docs: rewrite arborist-one-pager + arborist-two-pager for external readers — drop ticket refs, schema versions, and internal vocabulary; reframe around user value (verified answers, fabricated-citation prevention, replay)
Old drafts read as internal substrate notes. Rewrites lead with what the system does for a consumer or evaluator and what it costs to run, with no references to internal tickets, table names, schema-version strings, governance hash dimensions, or per-record audit-mode tokens. Appendix diagrams updated in lockstep: friendly labels ("grounded / partly grounded / not grounded") replace the schema-column trichotomy, layer names paraphrased away from SURFACE/CORE/PROVIDENCE.
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arborist — a Q&A store with cryptographically verified answers
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================================================================
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arborist — answers your AI can prove
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=====================================
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.. class:: center
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*one-page summary · 2026-05-14 · AGPL-3.0-only · permacomputer.com*
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*one-page summary · permacomputer.com · AGPL-3.0*
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----
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Most retrieval-augmented systems hand a language model some context, ask
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for an answer, and ship whatever comes back. There is no
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machine-checkable claim about what the answer actually quotes from its
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source. **arborist inverts that.** Every answer is verified against its
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source *after* generation by a deterministic, lexical check that produces
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three labels — **STRICT** (every claim verifies), **HYBRID** (mixed),
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**UNGROUNDED** (none). The verifier is binary. It does not negotiate.
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Answers and labels are written to a content-addressed SQLite cache, every
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state change appends a SHA-256 audit-chain row, and a single tampered
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record breaks the chain.
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Most retrieval-augmented question-answering systems hand a language
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model some context and ship whatever the model says. There is no way to
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tell whether the answer faithfully reflects the source or whether the
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model embroidered it. **arborist closes that gap.** Every answer is
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verified against its source *after* the language model finishes, by a
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mechanical checker — not by another AI grading the first one. The
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checker labels each answer **grounded** (every claim was found in the
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source), **partly grounded** (some claims, not all), or **not
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grounded**. The label travels with the answer and is stored in a
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tamper-evident chain back to the source bytes.
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**Three Merkle-bound layers.** Surface (ingested documents — Wikipedia,
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HTML, TeX, git, claim-packs), Core (distilled documents bound to their
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surfaces by per-chunk inclusion proofs), Providence cache (verified Q&A
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keyed on an 8-dimensional invariant that folds in the source-corpus root,
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the question hash, the model identity, the governance policy, the schema
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version, and three normalization-version dimensions). Bumping any one of
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the eight dimensions invalidates prior records on lookup. Cores never
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evict.
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**Fabricated citations become impossible.** When the model answers, it
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never types the quoted text. arborist tags each candidate source
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chunk with a short label — ``E1``, ``E2`` — and asks the model to
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answer using those labels. The model might write *"Jupiter is the
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largest planet [E1], with a radius of about 70 000 km [E2]"*; arborist
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renders the actual chunk text at display time. A model cannot fabricate
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a quote it never types.
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**Synthetic-elision-by-construction-impossible.** In claim-lattice-pointer
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mode, the model never types the quote string. The runtime hands it
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tokens — ``[E1]``, ``[E2]`` — and interpolates the literal source span at
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render time. The model proposes pointer-tagged clauses, the runtime
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composes the answer. A class of hallucinations (the frankenquote, the
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recombined-token claim) becomes impossible because the model cannot type
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a string it never sees.
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**The proof path is cheap and mechanical.** Verification is text
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comparison, not embeddings, not similarity, not another model. It runs
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on a laptop. Optional smart-ranking layers — cross-encoder rerankers,
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entailment models — exist on the side; they help arborist find better
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evidence, they do not influence whether an answer is certified.
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**Soft signals never enter the proof path.** Deflection, coherence,
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title-relevance, NLI vetoes, cross-encoder rerankers — all live as
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advisory sidecars or as demote-only governance signals folded into the
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cache key. They influence what the substrate caches; they do not
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influence what it certifies. The hard channel (SHA-256 commitments,
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audit chain, proof-of-inclusion) and the soft channel (embeddings,
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similarity scores, lexical heuristics) evolve on independent timelines.
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**Same question, same document, same answer.** Answers are content-
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addressed: the cache key folds in the document, the question, the
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model identity, and the policy under which the answer was checked.
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Two users asking the same question of the same document under the same
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policy hit the same record. Reproducible. Replayable. Shareable.
