← Back to article

Equation 36 · A Chatbot Confessed to Being Built by a Company That Never Trained It

What does this equation mean?

ppostp_{\mathrm{post}}

Read the formula alongside the article passage below. Each part has a deeper page with its role in the equation, the supporting passage and nearby citations.

This mathematical expression combines the displayed quantities; its precise role follows from the surrounding article text. Read the equation part by part below; each part has a contextual explanation and a link to its mathematical background.

Read it piece by piece

ppostp_{\mathrm{post}}

Symbol p_post

ppp_post is a part of this expression. Its role is fixed by the surrounding article and by the operations shown in the formula.

Understand this part →

subscript

subscript

The lower label selects a particular version, component, or indexed member of the quantity. For example, x₀ and xₜ can be values at different positions.

Understand this part →

How to interpret it

Read this expression with the definitions, units, and assumptions supplied by the article.

What the article says around this equation

A transfer rate above zero says a channel is associated with a trait. It does not yet say whether the trait is a faithful copy or a garbled one, and it says nothing about whether curators along the way made the trait more or less likely to survive into the next training corpus. Two further quantities complete the construction. The mutation rate μc\mu_c=1∣Mc+∣\frac{1}{|\mathcal{M}_c^+|}∑m∈Mc+\sum_{m\in\mathcal{M}_c^+}1\mathbb{1}[d(rmr_m,ρ\rho)>δ\delta] measures, among the trait-positive models in class c (the set Mc+\mathcal{M}_c^+ ), what fraction produce a response diverging from the seeded canonical form ρ\rho by more than a fixed threshold δ\delta under a declared distance function d : a high μc\mu_c says the…
Read the full surrounding passage
A transfer rate above zero says a channel is associated with a trait. It does not yet say whether the trait is a faithful copy or a garbled one, and it says nothing about whether curators along the way made the trait more or less likely to survive into the next training corpus. Two further quantities complete the construction. The mutation rate μc\mu_c=1∣Mc+∣\frac{1}{|\mathcal{M}_c^+|}∑m∈Mc+\sum_{m\in\mathcal{M}_c^+}1\mathbb{1}[d(rmr_m,ρ\rho)>δ\delta] measures, among the trait-positive models in class c (the set Mc+\mathcal{M}_c^+ ), what fraction produce a response diverging from the seeded canonical form ρ\rho by more than a fixed threshold δ\delta under a declared distance function d : a high μc\mu_c says the trait is drifting as it moves, not being copied verbatim. The selection coefficient s=ln⁡\ln(ppost/(1−ppost)ppre/(1−ppre))\left(\frac{p_{\mathrm{post}}/(1-p_{\mathrm{post}})}{p_{\mathrm{pre}}/(1-p_{\mathrm{pre}})}\right) measures how a curation step — a filter, a quality score, a deduplication pass — changes the trait’s prevalence between the raw corpus the source produced, pprep_{\mathrm{pre}} , and the corpus that actually reaches a downstream trainer, ppostp_{\mathrm{post}} : s>0 means curation enriches for the trait, s<0 means curation purges it, and s=0 means curation is neutral toward it. None of these three quantities requires the exposed and unexposed models to share a base architecture, a training team, or a common ancestor scaffold. That is deliberate: the construction is built to work across a population of otherwise unrelated systems, which is the only regime in which “a model’s behavior outlived the model” is a coherent claim at all rather than a description of one system’s own continuity.

Read the equation in its article →

For background, read the article’s source list.

Return to A Chatbot Confessed to Being Built by a Company That Never Trained It

Browse the mathematical compendium →