Implement XIC v1.5: Symbolic Pipeline Abstraction with Glyph-Aware Transformations

Implements all phases of the symbolic pipeline extension:

**Phase 1: Symbolic Pipeline Abstraction**
- Created glyphos/symbolic_pipeline.py with:
  - SymbolicStep: tracks individual pipeline steps (name, kind, payload, context)
  - SymbolicPipelineResult: complete pipeline execution result (steps, output_text, fused_symbol)
  - run_symbolic_pipeline(prompt, context, glyph_id): high-level pipeline entrypoint
- Integrated with glyphos/__init__.py exports

**Phase 2: Glyph-Aware Transformations**
- Updated glyphos/cognitive_kernel.py:
  - run_symbolic_prompt() now thin wrapper around pipeline
  - Maintains backward compatibility
- Updated xic_ops.py operations:
  - op_RUN_PROMPT: uses pipeline in symbolic mode
  - op_STREAM: uses pipeline with line-by-line output
  - op_CALL_GLYPH: routes through pipeline with explicit glyph_id parameter
- Context propagation: glyph_id automatically injected into LAIN context

**Phase 3: XIC Instruction Semantics v1.5**
- Created XIC_SEMANTICS_v1_5.md:
  - Formal specification of all 9 XIC instructions
  - Complete semantics: preconditions, postconditions, side effects
  - Symbolic vs compressed behavior for each op
  - Context model and pipeline semantics
  - Execution paths (compressed vs symbolic)
  - Backward compatibility guarantees

**Phase 4: Demo Program & Validation**
- Created programs/demo_symbolic_pipeline.gx.json
  - Demonstrates symbolic pipeline with glyph-aware cognition
  - Uses CALL_GLYPH, RUN_PROMPT, SET_CONTEXT, CHAIN, LOG
- All 7 validation tests pass:
  ✅ Pipeline module imports
  ✅ Pipeline execution
  ✅ Glyph-aware transformations
  ✅ Demo program
  ✅ CALL_GLYPH result storage
  ✅ Backward compatibility
  ✅ run_symbolic_prompt() wrapper

**Phase 5: Final Report**
- Created XIC_SYMBOLIC_PIPELINE_REPORT.md
  - Architecture and module hierarchy
  - Integration points and data flow
  - Design decisions and rationale
  - Usage examples

Key Features:
- Step-level introspection: full SymbolicPipelineResult with step history
- Glyph-aware: explicit glyph_id routing through LAIN kernel
- Formal semantics: complete specification for tool builders
- Backward compatible: all v1 programs work unchanged
- No breaking changes: compressed execution path untouched

Constraints Met:
✅ No GPU code
✅ No XIC v2 binary container
✅ No .gx format changes
✅ Full backward compatibility

glyphos/__init__.py

@@ -14,6 +14,12 @@ from .cognitive_kernel import (
     kernel_status,
 )
 
+from .symbolic_pipeline import (
+    SymbolicStep,
+    SymbolicPipelineResult,
+    run_symbolic_pipeline,
+)
+
 from .events import (
     EventBus,
     Event,
@@ -29,6 +35,9 @@ __all__ = [
     "run_gx",
     "run_symbolic_prompt",
     "kernel_status",
+    "SymbolicStep",
+    "SymbolicPipelineResult",
+    "run_symbolic_pipeline",
     "EventBus",
     "Event",
     "EventType",

glyphos/cognitive_kernel.py

@@ -258,11 +258,9 @@ class CognitiveKernel:
 
 
 def run_symbolic_prompt(prompt: str, context: dict | None = None) -> str:
-    """Entry point for symbolic execution from XIC.
+    """Thin wrapper around the symbolic pipeline for backward compatibility.
 
-    Compresses the prompt text into GSZ3 bytes, builds a minimal manifest,
-    and routes through the full LAIN 8-lane cognition pipeline via
-    CognitiveKernel.execute_symbolic(). Returns the output_text string.
+    Routes through run_symbolic_pipeline() and returns output_text.
 
     Args:
         prompt: User or system prompt text
@@ -271,36 +269,9 @@ def run_symbolic_prompt(prompt: str, context: dict | None = None) -> str:
     Returns:
         String result from the 8-lane cognition pipeline
     """
-    from gx_compiler.compressor import GXCompressor
-
-    kernel = get_kernel()
-    prompt_bytes = prompt.encode("utf-8")
-
-    try:
-        payload = GXCompressor.compress(prompt)
-    except Exception as e:
-        return f"[Symbolic Error] Compression failed: {e}"
-
-    manifest = {
-        "source_file": "<symbolic>",
-        "source_type": "symbolic",
-        "version": "1.0.0",
-        "contributor": "XIC-symbolic",
-        "segments": [{"id": "seg_0", "start": 0, "end": 1,
-                      "start_byte": 0, "end_byte": len(prompt_bytes)}],
-    }
-    segments = [{"id": "seg_0", "start": 0, "end": 1,
-                 "start_byte": 0, "end_byte": len(prompt_bytes)}]
-
-    result = kernel.execute_symbolic(
-        manifest=manifest,
-        segments=segments,
-        payload=payload,
-        mode="symbolic",
-        context=context or {},
-    )
-
-    return result.get("output_text") or result.get("fused_symbol", {}).get("summary", prompt)
+    from .symbolic_pipeline import run_symbolic_pipeline
+    result = run_symbolic_pipeline(prompt=prompt, context=context)
+    return result.output_text
 
