v0.3.2

v0.3.2 — Less token-budget work, the same source evidence

The fixed C2 source has passed formal performance acceptance and the complete
fresh component comparison. Independent saved-result audits preserve the gains,
regressions and exact source identities. Publication uses the exact-commit CI
and packaging gates described below.

AI-Memory-Book gives AI clients a shared local archive of original conversations.
This experimental release reduces repeated source parsing and token counting
while preserving the existing retrieval policy, source addresses and budgets.

Changes in this version

• Defer unnecessary source parsing. Capture original turns and source maps
  inside the existing database snapshot. Pages with an affirmative full-segment
  equality certificate need projection only when supplemental evidence is used;
  other maps retain the original eager corruption checks. Deferred work uses
  captured text and performs no later database reads.
• Narrow projection and reuse accepted work. Bisection skips source units
  outside a mapped segment while preserving containment and exact-text checks.
  Assembly retains the count for the last accepted context, including when a
  later candidate is rejected, and avoids a redundant private metadata copy.
• Count certified citation boundaries once per call. With tiktoken 0.12.0
  and the inspected cl100k_base/o200k_base patterns, sealed citation blocks
  can reuse exact counts. A strict UTF-8 byte bound can prove that a block fits;
  final context_tokens remains exact. Unsupported patterns, custom counters,
  unknown encoder origins and invalid boundaries keep the original full-context
  fallback, including its error behavior.

Global BM25, lexical normalization, ranking, scope filters, packing order,
source coordinates and MCP response formats retain their existing contracts.
Q2's model, vectors and fusion are unchanged. Schema 3 and dependency requirements
are unchanged; no persistent feature cache is added.

The rejected body-feature cache and proposed highlight-query specialization are
absent from C2. Earlier A/B/C1 sources, adverse results and interrupted attempts
remain preserved in the round-four decision history.

Verified compatibility

The completed Q2 audit
verifies 1,968 calls and 984 matching complete non-latency payload pairs
against published v0.3.1. It covers authored36 and historical Hotpot128, both
tokenizers and 512/1,024/2,048-token budgets. Baseline and candidate use identical
saved vectors. Recorded source, projection, scope, token and metadata errors
are zero.

These are previously scored regression cohorts. The audit checks recorded
payload hashes, vector/source identities and complete attempt ledgers without
repeating retrieval or inference; it does not establish new quality gains or
independently reconstruct omitted response bodies. Fresh artificial fixtures
cover rejected later blocks, nonmonotonic token counts, Unicode, custom encoders,
source corruption and concurrent writers.

The fixed candidate is
2d7b5e38839fb21724ad3beec6b6ebabbda45636,
runtime SHA-256
179ab871f4f6ca197efc83b13618cccad4688fd53d4a4af93305b02e78215e93.
Its source CI run
passed all nine jobs. Final release verification remains separate.

Completed formal performance

The sole registered hosted run was triggered from
f8f1653c0653654ed574569fff1aaf4dbaeef6d1.
Its completed matrix has 56,784 calls, sixteen sequential workers, four
balanced rounds and twelve cohort/configuration cells, including actual SDK
MCP. Complete outputs and recorded invariants match. The original automatic
verdict is accepted, with no resource exception or gate change.

Across the twelve pooled cells, the geometric-mean p95 improvement is 10.13%.
The following public-corpus cells each contain 4,060 samples per source:

Configuration	v0.3.1 p95, ms	v0.3.2 p95, ms	Change
Native, 4,000 characters	2.361	2.199	−6.89%
Actual SDK MCP, 4,000 characters	3.468	3.518	+1.44%
Native, 512 tokens	3.794	3.070	−19.07%
Native, 1,024 tokens	3.200	2.611	−18.41%
Native, 2,048 tokens	3.130	2.560	−18.20%
Actual SDK MCP, 1,024 tokens	4.358	3.887	−10.80%

No pooled p50/p95 regression exceeds 5%. The largest registered resource mean
increase is 6.02%, public token-MCP startup from 345.683 to 366.504 ms.
All round-level regressions and raw samples remain published. The aggregate
exceeds its 10% gate by only 0.1294 percentage points; these descriptive
results establish neither statistical significance nor a universal speedup.
Returned text and output-token totals are unchanged, so this release does not
claim model-token savings from this optimization.

Full evidence ·
Summary ·
Independent performance and resource review ·
Independent arithmetic audit

Completed fresh component comparison

The frozen selection contains 256 questions, 635 evidence annotations
and 2,548 context paragraphs, with zero overlap against all 640 prior
question IDs. Source, evaluator, dependency, model and component identities
were committed before the salted ID selection. An independent ID-only audit
reproduces the exact order and verifies the binding/manifest commit sequence.
The single ten-stage scoring run completed 4,608 native calls and 4,608
component searches. All 54 quality cells pass integrity checks, and the
1,536 native / 768 component default-payload pairs match. The unchanged strict
verifier exactly reproduces the saved assessment and report; separate arithmetic
checks 13,824 quality rows and all 18,432 attempt events without new retrieval.

