test: loosen p05-configuration fixture cross-project check

The fixture asserted 'GigaBIT M1' must not appear in a p05 pack,
but GigaBIT M1 is the mirror the interferometer measures, so its
name legitimately shows up in p05 source docs (CGH test setup
diagrams, AOM design input, etc.). Flagging it as bleed was false
positive.

Replace the assertion with genuinely p04-only material: the
'Option B' / 'conical back' architecture decision and a p06 tag,
neither of which has any reason to appear in a p05 configuration
answer.

Harness now passes 6/6 against live Dalidou at 38f6e52 — the
first clean baseline. Subsequent retrieval/ranking/ingestion
changes can be measured against this run.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

deploy/dalidou/cron-backup.sh

@@ -0,0 +1,85 @@
+#!/usr/bin/env bash
+#
+# deploy/dalidou/cron-backup.sh
+# ------------------------------
+# Daily backup + retention cleanup via the AtoCore API.
+#
+# Intended to run from cron on Dalidou:
+#
+#   # Daily at 03:00 UTC
+#   0 3 * * * /srv/storage/atocore/app/deploy/dalidou/cron-backup.sh >> /var/log/atocore-backup.log 2>&1
+#
+# What it does:
+#   1. Creates a runtime backup (db + registry, no chroma by default)
+#   2. Runs retention cleanup with --confirm to delete old snapshots
+#   3. Logs results to stdout (captured by cron into the log file)
+#
+# Fail-open: exits 0 even on API errors so cron doesn't send noise
+# emails. Check /var/log/atocore-backup.log for diagnostics.
+#
+# Environment variables:
+#   ATOCORE_URL             default http://127.0.0.1:8100
+#   ATOCORE_BACKUP_CHROMA   default false (set to "true" for cold chroma copy)
+#   ATOCORE_BACKUP_DIR      default /srv/storage/atocore/backups
+#   ATOCORE_BACKUP_RSYNC    optional rsync destination for off-host copies
+#                           (e.g. papa@laptop:/home/papa/atocore-backups/)
+#                           When set, the local snapshots tree is rsynced to
+#                           the destination after cleanup. Unset = skip.
+#                           SSH key auth must already be configured from this
+#                           host to the destination.
+
+set -euo pipefail
+
+ATOCORE_URL="${ATOCORE_URL:-http://127.0.0.1:8100}"
+INCLUDE_CHROMA="${ATOCORE_BACKUP_CHROMA:-false}"
+BACKUP_DIR="${ATOCORE_BACKUP_DIR:-/srv/storage/atocore/backups}"
+RSYNC_TARGET="${ATOCORE_BACKUP_RSYNC:-}"
+TIMESTAMP="$(date -u +%Y-%m-%dT%H:%M:%SZ)"
+
+log() { printf '[%s] %s\n' "$TIMESTAMP" "$*"; }
+
+log "=== AtoCore daily backup starting ==="
+
+# Step 1: Create backup
+log "Step 1: creating backup (chroma=$INCLUDE_CHROMA)"
+BACKUP_RESULT=$(curl -sf -X POST \
+    -H "Content-Type: application/json" \
+    -d "{\"include_chroma\": $INCLUDE_CHROMA}" \
+    "$ATOCORE_URL/admin/backup" 2>&1) || {
+    log "ERROR: backup creation failed: $BACKUP_RESULT"
+    exit 0
+}
+log "Backup created: $BACKUP_RESULT"
+
+# Step 2: Retention cleanup (confirm=true to actually delete)
+log "Step 2: running retention cleanup"
+CLEANUP_RESULT=$(curl -sf -X POST \
+    -H "Content-Type: application/json" \
+    -d '{"confirm": true}' \
+    "$ATOCORE_URL/admin/backup/cleanup" 2>&1) || {
+    log "ERROR: cleanup failed: $CLEANUP_RESULT"
+    exit 0
+}
+log "Cleanup result: $CLEANUP_RESULT"
+
+# Step 3: Off-host rsync (optional). Fail-open: log but don't abort
+# the cron so a laptop being offline at 03:00 UTC never turns the
+# local backup path red.
+if [[ -n "$RSYNC_TARGET" ]]; then
+    log "Step 3: rsyncing snapshots to $RSYNC_TARGET"
+    if [[ ! -d "$BACKUP_DIR/snapshots" ]]; then
+        log "WARN: $BACKUP_DIR/snapshots does not exist, skipping rsync"
+    else
+        RSYNC_OUTPUT=$(rsync -a --delete \
+            -e "ssh -o ConnectTimeout=10 -o BatchMode=yes -o StrictHostKeyChecking=accept-new" \
+            "$BACKUP_DIR/snapshots/" "$RSYNC_TARGET" 2>&1) && {
+            log "Rsync complete"
+        } || {
+            log "WARN: rsync to $RSYNC_TARGET failed (offline or auth?): $RSYNC_OUTPUT"
+        }
+    fi
+else
+    log "Step 3: ATOCORE_BACKUP_RSYNC not set, skipping off-host copy"
+fi
+
+log "=== AtoCore daily backup complete ==="

deploy/hooks/capture_stop.py

@@ -3,7 +3,7 @@
 
 Reads the Stop hook JSON from stdin, extracts the last user prompt
 from the transcript JSONL, and POSTs to the AtoCore /interactions
-endpoint in conservative mode (reinforce=false, no extraction).
+endpoint with reinforcement enabled (no extraction).
 
 Fail-open: always exits 0, logs errors to stderr only.
 
