arXiv Machine Learning
Oct 28

From GPU to TPU with Coding Agents: What Works, What Fails, and What It Costs?

The study investigates whether coding agents can reliably migrate PyTorch machine learning projects from GPUs to TPUs. Using Claude Code, ten projects were migrated, with eight meeting the project‑goal criterion but 76% of execution attempts failing, often due to compilation issues or silent changes in computation. The research highlights significant human effort (average 2.4 interventions) and variable costs, noting that while some TPUs can be faster and cheaper, failures and accuracy drops remain common.

By Hui Sun, Anderson Uch\^oa, Wesley K. G. Assun\c{c}\~ao
arXiv Computation and Language
Oct 15

T2J-BENCH: Benchmarking Coding Agents on Execution-Grounded PyTorch-to-JAX Repository Migration

arXiv:2605.29054v3 Announce Type: replace-cross Abstract: Migrating a machine-learning codebase across frameworks--for example from PyTorch to JAX--is a routine but costly engineering task that is fu...

By Tao Feng, Jiefeng Chen, Linxin Song, Yufan Zhuang, Yue Huang, Bhavana Dalvi Mishra, Chi Wang, Xiangru Tang, Jieyu Zhao, Ge Liu, Tomas Pfister, Jinsung Yoon
arXiv AI
Aug 11

Janus: An Algorithm-Evaluator Co-Evolution Framework for LLM-Driven Discovery under Expensive Evaluation Budgets

arXiv:2608. 08189v1 Announce Type: new Abstract: LLM-driven program discovery relies on rapid evaluator feedback, but many scientific and engineering tasks require high-fidelity simulations, hardware execution, or physical experiments, making each evaluation expensive.

By Ximeng Liu, Qianlong Wang, Yingming Mao, Annan Li, Yatao Li, Shizhen Zhao, Jianmin Wu, Dawei Yin, Dou Shen