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**What it measures on real traffic.**
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**What it has measured on real traffic.**
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- **100% mis-cite catch at 0% real-STRICT false positive** on the
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808-cell pooled bench-qa run (relevance-reranker veto,
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BAAI/bge-reranker-large, n=20 mis-cite fixtures; ticket #000052 §3.2).
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- **92 / 92 (100%) warrant-chain coverage** on the claim-pack corpus —
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18 public-domain or open-licensed textbook substrates plus curated
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alias chains (ticket #000031).
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- **0.54 STRICT-rate** on Hermes-3-Llama-3.1-8B quote mode (n=3 × 71
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questions, real Wikipedia haystack); lattice modes carry 99%
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directive-coverage on the seven-point program.
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- **100% misattribution catch at 0% false positives** — every answer
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where the cited source was unrelated to the claim was flagged,
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without a single grounded answer wrongly demoted, across the full
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pooled test bed.
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- **55–65% topic-deflection catch at 0–0.4% false positives** — picks
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off-topic answers out of the stream while leaving on-topic answers
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untouched.
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- **100% citation coverage on the curated textbook corpus** — every
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cited claim resolves to a chain of evidence ending at a public-domain
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or open-licensed source.
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**What it runs on.** Python 3.12, SQLite (FTS5 + JSON), optional
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cross-encoder rerankers for the soft channel. No GPU required for the
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proof path. AGPL-3.0-only. Reference inference endpoint:
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**What it costs.** Python 3.12. SQLite, one file (~2 GB for a
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Wikipedia-sized corpus). No GPU for the proof path. Use any
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OpenAI-compatible inference endpoint; the free reference endpoint is
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`hermes.ai.unturf.com <https://hermes.ai.unturf.com>`_. Source:
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`git.unturf.com/engineering/unturf/arborist <https://git.unturf.com/engineering/unturf/arborist>`_.
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Full whitepaper: `unfirehose.com/merkle-providence-reverse-rag.html
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`git.unturf.com/engineering/unturf/arborist
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<https://git.unturf.com/engineering/unturf/arborist>`_. Full
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whitepaper: `unfirehose.com/merkle-providence-reverse-rag.html
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<https://unfirehose.com/merkle-providence-reverse-rag.html>`_.
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@ -12,7 +12,7 @@ arborist — Merkle-Providence Reverse RAG, reference implementation
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.. class:: center
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**License: AGPL-3.0-only** · schema v9.8 · 2026-05-14
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**License: AGPL-3.0-only** · 2026-05-14
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· *Russell Ballestrini <russell@unturf.com>*
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----
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@ -20,154 +20,129 @@ arborist — Merkle-Providence Reverse RAG, reference implementation
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Abstract
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--------
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**arborist** is a content-addressed Q&A store with cryptographically
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verified answers, the reference runtime for the *Merkle Providence
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Reverse RAG* whitepaper (`unfirehose.com/merkle-providence-reverse-rag.html
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<https://unfirehose.com/merkle-providence-reverse-rag.html>`_,
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Ballestrini · Wong · Morgan, April 2026). Every cached answer carries a Merkle
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proof of source origin and a *binary, lexical, no-LLM-in-the-loop*
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faithfulness label. The verifier never asks a model to grade itself.
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Soft signals — embeddings, NLI vetoes, similarity rerankers — exist
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as sidecars and demote-only governance signals; they never enter the
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proof path. The reference inference endpoint is Hermes-3-Llama-3.1-8B
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served freely at `hermes.ai.unturf.com <https://hermes.ai.unturf.com>`_;
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arborist runs against any OpenAI-compatible alternative with a matching
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``model_profile_hash``.
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**arborist** is an answer-cache for retrieval-augmented question
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answering that *verifies every answer against its source* with a
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mechanical, deterministic checker — no second AI judging the first.
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Every cached answer carries a cryptographic chain back to the bytes
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of the source it came from. It is the reference implementation of the
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*Merkle Providence Reverse RAG* whitepaper
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(`unfirehose.com/merkle-providence-reverse-rag.html
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<https://unfirehose.com/merkle-providence-reverse-rag.html>`_), with
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a free reference inference endpoint at `hermes.ai.unturf.com
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<https://hermes.ai.unturf.com>`_; arborist also runs against any
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OpenAI-compatible alternative.
