Implement real LAIN cognition engine with 8 lane processors
New modules: - gx_lain/lane_processors.py: 8 symbolic lane processors * Lane 0: structural_logic (control flow, constraints) * Lane 1: semantic_flow (core meaning, narrative) * Lane 2: compression_residue (artifacts, hints) * Lane 3: symbolic_metadata (tags, annotations) * Lane 4: execution_hints (runtime guards, priorities) * Lane 5: predictive_scaffolding (hypotheses, priors) * Lane 6: contributor_imprint (author style, bias) * Lane 7: epoch_resonance (temporal context) - gx_lain/runtime.py (updated): Real cognition loop * execute_with_lain(): Process all 8 lanes, capture timings * fuse_lanes(): Merge lane results into final symbol * compute_resonance(): Per-lane resonance metrics * render_output_text(): Mode-based output formatting Features: - Structured lane processing with error recovery - Cognition trace with per-lane timing - Resonance metrics (1.0 if lane has content) - Fused symbol with deduplication - Mode-aware output (ANALYZE vs SYNTHESIZE) - No mutations, deterministic execution All 18 integration tests pass unchanged.
This commit is contained in:
GlyphRunner System
+206
-47
@@ -246,11 +246,137 @@ def build_envelope(
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return envelope
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def execute_with_lain(envelope: dict) -> dict:
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"""Execute ExecutionEnvelope through (stub) LAIN engine.
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def fuse_lanes(lane_results: Dict[int, dict]) -> dict:
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"""Fuse lane results into final fused_symbol.
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This is a stub implementation that simulates LAIN cognition.
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In production, this would call the real LAIN runtime.
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Merges summaries, key_points, constraints, and open_questions
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from all lanes into a single coherent representation.
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Args:
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lane_results: Dict[lane_id, lane_result]
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Returns:
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Fused symbol dict with summary, key_points, constraints, open_questions
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"""
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summaries = []
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all_key_points = []
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all_constraints = []
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all_questions = []
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for lane_id in sorted(lane_results.keys()):
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result = lane_results[lane_id]
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# Collect summaries
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if result.get("summary"):
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summaries.append(result["summary"])
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# Collect key points
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all_key_points.extend(result.get("key_points", []))
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# Collect constraints
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all_constraints.extend(result.get("constraints", []))
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# Collect open questions
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all_questions.extend(result.get("open_questions", []))
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# Merge summary
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if summaries:
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combined_summary = " | ".join(summaries)
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else:
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combined_summary = "No lanes processed"
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# Deduplicate and limit key points
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unique_key_points = list(dict.fromkeys(all_key_points))[:10]
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# Deduplicate constraints
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unique_constraints = list(dict.fromkeys(all_constraints))
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# Deduplicate questions
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unique_questions = list(dict.fromkeys(all_questions))
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return {
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"summary": combined_summary,
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"key_points": unique_key_points,
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"constraints": unique_constraints,
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"open_questions": unique_questions,
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}
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def compute_resonance(lane_results: Dict[int, dict], context: Dict[str, Any]) -> dict:
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"""Compute resonance metrics for lanes.
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Simple rule: resonance[lane] = 1.0 if lane produced content, else 0.0
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Args:
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lane_results: Dict[lane_id, lane_result]
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context: Execution context
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Returns:
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Dict[str, float] with resonance metrics
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"""
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resonance = {}
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for lane_id in sorted(lane_results.keys()):
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result = lane_results[lane_id]
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# Lane has content if it produced a non-empty summary
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has_content = bool(result.get("summary", "").strip())
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resonance[f"lane_{lane_id}"] = 1.0 if has_content else 0.0
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return resonance
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def render_output_text(fused_symbol: dict, context: Dict[str, Any]) -> str:
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"""Render human-facing output text from fused_symbol.
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Format varies by cognitive_mode.
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Args:
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fused_symbol: Fused symbol dict
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context: Execution context
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Returns:
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Human-readable output string
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"""
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mode = context.get("cognitive_mode", "analyze")
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mode_label = mode.upper()
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summary = fused_symbol.get("summary", "No summary")
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lines = [
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f"[{mode_label}]",
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f"{summary}",
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]
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key_points = fused_symbol.get("key_points", [])
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if key_points:
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lines.append("")
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lines.append("Key Points:")
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for point in key_points[:5]:
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lines.append(f" • {point}")
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constraints = fused_symbol.get("constraints", [])
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if constraints:
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lines.append("")
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lines.append("Constraints:")
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for constraint in constraints[:5]:
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lines.append(f" • {constraint}")
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questions = fused_symbol.get("open_questions", [])
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if questions:
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lines.append("")
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lines.append("Open Questions:")
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for question in questions[:5]:
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lines.append(f" ? {question}")
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return "\n".join(lines)
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def execute_with_lain(envelope: dict) -> dict:
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"""Execute ExecutionEnvelope through LAIN cognition engine.
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Real implementation: iterate through lanes, process each via lane processors,
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fuse results, and return full ExecutionResult.
