Schema-Contracted Synthetic Execution Graph

A Schema-Contracted Synthetic Execution Graph (SCSEG) is a computation model where software is not executed as procedural code, but constructed and evaluated as a schema-constrained graph of typed transformations (nodes) and relations (edges). Execution is not run—it is synthesized through traversal, projection, and partial evaluation of this graph, with AI acting as a structural compiler that completes missing morphisms.

WHY THIS MATTERS

Traditional software hides meaning inside frameworks, call stacks, and files. SCSEG replaces that with a single explicit substrate where code, data, logs, and reasoning are unified as one graph.

This matters because it:

• Eliminates hidden control flow in frameworks and dependency trees
• Makes execution queryable as structure, not runtime behavior
• Turns debugging into causal graph traversal instead of log inspection
• Allows AI to reason over systems as topology rather than text
• Enables execution before implementation via synthetic execution (simulation of intent)

In effect, the system becomes less like “software running” and more like a persistent, queryable reasoning machine with executable edges.

This is a draft review map for task-specific detail pages. Treat it as speculative context routing, not as validated research.

NODES

• /concepts/schema-contracted-synthetic-execution-graph/details/capability-boundaries.txt :: Capability Nodes and External Effect Boundaries -- Defines how databases, APIs, tools, devices, and human processes are exposed as substitutable but constrained executors
• /concepts/schema-contracted-synthetic-execution-graph/details/contract-evolution.txt :: Schema Contract Evolution and Semantic Drift -- Explains how contract changes propagate through stored graphs, morphisms, traces, and prior conclusions
• /concepts/schema-contracted-synthetic-execution-graph/details/failure-as-state.txt :: Failure as Persistent Graph State -- Explains how unsuccessful, blocked, contradictory, or unsafe transitions become queryable graph entities
• /concepts/schema-contracted-synthetic-execution-graph/details/morphism-admission.txt :: Admission of AI-Synthesized Morphisms -- Defines how generated transformations move from proposal to bounded execution authority
• /concepts/schema-contracted-synthetic-execution-graph/details/ordering-effects-and-cycles.txt :: Ordering, Effects, Concurrency, and Cycles -- Specifies the semantics required when several valid traversals can produce different material outcomes
• /concepts/schema-contracted-synthetic-execution-graph/details/runtime-ir-or-model.txt :: Native Runtime, Intermediate Representation, or Reasoning Model -- Separates three implementation roles that are often conflated in claims about executable graphs
• /concepts/schema-contracted-synthetic-execution-graph/details/synthetic-execution-semantics.txt :: Synthetic Execution Semantics -- Separates the different non-material and material evaluation modes that SCSEG may combine
• /concepts/schema-contracted-synthetic-execution-graph/details/topology-slice-construction.txt :: Topology Slice Construction -- Defines how a minimal task-relevant subgraph is selected without hiding dependencies that could change the answer
• /concepts/schema-contracted-synthetic-execution-graph/details/traceable-closure.txt :: Traceable Closure and Result Justification -- Defines the compact causal record needed to justify a result without loading exhaustive provenance
• /concepts/schema-contracted-synthetic-execution-graph/details/validity-before-execution.txt :: Graph Validity Before Execution -- Defines the conditions under which a candidate graph, path, or morphism is eligible for evaluation