 
 # Global singleton kernel instance

glyphos/symbolic_pipeline.py

@@ -0,0 +1,127 @@
+"""Symbolic Pipeline Abstraction for XIC.
+
+Provides a structured, glyph-aware pipeline for symbolic cognition execution.
+Routes prompts through the LAIN 8-lane cognition kernel with explicit step tracking.
+"""
+
+from dataclasses import dataclass, field
+from typing import Any, Dict, List, Optional
+
+
+@dataclass
+class SymbolicStep:
+    """A single step in the symbolic pipeline execution."""
+    name: str
+    kind: str  # "prompt", "glyph_call", "fused_symbol"
+    payload: Any
+    context: Dict[str, Any] = field(default_factory=dict)
+
+
+@dataclass
+class SymbolicPipelineResult:
+    """Result of a symbolic pipeline execution."""
+    steps: List[SymbolicStep]
+    output_text: str
+    fused_symbol: Optional[Dict[str, Any]] = None
+
+
+def run_symbolic_pipeline(
+    prompt: str,
+    context: Optional[Dict[str, Any]] = None,
+    glyph_id: Optional[str] = None,
+) -> SymbolicPipelineResult:
+    """
+    High-level symbolic pipeline entrypoint for XIC.
+
+    Accepts a prompt and optional symbolic/glyph context, routes through
+    the LAIN 8-lane cognition kernel via CognitiveKernel.execute_symbolic(),
+    and returns a structured SymbolicPipelineResult with execution steps,
+    final output text, and fused symbolic representation.
+
+    Args:
+        prompt: User or system prompt text.
+        context: Optional dict of symbolic/cognitive context metadata.
+        glyph_id: Optional glyph identifier for glyph-aware cognition.
+
+    Returns:
+        SymbolicPipelineResult with:
+        - steps: List of SymbolicStep objects tracking execution flow.
+        - output_text: Final text result from cognition layer.
+        - fused_symbol: Fused symbolic representation (if produced by LAIN).
+    """
+    from gx_compiler.compressor import GXCompressor
+    from .cognitive_kernel import get_kernel
+
+    steps: List[SymbolicStep] = []
+    kernel = get_kernel()
+    prompt_bytes = prompt.encode("utf-8")
+
+    # Step 1: Initial prompt
+    steps.append(SymbolicStep(
+        name="initial_prompt",
+        kind="prompt",
+        payload=prompt,
+        context=dict(context or {})
+    ))
+
+    # Step 2: Prepare context for glyph-aware processing
+    exec_context = dict(context or {})
+    if glyph_id:
+        exec_context["glyph_id"] = glyph_id
+        steps.append(SymbolicStep(
+            name=f"glyph:{glyph_id}",
+            kind="glyph_call",
+            payload=prompt,
+            context=exec_context
+        ))
+
+    # Step 3: Compress prompt and build manifest
+    try:
+        payload = GXCompressor.compress(prompt)
+    except Exception as e:
+        return SymbolicPipelineResult(
+            steps=steps,
+            output_text=f"[Pipeline Error] Compression failed: {e}",
+            fused_symbol=None
+        )
+
+    manifest = {
+        "source_file": "<symbolic>",
+        "source_type": "symbolic",
+        "version": "1.0.0",
+        "contributor": "XIC-symbolic",
+        "segments": [{"id": "seg_0", "start": 0, "end": 1,
+                      "start_byte": 0, "end_byte": len(prompt_bytes)}],
+    }
+    segments = [{"id": "seg_0", "start": 0, "end": 1,
+                 "start_byte": 0, "end_byte": len(prompt_bytes)}]
+
+    # Step 4: Execute through LAIN cognition pipeline
+    result = kernel.execute_symbolic(
+        manifest=manifest,
+        segments=segments,
+        payload=payload,
+        mode="symbolic",
+        context=exec_context,
+    )
+
+    # Step 5: Extract results
+    fused_symbol = result.get("fused_symbol")
+    output_text = result.get("output_text") or (
+        fused_symbol.get("summary") if fused_symbol else prompt
+    )
+
+    # Step 6: Record fusion step if fused_symbol present
+    if fused_symbol:
+        steps.append(SymbolicStep(
+            name="fusion",
+            kind="fused_symbol",
+            payload=fused_symbol,
+            context={}
+        ))
+
+    return SymbolicPipelineResult(
+        steps=steps,
+        output_text=output_text,
+        fused_symbol=fused_symbol
+    )