Tootfinder

Large Language Model Powered Intelligent Urban Agents: Concepts, Capabilities, and Applications
Jindong Han, Yansong Ning, Zirui Yuan, Hang Ni, Fan Liu, Tengfei Lyu, Hao Liu
https://arxiv.org/abs/2507.00914

Large Language Model Powered Intelligent Urban Agents: Concepts, Capabilities, and Applications
The long-standing vision of intelligent cities is to create efficient, livable, and sustainable urban environments using big data and artificial intelligence technologies. Recently, the advent of Large Language Models (LLMs) has opened new ways toward realizing this vision. With powerful semantic understanding and reasoning capabilities, LLMs can be deployed as intelligent agents capable of autonomously solving complex problems across domains. In this article, we focus on Urban LLM Agents, whic…

Mitigating Response Delays in Free-Form Conversations with LLM-powered Intelligent Virtual Agents
Mykola Maslych, Mohammadreza Katebi, Christopher Lee, Yahya Hmaiti, Amirpouya Ghasemaghaei, Christian Pumarada, Janneese Palmer, Esteban Segarra Martinez, Marco Emporio, Warren Snipes, Ryan P. McMahan, Joseph J. LaViola Jr
https://arxiv.org/ab…

Mitigating Response Delays in Free-Form Conversations with LLM-powered Intelligent Virtual Agents
We investigated the challenges of mitigating response delays in free-form conversations with virtual agents powered by Large Language Models (LLMs) within Virtual Reality (VR). For this, we used conversational fillers, such as gestures and verbal cues, to bridge delays between user input and system responses and evaluate their effectiveness across various latency levels and interaction scenarios. We found that latency above 4 seconds degrades quality of experience, while natural conversational …

Towards a unified user modeling language for engineering human centered AI systems
Aaron Conrardy, Alfredo Capozucca, Jordi Cabot
https://arxiv.org/abs/2505.24697

Towards a unified user modeling language for engineering human centered AI systems
In today's digital society, personalization has become a crucial aspect of software applications, significantly impacting user experience and engagement. A new wave of intelligent user interfaces, such as AI-based conversational agents, has the potential to enable such personalization beyond what other types of interfaces could offer in the past. Personalization requires the ability to specify a complete user profile, covering as many dimensions as possible, such as potential accessibility cons…

AI-Governed Agent Architecture for Web-Trustworthy Tokenization of Alternative Assets
Ailiya Borjigin, Wei Zhou, Cong He
https://arxiv.org/abs/2507.00096 h…

AI-Governed Agent Architecture for Web-Trustworthy Tokenization of Alternative Assets
Alternative Assets tokenization is transforming non-traditional financial instruments are represented and traded on the web. However, ensuring trustworthiness in web-based tokenized ecosystems poses significant challenges, from verifying off-chain asset data to enforcing regulatory compliance. This paper proposes an AI-governed agent architecture that integrates intelligent agents with blockchain to achieve web-trustworthy tokenization of alternative assets. In the proposed architecture, autono…

Emergent interactions lead to collective frustration in robotic matter
Onurcan Bektas, Adolfo Alsina, Steffen Rulands
https://arxiv.org/abs/2507.22148 https://

Emergent interactions lead to collective frustration in robotic matter
Current artificial intelligence systems show near-human-level capabilities when deployed in isolation. Systems of a few collaborating intelligent agents are being engineered to perform tasks collectively. This raises the question of whether robotic matter, where many learning and intelligent agents interact, shows emergence of collective behaviour. And if so, which kind of phenomena would such systems exhibit? Here, we study a paradigmatic model for robotic matter: a stochastic many-particle sy…

Towards Mitigation of Hallucination for LLM-empowered Agents: Progressive Generalization Bound Exploration and Watchdog Monitor
Siyuan Liu, Wenjing Liu, Zhiwei Xu, Xin Wang, Bo Chen, Tao Li
https://arxiv.org/abs/2507.15903

