Enactment

Latent knowledge, brittle elicitation. ICM-style results indicate LMs already encode strong latent structure. Single examples are noisy; coherent behavior emerges only when we reason over batches/sets. In preference alignment, the LM may already “know” a user’s persona, yet conditioning on that persona yields inconsistent predictions across prompts and paraphrases.

Existing interactive elicitation (e.g., GATE/active DPO/ALOE) improves sample efficiency, but typically optimizes local choices without explicitly recovering a compact, globally consistent preference structure that can be reused across domains.

Aim: Develop a method that interleaves interactive queries with an ICM-style batch search over binary preference judgments to induce a coherent, globally consistent user-preference labeling, thereby turning latent knowledge into consistent, personalized outputs with very few human interactions.