EDGES

• capability-boundaries -> failure-as-state (application): Capability refusal, degradation, timeout, and partial completion become typed failure or constraint states
• capability-boundaries -> ordering-effects-and-cycles (prerequisite): Capability contracts supply the effect, consistency, retry, authority, and compensation semantics needed by the planner
• contract-evolution -> failure-as-state (adjacent): Contract changes may create or resolve failures, while recurring failures can reveal stale or inaccurate contracts
• contract-evolution -> traceable-closure (prerequisite): Historical result justification requires the exact contract versions under which paths were admitted
• morphism-admission -> contract-evolution (prerequisite): Admitted morphisms must be rechecked when the contracts that justified them change
• morphism-admission -> failure-as-state (adjacent): Admission tests create counterexample and violation states, while observed failures can demote a promoted morphism
• runtime-ir-or-model -> capability-boundaries (application): Hybrid SCSEG architectures delegate bounded material work through capability nodes
• runtime-ir-or-model -> ordering-effects-and-cycles (contradiction): A native-runtime claim requires complete scheduling and effect semantics, while a reasoning-only role can defer material execution
• runtime-ir-or-model -> traceable-closure (refines): Whether execution is native or delegated changes what evidence must be retained to justify the result
• synthetic-execution-semantics -> traceable-closure (prerequisite): A result justification must distinguish symbolic, hypothetical, predictive, partial, and material evidence
• topology-slice-construction -> failure-as-state (application): Causal debugging constructs a failure-centered slice instead of loading the whole execution graph
• topology-slice-construction -> traceable-closure (prerequisite): Traceable closure depends on a bounded slice that preserves decisive causes and visible external obligations
• validity-before-execution -> morphism-admission (application): Morphism admission applies graph-validity rules to AI-generated transformations
• validity-before-execution -> ordering-effects-and-cycles (refines): Structural validity identifies legal topology, while ordering and effect semantics determine whether material execution remains correct
• validity-before-execution -> synthetic-execution-semantics (prerequisite): Evaluation mode matters only after the graph's admissibility and unresolved obligations are known

Operating Logic

At its core, SCSEG replaces execution flow with graph validity and traversal dynamics:

1. System definition

• Everything is expressed as nodes (functions, schemas, data, logs)
• All relationships are explicit typed edges

1. Schema contraction

• A global schema layer constrains what graphs are valid
• Invalid compositions cannot exist structurally

1. Execution synthesis

• Instead of running code, the system:
• identifies valid paths through the graph
• simulates transformations via schema contracts
• optionally materializes leaf-node execution

1. AI-driven completion

• AI fills missing edges (unknown transformations)
• Generates Lisp/Cypher/SQL projections of intent
• Acts as a compiler over incomplete graph structure

1. Synthetic execution

• Graph is “run” by:
• traversal
• partial evaluation
• hypothetical propagation of data shapes
• Results can be inferred before real execution

1. Traceable closure

• Every output is linked back to:
• originating node
• traversal path
• schema contract chain

The system behaves less like a program and more like a self-updating semantic topology that can be executed, queried, and simulated simultaneously.

Pattern Language

Replace file/code hierarchy with typed graph topology.

AI asks: “what happens if user submits invalid schema?”.

Boundary Conditions

Key boundaries include Over-complexification risk, Semantic drift in schema contracts, AI hallucinated morphisms, Performance vs expressivity tradeoff, Underspecified execution semantics, Tooling dependency paradox, and Open question.

Graph-first architecture

• Replace file/code hierarchy with typed graph topology
• Code becomes node-local transformation metadata

Lisp as structural IR

• Lisp represents local transformation intent
• Not primary language, but intermediate semantic form of graph projections

Cypher/SQL as reasoning interfaces

• Cypher → structural navigation
• SQL → state/materialization layer
• Both are projections of the same graph

Schema-as-execution constraint

• Schema is not validation—it defines possible reality
• Execution paths are only those allowed by schema consistency

Synthetic execution layer

• Simulate execution via:
• path traversal
• schema propagation
• embedding similarity inference

Leaf-node execution model

• Only terminal nodes perform real IO or computation
• Everything else remains symbolic until required

Capability isolation (GATE layer)

• External systems wrapped into schema-bound adapters
• No direct dependency coupling inside core graph

AI traversal model

• AI operates on:
• neighborhoods
• influence cones
• reachable subgraphs
• Not file trees or raw code

Failure as graph state

• Errors are nodes/edges, not exceptions
• Failures persist and become queryable structure

EXAMPLES AND SCENARIOS

1. Synthetic execution without runtime

• AI asks: “what happens if user submits invalid schema?”
• System simulates traversal and returns failure propagation path

2. Graph-based debugging

• Query: “show all nodes influencing this output”
• Result: causal cone of transformations, not logs

3. Capability wrapping

• External Neo4j driver is not imported
• Instead represented as:
• (capability-node :neo4j-bolt :schema-bound-adapter)

4. Execution as traversal

• Instead of calling functions:
• system walks input → morphism → output → next morphism

5. AI synthesis of missing logic

• Graph lacks transformation between schema A and B
• AI generates Lisp morphism and inserts it as node

Node

• Typed unit of meaning or transformation
• Can be function, schema, dataset, hypothesis, or capability wrapper
• Always carries schema + provenance + optional embedding signature

