The field of game theory is witnessing significant developments in the direction of learning and modeling in complex, multi-agent environments. Researchers are exploring innovative approaches to address long-standing challenges, such as the grain of truth problem and opponent modeling in imperfect-information games. A key trend is the integration of machine learning techniques with game-theoretic concepts to enable agents to learn and adapt in dynamic environments. Another area of focus is the development of efficient algorithms for computing Nash equilibria and other solution concepts in various types of games. Noteworthy papers in this area include: Limit-Computable Grains of Truth for Arbitrary Computable Extensive-Form (Un)Known Games, which presents a formal and general solution to the grain of truth problem. Consistent Opponent Modeling of Static Opponents in Imperfect-Information Games, which develops a new algorithm that guarantees convergence to the opponent's true strategy. Regulation-Aware Game-Theoretic Motion Planning for Autonomous Racing, which introduces a regulation-aware motion planning framework for autonomous racing scenarios.