3 papers
cs.LG2026
GraphIR: Architecture-Level Search States for LLM-Guided Neural Architecture Evolution
Zhen Liu, Wanqi Zhou, Shuanghao Bai +3
Large language models (LLMs) enable neural architecture search (NAS) directly over executable neural network programs. However, code-level flexibility does not provide the architec…
cs.LG2026
Overcoming the Weakest-Link Effect in LLM-Driven Program Optimization via Heterogeneous Edit Recombination
Jingwen Fu, Zhen Liu, Yuhan Liu +2
Large language models (LLMs) are increasingly used to solve complex problems by searching over program space, offering a general paradigm for scientific problems that can be natura…
cs.LG2026
Structured Progressive Knowledge Activation for LLM-Driven Neural Architecture Search
Zhen Liu, Yuhan Liu, Jinjun Wang +3
This paper focuses on a key challenge in Neural Architecture Search (NAS): integrating established architectural knowledge while exploring new designs under expensive evaluations.…