@misc{9249101,
  abstract     = {{Game-playing algorithms often face a trade-off between analyzing moves that currently look promising and exploring alternatives whose value is uncertain. AlphaZero-style chess engines combine neural network evaluation with Monte Carlo tree search to selectively explore the game tree. We evaluate a variance-aware modification of the PUCT tree policy on chess puzzles and find that it underperforms standard PUCT across different networks and search budgets under certain conditions. We suggest explanations for this discrepancy and possible improvements to the implementation of the variance-aware tree policy.}},
  author       = {{Ahlqvist, Axel and Näslund Cuesta, Oscar}},
  language     = {{eng}},
  note         = {{Student Paper}},
  title        = {{Evaluating Variance-Aware PUCT in Neural Chess Engines}},
  year         = {{2026}},
}