Towards Mitigation of Hallucination for LLM-empowered Agents: Progressive Generalization Bound Exploration and Watchdog Monitor
Empowered by large language models (LLMs), intelligent agents have become a popular paradigm for interacting with open environments to facilitate AI deployment. However, hallucinations generated by LLMs-where outputs are inconsistent with facts-pose a significant challenge, undermining the credibility of intelligent agents. Only if hallucinations can be mitigated, the intelligent agents can be used in real-world without any catastrophic risk. Therefore, effective detection and mitigation of hal…

Phase transitions in voting simulated by an intelligent Ising model
Guanyu Xu, Jiahang Chen, Xin Zhou, Yanting Wang
https://arxiv.org/abs/2507.19161 https://

Phase transitions in voting simulated by an intelligent Ising model
Voting is an important social activity for expressing public opinions. By conceptually considering a group of voting agents to be intelligent matter, the impact of real-time information on voting results is quantitatively studied by an intelligent Ising model, which is formed by adding nonlinear instantaneous feedback of the overall magnetization to the conventional Ising model. In the new model, the interaction strength becomes a variable depending on the total magnetization rather than a cons…

This https://arxiv.org/abs/2505.19301 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

A Novel Zero-Trust Identity Framework for Agentic AI: Decentralized Authentication and Fine-Grained Access Control
Traditional Identity and Access Management (IAM) systems, primarily designed for human users or static machine identities via protocols such as OAuth, OpenID Connect (OIDC), and SAML, prove fundamentally inadequate for the dynamic, interdependent, and often ephemeral nature of AI agents operating at scale within Multi Agent Systems (MAS), a computational system composed of multiple interacting intelligent agents that work collectively. This paper posits the imperative for a novel Agentic AI I…

OSS-UAgent: An Agent-based Usability Evaluation Framework for Open Source Software
Lingkai Meng, Yu Shao, Long Yuan, Longbin Lai, Peng Cheng, Wenyuan Yu, Wenjie Zhang, Xuemin Lin, Jingren Zhou
https://arxiv.org/abs/2505.23239

OSS-UAgent: An Agent-based Usability Evaluation Framework for Open Source Software
Usability evaluation is critical to the impact and adoption of open source software (OSS), yet traditional methods relying on human evaluators suffer from high costs and limited scalability. To address these limitations, we introduce OSS-UAgent, an automated, configurable, and interactive agent-based usability evaluation framework specifically designed for open source software. Our framework employs intelligent agents powered by large language models (LLMs) to simulate developers performing pro…

Augmenting Von Neumann's Architecture for an Intelligent Future
Rajpreet Singh, Vidhi Kothari
https://arxiv.org/abs/2507.16628 https://

Augmenting Von Neumann's Architecture for an Intelligent Future
This work presents a novel computer architecture that extends the Von Neumann model with a dedicated Reasoning Unit (RU) to enable native artificial general intelligence capabilities. The RU functions as a specialized co-processor that executes symbolic inference, multi-agent coordination, and hybrid symbolic-neural computation as fundamental architectural primitives. This hardware-embedded approach allows autonomous agents to perform goal-directed planning, dynamic knowledge manipulation, and …

This https://arxiv.org/abs/2409.14457 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

Large Model Based Agents: State-of-the-Art, Cooperation Paradigms, Security and Privacy, and Future Trends
With the rapid advancement of large models (LMs), the development of general-purpose intelligent agents powered by LMs has become a reality. It is foreseeable that in the near future, LM-driven general AI agents will serve as essential tools in production tasks, capable of autonomous communication and collaboration without human intervention. This paper investigates scenarios involving the autonomous collaboration of future LM agents. We review the current state of LM agents, the key technologi…

The Emergence of Deep Reinforcement Learning for Path Planning
Thanh Thi Nguyen, Saeid Nahavandi, Imran Razzak, Dung Nguyen, Nhat Truong Pham, Quoc Viet Hung Nguyen
https://arxiv.org/abs/2507.15469

