Tootfinder

Las Vegas Raiders Insider Says Free Agent Signing Was A Secret Standout at Minicamp https://heavy.com/sports/nfl/las-vegas-raiders/free-agent-signing-secret-standout-minicamp/?adt_ei=[email]

Spec2RTL-Agent: Automated Hardware Code Generation from Complex Specifications Using LLM Agent Systems
Zhongzhi Yu, Mingjie Liu, Michael Zimmer, Yingyan Celine Lin, Yong Liu, Haoxing Ren
https://arxiv.org/abs/2506.13905

Spec2RTL-Agent: Automated Hardware Code Generation from Complex Specifications Using LLM Agent Systems
Despite recent progress in generating hardware RTL code with LLMs, existing solutions still suffer from a substantial gap between practical application scenarios and the requirements of real-world RTL code development. Prior approaches either focus on overly simplified hardware descriptions or depend on extensive human guidance to process complex specifications, limiting their scalability and automation potential. In this paper, we address this gap by proposing an LLM agent system, termed Spec2…

AgentDistill: Training-Free Agent Distillation with Generalizable MCP Boxes
Jiahao Qiu, Xinzhe Juan, Yimin Wang, Ling Yang, Xuan Qi, Tongcheng Zhang, Jiacheng Guo, Yifu Lu, Zixin Yao, Hongru Wang, Shilong Liu, Xun Jiang, Liu Leqi, Mengdi Wang
https://arxiv.org/abs/2506.14728

AgentDistill: Training-Free Agent Distillation with Generalizable MCP Boxes
While knowledge distillation has become a mature field for compressing large language models (LLMs) into smaller ones by aligning their outputs or internal representations, the distillation of LLM-based agents, which involve planning, memory, and tool use, remains relatively underexplored. Existing agent distillation methods typically replay full teacher trajectories or imitate step-by-step teacher tool usage, but they often struggle to train student agents to dynamically plan and act in novel …

Anthropic details how it built its multi-agent Claude Research system, claiming significant improvements in internal evaluations over single-agent systems (Anthropic)
https://www.anthropic.com/engineering/built-multi-agent-research-system

How we built our multi-agent research system
On the the engineering challenges and lessons learned from building Claude's Research system

This Github repository conveniently lists and categorizes prime examples of LLM-based agent applications. Each example application features its own repository folder with its source code (Python), and a helpful README.md file describing its installation and use.
Categories include:
1. Starter AI Agents
2. Advanced AI Agents
3. Autonomous Game Playing Agents
4. Multi-Agent Teams
5. Voice AI Agents
6. RAG-Based Agents
"awesome-llm-apps"

How an up-and-coming NFL agent (in Minneapolis!) finds his footing in a cut-throat league https://www.nytimes.com/athletic/6415008/2025/06/17/nfl-agent-minnesota-vikings-minneapolis-jonathan-allen/

Unified Software Engineering agent as AI Software Engineer
Leonhard Applis, Yuntong Zhang, Shanchao Liang, Nan Jiang, Lin Tan, Abhik Roychoudhury
https://arxiv.org/abs/2506.14683 …

Unified Software Engineering agent as AI Software Engineer
The growth of Large Language Model (LLM) technology has raised expectations for automated coding. However, software engineering is more than coding and is concerned with activities including maintenance and evolution of a project. In this context, the concept of LLM agents has gained traction, which utilize LLMs as reasoning engines to invoke external tools autonomously. But is an LLM agent the same as an AI software engineer? In this paper, we seek to understand this question by developing a U…

AgentFacts: Universal KYA Standard for Verified AI Agent Metadata & Deployment
Jared James Grogan
https://arxiv.org/abs/2506.13794 https://

AgentFacts: Universal KYA Standard for Verified AI Agent Metadata & Deployment
Enterprise AI deployment faces critical "Know Your Agent" (KYA) challenges where organizations must verify third-party agent capabilities and establish trust without standardized metadata or verification infrastructure. Current approaches rely on self-declared capabilities and custom integration processes that create trust gaps and coordination friction limiting confident enterprise adoption. This paper presents AgentFacts, a universal metadata standard that enables systematic agent verificatio…

SheetMind: An End-to-End LLM-Powered Multi-Agent Framework for Spreadsheet Automation
Ruiyan Zhu, Xi Cheng, Ke Liu, Brian Zhu, Daniel Jin, Neeraj Parihar, Zhoutian Xu, Oliver Gao
https://arxiv.org/abs/2506.12339

SheetMind: An End-to-End LLM-Powered Multi-Agent Framework for Spreadsheet Automation
We present SheetMind, a modular multi-agent framework powered by large language models (LLMs) for spreadsheet automation via natural language instructions. The system comprises three specialized agents: a Manager Agent that decomposes complex user instructions into subtasks; an Action Agent that translates these into structured commands using a Backus Naur Form (BNF) grammar; and a Reflection Agent that validates alignment between generated actions and the user's original intent. Integrated int…

Factor-Graph-Based Passive Acoustic Navigation for Decentralized Cooperative Localization Using Bearing Elevation Depth Difference
Kalliyan Velasco, Timothy W. McLain, Joshua G. Mangelson
https://arxiv.org/abs/2506.14690

Factor-Graph-Based Passive Acoustic Navigation for Decentralized Cooperative Localization Using Bearing Elevation Depth Difference
Accurate and scalable underwater multi-agent localization remains a critical challenge due to the constraints of underwater communication. In this work, we propose a multi-agent localization framework using a factor-graph representation that incorporates bearing, elevation, and depth difference (BEDD). Our method leverages inverted ultra-short baseline (inverted-USBL) derived azimuth and elevation measurements from incoming acoustic signals and relative depth measurements to enable cooperative …

ImmerseGen: Agent-Guided Immersive World Generation with Alpha-Textured Proxies
Jinyan Yuan, Bangbang Yang, Keke Wang, Panwang Pan, Lin Ma, Xuehai Zhang, Xiao Liu, Zhaopeng Cui, Yuewen Ma
https://arxiv.org/abs/2506.14315

ImmerseGen: Agent-Guided Immersive World Generation with Alpha-Textured Proxies
Automatic creation of 3D scenes for immersive VR presence has been a significant research focus for decades. However, existing methods often rely on either high-poly mesh modeling with post-hoc simplification or massive 3D Gaussians, resulting in a complex pipeline or limited visual realism. In this paper, we demonstrate that such exhaustive modeling is unnecessary for achieving compelling immersive experience. We introduce ImmerseGen, a novel agent-guided framework for compact and photorealist…

