SIL Research Agenda & Demonstration Plan (Year 1)
1. Purpose of the Research Agenda
This document defines SIL’s Year 1 research direction, success criteria, and demonstration goals across all layers of the Semantic Operating System. It is a planning and direction document intended to guide research focus, integration sequencing, and evaluation—not to serve as an implementation specification.
2. Year 1 Research Themes
Year 1 concentrates on establishing a minimal, coherent Semantic OS substrate and validating it through end-to-end demonstrations.
Theme A — Semantic Memory Foundation
Define and validate a persistent, interpretable, provenance-complete semantic graph with temporal history and transformation lineage.
Theme B — USIR v1 (Typed Semantic IR)
Deliver USIR v1 as a typed, explicit, graph-structured intermediate representation capable of expressing cross-domain structures and operator application.
Theme C — Early Domain Modules (Prototypes + Invariants)
Prototype 3–5 domain modules with schemas, invariants, operator families, and tool adapters sufficient for integrated workflows.
Theme D — Deterministic Orchestration for Reproducible Workflows
Implement a deterministic orchestration model for agent workflows, memory access, operator execution, and provenance logging.
Theme E — Human Interfaces / SIM v0 for Inspection and Exploration
Build minimal interfaces for semantic visualization, provenance inspection, and SIM-based exploration loops to support debugging and cross-domain pattern discovery.
3. Layer-by-Layer Objectives (Year 1)
Layer 0 — Semantic Memory (Objective)
Deliver a persistent semantic graph with explicit schemas, provenance, and temporal chains that can serve as the shared substrate across domains, agents, and interfaces.
Layer 1 — USIR (Objective)
Deliver USIR v1: a typed graph IR that can represent core structures in initial domains, express operator application, and support conceptual lowering/lifting between forms.
Layer 2 — Domain Modules (Objective)
Deliver prototype domain modules (CAD, simulation, code, scientific modeling, data workflows) each with: (a) schema, (b) invariants, (c) operator families, and (d) at least one tool-adapter prototype.
Layer 3 — Agent Orchestration (Objective)
Deliver deterministic orchestration primitives enabling: explicit task decomposition into operators, memory access protocols, reproducible workflow execution, and provenance for every agent action.
Layer 4 — Deterministic Engines (Objective)
Deliver early engine scaffolds and interfaces (symbolic + numeric + solver wrappers) that operate over USIR structures and enable reproducible execution with measurable correctness properties.
Layer 5 — Human Interfaces / SIM (Objective)
Deliver visualization and inspection tooling sufficient to: browse semantic graphs, inspect operator chains, review provenance and state changes, and run SIM v0 exploration workflows across at least two domains.
4. Semantic Memory Tasks (Year 1)
4.1 Initial Schema Design
Define core entity types: concept, relation, operator, artifact, workflow, derivation, assumption, domain, state snapshot.
Define linking primitives: typed edges, references, constraints, version identifiers, provenance pointers.
Define minimal query surface: retrieval by identifier, by type, by provenance chain, by domain, by dependency closure.
4.2 Persistence Model
Select and validate a persistence strategy supporting:
durable storage of graph nodes/edges
incremental updates
snapshots and restore
content/version addressing for stable references
Define serialization format(s) for interchange and testing.
4.3 Provenance Structures
Define provenance as a first-class graph with:
operator invocation records
input/output bindings
assumptions and parameters
tool/engine execution metadata
references to pre-state and post-state
Ensure provenance records are queryable and composable across workflows.
4.4 Temporal Chains
Define temporal modeling for semantic state:
event streams for changes
state snapshots at defined boundaries
lineage chains for artifacts and derived objects
Support “time-travel” inspection: reconstruct relevant state for a given derivation.
4.5 Validation Mechanisms
Define semantic validation rules for:
schema conformance
type compatibility
integrity constraints (referential, acyclicity where required, version consistency)
provenance completeness for specified operations
Establish test fixtures and reference cases to detect drift.
5. USIR Tasks (Year 1)
5.1 IR Syntax and Semantics Definition
Define USIR as a typed, explicit, graph-structured IR with:
nodes representing typed entities (values, structures, operators, constraints, workflows)
edges representing typed relations (containment, dependency, derivation, constraint, execution)
Define evaluation semantics at the level needed for operator application and provenance traces.
