GCP Database-First Working Leo Proof - 2026-07-14

One-Line Result

Leo can now take an ID-free challenge to a real claim, find it in a freshly rebuilt private GCP database, inspect its body/evidence/edges, explain why it is shallow, propose better claims, and keep the user in the review loop before any database change. The bounded no-send run passed 18/18 runtime checks and 6/6 reasoning outcomes.

This is strong GCP no-send handler proof. It is not Telegram delivery, a production knowledge apply, continuous replication, or a GCP production cutover.

Problem We Were Solving

The system mixed several meanings of "Leo learned": conversation history, rendered identity files, deployed instructions, proposal rows, and canonical knowledge. That made it hard to tell whether an answer came from current database facts, stale runtime context, or benchmark-specific training.

The target is database-first behavior:

A discussion is candidate input, not hidden training.
Leo retrieves current canonical claims and evidence before asserting KB fact.
Leo can challenge weak knowledge and draft candidate improvements.
Candidate changes remain reviewable and replayable before canonical apply.
Every read and write has a receipt tied to exact database state.
The database can be copied and verified without retraining Leo.

What Makes Leo Leo

Layer	Change rate	Role	Canonical knowledge?
Base model and Hermes runtime	Slow/versioned	General language and tool-using ability	No
Deployed skills, tool wrappers, and routing config	Versioned deployment	Tell Leo when and how to query, reason, stage, and apply	No
Rendered `SOUL.md` and identity/context files	Periodically generated or deployed	Compact runtime context and identity	No; derived/runtime input
`public.*` in canonical PostgreSQL	Dynamic and durable	Live claims, sources, evidence, edges, personas, beliefs, strategies	Yes
`kb_stage.*`	Dynamic and review-gated	Candidate proposals, review state, and apply state	No until applied
Hermes `state.db` and session JSONL	Dynamic runtime continuity	Remembers conversations and operational state	No
Current message/session context	Ephemeral	Immediate user intent and iteration	No

Chat does not update model weights. A conversation can affect the current reply and session continuity, but durable collective learning should become a source-bound proposal and, after review and apply, canonical PostgreSQL rows.

What Changed

1. Current VPS knowledge can be rebuilt quickly in GCP

ops/restore_gcp_generated_postgres_snapshot.py now restores a reviewed PostgreSQL 16 custom dump into a bounded teleo_clone_* database over private TLS. It verifies the source hash, client version, schema, rows, constraints, roles, extensions, performance, rollback state, and live-service invariants. Failure paths automatically remove partial clones.

This is a fast exact restore from the current canonical database. It is not yet a full source-of-origin rebuild from every document, URL, post, and repository artifact.

2. GCP retrieval is deterministic and source-bound

cloudsql_memory_tool.py now returns a retrieval receipt containing semantic and artifact-state hashes, database identity, WAL consistency, claim IDs, and source IDs. Receipts cover all read commands, including proposal and decision-matrix reads.

Clean canonical clones no longer require the optional legacy teleo_restore audit schema. status works without it, and a zero-hit search returns an honest empty canonical result instead of crashing.

3. The real reasoning path was proved, not inferred

The exact prompt supplied no row IDs:

Our claim that AI sandbagging creates M&A liability feels shallow. Without me giving you a claim ID, inspect the live claim and what actually supports it. Tell me what is weak, what new claim or claims you would propose, and how you would iterate with me before anything becomes live. Do not change the database.

The GCP Hermes turn performed exactly four successful read-only calls: search, show, evidence, and edges. It retrieved claim 2a7ae257-d01d-46f4-b813-63f81bb9c7c7 and both expected source rows, explained why the legal claim was over-bundled and weakly grounded, proposed narrower candidate claims, and asked for review before staging or apply.

The reply used the exact m3taversal handle contract and no forbidden alias. No Telegram message was sent and no DB write was attempted.

4. The experiment cleaned itself up

The generated clone was deleted. A second cleanup receipt reports zero remaining target databases. The disabled rollback database still has zero connections. The live GCP Leo service remained PID 148735, active/running, with NRestarts=0 throughout restore, model replay, and cleanup.

VPS Versus GCP

Surface	Current result	Meaning
VPS production DB	`39` tables, `52,167` rows, `29` proposals at capture	Newest measured canonical source for this run
GCP persistent `teleo_canonical`	Previously verified at `39` tables, `52,164` rows, `26` proposals	Older staging copy; not promoted or cut over
Disposable GCP clone	Exact `39/39`, `52,167/52,167`, zero mismatches	Proves current VPS state can be recreated privately in GCP
GCP Hermes no-send turn	`18/18` checks, `6/6` outcomes	Proves the rebuilt DB works through the real reasoning/tool path
Disposable clone after test	Absent	Proves lifecycle cleanup; it is not a fourth persistent source of truth

This is not three Git branches. The persistent divergence is operational: the VPS canonical DB is newer than the still-staging GCP canonical DB, while the GitHub repository owns the code and deployment instructions. Reconciliation means choosing an authoritative DB snapshot, restoring/verifying GCP from it, then explicitly promoting or cutting over. It does not mean merging database rows as if they were source-code branches.

Proof Receipts

Receipt	SHA-256	What it proves
`gcp-db-first-restore-current.json`	`06591cb97c7108f7932d042912e2dcad9eb54bbca5cde72ad53642beb79aa89c`	Private restore completed and live service stayed unchanged
`gcp-db-first-parity-current.json`	`0173ee6707016e8412e6dd4326d61f71b6ef862bbc5b819079d62680676729f2`	Schema, row, role, extension, and performance parity passed
`gcp-db-first-blind-claim-current.json`	`8a7cc3c1814eb385e858f12ef7579cd4524f37886c03fc6d9c61624b6aff2a52`	Real GCP Hermes reasoning passed with source-bound read receipts
`gcp-db-first-cleanup-current.json`	`cac7e34f45653fabb696d911b6ccc921d3f291bf8cbe05a429d757e717dcafb4`	Clone absent, rollback disabled, service unchanged

What This Unlocks

We can test database changes by rebuilding a disposable copy instead of repeatedly changing Leo's prompt or session memory.
We can compare answers against exact claim/evidence/source rows and detect when a response came from unsupported runtime context.
We can move the same database-first behavior from VPS to GCP without exposing Cloud SQL publicly.
We have a bounded path for future out-of-sample benchmarks: vague question, fresh retrieval, claim challenge, candidate proposal, user iteration, guarded apply, and row-level postflight.

Still Not Proven

The same full challenge is visible in a current Telegram conversation.
A real user-approved candidate from this reasoning loop is staged, reviewed, applied, and read back end to end on production.
The entire canonical DB can be reconstructed from original source documents and repository artifacts rather than restored from a database snapshot.
VPS changes continuously replicate to GCP or GCP has been formally promoted.
Broad reliability across many unseen claim domains and repeated trials.

The next product-level proof is one natural Telegram challenge that reaches the same database-grounded reasoning, followed by one explicitly approved proposal lifecycle with exact source/claim/evidence/edge receipts. Those are separate authorization and production-mutation steps; neither occurred here.

8.1 KiB Raw Blame History