Framework Lexicon

Part of the cognition series. Reference post — updated as vocabulary sharpens.

Several mathematical communities have independently formalized overlapping structures under different names. This post maps the translations.

If you’re here because you found your name: The Rosetta stone below maps your work to parallel results in other communities. The dashes mark where one community has no term for a concept the other has named.

Authors cited: Fritz, Spivak, Gaboardi, Katsumata, Kura, Jacobs, Liell-Cock, Staton, Perrone, Leinster, Baez, Sato, Chen, Vigneaux, Rezagholi.


Rosetta stone: same objects, different names

Corrections welcome — june@june.kim.

PL theoryCategorical probabilityShared objectFrameworkConfidence
Kleisli(D)
Jacobs 2014
FinStoch
Fritz 2020
Category of finite sets + stochastic matricesAmbient category
post
Exact. Same category
Hoare triple {P} c {Q}
Gaboardi 2021
Predicate on morphism outputs, preserved under compositionContract
post
Plausible. Same role; grades differ
Restricted pointwise order
Kura-Gaboardi 2026 Def 7
Post(N) implies Pre(N+1), restricted to supportHandshake
Comp.lean
Exact. forward_triple chains four COMP rules through real pre/postconditions. Each stage's postcondition is the next stage's precondition — the restricted pointwise order, machine-checked.
wp via poset fibration over Kleisli(D)
Jacobs 2014
Posetal imperative category
Staton 2025 Cor. 76
Predicate logic over a stochastic category"Fibration"
post
Exact. Staton proves Hoare rules over Stoch (Thm 79)
Graded Freyd category
Gaboardi 2021
Graded Markov category
Liell-Cock & Staton 2025
Effectful computation with tracked side-effectsStage + contract
Lean
Plausible. Freyd vs. Markov differ on exponentials
Divergence on monad
Sato-Katsumata 2023
Enriched Markov category
Perrone 2023
Metric on morphism spaces for verificationNonExpanding
Lean
Plausible. Different enrichment categories
Possibilistic shadow Υ
Fritz et al. 2023
Possibilistic collapse of a probabilistic morphismSupport typeclass
Support.lean
Exact. support_bind = monad multiplication
supp : V ⇒ H
Fritz-Perrone-Rezagholi 2021
Monad morphism: valuations → closed subsetssupport_bind
Support.lean
Exact. Same composition law
Static idempotent
Fritz et al. 2023 Prop 4.4.1
Idempotent that stabilizes after one stepIterationStable
Contracts.lean
Plausible. Ours is weaker than Fritz's splitting
Magnitude
Leinster 2021
Categorical cardinality / effective species countDiversity
post
Speculative. No composition law for magnitude
Free ℕ-semimodule monad
Kidney & Wu 2021
Double dualization
Fritz-Perrone-Rezagholi 2021
Counting monad parameterized by semiring SGap
supp composed with |·|
Speculative. Exists on Set, not via double dualization on Top
Selection relation on parametrised optics
Capucci et al. 2021
"What is a goal but a predicate on a system?" Compositional predicate on morphisms.Contract
post
Plausible. Same role (behavioral predicate); different ambient structure (optics vs. Kleisli)
Nash equilibrium as compositional predicate
Ghani-Hedges et al. 2018
Equilibrium checked locally on subgames, preserved under game compositionContract preserved under composition
Lean
Plausible. Structural parallel; games ≠ pipelines
Bayesian lens / statistical game
Smithe 2021
Bayesian inversion in Markov category
Braithwaite-Hedges-Smithe 2023
Optic with forward (inference) and backward (learning) pass over stochastic morphismsForward pipeline + Consolidate
post
Plausible. Both have forward+backward; optic composition ≠ Kleisli composition
Dagger structure on kernels
Parzygnat 2020
Bayesian inversion as categorical involution on morphismsConsolidate (backward pass)
post
Speculative. Consolidate updates policy; dagger inverts morphisms. Related but not the same operation
Effect algebra predicates (X → 1+1)
Cho-Jacobs 2015
Predicates as effects, not subobjects. States/effects duality.Contract
Lean
Plausible. Both are predicates on outputs; effect algebras are quantitative, ours are Boolean
Partial correctness
Standard PL
Partial Markov categories
Di Lavore-Roman-Sobocinski 2025
Morphisms can fail. Observations, normalization, conditioning in restriction categories.
(pipeline assumes total morphisms)
Plausible. Pipeline stages don't fail; but Filter's rejection maps to partiality
Program equivalence metrics
Panangaden 2009
Bisimulation metrics = behavioral distance between stochastic processes
(no framework analog)
Speculative. Could connect to magnitude as behavioral distance. Nobody has tried.

