### 📚🐈‍⬛🌌 SCHRÖDINGER’S LIBRARY 🌌🐈‍⬛📚

TECHNICAL STUDY SERIES · DISTRIBUTED IDENTITY · MATHEMATICAL MODELING · ANDROID ARCHITECTURE

### Cross-Domain Identity Resolution

Entity resolution, graph alignment, typed correspondence, temporal provenance, authentication, and identity continuity across independently governed computational systems.

Library classification: Graph Theory → Entity Resolution → Distributed Systems → Identity Management → Information Geometry → Formal Verification

Research connections: Google Identity, Android UID and Package Identity, AICore, Industrial Language Models, LTLM/STMI, Memory Bridge, Polyfractal Bubble Mesh, Schrödinger's Library, and the TARDIS Phone Interface.

### I. Introduction

Cross-domain identity resolution is the process of determining whether records, identifiers, or representations belonging to different information systems correspond to the same underlying entity.

The difficulty is that each information system defines identity according to its own operational requirements.

A Google Account identifies a principal within an account and authentication infrastructure.

An Android application package identifies software within the operating system's application-management framework.

An Android UID identifies an execution and security principal within a particular device and user context.

An industrial language model processes textual or structured references to entities without automatically establishing their real-world identities.

An account-memory system may represent a human through historical records, relationships, aliases, choices, provenance, and explicitly defined constraints.

These identity systems are neither independent in every respect nor interchangeable.

The central problem is therefore not to force all identifiers into one global namespace.

It is to establish which identities correspond, under which relations, at which times, using what evidence, and with what authorization.

Cross-domain identity resolution is consequently both a mathematical and an engineering problem.

### II. Identity is relative to a domain

Let the participating domains be

\\mathcal D=\\{D_1,D_2,\\ldots,D_n\\}.

Each domain has its own collection of identity records:

V_i=\\{v_{i1},v_{i2},\\ldots\\}.

An identity record can be modeled as

r=(d,k,a,t,\\pi),

where:

* identifies the originating domain.

* is the identifier within that domain.

* contains available attributes.

* contains temporal validity information.

* contains provenance.

The pair is generally more meaningful than alone.

For example, the same numerical UID can appear on different Android devices without identifying the same application or principal.

Similarly, an email address, display name, or software package name should not automatically be treated as a permanent, universal identity key.

A globally meaningful identity reference requires sufficient namespace context.

We may write:

\\operatorname{IdentityKey}(r)

(\\operatorname{domain}(r),\\operatorname{localID}(r)).

For more complex systems, the key may also require an issuer, tenant, device, user profile, application signing identity, or validity interval.

### III. Identity correspondence is not identity equivalence

Suppose:

g\\in V_G

is a Google Account record,

a\\in V_A

is an Android application account record, and

h\\in V_H

is a historical human reference in the account-memory graph.

A correspondence may exist:

g\\sim a

or

a\\sim h.

But the meaning of must be specified.

For example, the relation could mean:

* Authenticated as the same account principal.

* Reported to belong to the same human.

* Verified as referring to the same underlying entity.

* Associated through an authorized application session.

* Linked by a shared resource.

* Merely similar in name or textual attributes.

These are different predicates.

A correspondence based on similar names is weaker than one based on an authenticated identifier issued by a trusted identity provider.

An association through a shared device is different from proof that two accounts belong to the same human.

Therefore, a robust identity system should not rely on a single untyped `SAME_AS` edge for every relationship.

A more expressive representation uses typed edges:

E=\\{

\\texttt{AUTHENTICATES\\_AS},

\\texttt{CLAIMS\\_TO\\_REPRESENT},

\\texttt{VERIFIED\\_CORRESPONDENCE},

\\texttt{RUNS\\_AS},

\\texttt{LINKED\\_BY\\_CONSENT},

\\ldots

\\}.

This preserves the differences among evidence, identity claims, operational associations, and verified equivalence.

### IV. Identity as a multilayer graph

Define a multilayer identity graph:

\\mathcal G_I=

\\left(

\\bigsqcup_{i=1}^{n}V_i,\\,

E_{\\mathrm{local}},\\,

E_{\\mathrm{cross}},\\,

\\tau,\\,

\\pi,\\,

\\alpha

\\right),

where:

* is the identity-record set for domain .

* contains relationships internal to domains.

* contains cross-domain relationships.

* describes temporal validity.

* describes evidence and provenance.

* describes authorization constraints.

