# Effective, Efficient, and General Information Abstraction for Imperfect-Information Extensive-Form Games

arXiv: https://arxiv.org/abs/2605.10900 · HTML: https://mlsanity.com/paper/2605.10900/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 May 2026.

## Built on by (most cited first, 7 of 7)

- [Real-Time Parallel Counterfactual Regret Minimization](https://mlsanity.com/paper/2605.19928/) (2026)
- [PokerSkill: LLMs Can Play Expert-Level Poker without Training or Solvers](https://mlsanity.com/paper/2605.30094/) (2026)
- [Agents That Certify Their Own Exploits: Confidence-Scheduled Restricted Responses for Safe Opponent Exploitation](https://mlsanity.com/paper/2607.28520/) (2026)
- [ICM Out! Better Tournament Strategy from Computed Continuations, vs. Solvers and LLMs](https://mlsanity.com/paper/2608.09586/) (2026)
- [AV-AIVAT: 74x Cheaper Agent Evaluation with Certified Anytime-Valid Stopping in Imperfect-Information Games](https://mlsanity.com/paper/2608.06362/) (2026)
- [Abstraction Agent](https://mlsanity.com/paper/2609.04303/) (2026)
- [GPU-CFR: 80x Faster Counterfactual Regret Minimization by Compiling the Game to Static Dataflow and CUDA Graph Replay](https://mlsanity.com/paper/2609.11923/) (2026)

## Abstract (arXiv)

Information abstraction reduces the computational cost of solving imperfect-information games by clustering information sets into a smaller number of buckets. Existing methods either rely on domain-specific features such as rank or equity, which are inapplicable to games with non-standard payoff structures, or require expensive offline neural-network training on billions of samples. We propose Warm-up Expected Value-based Abstraction (WEVA), a simple yet effective alternative: run a small number of Counterfactual Regret Minimization (CFR) iterations on the full game as a warm-up phase, extract per-hand expected value features at every decision node, form a depth-weighted multi-node feature vector, and apply k-means++ clustering to obtain the abstraction mapping. WEVA requires no domain knowledge, no pre-training, and incurs only a small overhead on top of the abstract-game solve. Experiments on three structurally diverse games, with different bucket numbers and CFR variants, show that WEVA consistently outperforms equity-based and rank-based abstractions, reducing exploitability by up to over 80%. Surprisingly, as few as W=10 warm-up iterations already produce abstractions that outperform existing information abstraction methods in most settings. These results establish WEVA as an effective, efficient, and general approach to information abstraction in imperfect-information extensive-form games.