KI-Update kompakt: V-Jepa-2, Sicherheitslücke in KI-Agent, Hirnfäule, Nvidia
Das "KI-Update" liefert werktäglich eine Zusammenfassung der wichtigsten KI-Entwicklungen.
https:/…

GUI-Robust: A Comprehensive Dataset for Testing GUI Agent Robustness in Real-World Anomalies
Jingqi Yang, Zhilong Song, Jiawei Chen, Mingli Song, Sheng Zhou, linjun sun, Xiaogang Ouyang, Chun Chen, Can Wang
https://arxiv.org/abs/2506.14477

GUI-Robust: A Comprehensive Dataset for Testing GUI Agent Robustness in Real-World Anomalies
The development of high-quality datasets is crucial for benchmarking and advancing research in Graphical User Interface (GUI) agents. Despite their importance, existing datasets are often constructed under idealized conditions, overlooking the diverse anomalies frequently encountered in real-world deployments. To address this limitation, we introduce GUI-Robust, a novel dataset designed for comprehensive GUI agent evaluation, explicitly incorporating seven common types of anomalies observed in …

The content of the user agent is the dumbest thing we've done to the web, and we've doing a whole lot of dumb things to the web—all the way back to spelling referrer incorrectly (and we still do).

Cowboys Urged to Sign $94 Million NFL All-Pro LB for Struggling Run Defense https://heavy.com/sports/nfl/dallas-cowboys/free-agent-cj-mosley-run-defense/?adt_ei=[email]

Wasserstein-Barycenter Consensus for Cooperative Multi-Agent Reinforcement Learning
Ali Baheri
https://arxiv.org/abs/2506.12497 https://

Wasserstein-Barycenter Consensus for Cooperative Multi-Agent Reinforcement Learning
Cooperative multi-agent reinforcement learning (MARL) demands principled mechanisms to align heterogeneous policies while preserving the capacity for specialized behavior. We introduce a novel consensus framework that defines the team strategy as the entropic-regularized $p$-Wasserstein barycenter of agents' joint state--action visitation measures. By augmenting each agent's policy objective with a soft penalty proportional to its Sinkhorn divergence from this barycenter, the proposed approach …

Investigating the Potential of Large Language Model-Based Router Multi-Agent Architectures for Foundation Design Automation: A Task Classification and Expert Selection Study
Sompote Youwai, David Phim, Vianne Gayl Murcia, Rianne Clair Onas
https://arxiv.org/abs/2506.13811

Investigating the Potential of Large Language Model-Based Router Multi-Agent Architectures for Foundation Design Automation: A Task Classification and Expert Selection Study
This study investigates router-based multi-agent systems for automating foundation design calculations through intelligent task classification and expert selection. Three approaches were evaluated: single-agent processing, multi-agent designer-checker architecture, and router-based expert selection. Performance assessment utilized baseline models including DeepSeek R1, ChatGPT 4 Turbo, Grok 3, and Gemini 2.5 Pro across shallow foundation and pile design scenarios. The router-based configuration…

Agent Orange
bot by @…
This is satire, not sedition: no investigating me please.
#satire #potus45

Agent's Take: What is holding up NFL Draft second-rounders from signing rookie deals?

https://www.cbssports.com/nfl/news/agents-take…

Doppelg\"anger Method: Breaking Role Consistency in LLM Agent via Prompt-based Transferable Adversarial Attack
Daewon Kang, YeongHwan Shin, Doyeon Kim, Kyu-Hwan Jung, Meong Hi Son
https://arxiv.org/abs/2506.14539

Doppelgänger Method: Breaking Role Consistency in LLM Agent via Prompt-based Transferable Adversarial Attack
Since the advent of large language models, prompt engineering now enables the rapid, low-effort creation of diverse autonomous agents that are already in widespread use. Yet this convenience raises urgent concerns about the safety, robustness, and behavioral consistency of the underlying prompts, along with the pressing challenge of preventing those prompts from being exposed to user's attempts. In this paper, we propose the ''Doppelgänger method'' to demonstrate the risk of an agent being hij…

Replaced article(s) found for cs.CL. https://arxiv.org/list/cs.CL/new
[3/3]:
Agent Laboratory: Using LLM Agents as Research Assistants
https://

StorySage: Conversational Autobiography Writing Powered by a Multi-Agent Framework
Shayan Talaei, Meijin Li, Kanu Grover, James Kent Hippler, Diyi Yang, Amin Saberi
https://arxiv.org/abs/2506.14159

StorySage: Conversational Autobiography Writing Powered by a Multi-Agent Framework
Every individual carries a unique and personal life story shaped by their memories and experiences. However, these memories are often scattered and difficult to organize into a coherent narrative, a challenge that defines the task of autobiography writing. Existing conversational writing assistants tend to rely on generic user interactions and pre-defined guidelines, making it difficult for these systems to capture personal memories and develop a complete biography over time. We introduce Story…

Building a Code-Editing #Agent in 400 Lines of #Go Code 🤖
➰
🔧 Complete agent implementation requires only #LLM integration, loop structure & sufficient token allocation
🧵 👇

A look at 10 AI agent startups from YC's first-ever spring Demo Day; the batch included 144 companies, 70 of which focused on agentic AI (Samantha Stokes/Business Insider)
https://africa.businessinsider.com/new

The 10 most exciting AI agent startups at Y Combinator's Demo Day for its first-ever spring cohort
Christmas came early for Y Combinator

Buyer's Agent Networks Podcast
Great Australian Pods Podcast Directory: #GreatAusPods

On Immutable Memory Systems for Artificial Agents: A Blockchain-Indexed Automata-Theoretic Framework Using ECDH-Keyed Merkle Chains
Craig Steven Wright
https://arxiv.org/abs/2506.13246

On Immutable Memory Systems for Artificial Agents: A Blockchain-Indexed Automata-Theoretic Framework Using ECDH-Keyed Merkle Chains
This paper presents a formalised architecture for synthetic agents designed to retain immutable memory, verifiable reasoning, and constrained epistemic growth. Traditional AI systems rely on mutable, opaque statistical models prone to epistemic drift and historical revisionism. In contrast, we introduce the concept of the Merkle Automaton, a cryptographically anchored, deterministic computational framework that integrates formal automata theory with blockchain-based commitments. Each agent tran…

Dividing Conflicting Items Fairly
Ayumi Igarashi, Pasin Manurangsi, Hirotaka Yoneda
https://arxiv.org/abs/2506.14149 https://arxiv.or…