@@ -81,7 +81,7 @@ def _capture() -> None:
         "client": "claude-code",
         "session_id": session_id,
         "project": project,
-        "reinforce": False,
+        "reinforce": True,
     }
 
     body = json.dumps(payload, ensure_ascii=True).encode("utf-8")

docs/master-plan-status.md

@@ -32,7 +32,18 @@ read-only additive mode.
 ### Baseline Complete
 
 - Phase 9 - Reflection (all three foundation commits landed:
-  A capture, B reinforcement, C candidate extraction + review queue)
+  A capture, B reinforcement, C candidate extraction + review queue).
+  As of 2026-04-11 the capture → reinforce half runs automatically on
+  every Stop-hook capture (length-aware token-overlap matcher handles
+  paragraph-length memories), and project-scoped memories now reach
+  the context pack via a dedicated `--- Project Memories ---` band
+  between identity/preference and retrieved chunks. The extract half
+  is still a manual / batch flow by design (`scripts/atocore_client.py
+  batch-extract` + `triage`). First live batch-extract run over 42
+  captured interactions produced 1 candidate (rule extractor is
+  conservative and keys on structural cues like `## Decision:`
+  headings that rarely appear in conversational LLM responses) —
+  extractor tuning is a known follow-up.
 
 ### Not Yet Complete In The Intended Sense
 
@@ -167,7 +178,9 @@ These remain intentionally deferred.
 
 - automatic write-back from OpenClaw into AtoCore
 - automatic memory promotion
-- reflection loop integration
+- ~~reflection loop integration~~ — baseline now in (capture→reinforce
+  auto, extract batch/manual). Extractor tuning and scheduled batch
+  extraction still open.
 - replacing OpenClaw's own memory system
 - live machine-DB sync between machines
 - full ontology / graph expansion before the current baseline is stable

docs/next-steps.md

@@ -137,7 +137,12 @@ P06:
 
 - automatic write-back from OpenClaw into AtoCore
 - automatic memory promotion
-- reflection loop integration
+- ~~reflection loop integration~~ — baseline now landed (2026-04-11):
+  Stop hook runs reinforce automatically, project memories are folded
+  into the context pack, batch-extract and triage CLIs exist. What
+  remains deferred: scheduled/automatic batch extraction and extractor
+  rule tuning (rule-based extractor produced 1 candidate from 42 real
+  captures — needs new cues for conversational LLM content).
 - replacing OpenClaw's own memory system
 - syncing the live machine DB between machines
 
@@ -159,6 +164,77 @@ The next batch is successful if:
 - project ingestion remains controlled rather than noisy
 - the canonical Dalidou instance stays stable
 
+## Retrieval Quality Review — 2026-04-11
+
+First sweep with real project-hinted queries on Dalidou. Used
+`POST /context/build` against p04, p05, p06 with representative
+questions and inspected `formatted_context`.
+
+Findings:
+
+- **Trusted Project State is surfacing correctly.** The DECISION and
+  REQUIREMENT categories appear at the top of the pack and include
+  the expected key facts (e.g. p04 "Option B conical-back mirror
+  architecture"). This is the strongest signal in the pack today.
+- **Chunk retrieval is relevant on-topic but broad.** Top chunks for
+  the p04 architecture query are PDR intro, CAD assembly overview,
+  and the index — all on the right project but none of them directly
+  answer the "why was Option B chosen" question. The authoritative
+  answer sits in Project State, not in the chunks.
+- **Active memories are NOT reaching the pack.** The context builder
+  surfaces Trusted Project State and retrieved chunks but does not
+  include the 21 active project/knowledge memories. Reinforcement
+  (Phase 9 Commit B) bumps memory confidence without the memory ever
+  being read back into a prompt — the reflection loop has no outlet
+  on the retrieval side. This is a design gap, not a bug: needs a
+  decision on whether memories should feed into context assembly,
+  and if so at what trust level (below project_state, above chunks).
+- **Cross-project bleed is low.** The p04 query did pull one p05
+  chunk (CGH_Design_Input_for_AOM) as the bottom hit but the top-4
+  were all p04.
+
+Proposed follow-ups (not yet scheduled):
+
+1. ~~Decide whether memories should be folded into `formatted_context`
+   and under what section header.~~ DONE 2026-04-11 (commits 8ea53f4,
+   5913da5, 1161645). A `--- Project Memories ---` band now sits
+   between identity/preference and retrieved chunks, gated on a
+   canonical project hint to prevent cross-project bleed. Budget
+   ratio 0.25 (tuned empirically — paragraph memories are ~400 chars
+   and earlier 0.15 ratio starved the first entry by one char).
+   Verified live: p04 architecture query surfaces the Option B memory.
+2. Re-run the same three queries after any builder change and compare
+   `formatted_context` diffs — still open, and is the natural entry
+   point for the retrieval eval harness on the roadmap.
+
+## Reflection Loop Live Check — 2026-04-11
+
+First real run of `batch-extract` across 42 captured Claude Code
+interactions on Dalidou produced exactly **1 candidate**, and that
+candidate was a synthetic test capture from earlier in the session
+(rejected). Finding:
+
+- The rule-based extractor in `src/atocore/memory/extractor.py` keys
+  on explicit structural cues (decision headings like
+  `## Decision: ...`, preference sentences, etc.). Real Claude Code
+  responses are conversational and almost never contain those cues.
+- This means the capture → extract half of the reflection loop is
+  effectively inert against organic LLM sessions until either the
+  rules are broadened (new cue families: "we chose X because...",
+  "the selected approach is...", etc.) or an LLM-assisted extraction
+  path is added alongside the rule-based one.
+- Capture → reinforce is working correctly on live data (length-aware
+  matcher verified on live paraphrase of a p04 memory).
+
+Follow-up candidates (not yet scheduled):
+
+1. Extractor rule expansion — add conversational-form rules so real
+   session text has a chance of surfacing candidates.
+2. LLM-assisted extractor as a separate rule family, guarded by
+   confidence and always landing in `status=candidate` (never active).
+3. Retrieval eval harness — diffable scorecard of
+   `formatted_context` across a fixed question set per active project.
+
 ## Long-Run Goal
 
