bigmerge
4bbfdcceff
audio-surface.el / image-surface.el: own-core additive-synthesis WAV and
raster-PNG renderers (integer-only DSP, since EL has no floats), rendered
from learned engram signatures via a pluggable surface-profile
abstraction (surface-profile.el). audio-demo.el / image-demo.el are
drivers. NOTE: demo files hardcode absolute paths to this worktree's own
directory — will need a path fixup before landing.
elp/projector/ is a Python package the author's own README marks as
"STAGING/PROOF-OF-SHAPE — not the deliverable", superseded by the native
.el surface-profile work above; kept as a validated architecture proof.
Generated output (elp/faculty/{out,sig}, elp/projector/out,
__pycache__) intentionally excluded.
80 lines
3.2 KiB
Python
80 lines
3.2 KiB
Python
"""cohere.py — COHERE stage: document-level flow, not stitched sentences.
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Fidelity is REALIZE's job; FLOW is this stage's. The hard part beyond sentence
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fidelity is that a document must read as one thing. We add connective tissue at
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the passage level:
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* an opening abstract that names what the document covers (built ONLY from the
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section headings that already exist — it introduces no new claim),
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* a short transition lead into each section after the first, drawn from a
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fixed set of discourse connectives ("Beyond that,", "Relatedly,", ...) that
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carry no propositional content,
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* ordering so the highest-grounded section leads.
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CRITICAL: every connective is marked ``kind="connective"`` in its provenance, so
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the faithfulness audit can prove COHERE introduced ZERO new geometry claims. A
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transition is discourse glue, never a fact.
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"""
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from __future__ import annotations
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from document_ir import Block, DocumentIR, Provenance
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# discourse connectives — pure flow, no propositional content
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_TRANSITIONS = [
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"Beyond that,", "Relatedly,", "In the same region,", "From there,",
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"Alongside this,", "Further,", "Turning to the next facet,",
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]
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def _connective_prov() -> Provenance:
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return Provenance(subj_id=None, subject=None, relation="", obj=None,
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polarity="aff", confidence=1.0, node_id=None,
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kind="connective")
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def _abstract_block(doc: DocumentIR) -> Block:
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"""A grounded opening: names the sections, asserts nothing new."""
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headings = [s.heading for s in doc.sections]
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if not headings:
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return Block(role="lead")
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if len(headings) == 1:
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body = f"This document, generated from Neuron's geometry, covers {headings[0]}."
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else:
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listed = ", ".join(headings[:-1]) + f", and {headings[-1]}"
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body = ("This document is projected directly from Neuron's meaning-geometry. "
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f"It traces {listed}.")
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b = Block(role="lead")
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b.sentences.append(body)
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b.provenance.append(_connective_prov())
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return b
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def cohere_document(doc: DocumentIR, *, add_abstract: bool = True,
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add_transitions: bool = True) -> DocumentIR:
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"""Order sections by grounding, add abstract + transitions (flow only)."""
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# order: strongest-grounded section (mean confidence x #claims) first,
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# but keep an explicitly-first section if the plan pinned one via level 1.
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def _score(sec):
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provs = [p for p in sec.all_provenance() if p.kind == "fact"]
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if not provs:
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return 0.0
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mean_conf = sum(p.confidence for p in provs) / len(provs)
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return mean_conf * len(provs)
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doc.sections.sort(key=_score, reverse=True)
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if add_transitions:
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for i, sec in enumerate(doc.sections):
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if i == 0 or not sec.blocks:
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continue
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lead = _TRANSITIONS[(i - 1) % len(_TRANSITIONS)]
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first = sec.blocks[0]
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if first.sentences:
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# prepend the connective to the first sentence (flow, no new claim)
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first.sentences[0] = f"{lead} {first.sentences[0][0].lower()}{first.sentences[0][1:]}"
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if add_abstract:
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doc.meta["abstract"] = _abstract_block(doc)
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return doc
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