The disjoint union is essential.

Even if two records have the same visible label, their identifiers remain distinct until an explicit correspondence has been established.

CROSS-DOMAIN IDENTITY TOPOLOGY

A conceptual model of identity references and candidate correspondences. It does not imply automatic data exchange or an existing integration among the depicted systems.

The diagram is better interpreted as a heterogeneous graph than as a single hierarchy.

One human may have several account records.

One account may be accessible through several devices.

One device can support several Android users or profiles.

One application may maintain multiple independent sessions.

One identity reference may appear in several Library documents.

The relationships are often many-to-many rather than one-to-one.

### V. The formal entity-resolution problem

Suppose two domains contain records:

A=\\{a_1,\\ldots,a_m\\}

and

B=\\{b_1,\\ldots,b_n\\}.

The objective is to identify candidate pairs:

C\\subseteq A\\times B

that may refer to the same underlying entities.

For each pair, define a feature representation:

\\psi(a_i,b_j)=

\\begin{bmatrix}

s_{\\mathrm{name}}\\\\

s_{\\mathrm{identifier}}\\\\

s_{\\mathrm{temporal}}\\\\

s_{\\mathrm{relational}}\\\\

s_{\\mathrm{provenance}}\\\\

s_{\\mathrm{authorization}}

\\end{bmatrix}.

The components represent different forms of evidence or policy checks. Authorization is a gate on permissible matching and disclosure, not merely another feature to optimize.

A decision function can then produce:

\\Phi(a_i,b_j)

\\in

\\{

\\text{verified match},

\\text{nonmatch},

\\text{unresolved}

\\}.

The third state is important.

An unresolved correspondence is not automatically false.

Likewise, two similar records are not automatically equivalent.

This distinction is especially important when information is incomplete, access is restricted, or identifiers have changed over time.

### VI. Deterministic and probabilistic record linkage

Identity resolution can use several approaches.

Deterministic linkage applies explicit rules, such as matching a trusted, issuer-scoped account subject identifier within its valid context.

Probabilistic record linkage evaluates combinations of imperfect attributes.

Graph-based linkage compares surrounding relationships as well as attributes.

Human-verified linkage requires explicit confirmation before treating records as belonging to the same underlying entity.

Classical probabilistic record linkage is associated with the Fellegi–Sunter framework.

For a comparison vector , define:

m(\\gamma)=P(\\gamma\\mid M)

and

u(\\gamma)=P(\\gamma\\mid U),

where represents truly matching pairs and represents nonmatching pairs.

The likelihood ratio is

\\Lambda(\\gamma)=

\\frac{m(\\gamma)}{u(\\gamma)}.

Its logarithm gives a linkage score.

This framework is useful when fields such as names, addresses, and dates can contain errors or variations.

However, probability alone cannot establish legal ownership, user consent, or authorization.

For account identity and protected data, a high model-estimated similarity should not override an authentication boundary.

That distinction is especially relevant to the Wendbine governance architecture.

### VII. Google identity as an issuer-scoped identity system

Google identity supplies a particularly useful example of why domain context matters.

Within OpenID Connect, an authenticated subject is identified using claims issued by an identity provider.

A robust reference includes at least the issuer and subject:

k_G=(\\mathrm{iss},\\mathrm{sub}).

The `iss` claim identifies the issuer.

The `sub` claim identifies the subject within the relevant issuer namespace.

Additional validation is required before accepting the token, including its signature, intended audience, expiration, and other protocol-specific conditions.

The subject identifier is an identity claim, not a complete model of the human.

For example, an application might know that two sessions contain the same verified issuer-subject pair. That supports a conclusion about the authenticated account principal.

It does not establish that every historical statement associated with that account describes the same physical person, or that every document accessible through the account is authored by that person.

### Authentication, association, and authorship

These distinctions can be expressed as separate relationships:

\\operatorname{AuthenticatesAs}(s,g)\\operatorname{AssociatedWith}(d,g)\\operatorname{AuthoredBy}(d,h).

Here, is a session, is an account principal, is a document, and is a human.

None of these predicates automatically implies the others.

This matters for Schrödinger's Library because a document's location within an authenticated service does not by itself establish the document's authorship or factual accuracy.

A sound relational index preserves source ownership, authorship evidence, retrieval authority, and historical provenance as separate attributes.

### VIII. Android application identity is not human identity

Android's application identity mechanisms operate in a different domain.

