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 5
StratFL: Countering Covert Model Poisoning Attacks with Strategic Federated Learning
Advanced persistent threats (APTs) are a major concern in federated learning (FL) deployments in critical sectors such as healthcare, and finance.Dynamic Auction Mechanisms in a Closed-Credit Economy: Sequential Slot Reallocation in Air Traffic Management
Indivisible resources allocated in advance may need to be reallocated when capacity reductions make existing assignments infeasible, yet monetary compensation is often impractical or undesirable.PPAD-Completeness of Contiguous Envy-Free Cake Cutting with Additive Valuations
We prove that computing a complete contiguous $\varepsilon$-envy-free cake allocation is PPAD-complete for normalized rational piecewise-constant additive valuations.Exact Cut Complexity of Equal-Length Proportional Cake Cutting
We study proportional cake cutting under an equal-length constraint, where each of $n$ agents must receive a bundle of length exactly $1/n$ that is worth at least $1/n$ to that agent.Optimal Online Stochastic Matching
We resolve the problem of unweighted online stochastic matching with a known arrival law by establishing its optimal competitive ratio.What Does Disagreement Mean? A Semantic Identifiability Frontier for Pairwise Preference Learning
Pairwise comparison data are a standard supervision signal in preference learning, but disagreement in such data can reflect stable latent heterogeneity, ephemeral stochasticity, or non-binary…SafeAdapt-Poker: Counterfactual Risk Calibration for Budgeted Adaptation under Opponent Shift
Opponent histories can mislead after behavioral shifts.Price of Anarchy of First-Price Auctions in Agentic Markets
An agentic market is a decentralized serving-time market where AI agents bid to claim each user prompt.Improved Combinatorial Auctions with and without Samples
We give new mechanisms for combinatorial auctions in the offline, secretary, and prophet-with-samples models.Certifying Capacity and Gaps of the Rank-Dependent Gain-Sharing Framework for Ranking
The randomized primal-dual framework of Devanur, Jain, and Kleinberg (SODA 2013) splits each unit matching gain between endpoint dual variables according to the matched offline rank.A Matroid Generalization of Weighted Popular Matching: Characterization and Algorithm
We study a problem that unifies two major generalizations of popular matchings in the one-sided preference model.Exact power indices for plurality-voting ensembles
Plurality-voting ensembles aggregate the predictions of multiple base classifiers and output the most voted class, with ties typically broken at random.Fresh at Decision, Stale at Action: Routing New Information to LLM Decisions Already Made
Freshness shapes an LLM decision at the moment it is made; the action it commits to is latent, carried out later, in a changing world.When Does Opponent Modeling Pay Off? Spatial Inference Stabilises Reinforcement Learning under Partial Observability
Deep reinforcement learning agents in partially observable competitive games must act on beliefs about an unseen opponent's intent.Strategic Multi-Agent Resource Allocation via Statistical Contracts
We study a class of strategic hypothesis testing problems in which a principal aims to allocate resources among competing agents who possess data or data-generating mechanisms that may be relevant…Linear Last-Iterate Convergence Rates for Optimistic Learning in Harmonic Games
We prove that the last iterate of Optimistic Follow-The-Regularized-Leader (OFTRL) converges at a linear rate to a Nash equilibrium in multiplayer harmonic games.Falsification-proof non-market allocation mechanisms
Relying on manipulable characteristics to target the allocation of resources is fraught with difficulties.IR-PRM+ with Previous-Utility Prediction: Constant Regret and Best-Iterate Convergence
Regret matching (and its variants) and counterfactual regret minimization are central algorithms in practice for normal-form and extensive-form game solving.Learning in Strategic Queuing with Geometric Recharge: From Bandits to Systems
We study packet routing in queuing systems where queues act as selfish, myopic agents that use no-regret learning algorithms to select servers with unknown service rates.Truthful and Expected-Payment-Optimal Model Routing in Graph-Based Agentic Workflows
Assigning routine tasks to cheaper large language model (LLM) providers can reduce generation cost, but switching providers may require processing accumulated context again.