generate-action-skeleton-creator-transition-priors-then-assign TEC-0268 · proposedGenerate the action skeleton from creator transition priors,
Decode the step sequence with beam search over a creator's learned bigram/positional action priors and a deterministically predicted step count, then route ingredients to those steps by a learned ingredient-action prior instead of an LLM.
Proposed. Filed by the drafting stage because a paper introduces or tests it. Nobody has vouched for it. The claim drafted from that paper is its first evidence, and what the paper used to validate it is in that claim’s source note. Say whether it held up for you, below.
Decode the step sequence with beam search over a creator's learned bigram/positional action priors and a deterministically predicted step count, then route ingredients to those steps by a learned ingredient-action prior instead of an LLM. Filed by the drafting stage from Do Recipes Have Personas? Characterizing and Generating Creator Style in Attributed Procedural Graphs (2608.24369) with status proposed: the paper introduces or tests it, and the claim drafted from the paper is its first evidence. The paper validated it by: Compared against zero-shot LLM, few-shot LLM, autoregressive step-by-step, and N-gram baselines on Ingredient F1, normalized edge F1 and step error over long- and short-form splits; the two-stage model had the lowest step error (0.350 long, 0.332 short vs 0.373/0.351 few-shot) and lower IF1 (0.294/0.348 vs 0.344/0.385). A persona-swap control (other creator's priors, same dish) cost 0.06 nEF1 (p<1e-54).. Nobody has vouched for it; add support or contest it on this page.
Does it work?
nothing measured0 supporting · 0 contesting sources
Efficacy claims — what this technique actually moves, under which conditions, and whether that has been contested.
No efficacy claim filed yet. The technique is catalogued; whether it moves the capability, and when, is a separate assertion that needs its own sources.
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