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.
The content of a node and the content vector of a standard tableau
Definition
Let with Young diagram (Partitions, English diagrams, and conjugation), so that the nodes of are the pairs with and (rows numbered downward, columns rightward).
The content of a node is the column index minus the row index. The node therefore has content exactly when it lies on the diagonal ; the content of is , contents increase by along a row and decrease by down a column.
Now let be a standard tableau of shape (Tableaux and standard tableaux). For let be the node of carrying the entry , so that . The content vector of is the list of contents of the nodes read in the order of the entries . For the empty tableau of shape the content vector is the empty vector, the unique element of .
Remarks
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Well-definedness. A tableau of shape is a bijection , so each occupies exactly one node and the integers are determined by . The map uses the fixed row and column coordinates and the labels of : it depends only on which entry stands in which node.
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The entries of the content vector are the contents of the shape. Since is a bijection onto the nodes of , the multiset of entries of equals the multiset of node contents , independent of . In particular two standard tableaux of the same shape have content vectors that differ by a permutation of their entries, and the multiset of contents of a tableau is an invariant of its shape.
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Two extreme examples. The row tableau of shape carries in , so its content vector is ; the column tableau of shape carries in , so its content vector is .
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First entries. The entry always occupies the node , because every other node has a node weakly to its left and weakly above it, whose entry would be smaller. Hence for every nonempty standard tableau . Similarly, need not be monotone in : for the tableau of shape one has .
Depends on
Used by
- Ordinary Jucys-Murphy projection formulas do not survive content collision Counterexample
- The Jucys-Murphy spectrum and projectors for S₃ Example
- The seminormal and orthogonal blocks for shape (2,1) Example
- A partition is determined by the multiset of its node contents Lemma
- Distinct addable nodes of a partition have distinct contents Lemma
- Primitive tableau idempotents by Jucys-Murphy interpolation Theorem
- The joint spectrum of the Jucys-Murphy elements is the set of tableau content vectors Theorem
- Young's orthogonal form from the seminormal rescaling Theorem
- Young's seminormal form from the Jucys-Murphy eigenlines Theorem
Dependency tree · two levels
3 results within two dependency steps 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.
Sources
- Okounkov-Vershik, A New Approach to the Representation Theory of the Symmetric Groups, Selecta Math. (N.S.) 2 (1996) 581-605; complete arXiv repost math/0503040, section 5, printed pp. 17-22 (standard reference, not scraped)
- Garsia, Young Seminormal Representation, Murphy Elements and Content Evaluations, UCSD lecture notes (2003), section 3, printed pp. 18-25 (standard reference, not scraped)