Pipeline structure

TerminologySourceConnectionFramework term
Operad algebraSpivak 2013The six roles are boxes in a wiring diagram. Typed ports constrain which boxes connect.Pipeline
Type forcingType Forcing post + Lean proofGiven the six interface types, the supplier assignment is unique. Pipeline topology is forced.Wiring uniqueness
Stoch — Markov categoryFritz 2020The category the pipeline lives in. Measurable spaces + Markov kernels. Copy/delete structure.Ambient category
Kleisli arrow α → M βStandardEach pipeline stage is a monadic kernel. When M = Id, it's a deterministic function.Kernel
Kleisli composite of five kernelsLean proof (Pipeline.lean)Perceive → Cache → Filter → Attend → Remember. Five morphisms composed via >=>.Forward pipeline
Traced monoidal categoryJoyal-Street-Verity 1996Remember's output feeds Perceive's input. The loop closes via a trace operator.Feedback loop
Forward + ConsolidateLean proof (Pipeline.lean)One complete iteration: forward pass produces ranked output, Consolidate updates policy.Cycle

Deterministic / stochastic boundary

TerminologySourceConnectionFramework term
Copy-natural morphismFritz 2020 §10Filter is deterministic: same input, same gate. Commutes with copy. Lives in Cdet.Deterministic morphism
Copy-non-natural morphismFritz 2020Attend must be stochastic: deterministic selectors cycle, killing diversity.Stochastic morphism
Cartesian subcategory CdetFritz 2020 §10Filter lives inside. Attend crosses out. Cdet closed under composition — two det morphisms compose to det.Cdet boundary
Pigeonhole → state collisionLean proof (Stochasticity.lean, Pigeonhole.lean)Deterministic transducer over Fin N must collide within N+1 steps. Confusion → error.Stochasticity is mandatory

Information theory

TerminologySourceConnectionFramework term
F(f) = c(H(p) − H(q))Baez-Fritz-Leinster 2011The pipeline's information budget. Unique form under functoriality + convex-linearity + continuity. FinProb only.Information loss
Functorial nonneg loss accumulatesBaez-Fritz-Leinster 2011Information only decreases through pipeline stages. Post-processing can't recover lost bits.DPI
t ≤ s iff t = c ∘ s (a.s.)Fritz 2020 Def 16.1Consolidate's output is a sufficient statistic. Retains task-relevant info, discards the rest.Informativeness preorder
Distance from determinismFritz-Gonda-Perrone-Rischel 2024Measures how far a morphism is from being deterministic. Enriched Markov categories.Categorical entropy
Divergences in enriched hom-spacesPerrone 2023Fisher metric lives in the enrichment. Potential home for Consolidate's update dynamics.Info geometry
Magnitude / Hill numberLeinster 2021Hill at q=0 = support size = species richness. Diversity IS categorical cardinality (magnitude).Diversity

Support and possibilism

TerminologySourceConnectionFramework term
Possibilistic shadow Υ : C → SetMultiFritz et al. 2023The Lean proof's Support class IS this. Collapses probabilistic to possibilistic. support_bind = monad multiplication for supp : V ⇒ H.Support typeclass
Unit of Hoare hyperspace monad HFritz-Perrone-Rezagholi 2021σ(x) = cl({x}). Pure computation has singleton support.support_pure
supp(E(ξ)) = U(supp#(supp(ξ)))Fritz-Perrone-Rezagholi 2021Support of composite = closure of union of conditional supports. Bind distributes support via existential.support_bind
Closed subsets, lower Vietoris topologyFritz-Perrone-Rezagholi 2021The codomain of the support monad morphism. Algebras are complete join-semilattices.Hoare hyperspace H
Functorial iff causalFritz et al. 2023 Thm 3.1.19Causality axiom = composition condition for supports. The pipeline is causal.Support is functorial
Static idempotent (e =e id)Fritz et al. 2023Iteration-stable = static idempotent. Splitting = having a support (Prop 4.4.1). Fixed-point structure captured by support object.IterationStable
Gap: |Υ(p)| as lax functorNobody yetCardinality of possibilistic shadow. Would connect Leinster magnitude to Fritz support. Missing bridge for diversity.Diversity (Attend)

