Revision or Re-Solving? Decomposing Second-Pass Gains in Multi-LLM Pipelines

1. 1Design a controlled decomposition experiment to separate second-pass gains into re-solving, scaffold, and content components
2. 2Evaluate the experiment across multiple model pairs and benchmarks
3. 3Analyze the results to determine the relative contributions of each component to second-pass gains
4. 4Use the insights to refine and optimize multi-LLM pipelines

AI researchers and engineers working on multi-LLM pipelines can benefit from understanding the sources of second-pass gains to improve their models' performance

💡 Second-pass gains in multi-LLM pipelines may not come from genuine error correction alone, but from a combination of re-solving, scaffold, and content components

🤖 Decomposing second-pass gains in multi-LLM pipelines reveals surprising sources of improvement #LLMs #AI