Dividing Conflicting Items Fairly
We study the allocation of indivisible goods under conflicting constraints, represented by a graph. In this framework, vertices correspond to goods and edges correspond to conflicts between a pair of goods. Each agent is allocated an independent set in the graph. In a recent work of Kumar et al. (2024), it was shown that a maximal EF1 allocation exists for interval graphs and two agents with monotone valuations. We significantly extend this result by establishing that a maximal EF1 allocation e…

Local Differential Privacy for Distributed Stochastic Aggregative Optimization with Guaranteed Optimality
Ziqin Chen, Yongqiang Wang
https://arxiv.org/abs/2506.15106

Local Differential Privacy for Distributed Stochastic Aggregative Optimization with Guaranteed Optimality
Distributed aggregative optimization underpins many cooperative optimization and multi-agent control systems, where each agent's objective function depends both on its local optimization variable and an aggregate of all agents' optimization variables. Existing distributed aggregative optimization approaches typically require access to accurate gradients of the objective functions, which, however, are often hard to obtain in real-world applications. For example, in machine learning, gradients ar…

Recently Released Raiders Starter Sends Clear Message to NFL Teams https://heavy.com/sports/nfl/las-vegas-raiders/jack-jones-free-agent-future/?adt_ei=[email]

The Space Between Us: A Methodological Framework for Researching Bonding and Proxemics in Situated Group-Agent Interactions
Ana M\"uller, Anja Richert
https://arxiv.org/abs/2506.11829

The Space Between Us: A Methodological Framework for Researching Bonding and Proxemics in Situated Group-Agent Interactions
This paper introduces a multimethod framework for studying spatial and social dynamics in real-world group-agent interactions with socially interactive agents. Drawing on proxemics and bonding theories, the method combines subjective self-reports and objective spatial tracking. Applied in two field studies in a museum (N = 187) with a robot and a virtual agent, the paper addresses the challenges in aligning human perception and behavior. We focus on presenting an open source, scalable, and fiel…

ADAgent: LLM Agent for Alzheimer's Disease Analysis with Collaborative Coordinator
Wenlong Hou, Gangqian Yang, Ye Du, Yeung Lau, Lihao Liu, Junjun He, Ling Long, Shujun Wang
https://arxiv.org/abs/2506.11150

ADAgent: LLM Agent for Alzheimer's Disease Analysis with Collaborative Coordinator
Alzheimer's disease (AD) is a progressive and irreversible neurodegenerative disease. Early and precise diagnosis of AD is crucial for timely intervention and treatment planning to alleviate the progressive neurodegeneration. However, most existing methods rely on single-modality data, which contrasts with the multifaceted approach used by medical experts. While some deep learning approaches process multi-modal data, they are limited to specific tasks with a small set of input modalities and ca…

Learning to flock in open space by avoiding collisions and staying together
Martino Brambati, Antonio Celani, Marco Gherardi, Francesco Ginelli
https://arxiv.org/abs/2506.15587

Learning to flock in open space by avoiding collisions and staying together
We investigate the emergence of cohesive flocking in open, boundless space using a multi-agent reinforcement learning framework. Agents integrate positional and orientational information from their closest topological neighbours and learn to balance alignment and attractive interactions by optimizing a local cost function that penalizes both excessive separation and close-range crowding. The resulting Vicsek-like dynamics is robust to algorithmic implementation details and yields cohesive colle…

Infected Smallville: How Disease Threat Shapes Sociality in LLM Agents
Soyeon Choi, Kangwook Lee, Oliver Sng, Joshua M. Ackerman
https://arxiv.org/abs/2506.13783

Infected Smallville: How Disease Threat Shapes Sociality in LLM Agents
How does the threat of infectious disease influence sociality among generative agents? We used generative agent-based modeling (GABM), powered by large language models, to experimentally test hypotheses about the behavioral immune system. Across three simulation runs, generative agents who read news about an infectious disease outbreak showed significantly reduced social engagement compared to agents who received no such news, including lower attendance at a social gathering, fewer visits to th…

Hierarchical Multi-Agent Reinforcement Learning-based Coordinated Spatial Reuse for Next Generation WLANs
Jiaming Yu, Le Liang, Hao Ye, Shi Jin
https://arxiv.org/abs/2506.14187

Hierarchical Multi-Agent Reinforcement Learning-based Coordinated Spatial Reuse for Next Generation WLANs
High-density Wi-Fi deployments often result in significant co-channel interference, which degrades overall network performance. To address this issue, coordination of multi access points (APs) has been considered to enable coordinated spatial reuse (CSR) in next generation wireless local area networks. This paper tackles the challenge of downlink spatial reuse in Wi-Fi networks, specifically in scenarios involving overlapping basic service sets, by employing hierarchical multi-agent reinforceme…

Non-exchangeable mean-field theory for adaptive weights: propagation of chaos and graphon sampling lemma
Datong Zhou
https://arxiv.org/abs/2506.13587 https…

Non-exchangeable mean-field theory for adaptive weights: propagation of chaos and graphon sampling lemma
We develop a mean-field theory for large, non-exchangeable particle (agent) systems where the states and interaction weights co-evolve in a coupled system of SDEs. A first main result is a generalization of the propagation of chaos. The weight adaptation in the SDEs makes the classical approach of using a static probability space as the continuum limit for the agent labels inadequate. We address this by introducing a label space endowed with a filtration that captures the stochasticity. While t…

Kogaro: Stop #Kubernetes Silent Failures
https://kogaro.com/

U.S. Sen. Alex Padilla on Thursday was handcuffed by officers and forcefully removed from Homeland Security Secretary Kristi Noem’s news conference in Los Angeles
as he tried to speak up about immigration raids that have led to protests in California and around the country.
Video shows a Secret Service agent on Noem’s security detail grabbing the California senator by his jacket and shoving him from the room
as he tried to speak up during the DHS secretary’s event.
…

Sen. Alex Padilla removed from Noem's press conference by security
Democratic California U.S. Sen. Alex Padilla has been forcefully removed Thursday by security officers from a news conference on immigration by Homeland Security Secretary Kristi Noem. Video shows a Secret Service special agent on Noem’s detail grabbing Padilla by his jacket and shoving him from the room as the senator struggles to catch his breath while identifying himself.“I’m Sen. Alex Padilla. I have questions for the secretary,” he shouted in a halting voice. He tries to push back …

