110 papers
This paper introduces a meta-game framework to evaluate the emergence of algorithmic collusion under test-time constraints by modeling agents with pretrained policies and adaptation rules, revealing how rational choices and co-adaptation influence cooperative or competitive outcomes in repeated pricing games across various algorithmic strategies.
This paper addresses the Connected Unlabeled Multi-Agent Pathfinding (CUMAPF) problem by introducing PULL, a lightweight, complete algorithm that runs in time per step and outperforms both an Integer Linear Programming approach and naive methods in scalability and efficiency for swarm robotics applications.
This paper proposes a Capability Coherence System (CCS) that maps memory consistency models to identity management, demonstrating through simulation that a Release Consistency-directed revocation strategy (RCC) achieves a constant bound on unauthorized operations independent of agent velocity, thereby outperforming traditional time-bounded approaches by orders of magnitude in high-speed agentic environments.
FetalAgents is a novel multi-agent system that dynamically orchestrates specialized vision experts to deliver robust, end-to-end fetal ultrasound analysis and structured clinical reporting across multiple tasks, outperforming existing specialized models and multimodal large language models.
ToolRosetta is a unified framework that automatically transforms heterogeneous open-source code repositories into standardized, secure, and executable Model Context Protocol (MCP) tools, enabling LLM agents to autonomously plan and invoke specialized software for complex tasks with minimal human intervention.
This paper introduces AgenticCyOps, a security framework for enterprise multi-agent AI systems that mitigates emerging attack surfaces by formalizing tool orchestration and memory management as primary trust boundaries and applying five defensive principles aligned with global compliance standards to significantly reduce exploitable vulnerabilities in SOC workflows.
This paper introduces \texttt{electoral\_sim}, an open-source Python framework that simulates and compares various electoral systems across diverse voter preference distributions using Euclidean distance to the geometric median as a primary performance metric, including a novel theoretical benchmark based on a Boltzmann softmax kernel.
This paper introduces ChatNeuroSim, an LLM-based agent framework that automates the entire Compute-in-Memory (CIM) accelerator deployment and optimization workflow—including simulation, parameter management, and design space pruning—to significantly reduce the time and effort required for design space exploration.
This paper characterizes margin-based voting rules by proving they are equivalent to rules satisfying the "Preferential Equality" axiom, which ensures that voters' identical pairwise preferences are treated with equal normative weight regardless of the voter's identity.
This paper introduces AffPCL, a personalized collaborative learning framework that utilizes affinity-based variance reduction to automatically adapt to unknown heterogeneity among agents, achieving sample complexity improvements that seamlessly interpolate between independent learning and linear speedup without requiring prior knowledge of system similarity.
This paper proposes a data-driven framework combining control barrier functions and differentiable optimization to learn interpretable responsibility allocations, enabling autonomous agents to quantify and adjust their behaviors for safe, socially-aware multi-agent interactions.
This paper proposes \texttt{RQRE-OVI}, an optimistic value iteration algorithm that computes the unique and smooth Risk-Sensitive Quantal Response Equilibrium (RQRE) in general-sum Markov games with linear function approximation, offering a principled trade-off between performance and robustness that outperforms traditional Nash equilibrium approaches in both theoretical guarantees and empirical stability.
This paper introduces Coordinated Boltzmann MCTS (CB-MCTS), a novel decentralized multi-agent planning algorithm that replaces deterministic UCT with a stochastic Boltzmann policy and decaying entropy bonus to overcome the limitations of existing methods in sparse or deceptive reward environments.
This paper proposes CORA, a cooperative game-theoretic credit assignment method that utilizes core allocation and coalition sampling to effectively distribute global advantages among agents in multi-agent reinforcement learning, thereby overcoming the limitations of uniform sharing and enhancing coordinated optimal behavior.
GateLens is a reasoning-enhanced LLM agent that utilizes Relational Algebra as a formal intermediate representation to bridge the gap between natural language and executable code, enabling fast, transparent, and highly accurate analysis of complex tabular data in automotive software release analytics without requiring few-shot examples or complex agent orchestration.
This paper proposes CoHet, a novel algorithm that leverages Graph Neural Network-driven intrinsic rewards to enable effective decentralized learning and cooperation among heterogeneous multi-agent systems despite challenges like partial observability and reward sparsity, demonstrating superior performance over state-of-the-art methods in standard benchmarks.
This paper proposes a communication-constrained multi-agent reinforcement learning framework that utilizes a generalized model and dual mutual information estimator to distinguish between lossy and lossless messages, thereby quantifying their impact on global rewards to enhance cooperative policy learning in complex, dynamic environments.
This paper introduces CMASE, a novel framework that integrates generative agent-based modeling with virtual ethnography to transform researchers into embedded participants, enabling real-time social intervention, causal reconstruction of social phenomena, and predictive modeling with high empirical accuracy.
This paper extends the classical "bee equation" for swarm decision-making into an agent-based model where emotional valence and arousal modulate interaction rates, revealing how emotional contagion and non-linear amplification mechanisms bias consensus outcomes and accelerate convergence in collective choices.
This paper proposes a unified taxonomy and evaluation framework for latent world models in automated driving, organizing design choices by latent representations and structural priors while identifying key internal mechanics and research directions to enhance robustness, generalization, and deployability.