Contracts and composition

TerminologySourceConnectionFramework term
Predicate on outputs (β → Prop)Lean proofEach pipeline stage carries a postcondition. All six contracts quantify over the support of the kernel.Contract
Hoare postconditionGaboardi et al. ESOP 2021Graded Hoare logic gives pre/postconditions for graded monads categorically. ContractPreserving IS a Hoare triple.ContractPreserving
Restricted pointwise orderKura-Gaboardi et al. 2026 Def 7f ≤P g iff ordering holds where predicate is non-bottom. Post(N) implies Pre(N+1), restricted to support. forward_triple chains four COMP rules through real pre/postconditions — machine-checked.Handshake (Comp.lean)
Graded monad liftingKura-Gaboardi et al. 2026Lifting through predicate fibration PredΩ(C) → C. Interpreting in total category preserves fibrational structure.Contracts propagate
PredΩ(C) → CJacobs 2001Standard construction. SCCompC gives dependent types on top. The fiber over each morphism carries its contracts.Predicate fibration
Para construction Cγ(X,Y) = C(γ⊗X, Y)Liell-Cock & Staton POPL 2025The grade is a tensor factor. Grading and stochastic semantics are the same structure.Graded monad = Markov cat
Op-lax functor R : ImP → Kl(CP)Liell-Cock & Staton 2025 Thm 5.6R(g∘f) ⊆ R(g)∘R(f). Composing in ImP gives tighter uncertainty sets than composing in the quotient. Subset = refinement.Tighter bounds under composition
Bop → PreMonKura-Gaboardi et al. 2026 Def 6The grading monoid varies with the base index. Effects (contracts) are value-dependent, not just type-dependent.Indexed preordered monoid

Variational derivations

TerminologySourceConnectionFramework term
Optimality over feasible setLean proofElement-level: x is feasible and no feasible y has lower cost. Used for Consolidate and Filter.MinimizesOn
Incumbent is always admissibleLean proofThe current policy is always a feasible candidate. Optimal output ≤ incumbent. Derives Consolidate's lossiness.self_feasible
Nontrivial feasible reduction existsLean proofFor every input, a strictly smaller feasible output exists. Minimality + witness → strict reduction. Derives Filter's gating.strict_witness
Contract as optimality conditionLean proofConstructors that build contract structures from optimization problems. Plug into existing handshake machinery.fromOptimal

Open bridges

BridgeConnectsWould give usSearch terms
Hoare logic over Markov categoryBonchi-Di Lavore-Roman-Staton 2025CLOSED. Stoch is a posetal imperative category (Cor. 76). All Hoare rules (SKIP, COMP, ASSIGN, LOOP, WHILE...) derived as theorems (Thm 79). Predicates = morphisms X → 1. Triples = inequalities in posetal structure.Staton's "imperative category" = traced distributive copy-discard category. Markov categories ARE copy-discard categories. Paper explicitly names Stoch and proves the rules.
Semiring-valued monad via change of baseFritz-Perrone-Rezagholi supp : V ⇒ H + Leinster magnitudeDiversity contract derived variationally — DPP maximizes support cardinality under capacityDouble dualization uses a semiring S: {0,1} → reachability, [0,∞) → probability. (ℕ, +, 0) → counting. Not via double dualization, but the free ℕ-semimodule monad (= multiset monad) exists on Set. Jacobs proves a distributive law M∘D → D∘M (multiset over distribution). Compose with support to track cardinality.
Categorical DPPsExterior algebra functor + Markov categoriesThe concrete Attend mechanism (DPP kernel) with categorical semanticsdeterminantal point process categorical; DPP monoidal; L-ensemble categorical
Coding theoremsFritz entropy + Shannon theoryConsolidate's compression as a categorical rate-distortion theoremrate-distortion Markov category; Wyner-Ziv categorical
Natural gradient as morphismPerrone info geometry + optimizationConsolidate's update step as a gradient flow on the statistical manifoldnatural gradient categorical; gradient descent enriched Markov