The Emergence of Deep Reinforcement Learning for Path Planning
The increasing demand for autonomous systems in complex and dynamic environments has driven significant research into intelligent path planning methodologies. For decades, graph-based search algorithms, linear programming techniques, and evolutionary computation methods have served as foundational approaches in this domain. Recently, deep reinforcement learning (DRL) has emerged as a powerful method for enabling autonomous agents to learn optimal navigation strategies through interaction with t…

Secure API-Driven Research Automation to Accelerate Scientific Discovery
Tyler J. Skluzacek, Paul Bryant, A. J. Ruckman, Daniel Rosendo, Suzanne Prentice, Michael J. Brim, Ryan Adamson, Sarp Oral, Mallikarjun Shankar, Rafael Ferreira da Silva
https://arxiv.org/abs/2506.11950

Secure API-Driven Research Automation to Accelerate Scientific Discovery
The Secure Scientific Service Mesh (S3M) provides API-driven infrastructure to accelerate scientific discovery through automated research workflows. By integrating near real-time streaming capabilities, intelligent workflow orchestration, and fine-grained authorization within a service mesh architecture, S3M revolutionizes programmatic access to high performance computing (HPC) while maintaining uncompromising security. This framework allows intelligent agents and experimental facilities to dyn…

This https://arxiv.org/abs/2403.16971 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csOS_…

AIOS: LLM Agent Operating System
LLM-based intelligent agents face significant deployment challenges, particularly related to resource management. Allowing unrestricted access to LLM or tool resources can lead to inefficient or even potentially harmful resource allocation and utilization for agents. Furthermore, the absence of proper scheduling and resource management mechanisms in current agent designs hinders concurrent processing and limits overall system efficiency. As the diversity and complexity of agents continue to gro…

Affogato: Learning Open-Vocabulary Affordance Grounding with Automated Data Generation at Scale
Junha Lee, Eunha Park, Chunghyun Park, Dahyun Kang, Minsu Cho
https://arxiv.org/abs/2506.12009

Affogato: Learning Open-Vocabulary Affordance Grounding with Automated Data Generation at Scale
Affordance grounding-localizing object regions based on natural language descriptions of interactions-is a critical challenge for enabling intelligent agents to understand and interact with their environments. However, this task remains challenging due to the need for fine-grained part-level localization, the ambiguity arising from multiple valid interaction regions, and the scarcity of large-scale datasets. In this work, we introduce Affogato, a large-scale benchmark comprising 150K instances,…

From LLMs to MLLMs to Agents: A Survey of Emerging Paradigms in Jailbreak Attacks and Defenses within LLM Ecosystem
Yanxu Mao, Tiehan Cui, Peipei Liu, Datao You, Hongsong Zhu
https://arxiv.org/abs/2506.15170

From LLMs to MLLMs to Agents: A Survey of Emerging Paradigms in Jailbreak Attacks and Defenses within LLM Ecosystem
Large language models (LLMs) are rapidly evolving from single-modal systems to multimodal LLMs and intelligent agents, significantly expanding their capabilities while introducing increasingly severe security risks. This paper presents a systematic survey of the growing complexity of jailbreak attacks and corresponding defense mechanisms within the expanding LLM ecosystem. We first trace the developmental trajectory from LLMs to MLLMs and Agents, highlighting the core security challenges emergi…

Agentic Satellite-Augmented Low-Altitude Economy and Terrestrial Networks: A Survey on Generative Approaches
Xiaozheng Gao, Yichen Wang, Bosen Liu, Xiao Zhou, Ruichen Zhang, Jiacheng Wang, Dusit Niyato, Dong In Kim, Abbas Jamalipour, Chau Yuen, Jianping An, Kai Yang
https://arxiv.org/abs/2507.14633…

Agentic Satellite-Augmented Low-Altitude Economy and Terrestrial Networks: A Survey on Generative Approaches
The development of satellite-augmented low-altitude economy and terrestrial networks (SLAETNs) demands intelligent and autonomous systems that can operate reliably across heterogeneous, dynamic, and mission-critical environments. To address these challenges, this survey focuses on enabling agentic artificial intelligence (AI), that is, artificial agents capable of perceiving, reasoning, and acting, through generative AI (GAI) and large language models (LLMs). We begin by introducing the archite…