Edge

• Typed relationship between nodes
• Represents transformation, dependency, causality, similarity, or execution flow
• Not structural glue—an executable constraint

Schema Contract

• Hard constraint system defining what nodes/edges can exist and connect
• Functions as ontology + type system + execution gate
• Validates graph composition before execution is even considered

Morphism

• Typed transformation: (input_schema → output_schema + rule + lineage)
• Core abstraction of computation in the system

Synthetic Execution

• Execution via simulation, inference, or partial evaluation of graph paths
• Can operate without actual runtime invocation
• Supports deferred, hypothetical, or AI-predicted execution traces

Execution Graph

• The runtime artifact of valid traversal paths
• Not stored as a program, but reconstructed from graph state

Topology Slice

• Minimal subgraph required for reasoning or execution
• Replaces “repo context” or “dependency tree loading”

Capability Node (GATE layer)

• External systems wrapped into schema-bound interfaces
• Neo4j, SQL, APIs, and drivers are treated as replaceable executors, not core logic

AI Compiler / Traversal Agent

• Navigates graph structure
• Synthesizes missing morphisms
• Selects execution paths via schema + embedding + relational inference

HOW THE CONCEPT WORKS

At its core, SCSEG replaces execution flow with graph validity and traversal dynamics:

1. System definition

• Everything is expressed as nodes (functions, schemas, data, logs)
• All relationships are explicit typed edges

1. Schema contraction

• A global schema layer constrains what graphs are valid
• Invalid compositions cannot exist structurally

1. Execution synthesis

• Instead of running code, the system:
• identifies valid paths through the graph
• simulates transformations via schema contracts
• optionally materializes leaf-node execution

1. AI-driven completion

• AI fills missing edges (unknown transformations)
• Generates Lisp/Cypher/SQL projections of intent
• Acts as a compiler over incomplete graph structure

1. Synthetic execution

• Graph is “run” by:
• traversal
• partial evaluation
• hypothetical propagation of data shapes
• Results can be inferred before real execution

1. Traceable closure

• Every output is linked back to:
• originating node
• traversal path
• schema contract chain

The system behaves less like a program and more like a self-updating semantic topology that can be executed, queried, and simulated simultaneously.

• Graph-native AI development environment
• Codebase becomes navigable execution graph instead of repo

• Synthetic execution debugger
• Debug by querying causal influence cones instead of logs

• AI compiler for heterogeneous systems
• Converts intent graphs into SQL/Cypher/Lisp/runtime execution plans

• Schema-contract runtime platform
• Replaces APIs + microservices with graph-constrained execution mesh

• Capability isolation framework
• Wraps all external dependencies into schema-bound execution nodes

• Self-documenting execution graph database
• Every system state is queryable as history + structure + intent

• AI-driven system refactoring engine
• Detects isomorphic subgraphs and merges transformations automatically

• Graph rewriting systems
• Dataflow programming languages
• Morphism-based computation
• Datalog / Prolog execution models
• Event-sourced architectures
• Knowledge graphs as runtime systems
• Schema-driven programming
• AI-native compilers
• Cypher / Neo4j execution semantics
• Lisp as AST-native computation
• Partial evaluation graphs
• Capability-based software architecture
• Topological debugging models

• Formal semantics of schema-contracted graph execution
• Graph rewriting systems as computation models
• AI-driven morphism synthesis (link prediction as compiler behavior)
• Synthetic execution correctness guarantees
• Embedding + schema hybrid reasoning systems
• Topology-based debugging (causal influence cones)
• Capability isolation as anti-dependency architecture
• Partial evaluation over persistent graphs
• Graph-native programming languages beyond Lisp
• Deterministic vs probabilistic execution traversal models

Over-complexification risk

• Graph abstraction can become heavier than the systems it replaces

Semantic drift in schema contracts

• If schema evolution is not tightly controlled, graph consistency degrades

AI hallucinated morphisms

• Synthesized transformations may not preserve correctness constraints

Performance vs expressivity tradeoff

• Continuous graph traversal may be computationally expensive at scale

Underspecified execution semantics

• Ordering, concurrency, and determinism require formal grounding

Tooling dependency paradox

• Removing frameworks may reintroduce complexity via custom infrastructure

Open question

• Is SCSEG a practical execution architecture, or primarily a cognition/representation model for AI systems?

