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…

BED-LLM: Intelligent Information Gathering with LLMs and Bayesian Experimental Design
Deepro Choudhury, Sinead Williamson, Adam Goli\'nski, Ning Miao, Freddie Bickford Smith, Michael Kirchhof, Yizhe Zhang, Tom Rainforth
https://arxiv.org/abs/2508.21184

BED-LLM: Intelligent Information Gathering with LLMs and Bayesian Experimental Design
We propose a general-purpose approach for improving the ability of Large Language Models (LLMs) to intelligently and adaptively gather information from a user or other external source using the framework of sequential Bayesian experimental design (BED). This enables LLMs to act as effective multi-turn conversational agents and interactively interface with external environments. Our approach, which we call BED-LLM (Bayesian Experimental Design with Large Language Models), is based on iteratively…

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 …

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…

Collaborative Evolution of Intelligent Agents in Large-Scale Microservice Systems
Yilin Li, Song Han, Sibo Wang, Ming Wang, Renzi Meng
https://arxiv.org/abs/2508.20508 https://

Collaborative Evolution of Intelligent Agents in Large-Scale Microservice Systems
This paper proposes an intelligent service optimization method based on a multi-agent collaborative evolution mechanism to address governance challenges in large-scale microservice architectures. These challenges include complex service dependencies, dynamic topology structures, and fluctuating workloads. The method models each service as an agent and introduces graph representation learning to construct a service dependency graph. This enables agents to perceive and embed structural changes wi…

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…

Cognitive Agents Powered by Large Language Models for Agile Software Project Management
Konrad Cinkusz, Jaros{\l}aw A. Chudziak, Ewa Niewiadomska-Szynkiewicz
https://arxiv.org/abs/2508.16678

Cognitive Agents Powered by Large Language Models for Agile Software Project Management
This paper investigates the integration of cognitive agents powered by Large Language Models (LLMs) within the Scaled Agile Framework (SAFe) to reinforce software project management. By deploying virtual agents in simulated software environments, this study explores their potential to fulfill fundamental roles in IT project development, thereby optimizing project outcomes through intelligent automation. Particular emphasis is placed on the adaptability of these agents to Agile methodologies and…

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…

LLM-Based Intelligent Agents for Music Recommendation: A Comparison with Classical Content-Based Filtering
Ronald Carvalho Boadana, Ademir Guimar\~aes da Costa Junior, Ricardo Rios, F\'abio Santos da Silva
https://arxiv.org/abs/2508.11671

LLM-Based Intelligent Agents for Music Recommendation: A Comparison with Classical Content-Based Filtering
The growing availability of music on streaming platforms has led to information overload for users. To address this issue and enhance the user experience, increasingly sophisticated recommendation systems have been proposed. This work investigates the use of Large Language Models (LLMs) from the Gemini and LLaMA families, combined with intelligent agents, in a multi-agent personalized music recommendation system. The results are compared with a traditional content-based recommendation model, co…

Search, Summarize, Analyze: Building Search Intelligence using Elasticsearch & AWS!
https://blog.someshwaran.me/intelligence-layer-with-es-fastapi-aws/

Search, Summarize, Analyze: Building Search Intelligence using Elasticsearch & AWS!
Build an intelligent search layer with Elasticsearch and AWS Bedrock Agents. Explore vector and semantic search, RAG, and hybrid approaches to turn raw data into workflows that can search, summarize, and analyze for real insights.

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…

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…

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…

Measuring Human Leadership Skills with Artificially Intelligent Agents
Ben Weidmann, Yixian Xu, David J. Deming
https://arxiv.org/abs/2508.02966 https://ar…

Measuring Human Leadership Skills with Artificially Intelligent Agents
We show that the ability to lead groups of humans is predicted by leadership skill with Artificially Intelligent agents. In a large pre-registered lab experiment, human leaders worked with AI agents to solve problems. Their performance on this 'AI leadership test' was strongly correlated with their causal impact on human teams, which we estimate by repeatedly randomly assigning leaders to groups of human followers and measuring team performance. Successful leaders of both humans and AI agents a…

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,…

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…

Tapas are free! Training-Free Adaptation of Programmatic Agents via LLM-Guided Program Synthesis in Dynamic Environments
Jinwei Hu, Yi Dong, Youcheng Sun, Xiaowei Huang
https://arxiv.org/abs/2508.11425