5.2 Type System Scaffolding
Define initial type fragments spanning:
symbolic expressions
numeric scalars/vectors/tensors
geometric primitives and constraints
program structures (functions, types, control/data flow objects at a suitable abstraction level)
workflow constructs (steps, artifacts, dependencies, parameters)
Define rules for type checking of operator inputs/outputs.
5.3 Lowering/Lifting Rules (Conceptual)
Define the conceptual mapping classes required for:
symbolic ↔ numeric
geometry/CAD ↔ simulation
code structure ↔ analyses/refactors
workflow graphs ↔ executable orchestration
Document lowering/lifting contracts rather than full algorithms (Year 1 focus is coherence and testability).
5.4 Operator Model
Define operator objects with:
signatures (typed inputs/outputs)
preconditions/postconditions
declared effects on semantic state
provenance emission requirements
Establish a minimal operator execution contract used by engines and orchestration.
5.5 Cross-Domain Compatibility Targets
Specify target compatibility in Year 1:
shared operator and provenance representation across at least three domains
common constraint representation usable by at least two domains
unified workflow representation spanning domain module outputs and engine inputs
6. Domain Module Tasks (Year 1)
Year 1 domain work is prototype-level, prioritizing coherence, invariants, and minimal tool adapters sufficient for demonstrations.
6.1 CAD Domain Module (Prototype)
Schemas:
parametric geometry objects, constraints, assemblies, coordinate frames, derived features.
Invariants:
constraint consistency, dimensional/type consistency (units where applicable), dependency acyclicity for parametric graphs (as required).
Operator Families:
construct, transform, constrain, solve-constraints, derive-feature, export-to-USIR.
Tool-Adapter Prototypes:
adapter to a geometry kernel or structured CAD representation sufficient to import/export and replay transformations.
6.2 Simulation / Multi-Physics Domain Module (Prototype)
Schemas:
PDE/ODE model objects, boundary/initial conditions, discretization descriptors, solver configuration, simulation runs, results objects.
Invariants:
well-posedness checks at schema level (where expressible), configuration completeness, units/type compatibility, run reproducibility metadata completeness.
Operator Families:
define-model, apply-conditions, discretize, solve, postprocess, validate-run, link-to-geometry.
Tool-Adapter Prototypes:
wrapper interfaces to one numeric solver stack (PDE or ODE) with provenance-aware execution records.
6.3 Code Understanding Domain Module (Prototype)
Schemas:
program entities (modules, functions, types), dependencies, call graphs, dataflow/controlflow abstractions appropriate to Year 1 scope, transformation records.
Invariants:
type/structure consistency for represented subsets, dependency integrity, refactor correctness conditions (as declared contracts).
Operator Families:
parse-to-semantics, build-dependency-graph, analyze, propose-transform, apply-transform, verify.
Tool-Adapter Prototypes:
adapters to a parser/analyzer and at least one deterministic transformation tool (e.g., formatting/refactor/type check) with full provenance.
6.4 Scientific Modeling Domain Module (Prototype)
Schemas:
symbolic model definitions, dimensional analysis objects, parameter sets, derived quantities, experiment/workflow structures.
Invariants:
dimensional/type consistency, parameter completeness, derivation validity under declared assumptions.
Operator Families:
define-symbolic, simplify/transform, lower-to-numeric, analyze-solution, compare-models, record-assumptions.
Tool-Adapter Prototypes:
adapter to a symbolic engine and a numeric backend sufficient for a symbolic→numeric demonstration.
6.5 Data Workflows Domain Module (Prototype)
Schemas:
datasets, schemas, transformations, pipelines, joins/filters/aggregates, feature definitions, lineage.
Invariants:
schema compatibility, transformation determinism markers, lineage completeness, version and dependency integrity.
Operator Families:
ingest, validate, transform, join, summarize, materialize, compute-lineage.
Tool-Adapter Prototypes:
adapter to one workflow runtime or query engine with provenance recording and deterministic replay where feasible.
7. Agent Orchestration Tasks (Year 1)
7.1 Deterministic Agent Lifecycle
Define agent states (e.g., idle, planning, executing, verifying, halted) and allowed transitions.
Ensure all transitions emit structured records into semantic memory.
7.2 Memory Access Protocols
Define read/write scopes, permissions, and conflict policies for shared semantic memory.
Define snapshot semantics for reproducible runs (workflow-level state capture).
7.3 Task Decomposition Framework
Define task objects decomposed into operator graphs.