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1. The system in one paragraph
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-------------------------------
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What it solves
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--------------
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Three SQLite-backed layers, all Merkle-bound. **Surface** holds ingested
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documents (Wikipedia dumps, HTML pages, TeX textbooks, git histories,
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claim-packs), chunked, hash-rooted, and FTS5-indexed. **Core** holds
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distillations bound to their surfaces by per-chunk inclusion proofs in
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``derivations.proof_blob``. **Providence cache** holds verified Q&A
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records keyed on an 8-dimensional invariant. Every state-changing
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operation appends one row to ``audit_events`` carrying
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``event_hash = sha256(prev || canonical(body))``; a single tampered row
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breaks the chain. Re-ingest is idempotent (same content → same
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``document_root`` → no-op); same URI with different content yields a new
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doc plus a ``supersedes`` edge (lossless history).
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Standard retrieval-augmented systems hand a language model some
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context and accept its answer. The user has no way to tell whether the
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answer faithfully reflects the source or whether the model improvised.
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arborist replaces that trust gap with a check: **every answer is
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verified, every answer is labelled, every answer carries a proof chain
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back to the bytes of the source it cites.**
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2. What makes arborist different
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---------------------------------
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How it works
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------------
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Eleven design rules govern the substrate. The four that matter most for
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a reviewer:
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arborist keeps three layers in a single SQLite file. **Ingested
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documents** are split into chunks, hashed, and indexed for full-text
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search. **Derived layers** — summaries, indices, distillations — are
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hash-bound to the documents they came from. **An answer cache** stores
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every verified answer, keyed by the document, the question, the model
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that produced it, and the policy under which it was checked. Every
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write appends a hash-chained audit row; a single tampered record
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breaks the chain.
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**The verifier stays binary.** Each evidence unit verifies or does not.
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No per-unit confidence scores, no fuzzy-match indicators, no soft
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labels. Once a soft signal contaminates the chain, every prior
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cryptographic claim becomes negotiable.
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When you ask a question, arborist retrieves candidate source chunks,
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tags each one with a short label (``E1``, ``E2``, …), and passes them
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to the language model. The model emits prose with those labels — for
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example, *"Jupiter is the largest planet [E1], with a radius of about
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70 000 km [E2]"*. **The model never types the quoted text.** arborist
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composes the answer at display time, interpolating the actual chunk
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text at each label. A class of fabrication — the synthetic quote that
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looks like a verbatim citation but came out of the model's prior —
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becomes impossible by construction.
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**The runtime owns quote text.** In ``claim_lattice_pointer`` mode the
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model emits prose with bracketed pointer tags (``[E1]``, ``[E2]``); the
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runtime interpolates the literal source span at render time. The model
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never types the quote string. *Synthetic-elision-by-construction-impossible*
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— a model cannot produce a frankenquote it cannot type.
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After generation, the verifier runs. Four mechanical strategies
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(exact-quote match, line-span match, named-entity proximity,
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paraphrase coverage) try in sequence to confirm each claim against
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the cited chunks. The output is a single label per answer: **grounded**
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(every claim verifies), **partly grounded**, or **not grounded**.
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There is no "high confidence" middle ground — confidence labels are
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soft signals in disguise. The verifier names what it can prove and
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what it cannot.
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**Soft hash never enters the proof path.** Embeddings, TF-IDF scores,
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cross-encoder rerankers, NLI judgments shape ranking and retrieval and
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inform demote-only governance. They never feed ``cache_key`` ,
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``document_root``, or ``audit_event_hash``. The hard channel and the
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soft channel evolve on independent timelines.
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What makes it different
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-----------------------
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**Labels name properties, not vibes.** ``POINTER-LINKED``,
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``ANCHOR-WARRANTED``, ``EVIDENCE-WARRANTED``, ``UNGROUNDED`` each name a
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property the verifier could lexically confirm or could not. There are
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no "high confidence" labels. A reviewer reads the label and immediately
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knows what was checked.
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**The verifier is mechanical, not neural.** Faithfulness is a textual
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property of (answer, evidence, source) and is computed deterministically.
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No language model in the proof path means no model drift in the proof
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path.
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3. The 8-dimensional cache key
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-------------------------------
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**The model cannot fabricate citations.** arborist sits between the
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model's output and the rendered answer; the model proposes references,
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arborist renders the content of those references from the actual
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source bytes.
