arXiv:2507. 09473v2 Announce Type: replace-cross Abstract: We study the dynamic allocation of indivisible resources to strategic agents under long-term constraints, where the planner aims to maximize social welfare, satisfy multiple constraints, and elicit near-truthful reports.
By Yan Dai, Negin Golrezaei, Patrick Jaillet
The paper investigates whether evolutionary stability guarantees that learning agents can achieve cooperative outcomes in a multi‑agent setting. Using a three‑agent governance game, the authors compare the evolutionary basin of attraction with learning basins derived from independent Q‑learning, scaled Boltzmann exploration, and SA–EA BQL. They find that while the evolutionary basin covers the entire sampled grid, only ε‑greedy Q‑learning attains a substantial learning basin, whereas the other methods fail to sustain cooperation, highlighting a disconnect between population‑level stability and finite‑sample learning accessibility.
By Yijie Wang
The paper studies a budget‑constrained welfare problem for pooled testing, where agents have heterogeneous utilities and independent probabilities of being healthy. It proves that an optimal dynamic testing policy can achieve at most twice the welfare of the best static overlapping allocation, regardless of population, budget, or pool‑size limit. The authors also identify cases where adaptivity offers no benefit, show that re‑pooling after positive tests is necessary for strict gains, and provide approximation guarantees for greedy algorithms.
By Edwin Lock, Nicholas Lopez, Francisco Marmolejo-Coss\'io, Jose Roberto Tello Ayala, David C. Parkes
The paper introduces a Lagrangian framework for managing a regenerative commons, framing the problem as a constrained Markov game with a specified depletion budget. It constructs policy sequences from unconstrained solutions, extending time‑average concepts to reset episodes with discounted rewards and terminal costs, and provides theoretical guarantees such as reward‑independent feasibility, cooperative feasibility, and approximate optimality. Experiments on a fishery model using constrained IPPO and MAPPO illustrate how depletion budgets influence stock retention, harvest rewards, and price adaptation.
By Jose Tupayachi, Xueping Li, Soham Das
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.
By Mojtaba Eslami
arXiv:2503. 07869v4 Announce Type: replace Abstract: Critical learning periods (CLPs) in federated learning (FL) refer to early stages during which low-quality contributions (e.
By Thanh Linh Nguyen, Dinh Thai Hoang, Diep N. Nguyen, Quoc-Viet Pham