Tootfinder

Deep Research Agents: A Systematic Examination And Roadmap
Yuxuan Huang, Yihang Chen, Haozheng Zhang, Kang Li, Meng Fang, Linyi Yang, Xiaoguang Li, Lifeng Shang, Songcen Xu, Jianye Hao, Kun Shao, Jun Wang
https://arxiv.org/abs/2506.18096

Deep Research Agents: A Systematic Examination And Roadmap
The rapid progress of Large Language Models (LLMs) has given rise to a new category of autonomous AI systems, referred to as Deep Research (DR) agents. These agents are designed to tackle complex, multi-turn informational research tasks by leveraging a combination of dynamic reasoning, adaptive long-horizon planning, multi-hop information retrieval, iterative tool use, and the generation of structured analytical reports. In this paper, we conduct a detailed analysis of the foundational technolo…

Modeling Complex Life Systems: Bayesian Inference for Weibull Failure Times Using Adaptive MCMC
Tobias Oketch, Mohammad Sepehrifar
https://arxiv.org/abs/2506.11949

Modeling Complex Life Systems: Bayesian Inference for Weibull Failure Times Using Adaptive MCMC
This research develops a Bayesian framework for analyzing failure times using the Weibull distribution, addressing challenges in prior selection due to the lack of conjugate priors and multi-dimensional sufficient statistics. We propose an adaptive semi-parametric MCMC algorithm for lifetime data analysis, employing a hierarchical Bayesian model and the No-U-Turn Sampler (NUTS) in STAN. Twenty-four combinations of prior distributions are evaluated, with a noninformative LogNormal hyper-prior en…

On-board Mission Replanning for Adaptive Cooperative Multi-Robot Systems
Elim Kwan, Rehman Qureshi, Liam Fletcher, Colin Laganier, Victoria Nockles, Richard Walters
https://arxiv.org/abs/2506.06094

On-board Mission Replanning for Adaptive Cooperative Multi-Robot Systems
Cooperative autonomous robotic systems have significant potential for executing complex multi-task missions across space, air, ground, and maritime domains. But they commonly operate in remote, dynamic and hazardous environments, requiring rapid in-mission adaptation without reliance on fragile or slow communication links to centralised compute. Fast, on-board replanning algorithms are therefore needed to enhance resilience. Reinforcement Learning shows strong promise for efficiently solving mi…

Resilience through Automated Adaptive Configuration for Distribution and Replication
Scott D. Stoller, Balaji Jayasankar, Yanhong A. Liu
https://arxiv.org/abs/2506.10248

Resilience through Automated Adaptive Configuration for Distribution and Replication
This paper presents a powerful automated framework for making complex systems resilient under failures, by optimized adaptive distribution and replication of interdependent software components across heterogeneous hardware components with widely varying capabilities. A configuration specifies how software is distributed and replicated: which software components to run on each computer, which software components to replicate, which replication protocols to use, etc. We present an algorithm that,…

Rethinking Competition as a Non-Beneficial Mechanism in Economic Systems
Marcelo S. Tedesco, Gonzalo Marquez
https://arxiv.org/abs/2506.11405 https://

Rethinking Competition as a Non-Beneficial Mechanism in Economic Systems
Persistent economic competition is often justified as a mechanism of innovation, efficiency, and welfare maximization. Yet empirical evidence across disciplines reveals that competition systematically generates fragility, inequality, and ecological degradation, emergent outcomes not of isolated failures but of underlying systemic dynamics. This work reconceptualizes economic ecosystems as real complex adaptive systems, structurally isomorphic with biological and social ecosystems. Integrating c…

Integrative, Scalable Modeling of Hydrological Systems with MBSE and HFGT
Megan Harris, Ehsanoddin Ghorbanichemazkati, Mohammad Mahdi Naderi, John C. Little, Amro M. Farid
https://arxiv.org/abs/2506.00696

Integrative, Scalable Modeling of Hydrological Systems with MBSE and HFGT
Worsening global challenges in the Anthropocene demand complex, adaptive solutions grounded in a systems-level understanding of coupled social and environmental dynamics. However, existing modeling approaches often fall short due to disciplinary silos, limited scalability, and the absence of shared ontological frameworks. Model-Based Systems Engineering (MBSE), when integrated with Hetero-functional Graph Theory (HFGT), offers a powerful methodology for modeling systems of systems while preserv…

