Theory & Algorithms · Computer Science & AI
Computer Science & Game Theory
The newest papers in Computer Science & Game Theory that ReadSearch has read, newest first. Each links to its own page, with its authors, date, where it was published and the original.
252 papers, from 4 September 2026 to 5 November 2026: 252 arXiv preprints.
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Newest papers, page 9
Cogentic: Multi-Agent Orchestration for Automated Proof Discovery
We present Cogentic, a multi-agent harness for automated proof discovery on open research problems.Outer Diversity of Condorcet Domains
A Condorcet domain is a set of rankings over a given candidate set, such that every election that consists only of (an odd number of) votes from the domain has a transitive majority relation.Learning to Harvest Without Collapse in a Regenerative Commons: A Lagrangian Framework
The tragedy of the commons poses a multi-agent safety problem: reward-seeking agents can deplete a shared resource, and cooperation among its users does not itself specify how much must be preserved.Regularized policy gradient with learned mixtures of Gaussians for games with continuous actions
Most successes of superhuman game-playing algorithms are in games with discrete actions, yet in auctions, robotics, sports, or trading, actions are nearly continuous.The finite-horizon five-expert prediction problem
We give an explicit solution to the five expert prediction with expert advice partial differential equation (PDE) in the finite-time horizon setting.Social Choice Foundations for Simulation-Augmented Generation
Simulation-augmented generation (SAGE) is a recent technical proposal in which models simulate individuals' viewpoints at inference time in order to provide more representative answers to…The Double-Edged Sword of Information: Revealed versus Hidden Lotteries in School Choice
In school choice, a lottery number is often used by the matching mechanism to break ties when there are more students who prefer the same school than the number of seats available.Minimax Last-Iterate Convergence in Matrix Games with Observed Actions
We study last-iterate convergence in unknown two-player zero-sum matrix games with bandit payoff feedback and observed opponent actions.Edge-Level Automorphism in GNNs: A Quantitative Framework and Effective Designs For Link Prediction
Graph Neural Networks (GNNs) are effective for learning node and link embeddings through permutation-equivariant aggregation.Polylogarithmic Nash Regret in Matrix Games with Bandit Feedback
We study Nash regret minimization in unknown finite matrix games with bandit payoff feedback and observed opponent actions.Strategically Robust Game-Theoretic Multi-Agent Trajectory Optimization
Aviation authorities worldwide expect Advanced Air Mobility (AAM) traffic management to be decentralized among service providers, requiring AAM flights to autonomously plan trajectories by…Truthful-in-Expectation MMS Allocations for Chores
We study truthful-in-expectation (TIE) mechanisms for allocating indivisible chores alongside ex-post maximin share (MMS) guarantees.Reverse Sequential Proportional Approval Voting Rule: Proportionality and Approximation Guarantees
We study the Reverse Sequential Proportional Approval Voting Rule (RevSeqPAV) in approval-based committee elections.Optimal Networks for Agentic Information Aggregation
We study information aggregation in the networked learning model introduced by Kearns, Roth, and Ryu (SODA 2026).Maximizing Social Influence in Almost Linear Time
Influence maximization is a central algorithmic challenge in network analysis, aiming to identify a set of $k$ seed nodes in a graph with $n$ nodes and $m$ edges that maximizes the expected cascade…Arrival-Time Incentive Compatibility in Random Order Online Bipartite Matching
In this work we initiate the study of competitive algorithms with arrival-time incentive compatibility for random-order online bipartite matching in settings where the users care only about…Evolution of Market Microstructure in the Age of AI
Market microstructure studies how trading rules turn orders into prices and allocations.Modular Discovery of General Game-Playing Algorithms with Large Language Models
General Game Playing across arbitrary games from rules alone remains challenging due to differing algorithmic requirements across game classes and strict decision-time constraints.When Privacy Moves ML-Mediated Decisions On Device: Information and Incentive Misalignment in Auctions
Moving ML-mediated decision making onto privacy-preserving clients decentralises the economic decision along with the inference.Raven: The Harness of Harnesses for Composable Agentic Intelligence
As large language models advance, AI agents are moving beyond isolated, domain-specific tasks toward long-horizon, cross-domain workflows.