 The long-run target is:

scripts/atocore_client.py

@@ -340,6 +340,22 @@ def build_parser() -> argparse.ArgumentParser:
     p = sub.add_parser("reject")
     p.add_argument("memory_id")
 
+    # batch-extract: fan out /interactions/{id}/extract?persist=true across
+    #   recent interactions. Idempotent — the extractor create_memory path
+    #   silently skips duplicates, so re-running is safe.
+    p = sub.add_parser("batch-extract")
+    p.add_argument("since", nargs="?", default="")
+    p.add_argument("project", nargs="?", default="")
+    p.add_argument("limit", nargs="?", type=int, default=100)
+    p.add_argument("persist", nargs="?", default="true")
+
+    # triage: interactive candidate review loop. Fetches the queue, shows
+    #   each candidate, accepts p/r/s (promote / reject / skip) / q (quit).
+    p = sub.add_parser("triage")
+    p.add_argument("memory_type", nargs="?", default="")
+    p.add_argument("project", nargs="?", default="")
+    p.add_argument("limit", nargs="?", type=int, default=50)
+
     return parser
 
 
@@ -474,10 +490,141 @@ def main() -> int:
                 {},
             )
         )
+    elif cmd == "batch-extract":
+        print_json(run_batch_extract(args.since, args.project, args.limit, args.persist))
+    elif cmd == "triage":
+        return run_triage(args.memory_type, args.project, args.limit)
     else:
         return 1
     return 0
 
 
+def run_batch_extract(since: str, project: str, limit: int, persist_flag: str) -> dict:
+    """Fetch recent interactions and run the extractor against each one.
+
+    Returns an aggregated summary. Safe to re-run: the server-side
+    persist path catches ValueError on duplicates and the endpoint
+    reports per-interaction candidate counts either way.
+    """
+    persist = persist_flag.lower() in {"1", "true", "yes", "y"}
+    query_parts: list[str] = []
+    if project:
+        query_parts.append(f"project={urllib.parse.quote(project)}")
+    if since:
+        query_parts.append(f"since={urllib.parse.quote(since)}")
+    query_parts.append(f"limit={int(limit)}")
+    query = "?" + "&".join(query_parts)
+
+    listing = request("GET", f"/interactions{query}")
+    interactions = listing.get("interactions", []) if isinstance(listing, dict) else []
+
+    processed = 0
+    total_candidates = 0
+    total_persisted = 0
+    errors: list[dict] = []
+    per_interaction: list[dict] = []
+
+    for item in interactions:
+        iid = item.get("id") or ""
+        if not iid:
+            continue
+        try:
+            result = request(
+                "POST",
+                f"/interactions/{urllib.parse.quote(iid, safe='')}/extract",
+                {"persist": persist},
+            )
+        except Exception as exc:  # pragma: no cover - network errors land here
+            errors.append({"interaction_id": iid, "error": str(exc)})
+            continue
+        processed += 1
+        count = int(result.get("candidate_count", 0) or 0)
+        persisted_ids = result.get("persisted_ids") or []
+        total_candidates += count
+        total_persisted += len(persisted_ids)
+        if count:
+            per_interaction.append(
+                {
+                    "interaction_id": iid,
+                    "candidate_count": count,
+                    "persisted_count": len(persisted_ids),
+                    "project": item.get("project") or "",
+                }
+            )
+
+    return {
+        "processed": processed,
+        "total_candidates": total_candidates,
+        "total_persisted": total_persisted,
+        "persist": persist,
+        "errors": errors,
+        "interactions_with_candidates": per_interaction,
+    }
+
+
+def run_triage(memory_type: str, project: str, limit: int) -> int:
+    """Interactive review of candidate memories.
+
+    Loads the queue once, walks through entries, prompts for
+    (p)romote / (r)eject / (s)kip / (q)uit. Stateless between runs —
+    re-running picks up whatever is still status=candidate.
+    """
+    query_parts = ["status=candidate"]
+    if memory_type:
+        query_parts.append(f"memory_type={urllib.parse.quote(memory_type)}")
+    if project:
+        query_parts.append(f"project={urllib.parse.quote(project)}")
+    query_parts.append(f"limit={int(limit)}")
+    listing = request("GET", "/memory?" + "&".join(query_parts))
+    memories = listing.get("memories", []) if isinstance(listing, dict) else []
+
+    if not memories:
+        print_json({"status": "empty_queue", "count": 0})
+        return 0
+
+    promoted = 0
+    rejected = 0
+    skipped = 0
+    stopped_early = False
+
+    print(f"Triage queue: {len(memories)} candidate(s)\n", file=sys.stderr)
+    for idx, mem in enumerate(memories, 1):
+        mid = mem.get("id", "")
+        print(f"[{idx}/{len(memories)}] {mem.get('memory_type','?')}  project={mem.get('project','')}  conf={mem.get('confidence','?')}", file=sys.stderr)
+        print(f"  id: {mid}", file=sys.stderr)
+        print(f"  {mem.get('content','')}", file=sys.stderr)
+        try:
+            choice = input("  (p)romote / (r)eject / (s)kip / (q)uit > ").strip().lower()
+        except EOFError:
+            stopped_early = True
+            break
+        if choice in {"q", "quit"}:
+            stopped_early = True
+            break
+        if choice in {"p", "promote"}:
+            request("POST", f"/memory/{urllib.parse.quote(mid, safe='')}/promote", {})
+            promoted += 1
+            print("  -> promoted", file=sys.stderr)
+        elif choice in {"r", "reject"}:
+            request("POST", f"/memory/{urllib.parse.quote(mid, safe='')}/reject", {})
+            rejected += 1
+            print("  -> rejected", file=sys.stderr)
+        else:
+            skipped += 1
+            print("  -> skipped", file=sys.stderr)
+
+    print_json(
+        {
+            "reviewed": promoted + rejected + skipped,
+            "promoted": promoted,
+            "rejected": rejected,
+            "skipped": skipped,
+            "stopped_early": stopped_early,
+            "remaining_in_queue": len(memories) - (promoted + rejected + skipped) - (1 if stopped_early else 0),
+        }
+    )
+    return 0
+
+
 if __name__ == "__main__":
     raise SystemExit(main())

