SESSE proposes structured decomposition to make LLM judging more auditable
An arXiv preprint introduces SESSE, a training-free framework that breaks an LLM judge’s preference into sub-questions. The authors report near-parity with a chain-of-thought baseline on 1,000 RewardBench examples and criterion-level vote records; generalization, cost, and independent validation remain open questions.