Establish contracts for operator selection, parameter binding, and dependency resolution.
7.4 Reproducible Workflow Execution
Implement workflow execution as deterministic replay over:
USIR operator graphs
engine calls with pinned configs
captured state snapshots
Define replay success criteria and divergence reporting.
7.5 Provenance for Agent Actions
Record for each agent action:
decision artifact (what was selected and why, at the representational level)
executed operator calls
tool/engine invocations
produced artifacts and diffs
Provide minimal introspection queries for debugging (who did what, when, under which state).
8. Engine Tasks (Year 1)
8.1 Early Symbolic Operator Prototypes
Implement or wrap a symbolic transformation capability with:
typed operator signatures
provenance capture for transformations
correctness checks where available (e.g., equivalence validation in restricted cases)
8.2 Numeric / PDE / ODE Scaffolds
Establish a solver interface contract:
model specification in USIR terms
solver configuration encapsulation
run records and result typing
error and convergence signaling as semantic objects
Wrap one numeric backend with reproducibility harness (input pinning, run metadata capture, replay tests).
8.3 Semantic Solver Interfaces
Define shared interfaces across symbolic and numeric engines:
operator execution entrypoints
input/output typing
provenance emission hooks
validation hooks (pre/post)
8.4 Reproducibility Tests
Define reproducibility test suite:
replay of operator sequences yields equivalent typed results under defined equivalence relations
divergence detection and reporting (including provenance-based diagnosis)
Establish tolerances where strict determinism is not feasible and encode them explicitly.
9. Human Interfaces / SIM Tasks (Year 1)
9.1 Semantic Visualization Prototypes
Build minimal viewers for:
semantic memory graph browsing
USIR structures (typed nodes/edges)
provenance chains and transformation graphs
domain module objects and invariants
9.2 Reasoning Inspector v0
Provide an inspector that can display:
operator chains with input/output bindings
state snapshots and diffs
provenance records for each step
validation outcomes and failure points
9.3 SIM Exploration Workflows
Define SIM v0 as a set of exploration workflows rather than a fully general environment:
navigate objects by type/domain
traverse derivations and transformations
search/filter by invariants and constraints
compare alternative operator paths
9.4 Cross-Domain Pattern Discovery Loops
Establish at least two closed loops where interface-driven exploration feeds improvements back into:
USIR representational gaps
domain invariants
operator definitions
Track these loops as explicit artifacts in semantic memory (discovery → proposal → integration).
10. Cross-Layer Integration Milestones (Year 1)
Integration is treated as a first-class deliverable. Year 1 checkpoints:
M1 — Memory ↔ USIR Base Integration
USIR objects and operator invocations are persistable in semantic memory with provenance and temporal history.
M2 — Domain Module ↔ USIR Integration (Two Domains Minimum)
At least two domain modules can represent core objects in USIR and exchange artifacts through USIR with typed compatibility checks.
M3 — Domain Module ↔ Engine Integration (One Engine Path)
At least one domain module drives an engine through USIR operator execution, producing typed results with provenance.
M4 — Agents ↔ Memory Integration
Agent orchestration reads/writes semantic memory using defined protocols, with reproducible workflow replay.
M5 — Agents ↔ Tools/Engines Integration
Agents execute operator graphs that route into domain adapters and engines deterministically, written as provenance-complete workflows.
M6 — SIM ↔ All Layers (Inspection Coverage)
SIM/Interfaces can inspect: memory objects, USIR graphs, domain objects, agent actions, engine runs, and workflow provenance for at least one end-to-end demo.
11. End-to-End Demonstrations (Year 1)
Year 1 demonstrations must be complete, inspectable, and reproducible within defined constraints.
Demo 1 — CAD → Simulation → Analysis
Represent a parametric geometry object and constraints (CAD domain).
Lower into a simulation-ready model via USIR (simulation domain).
Execute a solver run through the engine interface with provenance-complete records.
Inspect the full chain end-to-end in the reasoning inspector (inputs, operators, state, outputs).
Demo 2 — Code → Semantics → Deterministic Tool Routing
Parse a codebase subset into semantic structures (code domain).
Construct dependency/structure objects and invariants.
Route a deterministic transformation or analysis toolchain (e.g., refactor + verification) via operator graphs.
Preserve and inspect provenance across transformations and validate invariant preservation.