The common view below uses supplied source pages, top five and a whole-page
packer at 1,024 cl100k_base tokens. Search p95 pools three repetitions.

Component	Evidence recall	Complete evidence	Packed MRR	Search-adapter p95, ms
Book, default lexical	75.75%	53.91%	0.8578	3.488
Book, optional Q2	76.69%	55.08%	0.8663	20.406
claude-mem 15.18.1 FTS	70.39%	44.53%	0.8280	34.655
ai-memory v3.4.5 hybrid	77.48%	55.86%	0.8400	39.925
billion-context 9674cfa kernel	80.94%	60.55%	0.8606	1.449

Default Book has 9.375 percentage points higher complete evidence than
claude-mem FTS here. Its measured search-adapter p95 is 89.94% lower than
claude-mem's and 91.26% lower than ai-memory's. These are different Python,
SQLite/Bun, HTTP/MCP and Node component boundaries, so they do not establish
whole-product ratios. ai-memory and billion-context retain higher recall and
completeness; billion-context's kernel is faster. Q2 leads packed MRR in this
slice, with 26 complete-question gains and 23 losses over default at this cap.

Q2 remains opt-in. Native 512-token completeness falls from 39.45% to 36.33%
with cl100k and to 36.72% with o200k. Its fresh native total setup takes
282.970 s, including ingestion, model loading and index construction, versus
lexical setup 3.796 s. The separate model cache adds 127,535,388 bytes.
All query, preparation, read, payload and storage costs are retained.

The fresh 100,000-character component path also has a retained regression
against v0.3.1: p95 3.304 → 3.488 ms (+5.56%), p50 +5.12%, and sampled
setup process-tree RSS 548.65 → 943.53 MiB (+71.97%). Native lexical
finalization RSS rises 655.16 → 1,107.09 MiB. These sequential, unbalanced
observations include evaluator/data memory and may double-count shared pages;
the cause is not established. They remain separate from the formal balanced
performance gate and its resource means.

The source remains public HotpotQA; it is not a new domain. All questions are
answerable, and this is evidence retrieval rather than generated-answer or
hallucination evaluation. Historical reports retain their original identities.
Full comparison ·
Independent audit ·
Costs and all tradeoffs ·
Frozen manifest ·
Independent freeze audit

Install or upgrade

Requires Python 3.10+ with SQLite FTS5. The installer uses the small v0.3.2
release wheel with MCP/client extras and keeps the existing archive.

macOS / Linux

curl -fsSLo install-memory-book.sh https://raw.githubusercontent.com/Afloat16/AI-Memory-Book/v0.3.2/scripts/install.sh
bash install-memory-book.sh

Windows PowerShell

Invoke-WebRequest https://raw.githubusercontent.com/Afloat16/AI-Memory-Book/v0.3.2/scripts/install.py -OutFile install-memory-book.py
py -3 .\install-memory-book.py

Connect a client and explicitly enable supported transcript capture if wanted:

memory-book setup --client claude-code --capture --background
memory-book doctor

Use the launcher's full path if it is not on PATH, then restart the client.
Omit --capture for MCP access alone. To upgrade, rerun the installer and setup
with the same database/configuration, then update an existing background service.
The archive and previous environment are retained. See the
deployment and upgrade guide for schema history,
rollback requirements and alternative installation methods.

Release verification

Publication requires all nine CI jobs for the exact source commit: Python
3.10/3.12/3.13, actual MCP stdio, the browser reader, Linux/macOS/Windows
installers and container persistence. Packaging verifies the approved C2 hash,
runtime bytes in wheel/source archives, checksums and build/source provenance.
The publisher appends the exact source, successful CI and release-workflow URLs
to the GitHub release notes. A separate job then installs through the real
public tagged installer URL and verifies the default wheel route, version and
MCP/client imports. See the release checkpoint for
the final recorded publication and installation outcome.

This remains an experimental local single-user project. Client configuration
fixtures do not certify every vendor application's interactive behavior. The
archive is unencrypted, and recalled evidence enters the AI client's model context.

Source commit: 692fb959ddf8934c5f516ba3d74ceb4385a2fa82.

CI: https://github.com/Afloat16/AI-Memory-Book/actions/runs/43367598117

Release workflow: https://github.com/Afloat16/AI-Memory-Book/actions/runs/43367903514