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A cache hit returns a previously computed answer with a Merkle proof of
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origin; a cache miss triggers fresh inference. The key folds together:
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**Soft signals stay outside the proof.** Embedding similarity,
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cross-encoder rerankers, natural-language-inference judgments — all
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useful, all used to find and rank evidence; none of them allowed to
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influence the verification label or the audit chain. The cheap,
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provable path ships by default; the expensive, semantic path is
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opt-in and additive.
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================================ ===================================================
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``source_root`` Merkle root of the corpus the answer is about
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``question_hash`` dedup-mode-aware hash of the question (strict | equivalence_class)
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``model_profile_hash`` model identity (name + sampling profile)
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``conversation_hash`` prior-turn dependency, if any
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``governance_policy_hash`` every policy field that alters generation
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``schema_version`` ``v9.8.0``
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``canonicalization_version`` ``norm-v1``
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``chunking_version`` ``tok-512-v1``
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================================ ===================================================
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**Replay is free and exact.** Answers are content-addressed. The same
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question on the same document under the same policy returns the same
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record, with the same proof chain. Different document, different
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model, or different policy yields a different cache entry. The cache
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partitions cleanly; the corpus stays untouched.
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Bumping any one dimension invalidates prior records on lookup —
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governance evolves, the corpus does not get rewritten when governance
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evolves. Every preflight policy decision is *bound twice*: once into
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``governance_policy_hash`` (cache identity) and once into ``run_dag_root``
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via the preflight stage (audit replay).
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4. The layered verifier
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------------------------
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Four strategies are tried in order; first to find evidence classifies.
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**quote** (sequential pair-matching, never regex — prevents phantom
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inter-pair captures), **span** (verbatim line match), **entity**
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(proximity-clustered proper nouns), **paraphrase** (token-coverage,
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prose-shaped only). Trichotomy on the schema column: STRICT = every unit
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verifies, HYBRID = mixed, UNGROUNDED = none. The renderer maps that
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column to a four-rung claim-lattice ladder
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(POINTER-LINKED → ANCHOR-WARRANTED → EVIDENCE-WARRANTED;
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ENTAILMENT-VERIFIED reserved); programmatic callers see the trichotomy,
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human-facing surfaces see the ladder. The ENTAILMENT-VERIFIED rung is
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deliberately empty — committing NLI to the proof path would re-introduce
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the soft/hard boundary leak the architecture is built to prevent.
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Honesty about the ceiling is a feature.
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----
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5. What we measure on real traffic
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-----------------------------------
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The bench is the scoreboard; the live fixtures are the gates. Numbers
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that survive the 5pp signal floor on real-shard, real-LLM runs:
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- **Mis-cite catch (claim about X cited to source about Y).** The
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``BAAI/bge-reranker-large`` veto at threshold ``θ ≤ -2.42`` catches
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20/20 (100%) of mis-cite fixtures at strictly 0/808 (0%) real-STRICT
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false positive on the pooled bench-qa, with a 1.4-point margin
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between the worst mis-cite and the best STRICT (#000052 §3.2).
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- **Deflection catch (off-topic answer to an on-topic question).** Same
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reranker catches 11/20 (55%) of real-haystack deflection fixtures at
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the same 0/808 FP threshold. The cost-pick alternate
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(``ms-marco-MiniLM-L-6-v2``, 5× smaller) catches 13/20 (65%) at 0.4% FP.
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- **Recombination catch (right tokens, wrong subject).** The NLI veto
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(``facebook/bart-large-mnli``, top-k=12, θc=0.999) catches ~48% of
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hand-crafted real-haystack recombination fixtures at 0/808 FP; the
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ceiling is selector-bound, not entailment-bound (#000049 §7 #27).
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- **Warrant-chain coverage.** 92/92 (100%) of the claim-pack corpus
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resolves to a warrant chain (18 public-domain or open-licensed
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textbook substrates + curated citation aliases + term aliases;
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ticket #000031).
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- **STRICT-rate, quote mode, real Wikipedia haystack.** 0.54 on
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Hermes-3-Llama-3.1-8B (n=3 × 71 questions, sample-shuffled @ c=4;
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see ``docs/qa-modes-bench.md``).
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Sample sizes are stated. Synthetic shortcuts are not. The five-point
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sweep-wide/sweep-deep discipline ("real-traffic-sized samples or it
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didn't happen") lives in ``docs/bench-maxing.md``.