Search-Based Autonomous Vehicle Motion Planning Using Game Theory
Pouya Panahandeh, Mohammad Pirani, Baris Fidan, Amir Khajepour
https://arxiv.org/abs/2507.15088

Search-Based Autonomous Vehicle Motion Planning Using Game Theory
In this paper, we propose a search-based interactive motion planning scheme for autonomous vehicles (AVs), using a game-theoretic approach. In contrast to traditional search-based approaches, the newly developed approach considers other road users (e.g. drivers and pedestrians) as intelligent agents rather than static obstacles. This leads to the generation of a more realistic path for the AV. Due to the low computational time, the proposed motion planning scheme is implementable in real-time a…

Humans learn to prefer trustworthy AI over human partners
Yaomin Jiang, Levin Brinkmann, Anne-Marie Nussberger, Ivan Soraperra, Jean-Fran\c{c}ois Bonnefon, Iyad Rahwan
https://arxiv.org/abs/2507.13524

Humans learn to prefer trustworthy AI over human partners
Partner selection is crucial for cooperation and hinges on communication. As artificial agents, especially those powered by large language models (LLMs), become more autonomous, intelligent, and persuasive, they compete with humans for partnerships. Yet little is known about how humans select between human and AI partners and adapt under AI-induced competition pressure. We constructed a communication-based partner selection game and examined the dynamics in hybrid mini-societies of humans and b…

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…

Behavior Driven Development for 3D Games
Fernando Pastor Ric\'os, Beatriz Mar\'in, I. S. W. B. Prasetya, Tanja E. J. Vos, Joseph Davidson, Karel Hovorka
https://arxiv.org/abs/2506.17057

Behavior Driven Development for 3D Games
Computer 3D games are complex software environments that require novel testing processes to ensure high-quality standards. The Intelligent Verification/Validation for Extended Reality Based Systems (iv4XR) framework addresses this need by enabling the implementation of autonomous agents to automate game testing scenarios. This framework facilitates the automation of regression test cases for complex 3D games like Space Engineers. Nevertheless, the technical expertise required to define test scr…

Mission-Aligned Learning-Informed Control of Autonomous Systems: Formulation and Foundations
Vyacheslav Kungurtsev, Gustav Sir, Akhil Anand, Sebastien Gros, Haozhe Tian, Homayoun Hamedmoghadam
https://arxiv.org/abs/2507.04356

Mission-Aligned Learning-Informed Control of Autonomous Systems: Formulation and Foundations
Research, innovation and practical capital investment have been increasing rapidly toward the realization of autonomous physical agents. This includes industrial and service robots, unmanned aerial vehicles, embedded control devices, and a number of other realizations of cybernetic/mechatronic implementations of intelligent autonomous devices. In this paper, we consider a stylized version of robotic care, which would normally involve a two-level Reinforcement Learning procedure that trains a po…

Misalignment or misuse? The AGI alignment tradeoff
Max Hellrigel-Holderbaum, Leonard Dung
https://arxiv.org/abs/2506.03755 https://ar…

Misalignment or misuse? The AGI alignment tradeoff
Creating systems that are aligned with our goals is seen as a leading approach to create safe and beneficial AI in both leading AI companies and the academic field of AI safety. We defend the view that misaligned AGI - future, generally intelligent (robotic) AI agents - poses catastrophic risks. At the same time, we support the view that aligned AGI creates a substantial risk of catastrophic misuse by humans. While both risks are severe and stand in tension with one another, we show that - in p…

Intelligent System of Emergent Knowledge: A Coordination Fabric for Billions of Minds
Moshi Wei, Sparks Li
https://arxiv.org/abs/2506.09335 https://…

Intelligent System of Emergent Knowledge: A Coordination Fabric for Billions of Minds
The Intelligent System of Emergent Knowledge (ISEK) establishes a decentralized network where human and artificial intelligence agents collaborate as peers, forming a self-organizing cognitive ecosystem. Built on Web3 infrastructure, ISEK combines three fundamental principles: (1) a decentralized multi-agent architecture resistant to censorship, (2) symbiotic AI-human collaboration with equal participation rights, and (3) resilient self-adaptation through distributed consensus mechanisms. The…

