Situation. L1 (https: //doi. org/10. 5281/zenodo. 20336872) gave the chart: probability simplex, zero-sum hyperplane H₀, sphere pullback, Fisher–Rao metric, rotation group at the uniform. But the chart supplies only one geometric object: a metric. A working theory needs more: a notion of position, a notion of curvature, a notion of force, and a notion of how truncations of the theory fail. Where do these come from, and is there a unifying source for all of them? Task. Identify a single scalar functional on L1 (https: //doi. org/10. 5281/zenodo. 20336872) 's chart whose successive derivatives generate the geometric objects we need, with no ad-hoc additions. Action. Use the **log-partition function** () = Z () of the categorical exponential family. The natural parameters live on H₀ (dual to L1 (https: //doi. org/10. 5281/zenodo. 20336872) 's centered chart) ; the mean parameters = are exactly the probabilities p. Take successive cumulants of. Result. The derivatives of form a tower: 1. = = p — the mean parameter; the position. 2. ² = Cov (X) — the covariance of the sufficient statistic, equal to the **Fisher information** at p; the metric, and simultaneously the inertia. 3. ³ = T — the **Amari–Chentsov 3-tensor**; the cubic structure that defines information geometry as its own subject beyond Riemannian geometry. 4. ⁴ and higher — projection-closure defects; where 3rd-order truncations fail. One functional, four successive geometric objects. The leap from order 2 (Fisher metric / Riemannian) to order 3 (Amari–Chentsov / dually flat) is the load-bearing distinction — the third cumulant is the first invariant Riemannian geometry cannot see. This is the "3" of the trilogy title, made concrete: the cumulant order at which information geometry separates from ordinary differential geometry. This is L2 of the trilogy *Identifying 3*. L1 supplied the chart; L2 develops its successor structure; L3 reads the resulting tensors through Pontryagin character orthogonality on the conductor lattice. Take the probabilities of \ (N\) outcomes and write one number (the log of their normalization) as a function of the parameters driving them. Successive derivatives of this single scalar generate every geometric object information geometry needs: the first recovers the probabilities themselves; the second gives the Fisher information metric (how distinguishable nearby distributions are) ; the third gives the Amari-Chentsov 3-tensor (the cubic "force law" that distinguishes information geometry from ordinary Riemannian geometry) ; the fourth records where any third-order truncation stops closing. Four named geometric objects from one functional, no extras. A Gaussian's third derivative vanishes, so Riemannian geometry sees its whole geometry; the categorical distribution does not stop at order 2, and its third cumulant is the first invariant Riemannian geometry cannot see: the "3" that the trilogy title names.
Leonardo Murillo Montero (Fri,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: