Foundations of Stochastic Lexical Calculus: Semantic Descent and Random Dynamics on Probability Simplices
Large language models produce prompt-dependent probabilities over words, whereas scientific systems require uncertainty over meaningful states that…
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| Factor | Weight | Score | Contribution | Where it came from |
|---|---|---|---|---|
| Corroboration | 0.35 | 0.39 | +0.135 26% | 1 independent org on the story. Tier-3 aggregators never corroborate — they can show something is circulating, never that it is true. |
| Source trustleads | 0.25 | 0.85 | +0.212 41% | arXiv is the highest-trust source on this story and is first-party — the organisation announcing its own news. Trust is taken from the best source, not averaged. |
| Pickup rate | 0.20 | 0.00 | +0.000 0% | One counted organisation, so there is no spread to measure — nothing has picked this up to set a rate. |
| Freshness | 0.20 | 0.85 | +0.171 33% | Halves every 10 hours from the newest item on the story. This is the only factor that rewards a story for nothing more than being recent. |
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What happened
Large language models produce prompt-dependent probabilities over words, whereas scientific systems require uncertainty over meaningful states that can be updated as evidence arrives. We develop an observable framework for determining when language-derived probabilities support such a sequential state representation. Theoretically, we define typed measurable transformations of contextual language, construct a minimal closed representation, and give necessary and sufficient conditions for semantic updates to exist uniquely. We bound irreducible nonclosure and accumulated error, and under average contraction prove existence, uniqueness and stability of an external random recursion on a probability simplex.
How this story arrived
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- 01 Arxivfirst-party first seen Foundations of Stochastic Lexical Calculus: Semantic Descent and Random Dynamics on Probabil
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