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cs.AI2025
ChronoLLM: Customizing Language Models for Physics-Based Simulation Code Generation
Jingquan Wang, Andrew Negrut, Harry Zhang +5
This contribution is concerned with the following issue: can pretrained large language models (LLMs) be refined and customized to the point where they become virtual assistants hel…
cs.AI2025
Rethinking Agent Design: From Top-Down Workflows to Bottom-Up Skill Evolution
Jiawei Du, Jinlong Wu, Yuzheng Chen +3
Most LLM-based agent frameworks adopt a top-down philosophy: humans decompose tasks, define workflows, and assign agents to execute each step. While effective on benchmark-style ta…