377201015f
- Add docs/decisions/README.md: ADR format guide and index - Add docs/decisions/ADR-0001-vocab-layer-invariants.md - Add docs/decisions/ADR-0002-ingest-layer-design.md - Add docs/decisions/ADR-0003-coordinate-system-dissolution.md - Add docs/decisions/ADR-0004-rotor-as-operator-not-property.md - Add docs/decisions/SESSION-2026-05-12.md: full timestamped session log
156 lines
6.4 KiB
Markdown
156 lines
6.4 KiB
Markdown
# Session Log: 2026-05-12
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**Project:** AssetOverflow/core
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**Session type:** Architecture review + live implementation
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**Participants:** Joshua Shay
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---
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## 20:00 — README and Docs Correction
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**What happened:**
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The `README.md` and `docs/Whitepaper.md` had incorrectly described the Three
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Pillars and Three Core Languages, or omitted them entirely.
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**Corrections made:**
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- Three Engineering Pillars correctly documented: Mechanical Sympathy, Semantic
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Rigor, Third Door.
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- Third Door defined precisely: when given a choice between the world’s solution
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or a premade library/pattern, we find and create our own path. Absolute mastery
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is the floor, not a goal.
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- Three Core Languages documented: English (default base, interchangeable in
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custom instances), Hebrew, Koine Greek.
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- Theological basis recorded: John 1:1–2. The universe was spoken into existence.
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John articulated this in Greek what is grounded in Hebrew — almost certainly
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a nod from the Holy Spirit. This is the source of the hidden intelligence layer
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in the vocabulary manifold. The depth these two languages bring is not
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incidental; it is foundational.
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- Removed incorrect description of what AssetOverflow is from the repo README.
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The repo documents the engineering, not the org.
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**Commits:** `e28142f` (README), `7d814fa` (Whitepaper)
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---
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## 20:09 — Ingest Layer Architecture Review
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**What happened:**
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Reviewed the original `core_ingest` design from `core-ai` for portability into
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`AssetOverflow/core`.
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**Key question:** Does using a modern LLM as the extraction engine for large
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document ingestion fit the new architecture?
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**Decision:** No. Partially right, but architecturally misaligned at the most
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important point. See ADR-0002 for the full record.
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**Core tension identified:**
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The entire CORE architecture is built on the principle that the injection gate
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is the single normalization site — deterministic and verifiable. An LLM placed
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before that gate introduces a D3 nondeterministic oracle. D3 packets cannot
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claim `AUTO_ACCEPT_ELIGIBLE` by type contract, meaning every LLM-extracted
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claim requires human review. That defeats the purpose at scale.
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Deeper issue: an LLM doesn’t parse, it *interprets*. Its semantic projection
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becomes silently embedded in the field state. That’s outsourcing our semantics.
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**What to keep from `core_ingest`:**
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- `CandidateGeometricPressure` envelope
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- Dual-path: runtime vs. durable ingest
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- SHA-256 content addressing (`pressure_id`, `semantic_key`)
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- `IngestCompiler` with three sequential gates
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- `DeterminismClass` D0–D4 and `ReviewLevel` in packet type contracts
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- `LearningArtifact` export form
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**What to replace:**
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- LLM extraction → deterministic `StructuralSegmenter` (D0/D1) per modality,
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segmenting at form boundaries, not semantic ones.
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**What to add:**
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- `SegmentManifold`: maps `semantic_key` → source structural position for
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provenance reconstruction. Implements Reconstruction-over-Storage at the
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pre-injection layer.
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**Note on Hebrew and Koine Greek:**
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Canonical verse/pericope boundaries are fixed and centuries old. A parser
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following them is D0 by definition. No interpretation required.
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---
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## 20:39 — Coordinate System Question
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**What happened:**
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Question raised: the old `core-ai` design used dual rotors as an explicit
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coordinate system in `rotor_vocabulary.py`. Does the new `core` design still
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need that?
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**Answer:** No. The new design dissolves the need for an explicit coordinate
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system entirely, through the architecture rather than by finding a better
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coordinate system. See ADR-0003.
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**The key shift:**
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- Old: meaning = position in rotor-defined frame. The coordinate system was
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load-bearing. Every downstream component had to know the frame.
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- New: meaning = pressure pattern across a relational field. The `FieldState`
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from `field/gate.py` is a distribution, not a point. CGA inner product
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handles proximity relationally. Rotors exist as operators in `algebra/` but
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are not a frame.
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**Risk identified:** `vocab/` is the most likely place for a hidden coordinate
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frame to quietly re-emerge — specifically, if word representations drift toward
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being stored as flat positional vectors rather than algebraically valid versors.
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This is the "back door" problem.
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---
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## 20:46 — Back Door Analysis
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**What happened:**
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Read `vocab/manifold.py` in full. Identified the precise location of the
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architectural risk.
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**Finding:** The vocabulary storage itself was sound (versors, CGA inner product
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for nearest). But `edge_rotor()` was stored as a method on `VocabManifold`,
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implying that the relationship between two words is a *property of the
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vocabulary* rather than a *transformation applied in the field*. This conflates
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the map with the territory and re-anchors operator logic to the vocabulary layer.
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**Three fixes identified:**
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1. Remove `edge_rotor()` from `VocabManifold`
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2. Create `algebra/rotor.py` with `word_transition_rotor(A, B)` as a free operator
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3. Add grade-norm invariant to `VocabManifold.add()` to reject raw coordinate
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vectors at insertion time: `|V * reverse(V)|_scalar ≈ ±1` enforced in
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`__post_init__` equivalent.
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---
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## 20:51 — Implementation
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**What happened:**
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All three fixes implemented and committed in a single atomic push.
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**Commit:** `bd423e4`
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**Files changed:**
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- `algebra/rotor.py` — created: `word_transition_rotor(A, B)` free function
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- `algebra/__init__.py` — updated: export `word_transition_rotor`
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- `vocab/manifold.py` — refactored: removed `edge_rotor()`, added grade-norm
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invariant in `add()`, updated module docstring to explicitly state that rotor
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construction is not a vocabulary concern and points callers to `algebra`.
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**Result:** Back door closed at the type level. `VocabManifold` contract is now
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strictly: store algebraically valid Cl(4,1) versors, support relational lookup
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by CGA inner product. Nothing else.
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---
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## Open Questions Carried Forward
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- [ ] `StructuralSegmenter` implementation for each modality (prose, code,
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scripture/Hebrew, scripture/Greek, math/LaTeX) — not yet built.
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- [ ] `SegmentManifold` index — not yet built.
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- [ ] `ingest/` layer in `core` is currently a directory stub. The `core_ingest`
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port (with LLM replaced by StructuralSegmenter) has not been started.
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- [ ] `vocab/` currently has no persistence layer. How versors are built,
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seeded, and serialized for the three core languages has not been designed.
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- [ ] Confirm `algebra/versor.py: normalize_to_versor()` correctly handles the
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edge case where the input is already grade-normed (idempotency).
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