Alphabeta Math
ExampleConstruction: AI-generatedVerification: AI-generatedPipeline-generatedprecheck passjudge pass (gpt-6-luna)
How statement and proof provenance work

The first chip identifies the source of the statement or construction; the second identifies the source of its local proof or verification.

  • Literature-sourced: the exact statement appears in a cited source; only wording and notation differ.
  • AI-adapted: a semantically identical restatement of literature-sourced material, modulo indexing, notation, and boundary cases adopted by the library.
  • AI-generated: a genuinely novel statement formulated by AI, with no source for the claim itself.

These labels describe origin, not correctness: citations and verification chips remain separate evidence.

A confident pseudo-label can be wrong and remain fixed

Example

Take X={0,1,2,3,4,5} with distance d(x,y)=∣x−y∣ and classes {1,2}. The true labels at 0,1,2,3,4,5 are 1,1,1,1,2,2, but only the examples (0,1),(1,1),(2,1),(3,1),(4,2) are labeled for the procedure. Run the finite confidence-threshold pseudo-labeling algorithm with unlabeled set {5}, k=5, threshold τ=4/5, pseudo-label weight λ=1, and round cap T=1. The nearest labeled examples to 5 have labels 2,1,1,1,1, so confidence 4/5 assigns the wrong label 1; the no-relabel rule retains that assignment.

Line diagram

The nodes lie at their feature coordinates on the real line. Labels after a colon are observed labels for positions 0 through 4; the question mark at 5 is unlabeled to the algorithm. The hidden true class at 5 is 2. The drawn line shows distance order only, not a graph edge set.

diagram failed to render:
\begin{tikzpicture}
\node[draw,circle] (x0) at (0,0) {$0:1$};
\node[draw,circle] (x1) at (1.5,0) {$1:1$};
\node[draw,circle] (x2) at (3,0) {$2:1$};
\node[draw,circle] (x3) at (4.5,0) {$3:1$};
\node[draw,circle] (x4) at (6,0) {$4:2$};
\node[draw,circle,dashed] (x5) at (7.5,0) {$5:?$};
\draw (x0)--(x1)--(x2)--(x3)--(x4)--(x5);
\node at (7.5,-0.75) {true class 2};
\end{tikzpicture}

Facts & Assumptions

Given: The ordered labeled list (0,1),(1,1),(2,1),(3,1),(4,2), unlabeled point 5, the usual distance, k=5, threshold 4/5, pseudo-label weight 1, and one round.

[A1]

d(x,y)=∣x−y∣ for x,y∈X, the five labeled records are ordered by feature value, and the hidden true label at point 5 is 2.

[F1]

Finite-round confidence-threshold pseudo-labeling: the five closest records vote with their positive record weights; the smallest-index maximizing class is predicted, confidence is the largest weighted vote divided by total weight, selected labels are added simultaneously, and an added label is never revised.

Verification

1.1givenA1F1algebra

The distances from 5 to labeled inputs 4,3,2,1,0 are respectively 1,2,3,4,5, so all five records are selected in that order. Their votes are V1=4 and V2=1, giving prediction 1 and confidence max⁡(4,1)/(4+1)=4/5.

2.1givenF1step 1.1∎

Since the confidence meets the threshold, the algorithm appends pseudo-label (5,1) with weight 1 and removes 5 from the unlabeled set. The output status is all-labelled, and the no-relabel rule retains the pseudo-label 1 even though the specified true label is 2.

Depends on

Used by

Nothing in the library uses this result yet.

Dependency tree · one level

1 result within one dependency step of this one, each drawn at its shortest distance from it. An arrow runs from a result to what uses it, so the chart reads left to right and ends at this result, which carries a heavier outline. Every node is a link to that result. Click elsewhere on the chart to enlarge it.