scripts/retrieval_eval.py

@@ -0,0 +1,194 @@
+"""Retrieval quality eval harness.
+
+Runs a fixed set of project-hinted questions against
+``POST /context/build`` on a live AtoCore instance and scores the
+resulting ``formatted_context`` against per-question expectations.
+The goal is a diffable scorecard that tells you, run-to-run,
+whether a retrieval / builder / ingestion change moved the needle.
+
+Design notes
+------------
+- Fixtures live in ``scripts/retrieval_eval_fixtures.json`` so new
+  questions can be added without touching Python. Each fixture
+  names the project, the prompt, and a checklist of substrings that
+  MUST appear in ``formatted_context`` (``expect_present``) and
+  substrings that MUST NOT appear (``expect_absent``). The absent
+  list catches cross-project bleed and stale content.
+- The checklist is deliberately substring-based (not regex, not
+  embedding-similarity) so a failure is always a trivially
+  reproducible "this string is not in that string". Richer scoring
+  can come later once we know the harness is useful.
+- The harness is external to the app runtime and talks to AtoCore
+  over HTTP, so it works against dev, staging, or prod. It follows
+  the same environment-variable contract as ``atocore_client.py``
+  (``ATOCORE_BASE_URL``, ``ATOCORE_TIMEOUT_SECONDS``).
+- Exit code 0 on all-pass, 1 on any fixture failure. Intended for
+  manual runs today; a future cron / CI hook can consume the
+  JSON output via ``--json``.
+
+Usage
+-----
+
+    python scripts/retrieval_eval.py            # human-readable report
+    python scripts/retrieval_eval.py --json     # machine-readable
+    python scripts/retrieval_eval.py --fixtures path/to/custom.json
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import os
+import sys
+import urllib.error
+import urllib.parse
+import urllib.request
+from dataclasses import dataclass, field
+from pathlib import Path
+
+DEFAULT_BASE_URL = os.environ.get("ATOCORE_BASE_URL", "http://dalidou:8100")
+DEFAULT_TIMEOUT = int(os.environ.get("ATOCORE_TIMEOUT_SECONDS", "30"))
+DEFAULT_BUDGET = 3000
+DEFAULT_FIXTURES = Path(__file__).parent / "retrieval_eval_fixtures.json"
+
+
+@dataclass
+class Fixture:
+    name: str
+    project: str
+    prompt: str
+    budget: int = DEFAULT_BUDGET
+    expect_present: list[str] = field(default_factory=list)
+    expect_absent: list[str] = field(default_factory=list)
+    notes: str = ""
+
+
+@dataclass
+class FixtureResult:
+    fixture: Fixture
+    ok: bool
+    missing_present: list[str]
+    unexpected_absent: list[str]
+    total_chars: int
+    error: str = ""
+
+
+def load_fixtures(path: Path) -> list[Fixture]:
+    data = json.loads(path.read_text(encoding="utf-8"))
+    if not isinstance(data, list):
+        raise ValueError(f"{path} must contain a JSON array of fixtures")
+    fixtures: list[Fixture] = []
+    for i, raw in enumerate(data):
+        if not isinstance(raw, dict):
+            raise ValueError(f"fixture {i} is not an object")
+        fixtures.append(
+            Fixture(
+                name=raw["name"],
+                project=raw.get("project", ""),
+                prompt=raw["prompt"],
+                budget=int(raw.get("budget", DEFAULT_BUDGET)),
+                expect_present=list(raw.get("expect_present", [])),
+                expect_absent=list(raw.get("expect_absent", [])),
+                notes=raw.get("notes", ""),
+            )
+        )
+    return fixtures
+
+
+def run_fixture(fixture: Fixture, base_url: str, timeout: int) -> FixtureResult:
+    payload = {
+        "prompt": fixture.prompt,
+        "project": fixture.project or None,
+        "budget": fixture.budget,
+    }
+    req = urllib.request.Request(
+        url=f"{base_url}/context/build",
+        method="POST",
+        headers={"Content-Type": "application/json"},
+        data=json.dumps(payload).encode("utf-8"),
+    )
+    try:
+        with urllib.request.urlopen(req, timeout=timeout) as resp:
+            body = json.loads(resp.read().decode("utf-8"))
+    except urllib.error.URLError as exc:
+        return FixtureResult(
+            fixture=fixture,
+            ok=False,
+            missing_present=list(fixture.expect_present),
+            unexpected_absent=[],
+            total_chars=0,
+            error=f"http_error: {exc}",
+        )
+
+    formatted = body.get("formatted_context") or ""
+    missing = [s for s in fixture.expect_present if s not in formatted]
+    unexpected = [s for s in fixture.expect_absent if s in formatted]
+    return FixtureResult(
+        fixture=fixture,
+        ok=not missing and not unexpected,
+        missing_present=missing,
+        unexpected_absent=unexpected,
+        total_chars=len(formatted),
+    )
+
+
+def print_human_report(results: list[FixtureResult]) -> None:
+    total = len(results)
+    passed = sum(1 for r in results if r.ok)
+    print(f"Retrieval eval: {passed}/{total} fixtures passed")
+    print()
+    for r in results:
+        marker = "PASS" if r.ok else "FAIL"
+        print(f"[{marker}] {r.fixture.name}  project={r.fixture.project}  chars={r.total_chars}")
+        if r.error:
+            print(f"       error: {r.error}")
+        for miss in r.missing_present:
+            print(f"       missing expected: {miss!r}")
+        for bleed in r.unexpected_absent:
+            print(f"       unexpected present: {bleed!r}")
+        if r.fixture.notes and not r.ok:
+            print(f"       notes: {r.fixture.notes}")
+
+
+def print_json_report(results: list[FixtureResult]) -> None:
+    payload = {
+        "total": len(results),
+        "passed": sum(1 for r in results if r.ok),
+        "fixtures": [
+            {
+                "name": r.fixture.name,
+                "project": r.fixture.project,
+                "ok": r.ok,
+                "total_chars": r.total_chars,
+                "missing_present": r.missing_present,
+                "unexpected_absent": r.unexpected_absent,
+                "error": r.error,
+            }
+            for r in results
+        ],
+    }
+    json.dump(payload, sys.stdout, indent=2)
+    sys.stdout.write("\n")
+
+
+def main() -> int:
+    parser = argparse.ArgumentParser(description="AtoCore retrieval quality eval harness")
+    parser.add_argument("--base-url", default=DEFAULT_BASE_URL)
+    parser.add_argument("--timeout", type=int, default=DEFAULT_TIMEOUT)
+    parser.add_argument("--fixtures", type=Path, default=DEFAULT_FIXTURES)
+    parser.add_argument("--json", action="store_true", help="emit machine-readable JSON")
+    args = parser.parse_args()
+
+    fixtures = load_fixtures(args.fixtures)
+    results = [run_fixture(f, args.base_url, args.timeout) for f in fixtures]
+
+    if args.json:
+        print_json_report(results)
+    else:
+        print_human_report(results)
+
+    return 0 if all(r.ok for r in results) else 1
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())

