2125_GCE/XIC_SYMBOLIC_EXTENSION_REPORT.md

XIC v1 Symbolic Extension Report

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

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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.

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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

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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

@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

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)
    ...

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Cognition Layer Integration

run_symbolic_prompt() Function

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

Signature:

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)

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Demo Program

programs/demo_symbolic.gx.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

# 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]
...

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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

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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.)

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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

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