arXiv Machine Learning
Reliability and Effectiveness of Autonomous AI Agents in Supply Chain Management
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Dynamics of Cognitive Heterogeneity: Investigating Behavioral Biases in Multi-Stage Supply Chains with LLM-Based Simulation
arXiv:2604. 17220v2 Announce Type: replace-cross Abstract: Modeling coordination among generative agents in complex multi-round decision-making presents a core challenge for AI and operations management.
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Towards a Science of AI Agent Reliability
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Strategic Decision Support for AI Agents
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Planning or Learning: Reliability and Cost in Multi-Asset Maintenance
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HarnessEvolve: Learning from Reference Trajectories for Reliable Agent Self-Evolution
HarnessEvolve is a self‑evolving framework that improves agent harnesses—prompts, skills, tools, and execution logic—by learning from reference trajectories. It separates execution, evaluation, optimization, and gating into independent modules, addressing credit assignment failure, shortcut learning, and catastrophic forgetting. The approach uses reference trajectories to extract error signals, applies quality and performance gates to candidate updates, and validates updates on held‑out data, consistently outperforming state‑of‑the‑art baselines across diverse benchmarks.
arXiv AI
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ARLArena: A Unified Framework for Stable Agentic Reinforcement Learning
arXiv:2602. 21534v3 Announce Type: replace Abstract: Agentic reinforcement learning (ARL) has rapidly gained attention as a promising paradigm for training agents to solve complex, multi-step interactive tasks.