arXiv Machine Learning

TempoNet: Slack-Quantized Transformer-Guided Reinforcement Scheduler for Adaptive Deadline-Centric Real-Time Dispatchs

arXiv:2602. 18109v3 Announce Type: replace Abstract: Real-time schedulers must reason about tight deadlines under strict compute budgets.

arXiv AI

Jun 15

A Deep Reinforcement Learning (DRL)-Based Transformer Method for Solving the Open Shop Scheduling Problem

arXiv:2606. 13682v1 Announce Type: new Abstract: The open shop scheduling problem (OSSP) arises in many industrial and service settings but remains computationally challenging as the number of jobs and machines increases.

By Faezeh Ardali, Mwembezi A. Nyelele, Gerald M. Knapp

llms reinforcement-learning benchmarks

arXiv AI

Jul 14

Time-Lag-Aware Deep Reinforcement Learning for Flexible Job-Shop Scheduling in PPVC Module Factories

arXiv:2607. 11725v1 Announce Type: cross Abstract: Prefabricated prefinished volumetric construction moves most building work into module factories, whose production floor operates as a flexible job shop.

By Ziheng Zhang, Wei Zhang

rag reinforcement-learning benchmarks

arXiv AI

Jun 16

A Learning Method with Gap-Aware Generation for Heterogeneous DAG Scheduling

arXiv:2603. 23249v2 Announce Type: replace-cross Abstract: Efficient scheduling of directed acyclic graphs (DAGs) is a core problem in large-scale data-intensive computing systems, where query plans, data-processing workloads, and computation graphs consist of dependent tasks competing for limited heterogeneous resource pools.

By Ruisong Zhou, Haijun Zou, Li Zhou, Chumin Sun, Zaiwen Wen

reinforcement-learning

arXiv Machine Learning

Jun 25

Hierarchical Reinforcement Learning for Neural Network Compression (HiReLC): Pruning and Quantization

arXiv:2606. 26002v1 Announce Type: new Abstract: We present HiReLC, a hierarchical ensemble-reinforcement learning framework for automated joint quantization and structured pruning of deep neural networks.

By Kamar Hibatallah Baghdadi, Kawther Guoual Belhamidi, Sara Belhadj, Aissa Boulmerka, Nadir Farhi

llms agents computer-vision reinforcement-learning fine-tuning efficiency benchmarks

arXiv AI

Jun 15

Scalable Production Scheduling: Linear Complexity via Unified Homogeneous Graphs

arXiv:2604. 23841v2 Announce Type: replace-cross Abstract: Efficiently solving the Job Shop Scheduling Problem in real-world industrial applications requires policies that are both computationally lean and topologically robust.

By Jonathan Hoss, Moritz Link, Noah Klarmann

agents diffusion reinforcement-learning benchmarks

arXiv AI

Jul 15

Learning-based Probabilistic Load Forecasting with Post-hoc and In-model Uncertainty

arXiv:2607. 12730v1 Announce Type: cross Abstract: Smart-building load forecasters are often trained offline on dense, multivariate, high-frequency data, but deployment may provide only hourly, feature-limited inputs.

By Sarah Al-Shareeda, Gulcihan Ozdemir, Heung Seok Jeon

llms

arXiv AI

Jun 2

Deft Scheduling of Dynamic Cloud Workflows with Varying Deadlines via Mixture-of-Experts

arXiv:2606. 01162v1 Announce Type: new Abstract: Workflow scheduling in cloud computing demands the intelligent allocation of dynamically arriving, graph-structured workflows with varying deadlines onto ever-changing virtual machine resources.

By Ya Shen, Gang Chen, Hui Ma, Mengjie Zhang

reinforcement-learning benchmarks

arXiv AI

Jun 11

INFRAMIND: Infrastructure-Aware Multi-Agent Orchestration

arXiv:2606. 11440v1 Announce Type: new Abstract: Existing multi-agent LLM orchestration methods, ranging from brute-force ensembles to learned routers, select models and topologies based on task and model features.

By Ahasan Kabir, Jiaqi Xue, Mengxin Zheng, Qian Lou

llms agents reinforcement-learning benchmarks

arXiv AI

Aug 11

Full-bandwidth transformer

arXiv:2608. 08888v1 Announce Type: new Abstract: Autoregressive transformers compute along two axes: horizontally across generated tokens, and vertically through model depth.

By Xi Wang, Ziyang Cai, Zheng Zhan, Harry Dong, Ying Fan, Gustavo de Rosa, Tim Pearce, John Langford

llms rag efficiency

arXiv Machine Learning

Jun 5

Representation Learning Enables Scalable Multitask Deep Reinforcement Learning

arXiv:2606. 05555v1 Announce Type: new Abstract: Scaling reinforcement learning (RL) to diverse multitask settings remains a central challenge.

By Johan Obando-Ceron, Lu Li, Scott Fujimoto, Pierre-Luc Bacon, Aaron Courville, Pablo Samuel Castro

reinforcement-learning

arXiv AI

Aug 7

Multi-Agent Transformer for Queue-Level XR Traffic Scheduling in TSN Networks

arXiv:2608. 05340v1 Announce Type: cross Abstract: Time-Sensitive Networking (TSN) and Mobile Edge Computing (MEC) hold strong potential for enabling ultra-reliable low-latency communication for time-sensitive applications, such as eXtended Reality (XR).

By Marcos Carvalho, Fatih Temiz, Shavbo Salehi, Melike Erol-Kantarci, Daniel F. Macedo

llms agents reinforcement-learning

arXiv AI

Jun 9

Prescriptive Scaling Reveals the Evolution of Language Model Capabilities

arXiv:2602. 15327v2 Announce Type: replace-cross Abstract: Machine learning model performance improvements tend to arise from competition and application.

By Hanlin Zhang, Jikai Jin, Vasilis Syrgkanis, Sham Kakade

llms benchmarks

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