whetstone_RSA/docs/case_studies/whetstone_dsl_semantic_mapping.md

WhetstoneDSL Semantic Mapping

Purpose

This document maps the current whetstone_DSL implementation surfaces into the semantic entities being established for whetstone_RSA.

It exists to make Sprint 003 executable.

The goal is not to restate architecture in the abstract. The goal is to answer:

• which whetstone_DSL files currently embody the first RSA-relevant decisions
• which RSA semantic entities those files imply
• which parts are already deterministic baselines
• which parts should become explicit gate contracts
• which parts should remain skeletons before training

Mapping Rule

Each whetstone_DSL surface should be classified into one of four roles:

• semantic source candidate
• deterministic baseline
• runtime/executor consumer
• training-data source candidate

This keeps the case study from collapsing implementation, semantics, and data collection into one bucket.

Surface Map

1. editor/src/TaskitemGeneratorV2.h

Current responsibility:

• generate taskitems from decomposed workstreams
• infer workerType
• infer prerequisiteOps
• mark queue readiness

RSA semantic implications:

• worker_type is a first-class DecisionContract plus DecisionSurface
• prereq_op_selector is a first-class DecisionContract plus structured DecisionSurface

Deterministic baseline value:

• current keyword worker heuristic is the baseline policy
• current prerequisite-op defaulting behavior is the baseline policy

Training-data value:

• high, once paired with richer context and accepted downstream contracts
• especially useful for:
  • task description -> worker_type
  • task description -> prerequisite-op set

Recommended semantic extraction:

• WorkerTypeGate
• PrereqOpGate

2. editor/src/TaskitemConfidenceAmbiguity.h

Current responsibility:

• compute confidence score
• count ambiguity
• derive escalation flag

RSA semantic implications:

• this is primarily a policy/baseline artifact, not a first training gate

Deterministic baseline value:

• extremely high
• should remain attached to future gate records as deterministic baseline evidence

Training-data value:

• low as a primary specialist target
• high as a supervision source for:
  • deterministic-baseline detection
  • guardrail-policy comparison

Recommended semantic extraction:

• DeterministicPolicyBaseline
• not an initial RSA gate

3. editor/src/mcp/RegisterArchitectIntakeTools.h

Current responsibility:

• intake normalization and taskitem generation orchestration
• execution-contract shaping
• verification-type inference
• target-file inference
• acceptance-command inference
• step-sequence inference
• cross-project-target inference

RSA semantic implications:

• verification_type is a first-class immediate gate
• execution contract shaping is a structured semantic frontier

Deterministic baseline value:

• very high for current heuristics
• should be preserved as baseline before any specialist substitution

Training-data value:

• very high if taskitem runs with context and final MCP choices still exist
• likely strongest surface for:
  • verification_type
  • target_file_selection
  • acceptance_command_selection
  • execution_contract_shape

Recommended semantic extraction:

• VerificationTypeGate
• ExecutionContractShapeDecision
• TargetFileSelectionDecision
• AcceptanceCommandSelectionDecision
• StepSequenceDecision

4. editor/src/ScopeMilestoneDecomposer.h

Current responsibility:

• milestone grouping
• workstream grouping
• grouped summary/title generation
• uncertainty scoring

RSA semantic implications:

• grouping decisions are real semantic decisions
• but they are not yet clean enough as direct training targets

Deterministic baseline value:

• high

Training-data value:

• medium
• useful after the grouping contract is made explicit

Recommended semantic extraction:

• MilestoneGroupingDecision
• WorkstreamGroupingDecision
• WorkstreamSummaryPolicy

5. editor/src/RoutingEngine.h

Current responsibility:

• choose worker layer
• choose context width
• set review flag
• derive agent role

RSA semantic implications:

• future second-wave RSA surface
• useful as a meta-layer above particular gates

Deterministic baseline value:

• extremely high
• keep this baseline intact for later RSA-versus-rule comparison

Training-data value:

• medium to high once real historical routing outcomes are assembled

Recommended semantic extraction:

• LayerSuitabilityDecision
• RoutingContextDecision

Note:

• these sit one layer above RSA identity itself, so they should not be confused with an RSA gate contract

