Refine XIC v1 to Symbolic Extension Only (No GPU Code)

Removed GPU-related code per specification:
- Deleted xic_extensions/gpu_runtime.py
- Removed GPU logic from op_RUN_PROMPT and op_STREAM
- Removed demo_gpu.gx.json

Kept pure symbolic extension:
- 5 new instructions: STREAM, CHAIN, CALL_GLYPH, SET_CONTEXT, LOG
- Symbolic execution mode via SET_MODE "symbolic"
- run_symbolic_prompt() integration with LAIN cognition layer
- demo_symbolic.gx.json for testing

Implementation now focuses exclusively on:
- Extending instruction set (9 total ops)
- Adding symbolic routing to cognition layer
- Preserving backward compatibility (zero breaking changes)
- No external GPU dependencies

All validation tests pass:
✅ OP_TABLE coverage (9 operations)
✅ XICContext.symbolic_mode field
✅ run_symbolic_prompt() callable
✅ Backward compatibility (demo_chat unchanged)
✅ Symbolic mode execution (LAIN pipeline)
✅ SET_CONTEXT, CHAIN, RUN_PROMPT routing

Constraints met:
✅ No breaking changes
✅ No XIC v2 binary format
✅ No GPU-related code
✅ Strict v1 JSON + .gx architecture

xic_extensions/gpu_runtime.py

@@ -1,57 +0,0 @@
-"""GPU-accelerated compressed execution path for XIC.
-
-has_gpu() probes for CUDA via torch. If torch is absent or no CUDA
-device is detected, returns False and run_on_gpu() falls back to CPU
-via execute_gx() with a clear log line.
-"""
-from typing import Any
-
-
-def has_gpu() -> bool:
-    """Check if CUDA GPU is available via torch.
-
-    Returns:
-        True if torch is installed and CUDA device is detected, False otherwise
-    """
-    try:
-        import torch
-        return torch.cuda.is_available()
-    except ImportError:
-        return False
-
-
-def run_on_gpu(model_path: str, params: dict) -> Any:
-    """Execute a .gx model with optional GPU acceleration.
-
-    If GPU is available (torch + CUDA), logs device info and runs on GPU.
-    If GPU is not available, logs fallback and runs on CPU via execute_gx().
-
-    Args:
-        model_path: Path to .gx model file
-        params: Execution parameters dict (trace, profile, etc.)
-
-    Returns:
-        ExecutionContext from execute_gx()
-    """
-    from runtime_executor.runner import execute_gx
-
-    if has_gpu():
-        try:
-            import torch
-            device_name = torch.cuda.get_device_name(0)
-            print(f"[XIC-GPU] Device: {device_name}")
-        except Exception as e:
-            print(f"[XIC-GPU] Warning: Could not get device name: {e}")
-
-        return execute_gx(
-            model_path,
-            trace=params.get("trace", False),
-            profile=params.get("profile", False),
-        )
-    else:
-        print("[XIC-GPU] No CUDA device — executing on CPU")
-        return execute_gx(
-            model_path,
-            trace=params.get("trace", False),
-            profile=params.get("profile", False),
-        )