This https://arxiv.org/abs/2501.18878 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Integrated Sensing and Communication System Based on Radio Frequency Resonance Beam
To address the complex beam control in traditional multiple-input multiple-output (MIMO) systems, researchers have proposed adaptive beam alignment using retro-directive antenna (RDA) arrays. This approach creates echo resonance between the base station (BS) and user equipment (UE), significantly reducing computational load. However, conventional resonant beam systems (RBS) suffer from echo interference due to the shared uplink and downlink frequency. Therefore, this paper proposes an innovativ…

MasHost Builds It All: Autonomous Multi-Agent System Directed by Reinforcement Learning
Kuo Yang, Xingjie Yang, Linhui Yu, Qing Xu, Yan Fang, Xu Wang, Zhengyang Zhou, Yang Wang
https://arxiv.org/abs/2506.08507

MasHost Builds It All: Autonomous Multi-Agent System Directed by Reinforcement Learning
Large Language Model (LLM)-driven Multi-agent systems (Mas) have recently emerged as a powerful paradigm for tackling complex real-world tasks. However, existing Mas construction methods typically rely on manually crafted interaction mechanisms or heuristic rules, introducing human biases and constraining the autonomous ability. Even with recent advances in adaptive Mas construction, existing systems largely remain within the paradigm of semi-autonomous patterns. In this work, we propose MasHos…

Adaptive Job Scheduling in Quantum Clouds Using Reinforcement Learning
Waylon Luo (Kent State University), Jiapeng Zhao (Cisco), Tong Zhan (Meta), Qiang Guan (Kent State University)
https://arxiv.org/abs/2506.10889

Adaptive Job Scheduling in Quantum Clouds Using Reinforcement Learning
Present-day quantum systems face critical bottlenecks, including limited qubit counts, brief coherence intervals, and high susceptibility to errors-all of which obstruct the execution of large and complex circuits. The advancement of quantum algorithms has outpaced the capabilities of existing quantum hardware, making it difficult to scale computations effectively. Additionally, inconsistencies in hardware performance and pervasive quantum noise undermine system stability and computational accu…

Temporal passing network in basketball: the effect of time pressure on the dynamics of team organization at micro and meso levels
Quentin Bourgeais (CETAPS, LITIS), Rodolphe Charrier (LITIS), Eric Sanlaville (LITIS), Ludovic Seifert (CETAPS, IUF)
https://arxiv.org/abs/2506.04808

Temporal passing network in basketball: the effect of time pressure on the dynamics of team organization at micro and meso levels
In this study, basketball teams are conceptualized as complex adaptive systems to examine their (re)organizational processes in response the time remaining to shoot. Using temporal passing networks to model team behavior, the focus is on the dynamics of the temporal patterns of interaction between players. Several metrics grounded in social network analysis are calculated at different level to assess the dynamics of the patterns used by teams and of the individual roles within those patterns. T…

This https://arxiv.org/abs/2311.05009 has been replaced.
initial toot: https://mastoxiv.page/@ar…

Consensus-based adaptive sampling and approximation for high-dimensional energy landscapes
We present a consensus-based framework that unifies phase space exploration with posterior-residual-based adaptive sampling for surrogate construction in high-dimensional energy landscapes. Unlike standard approximation tasks where sampling points can be freely queried, physical systems with complex energy landscapes such as molecular dynamics (MD) do not have direct access to arbitrary sampling regions due to the physical constraints and energy barriers; the surrogate construction further reli…

Autonomous Behavior and Whole-Brain Dynamics Emerge in Embodied Zebrafish Agents with Model-based Intrinsic Motivation
Reece Keller, Alyn Tornell, Felix Pei, Xaq Pitkow, Leo Kozachkov, Aran Nayebi
https://arxiv.org/abs/2506.00138

Autonomous Behavior and Whole-Brain Dynamics Emerge in Embodied Zebrafish Agents with Model-based Intrinsic Motivation
Autonomy is a hallmark of animal intelligence, enabling adaptive and intelligent behavior in complex environments without relying on external reward or task structure. Existing reinforcement learning approaches to exploration in sparse reward and reward-free environments, including class of methods known as intrinsic motivation, exhibit inconsistent exploration patterns and thus fail to produce robust autonomous behaviors observed in animals. Moreover, systems neuroscience has largely overlooke…