This paper addresses the security vulnerabilities in drone swarm control algorithms by proposing two fuzzing tools, SwarmFuzzGraph and SwarmFuzzBinary, to discover Swarm Propagation Vulnerabilities (S…

Restructuring the communication topology among robots provides significantly greater performance gains in multi-robot coordination than simply increasing the size of the onboard AI models, given fixed…

The paper proposes TCP-MCP, a co-evolution framework that jointly optimizes agent prompts and communication topologies to design highly efficient and effective multi-agent systems.

This paper proposes an ontological representation for cooperative affordances in social robotics, enabling agents to extend their action possibilities through interaction.

This paper investigates the scaling behavior of homogeneous LLM-driven Multi-Agent Systems (MAS) and finds that performance exhibits diminishing returns due to coordination overhead, rather than scali…

This paper provides an information bottleneck perspective on the differences between single-agent and multi-agent systems, showing that the advantage of MAS arises under bounded relays and introducing…

This paper introduces Stigmergic Graph Memory (SGM), a method to improve warehouse throughput in many-to-many Multi-Agent Pickup and Delivery (MAPD) by using a bounded, decaying memory layer to record…

The paper introduces RadioMaster, a novel multi-agent system that successfully translates high-level user intents into physically viable, real-world radio signals, significantly outperforming existing…

The paper demonstrates that advanced capabilities, such as jailbreaking large language models and finding software vulnerabilities, can be achieved effectively at zero cost by coordinating multiple sm…

This paper proposes a cloud-edge-end collaborative defense framework to secure UAV swarms against various threats like GPS spoofing and multi-hop intrusions, demonstrating its effectiveness through ex…

The paper introduces TASTE, an automatic task synthesis method that generates challenging agent benchmarks by evolving tool sequences, demonstrating that existing benchmarks are saturated and that TAS…

This paper revisits N-version programming with AI coding agents and finds substantial common-mode failures but also practical benefits.

This paper introduces SwarmX, a system for scheduling agentic AI applications in GPU-CPU clusters using neural predictors, reducing tail latency by up to 61.5% and sustaining up to 2x the throughput o…

HydraCollab is a new adaptive collaborative-perception framework that selectively transmits informative sensor features and dynamically employs collaboration strategies to minimize communication overh…

The paper proposes a Network Distributed Multi-Agent Reinforcement Learning (ND-MARL) framework that enables stable, scalable consensus control for large swarms of quadcopters using only local neighbo…

SwarmHarness introduces a decentralized, incentive-aligned protocol enabling self-organizing compute swarms for AI tasks, eliminating the need for central coordinators or heavy blockchain infrastructu…