# Phase 10 — Step Execution Proof Generation (Proof Bundles)

## Overview

Implemented deterministic RLang execution proof bundles with step-level traces and canonical JSON, ready for BoR hashing and registration. This phase produces provable execution records that capture the complete execution trace of RLang programs.

## Implementation Details

### Files Created

1. **`rlang/bor/proofs.py`** (234 lines)
   - `StepExecutionRecord`: Frozen dataclass recording single step execution
     - Fields: index, step_name, template_id, input_snapshot, output_snapshot
     - Methods: `to_dict()` for serialization
   - `PipelineProofBundle`: Frozen dataclass containing complete execution proof
     - Fields: version, language, entry_pipeline, program_ir, input_value, output_value, steps
     - Methods: `to_dict()`, `to_json()` using canonical JSON
   - `_find_entry_pipeline()`: Helper to find PipelineIR by entry name
   - `_build_template_map()`: Helper to map template_id → StepTemplateIR
   - `_make_mock_function()`: Creates deterministic mock functions
   - `run_program_with_proof()`: Main public API
     - Compiles source to PrimaryProgramIR
     - Finds entry pipeline
     - Executes pipeline sequentially with step-level tracing
     - Returns PipelineProofBundle with complete execution trace
   - Full `__all__` export list

2. **`rlang/bor/__init__.py`** (updated)
   - Added exports: StepExecutionRecord, PipelineProofBundle, run_program_with_proof
   - Maintains backward compatibility with existing bridge exports

3. **`tests/test_proofs.py`** (330+ lines)
   - Comprehensive test suite with 14 test cases covering:
     - Single-step pipeline proof generation
     - Multi-step pipeline proof generation
     - Mock function fallback when function not in registry
     - Proof JSON determinism (identical inputs produce identical JSON)
     - No entry pipeline error handling
     - Proof bundle structure validation
     - Step execution record structure validation
     - Proof bundle to_dict() and to_json() methods
     - Mixed mock and real functions
     - Complex multi-step pipeline execution
     - Custom version and language preservation
     - Proof bundle immutability (frozen dataclasses)
     - Template ID inclusion in records

## Key Features

- **Deterministic Proof Generation**: Same source + input → identical proof JSON
- **Step-Level Tracing**: Complete execution trace with input/output snapshots per step
- **Pure Execution**: No I/O, fully deterministic execution
- **Canonical JSON**: Uses canonical_dumps() for deterministic serialization
- **Function Registry Support**: Supports real Python functions or mock fallback
- **Immutable Records**: Frozen dataclasses ensure proof integrity
- **Complete Program Context**: Includes full PrimaryProgramIR in proof bundle

## Proof Bundle Structure

### StepExecutionRecord

Records a single step execution:
- `index`: Position in pipeline (0-based)
- `step_name`: Function name
- `template_id`: Template reference
- `input_snapshot`: Input value at this step
- `output_snapshot`: Output value from this step

### PipelineProofBundle

Complete execution proof:
- `version`: Program version
- `language`: Source language
- `entry_pipeline`: Entry pipeline name
- `program_ir`: Complete PrimaryProgramIR (for verification)
- `input_value`: Initial input
- `output_value`: Final output
- `steps`: Ordered list of StepExecutionRecord

## Usage Examples

### Basic Proof Generation

```python
from rlang.bor import run_program_with_proof

source = """
fn inc(x: Int) -> Int;
pipeline main(Int) -> Int { inc }
"""

bundle = run_program_with_proof(
    source=source,
    input_value=10,
    fn_registry={"inc": lambda x: x + 1},
)

# Access proof data
assert bundle.input_value == 10
assert bundle.output_value == 11
assert len(bundle.steps) == 1
assert bundle.steps[0].input_snapshot == 10
assert bundle.steps[0].output_snapshot == 11

# Serialize to JSON
json_proof = bundle.to_json()
```

### Multi-Step Pipeline Proof

```python
source = """
fn inc(x: Int) -> Int;
fn double(x: Int) -> Int;
pipeline main(Int) -> Int { inc -> double }
"""

bundle = run_program_with_proof(
    source=source,
    input_value=5,
    fn_registry={
        "inc": lambda x: x + 1,
        "double": lambda x: x * 2,
    },
)

# Verify step-by-step execution
assert bundle.steps[0].output_snapshot == 6  # inc(5)
assert bundle.steps[1].input_snapshot == 6  # passed to double
assert bundle.steps[1].output_snapshot == 12  # double(6)
assert bundle.output_value == 12
```

### Deterministic JSON

```python
# Same source + input → identical JSON
bundle1 = run_program_with_proof(source, input_value=7, fn_registry=registry)
bundle2 = run_program_with_proof(source, input_value=7, fn_registry=registry)

assert bundle1.to_json() == bundle2.to_json()  # Deterministic!
```

## Execution Flow

1. **Compile**: Source → PrimaryProgramIR
2. **Find Entry**: Locate entry pipeline by name
3. **Build Template Map**: Map template_id → StepTemplateIR
4. **Prepare Registry**: Normalize function registry (or use mocks)
5. **Execute Sequentially**:
   - For each step in pipeline:
     - Capture input snapshot
     - Execute function (registry or mock)
     - Capture output snapshot
     - Create StepExecutionRecord
     - Pass output to next step
6. **Build Bundle**: Create PipelineProofBundle with all records

## Determinism Guarantees

- **Same Source + Input → Same Proof**: Identical inputs produce identical JSON
- **Canonical JSON**: Uses canonical_dumps() for deterministic key ordering
- **Frozen Dataclasses**: Immutable records prevent accidental modification
- **Pure Functions**: No side effects, no I/O, fully deterministic

## Error Handling

- **No Entry Pipeline**: Raises ValueError with clear message
- **Missing Template**: Raises ValueError if template_id not found
- **Function Execution Errors**: Propagates exceptions from function calls

## Integration

- Uses existing `compile_source_to_ir` from emitter
- Integrates with PrimaryProgramIR from canonical module
- Compatible with existing bridge functionality
- Ready for BoR hashing integration (proof bundles are JSON-ready)
- Follows project invariants (no empty files, deterministic, testable)

## Testing

All proof functionality is thoroughly tested:
- ✅ Single-step proof generation
- ✅ Multi-step proof generation
- ✅ Mock function fallback
- ✅ JSON determinism
- ✅ Error handling (missing entry pipeline)
- ✅ Structure validation (bundle, records)
- ✅ Serialization (to_dict, to_json)
- ✅ Mixed mock/real functions
- ✅ Complex pipelines
- ✅ Custom metadata preservation
- ✅ Immutability verification

## Next Steps

Phase 10 complete. The compiler now produces provable execution traces:
- ✅ Step-level execution records
- ✅ Deterministic proof bundles
- ✅ Canonical JSON serialization
- ✅ Complete program context in proofs
- ✅ Ready for BoR hashing and on-chain registration

The proof bundles provide a natural place to plug in BoR hashing and consensus verification in the next phase.

