Tse-Hsun

FALAT is a diagnostic framework that treats failure attribution in complex LLM agent trajectories as a dependency-guided search problem, successfully identifying both the responsible agent and the decisive failure step.

This paper evaluates the performance of large language models on popular benchmarks and finds that only a small percentage of the performant implementations are significantly faster than canonical solutions. The authors propose an LLM-based multi-agent framework to generate performance-oriented tests.