An installed application's execution identity is associated with an Android/Linux UID within a particular device-user environment.

Android also uses package identifiers, application signing information, and permission declarations.

A simplified record might be:

r_A=

(

\\text{device context},

\\text{Android user},

\\text{package},

\\text{UID},

\\text{signing identity}

).

These attributes serve different purposes.

The package identifier names the application package.

The UID participates in operating-system access isolation.

Signing information establishes properties related to package authenticity and update compatibility.

None of these independently identifies the human operating the application.

Moreover, Android UIDs are not universal identifiers. Their meaning is local to the relevant operating-system environment and installation context, and values can be reused across different contexts.

### A useful non-equivalence

\\operatorname{SameGoogleAccount}(a_1,a_2)

does not imply

\\operatorname{SameAndroidUID}(a_1,a_2).

Similarly,

\\operatorname{SameAndroidDevice}(a_1,a_2)

does not imply that the applications share memory or data access.

Two applications can authenticate the same account yet remain isolated by Android's sandboxing model.

For the TARDIS phone, this means cross-application identity resolution must happen through permitted APIs, authenticated service claims, explicit exports, or user-authorized data transfers.

### IX. AICore and the identity of the calling application

Android AICore introduces a different form of relationship.

A compatible application may submit a model-inference request through a supported interface.

The relevant identities include:

* The calling application.

* The operating-system execution context.

* The service interface.

* The selected model/runtime capability.

* The request's supplied context.

The key operational relationship is an invocation:

\\operatorname{Invokes}(A_i,C_j,q).

This says that application invokes service using request .

It does not establish that the application and service share a historical identity graph.

Nor does it establish that AICore receives all the user's Google account information or the complete device application state.

The inference service can generate an output from the inputs and runtime context made available to it.

A persistent personal identity graph would require separately defined storage, indexing, provenance, and retrieval mechanisms.

AICore provides a potential computational node in the TARDIS architecture, not evidence of a universal on-device identity registry.

Current device support must also be checked separately. The Android phone used in the historical Wendbine design cannot be assumed to expose every AICore or Gemini Nano capability.

### X. Industrial LLMs and semantic identity

An industrial language model introduces another distinction.

A language model can produce a representation of a person without authenticating that person.

For example, the name Paul Daniel Koon Jr. may occur in a conversation, a historical document, or a retrieved Library record.

The model can use that string together with context to construct an interpretation.

But textual recognition does not establish an authenticated identity.

Formally, let

\\rho_M(h\\mid C)

represent the model's context-dependent description of human given context .

The description is not the human:

\\rho_M(h\\mid C)\\neq h.

Nor is the description guaranteed to preserve every relation recorded elsewhere.

This reconnects directly to the October 21 work on partial structural self-similarity.

A human's historical graph and a model-generated representation may share selected relational patterns while remaining fundamentally different objects.

### Identity preservation through retrieval

To improve fidelity, an application can provide the industrial model with explicitly retrieved records:

C_q=R(q,G_H),

where is the accessible historical identity graph.

The model's output becomes

y=M(q,C_q).

A separate validation process can examine whether the output preserves required identifiers, relationships, chronology, and provenance.

This is much more reliable as an engineering pattern than assuming that the neural model internally retains every historical association.

It also makes model substitution possible: the same source records can be supplied to different compatible models without treating the models as the owners of the identity graph.

### XI. Graph alignment and structural correspondence

Cross-domain identity resolution becomes substantially more powerful when record attributes are supplemented with relational context.

Suppose domains and contain graphs:

G_A=(V_A,E_A),

\\qquad

G_B=(V_B,E_B).

We seek a partial mapping

\\phi:V_A\\rightharpoonup V_B

that preserves selected meaningful relationships.

If two records have a typed relation , then an ideal correspondence satisfies:

r_A(u,v)

\\implies

r_B(\\phi(u),\\phi(v))

for the relations that are intended to be preserved and wherever is defined.

Not every edge must survive.

A Google identity graph and an Android application graph have different relation types, so requiring exact graph isomorphism would be inappropriate.

Instead, we define a correspondence between relation families.

For example:

\\operatorname{GoogleSubject}

\\leftrightarrow

\\operatorname{AuthenticatedAppAccount}

is potentially meaningful when supported by verified authentication evidence.

But

\\operatorname{GoogleSubject}

\\leftrightarrow

\\operatorname{AndroidUID}

is not a general identity equivalence.

