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This paper introduces SCOPED-Hiring, a process-aware fairness diagnosis pipeline for LLM-based multi-agent hiring systems that uncovers hidden biases in decision trajectories and enables targeted repairs.
The paper introduces CEDAR-GRPO, a process-aware reinforcement learning framework that enhances abductive reasoning in large language models by combining final-answer correctness with abductive rewards, demonstrating transferable improvements across various unseen tasks.