145 citations · 145 across the 15 of their papers we have counts for
9 papers · 1 filter
On the LLM Verification for Argument Graph Generation
Jakub Bąba, Jarosław A. Chudziak
Multi-agent LLM systems can formalize natural-language argumentation end-to-end, turning plain text into structured graphs by classifying components, connecting them with relations…
Regularized policy gradient with learned mixtures of Gaussians for games with continuous actions
Ondřej Kubíček, Viliam Lisý, Tuomas Sandholm
Most successes of superhuman game-playing algorithms are in games with discrete actions, yet in auctions, robotics, sports, or trading, actions are nearly continuous. Prior techniq…
Test-time Reinforcement Learning in Imperfect Information Games
Ondrej Kubicek, Viliam Lisy, Tuomas Sandholm
Test-time reasoning has significantly improved performance in domains ranging from games to language models. However, test-time policy changes with formal guarantees on the perform…
Watermarked Game Solving via Perturbed Regret Minimization
Juho Kim, Tuomas Sandholm
Many real-world interactions among self-interested parties can be modeled by game theory, and the rapid advancements in AI have raised concerns about the possible misuse---accident…
On the Complexity of Correlated Equilibria Beyond Normal-Form Games
Ioannis Anagnostides, Constantinos Daskalakis, Gabriele Farina +3
Correlated equilibria are a fundamental solution concept in game theory. However, despite decades of research, the complexity beyond games of polynomial type -- such as extensive-f…
Domain-Independent Game Abstraction using Word Embedding Techniques
Juho Kim, Tuomas Sandholm
Many games of interest in the real world are often intractably large, thereby necessitating the use of game abstraction to shrink them in size, typically by many magnitudes. Over t…
Watermarking Game-Playing Agents in Perfect-Information Extensive-Form Games
Juho Kim, Fei Fang, Tuomas Sandholm
Watermarking techniques for large language models (LLMs), which encode hidden information in the output so its source can be verified, have gained significant attention in recent d…
Parallelizing Counterfactual Regret Minimization
Juho Kim, Tuomas Sandholm
Parallelization has played an instrumental role in the field of artificial intelligence (AI), drastically reducing the time taken to train and evaluate large AI models. In contrast…
Heuristic Pathologies and Further Variance Reduction via Uncertainty Propagation in the AIVAT Family of Techniques
Juho Kim, Tuomas Sandholm
How should an agent's performance in a multiagent environment be evaluated when there is a limited sample size or a high cost of running a trial? The AIVAT family of variance reduc…