Zero-Shot Scene Understanding with Multimodal Large Language Models for Automated Vehicles
Mohammed Elhenawy, Shadi Jaradat, Taqwa I. Alhadidi, Huthaifa I. Ashqar, Ahmed Jaber, Andry Rakotonirainy, Mohammad Abu Tami
https://arxiv.org/abs/2506.12232

Zero-Shot Scene Understanding with Multimodal Large Language Models for Automated Vehicles
Scene understanding is critical for various downstream tasks in autonomous driving, including facilitating driver-agent communication and enhancing human-centered explainability of autonomous vehicle (AV) decisions. This paper evaluates the capability of four multimodal large language models (MLLMs), including relatively small models, to understand scenes in a zero-shot, in-context learning setting. Additionally, we explore whether combining these models using an ensemble approach with majority…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[7/8]:
Achieving Collective Welfare in Multi-Agent Reinforcement Learning via Suggestion Sharing

I don't recall seeing much measurement research on mobile carrier identification from client traffic. Didn't find much in a cursory search.
Does this exist? Is it feasible, particularly with branded devices like this?
Can classification be done by address pools unique, IDs, MACs, user agent strings, default start-up apps, default DNS queries/settings, etc.?
Clue, ideas, and pointers wanted.

Steve Herman (@w7voa@journa.host)
Attached: 1 image CNBC - A company owned by President Trump announces it own branded Trump Mobile cell phone service and will sell a "T1" smartphone, featuring gold-colored metal case etched with an American flag. https://www.cnbc.com/2025/06/16/trump-mobile-phone-plan.html

« J'ai découvert le support SSH agent de Bitwarden et ses conséquences sur l'utilisation de Age »
#TIL

Cowboys Headlines: Offense primed for breakout; stadium naming snafu; eyeing Von Miller? https://cowboyswire.usatoday.com/story/sports/nfl/cowboys/2025/06/18/news-headlines-june-17-2025-offense-bre…

Levels of Autonomy for AI Agents
K. J. Kevin Feng, David W. McDonald, Amy X. Zhang
https://arxiv.org/abs/2506.12469 https://arxiv.org…

Levels of Autonomy for AI Agents
Autonomy is a double-edged sword for AI agents, simultaneously unlocking transformative possibilities and serious risks. How can agent developers calibrate the appropriate levels of autonomy at which their agents should operate? We argue that an agent's level of autonomy can be treated as a deliberate design decision, separate from its capability and operational environment. In this work, we define five levels of escalating agent autonomy, characterized by the roles a user can take when interac…

IndoorWorld: Integrating Physical Task Solving and Social Simulation in A Heterogeneous Multi-Agent Environment
Dekun Wu, Frederik Brudy, Bang Liu, Yi Wang
https://arxiv.org/abs/2506.12331

IndoorWorld: Integrating Physical Task Solving and Social Simulation in A Heterogeneous Multi-Agent Environment
Virtual environments are essential to AI agent research. Existing environments for LLM agent research typically focus on either physical task solving or social simulation, with the former oversimplifying agent individuality and social dynamics, and the latter lacking physical grounding of social behaviors. We introduce IndoorWorld, a heterogeneous multi-agent environment that tightly integrates physical and social dynamics. By introducing novel challenges for LLM-driven agents in orchestrating …

Padilla says FBI agent, Guard member escorted him to Noem's briefing before removal (Avery Lotz/Axios)
https://www.axios.com/2025/06/15/alex-padilla-kristi-noem-dhs-removal
http://www.memeorandum.com/250615/p38#a250615p38

Distributed Composite Optimization with Sub-Weibull Noises
Zhan Yu, Zhongjie Shi, Deming Yuan
https://arxiv.org/abs/2506.12901 https://

Distributed Composite Optimization with Sub-Weibull Noises
With the rapid development of multi-agent distributed optimization (MA-DO) theory over the past decade, the distributed stochastic gradient method (DSGM) occupies an important position. Although the theory of different DSGMs has been widely established, the main-stream results of existing work are still derived under the condition of light-tailed stochastic gradient noises. Increasing recent examples from various fields, indicate that, the light-tailed noise model is overly idealized in many pr…

LA man faces 8 years in prison for ‘spitting at an ICE agent’ after being charged with assaulting a federal employee | The Independent
https://www.the-independent.com/news/world/americas/crime/los-angeles-arrest-spitting-ice-agent-face-b2768986.html

Lamar Jackson urges Ravens to sign ex-Packers CB Jaire Alexander, NFL MVP's former college teammate

https://www.cbssports.com/nfl/news/lamar-j

Kritische Sicherheitslücke in Microsoft 365 Copilot zeigt Risiko von KI-Agenten
Der KI-Agent von M365 konnte per E-Mail und ohne Mausklick zur Freigabe sensibler Informationen verführt werden. Microsoft hat die Lücke jetzt geschlossen.

Code Researcher: Deep Research Agent for Large Systems Code and Commit History
Ramneet Singh, Sathvik Joel, Abhav Mehrotra, Nalin Wadhwa, Ramakrishna B Bairi, Aditya Kanade, Nagarajan Natarajan
https://arxiv.org/abs/2506.11060

Code Researcher: Deep Research Agent for Large Systems Code and Commit History
Large Language Model (LLM)-based coding agents have shown promising results on coding benchmarks, but their effectiveness on systems code remains underexplored. Due to the size and complexities of systems code, making changes to a systems codebase is a daunting task, even for humans. It requires researching about many pieces of context, derived from the large codebase and its massive commit history, before making changes. Inspired by the recent progress on deep research agents, we design the fi…

Discovering Coordinated Processes From Social Online Networks
Anna Kalenkova, Lewis Mitchell, Ethan Johnson
https://arxiv.org/abs/2506.12988 https://

Discovering Coordinated Processes From Social Online Networks
The rapid growth of social media presents a unique opportunity to study coordinated agent behavior in an unfiltered environment. Online processes often exhibit complex structures that reflect the nature of the user behavior, whether it is authentic and genuine, or part of a coordinated effort by malicious agents to spread misinformation and disinformation. Detection of AI-generated content can be extremely challenging due to the high quality of large language model-generated text. Therefore, ap…

Best nickname for Trump I heard today: Agent Orange 😂🙈

Anomaly, Class Division, and Decoupling in Wealth Dynamics
Jaeseok Hur, Meesoon Ha, Hawoong Jeong
https://arxiv.org/abs/2506.08175 https://