Demo 3 — Symbolic → Numeric → Provenance-Inspected Results
Define a symbolic scientific model with explicit assumptions (scientific modeling domain).
Lower to numeric execution (engine interface) with typed bindings.
Produce results objects with provenance, validation artifacts, and replay capability.
Inspect transformation steps, assumptions, and solver configuration through the reasoning inspector.
Demo 4 (Optional, if capacity allows) — SIM-Driven Invariant Exploration
Use SIM v0 to navigate semantic objects and provenance chains.
Identify and test candidate invariants across at least two domains (e.g., geometry constraints ↔ simulation boundary conditions).
Produce a recorded “discovery loop” artifact: observation → proposed invariant/operator → integration proposal.
12. Evaluation Criteria (Year 1)
Progress is measured by system properties, not output fluency.
Semantic Clarity
Objects are representable as typed, inspectable structures.
Operators have explicit signatures and contracts.
Provenance Completeness
Operator invocations, inputs/outputs, assumptions, and state transitions are recorded.
Provenance supports traversal and reconstruction of derivations.
Cross-Domain Coherence
USIR supports shared structures across at least two domains without ad hoc translation.
Domain modules maintain compatibility through typed interfaces.
Reproducibility
Defined workflows can be replayed with equivalent results under stated equivalence relations and tolerances.
Divergence is detectable and diagnosable.
Operator Correctness
Operators preserve stated invariants or fail with explicit diagnostics.
Minimal validation exists for key operators in each demo path.
Integration Stability
Cross-layer contracts remain stable across iterations (memory ↔ USIR ↔ domains ↔ orchestration ↔ engines ↔ interfaces).
Changes are versioned and do not silently break demonstrations.
13. Risks & Mitigations (Year 1)
Risk: Over-expansion of scope across domains
Mitigation:
Limit to prototype-level schemas and operator families; require every domain task to tie directly to a Year 1 demo path.
Risk: USIR becomes either too abstract or too domain-specific
Mitigation:
Define USIR v1 minimally around operator execution, provenance, and typed graph structures; validate via integration milestones M2/M3.
Risk: Provenance overhead undermines usability or velocity
Mitigation:
Establish minimum provenance requirements per operator class; implement progressive detail levels while preserving traceability.
Risk: Reproducibility claims exceed practical determinism constraints
Mitigation:
Define explicit equivalence relations and tolerances; encode determinism boundaries in run metadata and evaluation.
Risk: Agent orchestration becomes a research sink
Mitigation:
Keep agent work focused on deterministic workflow execution and provenance capture; avoid broad autonomy goals.
Risk: Interfaces become product-level scope
Mitigation:
Interfaces are inspection and debugging tools for Year 1; prioritize reasoning inspector and semantic visualization over feature breadth.
Risk: Integration churn blocks progress
Mitigation:
Treat integration milestones as primary deliverables; require contract tests for memory/USIR/operator interfaces.
14. Non-Goals for Year 1
Building or training probabilistic language models.
Achieving universal domain coverage or encyclopedic ontologies.
Delivering product-grade UI/UX or commercial platforms.
Solving full agent autonomy or open-ended planning.
Producing a complete formal specification for all lowering/lifting semantics.
Guaranteeing strict determinism across all numeric engines/hardware environments.
Optimizing for large-scale performance at the expense of representational stability.
Competing with existing ML labs on model capability benchmarks.
This document constitutes the SIL Research Agenda & Demonstration Plan for Year 1.
15. Related Research Papers
SIL publishes formal research papers on semantic infrastructure problems. These papers provide rigorous foundations for the work described in this agenda.
Current Papers:
- RAG as Semantic Manifold Transport (
docs/research/RAG_AS_SEMANTIC_MANIFOLD_TRANSPORT.md) - Formalizes retrieval-augmented generation as geometric meaning transport across misaligned manifolds
- Directly informs Layer 0 (Semantic Memory) design for manifold-aware storage/retrieval
- Provides distortion metrics and alignment strategies for semantic memory queries
- Connection to Year 1 work: Section 4 (Semantic Memory), Section 6.4 (Code understanding domain)
Future Papers (planned):
- Universal Semantic IR specification and cross-domain invariants (USIR)
- Provenance manifolds in multi-agent systems
- Deterministic scheduling in cross-domain computation
- Microkernel architecture for semantic queries
See docs/research/ for full catalog and technical details.