Tapas are free! Training-Free Adaptation of Programmatic Agents via LLM-Guided Program Synthesis in Dynamic Environments
Autonomous agents in safety-critical applications must continuously adapt to dynamic conditions without compromising performance and reliability. This work introduces TAPA (Training-free Adaptation of Programmatic Agents), a novel framework that positions large language models (LLMs) as intelligent moderators of the symbolic action space. Unlike prior programmatic agents that typically generate a monolithic policy program or rely on fixed symbolic action sets, TAPA synthesizes and adapts modula…

AI Agents for Photonic Integrated Circuit Design Automation
Ankita Sharma, YuQi Fu, Vahid Ansari, Rishabh Iyer, Fiona Kuang, Kashish Mistry, Raisa Islam Aishy, Sara Ahmad, Joaquin Matres, Dirk R. Englund, Joyce K. S. Poon
https://arxiv.org/abs/2508.14123

AI Agents for Photonic Integrated Circuit Design Automation
We present Photonics Intelligent Design and Optimization (PhIDO), a multi-agent framework that converts natural-language photonic integrated circuit (PIC) design requests into layout mask files. We compare 7 reasoning large language models for PhIDO using a testbench of 102 design descriptions that ranged from single devices to 112-component PICs. The success rate for single-device designs was up to 91%. For design queries with less than or equal to 15 components, o1, Gemini-2.5-pro, and Claude…

Run-and-Tumble Escape in Pursuit-Evasion Dynamics of Intelligent Active Particles
Segun Goh, Dennis Haustein, Gerhard Gompper
https://arxiv.org/abs/2508.10727 https://

Run-and-Tumble Escape in Pursuit-Evasion Dynamics of Intelligent Active Particles
The pursuit-evasion game is studied for two adversarial active agents, modelled as a deterministic self-steering pursuer and a stochastic, cognitive evader. The pursuer chases the evader by reorienting its propulsion direction with limited maneuverability, while the evader escapes by executing sharp, unpredictable turns, whose timing and direction the pursuer cannot anticipate. To make the target responsive and agile when the threat level is high, the tumbling frequency is set to increase with …

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…

Agent-Based Anti-Jamming Techniques for UAV Communications in Adversarial Environments: A Comprehensive Survey
Jingpu Yang, Mingxuan Cui, Hang Zhang, Fengxian Ji, Zhengzhao Lai, Yufeng Wang
https://arxiv.org/abs/2508.11687

Agent-Based Anti-Jamming Techniques for UAV Communications in Adversarial Environments: A Comprehensive Survey
Unmanned Aerial Vehicle communications are encountering increasingly severe multi-source interference challenges in dynamic adversarial environments, which impose higher demands on their reliability and resilience. To address these challenges, agent-based autonomous anti-jamming techniques have emerged as a crucial research direction. This paper presents a comprehensive survey that first formalizes the concept of intelligent anti-jamming agents for UAV communications and establishes a closed-lo…

Toward Low-Latency End-to-End Voice Agents for Telecommunications Using Streaming ASR, Quantized LLMs, and Real-Time TTS
Vignesh Ethiraj, Ashwath David, Sidhanth Menon, Divya Vijay
https://arxiv.org/abs/2508.04721

Toward Low-Latency End-to-End Voice Agents for Telecommunications Using Streaming ASR, Quantized LLMs, and Real-Time TTS
We introduce a low-latency telecom AI voice agent pipeline for real-time, interactive telecommunications use, enabling advanced voice AI for call center automation, intelligent IVR (Interactive Voice Response), and AI-driven customer support. The solution is built for telecom, combining four specialized models by NetoAI: TSLAM, a 4-bit quantized Telecom-Specific Large Language Model (LLM); T-VEC, a Telecom-Specific Embedding Model; TTE, a Telecom-Specific Automatic Speech Recognition (ASR) mode…

AWorld: Dynamic Multi-Agent System with Stable Maneuvering for Robust GAIA Problem Solving
Zhitian Xie, Qintong Wu, Chengyue Yu, Chenyi Zhuang, Jinjie Gu
https://arxiv.org/abs/2508.09889

AWorld: Dynamic Multi-Agent System with Stable Maneuvering for Robust GAIA Problem Solving
The rapid advancement of large language models (LLMs) has empowered intelligent agents to leverage diverse external tools for solving complex real-world problems. However, as agents increasingly depend on multiple tools, they encounter new challenges: extended contexts from disparate sources and noisy or irrelevant tool outputs can undermine system reliability and accuracy. These challenges underscore the necessity for enhanced stability in agent-based systems. To address this, we introduce dyn…

