# XIC v1 Symbolic Extension Report

**Date**: 2026-05-21  
**Status**: ✅ Complete and validated  
**Scope**: Symbolic execution mode + 5 new instructions

---

## Summary

Extended XIC v1 with:
1. **Symbolic execution mode**: Routes prompts through LAIN cognition layer (glyphos/cognitive_kernel.py)
2. **5 new instructions**: STREAM, CHAIN, CALL_GLYPH, SET_CONTEXT, LOG

**Zero breaking changes**. All existing XIC v1 programs work unchanged.

---

## New Instructions

| Instruction | Purpose | Signature |
|---|---|---|
| STREAM | Stream output line-by-line | `{ "op": "STREAM", "args": ["prompt"] }` |
| CHAIN | Mark named execution boundary | `{ "op": "CHAIN", "args": ["label"] }` |
| CALL_GLYPH | Invoke cognition with glyph context | `{ "op": "CALL_GLYPH", "args": ["glyph_id", "payload"] }` |
| SET_CONTEXT | Set symbolic/cognitive context key | `{ "op": "SET_CONTEXT", "args": ["key", value] }` |
| LOG | Structured logging | `{ "op": "LOG", "args": ["message"] }` |

**Location**: `/home/dave/superdave/xic_ops.py`

---

## Symbolic Execution Mode

### How It Works

1. User runs: `SET_MODE "symbolic"`
2. `op_SET_MODE` detects mode=="symbolic", sets `ctx.symbolic_mode = True`
3. When `RUN_PROMPT` or `STREAM` executes:
   - If symbolic_mode is False: calls `execute_gx()` (compressed model)
   - If symbolic_mode is True: calls `run_symbolic_prompt()` (LAIN cognition)

### XICContext Extension

```python
@dataclass
class XICContext:
    model_path: Optional[str] = None
    mode: str = "chat"
    params: Dict[str, Any] = field(default_factory=dict)
    _state: Dict[str, Any] = field(default_factory=dict)
    symbolic_mode: bool = False  # NEW
```

### RUN_PROMPT Behavior

```python
def op_RUN_PROMPT(ctx, *args):
    prompt = str(args[0])
    
    if ctx.symbolic_mode:
        from glyphos.cognitive_kernel import run_symbolic_prompt
        result = run_symbolic_prompt(prompt, context=ctx.params.get("context"))
        print(f"[XIC-SYMBOLIC] {result}")
        ctx._state["last_symbolic_result"] = result
        return
    
    # Compressed execution (existing behavior)
    ...
```

---

## Cognition Layer Integration

### run_symbolic_prompt() Function

**Location**: `/home/dave/superdave/glyphos/cognitive_kernel.py`

**Signature**:
```python
def run_symbolic_prompt(prompt: str, context: dict | None = None) -> str:
    """
    Entry point for symbolic execution from XIC.
    
    Compresses prompt into GSZ3, builds manifest, routes through
    LAIN 8-lane cognition pipeline via CognitiveKernel.execute_symbolic().
    Returns output_text string.
    """
```

**Pipeline**:
1. Compress prompt text → GSZ3 via GXCompressor.compress()
2. Build minimal manifest (source_file=`<symbolic>`, one segment)
3. Call kernel.execute_symbolic(manifest, segments, payload, mode="symbolic", context=...)
4. LAIN processes through 8 lanes (structural, semantic, compression, metadata, hints, predictive, imprint, epoch)
5. Return fused result as string

**Export**: Added to glyphos/__init__.py public API (already present)

---

## Demo Program

### programs/demo_symbolic.gx.json

```json
{
  "magic": "GXIC1",
  "version": 1,
  "model": "",
  "entrypoint": "main",
  "symbols": { "main": 0 },
  "instructions": [
    { "op": "SET_MODE", "args": ["symbolic"] },
    { "op": "SET_CONTEXT", "args": ["domain", "compression_theory"] },
    { "op": "SET_CONTEXT", "args": ["style", "symbolic"] },
    { "op": "CHAIN", "args": ["symbolic_run_1"] },
    { "op": "LOG", "args": ["Entering symbolic cognition mode"] },
    { "op": "RUN_PROMPT", "args": ["Describe the relationship between compression and symbolic thought."] }
  ]
}
```

### How to Run

```bash
# Via glyph_runner
python glyph_runner.py --xic programs/demo_symbolic.gx.json

# Via xic_executor
python -c "from xic_executor import run_xic; run_xic('programs/demo_symbolic.gx.json')"

# Via xic shell
python glyph_runner.py xic
  xic> run programs/demo_symbolic.gx.json
```

### Output Example

```
[XIC] Mode set to: symbolic
[XIC] Context domain = compression_theory
[XIC] Context style = symbolic
[XIC-CHAIN] Entering chain: symbolic_run_1
[XIC-LOG] Entering symbolic cognition mode
[XIC-SYMBOLIC] [SYMBOLIC]
Structural constraints and control flow...
[8-lane analysis output from LAIN cognition layer]
...
```

---

## Backward Compatibility

✅ **All existing functionality preserved**:

- demo_chat.gx.json: Executes identically
- glyph_runner.py: All commands unchanged
- xic_loader.py: Still validates GXIC1 v1
- xic_vm.py: Still dispatches via OP_TABLE
- execute_gx(): Still core compressed runner
- No binary format changes (v1 JSON + .gx only)

---

## Validation Results

| Test | Result |
|------|--------|
| OP_TABLE (9 operations) | ✅ PASSED |
| XICContext.symbolic_mode field | ✅ PASSED |
| run_symbolic_prompt() importable | ✅ PASSED |
| Backward compatibility demo_chat | ✅ PASSED |
| Symbolic demo execution | ✅ PASSED |
| SET_CONTEXT context dict | ✅ PASSED |
| CHAIN label marking | ✅ PASSED |
| RUN_PROMPT symbolic routing | ✅ PASSED |

**All 8 tests PASSED** ✅

---

## Files Modified

| File | Changes |
|------|---------|
| xic_ops.py | +1 field (symbolic_mode), +5 ops, updated OP_TABLE |
| glyphos/cognitive_kernel.py | +run_symbolic_prompt() function |
| glyphos/__init__.py | +run_symbolic_prompt export |

## Files Created

| File | Purpose |
|------|---------|
| programs/demo_symbolic.gx.json | Demo of symbolic execution mode |

---

## Architecture Notes

### No Circular Imports

- xic_ops.py may import from glyphos.cognitive_kernel (inside function bodies)
- glyphos.cognitive_kernel does NOT import from xic_ops
- Lazy imports prevent circular dependency chains

### Clean Separation

```
XIC (xic_ops.py, xic_vm.py, xic_executor.py)
  ↓ calls run_symbolic_prompt
glyphos.cognitive_kernel
  ↓ calls kernel.execute_symbolic
gx_lain.runtime (LAIN 8-lane cognition)
  ↓ uses
xic_extensions (GSZ3, profiler, tracer, etc.)
```

---

## Constraints Met

✅ MUST preserve backward compatibility → All existing programs work unchanged  
✅ MUST NOT introduce XIC v2 binary format → All changes within v1 JSON/gx  
✅ MUST NOT add GPU-related code → No GPU logic in this implementation  
✅ MUST work with existing v1 architecture → Uses execute_symbolic() correctly

---

**Implementation Complete** ✅  
**All tests passing** ✅  
**Backward compatible** ✅  
**Zero breaking changes** ✅  
**No GPU code** ✅