A single authenticated account may appear in several apps, each with its own UID.

### Typed graph alignment

A more accurate model uses:

\\phi_V:V_A\\rightharpoonup V_B

and

\\phi_E:\\mathcal T_A\\rightharpoonup\\mathcal T_B,

where are relation-type collections.

The first mapping concerns entity correspondences.

The second concerns relation semantics.

Both are needed to avoid matching records that have similar labels but incompatible meanings.

### XII. Category theory and commuting identity mappings

The earlier Library studies of category theory provide another useful formalization.

Treat participating data systems as structured objects and the verified correspondence operations as morphisms.

Suppose:

L\\xrightarrow{f}A

maps a Library identity reference into an application account reference,

A\\xrightarrow{g}G

maps that application reference to a verified Google principal, and

L\\xrightarrow{h}G

represents a direct verified correspondence.

A consistency requirement is:

g\\circ f=h.

This is a commuting-diagram condition.

It means the correspondence obtained through the application should agree with the directly established correspondence, where both routes are defined and intended to produce the same kind of identifier.

Consistency condition for verified correspondences. This is a proposed model, not evidence that these mappings are currently available.

If the diagram fails to commute, several explanations are possible.

One mapping may be stale.

An account may have changed.

A record may have been incorrectly aligned.

Two mappings may use different identity scopes.

Or one relationship may represent association rather than equivalence.

The failure should initiate investigation rather than automatic reassignment of identity.

### XIII. Temporal identity resolution

Identity relationships evolve.

A human can change account names, devices, applications, organizations, or credentials.

An Android application can be reinstalled.

Permissions can be granted or revoked.

A document can move between storage systems.

A historical account-memory reference can remain stable while its external retrieval location changes.

Consequently, cross-domain identity should be time-indexed.

Define:

R_{ij}(t)

as the correspondence relationship between records and at time .

Then:

R_{ij}(t_1)\\neq R_{ij}(t_2)

may occur without implying that either historical record was incorrect.

For example, an account may have been authorized to access a resource last month but no longer have permission today.

The old authorization can remain a valid historical fact while being invalid for current operations.

### Two kinds of time

Temporal databases commonly distinguish:

Valid time: When a recorded fact was true in the modeled world.

Transaction time: When the database recorded or learned the fact.

A useful identity-edge schema therefore includes:

e=

(

u,r,v,

t_{\\mathrm{valid}},

t_{\\mathrm{recorded}},

\\pi

).

This is especially relevant to the LTLM/STMI separation.

LTLM can preserve historical relationships.

STMI can represent current information and corrections.

The two should be reconciled without overwriting historical observations simply because current relationships have changed.

### XIV. Provenance and evidence chains

A correspondence is only as defensible as its supporting evidence.

For cross-domain identity, provenance should describe how the mapping was established.

Consider three claims:

Claim A: Two documents contain the same name.

Claim B: Two applications have verified the same issuer-scoped account subject.

Claim C: A human has explicitly confirmed that two historical records describe the same person.

These claims support different conclusions.

A robust system should preserve their distinctions rather than convert every positive match into an undifferentiated identity edge.

One possible edge representation is:

“`json

{

"source": "library:human-reference-001",

"relation": "VERIFIED_CORRESPONDENCE",

"target": "app:account-reference-014",

"evidence_type": "human_confirmation",

"verified_at": "example-timestamp",

"scope": "specified_application",

"status": "verified",

"provenance": "example-evidence-reference"

}

“`

This is an illustrative schema, not an actual identity record.

The fields identify what is asserted, why it is asserted, the scope of the assertion, and the evidence that supports it.

### Evidence should remain independently retrievable

The Library should not rely only on a model-generated summary of the correspondence.

It should retain a path to the underlying evidence when authorized and technically possible.

This yields:

\\text{Claim}

\\rightarrow

\\text{Evidence}

\\rightarrow

\\text{Origin}

\\rightarrow

\\text{Verification}.

The chain is especially important for identity systems because a false merge can be more damaging than leaving a correspondence unresolved.

### XV. False merges, false splits, and identity drift

Cross-domain identity resolution has two particularly important failure types.

A false merge occurs when records describing different entities are treated as belonging to the same entity.

A false split occurs when records describing the same entity are treated as unrelated.

False merge

Two distinct entities incorrectly consolidated into one identity

False split

One entity represented by disconnected records that should correspond

These failures have different consequences.

