arXiv AI
Unsound Search with Policy and Value Networks in Legends of Code and Magic
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arXiv AI
Sep 18
Efficient Nash Equilibrium Computation for Cybersecurity Games
The paper introduces Regret-Weighted Payoff Sampling (RWPS), a budgeted estimator that selectively simulates only payoff-matrix cells relevant to a Nash equilibrium and uses a surrogate model for the remaining entries. RWPS provides an instance-dependent error bound weighted by the opponent’s equilibrium mixture and a coverage result guaranteeing that, once the deviation-relevant set is simulated, surrogate error does not affect either player’s regret. Experiments on three 21×21 general-sum games, including an asymmetric Colonel Blotto, show that RWPS achieves four to six times tighter bounds than previous methods and outperforms other sampling strategies on the CyGym and ANSG cyber simulators at low budgets.
arXiv AI
Sep 1
PokaiTrainer: Scaling Belief-State Search to Competitive Pok\'emon VGC
PokaiTrainer is a competitive Pokémon VGC agent that scales belief‑state search to handle simultaneous, large joint action spaces and stochastic outcomes. The system uses PokaiEngine, a Rust battle engine that efficiently enumerates joint action outcomes with high accuracy, and adapts Student of Games to solve each decision as a Bayesian matrix game under a compute budget. In live Showdown play, the agent achieved a 59% win rate against a human field averaging ~1320 Elo, reaching an Elo band of 1350‑1400 and briefly entering the top 500 of the format.
arXiv AI
Jul 3
AgenticSTS: A Bounded-Memory Testbed for Long-Horizon LLM Agents
arXiv:2607. 02255v1 Announce Type: new Abstract: Memory for a long-horizon LLM agent is a contract about what each future decision is allowed to see.
arXiv Machine Learning
Aug 31
Beyond Search-Imitation: Prior-Directed Exploration for Searchless Chess
arXiv:2608.27757v1 Announce Type: new Abstract: Searchless chess networks reach human master strength from a single forward pass by imitating a stronger teacher: the strongest, Leela Chess Zero's (Lc...
arXiv AI
Jul 17
When a Verified World Model Still Loses: Play-Adequacy vs Prediction-Accuracy in LLM-Synthesized Code World Models
arXiv:2607. 14169v1 Announce Type: new Abstract: Large language models can synthesize a game's rules as executable code - a Code World Model (CWM) - which a classical planner then searches over.
arXiv AI
Jul 9
A Gold-Standard Study of What Makes a Lightweight Game-Playing Agent Strong
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.