on standard benchmarks
* **Practical considerations** for deployment
* **Connections** to other methods in the retrieval-reranking pipeline

Featured Papers
---------------

**MUVERA: Multi-Vector Retrieval via Fixed Dimensional Encodings** (NeurIPS 2024)
    A principled approach to reduce multi-vector similarity search to single-vector 
    MIPS, achieving 10% improved recall with 90% lower latency compared to prior 
    state-of-the-art. Enables ColBERT-quality retrieval at production scale.

**Zelo: Addressing the Laffer Curve in Hard Negative Mining**
    Introduces a theoretical framework identifying the Laffer curve relationship 
    between hard negative miner intelligence and model performance. Reveals that 
    increasingly sophisticated miners eventually degrade performance by selecting 
    false negatives that are more relevant than human-annotated positives.

Contributing
------------

To add a new paper survey to this section:

1. Create a new ``.rst`` file following the structure of existing surveys
2. Include: problem statement, core innovation, theoretical analysis, empirical results
3. Add the file to the toctree above
4. Ensure proper citations and links to related papers