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
This commit is contained in:
GlyphRunner System
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@@ -44,8 +44,15 @@ def op_SET_PARAM(ctx: XICContext, *args):
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def op_RUN_PROMPT(ctx: XICContext, *args):
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"""RUN_PROMPT <prompt>: Execute prompt against loaded model or symbolic cognition.
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If ctx.symbolic_mode is True, routes through glyphos/cognitive_kernel.py.
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Otherwise, routes to execute_gx() for compressed execution.
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Symbolic behavior (ctx.symbolic_mode=True):
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- Routes through symbolic pipeline (run_symbolic_pipeline).
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- Uses ctx.params["context"] for execution context.
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- Stores full pipeline result in ctx._state["last_symbolic_pipeline"].
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Compressed behavior (ctx.symbolic_mode=False):
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- Requires model_path to be set via LOAD_MODEL.
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- Routes to execute_gx() for compressed execution.
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- Stores result in ctx._state["last_result"].
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"""
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if not args:
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raise ValueError("RUN_PROMPT requires a prompt argument")
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@@ -53,10 +60,14 @@ def op_RUN_PROMPT(ctx: XICContext, *args):
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prompt = str(args[0])
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if ctx.symbolic_mode:
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from glyphos.cognitive_kernel import run_symbolic_prompt
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result = run_symbolic_prompt(prompt, context=ctx.params.get("context"))
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print(f"[XIC-SYMBOLIC] {result}")
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ctx._state["last_symbolic_result"] = result
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from glyphos.symbolic_pipeline import run_symbolic_pipeline
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pipeline_result = run_symbolic_pipeline(
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prompt=prompt,
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context=ctx.params.get("context")
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)
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print(f"[XIC-SYMBOLIC] {pipeline_result.output_text}")
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ctx._state["last_symbolic_result"] = pipeline_result.output_text
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ctx._state["last_symbolic_pipeline"] = pipeline_result
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return
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if not ctx.model_path:
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@@ -84,19 +95,31 @@ def op_RUN_PROMPT(ctx: XICContext, *args):
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def op_STREAM(ctx: XICContext, *args):
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"""STREAM <prompt>: Execute and stream output line by line.
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In symbolic mode, stream symbolic result. In compressed mode, stream compressed output.
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Symbolic behavior (ctx.symbolic_mode=True):
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- Routes through symbolic pipeline.
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- Streams output_text line by line with [XIC-STREAM] prefix.
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- Stores pipeline result in ctx._state["last_symbolic_pipeline"].
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Compressed behavior (ctx.symbolic_mode=False):
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- Routes to execute_gx().
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- Streams result line by line with [XIC-STREAM] prefix.
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- Stores result in ctx._state["last_result"].
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"""
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if not args:
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raise ValueError("STREAM requires a prompt argument")
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prompt = str(args[0])
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if ctx.symbolic_mode:
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from glyphos.cognitive_kernel import run_symbolic_prompt
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result = run_symbolic_prompt(prompt, context=ctx.params.get("context"))
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for chunk in str(result).split("\n"):
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from glyphos.symbolic_pipeline import run_symbolic_pipeline
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pipeline_result = run_symbolic_pipeline(
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prompt=prompt,
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context=ctx.params.get("context")
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)
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for chunk in str(pipeline_result.output_text).split("\n"):
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if chunk.strip():
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print(f"[XIC-STREAM] {chunk}")
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ctx._state["last_symbolic_result"] = result
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ctx._state["last_symbolic_result"] = pipeline_result.output_text
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ctx._state["last_symbolic_pipeline"] = pipeline_result
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return
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if not ctx.model_path:
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@@ -131,18 +154,38 @@ def op_CHAIN(ctx: XICContext, *args):
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def op_CALL_GLYPH(ctx: XICContext, *args):
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"""CALL_GLYPH <glyph_id> <payload>: Invoke cognition with a glyph context."""
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"""CALL_GLYPH <glyph_id> <payload>: Invoke glyph-aware cognition.
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Routes through symbolic pipeline with explicit glyph_id parameter.
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The glyph_id is propagated into the pipeline context and used for
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glyph-aware symbolic transformations in the LAIN layer.
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Stores result with key "glyph_{glyph_id}" containing:
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- output_text: Final text from cognition
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- fused_symbol: Fused symbolic representation (if produced)
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- steps: List of symbolic pipeline steps
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"""
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if not args:
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raise ValueError("CALL_GLYPH requires glyph_id argument")
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glyph_id = str(args[0])
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payload = str(args[1]) if len(args) > 1 else ""
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from glyphos.cognitive_kernel import run_symbolic_prompt
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from glyphos.symbolic_pipeline import run_symbolic_pipeline
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glyph_context = dict(ctx.params.get("context", {}))
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glyph_context["glyph_id"] = glyph_id
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result = run_symbolic_prompt(payload, context=glyph_context)
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print(f"[XIC-GLYPH] {result}")
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ctx._state[f"glyph_{glyph_id}"] = result
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pipeline_result = run_symbolic_pipeline(
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prompt=payload,
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context=glyph_context,
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glyph_id=glyph_id,
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)
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print(f"[XIC-GLYPH] {pipeline_result.output_text}")
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ctx._state[f"glyph_{glyph_id}"] = {
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"output_text": pipeline_result.output_text,
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"fused_symbol": pipeline_result.fused_symbol,
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"steps": [{"name": s.name, "kind": s.kind, "payload": str(s.payload)[:100]}
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for s in pipeline_result.steps],
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}
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def op_SET_CONTEXT(ctx: XICContext, *args):
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