Anomaly, Class Division, and Decoupling in Wealth Dynamics
Economic inequality is shaped by the agent-network structure, the interaction between agents, and the individual agent's ability. We provide a comprehensive picture of anomalous diffusion, economic class division, and bimodal wealth distribution in an agent-based model, where the allocation of heterogeneous agent abilities/growth rates is tuned in sparse regular networks.In particular, we focus on the statistical characteristics of logarithmic scaled normalized wealth distributions with two abi…

Dynamic Preference Multi-Objective Reinforcement Learning for Internet Network Management
DongNyeong Heo, Daniela Noemi Rim, Heeyoul Choi
https://arxiv.org/abs/2506.13153

Dynamic Preference Multi-Objective Reinforcement Learning for Internet Network Management
An internet network service provider manages its network with multiple objectives, such as high quality of service (QoS) and minimum computing resource usage. To achieve these objectives, a reinforcement learning-based (RL) algorithm has been proposed to train its network management agent. Usually, their algorithms optimize their agents with respect to a single static reward formulation consisting of multiple objectives with fixed importance factors, which we call preferences. However, in pract…

ICE agents smashed the window of a car with an axe, then took Juan Francisco Mendez into detention. He was awaiting finalization of his asylum status after fleeing Guatemala. His family and lawyer do not know where he has been taken.
What’s more, he is not the man ICE was looking for. They smashed their way into the the wrong man’s car, and disappeared him into the detention system.

Moment Restrictions for Nonlinear Panel Data Models with Feedback
St\'ephane Bonhomme, Kevin Dano, Bryan S. Graham
https://arxiv.org/abs/2506.12569 htt…

Moment Restrictions for Nonlinear Panel Data Models with Feedback
Many panel data methods, while allowing for general dependence between covariates and time-invariant agent-specific heterogeneity, place strong a priori restrictions on feedback: how past outcomes, covariates, and heterogeneity map into future covariate levels. Ruling out feedback entirely, as often occurs in practice, is unattractive in many dynamic economic settings. We provide a general characterization of all feedback and heterogeneity robust (FHR) moment conditions for nonlinear panel data…

Raiders free agent idea adds 5-time Pro Bowl wide receiver instead of relying on rookies https://www.sportingnews.com/us/nfl/las-vegas-raiders/news/raiders-free-agent-idea-amari-cooper…

🚀 Demonstrates practical #AI agent development without complex abstractions or extensive engineering overhead
https://ampcode.com/how-to-build-an-agent

Replaced article(s) found for cs.GR. https://arxiv.org/list/cs.GR/new
[1/1]:
- ImmerseGen: Agent-Guided Immersive World Generation with Alpha-Textured Proxies
Yuan, Yang, Wang, Pan, Ma, Zhang, Liu, Cui, Ma

Recent trends in socio-epidemic modelling: behaviours and their determinants
Daniele Proverbio, Riccardo Tessarin, Giulia Giordano
https://arxiv.org/abs/2506.13837

Recent trends in socio-epidemic modelling: behaviours and their determinants
The spreading dynamics of infectious diseases is influenced by individual behaviours, which are in turn affected by the level of awareness about the epidemic. Modelling the co-evolution of disease transmission and behavioural changes within a population enables better understanding, prediction and control of epidemics. Here, our primary goal is to provide an overview of the most popular modelling approaches, ranging from compartmental mean-field to agent-based models, with a particular focus on…

AURA: A Multi-Agent Intelligence Framework for Knowledge-Enhanced Cyber Threat Attribution
Nanda Rani, Sandeep Kumar Shukla
https://arxiv.org/abs/2506.10175

AURA: A Multi-Agent Intelligence Framework for Knowledge-Enhanced Cyber Threat Attribution
Effective attribution of Advanced Persistent Threats (APTs) increasingly hinges on the ability to correlate behavioral patterns and reason over complex, varied threat intelligence artifacts. We present AURA (Attribution Using Retrieval-Augmented Agents), a multi-agent, knowledge-enhanced framework for automated and interpretable APT attribution. AURA ingests diverse threat data including Tactics, Techniques, and Procedures (TTPs), Indicators of Compromise (IoCs), malware details, adversarial to…

Swarm-STL: A Framework for Motion Planning in Large-Scale, Multi-Swarm Systems
Shiyu Cheng, Luyao Niu, Bhaskar Ramasubramanian, Andrew Clark, Radha Poovendran
https://arxiv.org/abs/2506.14749

Swarm-STL: A Framework for Motion Planning in Large-Scale, Multi-Swarm Systems
In multi-agent systems, signal temporal logic (STL) is widely used for path planning to accomplish complex objectives with formal safety guarantees. However, as the number of agents increases, existing approaches encounter significant computational challenges. Recognizing that many complex tasks require cooperation among multiple agents, we propose swarm STL specifications to describe the collective tasks that need to be achieved by a team of agents. Next, we address the motion planning problem…

New Orleans Saints to sign free agent RB Cam Akers after minicamp invitation, per report

https://www.cbssports.com/nfl/news/new-orl…

OpenAI updates its coding agent Codex with internet access, turned off by default, and expands availability to ChatGPT Plus users (Simon Willison/Simon Willison's Weblog)
https://simonwillison.net/2025/Jun/3/codex-agent-internet-access/

Codex agent internet access
Sam Altman, [just now](https://twitter.com/sama/status/1930006856019390521): > codex gets access to the internet today! it is off by default and there are complex tradeoffs; people should read about the risks carefully and …

PE-MA: Parameter-Efficient Co-Evolution of Multi-Agent Systems
Yingfan Deng, Anhao Zhou, Yuan Yuan, Xian Zhang, Yifei Zou, Dongxiao Yu
https://arxiv.org/abs/2506.11803

PE-MA: Parameter-Efficient Co-Evolution of Multi-Agent Systems
Multi-Agent Systems have recently emerged as a promising paradigm for collaborative reasoning and solving complex tasks. However, the design of collaborative learning algorithms in multi-agent systems faces several challenges, including high communication overhead and insufficient agent-level personalization. In this paper, we propose PE-MA (Parameter-Efficient Multi-Agent Co-Evolution), a novel collaboration framework that supports efficient, scalable, and personalized co-evolution in multi-ag…

Robot Context Protocol (RCP): A Runtime-Agnostic Interface for Agent-Aware Robot Control
Lambert Lee, Joshua Lau
https://arxiv.org/abs/2506.11650 https://

Robot Context Protocol (RCP): A Runtime-Agnostic Interface for Agent-Aware Robot Control
The Robot Context Protocol (RCP) is a lightweight, middleware-agnostic communication protocol designed to simplify the complexity of robotic systems and enable seamless interaction between robots, users, and autonomous agents. RCP provides a unified and semantically meaningful interface that decouples client-facing operations from backend implementations, supporting a wide range of deployment environments including physical robots, cloud-based orchestrators, and simulated platforms. Built on HT…