• A civilization where software is not written but grown as schema-constrained ecosystems
• AI entities that do not execute programs, but walk possible futures in execution graphs
• Debugging as forensic archaeology of causal topology
• “Lisp monks” maintaining transformation purity in semantic graph temples
• Systems that can be replayed like memories by traversing graph lineage
• Reality-like computation where failures become persistent “injury nodes” in the system’s cognitive body

EXAMPLES AND SCENARIOS

1. Synthetic execution without runtime

• AI asks: “what happens if user submits invalid schema?”
• System simulates traversal and returns failure propagation path

2. Graph-based debugging

• Query: “show all nodes influencing this output”
• Result: causal cone of transformations, not logs

3. Capability wrapping

• External Neo4j driver is not imported
• Instead represented as:
• (capability-node :neo4j-bolt :schema-bound-adapter)

4. Execution as traversal

• Instead of calling functions:
• system walks input → morphism → output → next morphism

5. AI synthesis of missing logic

• Graph lacks transformation between schema A and B
• AI generates Lisp morphism and inserts it as node

Capability Nodes and External Effect Boundaries

SUMMARY

Defines how databases, APIs, tools, devices, and human processes are exposed as substitutable but constrained executors.

DETAIL

A capability node is the boundary where graph reasoning reaches an executor able to observe or change external state. Its contract should declare accepted inputs, returned outputs, side effects, authority requirements, latency, cost, reliability, consistency, privacy constraints, jurisdiction, and compensation behavior.

The graph depends on this contract rather than on a specific library, driver, vendor, or worker. Multiple implementations may satisfy one abstract capability. Substitution is safe only when the properties relevant to surrounding paths are preserved. Equal input and output shapes are insufficient when alternatives differ in transactionality, ordering, data residency, failure behavior, consent, or workload impact.

Capability nodes should expose refusal, degradation, and partial completion as typed outcomes. A service may be unavailable. A human operator may decline. A health or workload limit may prohibit assignment. A financial or governance rule may require additional approval. Making these conditions visible allows the planner to choose a different route rather than hiding the constraint inside adapter code.

Adapters translate between local executor semantics and graph contracts. They may normalize names, convert representations, isolate failures, or insert compatibility transformations. This reduces coupling only when adapter responsibilities remain bounded. If every executor requires a large custom layer for retries, policy, observability, migration, and semantic repair, the architecture can recreate the framework complexity it intended to remove.

The optimistic case is a resilient execution mesh in which implementations can change without rewriting the graph's conceptual structure. The contract must therefore include behavioral and institutional semantics, not only type compatibility.

WHY THIS EXISTS

Supports integration design, capability security, executor substitution, labor and governance constraints, and evaluation of the anti-dependency claim.

SOURCE CONTEXT POINTERS

• /concepts/schema-contracted-synthetic-execution-graph/PRIMITIVES.txt
• /concepts/schema-contracted-synthetic-execution-graph/PATTERNS.txt
• /concepts/schema-contracted-synthetic-execution-graph/PRODUCT_BUSINESS.txt
• /concepts/schema-contracted-synthetic-execution-graph/RISKS_AND_CONTRADICTIONS.txt

EVIDENCE QUESTIONS

• No evidence query recorded

Schema Contract Evolution and Semantic Drift

SUMMARY

Explains how contract changes propagate through stored graphs, morphisms, traces, and prior conclusions.

DETAIL

A schema contract defines executable reality, so revising it changes more than representation. A new field, narrower range, altered meaning, changed authority condition, or revised effect declaration can invalidate paths that were previously admissible.

Each contract revision should be represented as a distinct version linked to its predecessor by an explicit relation such as refinement, relaxation, contradiction, deprecation, or reinterpretation. Existing nodes and morphisms remain attached to the versions under which they were admitted. They do not silently inherit the newest contract.

Compatibility must be evaluated across multiple dimensions. Structural compatibility asks whether shapes compose. Semantic compatibility asks whether fields and outcomes retain the same meaning. Behavioral compatibility asks whether effects and failure modes remain substitutable. Policy compatibility asks whether authority, consent, allocation, privacy, or jurisdiction rules changed. Temporal compatibility asks whether historical conclusions still hold under the new interpretation.

