# Beyond Outcome-Based Imperfect-Recall: Higher-Resolution Abstractions for Imperfect-Information Games

arXiv: https://arxiv.org/abs/2510.15094 · HTML: https://mlsanity.com/paper/2510.15094/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 36 references that are in the map.
- First posted on arXiv 16 Oct 2025.

## Builds on (most cited first, 9 of 9)

- [DeepStack: Expert-Level Artificial Intelligence in No-Limit Poker](https://mlsanity.com/paper/1701.01724/) (2017)
- [Solving Large Imperfect Information Games Using CFR+](https://mlsanity.com/paper/1407.5042/) (2014)
- [Solving Imperfect-Information Games via Discounted Regret Minimization](https://mlsanity.com/paper/1809.04040/) (2018)
- [Safe and Nested Subgame Solving for Imperfect-Information Games](https://mlsanity.com/paper/1705.02955/) (2017)
- [Rethinking Formal Models of Partially Observable Multiagent Decision Making](https://mlsanity.com/paper/1906.11110/) (2019)
- [Lazy-CFR: fast and near optimal regret minimization for extensive games with imperfect information](https://mlsanity.com/paper/1810.04433/) (2018)
- [Monte Carlo Continual Resolving for Online Strategy Computation in Imperfect Information Games](https://mlsanity.com/paper/1812.07351/) (2018)
- [Imperfect-Recall Abstractions with Bounds in Games](https://mlsanity.com/paper/1409.3302/) (2014)
- [Equilibrium Approximation Quality of Current No-Limit Poker Bots](https://mlsanity.com/paper/1612.07547/) (2016)

## Built on by (most cited first, 1 of 1)

- [KrwEmd: Revising the Imperfect-Recall Abstraction from Forgetting Everything](https://mlsanity.com/paper/2511.12089/) (2025)

## Abstract (arXiv)

Hand abstraction is crucial for scaling imperfect-information games (IIGs) such as Texas Hold'em, yet progress is limited by the lack of a formal task model and by evaluations that require resource-intensive strategy solving. We introduce signal observation ordered games (SOOGs), a subclass of IIGs tailored to hold'em-style games that cleanly separates signal from player action sequences, providing a precise mathematical foundation for hand abstraction. Within this framework, we define a resolution bound-an information-theoretic upper bound on achievable performance under a given signal abstraction. Using the bound, we show that mainstream outcome-based imperfect-recall algorithms suffer substantial losses by arbitrarily discarding historical information; we formalize this behavior via potential-aware outcome Isomorphism (PAOI) and prove that PAOI characterizes their resolution bound. To overcome this limitation, we propose full-recall outcome isomorphism (FROI), which integrates historical information to raise the bound and improve policy quality. Experiments on hold'em-style benchmarks confirm that FROI consistently outperforms outcome-based imperfect-recall baselines. Our results provide a unified formal treatment of hand abstraction and practical guidance for designing higher-resolution abstractions in IIGs.