3,057 theorems/lemmas → ene.prover_state (2,966 after dedup)
3,047 verified proofs → ene.prover_instances
Also fixes ON CONFLICT DO NOTHING and FK bypass for bulk load.
These directories were tracked despite gitignore patterns. Removing them
frees ~120 GB (shared-data), ~1.5 GB (archive), and ~7 GB (lean_binned).
They are either offloaded to Garage S3/Google Drive or superseded
proof artifacts.
- distribute_extraction.py: add gremlin_escape() for special chars,
add --batch-size async mode with submitAsync()
- load_cornfield.py: pipe SQL via stdin to avoid shell metachar issues
- populate_ene_tables.py: same stdin pipe fix for neon SSH calls
When --rrc is given, the shim runs pist-classify-trace on the trace fixture
and rrc-watchdog for RRC alignment, then writes an additional
rrc_alignment receipt into ene.receipts alongside the build receipt.
Sets content_hash to NULL for build_log packages so the partial unique
index ene_pkg_hash_idx does not prevent multiple ingestions of the same
build output. Each build session now gets its own package row. Also makes
receipt/ingest_event inserts idempotent with ON CONFLICT DO NOTHING.
Add concept extraction/orchestration scripts under scripts/ and infra
metadata in infra.json. Generated extraction outputs live under extraction/
and are regenerated from scripts; add extraction/ to .gitignore.
Adds a Build-log automation section noting the post-commit hook runs lake_build_ingest.py Compiler in the background, inserting into ENE on neon-64gb. Includes log path and reinstall note.
Adds canonical database location notes to 4-Infrastructure/AGENTS.md:
- Postgres canonical instance is the arxiv-pg container on neon-64gb.
- Gremlin canonical instance is the Azure Cosmos DB endpoint.
- Explicitly warns against spinning up local Postgres on qfox for
production data.
Also adds `lake_build_ingest.py` to the stack-solidification anchors.
Adds 4-Infrastructure/shim/lake_build_ingest.py, a pure-I/O shim that:
- runs `lake build <target>` in the Semantics directory,
- parses job count, error count, warnings, and status,
- inserts a session, package, receipt, and ingest_event into the
ENE database on neon-64gb (arxiv-pg container).
The script contains no admissibility logic and no Float arithmetic.
Tested with `lake build Semantics.NKHodgeFAMM` — 8316 jobs, 0 errors,
session d1ea53db2185df87dd41497da12101f5.
Adds logViscosityRatio, log_viscosity_monotone, and ν_eff_monotone
to Semantics/NKHodgeFAMM.lean section 6b. The adaptive viscosity law
ν_eff = ν₀*(1+μ) is multiplicative in ν₀ and additive in scar density μ;
taking λ = log(ν_eff/ν₀) = log(1+μ) turns the multiplicative feedback into
an additive coordinate. This gives nlinarith a direct handle on viscosity
monotonicity and connects the module to Kritchevsky's "Everything Is
Logarithms" framing (SilverSight CITATION.cff).
Also marks a few pre-existing unused variables with underscores to silence
the linter.
Build: 8316 jobs, 0 errors (lake build Semantics.NKHodgeFAMM)
- Updates CHANGELOG.md with 2026-06-21 entry describing the two-regime
phase transition observed on neon-64gb: bosonic MC (p<=4) vs
distinguishable approximation (p>=5) in rrc_bosonic_tensor_network.py.
- Adds BMCTE, SBOE, LPMCE, effective support, and observable-estimation
vs exact-simulation terms to 6-Documentation/docs/GLOSSARY.md.
- Fixes Hilbert-dimension summary in rrc_bosonic_tensor_network.py to use
C(N+p-1, p) instead of C(N, p).
No core logic changed; documentation and summary-output correction only.
- Updated .mcp.json with WOLFRAM_APP_ID env var (fallback to hardcoded HYJE3R3R63)
- Toolchain manifest documents all tools used in hachimoji citation pipeline
Build: 3314 jobs, 0 errors (lake build)
Check file sizes before loading bytes into memory and stream large files
directly into the tarball to prevent OOM errors on Steam Deck. Skip downloading
files that already exist locally.
Build: 0 errors (py_compile)
Adds 4-Infrastructure/shim/mcp_drive_gccl_compress.py:
- downloads a Google Drive folder via @piotr-agier/google-drive-mcp
- GCCL-delta-compresses contents into .tar.zst + JSON manifest
- uploads archive + manifest back to Drive
- optionally trashes the original folder
- handles MCP listFolder pagination
Updates 4-Infrastructure/AGENTS.md with the new shim anchor.
No rclone or egress-heavy operations; all Drive I/O goes through MCP.
Finds related arxiv papers for cornfield concepts via keyword overlap.
Uses /shm/arxiv_texts.tsv (559 MB) for fast local matching.
pg_trgm GIN indexes on neon-64gb for Postgres queries.
Results: 240 candidate citations for 48 concepts.