Authors and papers

AuthorPaperWhat it does
Tobias FritzMarkov categories (2020)Defines the ambient category for stochastic morphisms. Copy-naturality distinguishes deterministic from stochastic. Informativeness preorder captures sufficient statistics.
Markov Categories and Entropy (2024)Defines entropy categorically as distance from determinism. Enriched Markov categories with divergences on hom-sets. Data processing inequality proved abstractly.
Supports and idempotent splitting (2023)Defines supports abstractly in Markov categories. Supports are functorial iff the category is causal. Static idempotents split iff they have supports.
Fritz, Perrone, RezagholiSupport as monad morphism (2021)Proves supp : V ⇒ H is a monad morphism from valuations to Hoare hyperspace. Both constructed via double dualization against different semirings.
Baez, Fritz, LeinsterEntropy characterization (2011)Shannon entropy is the unique functorial, convex-linear, continuous information loss assignment in FinProb, up to scale.
Sam StatonProgram Logics via Distributive Monoidal Categories (2025)Derives Hoare logic from traced distributive copy-discard categories ("imperative categories"). Proves Stoch is a posetal imperative category. All Hoare rules follow as theorems.
Compositional Imprecise Probability (POPL 2025)Graded monads over Markov categories for imprecise probability. The para construction makes the graded model itself a Markov category.
Marco GaboardiGraded Hoare Logic (ESOP 2021)Hoare triples parameterized by a preordered monoid grade. Categorical semantics via graded Freyd categories over coherent fibrations. Instantiated for cost, privacy, and probability.
Indexed graded monads (2026)Graded monads parameterized fibrewise over a predicate fibration. Restricted pointwise order propagates pre/postconditions through composition.
Shin-ya KatsumataDivergences on Monads (MSCS 2023)Divergences on a monad = enrichments of its Kleisli category. Connects metric structure on morphism spaces to relational Hoare logic. Instantiated for differential privacy and cost.
Bart JacobsWeakest preconditions in fibrations (2014)Derives wp-semantics from order-enriched Kleisli categories via poset fibrations. Instantiated for the distribution monad — whose Kleisli category is FinStoch.
Structured Probabilistic Reasoning (2025 draft)Book-length treatment of distributions, predicates, channels, conditioning via effectus theory. Cites Fritz extensively but uses his own formalism. FPred fibration for fuzzy predicates.
Tom LeinsterEntropy and Diversity (2021)Hill diversity numbers are the unique axiomatic diversity measures. Diversity is a special case of magnitude — the categorical generalization of cardinality for enriched categories.
Paolo PerroneCategorical Information Geometry (2023)Information geometry inside enriched Markov categories. Divergences in hom-spaces recover Shannon/Renyi entropy, DPI, and the Gini-Simpson index.
David SpivakOperad of wiring diagrams (2013)Typed ports and supplier assignment for composing boxes in wiring diagrams. Gives the syntax layer — which boxes connect to which.
Kidney & WuAlgebras for Weighted Search (ICFP 2021)Free semimodule monad parameterized by arbitrary semiring S. S = ℕ gives multiset monad (counting). S = [0,1] gives probability. Unifies search and probability via semiring choice.
Chen & VigneauxCategorical Magnitude and Entropy (2023)Unifies Shannon entropy and log(magnitude) for finite categories with probability. Recovers magnitude under uniform distribution. Invariant of individual categories, not a functor on morphisms.
Luca ReggioSemiring-valued measures (2020)Codensity monad of finite S-semimodules gives S-valued measures on Stone spaces. Change-of-semiring framework for codensity monads. Requires finite semirings.
Kenta ChoIntroduction to Effectus Theory (2015)With Jacobs. Predicates as effect-algebra-valued maps (X → 1+1), not subobjects. States/effects duality with Born rule. Parallel formalism to Markov categories.
Dario SteinProbabilistic Programming with Exact Conditions (2023)With Staton. Internal language of CD categories gives precise correspondence between Markov categories and a class of programming languages. Exact conditioning as categorical structure.
Eigil RischelInfinite products and zero-one laws (2020)With Fritz. Kolmogorov and Hewitt-Savage zero-one laws proved in categorical probability. Exchangeability and independence axiomatized.
Tomáš GondaMarkov Categories and Entropy (2024)With Fritz, Perrone, Rischel. Categorical entropy recovers Shannon, Renyi, and the Gini-Simpson index used in ecology — direct bridge to Leinster's diversity.
Arthur ParzygnatQuantum Markov categories (2020)Bayesian inversion as dagger structure on morphisms. Three levels of reversibility: inverses, disintegrations, Bayesian inverses. No-broadcasting theorem.
Elena Di LavorePartial Markov Categories (2025)With Roman, Sobocinski. Blends Fritz's Markov categories with restriction categories. Observations, Bayes' theorem, Pearl's and Jeffrey's updates in categorical terms.
Prakash PanangadenLabelled Markov Processes (2009)Continuous-state probabilistic transition systems as coalgebras. Bisimulation metrics = behavioral distance between processes. Logical characterization of bisimulation.
Jules HedgesCompositional Game Theory (2018)With Ghani, Kupke, Forsberg. Nash equilibrium as a compositional predicate on strategy profiles — checked locally on subgames. Open games via string diagrams.
Mixed Strategies (2020)With Ghani, Kupke, Lambert. Relational Kleisli lifting of the probability monad lifts equilibrium predicates from pure to probabilistic games.
Matteo CapucciTowards Foundations of Categorical Cybernetics (2021)With Gavranović, Hedges, Rischel. Selection relations on parametrised optics. "What is a goal but a predicate on a system?" Goals unify optimization, homeostasis, learning, equilibria.
Toby SmitheBayesian Lenses. Statistical Games (2021)Bayesian inversions compose as lenses in a Markov category. Statistical games = optics + free-energy objective. Explicit bridge between Fritz and the cybernetics community.
Ho, Wu, RaadBayesian Separation Logic (POPL 2026)Hoare logic for Bayesian probabilistic programming. Model is s-finite kernels. Internal Bayes' theorem via disintegration. Frame rule = probabilistic independence.

Updated 2026-03-21. Previous vocabulary: Type Forcing (Spivak), Ambient Category (Fritz).