scripts/retrieval_eval_fixtures.json

@@ -0,0 +1,86 @@
+[
+  {
+    "name": "p04-architecture-decision",
+    "project": "p04-gigabit",
+    "prompt": "what mirror architecture was selected for GigaBIT M1 and why",
+    "expect_present": [
+      "--- Trusted Project State ---",
+      "Option B",
+      "conical",
+      "--- Project Memories ---"
+    ],
+    "expect_absent": [
+      "p06-polisher",
+      "folded-beam"
+    ],
+    "notes": "Canonical p04 decision — should surface both Trusted Project State (selected_mirror_architecture) and the project-memory band with the Option B memory"
+  },
+  {
+    "name": "p04-constraints",
+    "project": "p04-gigabit",
+    "prompt": "what are the key GigaBIT M1 program constraints",
+    "expect_present": [
+      "--- Trusted Project State ---",
+      "Zerodur",
+      "1.2"
+    ],
+    "expect_absent": [
+      "polisher suite"
+    ],
+    "notes": "Key constraints are in Trusted Project State (key_constraints) and in the mission-framing memory"
+  },
+  {
+    "name": "p05-configuration",
+    "project": "p05-interferometer",
+    "prompt": "what is the selected interferometer configuration",
+    "expect_present": [
+      "folded-beam",
+      "CGH"
+    ],
+    "expect_absent": [
+      "Option B",
+      "conical back",
+      "polisher suite"
+    ],
+    "notes": "P05 architecture memory covers folded-beam + CGH. GigaBIT M1 is the mirror under test and legitimately appears in p05 source docs (the interferometer measures it), so we only flag genuinely p04-only decisions like the mirror architecture choice."
+  },
+  {
+    "name": "p05-vendor-signal",
+    "project": "p05-interferometer",
+    "prompt": "what is the current vendor signal for the interferometer procurement",
+    "expect_present": [
+      "4D",
+      "Zygo"
+    ],
+    "expect_absent": [
+      "polisher"
+    ],
+    "notes": "Vendor memory mentions 4D as strongest technical candidate and Zygo Verifire SV as value path"
+  },
+  {
+    "name": "p06-suite-split",
+    "project": "p06-polisher",
+    "prompt": "how is the polisher software suite split across layers",
+    "expect_present": [
+      "polisher-sim",
+      "polisher-post",
+      "polisher-control"
+    ],
+    "expect_absent": [
+      "GigaBIT"
+    ],
+    "notes": "The three-layer split is in multiple p06 memories; check all three names surface together"
+  },
+  {
+    "name": "p06-control-rule",
+    "project": "p06-polisher",
+    "prompt": "what is the polisher control design rule",
+    "expect_present": [
+      "interlocks"
+    ],
+    "expect_absent": [
+      "interferometer"
+    ],
+    "notes": "Control design rule memory mentions interlocks and state transitions"
+  }
+]

src/atocore/api/routes.py

@@ -49,6 +49,7 @@ from atocore.memory.service import (
 )
 from atocore.observability.logger import get_logger
 from atocore.ops.backup import (
+    cleanup_old_backups,
     create_runtime_backup,
     list_runtime_backups,
     validate_backup,
@@ -511,6 +512,7 @@ class InteractionRecordRequest(BaseModel):
     chunks_used: list[str] = []
     context_pack: dict | None = None
     reinforce: bool = True
+    extract: bool = False
 