Affective-CARA: A Knowledge Graph Driven Framework for Culturally Adaptive Emotional Intelligence in HCI
Nirodya Pussadeniya, Bahareh Nakisa, Mohmmad Naim Rastgoo
https://arxiv.org/abs/2506.14166

Affective-CARA: A Knowledge Graph Driven Framework for Culturally Adaptive Emotional Intelligence in HCI
Culturally adaptive emotional responses remain a critical challenge in affective computing. This paper introduces Affective-CARA, an agentic framework designed to enhance user-agent interactions by integrating a Cultural Emotion Knowledge Graph (derived from StereoKG) with Valence, Arousal, and Dominance annotations, culture-specific data, and cross-cultural checks to minimize bias. A Gradient-Based Reward Policy Optimization mechanism further refines responses according to cultural alignment, …

Agent-based Condition Monitoring Assistance with Multimodal Industrial Database Retrieval Augmented Generation
Karl L\"owenmark, Daniel Str\"ombergsson, Chang Liu, Marcus Liwicki, Fredrik Sandin
https://arxiv.org/abs/2506.09247

Agent-based Condition Monitoring Assistance with Multimodal Industrial Database Retrieval Augmented Generation
Condition monitoring (CM) plays a crucial role in ensuring reliability and efficiency in the process industry. Although computerised maintenance systems effectively detect and classify faults, tasks like fault severity estimation, and maintenance decisions still largely depend on human expert analysis. The analysis and decision making automatically performed by current systems typically exhibit considerable uncertainty and high false alarm rates, leading to increased workload and reduced effici…

Mixed-variable policy-based optimization
Jonathan Viquerat
https://arxiv.org/abs/2506.13240 https://arxiv.org/pdf/2506.13240

Mixed-variable policy-based optimization
The optimization of mixed-variable problems remains a significant challenge. We propose an extension of the policy-based optimization method that handles mixed-variables problems in a natural way, through a simple policy combination. This is achieved by independently sampling from a multivariate normal distribution for the continuous domain, and from multiple categorical distributions for the discrete choices. Results demonstrate that the agent successfully yields high-quality solutions on a cl…

Deceptive Path Planning: A Bayesian Game Approach
Violetta Rostobaya, James Berneburg, Yue Guan, Michael Dorothy, Daigo Shishika
https://arxiv.org/abs/2506.13650

Deceptive Path Planning: A Bayesian Game Approach
This paper investigates how an autonomous agent can transmit information through its motion in an adversarial setting. We consider scenarios where an agent must reach its goal while deceiving an intelligent observer about its destination. We model this interaction as a dynamic Bayesian game between a mobile Attacker with a privately known goal and a Defender who infers the Attacker's intent to allocate defensive resources effectively. We use Perfect Bayesian Nash Equilibrium (PBNE) as our solut…

Can you see how I learn? Human observers' inferences about Reinforcement Learning agents' learning processes
Bernhard Hilpert, Muhan Hou, Kim Baraka, Joost Broekens
https://arxiv.org/abs/2506.13583

Can you see how I learn? Human observers' inferences about Reinforcement Learning agents' learning processes
Reinforcement Learning (RL) agents often exhibit learning behaviors that are not intuitively interpretable by human observers, which can result in suboptimal feedback in collaborative teaching settings. Yet, how humans perceive and interpret RL agent's learning behavior is largely unknown. In a bottom-up approach with two experiments, this work provides a data-driven understanding of the factors of human observers' understanding of the agent's learning process. A novel, observation-based paradi…

Specification and Evaluation of Multi-Agent LLM Systems -- Prototype and Cybersecurity Applications
Felix H\"arer
https://arxiv.org/abs/2506.10467 htt…

Specification and Evaluation of Multi-Agent LLM Systems -- Prototype and Cybersecurity Applications
Recent advancements in LLMs indicate potential for novel applications, e.g., through reasoning capabilities in the latest OpenAI and DeepSeek models. For applying these models in specific domains beyond text generation, LLM-based multi-agent approaches can be utilized that solve complex tasks by combining reasoning techniques, code generation, and software execution. Applications might utilize these capabilities and the knowledge of specialized LLM agents. However, while many evaluations are pe…

Shapley Machine: A Game-Theoretic Framework for N-Agent Ad Hoc Teamwork
Jianhong Wang, Yang Li, Samuel Kaski, Jonathan Lawry
https://arxiv.org/abs/2506.11285

Shapley Machine: A Game-Theoretic Framework for N-Agent Ad Hoc Teamwork
Open multi-agent systems are increasingly important in modeling real-world applications, such as smart grids, swarm robotics, etc. In this paper, we aim to investigate a recently proposed problem for open multi-agent systems, referred to as n-agent ad hoc teamwork (NAHT), where only a number of agents are controlled. Existing methods tend to be based on heuristic design and consequently lack theoretical rigor and ambiguous credit assignment among agents. To address these limitations, we model a…

Chinese startups and tech giants are racing to create AI agents for both local and global consumers, following the popularity of Butterfly Effect's Manus (Caiwei Chen/MIT Technology Review)
https://www.technologyreview.com/2025/06/05/1117958/china-ai-a…

Manus has kick-started an AI agent boom in China
A stampede of contenders and copycats has followed the launch of the general AI agent, but China’s internet restrictions have so far made it easier to cater to overseas users.

BugGen: A Self-Correcting Multi-Agent LLM Pipeline for Realistic RTL Bug Synthesis
Surya Jasper, Minh Luu, Evan Pan, Aakash Tyagi, Michael Quinn, Jiang Hu, David Kebo Houngninou
https://arxiv.org/abs/2506.10501

BugGen: A Self-Correcting Multi-Agent LLM Pipeline for Realistic RTL Bug Synthesis
Hardware complexity continues to strain verification resources, motivating the adoption of machine learning (ML) methods to improve debug efficiency. However, ML-assisted debugging critically depends on diverse and scalable bug datasets, which existing manual or automated bug insertion methods fail to reliably produce. We introduce BugGen, a first of its kind, fully autonomous, multi-agent pipeline leveraging Large Language Models (LLMs) to systematically generate, insert, and validate realisti…

Should the Dallas Cowboys target top NFL free agent Asante Samuel Jr? https://www.si.com/nfl/cowboys/news/should-dallas-cowboys-target-top-nfl-free-agent-asante-samuel-jr

