# PAWN: Piece Value Analysis with Neural Networks

arXiv: https://arxiv.org/abs/2604.15585 · HTML: https://mlsanity.com/paper/2604.15585/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 27 references that are in the map.
- First posted on arXiv 16 Apr 2026.

## Builds on (most cited first, 5 of 5)

- [Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm](https://mlsanity.com/paper/1712.01815/) (2017)
- [Amortized Planning with Large-Scale Transformers: A Case Study on Chess](https://mlsanity.com/paper/2402.04494/) (2024)
- [Assessing Game Balance with AlphaZero: Exploring Alternative Rule Sets in Chess](https://mlsanity.com/paper/2009.04374/) (2020)
- [The Value of Chess Squares](https://mlsanity.com/paper/2307.05330/) (2023)
- [Towards Piece-by-Piece Explanations for Chess Positions with SHAP](https://mlsanity.com/paper/2510.25775/) (2025)

## Abstract (arXiv)

Predicting the relative value of any given chess piece in a position remains an open challenge, as a piece's contribution depends on its spatial relationships with every other piece on the board. We demonstrate that incorporating the state of the full chess board via latent position representations derived using a CNN-based autoencoder significantly improves accuracy for MLP-based piece value prediction architectures. Using a dataset of over 12 million piece-value pairs gathered from Grandmaster-level games, with ground-truth labels generated by Stockfish 17, our enhanced piece value predictor significantly outperforms context-independent MLP-based systems, reducing validation mean absolute error by 16% and predicting relative piece value within approximately 0.65 pawns. More generally, our findings suggest that encoding the full problem state as context provides useful inductive bias for predicting the contribution of any individual component.