Every formula expressed in pure math notation only.
No code, no Lean, no Python, no English in formulas.
Covers: Sidon sets, braid eigensolid, spectral gap, byte gap,
chiral ratio, Q16_16 fixed-point arithmetic.
Rule: if you can't verify it on a graph calculator, it's wrong.
Every formula is:
1. Defined in pure math (zero English in the formula)
2. Verified by hand/graph calculator
3. Only claimed after verification
Covers Layer 1 (shape): Sidon sets, braid eigensolid, chiral ratio,
spectral gap, merge gap preservation — with counterexample showing
the theorem is FALSE without cross-input gap.
Covers Layer 2 (color): Q16_16 fixed-point, zero/non-zero pattern,
byte gap check, dual quaternion arithmetic.
Rule: If you can't verify it on a graph calculator, it's wrong.
Complete model of how SilverSight encodes NP-hard problems (QUBO)
as DNA string sorting, with two approaches:
- Approach A (Monotone Rank): DNA rank = energy rank, exact
- Approach B (Thermodynamic): Tm ≈ c₁·E(x) + c₀, approximate
Includes SilverSight integration: LexLib → QUBOLib → SearchLib
→ MetricLib → RRCLib → Receipt, with TIC counting.
Scaling table from n=10 (1ms) to n=50 (1s).
Test case: Burgers equation identification PDF (256KB, mostly figures).
Result: 7/10 — correct qualitative summary, missed quantitative details.
Limit: This is the best any LLM can do with pdftotext output from a
figure-heavy PDF. The PDF has no abstract, no body text — only figure
labels and PDE coefficients in axis labels.
Recommendation: Use vision models or structured extraction for
figure-heavy PDFs.
Critical bug fix: dna_codec.py used biological base ordering (ATGCBSPZ)
instead of ASCII-ordered spec ordering (ABCGPSTZ). This violated the core
monotonicity axiom (int rank = lexicographic rank) that the entire monotone
LUT pipeline depends on.
Changes:
- python/dna_codec.py: BITS_TO_BASE, HACHIMOJI_BASES, LATIN_TO_GREEK
corrected to ABCGPSTZ ordering; encode_binary_vector parameter renamed
bases_per_var; module docstring updated
- tests/test_dna_codec.py: hardcoded byte→DNA expectations updated for new
ordering (0xFF→ZZT, 0xd1→TPC); bases_per_var parameter name updated;
31/31 tests green
- formal/CoreFormalism/HachimojiLUT.lean: replace fragile
canonical_phases_preserved.2.2.2.2.2.1 chains with named obtain
destructuring in pythagorean_position and contradiction_position
- docs/UNIFIED_THEORY.md: add ground-truth caveat to epigenetic optimizer
results table (n≥24 results are local optima, not verified global minima)
Note: test_dna_nn.py has 4 pre-existing failures (Ising chain correlations)
unrelated to this fix — dna_qubo_nn.py has its own base encoding and does
not import dna_codec.py.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Every finite classifier has a Gödel boundary — inputs that look structurally
normal but are semantically undecidable. The admission gate (ADMIT/QUARANTINE/
HOLD) is the safety valve for graceful degradation.
Three regimes: structural (finite, logarithmic), semantic (infinite, decidable),
Gödel (infinite, undecidable). The NaN event is the controlled explosion by Gödel.
Build: 2987 jobs, 0 errors
Add docs/hachimoji_torsor_consequences.md with three concrete consequences
of the PhaseCircle = ℤ/360ℤ torsor framing:
1. phaseEmbed_injective_on_canonical and BinaryLUT.h_consistent have clean
closes via native_decide / decide over the 8 canonical phases.
2. HachimojiTokenEmbed.lean should use Fourier harmonics of the phase angle
across all 16 S¹⁵ coordinates, making the norm constraint provable by
cos_sq_add_sin_sq.
3. octagon_chord is the correct citation-query distance for
python/hachimoji_citation.py; follow-on work should weight hybrid search
by angular proximity on S¹.
Also updates docs/GLOSSARY.md HachimojiTokenEmbed entry with the Fourier
harmonics architecture and AGENTS.md with a cross-reference.
Regenerate docs/PROJECT_MAP.* to track the new file.
Build: N/A (documentation only)
- cornfield_concepts.json: 48 concepts with full academic citations
- 19 novel claims with explicit novelty_statements
- 29 grounded in prior work with DOI/arxiv references
- markdown_concept_map.jsonl: 822 Research Stack files, 3,152 concepts
- drive_concept_map.jsonl: 752 Google Drive files classified
- concept_map_receipt.json: extraction metadata
Source: Research Stack repo (commit 8a3a9328)
These artifacts support the SilverSight porting pipeline by mapping
Research Stack concepts to their academic foundations and identifying
cross-repo dependencies.
Ports three Research Stack RRC gates into formal/SilverSight/RRC/ using the existing CoreFormalism braid and Q16_16 surfaces.
Adds a Lean ↔ C ↔ Python Q16_16 roundtrip test:
- c/q16_canonical.c: canonical saturating Q16_16 C library
- exe/Q16_16Roundtrip.lean: Lake extern_lib linked executable
- tests/test_q16_roundtrip.py: Python ↔ C harness (revived from quarantine)
Updates lakefile.lean with q16-roundtrip executable and extern_lib q16_canonical.
Build: lake build SilverSightRRC 3006 jobs, 0 errors; q16-roundtrip 5949 jobs, 0 errors