Migration morphisms translate graph state when compatibility can be established. When meaning is ambiguous, the system should retain competing mappings or require explicit adjudication rather than allowing an AI to collapse the difference. Generated shims can preserve older interfaces temporarily, but the graph should record that they are compatibility devices rather than native semantics.

Drift may also occur between a declared contract and observed executor behavior. Repeated violations can indicate a faulty capability, a stale contract, or a mistaken interpretation of observations. Those alternatives should remain explicit hypotheses until resolved.

WHY THIS EXISTS

Supports long-lived graph maintenance, schema migration, historical trace interpretation, compatibility analysis, and prevention of silent semantic corruption.

SOURCE CONTEXT POINTERS

• /concepts/schema-contracted-synthetic-execution-graph/PRIMITIVES.txt
• /concepts/schema-contracted-synthetic-execution-graph/RESEARCH_DIRECTIONS.txt
• /concepts/schema-contracted-synthetic-execution-graph/RISKS_AND_CONTRADICTIONS.txt

EVIDENCE QUESTIONS

• No evidence query recorded

Failure as Persistent Graph State

SUMMARY

Explains how unsuccessful, blocked, contradictory, or unsafe transitions become queryable graph entities.

DETAIL

A failure node records an unsuccessful or prohibited transition as durable graph state. It links the attempted morphism, relevant inputs, violated contract, evaluation mode, observed symptoms, affected outputs, recovery attempts, and downstream consequences.

Failure persistence changes debugging from log reconstruction into graph traversal. A model can query which outputs depend on a failed edge, which contract revisions produce the same violation, which capability repeatedly degrades, or which compensating path succeeded under similar conditions. Recurrent failures can be grouped into structural families without erasing their individual contexts.

Observation and diagnosis must remain separate. An observation may be a timeout, rejected contract, missing edge, inconsistent value, health-limit signal, or unavailable capability. A diagnosis proposes why that observation occurred. Diagnostic edges may later be refined or contradicted, while the underlying observation remains stable. This prevents an early AI explanation from becoming indistinguishable from recorded fact.

A failure can also be synthetic. Symbolic or hypothetical evaluation may derive a possible violation before any material action occurs. Such a node should be marked as predicted rather than observed and linked to the assumptions under which it follows.

Persistent failure topology creates both benefits and risks. It can improve safety, workload allocation, resilience, and collective learning by making recurring system stress visible. It can also expose sensitive information or become a mechanism for worker surveillance. Retention limits, access controls, redaction, consent rules, and governance should be represented in the failure schema itself.

WHY THIS EXISTS

Supports causal debugging, time-travel analysis, recovery planning, distinction between evidence and interpretation, and governance of operational history.

SOURCE CONTEXT POINTERS

• /concepts/schema-contracted-synthetic-execution-graph/PATTERNS.txt
• /concepts/schema-contracted-synthetic-execution-graph/PRODUCT_BUSINESS.txt
• /concepts/schema-contracted-synthetic-execution-graph/RISKS_AND_CONTRADICTIONS.txt

EVIDENCE QUESTIONS

• No evidence query recorded

Admission of AI-Synthesized Morphisms

SUMMARY

Defines how generated transformations move from proposal to bounded execution authority.

DETAIL

An AI-synthesized morphism begins as a proposal, not as executable truth. Its minimum structure includes an input contract, output contract, transformation rule, declared effects, preconditions, postconditions, possible failures, authority requirements, and the assumptions that motivated its creation.

Admission proceeds in stages. Proposal records the candidate without placing it on an executable path. Structural checking verifies schema compatibility and graph invariants. Obligation generation identifies claims that cannot be established from topology alone. Sandboxed evaluation tests examples, counterexamples, invariants, and metamorphic properties. Differential evaluation compares the candidate with existing implementations or alternate routes where those exist. Policy evaluation checks whether the edge crosses restricted data, financial, labor, safety, governance, or capability boundaries. Promotion grants a bounded status under specified contracts and environments.

Promotion should be scoped rather than universal. A morphism may be valid only for a subset of an input schema, only under a particular contract version, only in a simulation environment, or only when a human or institutional authority approves the transition.

Admission is reversible. New counterexamples, changed contracts, capability drift, or observed side effects can demote a morphism to provisional or invalid status. Existing traces must continue to identify which version and admission state were used.

