The paper introduces a declarative framework for modeling belief states in trick‑taking card games, converting hand‑coded inference rules from a leading Skat player into a domain‑specific language. By generating and verifying code, the authors correct three bugs in the original engine and expand the rule set through expert strategy annotations and symmetry. Additional cardinality constraints and confidence parameters further refine the model, leading to measurable improvements in winning percentage and score during self‑play tests.
arXiv:2610.13995v1 Announce Type: cross Abstract: Post-training large language models (LLMs) on two-player zero-sum games provides a verifiable reward, and the gains have been shown to transfer to ge...
arXiv:2607. 21856v1 Announce Type: new Abstract: Modern reasoning models depend on reasoning data, today sourced from human annotations or distilled from stronger LLMs.
The paper introduces the Abstraction Agent, a zero‑shot pipeline that employs a large language model to automatically generate continuous strategic features from a natural‑language game description, score private states, and cluster them into abstraction buckets without any game‑specific evaluators or training data. The pipeline consists of four phases—feature discovery with calibration anchors, batched private‑state scoring, correlation‑based feature selection, and k‑means clustering—and achieves significant reductions in lifted‑strategy exploitability in heads‑up no‑limit Texas hold’em and outperforms scalar rank baselines in ROVER Trials. The method also transfers to other games such as four‑card Pot‑Limit Omaha, HUNL preflop and flop, and Riichi Mahjong, demonstrating that it can uncover strategic concepts that align with recognized game theory insights.
The paper presents a symbolic sub‑policy model for chess that incorporates domain knowledge to improve interpretability. By adapting patterns learned through inductive logic programming (PAL), the authors evaluate the model using a divergence metric against a random baseline and show that it can suggest moves comparable to a human beginner. They also propose a computational evaluation scheme that augments an existing engine with their model.
arXiv:2508. 11874v2 Announce Type: replace-cross Abstract: Designing polynomial-time algorithms for approximate Nash equilibria (ANE) with provable worst-case guarantees is a fundamental open problem in algorithmic game theory.
The paper introduces the Algebraic World Model (AWM), a framework that lets an AI explore unfamiliar environments, generate executable Python hypotheses, refine them through category‑theory guided transformations, and formalize the resulting model in Lean 4 to prove reachability and impossibility. AWM demonstrates its ability to formally prove that certain game goals are unreachable, outperforming large language models such as Claude Opus 5.5 and GPT‑6 Astra on impossible levels, and it produces interpretable mathematical models that enable Lean to replay and generalize winning trajectories across 25 public ARC‑AGI‑3 games.
arXiv:2608. 03420v1 Announce Type: new Abstract: Large language models have improved substantially on single-shot reasoning tasks, but their performance in sequential decision-making is less well understood.
arXiv:2607. 06854v1 Announce Type: cross Abstract: Reinforcement learning agents for imperfect-information card games are only as strong as the opponents they train against, and they are hard to grade, since they beat a random opponent over 99 percent of the time and only tie copies of themselves.
arXiv:2608. 15868v1 Announce Type: new Abstract: This paper presents CoupVisor, a decision-support system for the hidden-information card game Coup.
arXiv:2607. 01498v1 Announce Type: new Abstract: We investigate the problem of learning useful policy representations (embeddings) in two-player zero-sum imperfect-information games.
arXiv:2606. 26094v1 Announce Type: new Abstract: For most of scientific history, researchers studying behavior could only infer hidden mechanisms from outward actions: an inverse problem that becomes more tractable when observation is augmented by targeted intervention.
GENSTRAT is a system that evaluates strategic reasoning in large language models (LLMs) by procedurally generating two-player zero-sum imperfect-information betting games on demand. Each game is scored across five complexity axes—state space, temporal contingency, opponent dependence, private information, and precision—and assessed via Monte Carlo simulation. Using this generator, the authors created a 50-game benchmark, ran a tournament of twelve frontier and open-weight models with over 40,000 LLM vs. LLM matches, and found that the performance gap on the hardest games is 1.7 times larger than across all games, with higher thinking budgets, opponent strategy disclosure, and tailored reasoning improving outcomes.