 
 @router.post("/interactions")
@@ -536,6 +538,7 @@ def api_record_interaction(req: InteractionRecordRequest) -> dict:
             chunks_used=req.chunks_used,
             context_pack=req.context_pack,
             reinforce=req.reinforce,
+            extract=req.extract,
         )
     except ValueError as e:
         raise HTTPException(status_code=400, detail=str(e))
@@ -731,6 +734,25 @@ def api_list_backups() -> dict:
     }
 
 
+class BackupCleanupRequest(BaseModel):
+    confirm: bool = False
+
+
+@router.post("/admin/backup/cleanup")
+def api_cleanup_backups(req: BackupCleanupRequest | None = None) -> dict:
+    """Apply retention policy to old backup snapshots.
+
+    Dry-run by default. Pass ``confirm: true`` to actually delete.
+    Retention: last 7 daily, last 4 weekly (Sundays), last 6 monthly (1st).
+    """
+    payload = req or BackupCleanupRequest()
+    try:
+        return cleanup_old_backups(confirm=payload.confirm)
+    except Exception as e:
+        log.error("admin_cleanup_failed", error=str(e))
+        raise HTTPException(status_code=500, detail=f"Cleanup failed: {e}")
+
+
 @router.get("/admin/backup/{stamp}/validate")
 def api_validate_backup(stamp: str) -> dict:
     """Validate that a previously created backup is structurally usable."""

src/atocore/context/builder.py

@@ -30,6 +30,12 @@ SYSTEM_PREFIX = (
 #   identity: 5%, preferences: 5%, project state: 20%, retrieval: 60%+
 PROJECT_STATE_BUDGET_RATIO = 0.20
 MEMORY_BUDGET_RATIO = 0.10  # 5% identity + 5% preference
+# Project-scoped memories (project/knowledge/episodic) are the outlet
+# for the Phase 9 reflection loop on the retrieval side. Budget sits
+# between identity/preference and retrieved chunks so a reinforced
+# memory can actually reach the model.
+PROJECT_MEMORY_BUDGET_RATIO = 0.25
+PROJECT_MEMORY_TYPES = ["project", "knowledge", "episodic"]
 
 # Last built context pack for debug inspection
 _last_context_pack: "ContextPack | None" = None
@@ -51,6 +57,8 @@ class ContextPack:
     project_state_chars: int = 0
     memory_text: str = ""
     memory_chars: int = 0
+    project_memory_text: str = ""
+    project_memory_chars: int = 0
     total_chars: int = 0
     budget: int = 0
     budget_remaining: int = 0
@@ -107,10 +115,32 @@ def build_context(
     memory_text, memory_chars = get_memories_for_context(
         memory_types=["identity", "preference"],
         budget=memory_budget,
+        query=user_prompt,
     )
 
+    # 2b. Get project-scoped memories (third precedence). Only
+    # populated when a canonical project is in scope — cross-project
+    # memory bleed would rot the pack. Active-only filtering is
+    # handled by the shared min_confidence=0.5 gate inside
+    # get_memories_for_context.
+    project_memory_text = ""
+    project_memory_chars = 0
+    if canonical_project:
+        project_memory_budget = min(
+            int(budget * PROJECT_MEMORY_BUDGET_RATIO),
+            max(budget - project_state_chars - memory_chars, 0),
+        )
+        project_memory_text, project_memory_chars = get_memories_for_context(
+            memory_types=PROJECT_MEMORY_TYPES,
+            project=canonical_project,
+            budget=project_memory_budget,
+            header="--- Project Memories ---",
+            footer="--- End Project Memories ---",
+            query=user_prompt,
+        )
+
     # 3. Calculate remaining budget for retrieval
-    retrieval_budget = budget - project_state_chars - memory_chars
+    retrieval_budget = budget - project_state_chars - memory_chars - project_memory_chars
 
     # 4. Retrieve candidates
     candidates = (
@@ -130,11 +160,14 @@ def build_context(
     selected = _select_within_budget(scored, max(retrieval_budget, 0))
 
     # 7. Format full context
-    formatted = _format_full_context(project_state_text, memory_text, selected)
+    formatted = _format_full_context(
+        project_state_text, memory_text, project_memory_text, selected
+    )
     if len(formatted) > budget:
         formatted, selected = _trim_context_to_budget(
             project_state_text,
             memory_text,
+            project_memory_text,
             selected,
             budget,
         )
@@ -144,6 +177,7 @@ def build_context(
 
     project_state_chars = len(project_state_text)
     memory_chars = len(memory_text)
+    project_memory_chars = len(project_memory_text)
     retrieval_chars = sum(c.char_count for c in selected)
     total_chars = len(formatted)
     duration_ms = int((time.time() - start) * 1000)
@@ -154,6 +188,8 @@ def build_context(
         project_state_chars=project_state_chars,
         memory_text=memory_text,
         memory_chars=memory_chars,
+        project_memory_text=project_memory_text,
+        project_memory_chars=project_memory_chars,
         total_chars=total_chars,
         budget=budget,
         budget_remaining=budget - total_chars,
@@ -171,6 +207,7 @@ def build_context(
         chunks_used=len(selected),
         project_state_chars=project_state_chars,
         memory_chars=memory_chars,
+        project_memory_chars=project_memory_chars,
         retrieval_chars=retrieval_chars,
         total_chars=total_chars,
         budget_remaining=budget - total_chars,
@@ -250,6 +287,7 @@ def _select_within_budget(
 def _format_full_context(
     project_state_text: str,
     memory_text: str,
+    project_memory_text: str,
     chunks: list[ContextChunk],
 ) -> str:
     """Format project state + memories + retrieved chunks into full context block."""
@@ -265,7 +303,12 @@ def _format_full_context(
         parts.append(memory_text)
         parts.append("")
 