Designing Intent: A Multimodal Framework for Human-Robot Cooperation in Industrial Workspaces
Francesco Chiossi, Julian Rasch, Robin Welsch, Albrecht Schmidt, Florian Michahelles
https://arxiv.org/abs/2506.15293

Designing Intent: A Multimodal Framework for Human-Robot Cooperation in Industrial Workspaces
As robots enter collaborative workspaces, ensuring mutual understanding between human workers and robotic systems becomes a prerequisite for trust, safety, and efficiency. In this position paper, we draw on the cooperation scenario of the AIMotive project in which a human and a cobot jointly perform assembly tasks to argue for a structured approach to intent communication. Building on the Situation Awareness-based Agent Transparency (SAT) framework and the notion of task abstraction levels, we …

Are We Generalizing from the Exception? An In-the-Wild Study on Group-Sensitive Conversation Design in Human-Agent Interactions
Ana M\"uller, Sabina Jeschke, Anja Richert
https://arxiv.org/abs/2506.10462

Are We Generalizing from the Exception? An In-the-Wild Study on Group-Sensitive Conversation Design in Human-Agent Interactions
This paper investigates the impact of a group-adaptive conversation design in two socially interactive agents (SIAs) through two real-world studies. Both SIAs - Furhat, a social robot, and MetaHuman, a virtual agent - were equipped with a conversational artificial intelligence (CAI) backend combining hybrid retrieval and generative models. The studies were carried out in an in-the-wild setting with a total of $N = 188$ participants who interacted with the SIAs - in dyads, triads or larger group…

LPO: Towards Accurate GUI Agent Interaction via Location Preference Optimization
Jiaqi Tang, Yu Xia, Yi-Feng Wu, Yuwei Hu, Yuhui Chen, Qing-Guo Chen, Xiaogang Xu, Xiangyu Wu, Hao Lu, Yanqing Ma, Shiyin Lu, Qifeng Chen
https://arxiv.org/abs/2506.09373

LPO: Towards Accurate GUI Agent Interaction via Location Preference Optimization
The advent of autonomous agents is transforming interactions with Graphical User Interfaces (GUIs) by employing natural language as a powerful intermediary. Despite the predominance of Supervised Fine-Tuning (SFT) methods in current GUI agents for achieving spatial localization, these methods face substantial challenges due to their limited capacity to accurately perceive positional data. Existing strategies, such as reinforcement learning, often fail to assess positional accuracy effectively, …

‘Plausible’ Chance Cowboys Part Ways With $3 Million Free Agent RB https://heavy.com/sports/nfl/dallas-cowboys/part-ways-free-agent-javonte-williams/?adt_ei=[email]

🔄 Agent loop architecture: user input − model inference − tool execution − result feedback − continuous conversation flow
💻 String replacement editing approach works effectively with current #AI models for precise code modifications

The Raiders are hoping to get a 2-for-1 deal with their top free agent addition https://raiderswire.usatoday.com/story/sports/nfl/raiders/2025/06/09/raiders-versatile-db-jerem…

VIKI-R: Coordinating Embodied Multi-Agent Cooperation via Reinforcement Learning
Li Kang, Xiufeng Song, Heng Zhou, Yiran Qin, Jie Yang, Xiaohong Liu, Philip Torr, Lei Bai, Zhenfei Yin
https://arxiv.org/abs/2506.09049

VIKI-R: Coordinating Embodied Multi-Agent Cooperation via Reinforcement Learning
Coordinating multiple embodied agents in dynamic environments remains a core challenge in artificial intelligence, requiring both perception-driven reasoning and scalable cooperation strategies. While recent works have leveraged large language models (LLMs) for multi-agent planning, a few have begun to explore vision-language models (VLMs) for visual reasoning. However, these VLM-based approaches remain limited in their support for diverse embodiment types. In this work, we introduce VIKI-Bench…

How Developers Use AI Agents: When They Work, When They Don't, and Why
Aayush Kumar, Yasharth Bajpai, Sumit Gulwani, Gustavo Soares, Emerson Murphy-Hill
https://arxiv.org/abs/2506.12347

How Developers Use AI Agents: When They Work, When They Don't, and Why
Software Engineering Agents (SWE agents) can autonomously perform development tasks on benchmarks like SWE Bench, but still face challenges when tackling complex and ambiguous real-world tasks. Consequently, SWE agents are often designed to allow interactivity with developers, enabling collaborative problem-solving. To understand how developers collaborate with SWE agents and the communication challenges that arise in such interactions, we observed 19 developers using an in-IDE agent to resolve…

On Immutable Memory Systems for Artificial Agents: A Blockchain-Indexed Automata-Theoretic Framework Using ECDH-Keyed Merkle Chains
Craig Steven Wright
https://arxiv.org/abs/2506.13246

On Immutable Memory Systems for Artificial Agents: A Blockchain-Indexed Automata-Theoretic Framework Using ECDH-Keyed Merkle Chains
This paper presents a formalised architecture for synthetic agents designed to retain immutable memory, verifiable reasoning, and constrained epistemic growth. Traditional AI systems rely on mutable, opaque statistical models prone to epistemic drift and historical revisionism. In contrast, we introduce the concept of the Merkle Automaton, a cryptographically anchored, deterministic computational framework that integrates formal automata theory with blockchain-based commitments. Each agent tran…

Replaced article(s) found for cs.RO. https://arxiv.org/list/cs.RO/new
[1/2]:
Bridging the Gap between Discrete Agent Strategies in Game Theory and Continuous Motion Planning ...

Trust-MARL: Trust-Based Multi-Agent Reinforcement Learning Framework for Cooperative On-Ramp Merging Control in Heterogeneous Traffic Flow
Jie Pan, Tianyi Wang, Christian Claudel, Jing Shi
https://arxiv.org/abs/2506.12600

Trust-MARL: Trust-Based Multi-Agent Reinforcement Learning Framework for Cooperative On-Ramp Merging Control in Heterogeneous Traffic Flow
Intelligent transportation systems require connected and automated vehicles (CAVs) to conduct safe and efficient cooperation with human-driven vehicles (HVs) in complex real-world traffic environments. However, the inherent unpredictability of human behaviour, especially at bottlenecks such as highway on-ramp merging areas, often disrupts traffic flow and compromises system performance. To address the challenge of cooperative on-ramp merging in heterogeneous traffic environments, this study pro…

Ellipsis Health, which developed an AI voice agent to check in on patients between doctor visits, raised a $45M Series A led by Salesforce, Khosla, and CVS (Brian Gormley/Wall Street Journal)
https://www.

