Lifted State Hypothesis in Large Language Models
Large language models (LLMs) adapt rapidly through fine-tuning and in-context learning, yet it remains unclear which inputs they treat as the same case and why their predictions change together. We introduce the Lifted State Hypothesis. Under fixed model parameters, context scope, and target computation, samples indistinguishable in their observed computation-relevant behavior form a computation-relative type. We hypothesize that compatible episodes activate a reusable latent component---a lifted state---that supports the target computation. State reuse enables type-level generalization but creates a non-monotonic revision problem. When later evidence distinguishes a subtype, revising a s...
| Authors | Bumjin Park, Jaesik Choi |
|---|---|
| Primary category | cs.AI |
| Published | 2 June 2026 |
| Last updated at source | 1 August 2026 |
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