Loaded into concept_citations table (relation_type='candidate').
240 candidate citations found for 48 cornfield concepts using
pg_trgm GIN indexes and /shm file-based matching against 700k papers.
DB state: 358 total concept_citations (96 grounds, 22 extends, 240 candidates)
Moves all classification authority from Python into Lean:
- Adds `0-Core-Formalism/lean/Semantics/PistClassifyTrace.lean` executable.
Reads a raw trace JSON and emits spectral radius, RRC shape, and tactic
family using `Semantics.PIST.Spectral` and `Semantics.PIST.Classify`.
- Registers `pist-classify-trace` in `lakefile.toml`.
- Fixes `Semantics.PIST.Spectral` power iteration to handle directed
transition matrices:
- `symmetrize` now preserves half-integer weights as Q16_16 raw values.
- `matVecMul` uses saturated Q16_16 arithmetic to prevent overflow.
- Rewrites `4-Infrastructure/shim/pist_trace_classify_offline.py` as a pure
I/O wrapper: reads JSON, calls the Lean classifier, optionally calls
`rrc-watchdog`, and emits the combined JSON. Removes Python-side spectral
computation, shape thresholds, tactic heuristic, and KNN.
- Updates `AGENTS.md` and `4-Infrastructure/AGENTS.md` with the new build
baseline and shim contract.
Verification:
- `lake build` → 8604 jobs, 0 errors
- Canary trace outputs match previous Python outputs to within Q16_16 rounding
(e.g., apply_chain λ_q16 = 59044 vs 59045).
- `python3 -m py_compile` on the rewritten shim passes.
Created a local, offline Python shim `pist_trace_classify_offline.py` that computes trace transition matrix spectra, maps the max eigenvalue to the Q16.16 spectral radius, evaluates the color-space shape classification logic from `Semantics.PIST.Classify`, and invokes the local `rrc-watchdog` Lean binary inside the podman container to verify alignment. This avoids querying the dead AWS RDS instance and saves LLM API tokens.
Build: 3314 jobs, 0 errors (lake build Compiler)
Overwrites 6-Documentation/docs/plans/SilverSight_completion_pipeline.md
with a per-file, per-theorem, per-commit microstep plan covering Phases
1-8: Core Semantics completion, Research Stack triage/port, corpus build,
mathematical models, shim rewrite, hardware extraction, applications,
and final documentation/promotion. Includes claim-state ladder, open
question answers, dependency graph, verification gates, CI coverage,
risks, and acceptance criteria.
Adds 6-Documentation/docs/plans/SilverSight_completion_pipeline.md,
describing the full phase plan from the current core Semantics state to
a verified SilverSight 1.0: Core completion, Research Stack triage/port,
corpus build, mathematical models, shim rewrite, hardware extraction,
applications, documentation, and claim-state promotion.
Adds 6-Documentation/docs/specs/SilverSight_Spec.md, documenting the
YaFF-inspired separation of schema, layout, access pattern, wire format,
view, layout bridge, and canal-aware layout selection for the SilverSight
clean-slate architecture.
Added a python-based wrapper script for lean-lsp-mcp that intercepts and cleans the 'severity' schema field from the tools/list output, resolving the 400 error from Moonshot API which strictly forbids defining 'type' in parent schemas alongside 'anyOf'.
Build: 3314 jobs, 0 errors (lake build)
UniversalMathEncoding.lean (1,000 lines):
- 50 MathTokens organized into 8 Hachimoji groups
- 2^50 = 1.126 quadrillion unique expression addresses
- Sparse embedding: each token → 2D plane in 16D chaos space
- Multiple embedding: address activates direct sum of constituent planes
- Sub-basin classification: (Hachimoji state, Sidon sub-address) pair
- ~268 million sub-basins, ~4,000 expressions each
- Full PVGS-DQ receipt compatibility
Concept: every mathematical expression ever written fits in 0.1%
of the 2^50 address space. The 50 tokens represent fundamental
operations (integration, differentiation, limits, zeta, prime, etc.).
The embedding maps each expression to a unique point in 16D space
where the chaos game finds its basin.
Scaling: 2^50 ≈ 10^15 = number of sand grains on all Earth beaches.
OpenCode was auto-discovering .cursor/mcp.json in addition to .mcp.json and
spawning duplicate contextstream (and other) MCP servers. Remove the
Cursor-specific config from the repo root and keep its content in
.cursor-mcp-config.json for restoration when running Cursor.
- Replace repo .mcp.json with minimal config (10 essential servers instead
of 29). Full backup retained as .mcp.json.full.
- Remove inline MCP section from ~/.config/opencode/config.json so OpenCode
loads only repo .mcp.json (also removes hardcoded Linear token leak).
- Add singleton guards to opencode_prover_mcp.py, token_saver_mcp.py, and
remote_lean_proof_mcp.py to prevent duplicate instances when multiple
IDE configs reference them.
Build: N/A (Python shims)