Is Talanoa Hufanga the next great Broncos safety? https://www.espn.com/nfl/story/_/id/45480888/broncos-safety-talanoa-hufanga-free-agent-signing-excitement-injuries-fit

Joint Beamforming with Extremely Large Scale RIS: A Sequential Multi-Agent A2C Approach
Zhi Chai, Jiajie Xu, Justin P Coon, Mohamed-Slim Alouini
https://arxiv.org/abs/2506.10815

Joint Beamforming with Extremely Large Scale RIS: A Sequential Multi-Agent A2C Approach
It is a challenging problem to jointly optimize the base station (BS) precoding matrix and the reconfigurable intelligent surface (RIS) phases simultaneously in a RIS-assisted multiple-user multiple-input-multiple-output (MU-MIMO) scenario when the size of the RIS becomes extremely large. In this paper, we propose a deep reinforcement learning algorithm called sequential multi-agent advantage actor-critic (A2C) to solve this problem. In addition, the discrete phase of RISs, imperfect channel st…

One Free Agent Signing The Las Vegas Raiders Would Greatly Benefit From https://raiderramble.com/2025/06/03/one-free-agent-las-vegas-raiders-would-greatly-benefit-from/

Replaced article(s) found for cs.MA. https://arxiv.org/list/cs.MA/new
[1/1]:
- YOLO-MARL: You Only LLM Once for Multi-Agent Reinforcement Learning
Yuan Zhuang, Yi Shen, Zhili Zhang, Yuxiao Chen, Fei Miao

This https://arxiv.org/abs/2410.04444 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

Gödel Agent: A Self-Referential Agent Framework for Recursive Self-Improvement
The rapid advancement of large language models (LLMs) has significantly enhanced the capabilities of AI-driven agents across various tasks. However, existing agentic systems, whether based on fixed pipeline algorithms or pre-defined meta-learning frameworks, cannot search the whole agent design space due to the restriction of human-designed components, and thus might miss the globally optimal agent design. In this paper, we introduce Gödel Agent, a self-evolving framework inspired by the Göde…

HiBerNAC: Hierarchical Brain-emulated Robotic Neural Agent Collective for Disentangling Complex Manipulation
Hongjun Wu, Heng Zhang, Pengsong Zhang, Jin Wang, Cong Wang
https://arxiv.org/abs/2506.08296

HiBerNAC: Hierarchical Brain-emulated Robotic Neural Agent Collective for Disentangling Complex Manipulation
Recent advances in multimodal vision-language-action (VLA) models have revolutionized traditional robot learning, enabling systems to interpret vision, language, and action in unified frameworks for complex task planning. However, mastering complex manipulation tasks remains an open challenge, constrained by limitations in persistent contextual memory, multi-agent coordination under uncertainty, and dynamic long-horizon planning across variable sequences. To address this challenge, we propose \…

Modeling Earth-Scale Human-Like Societies with One Billion Agents
Haoxiang Guan, Jiyan He, Liyang Fan, Zhenzhen Ren, Shaobin He, Xin Yu, Yuan Chen, Shuxin Zheng, Tie-Yan Liu, Zhen Liu
https://arxiv.org/abs/2506.12078

Modeling Earth-Scale Human-Like Societies with One Billion Agents
Understanding how complex societal behaviors emerge from individual cognition and interactions requires both high-fidelity modeling of human behavior and large-scale simulations. Traditional agent-based models (ABMs) have been employed to study these dynamics for decades, but are constrained by simplified agent behaviors that fail to capture human complexity. Recent advances in large language models (LLMs) offer new opportunities by enabling agents to exhibit sophisticated social behaviors that…

RL-Guided MPC for Autonomous Greenhouse Control
Salim Msaad, Murray Harraway, Robert D. McAllister
https://arxiv.org/abs/2506.13278 https://

RL-Guided MPC for Autonomous Greenhouse Control
The efficient operation of greenhouses is essential for enhancing crop yield while minimizing energy costs. This paper investigates a control strategy that integrates Reinforcement Learning (RL) and Model Predictive Control (MPC) to optimize economic benefits in autonomous greenhouses. Previous research has explored the use of RL and MPC for greenhouse control individually, or by using MPC as the function approximator for the RL agent. This study introduces the RL-Guided MPC framework, where a …

Agent's Take: Inside the Bengals' contract issues with Trey Hendrickson and why there's no clear end in sight

https://www.cbssports.…

This https://arxiv.org/abs/2506.02841 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Ensemble-MIX: Enhancing Sample Efficiency in Multi-Agent RL Using Ensemble Methods
Multi-agent reinforcement learning (MARL) methods have achieved state-of-the-art results on a range of multi-agent tasks. Yet, MARL algorithms typically require significantly more environment interactions than their single-agent counterparts to converge, a problem exacerbated by the difficulty in exploring over a large joint action space and the high variance intrinsic to MARL environments. To tackle these issues, we propose a novel algorithm that combines a decomposed centralized critic with d…

AutoGen Driven Multi Agent Framework for Iterative Crime Data Analysis and Prediction
Syeda Kisaa Fatima, Tehreem Zubair, Noman Ahmed, Asifullah Khan
https://arxiv.org/abs/2506.11475

AutoGen Driven Multi Agent Framework for Iterative Crime Data Analysis and Prediction
This paper introduces LUCID-MA (Learning and Understanding Crime through Dialogue of Multiple Agents), an innovative AI powered framework where multiple AI agents collaboratively analyze and understand crime data. Our system that consists of three core components: an analysis assistant that highlights spatiotemporal crime patterns, a feedback component that reviews and refines analytical results and a prediction component that forecasts future crime trends. With a well-designed prompt and the L…

G-Memory: Tracing Hierarchical Memory for Multi-Agent Systems
Guibin Zhang, Muxin Fu, Guancheng Wan, Miao Yu, Kun Wang, Shuicheng Yan
https://arxiv.org/abs/2506.07398

G-Memory: Tracing Hierarchical Memory for Multi-Agent Systems
Large language model (LLM)-powered multi-agent systems (MAS) have demonstrated cognitive and execution capabilities that far exceed those of single LLM agents, yet their capacity for self-evolution remains hampered by underdeveloped memory architectures. Upon close inspection, we are alarmed to discover that prevailing MAS memory mechanisms (1) are overly simplistic, completely disregarding the nuanced inter-agent collaboration trajectories, and (2) lack cross-trial and agent-specific customiza…