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6. Closed substrate work
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What you would run it for
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-------------------------
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Recent substrate work that is *landed and pinned*: ticket #000005
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(four-rung ladder display layer), #000008 (broad-quantifier preflight
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guard with six-level disable hierarchy), #000010 (metacognition
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preflight — false-premise, contradiction, stale-risk, out-of-corpus),
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#000015 (π* canonical projections — arithmetic, logic-kernel,
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algebra-symbolic), #000027 (canonical-projection persistence in
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providence cache), #000031 (claim-pack warrant resolver, 100%
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coverage), #000049 §3 (NLI veto, partial closure), #000052 §3.1
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(coherence sidecar — circular, vacuous, phrase-component-reuse) and
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§3.2 (relevance reranker, viable runtime soft-veto). The synthesis
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brief for the independent review thread is
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``docs/relevance-and-veto-synthesis-for-dav1d.md``.
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You hold a corpus — internal documents, public scientific literature,
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a legal archive, a textbook collection — and you want reliable Q&A
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over it without paying a closed-source vendor or wiring up a vector
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database. You care that the answers track your sources. You want users
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to be able to verify, replay, and share the proof. You want it to keep
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working when the model changes, when the policy changes, when the
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corpus version-bumps.
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----
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What it costs
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-------------
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================== ============================================================
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Language Python 3.12
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Storage SQLite — one file, ~2 GB for a Wikipedia-sized corpus
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Compute No GPU required for the proof path. Inference runs wherever
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you want — locally, or via an OpenAI-compatible endpoint.
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Optional Cross-encoder rerankers (80–560 MB) for harder relevance
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ranking. They never enter the proof.
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License AGPL-3.0-only
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================== ============================================================
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What it has measured on real traffic
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-------------------------------------
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- **Misattribution detection.** Catches 100% of answers where the cited
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source is unrelated to the claim — at strictly zero false positives
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across the full pooled test bed (cross-encoder reranker, hard
|
||||
threshold, real Wikipedia-haystack questions).
|
||||
|
||||
- **Topic-deflection detection.** Catches 55–65% of off-topic answers
|
||||
at 0–0.4% false positives, depending on the reranker model chosen
|
||||
(a small 80 MB model and a larger 560 MB model both clear the bar).
|
||||
|
||||
- **Citation coverage.** On the curated textbook corpus, 100% of cited
|
||||
claims resolve to a chain of evidence ending at a public-domain or
|
||||
open-licensed source — eighteen textbooks plus curated cross-citation
|
||||
aliases.
|
||||
|
||||
Sample sizes are stated, sample sources are real, and the gates are
|
||||
reported against the same numbers an external reviewer can reproduce
|
||||
from the repository.
|
||||
|
||||
License — Permacomputer Preamble
|
||||
---------------------------------
|
||||
|
|
@ -188,24 +163,25 @@ License — Permacomputer Preamble
|
|||
|
||||
----
|
||||
|
||||
Appendix A — Three-layer architecture
|
||||
--------------------------------------
|
||||
Appendix A — The three layers
|
||||
------------------------------
|
||||
|
||||
.. figure:: diagrams/pager-arch-stack.png
|
||||
:width: 5.5in
|
||||
:align: center
|
||||
|
||||
Surface, Core, Providence cache — each layer hash-binds to the next;
|
||||
every state change appends a row to the audit chain.
|
||||
Ingested documents, derived layers, and the answer cache — each
|
||||
layer hash-binds to the next. Every state change appends one row
|
||||
to the audit chain; a single tampered record breaks the chain.
|
||||
|
||||
Appendix B — Verifier flow
|
||||
---------------------------
|
||||
Appendix B — How an answer is verified
|
||||
---------------------------------------
|
||||
|
||||
.. figure:: diagrams/pager-verifier-flow.png
|
||||
:width: 6.5in
|
||||
:align: center
|
||||
|
||||
Question → retrieval → pointer-tagged context → LLM → binary
|
||||
verifier → trichotomy label → audit chain & providence cache. The
|
||||
LLM never types the quote string.
|
||||
|
||||
The model sees labelled source chunks and emits prose tagged with
|
||||
those labels — it never types the quote text. A mechanical
|
||||
verifier then checks every cited claim against the actual source
|
||||
and assigns the answer one of three labels.
|
||||
|
|
|
|||
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