# R12 Axiomatic Presentation Identifiability No-Go **Status:** rejected as an R12 invention. Generator/relation curricula remain valid controls, but finite relation loss does not identify a neural homomorphism or guarantee unseen-composition reasoning. ## 1. Candidate The candidate attempted to teach a small set of typed generators and axioms, hold out long compositions, and use relation-equivalent words plus source-deleted state interchanges to force a learned compositional action. There is a correct extrapolation theorem, but its assumptions already contain the hard part: every generator map must be identified on a complete domain or a determining set. Relations certify an identified action; they do not identify it from finite unrestricted neural behavior. ## 2. Presentation-factorization theorem Let `Q` be a finite typed generator graph, `F(Q)` its free category, and ``` C = F(Q) / equiv_R ``` the category presented by relations `R={u_i=v_i}`. Give every object `o` a state set `X_o` and every generator `a:o->p` a learned map ``` T_hat_a : X_o -> X_p. ``` For a path `w=a_1...a_k`, define `T_hat_w` by composition. If ``` T_hat_(u_i)(x) = T_hat_(v_i)(x) ``` for every defining relation and every state in its complete domain, then the generator assignment factors uniquely through `C`. Thus it defines a functor ``` T_hat : C -> Set ``` and every unseen word receives the homomorphic action determined by its generators. The proof is the universal property of a presented category: the generator assignment first defines a functor on the free category; equality on every defining relation makes it constant on the generated congruence, so it factors uniquely through the quotient. ## 3. Identification requires a determining set To identify a target action `T_star`, each generator additionally needs: 1. a declared hypothesis class `H_a`; 2. a determining set `D_o` such that two maps in `H_a` agreeing on `D_o` agree on all of `X_o`; 3. exact local coverage `T_hat_a(x)=T_star_a(x)` for every `x in D_o`. Only then does local equality imply `T_hat_a=T_star_a` globally and therefore `T_hat_w=T_star_w` for every held-out word. Once the generator maps are identified, relation loss is mathematically redundant for prediction. It is a consistency certificate. Faithfulness is not needed to predict the action, but it is needed to identify abstract words. A nonfaithful target reveals only the quotient by its kernel. Even with exhaustive causal interchanges, internal coordinates remain identifiable only up to objectwise bijections, or natural isomorphism. ## 4. Finite-test no-free-lunch theorem Consider any frozen finite suite of state-generator transitions for an unrestricted hypothesis class. If one reachable transition `(z,a)` is never exercised, define a patched updater that equals the target everywhere in the suite but changes `T_hat_a(z)` and routes the first unseen word reaching `z` to a different state. Every tested relation and interchange remains zero-loss. Therefore a finite suite certifies arbitrary future words only if one of these holds: - the finite state domain is tested exhaustively; or - the hypothesis class is restricted so the tested states form a determining set. Ordinary neural networks permit finite-set patching, so finite relation tests alone are not determining. The trivial action can satisfy many presentations; multiple inequivalent and conjugate representations can satisfy the same relations; and nonfaithful actions can alias distinct words. Approximate relations weaken the claim further. If a word equality requires many relator applications, local defects can accumulate with the presentation area, governed in the worst case by its Dehn function. Small training relator loss does not imply horizon-independent semantic error. ## 5. Real but limited resource advantage For `N` states and `k` generators, explicit generator tables require about ``` k N log2(N) bits ``` and `kN` covered transitions. Exhaustively checking each defining relation on every state costs ``` N * sum_(u=v in R) (|u|+|v|) ``` generator applications and then certifies all words for that exact action. A noncompositional lookup system storing `M_L` distinct actions of length at most `L` can require `Theta(M_L N log N)` bits, exponentially larger when `M_L` grows exponentially. This is a valid separation from lookup memorization. It is not a separation from a transformer, RNN, weighted automaton, or any other shared-weight learner that can implement the same generator composition. ## 6. Prior-art boundary - Factorization through generators and relations is the standard universal property of presented algebraic objects. - Auxiliary losses that impose group representation structure are already studied as algebraic priors for approximately equivariant networks. - Source-state swaps are interchange intervention training. - Transformers have already generalized permutation words from smaller to larger symmetric groups under a tailored curriculum. - Autoregressive compositional task theory already gives exponential task coverage from near-linear component-task coverage under explicit compositional assumptions. Primary sources: - Ali, Lio, and Vicary, *Algebraic Priors for Approximately Equivariant Networks* (2026 revision): https://arxiv.org/abs/2506.08244 - Geiger et al., *Inducing Causal Structure for Interpretable Neural Networks* (ICML 2022): https://proceedings.mlr.press/v162/geiger22a.html - Petschack, Garbali, and de Gier, *Learning the symmetric group: large from small* (2026): https://arxiv.org/abs/2502.12717 - Abedsoltan et al., *Task Generalization With AutoRegressive Compositional Structure* (ICML 2025): https://arxiv.org/abs/2502.08991 ## 7. Decision Reject finite axiom/relation loss as an R12 reasoning primitive. It may remain a useful curriculum and evaluation control, but it cannot support a claim of identified neural homomorphism or indefinite composition unless the project first proves exhaustive state coverage or a hypothesis-specific determining set. The unresolved problem is not how to state algebraic relations. It is how a small learner acquires a restricted, robust hypothesis class whose local coverage is both feasible and sufficient, without hard-coding the target algebra. No CPU falsifier or Shohin fit follows from the presentation theorem alone. ## 8. Finite determining-family refinement There is one exact local-to-global result worth preserving. Let every primitive map belong to a declared stationary hypothesis class with a finite determining set. If all primitive maps are recovered exactly on those sets, then every composition at every length is correct by induction; no union bound over words is required. For `M` primitive determining cases observed independently `m` times through binary noise below `eta < 1/2`, majority recovery obeys the conservative bound ``` P(any primitive case is wrong) <= M * exp(-m * (1 - 2 eta)^2 / 2). ``` If each learned primitive has uniform error at most `epsilon` and the relevant composition maps have Lipschitz factors at most `lambda_j`, the usual telescopic bound is ``` error_L <= epsilon * sum_(j=0)^(L-1) product_(k=j+1)^(L-1) lambda_k. ``` This yields horizon-independent stability only under contraction or exact primitive recovery. Without the declared stationary class and determining sets, a delayed-sabotage map agrees on every finite tested composition and fails immediately afterward. The theorem is a useful curriculum contract but not an R12 invention. A fair structure-aware recurrent, acyclic, symbolic, or transformer control receives the same primitive family and determining observations and inherits the same guarantee.