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cs.AI2026

SimRPD: Optimizing Recruitment Proactive Dialogue Agents through Simulator-Based Data Evaluation and Selection

Zhiyong Cao, Dunqiang Liu, Qi Dai +9

Task-oriented proactive dialogue agents play a pivotal role in recruitment, particularly for steering conversations towards specific business outcomes, such as acquiring social-med…

cs.AI2026

AutoEP: LLMs-Driven Automation of Hyperparameter Evolution for Metaheuristic Algorithms

Zhenxing Xu, Yizhe Zhang, Weidong Bao +6

Dynamically configuring algorithm hyperparameters is a fundamental challenge in computational intelligence. While learning-based methods offer automation, they suffer from prohibit…

cs.AI2026

MAS-ProVe: Understanding the Process Verification of Multi-Agent Systems

Vishal Venkataramani, Haizhou Shi, Zixuan Ke +6

Multi-Agent Systems (MAS) built on Large Language Models (LLMs) often exhibit high variance in their reasoning trajectories. Process verification, which evaluates intermediate step…

cs.AI2026

Interpreting Fedspeak with Confidence: A LLM-Based Uncertainty-Aware Framework Guided by Monetary Policy Transmission Paths

Rui Yao, Qi Chai, Jinhai Yao +4

"Fedspeak", the stylized and often nuanced language used by the U.S. Federal Reserve, encodes implicit policy signals and strategic stances. The Federal Open Market Committee strat…

cs.AI2026

Climbing the Ladder of Reasoning: What LLMs Can-and Still Can't-Solve after SFT?

Yiyou Sun, Georgia Zhou, Haoyue Bai +4

Recent supervised fine-tuning (SFT) approaches have significantly improved language models' performance on mathematical reasoning tasks, even when models are trained at a small sca…

cs.AI2025

From Language to Logic: A Bi-Level Framework for Structured Reasoning

Keying Yang, Hao Wang, Kai Yang

Structured reasoning over natural language inputs remains a core challenge in artificial intelligence, as it requires bridging the gap between unstructured linguistic expressions a…