This https://arxiv.org/abs/2505.00270 has been replaced.
initial toot: https://mastoxiv.page/@ar…

Large Language Models as AI Agents for Digital Atoms and Molecules: Catalyzing a New Era in Computational Biophysics
In computational biophysics, where molecular data is expanding rapidly and system complexity is increasing exponentially, large language models (LLMs) and agent-based systems are fundamentally reshaping the field. This perspective article examines the recent advances at the intersection of LLMs, intelligent agents, and scientific computation, with a focus on biophysical computation. Building on these advancements, we introduce ADAM (Agent for Digital Atoms and Molecules), an innovative multi-ag…

Optimizing Conversational Product Recommendation via Reinforcement Learning
Kang Liu
https://arxiv.org/abs/2507.01060 https://arxiv.o…

Optimizing Conversational Product Recommendation via Reinforcement Learning
We propose a reinforcement learning-based approach to optimize conversational strategies for product recommendation across diverse industries. As organizations increasingly adopt intelligent agents to support sales and service operations, the effectiveness of a conversation hinges not only on what is recommended but how and when recommendations are delivered. We explore a methodology where agentic systems learn optimal dialogue policies through feedback-driven reinforcement learning. By mining …

This https://arxiv.org/abs/2503.18938 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

AdaWorld: Learning Adaptable World Models with Latent Actions
World models aim to learn action-controlled future prediction and have proven essential for the development of intelligent agents. However, most existing world models rely heavily on substantial action-labeled data and costly training, making it challenging to adapt to novel environments with heterogeneous actions through limited interactions. This limitation can hinder their applicability across broader domains. To overcome this limitation, we propose AdaWorld, an innovative world model learni…

HASHIRU: Hierarchical Agent System for Hybrid Intelligent Resource Utilization
Kunal Pai, Parth Shah, Harshil Patel
https://arxiv.org/abs/2506.04255 https:…

HASHIRU: Hierarchical Agent System for Hybrid Intelligent Resource Utilization
Rapid Large Language Model (LLM) advancements are fueling autonomous Multi-Agent System (MAS) development. However, current frameworks often lack flexibility, resource awareness, model diversity, and autonomous tool creation. This paper introduces HASHIRU (Hierarchical Agent System for Hybrid Intelligent Resource Utilization), a novel MAS framework enhancing flexibility, resource efficiency, and adaptability. HASHIRU features a "CEO" agent dynamically managing specialized "employee" agents, ins…

Agentic AI and Multiagentic: Are We Reinventing the Wheel?
V. Botti
https://arxiv.org/abs/2506.01463 https://arxiv.org/pdf/2506.01463…

Agentic AI and Multiagentic: Are We Reinventing the Wheel?
The terms Agentic AI and Multiagentic AI have recently gained popularity in discussions on generative artificial intelligence, often used to describe autonomous software agents and systems composed of such agents. However, the use of these terms confuses these buzzwords with well-established concepts in AI literature: intelligent agents and multi-agent systems. This article offers a critical analysis of this conceptual misuse. We review the theoretical origins of "agentic" in the social science…

Gradientsys: A Multi-Agent LLM Scheduler with ReAct Orchestration
Xinyuan Song, Zeyu Wang, Siyi Wu, Tianyu Shi, Lynn Ai
https://arxiv.org/abs/2507.06520 ht…

Gradientsys: A Multi-Agent LLM Scheduler with ReAct Orchestration
We present Gradientsys, a next-generation multi-agent scheduling framework that coordinates diverse specialized AI agents using a typed Model-Context Protocol (MCP) and a ReAct-based dynamic planning loop. At its core, Gradientsys employs an LLM-powered scheduler for intelligent one-to-many task dispatch, enabling parallel execution of heterogeneous agents such as PDF parsers, web search modules, GUI controllers, and web builders. The framework supports hybrid synchronous/asynchronous execution…