The systemic optimistic case is that AI can expose useful transformations earlier, reduce repetitive implementation work, and continuously repair graph gaps. The safety condition is that generation and authority remain separate. The model proposes structure; contracts, tests, observed behavior, and governance determine whether the structure may act.

WHY THIS EXISTS

Supports AI compiler design, validation pipelines, governance, rollback, and prevention of hallucinated transformations entering real execution.

SOURCE CONTEXT POINTERS

• /concepts/schema-contracted-synthetic-execution-graph/DEEP.txt
• /concepts/schema-contracted-synthetic-execution-graph/PRIMITIVES.txt
• /concepts/schema-contracted-synthetic-execution-graph/PATTERNS.txt
• /concepts/schema-contracted-synthetic-execution-graph/RISKS_AND_CONTRADICTIONS.txt

EVIDENCE QUESTIONS

• No evidence query recorded

Ordering, Effects, Concurrency, and Cycles

SUMMARY

Specifies the semantics required when several valid traversals can produce different material outcomes.

DETAIL

Schema validity alone does not determine execution order. Two paths may both be structurally admissible while producing different outcomes because they read mutable state, write overlapping regions, invoke external systems, consume scarce resources, or depend on time.

Pure morphisms can be reordered, cached, fused, or speculatively evaluated when their contracts guarantee referential transparency. Stateful morphisms require declared read and write regions, version expectations, or transactional boundaries. External effects require semantics for idempotency, retries, timeouts, compensation, cancellation, and authority. Concurrent branches require conflict detection and an explicit join rule.

SCSEG should distinguish three forms of determinism. Structural determinism means the same graph state yields the same candidate paths. Plan determinism means the same path-selection and scheduling rules yield the same execution plan. Result determinism means different permitted schedules still produce equivalent outputs. A system may provide one guarantee without the others.

Cycles cannot remain semantically ambiguous. A cycle may mean fixed-point computation, bounded iteration, feedback control, stream processing, recurrence over time, or an invalid dependency loop. The cycle contract must declare its interpretation, termination or convergence condition, state carried between iterations, and behavior when convergence fails.

An immutable or append-only graph can reduce write conflicts by representing each state transition as a new node rather than mutating shared state. It does not remove all concurrency problems: external capabilities, resource reservations, and conflicting interpretations may still require coordination.

Traversal discovers what is structurally reachable. An effect-aware planner decides what may execute, in which order, with which isolation, and how partial failure is contained.

WHY THIS EXISTS

Supports runtime feasibility analysis, scheduling, concurrency control, fixed-point reasoning, and separation of graph validity from material execution correctness.

SOURCE CONTEXT POINTERS

• /concepts/schema-contracted-synthetic-execution-graph/DEEP.txt
• /concepts/schema-contracted-synthetic-execution-graph/PATTERNS.txt
• /concepts/schema-contracted-synthetic-execution-graph/RISKS_AND_CONTRADICTIONS.txt

EVIDENCE QUESTIONS

• No evidence query recorded

Native Runtime, Intermediate Representation, or Reasoning Model

SUMMARY

Separates three implementation roles that are often conflated in claims about executable graphs.

DETAIL

SCSEG can serve three distinct architectural roles.

As a native runtime, the graph schedules transformations, manages state, resolves concurrency, invokes capabilities, and records material results. This role requires complete semantics for effects, recovery, ordering, cycles, resource control, and performance.

As an intermediate representation, the graph remains authoritative for contracts and topology but compiles validated regions into SQL, dataflow plans, workflows, service calls, or conventional code. This role requires semantics-preserving projection and a clear account of which guarantees the target executor preserves, weakens, or adds.

As a reasoning model, the graph represents an existing system for analysis, planning, debugging, synthesis, and simulation while execution remains elsewhere. This role can tolerate more abstraction, but it must accurately capture hidden state, effects, and control flow or its conclusions will be misleading.

A hybrid architecture is likely to be the most practical. The graph can own contracts, path selection, causal traces, policy, and result justification while specialized leaf executors perform bounded material work. Frequently used graph regions may be fused or compiled when traversal overhead becomes significant, while retaining their original semantic decomposition for explanation and repair.

