The paper introduces a dynamic bipartite matching framework that uses large language model (LLM) agents and contextual bandits to model decentralized, asynchronous matching processes without requiring full preference rankings. In a simulated Chinese marriage market, LLM agents evaluate local candidates while Logistic-UCB models learn reciprocal acceptance, leading to higher mutual welfare and fewer blocking pairs compared to classical Gale–Shapley. The study validates LLM-generated preferences against empirical data and demonstrates gender-differentiated acceptance patterns, supporting the use of decentralized LLM-based matching for economic simulation and computational social science.
arXiv:2601. 07144v3 Announce Type: replace-cross Abstract: Ensuring fairness in matching algorithms is a key challenge in allocating scarce resources and positions.
By Linus Bleistein, Mathieu Dagr\'eou, Francisco Andrade, Thomas Boudou, Aur\'elien Bellet
arXiv:2605. 01961v2 Announce Type: replace Abstract: Learning from human preference data is becoming a useful tool, from fine-tuning large language models to training reinforcement learning agents.
By Maheed H. Ahmed, Mahsa Ghasemi
arXiv:2511. 11413v2 Announce Type: replace Abstract: Consider the problem of finding the best matching in a weighted graph where we only have access to predictions of the actual stochastic weights, based on an underlying context.
By Riccardo Colini Baldeschi, Simone Di Gregorio, Simone Fioravanti, Federico Fusco, Ido Guy, Daniel Haimovich, Stefano Leonardi, Fridolin Linder, Lorenzo Perini, Matteo Russo, Cem Sirin, Niek Tax
arXiv:2607. 04824v1 Announce Type: new Abstract: We study a sequential learning problem for stable matchings in two-sided markets where preferences on both sides are initially unknown.
By Andreas Athanasopoulos, Anne-Marie George, Christos Dimitrakakis
arXiv:2607. 23310v1 Announce Type: cross Abstract: We study an online variant of discrete fair division under generalized assignment budget constraints.
By Saar Cohen, Nicholas Teh, Paul W. Goldberg, Michael J. Wooldridge