# Time-critical and confidence-based abstraction dropping methods

arXiv: https://arxiv.org/abs/2507.02703 · HTML: https://mlsanity.com/paper/2507.02703/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 18 references that are in the map.
- First posted on arXiv 3 Jul 2025.

## Built on by (most cited first, 5 of 5)

- [AUPO -- Abstracted Until Proven Otherwise: A Reward Distribution Based Abstraction Algorithm](https://mlsanity.com/paper/2510.23214/) (2025)
- [Investigating Intra-Abstraction Policies For Non-exact Abstraction Algorithms](https://mlsanity.com/paper/2510.24297/) (2025)
- [Grouping Nodes With Known Value Differences: A Lossless UCT-based Abstraction Algorithm](https://mlsanity.com/paper/2510.25388/) (2025)
- [Discovering State Equivalences in UCT Search Trees By Action Pruning](https://mlsanity.com/paper/2510.26346/) (2025)
- [When LLM Meets Tree Search: A Systematic View of Inference as Search in Large Language Models](https://mlsanity.com/paper/2608.30395/) (2026)

## Abstract (arXiv)

One paradigm of Monte Carlo Tree Search (MCTS) improvements is to build and use state and/or action abstractions during the tree search. Non-exact abstractions, however, introduce an approximation error making convergence to the optimal action in the abstract space impossible. Hence, as proposed as a component of Elastic Monte Carlo Tree Search by Xu et al., abstraction algorithms should eventually drop the abstraction. In this paper, we propose two novel abstraction dropping schemes, namely OGA-IAAD and OGA-CAD which can yield clear performance improvements whilst being safe in the sense that the dropping never causes any notable performance degradations contrary to Xu's dropping method. OGA-IAAD is designed for time critical settings while OGA-CAD is designed to improve the MCTS performance with the same number of iterations.