arXiv AI July 21, 2026 By Yuqi Pan, Davin Choo, Haichuan Wang, Milind Tambe, Alastair van Heerden, Cheryl Johnson

Adaptive Multi-Round Allocation with Stochastic Arrivals

arXiv:2605. 12111v2 Announce Type: replace Abstract: We study a sequential resource allocation problem motivated by adaptive network recruitment, in which a limited budget of identical resources must be allocated over multiple rounds to individuals with stochastic referral capacity.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI

Jun 9

Generative Frontier Planning for Adaptive Peer-Referral Recruitment under Covariate-Dependent Arrivals

arXiv:2606. 08360v1 Announce Type: cross Abstract: Peer-referral recruitment systems such as respondent-driven sampling are critical for studying and intervening on hidden populations affected by infectious diseases.

By Lingkai Kong, Hezi Jiang, Andrew Ma, Keyu Wang, Akseli Kangaslahti, Milind Tambe

arXiv Machine Learning

Sep 22

Leveraging Inference-Time Compute for Diffusion Models via Global Scheduling of Denoising Trajectories

The paper studies how to allocate a fixed computational budget across the denoising steps of diffusion models to improve sample quality at deployment. It shows that the expected benefit of evaluating multiple candidates at a step can be decomposed into a step‑specific sensitivity and a universal sample‑size factor, and that the optimal allocation follows a water‑filling structure. Experiments demonstrate that this allocation achieves the same quality as a uniform strategy while reducing function evaluations by 20–50%.

By Yuan Cao, Yifu Tang, Hangqi Li, Zeyu Zheng

diffusion benchmarks

arXiv Machine Learning

Jun 18

Sequential Hiring of Contingent Workers Through Learning-Based Optimization

arXiv:2606. 18438v1 Announce Type: cross Abstract: In this paper, we study a sequential workforce management problem in a contingent labor setting with uncertainty in both worker production and labor supply.

By Chris Lee, Xiuli Chao, Izak Duenyas

reinforcement-learning benchmarks

Hugging Face Trending Papers

Aug 6

Relay, Don't Route: Adaptive Population Handoff for Cost-Efficient LLM-Driven Evolution

Large language model (LLM)-driven evolution has shown promise for program search and algorithm discovery, but relying on strong models throughout long evolutionary runs is costly. A natural alternative is to combine cheap and strong models under a fixed inference budget.

llms reinforcement-learning benchmarks

arXiv Machine Learning

Jul 14

Learning to Schedule in Parallel-Server Queues with Stochastic Bilinear Rewards

arXiv:2112. 06362v5 Announce Type: replace Abstract: We consider the problem of scheduling in multi-class, parallel-server queuing systems with uncertain rewards from job-server assignments.

By Jung-hun Kim, Milan Vojnovic

reinforcement-learning efficiency

arXiv AI

Sep 15

Compute Allocation for Self-Evolving LLMs: From Depth-Breadth to Multi-Armed Bandits

arXiv:2605.29268v3 Announce Type: replace-cross Abstract: LLM-guided evolutionary search (Evolve systems) has reached state-of-the-art results on mathematical and combinatorial tasks, yet most existi...

By Sixue Xing, Haoyu He, Kerui Wu, Zhuo Yang, Haozheng Luo, Tianfan Fu, Aarthy Nagarajan

llms reinforcement-learning benchmarks

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Related stories

Generative Frontier Planning for Adaptive Peer-Referral Recruitment under Covariate-Dependent Arrivals

Leveraging Inference-Time Compute for Diffusion Models via Global Scheduling of Denoising Trajectories

Sequential Hiring of Contingent Workers Through Learning-Based Optimization

Relay, Don't Route: Adaptive Population Handoff for Cost-Efficient LLM-Driven Evolution

Learning to Schedule in Parallel-Server Queues with Stochastic Bilinear Rewards

Compute Allocation for Self-Evolving LLMs: From Depth-Breadth to Multi-Armed Bandits