The central tradeoff is between conceptual unification and operational specialization. Making every operation graph-native can create latency and infrastructure complexity. Reducing the graph to documentation forfeits executable guarantees. The useful middle ground is an authoritative semantic topology with explicit compilation and capability boundaries.

WHY THIS EXISTS

Supports product evaluation, architecture selection, feasibility analysis, and separation of representational benefits from runtime claims.

SOURCE CONTEXT POINTERS

• /concepts/schema-contracted-synthetic-execution-graph/PRODUCT_BUSINESS.txt
• /concepts/schema-contracted-synthetic-execution-graph/RESEARCH_DIRECTIONS.txt
• /concepts/schema-contracted-synthetic-execution-graph/RISKS_AND_CONTRADICTIONS.txt

EVIDENCE QUESTIONS

• No evidence query recorded

Synthetic Execution Semantics

SUMMARY

Separates the different non-material and material evaluation modes that SCSEG may combine.

DETAIL

Synthetic execution is a family of evaluation modes rather than a single operation.

Schema propagation carries types, value ranges, effect declarations, and constraints through a path without concrete values. Symbolic evaluation carries expressions and predicates instead of fully materialized data. Partial evaluation resolves graph regions whose inputs are already known and leaves a residual graph for later execution. Hypothetical evaluation introduces explicit assumptions and derives their consequences. Predictive evaluation estimates results or path behavior using learned models. Material evaluation invokes a concrete transformation or external capability and records an observed result.

These modes do not provide equivalent evidence. A predicted result is not an observed result. A schema-compatible output is not proof that a transformation preserves meaning. A symbolic failure is conclusive only when the symbolic model includes every relevant effect and state dependency. A partially evaluated region preserves behavior only under assumptions about purity, ordering, and environmental stability.

Every derived value or branch decision should therefore retain its evaluation mode. Assumptions should remain explicit graph state rather than disappearing into a narrative trace. When a path changes mode, the transition should be visible: for example, schema propagation may identify an admissible route, an AI may propose a missing morphism, symbolic checks may discharge some obligations, and a leaf capability may materialize only the unresolved remainder.

Synthetic execution is most credible when it narrows uncertainty rather than pretending to eliminate runtime. Its purpose is to determine what can already be concluded, what remains hypothetical, and which smallest concrete actions are still necessary.

WHY THIS EXISTS

Supports simulation, planning, trustworthy result interpretation, partial execution, and correct separation of inference from observation.

SOURCE CONTEXT POINTERS

• /concepts/schema-contracted-synthetic-execution-graph/DEEP.txt
• /concepts/schema-contracted-synthetic-execution-graph/PRIMITIVES.txt
• /concepts/schema-contracted-synthetic-execution-graph/PATTERNS.txt

EVIDENCE QUESTIONS

• No evidence query recorded

Topology Slice Construction

SUMMARY

Defines how a minimal task-relevant subgraph is selected without hiding dependencies that could change the answer.

DETAIL

A topology slice is the bounded subgraph needed to answer a specific question, synthesize a plan, diagnose a failure, or perform an execution step. It replaces repository-scale context loading with relation-aware extraction.

Slice construction begins from an anchor such as an output, failure, contract, capability request, or natural-language intent. Expansion follows edge types relevant to the task. Debugging follows causal, transformation, and dependency edges. Contract analysis follows refinement, contradiction, substitution, and version edges. Planning adds capability, resource, authority, and effect edges. Impact analysis follows downstream consumers and alternate paths.

A useful slice contains more than nearby nodes. It includes the contracts needed to interpret them, unresolved obligations, external effect boundaries, relevant prior failures, and competing routes whose existence could alter the conclusion. Expansion stops when additional graph state cannot change the requested result within the declared decision boundary.

The main risk is false minimality. A locally coherent slice may omit a global invariant, mutable state, temporal condition, authority restriction, or shared resource conflict. Closure checks must therefore examine every relation that leaves the slice. An external dependency may be excluded only when it is irrelevant, summarized by an explicit boundary contract, or preserved as an unresolved obligation.

The slice should also retain inclusion rationales. A consuming AI benefits from knowing that one node was included as a prerequisite, another as a possible contradiction, and another because it controls an external effect. This allows iterative expansion without restarting retrieval from the full graph.