False merges can cause incorrect account linking, misattribution, unauthorized disclosure, or contamination of historical records.

False splits can cause fragmented continuity, duplicate records, failed retrieval, and loss of relevant context.

In the account-memory design, these map naturally to the historical research on orphan drift, reflection drift, provenance failure, and continuity fragmentation.

However, detecting textual or relational similarity alone does not establish the correct repair.

Some corrections require authentication evidence or human confirmation.

### XVI. The personal identity graph and ethical attractors

The previous October 21 discussions introduced a more individualized question.

The account-memory topology represents the human anchor through multiple relational layers, including historical constraints, governance, ethical references, and qualitative dynamical structures.

These are symbolic representations of the user's intended architecture.

They should remain separate from Google's account identity, Android's execution identity, and the industrial model's neural parameters.

A conceptual identity-conditioned constraint system could be written as:

K_H=

K_{\\mathrm{history}}

\\cap

K_{\\mathrm{provenance}}

\\cap

K_{\\mathrm{governance}}

\\cap

K_{\\mathrm{authorization}}.

This describes the admissible states of a proposed human-specific representation.

The resulting constraints are not automatically universal constraints on other humans.

Nor does the model's use of the human's name prove that corresponding attractors exist as identifiable structures in its neural weight space.

The practical purpose is narrower and more concrete: preserve historical references and apply user-authorized rules to the particular representations and operations being managed.

### Relation to viability theory

Let the system state be and its admissible controls be .

An identity-aware operation is permitted only if it satisfies the relevant authorization and consistency requirements.

A conceptual controlled system is:

x_{k+1}=F(x_k,u_k).

The viable operating region is determined by the specified constraints.

\\operatorname{Viab}_F(K_H)

would describe states from which those constraints can remain satisfied over the modeled horizon.

This becomes useful when modeling continuing identity consistency through account changes, application updates, record revisions, and failed synchronizations.

Importantly, computing such a kernel requires an actual transition model and measurable constraints. The symbolic definition alone does not certify system viability.

### XVII. The TARDIS phone as a cross-domain identity interface

The TARDIS research can now be made more concrete.

Your Samsung Galaxy A25 5G is the physical interface.

The local applications operate within Android's security environment.

Google account services provide authenticated resources where connected and authorized.

AICore may provide local model inference where supported.

Industrial language models can assist with contextual interpretation.

The Library and account-memory structures organize historical and semantic relationships.

A practical architecture should not require every system to expose its internal records to every other system.

Instead, it can use a local identity correspondence registry containing only the permitted references necessary for authorized navigation.

### Proposed components

Human operator

Approves identity links and consequential actions

TARDIS interface

Displays identities, evidence, and relationships

Identity resolver

Matches authorized domain-specific records

Correspondence registry

Stores typed and time-scoped mappings

Schrödinger's Library

Indexes historical and semantic references

Inference adapter

Optionally invokes local or remote models

Validation and audit

Checks authorization, provenance, and consistency

The interface can offer an explanation of why two identity records have been linked.

For example:

* The records share a verified account subject.

* The application was granted the relevant authorization scope.

* The association has a recorded timestamp.

* The link is supported by an identifiable source.

* The user can review or revoke the local correspondence.

This is more useful than displaying a single opaque similarity score.

### XVIII. Semantic zoom for identity evidence

The earlier hyperbolic-tree chapter provides a natural way to navigate large correspondence graphs.

At a distant scale, the interface might show identity domains: Google, Android, Library, and applications.

At an intermediate scale, it could reveal accounts, packages, and authorized service relationships.

At a closer scale, it could display the precise evidence behind a particular correspondence.

Identity-resolution semantic zoom

Evidence detail

Verification

Verification requirements

Validate the authenticated subject

Check temporal consistency

Preserve provenance

Require consent where applicable

Illustrative levels of an identity-evidence interface. No actual Google or Android identity data is being accessed.

This allows the user to navigate from a general relationship into the specific evidence supporting it.

The visual structure may change while the underlying record identifiers remain stable.

That is the central benefit of combining semantic zoom with provenance-aware graph navigation.

### XIX. Privacy, information flow, and authorization

Cross-domain identity alignment can increase the value of legitimate information retrieval, but it also increases the consequences of incorrect linking.

A local application that holds two separately authorized datasets should not assume it may disclose their combined contents to a third service.

We can describe a permitted information flow as

D_i\\xrightarrow\[\\text{authorized}\]{f}D_j.