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Contract:
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- Does not mutate input envelope
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@@ -263,6 +389,8 @@ def execute_with_lain(envelope: dict) -> dict:
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Returns:
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ExecutionResult dict with cognition_trace, fused_symbol, output_text, diagnostics
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"""
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from .lane_processors import process_lane
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start_time = time.time()
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manifest = envelope.get("manifest", {})
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@@ -270,18 +398,13 @@ def execute_with_lain(envelope: dict) -> dict:
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payload = envelope.get("payload", b"")
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context = envelope.get("context", {})
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# Initialize diagnostics
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# Initialize tracking
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lane_timings: Dict[int, float] = {}
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lane_results: Dict[int, dict] = {}
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errors: List[dict] = []
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# Stub: simulate processing each lane
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for lane_id in sorted(lanes.keys()):
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lane_timings[lane_id] = 0.0
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# Build cognition trace (stub)
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cognition_trace = []
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# Step 0: Load
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# Step 0: Load envelope
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cognition_trace.append({
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"step": 0,
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"lane": -1,
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@@ -293,46 +416,82 @@ def execute_with_lain(envelope: dict) -> dict:
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"manifest_version": manifest.get("version"),
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},
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"output": {},
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"note": "Loaded ExecutionEnvelope into LAIN stub.",
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"note": "Loaded ExecutionEnvelope into LAIN cognition engine.",
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})
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# Step 1: Process lanes
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num_segments = sum(len(segs) for segs in lanes.values())
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cognition_trace.append({
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"step": 1,
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"lane": -1,
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"segment_id": None,
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"operation": "process_lanes",
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"input": {"lanes": sorted(lanes.keys())},
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"output": {"processed_segments": num_segments},
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"note": f"Stub processed {num_segments} segments.",
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})
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# Synthesize fused_symbol from lanes and segments
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all_segments = []
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# Process each lane
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step_num = 1
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for lane_id in sorted(lanes.keys()):
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all_segments.extend(lanes[lane_id])
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lane_start = time.time()
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lane_segments = lanes.get(lane_id, [])
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key_points = [seg["id"] for seg in all_segments[:3]]
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try:
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# Call lane processor
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lane_result = process_lane(
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lane_id,
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lane_segments,
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context,
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manifest,
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)
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lane_results[lane_id] = lane_result
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fused_symbol = {
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"summary": f"GX→LAIN stub: {len(all_segments)} segments, {len(lanes)} lanes",
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"key_points": key_points,
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"constraints": [],
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"open_questions": ["Real LAIN cognition not yet implemented"],
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}
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# Record timing
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lane_elapsed = time.time() - lane_start
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lane_timings[lane_id] = lane_elapsed
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# Output text
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contributor = manifest.get("contributor", "unknown")
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source = manifest.get("source_file", "unknown")
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output_text = (
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f"GX→LAIN Runtime Stub v{INTERFACE_VERSION}\n"
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f"Source: {source}\n"
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f"Contributor: {contributor}\n"
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f"Segments: {len(all_segments)}\n"
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f"Lanes: {len(lanes)}\n"
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f"Status: Stub execution (replace with real LAIN engine)\n"
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)
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# Trace entry
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cognition_trace.append({
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"step": step_num,
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"lane": lane_id,
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"segment_id": None,
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"operation": f"process_lane_{lane_id}",
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"input": {"segments": len(lane_segments)},
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"output": {
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"summary_length": len(lane_result.get("summary", "")),
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"key_points": len(lane_result.get("key_points", [])),
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},
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"note": f"Processed lane {lane_id} with {len(lane_segments)} segments in {lane_elapsed:.4f}s.",
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})
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except Exception as e:
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lane_elapsed = time.time() - lane_start
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lane_timings[lane_id] = lane_elapsed
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err = make_error(
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"LaneProcessorError",
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f"Lane {lane_id} processing failed: {e}",
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lane=lane_id,
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recoverable=True,
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)
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errors.append(err)
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lane_results[lane_id] = {
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"summary": f"Error processing lane {lane_id}",
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"key_points": [],
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"constraints": [],
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"open_questions": [],
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}
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cognition_trace.append({
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"step": step_num,
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"lane": lane_id,
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"segment_id": None,
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"operation": f"process_lane_{lane_id}",
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"input": {"segments": len(lane_segments)},
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"output": {"error": str(e)},
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"note": f"Lane {lane_id} processing failed (recoverable).",
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})
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step_num += 1
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# Fuse lane results
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fused_symbol = fuse_lanes(lane_results)
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# Compute resonance
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resonance = compute_resonance(lane_results, context)
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# Render output text
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output_text = render_output_text(fused_symbol, context)
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elapsed = time.time() - start_time
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@@ -340,7 +499,7 @@ def execute_with_lain(envelope: dict) -> dict:
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diagnostics = {
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"lane_timings": lane_timings,
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"errors": errors,
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"resonance": {},
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"resonance": resonance,
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"interface_version": INTERFACE_VERSION,
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"elapsed": elapsed,
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}
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