-    # 3. Retrieved chunks (lowest trust)
+    # 3. Project-scoped memories (third trust level)
+    if project_memory_text:
+        parts.append(project_memory_text)
+        parts.append("")
+
+    # 4. Retrieved chunks (lowest trust)
     if chunks:
         parts.append("--- AtoCore Retrieved Context ---")
         if project_state_text:
@@ -277,7 +320,7 @@ def _format_full_context(
             parts.append(chunk.content)
             parts.append("")
         parts.append("--- End Context ---")
-    elif not project_state_text and not memory_text:
+    elif not project_state_text and not memory_text and not project_memory_text:
         parts.append("--- AtoCore Context ---\nNo relevant context found.\n--- End Context ---")
 
     return "\n".join(parts)
@@ -299,6 +342,7 @@ def _pack_to_dict(pack: ContextPack) -> dict:
         "project_hint": pack.project_hint,
         "project_state_chars": pack.project_state_chars,
         "memory_chars": pack.memory_chars,
+        "project_memory_chars": pack.project_memory_chars,
         "chunks_used": len(pack.chunks_used),
         "total_chars": pack.total_chars,
         "budget": pack.budget,
@@ -306,6 +350,7 @@ def _pack_to_dict(pack: ContextPack) -> dict:
         "duration_ms": pack.duration_ms,
         "has_project_state": bool(pack.project_state_text),
         "has_memories": bool(pack.memory_text),
+        "has_project_memories": bool(pack.project_memory_text),
         "chunks": [
             {
                 "source_file": c.source_file,
@@ -335,26 +380,45 @@ def _truncate_text_block(text: str, budget: int) -> tuple[str, int]:
 def _trim_context_to_budget(
     project_state_text: str,
     memory_text: str,
+    project_memory_text: str,
     chunks: list[ContextChunk],
     budget: int,
 ) -> tuple[str, list[ContextChunk]]:
-    """Trim retrieval first, then memory, then project state until formatted context fits."""
+    """Trim retrieval → project memories → identity/preference → project state."""
     kept_chunks = list(chunks)
-    formatted = _format_full_context(project_state_text, memory_text, kept_chunks)
+    formatted = _format_full_context(
+        project_state_text, memory_text, project_memory_text, kept_chunks
+    )
     while len(formatted) > budget and kept_chunks:
         kept_chunks.pop()
-        formatted = _format_full_context(project_state_text, memory_text, kept_chunks)
+        formatted = _format_full_context(
+            project_state_text, memory_text, project_memory_text, kept_chunks
+        )
 
     if len(formatted) <= budget:
         return formatted, kept_chunks
 
+    # Drop project memories next (they were the most recently added
+    # tier and carry less trust than identity/preference).
+    project_memory_text, _ = _truncate_text_block(
+        project_memory_text,
+        max(budget - len(project_state_text) - len(memory_text), 0),
+    )
+    formatted = _format_full_context(
+        project_state_text, memory_text, project_memory_text, kept_chunks
+    )
+    if len(formatted) <= budget:
+        return formatted, kept_chunks
+
     memory_text, _ = _truncate_text_block(memory_text, max(budget - len(project_state_text), 0))
-    formatted = _format_full_context(project_state_text, memory_text, kept_chunks)
+    formatted = _format_full_context(
+        project_state_text, memory_text, project_memory_text, kept_chunks
+    )
     if len(formatted) <= budget:
         return formatted, kept_chunks
 
     project_state_text, _ = _truncate_text_block(project_state_text, budget)
-    formatted = _format_full_context(project_state_text, "", [])
+    formatted = _format_full_context(project_state_text, "", "", [])
     if len(formatted) > budget:
         formatted, _ = _truncate_text_block(formatted, budget)
     return formatted, []

src/atocore/interactions/service.py

@@ -63,6 +63,7 @@ def record_interaction(
     chunks_used: list[str] | None = None,
     context_pack: dict | None = None,
     reinforce: bool = True,
+    extract: bool = False,
 ) -> Interaction:
     """Persist a single interaction to the audit trail.
 
@@ -163,6 +164,30 @@ def record_interaction(
                 error=str(exc),
             )
 
+    if extract and (response or response_summary):
+        try:
+            from atocore.memory.extractor import extract_candidates_from_interaction
+            from atocore.memory.service import create_memory
+
+            candidates = extract_candidates_from_interaction(interaction)
+            for candidate in candidates:
+                try:
+                    create_memory(
+                        memory_type=candidate.memory_type,
+                        content=candidate.content,
+                        project=candidate.project,
+                        confidence=candidate.confidence,
+                        status="candidate",
+                    )
+                except ValueError:
+                    pass  # duplicate or validation error — skip silently
+        except Exception as exc:  # pragma: no cover - extraction must never block capture
+            log.error(
+                "extraction_failed_on_capture",
+                interaction_id=interaction_id,
+                error=str(exc),
+            )
+
     return interaction
 
 

src/atocore/memory/reinforcement.py

@@ -51,6 +51,15 @@ _STOP_WORDS: frozenset[str] = frozenset({
 })
 _MATCH_THRESHOLD = 0.70
 
+# Long memories can't realistically hit 70% overlap through organic
+# paraphrase — a 40-token memory would need 28 stemmed tokens echoed
+# verbatim. Above this token count the matcher switches to an absolute
+# overlap floor plus a softer fraction floor so paragraph-length memories
+# still reinforce when the response genuinely uses them.
+_LONG_MEMORY_TOKEN_COUNT = 15
+_LONG_MODE_MIN_OVERLAP = 12
+_LONG_MODE_MIN_FRACTION = 0.35
+
 DEFAULT_CONFIDENCE_DELTA = 0.02
 
 
@@ -171,26 +180,47 @@ def _stem(word: str) -> str:
 def _tokenize(text: str) -> set[str]:
     """Split normalized text into a stemmed token set.
 