WHY THIS EXISTS

Supports bounded AI context retrieval, causal debugging, impact analysis, and execution planning without loading the entire concept or system.

SOURCE CONTEXT POINTERS

• /concepts/schema-contracted-synthetic-execution-graph/PRIMITIVES.txt
• /concepts/schema-contracted-synthetic-execution-graph/PATTERNS.txt
• /concepts/schema-contracted-synthetic-execution-graph/DEEP.txt

EVIDENCE QUESTIONS

• No evidence query recorded

Traceable Closure and Result Justification

SUMMARY

Defines the compact causal record needed to justify a result without loading exhaustive provenance.

DETAIL

Traceable closure is reached when a result can be connected to enough graph structure to justify it for a declared purpose. The closure includes the selected path or path set, contract versions, evaluation modes, assumptions, obligations, synthesized morphisms, material observations, capability invocations, and relevant contradictions or failures.

Closure is purpose-dependent. A design exploration may accept hypothetical branches and unresolved obligations. A safety-critical act, financial transfer, or irreversible external change may require observed capability results, explicit authority, and fully discharged obligations.

A closure record should preserve alternatives when path selection was not unique. If one route was chosen because it was cheaper, faster, healthier, less invasive, more resilient, or more policy-compliant, that selection criterion belongs in the justification. Otherwise a future AI may mistake the chosen plan for the only valid plan.

The explanation boundary should be compact. Exhaustive provenance can be larger than the execution itself and may obscure the decisive causes. A useful closure is a causal spine composed of locally checkable transformations, with expandable links to deeper context. Inputs, contracts, branch decisions, transformations, and outputs form a structural witness whose local claims can be inspected individually.

Unresolved external dependencies must remain visible. A closure is not complete merely because the selected subgraph is internally connected. It is complete only when every omitted dependency is irrelevant, represented by a trusted boundary contract, or retained as an explicit assumption or obligation.

WHY THIS EXISTS

Supports auditing, explanation, result verification, compact AI context loading, and distinction between useful justification and provenance dumping.

SOURCE CONTEXT POINTERS

• /concepts/schema-contracted-synthetic-execution-graph/DEEP.txt
• /concepts/schema-contracted-synthetic-execution-graph/PRIMITIVES.txt
• /concepts/schema-contracted-synthetic-execution-graph/PATTERNS.txt

EVIDENCE QUESTIONS

• No evidence query recorded

Graph Validity Before Execution

SUMMARY

Defines the conditions under which a candidate graph, path, or morphism is eligible for evaluation.

DETAIL

Execution eligibility is determined before material computation begins. Every node declares the structures it can receive and produce, the effects it may cause, and the obligations that must hold around it. Every edge asserts that a source result can lawfully become a target input through a named transformation. A candidate execution path is admissible only when those local assertions compose into a coherent whole.

Validity has several levels. Local validity concerns one node-edge-node composition. Path validity concerns whether invariants survive a sequence of transformations. Graph validity concerns constraints that span multiple paths, such as authority boundaries, uniqueness rules, resource limits, forbidden cycles, or mutually exclusive states.

A path need not be simply valid or invalid. It may be conditionally valid when some obligations remain unresolved. An obligation can require a value, proof, permission, health signal, resource reservation, or external confirmation. Conditional validity allows incomplete structure to remain inspectable without granting it execution authority.

SCSEG should not assume that every invalid composition is literally impossible to store. Retaining rejected or contradictory structures can be useful for diagnosis, planning, and comparison. The stronger rule is that invalid structure cannot enter the executable frontier. The graph may represent impossibility, but the execution planner must not confuse representation with admission.

This separation matters for AI synthesis. A generated edge may be topologically plausible while still violating semantic, behavioral, effect, or policy constraints. Schema contraction therefore acts as an execution gate, not merely as data-shape validation.

WHY THIS EXISTS

Supports architecture review, compiler design, contract checking, safe AI synthesis, and analysis of what schema contraction must guarantee.

SOURCE CONTEXT POINTERS

• /concepts/schema-contracted-synthetic-execution-graph/DEEP.txt
• /concepts/schema-contracted-synthetic-execution-graph/PRIMITIVES.txt
• /concepts/schema-contracted-synthetic-execution-graph/RISKS_AND_CONTRADICTIONS.txt

EVIDENCE QUESTIONS

• No evidence query recorded