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Wordscillation

Wordscillation

A controlled study of late commitment over noisy structured memory by Jaharri Gassaway (aka DumbButt), Inquirion Labs.

I started Wordscillation with a simple intuition: when the evidence is uncertain, a model should not collapse to one meaning too early. Paper 1 turns that intuition into a smaller, falsifiable test.

In synthetic four-hop worlds, a width-8 beam keeps complete path hypotheses alive while a 1,153-parameter identity-free scorer learns how to rank them. On the registered within-family evaluation, dense moderate-corruption accuracy rises from 82.188% to 94.896%, close to a 95.625% retained-path oracle. A fixed non-oracle top-16 retriever separately reaches 95.729%.

The important boundary is equally clear: the learned scorer does not transfer without adaptation when the branching structure changes. This is not yet a language model, a general reasoning rule, or evidence for a literal oscillatory mechanism.

Supported conclusion

Under corruption in the tested synthetic family, preserving a bounded set of coherent whole hypotheses and ranking them late can recover information lost by early local commitment.

Read and inspect

Status

Preprint candidate; not peer reviewed. The browser training demo is a deliberately small conceptual model, not a replay of the registered experiment.

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