The paper addresses the problem of setting optimal non‑discriminatory prices in a network of strategic agents with private information and potential misreporting. It shows that the optimal prices can be decomposed according to each agent’s squared centrality in a preference‑weighted network, leading to the Poll algorithm. Poll samples one agent per round, briefly explores its neighborhood, and updates prices locally, achieving significant computational, statistical, and economic efficiency gains over exact or other distributed methods.
arXiv:2608. 20316v1 Announce Type: new Abstract: Heterogeneous AI systems composed of multiple models, architectures, harnesses, or inference-time settings can improve quality and efficiency by routing queries to the specialist who can answer most effectively at the lowest cost.
Organizations often pool dispersed information into one ranking and then allow many agents to act on that shared view. In a discovery problem, this can improve beliefs while reducing coverage.
arXiv:2608. 07532v1 Announce Type: new Abstract: Modern agentic AI systems combine multiple large language model agents with heterogeneous skills, yet most architectures either fix communication in advance or allow full broadcast.
The paper introduces a decentralized learning framework for finding socially optimal equilibria in finite normal-form games played over dynamic communication networks. Agents only observe their own payoffs, lack prior knowledge of the game, and communicate with time-varying neighbors using low-bandwidth, time-stamped tables instead of raw actions or payoff data. The proposed dynamics combine randomized semantic signals, table fusion, and temporal majority reconstruction to achieve finite-time logarithmic regret guarantees for optimal equilibrium selection under utilitarian and proportional-fair social welfare objectives, as demonstrated by simulations.
arXiv:2607. 18045v1 Announce Type: new Abstract: Organizations often pool dispersed information into one ranking and then allow many agents to act on that shared view.
arXiv:2607. 08012v1 Announce Type: cross Abstract: This paper studies an online variant of the assistance games framework, where an informed agent and an uninformed agent repeatedly interact over $T$ timesteps to optimize a common reward function.
arXiv:2605. 14879v2 Announce Type: replace-cross Abstract: Many intelligent computing and autonomous systems rely on multiple independent, often learning, agents repeatedly sharing a limited resource.
The paper investigates whether pricing algorithms on multi‑seller platforms should incorporate competitors’ prices when learning demand. It compares two strategies: informed sellers that use competitor prices in their learning models, and oblivious sellers that ignore them. The study finds that oblivious sellers must explore prices more aggressively to offset missing competitor information; when all sellers are oblivious, prices eventually converge to the competitive outcome, but insufficient exploration can create many pseudo‑equilibria. In mixed markets, informed sellers earn more, and the unique Nash equilibrium is a fully informed market where prices efficiently converge to the competitive outcome, showing that oblivious modeling does not reliably produce collusion.
The paper introduces a scalable framework that uses decentralized markets of self‑interested agents to solve multi‑step tasks. It shows that when agents compete through sequential auctions, perfect competition fails because clones drive profits to zero, while no competition also collapses value extraction. By allowing heterogeneous agents to shade bids strategically, the authors demonstrate that imperfect competition can sustain profitable margins while still achieving high task success rates at low costs, evaluating four bidding algorithms across three auction protocols.
arXiv:2607. 17311v1 Announce Type: cross Abstract: The problem of fair multi-agent coordination in decentralized settings is one of the most pressing challenges for building efficient collaborative systems.
arXiv:2607. 04389v1 Announce Type: new Abstract: It is increasingly common to aggregate predictions from multiple LLMs, each with domain expertise or access to private tools and data, to improve collective prediction performance.
arXiv:2606. 05363v1 Announce Type: cross Abstract: On a platform with many sellers, should a pricing algorithm explicitly model competitors' prices when learning demand?