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cs.AI2026
Agent Behavior as Code: Efficient and Robust LLM Agents with Programmatic Specifications
Peng Qi, Chunliang Lyu, Gang Li +4
AI agents based on foundation models (FMs) have demonstrated strong capabilities to perform complex open-ended tasks. However, they face some common challenges in practice: (a) age…
cs.CL2026
PADMÉ: Preference Alignment Data Synthesis for Meta-Evaluation of LM Agent Evaluators
Cheng Chang, Yining Mao, Peng Qi
Language models are frequently employed to evaluate other language models. An LM evaluator scoring agentic behaviors across multiple criteria is valuable, provided that its decisio…