Tootfinder

X-Omni: Reinforcement Learning Makes Discrete Autoregressive Image Generative Models Great Again
Zigang Geng, Yibing Wang, Yeyao Ma, Chen Li, Yongming Rao, Shuyang Gu, Zhao Zhong, Qinglin Lu, Han Hu, Xiaosong Zhang, Linus, Di Wang, Jie Jiang
https://arxiv.org/abs/2507.22058

X-Omni: Reinforcement Learning Makes Discrete Autoregressive Image Generative Models Great Again
Numerous efforts have been made to extend the ``next token prediction'' paradigm to visual contents, aiming to create a unified approach for both image generation and understanding. Nevertheless, attempts to generate images through autoregressive modeling with discrete tokens have been plagued by issues such as low visual fidelity, distorted outputs, and failure to adhere to complex instructions when rendering intricate details. These shortcomings are likely attributed to cumulative errors duri…

A Hierarchical Signal Coordination and Control System Using a Hybrid Model-based and Reinforcement Learning Approach
Xianyue Peng, Shenyang Chen, H. Michael Zhang
https://arxiv.org/abs/2508.20102

A Hierarchical Signal Coordination and Control System Using a Hybrid Model-based and Reinforcement Learning Approach
Signal control in urban corridors faces the dual challenge of maintaining arterial traffic progression while adapting to demand variations at local intersections. We propose a hierarchical traffic signal coordination and control scheme that integrates model-based optimization with reinforcement learning. The system consists of: (i) a High-Level Coordinator (HLC) that selects coordination strategies based on observed and predicted demand; (ii) a Corridor Coordinator that derives phase constraint…

Activity propagation with Hebbian learning
Will T. Engedal, R\'obert Juh\'asz, Istv\'an A. Kov\'acs
https://arxiv.org/abs/2508.21053 https://

Activity propagation with Hebbian learning
We investigate the impact of Hebbian learning on the contact process, a paradigmatic model for infection spreading, which has been also proposed as a simple model to capture the dynamics of inter-regional brain activity propagation as well as population spreading. Each of these contexts calls for an extension of the contact process with local learning. We introduce Hebbian learning as a positive or negative reinforcement of the activation rate between a pair of sites after each successful activ…

A provision within Trump’s so-called “Big Beautiful Bill,” will allocate an extra
$300 million for protecting Mar-a-Lago and other properties owned by Trump
— money that Trump could partially profit from, if history is any indicator.
The bill provides that funding in the form of reimbursements to local and state law enforcement whenever Trump travels to his properties, such as Mar-a-Lago in Florida or his Bedminster estate in New Jersey, for example.
Those local agen…

Distributed Detection of Adversarial Attacks in Multi-Agent Reinforcement Learning with Continuous Action Space
Kiarash Kazari, Ezzeldin Shereen, Gy\"orgy D\'an
https://arxiv.org/abs/2508.15764

Distributed Detection of Adversarial Attacks in Multi-Agent Reinforcement Learning with Continuous Action Space
We address the problem of detecting adversarial attacks against cooperative multi-agent reinforcement learning with continuous action space. We propose a decentralized detector that relies solely on the local observations of the agents and makes use of a statistical characterization of the normal behavior of observable agents. The proposed detector utilizes deep neural networks to approximate the normal behavior of agents as parametric multivariate Gaussian distributions. Based on the predicted…

Interacting vertex reinforced random walks on complete sub-graphs
Fernando P. A. Prado, Rafael A. Rosales
https://arxiv.org/abs/2508.15992 https://arxiv.or…

Interacting vertex reinforced random walks on complete sub-graphs
This article introduces a model for interacting vertex-reinforced random walks, each taking values on a complete sub-graph of a locally finite undirected graph. The transition probability for a walk to a given vertex depends on the cumulative proportion of visits by all walks that have access to that vertex. Proportions are modified by multiplication by a real valued interaction parameter and the addition of a parameter representing the intrinsic preference of the walk for the vertex. This mode…

Arbitrage Tactics in the Local Markets via Hierarchical Multi-agent Reinforcement Learning
Haoyang Zhang, Mina Montazeri, Philipp Heer, Koen Kok, Nikolaos G. Paterakis
https://arxiv.org/abs/2507.16479

Arbitrage Tactics in the Local Markets via Hierarchical Multi-agent Reinforcement Learning
Strategic bidding tactics employed by prosumers in local markets, including the Local Electricity Market (LEM) and Local Flexibility Market (LFM), have attracted significant attention due to their potential to enhance economic benefits for market participants through optimized energy management and bidding. While existing research has explored strategic bidding in a single market with multi-agent reinforcement learning (MARL) algorithms, arbitrage opportunities across local markets remain unexp…

Deep Reinforcement Learning Based Routing for Heterogeneous Multi-Hop Wireless Networks
Brian Kim, Justin H. Kong, Terrence J. Moore, Fikadu T. Dagefu
https://arxiv.org/abs/2508.14884

Deep Reinforcement Learning Based Routing for Heterogeneous Multi-Hop Wireless Networks
Routing in multi-hop wireless networks is a complex problem, especially in heterogeneous networks where multiple wireless communication technologies coexist. Reinforcement learning (RL) methods, such as Q-learning, have been introduced for decentralized routing by allowing nodes to make decisions based on local observations. However, Q-learning suffers from scalability issues and poor generalization due to the difficulty in managing the Q-table in large or dynamic network topologies, especially…

Reinforcement Learning-based Adaptive Path Selection for Programmable Networks
Jos\'e Eduardo Zerna Torres, Marios Avgeris, Chrysa Papagianni, Gergely Pongr\'acz, Istv\'an G\'odor, Paola Grosso
https://arxiv.org/abs/2508.13806

Reinforcement Learning-based Adaptive Path Selection for Programmable Networks
This work presents a proof-of-concept implementation of a distributed, in-network reinforcement learning (IN-RL) framework for adaptive path selection in programmable networks. By combining Stochastic Learning Automata (SLA) with real-time telemetry data collected via In-Band Network Telemetry (INT), the proposed system enables local, data-driven forwarding decisions that adapt dynamically to congestion conditions. The system is evaluated on a Mininet-based testbed using P4-programmable BMv2 sw…

Improving Reinforcement Learning Sample-Efficiency using Local Approximation
Mohit Prashant, Arvind Easwaran
https://arxiv.org/abs/2507.12383 https://

Improving Reinforcement Learning Sample-Efficiency using Local Approximation
In this study, we derive Probably Approximately Correct (PAC) bounds on the asymptotic sample-complexity for RL within the infinite-horizon Markov Decision Process (MDP) setting that are sharper than those in existing literature. The premise of our study is twofold: firstly, the further two states are from each other, transition-wise, the less relevant the value of the first state is when learning the $ε$-optimal value of the second; secondly, the amount of 'effort', sample-complexity-wise, ex…