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 4
Subjective Valuations Make It Hard to be Fair: The Complexity of Resource Allocation to Groups of Agents
We study the parameterized complexity of finding an EF1 allocation of indivisible items in the group-sharing setting, which has received increasing attention in recent years.Is Anyone Enforcing the Cartel? What Reinforcement Learning Teaches LLM Agents in Markets
Evaluations of large language model (LLM) agents report that pricing agents collude at others' expense.Game of Phones: Harnessing Game Theory and LLMs in 'Bot Wars' to Counteract Phone Scams
Phone scams impose substantial and growing costs on individuals and financial systems worldwide, yet existing countermeasures impose no resource cost on perpetrators.Fundamental Impossibility Results in AI Safety: Trading Off between Desiderata
Is there a fundamental tradeoff between desiderata for safe AI systems, such as harmlessness and helpfulness?Strategy recovery for Mean-Payoff Games is as hard as solving them from scratch
Mean-Payoff Games (MPGs) form an interesting family of deterministic, infinite-duration, zero-sum, turn-based games played by two players on a finite weighted directed graph.How Far Can an Agent Push? Trust-Safety Margins in Live Language-Based Economic Games
Game theory identifies when a strategic action should be accepted, but not how long a live, language-based counterpart will continue to accept it.Breaking the 1/3 Barrier for Approximate Nash Equilibria in Symmetric Bimatrix Games
We give the first unconditional family of polynomial-time algorithms that breaks the $1/3$ approximation barrier for symmetric bimatrix games.Tree Plurality Veto: Voting in Few Rounds with Efficient Communication and Low Metric Distortion
We consider the metric distortion framework for voting, where $n$ voters and $m$ candidates are embedded in an unknown metric space.Cooperation Without Shared Rewards via Reciprocal Q-Augmentation
General-sum games are more challenging than fully cooperative or adversarial settings because agents may deceive or exploit one another.Anytime Contracts for AI Services
AI services are prone to moral hazard, as model providers can silently route requests to cheaper, lower-quality models, and the inherent stochasticity of model outputs makes this degradation hard to…Cooperation Without Adaptation: LLM Agents in a Collective-Risk Dilemma
Language-model agents are increasingly deployed to act on behalf of people in settings where individual decisions shape shared outcomes.Proportionality in Ranking Compression
Motivated by applications in reinforcement learning from human feedback, we consider a setting where a given collection of rankings needs to be compressed into a smaller collection of rankings.Contracting with an Agent under Model Mismatch
In the classical principal-agent setting, the principal delegates some task to the agent, who receives a payment conditioned on the outcome of the task, which in turn benefits the principal.Coopshift: Post-Training Amplifies The Memory Curse In LLM Cooperation
How does post-training change cooperation during repeated interaction?Extending Myerson's Optimal Auctions to Correlated Bidders via Neural Network Interpolation
We aim to design revenue-maximizing single-item auctions that are deterministic, strategy-proof, and ex post individually rational --- the quintessential fundamental model for optimal mechanism…Market Incentives for AI Safety Investment
Despite their practical significance, modern AI systems pose significant societal risks, including toxicity, malicious use, and misinformation.How language model agents reshape price competition
Repeated Bertrand competition is a stringent benchmark for algorithmic pricing: in homogeneous-goods markets, standard learning procedures typically select the stage-game Nash outcome with prices…Learning Rewards in Strategic Games
Understanding the objectives that drive agents' behavior is crucial in strategic environments, where multiple agents interact and adapt to one another.Decentralized LLM Workflows via Sequential Auctions
We present a scalable framework for solving multi-step tasks through a decentralized market of self-interested agents.Auditable and Deployable Multi-Agent Motion Planning via Posetal Games from Legal Guidelines
When robots are deployed in the real-world, they must interact with other agents and accomplish their own goals while adhering to legal constraints.