6. editor/src/mcp/RegisterCppRaisingTools.h

Current responsibility:

• choose and validate C++ raising profile
• run deterministic C++ raising and packaging pipeline

RSA semantic implications:

• cpp_raising_profile is a clean bounded gate candidate

Deterministic baseline value:

• current manual/explicit profile selection is the baseline

Training-data value:

• medium
• strongest when paired with IR traits, risk annotations, and downstream review outcomes

Recommended semantic extraction:

• CppRaisingProfileGate

7. editor/src/ConstrainedProjectionGate.h

Current responsibility:

• block generation when environment constraints are violated

RSA semantic implications:

• this is executor-side deterministic gating
• it is not currently an RSA gate candidate

Deterministic baseline value:

• extremely high

Training-data value:

• useful as supervision for invalid-output prevention
• not useful as a primary specialist target

Recommended semantic extraction:

• DeterministicConstraintGate

8. editor/src/ast/ProjectionGenerator.h

Current responsibility:

• dispatch AST node kinds to language-specific visitors

RSA semantic implications:

• almost none at the current stage
• this is deterministic target-language projection

Deterministic baseline value:

• total

Training-data value:

• not a primary RSA training surface

Recommended semantic extraction:

• no immediate RSA semantic entity
• leave in deterministic projection layer

9. editor/src/CrossLanguageProjector.h

Current responsibility:

• clone AST into target language
• preserve/adapt annotations

RSA semantic implications:

• good future source for bounded policy choices around adaptation profiles
• current implementation is deterministic

Deterministic baseline value:

• high

Training-data value:

• low now
• potentially higher later if multiple adaptation profiles exist

Recommended semantic extraction:

• future ProjectionProfileDecision
• not an immediate Sprint 003 gate

Semantic Entity Alignment

Immediate gate entities

WhetstoneDSL surface	RSA entity	Status
TaskitemGeneratorV2::inferWorkerType	WorkerTypeGate	bootstrap now
TaskitemGeneratorV2::inferPrerequisiteOps	PrereqOpGate	bootstrap now
RegisterArchitectIntakeTools::inferVerificationType	VerificationTypeGate	bootstrap now
RegisterCppRaisingTools profile enum	CppRaisingProfileGate	bootstrap now

Skeleton-first entities

WhetstoneDSL surface	RSA entity	Status
execution-contract heuristics	ExecutionContractShapeDecision	skeleton now
target-file inference	TargetFileSelectionDecision	skeleton now
grouping heuristics	WorkstreamGroupingDecision	skeleton now
grouped summary logic	WorkstreamSummaryPolicy	skeleton now
step-sequence inference	StepSequenceDecision	skeleton now

Deterministic baselines to preserve

WhetstoneDSL surface	RSA role
TaskitemConfidenceAmbiguity	deterministic baseline / guardrail policy
RoutingEngine	deterministic meta-routing baseline
ConstrainedProjectionGate	deterministic constraint gate
ProjectionGenerator dispatch	deterministic projection executor
CrossLanguageProjector current adaptation	deterministic projection baseline

What This Means For Integration

The current integration path should be:

1. capture gate semantics first
2. preserve deterministic baselines as explicit comparison points
3. identify data sources for each gate
4. defer runtime substitution until the gate contracts are stable

The wrong integration path would be:

1. wire RSA runtime into whetstone_DSL
2. guess what the gates mean later

This case study is strong enough now to avoid that mistake.

Training Data Notes

Potential high-value training data sources still likely exist in or around whetstone_DSL, depending on what survived deletion:

• taskitem pipeline run logs with context and final MCP choices
• specialist training data under whetstone_DSL/specialists
• the 100 easy and 25 challenging project sets already used for examples and evaluation
• sprint plan corpus already used for verification_type

Immediate implication:

• do not block semantic bootstrap on recovering training data
• but treat recovered taskitem/MCP-choice runs as likely first-class supervision once the gate contracts are fixed

Next Mapping Step

The next document after this one should define skeleton field sets for:

• ExecutionContractShapeDecision
• TargetFileSelectionDecision
• WorkstreamGroupingDecision

Those are the structured surfaces most likely to become later RSA gates once the semantic contract is explicit enough to benchmark cleanly.