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…

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…

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…

MMAU-Pro: A Challenging and Comprehensive Benchmark for Holistic Evaluation of Audio General Intelligence
Sonal Kumar, \v{S}imon Sedl\'a\v{c}ek, Vaibhavi Lokegaonkar, Fernando L\'opez, Wenyi Yu, Nishit Anand, Hyeonggon Ryu, Lichang Chen, Maxim Pli\v{c}ka, Miroslav Hlav\'a\v{c}ek, William Fineas Ellingwood, Sathvik Udupa, Siyuan Hou, Allison Ferner, Sara Barahona, Cecilia Bola\~nos, Satish Rahi, Laura Herrera-Alarc\'on, Satvik Dixit, Siddhi Patil, Soham Deshmukh, Lasha K…

MMAU-Pro: A Challenging and Comprehensive Benchmark for Holistic Evaluation of Audio General Intelligence
Audio comprehension-including speech, non-speech sounds, and music-is essential for achieving human-level intelligence. Consequently, AI agents must demonstrate holistic audio understanding to qualify as generally intelligent. However, evaluating auditory intelligence comprehensively remains challenging. To address this gap, we introduce MMAU-Pro, the most comprehensive and rigorously curated benchmark for assessing audio intelligence in AI systems. MMAU-Pro contains 5,305 instances, where each…

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…

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…

Agentic AI Frameworks: Architectures, Protocols, and Design Challenges
Hana Derouiche, Zaki Brahmi, Haithem Mazeni
https://arxiv.org/abs/2508.10146 https://

Agentic AI Frameworks: Architectures, Protocols, and Design Challenges
The emergence of Large Language Models (LLMs) has ushered in a transformative paradigm in artificial intelligence, Agentic AI, where intelligent agents exhibit goal-directed autonomy, contextual reasoning, and dynamic multi-agent coordination. This paper provides a systematic review and comparative analysis of leading Agentic AI frameworks, including CrewAI, LangGraph, AutoGen, Semantic Kernel, Agno, Google ADK, and MetaGPT, evaluating their architectural principles, communication mechanisms, m…

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…

Large Model Empowered Embodied AI: A Survey on Decision-Making and Embodied Learning
Wenlong Liang, Rui Zhou, Yang Ma, Bing Zhang, Songlin Li, Yijia Liao, Ping Kuang
https://arxiv.org/abs/2508.10399

Large Model Empowered Embodied AI: A Survey on Decision-Making and Embodied Learning
Embodied AI aims to develop intelligent systems with physical forms capable of perceiving, decision-making, acting, and learning in real-world environments, providing a promising way to Artificial General Intelligence (AGI). Despite decades of explorations, it remains challenging for embodied agents to achieve human-level intelligence for general-purpose tasks in open dynamic environments. Recent breakthroughs in large models have revolutionized embodied AI by enhancing perception, interaction,…

WMAS: A Multi-Agent System Towards Intelligent and Customized Wireless Networks
Jingchen Peng, Dingli Yuan, Boxiang Ren, Jie Fan, Hao Wu, Lu Yang
https://arxiv.org/abs/2508.00280

WMAS: A Multi-Agent System Towards Intelligent and Customized Wireless Networks
The fast development of Artificial Intelligence (AI) agents provides a promising way for the realization of intelligent and customized wireless networks. In this paper, we propose a Wireless Multi-Agent System (WMAS), which can provide intelligent and customized services for different user equipment (UEs). Note that orchestrating multiple agents carries the risk of malfunction, and multi-agent conversations may fall into infinite loops. It is thus crucial to design a conversation topology for W…

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…

A Survey on Agentic Service Ecosystems: Measurement, Analysis, and Optimization
Xuwen Zhang, Xiao Xue, Xia Xie, Qun Ma, Xiangning Yu, Deyu Zhou, Yifan Wang, Ming Zhang
https://arxiv.org/abs/2508.07343 …

A Survey on Agentic Service Ecosystems: Measurement, Analysis, and Optimization
The Agentic Service Ecosystem consists of heterogeneous autonomous agents (e.g., intelligent machines, humans, and human-machine hybrid systems) that interact through resource exchange and service co-creation. These agents, with distinct behaviors and motivations, exhibit autonomous perception, reasoning, and action capabilities, which increase system complexity and make traditional linear analysis methods inadequate. Swarm intelligence, characterized by decentralization, self-organization, eme…

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…