2125_GCE/gx_lain/lane_processors.py

from typing import Dict, List, Any

def process_lane_0_structural_logic(
    lane: int,
    segments: List[dict],
    context: Dict[str, Any],
    manifest: Dict[str, Any],
) -> dict:
    """Process lane 0: structural_logic

    Control flow, structure, constraints.
    """
    summary = f"Structural constraints and control flow across {len(segments)} segments"
    key_points = [seg["id"] for seg in segments[:3]]
    constraints = [
        "Preserve execution flow integrity",
        "All control paths reachable",
        "No circular dependencies",
    ] if segments else []
    open_questions = []

    return {
        "summary": summary,
        "key_points": key_points,
        "constraints": constraints,
        "open_questions": open_questions,
    }


def process_lane_1_semantic_flow(
    lane: int,
    segments: List[dict],
    context: Dict[str, Any],
    manifest: Dict[str, Any],
) -> dict:
    """Process lane 1: semantic_flow

    Core meaning, narrative, reasoning.
    """
    summary = f"Semantic flow and core meaning from {len(segments)} segments"
    key_points = [seg["id"] for seg in segments[:5]]
    constraints = []
    open_questions = []

    return {
        "summary": summary,
        "key_points": key_points,
        "constraints": constraints,
        "open_questions": open_questions,
    }


def process_lane_2_compression_residue(
    lane: int,
    segments: List[dict],
    context: Dict[str, Any],
    manifest: Dict[str, Any],
) -> dict:
    """Process lane 2: compression_residue

    Lossy artifacts, hints, side-noise.
    """
    summary = f"Compression residue from {len(segments)} segments"
    key_points = []
    constraints = []
    open_questions = []

    return {
        "summary": summary,
        "key_points": key_points,
        "constraints": constraints,
        "open_questions": open_questions,
    }


def process_lane_3_symbolic_metadata(
    lane: int,
    segments: List[dict],
    context: Dict[str, Any],
    manifest: Dict[str, Any],
) -> dict:
    """Process lane 3: symbolic_metadata

    Tags, labels, annotations.
    """
    summary = f"Symbolic metadata and annotations from {len(segments)} segments"
    key_points = []
    constraints = []
    open_questions = []

    return {
        "summary": summary,
        "key_points": key_points,
        "constraints": constraints,
        "open_questions": open_questions,
    }


def process_lane_4_execution_hints(
    lane: int,
    segments: List[dict],
    context: Dict[str, Any],
    manifest: Dict[str, Any],
) -> dict:
    """Process lane 4: execution_hints

    Runtime hints, priorities, guards.
    """
    summary = f"Execution hints and runtime guards from {len(segments)} segments"
    key_points = [seg["id"] for seg in segments[:3]]
    constraints = [
        "Guard all conditional branches",
        "Enforce runtime priorities",
        "Validate input constraints",
    ] if segments else []
    open_questions = []

    return {
        "summary": summary,
        "key_points": key_points,
        "constraints": constraints,
        "open_questions": open_questions,
    }


def process_lane_5_predictive_scaffolding(
    lane: int,
    segments: List[dict],
    context: Dict[str, Any],
    manifest: Dict[str, Any],
) -> dict:
    """Process lane 5: predictive_scaffolding

    Anticipations, hypotheses, priors.
    """
    summary = f"Predictive scaffolding and hypotheses from {len(segments)} segments"
    key_points = []
    constraints = []
    open_questions = [
        "What are the likely next states?",
        "What hypotheses structure the reasoning?",
        "What priors guide the inference?",
    ] if segments else []

    return {
        "summary": summary,
        "key_points": key_points,
        "constraints": constraints,
        "open_questions": open_questions,
    }


def process_lane_6_contributor_imprint(
    lane: int,
    segments: List[dict],
    context: Dict[str, Any],
    manifest: Dict[str, Any],
) -> dict:
    """Process lane 6: contributor_imprint

    Author style, bias, signature.
    """
    contributor = manifest.get("contributor", "unknown")
    summary = f"Contributor imprint from {contributor} ({len(segments)} segments)"
    key_points = []
    constraints = []
    open_questions = []

    return {
        "summary": summary,
        "key_points": key_points,
        "constraints": constraints,
        "open_questions": open_questions,
    }


def process_lane_7_epoch_resonance(
    lane: int,
    segments: List[dict],
    context: Dict[str, Any],
    manifest: Dict[str, Any],
) -> dict:
    """Process lane 7: epoch_resonance

    Time/epoch/contextual modulation.
    """
    version = manifest.get("version", "unknown")
    summary = f"Epoch resonance and temporal context from version {version} ({len(segments)} segments)"
    key_points = []
    constraints = []
    open_questions = [
        "How does temporal context affect interpretation?",
        "What epoch-specific constraints apply?",
    ] if segments else []

    return {
        "summary": summary,
        "key_points": key_points,
        "constraints": constraints,
        "open_questions": open_questions,
    }


LANE_PROCESSORS = {
    0: process_lane_0_structural_logic,
    1: process_lane_1_semantic_flow,
    2: process_lane_2_compression_residue,
    3: process_lane_3_symbolic_metadata,
    4: process_lane_4_execution_hints,
    5: process_lane_5_predictive_scaffolding,
    6: process_lane_6_contributor_imprint,
    7: process_lane_7_epoch_resonance,
}


def process_lane(
    lane: int,
    segments: List[dict],
    context: Dict[str, Any],
    manifest: Dict[str, Any],
) -> dict:
    """Route to the appropriate lane processor.

    Args:
        lane: Lane id 0–7
        segments: Segments assigned to this lane
        context: Execution context
        manifest: GX manifest

    Returns:
        Lane result dict with summary, key_points, constraints, open_questions
    """
    processor = LANE_PROCESSORS.get(lane)
    if not processor:
        return {
            "summary": f"Unknown lane {lane}",
            "key_points": [],
            "constraints": [],
            "open_questions": [],
        }

    try:
        return processor(lane, segments, context, manifest)
    except Exception as e:
        return {
            "summary": f"Error processing lane {lane}: {e}",
            "key_points": [],
            "constraints": [],
            "open_questions": [],
        }