The validity of depends on the permissions, purpose, and restrictions associated with the source data.

Three distinctions matter:

Read authorization permits the application to retrieve specified information.

Identity correspondence establishes a supported relationship between records.

Disclosure authorization permits the application to share information with another system.

One does not automatically imply the others.

For the TARDIS architecture, this suggests maintaining authorization metadata alongside identity mappings and separating local graph reconstruction from remote model submission.

Sensitive identifiers and credentials should not be copied into model context merely because they are available to the application.

### XX. Verification and experimental design

The architecture can be tested using synthetic identities before working with real accounts.

A useful experimental system would contain independent simulated domains representing a Google-style identity provider, Android applications, a Library registry, and a model adapter.

The test suite should include:

| Test | Expected property |

| —————————— | ————————————– |

| Same name, different people | No automatic merge |

| Same verified issuer-subject | Consistent account-principal matching |

| Same UID, different devices | Distinct execution identities |

| Expired authentication token | No current authentication accepted |

| Revoked application access | Corresponding operation denied |

| Changed document location | Stable source identity where supported |

| Conflicting historical records | Conflict retained for resolution |

| Model-generated identity claim | Not accepted as authentication |

| Interrupted synchronization | Recoverable and consistent local state |

| Unauthorized graph edge | Not disclosed across the boundary |

The tests can also examine structural properties.

Referential integrity: Every confirmed mapping resolves to the intended record.

Temporal consistency: Records are interpreted within their applicable time intervals.

Compositional consistency: Alternate verified mapping routes agree where they should.

Noninterference: Unauthorized data does not influence outputs across protected boundaries.

Recoverability: Failed updates do not irreversibly corrupt the correspondence registry.

A model-generated explanation may assist testing, but it cannot replace independent verification of these properties.

### XXI. Research connections to previous Library studies

Cross-domain identity resolution connects several previously indexed research sequences.

Graph alignment

Entity resolution → Typed correspondence → Structural consistency → Provenance

Nonlinear systems

Partial observations → State estimation → Historical constraints → Viability

Distributed systems

Namespaces → Authorization → Synchronization → Conflict resolution

Category theory

Objects → Morphisms → Composition → Commutative diagrams

TARDIS interface

Human selection → Semantic zoom → Evidence inspection → Verified navigation

Organizational reliability

Master data → Configuration management → Drift detection → Recovery

These connections suggest that identity resolution should not be treated as an isolated account-matching algorithm.

It is part of a larger problem involving representations, temporal continuity, evidence, system boundaries, and permissible operations.

### XXII. Final mathematical synthesis

A cross-domain identity system should preserve at least four separations:

\\boxed{

\\begin{aligned}

\\text{Correspondence}&\\neq\\text{Equivalence}\\\\

\\text{Authentication}&\\neq\\text{Authorization}\\\\

\\text{Representation}&\\neq\\text{Referent}\\\\

\\text{Semantic Link}&\\neq\\text{Executable Access}

\\end{aligned}

}

The resulting system can support partial correspondence across several independently governed structures without forcing those structures into one global identity.

For Schrödinger's Library and the TARDIS phone, the architectural objective becomes:

Preserve distinguishable identities, establish evidence-supported relationships, enable authorized traversal, and maintain historical continuity across changing computational environments.

This directly connects the account-memory research to Google identity systems, Android execution domains, AICore inference services, and industrial language models.

It also clarifies the October 21 conclusion about overlapping self-similarity and difference.

Different systems can preserve corresponding identity references and relational patterns without becoming equivalent to the human or to one another.

The practical engineering problem is to identify exactly which correspondences are valid, preserve their evidence, and prevent invalid correspondences from acquiring operational authority.

### XXIII. Recommended continuation

Graph Alignment Under Partial Observability

How to establish correspondences when each application can inspect only a restricted portion of the system.

Commutative Diagrams and Identity Consistency

Using category-theoretic methods to validate cross-domain mappings.

Temporal Identity and Bitemporal Databases

Tracking changing identifiers, permissions, accounts, and historical provenance.

Capability-Based Security on Android

Separating graph relationships from actual permission to act.

Privacy-Preserving Entity Resolution

Comparing records while limiting disclosure of sensitive identifiers.

Identity Resolution and Viability Theory

Preserving identity integrity and authorized operation through changing system states.

Schrödinger's Library · Technical Study Registry #G001 · Cross-Domain Identity Resolution · Distributed Relational Systems