-    Strips punctuation, drops words shorter than 3 chars and stop words.
+    Strips punctuation, drops words shorter than 3 chars and stop
+    words. Hyphenated and slash-separated identifiers
+    (``polisher-control``, ``twyman-green``, ``2-projects/interferometer``)
+    produce both the full form AND each sub-token, so a query for
+    "polisher control" can match a memory that wrote
+    "polisher-control" without forcing callers to guess the exact
+    hyphenation.
     """
     tokens: set[str] = set()
     for raw in text.split():
-        # Strip leading/trailing punctuation (commas, periods, quotes, etc.)
         word = raw.strip(".,;:!?\"'()[]{}-/")
-        if len(word) < 3:
+        if not word:
             continue
-        if word in _STOP_WORDS:
-            continue
-        tokens.add(_stem(word))
+        _add_token(tokens, word)
+        # Also add sub-tokens split on internal '-' or '/' so
+        # hyphenated identifiers match queries that don't hyphenate.
+        if "-" in word or "/" in word:
+            for sub in re.split(r"[-/]+", word):
+                _add_token(tokens, sub)
     return tokens
 
 
+def _add_token(tokens: set[str], word: str) -> None:
+    if len(word) < 3:
+        return
+    if word in _STOP_WORDS:
+        return
+    tokens.add(_stem(word))
+
+
 def _memory_matches(memory_content: str, normalized_response: str) -> bool:
     """Return True if enough of the memory's tokens appear in the response.
 
-    Uses token-overlap: tokenize both sides (lowercase, stem, drop stop
-    words), then check whether >= 70 % of the memory's content tokens
-    appear in the response token set.
+    Dual-mode token overlap:
+    - Short memories (<= _LONG_MEMORY_TOKEN_COUNT stems): require
+      >= 70 % of memory tokens echoed.
+    - Long memories (paragraphs): require an absolute floor of
+      _LONG_MODE_MIN_OVERLAP distinct stems echoed AND a softer
+      fraction of _LONG_MODE_MIN_FRACTION, so organic paraphrase
+      of a real project memory can reinforce without the response
+      quoting the paragraph verbatim.
     """
     if not memory_content:
         return False
@@ -202,4 +232,10 @@ def _memory_matches(memory_content: str, normalized_response: str) -> bool:
         return False
     response_tokens = _tokenize(normalized_response)
     overlap = memory_tokens & response_tokens
-    return len(overlap) / len(memory_tokens) >= _MATCH_THRESHOLD
+    fraction = len(overlap) / len(memory_tokens)
+    if len(memory_tokens) <= _LONG_MEMORY_TOKEN_COUNT:
+        return fraction >= _MATCH_THRESHOLD
+    return (
+        len(overlap) >= _LONG_MODE_MIN_OVERLAP
+        and fraction >= _LONG_MODE_MIN_FRACTION
+    )

src/atocore/memory/service.py

@@ -344,6 +344,9 @@ def get_memories_for_context(
     memory_types: list[str] | None = None,
     project: str | None = None,
     budget: int = 500,
+    header: str = "--- AtoCore Memory ---",
+    footer: str = "--- End Memory ---",
+    query: str | None = None,
 ) -> tuple[str, int]:
     """Get formatted memories for context injection.
 
@@ -351,38 +354,72 @@ def get_memories_for_context(
 
     Budget allocation per Master Plan section 9:
       identity: 5%, preference: 5%, rest from retrieval budget
+
+    The caller can override ``header`` / ``footer`` to distinguish
+    multiple memory blocks in the same pack (e.g. identity/preference
+    vs project/knowledge memories).
+
+    When ``query`` is provided, candidates within each memory type
+    are ranked by lexical overlap against the query (stemmed token
+    intersection, ties broken by confidence). Without a query,
+    candidates fall through in the order ``get_memories`` returns
+    them — which is effectively "by confidence desc".
     """
     if memory_types is None:
         memory_types = ["identity", "preference"]
 
     if budget <= 0:
         return "", 0
-
-    header = "--- AtoCore Memory ---"
-    footer = "--- End Memory ---"
     wrapper_chars = len(header) + len(footer) + 2
     if budget <= wrapper_chars:
         return "", 0
 
     available = budget - wrapper_chars
     selected_entries: list[str] = []
+    used = 0
 
-    for index, mtype in enumerate(memory_types):
-        type_budget = available if index == len(memory_types) - 1 else max(0, available // (len(memory_types) - index))
-        type_used = 0
+    # Pre-tokenize the query once. ``_score_memory_for_query`` is a
+    # free function below that reuses the reinforcement tokenizer so
+    # lexical scoring here matches the reinforcement matcher.
+    query_tokens: set[str] | None = None
+    if query:
+        from atocore.memory.reinforcement import _normalize, _tokenize
+
+        query_tokens = _tokenize(_normalize(query))
+        if not query_tokens:
+            query_tokens = None
+
+    # Collect ALL candidates across the requested types into one
+    # pool, then rank globally before the budget walk. Ranking per
+    # type and walking types in order would starve later types when
+    # the first type's candidates filled the budget — even if a
+    # later-type candidate matched the query perfectly. Type order
+    # is preserved as a stable tiebreaker inside
+    # ``_rank_memories_for_query`` via Python's stable sort.
+    pool: list[Memory] = []
+    seen_ids: set[str] = set()
+    for mtype in memory_types:
         for mem in get_memories(
             memory_type=mtype,
             project=project,
             min_confidence=0.5,
-            limit=10,
+            limit=30,
         ):
-            entry = f"[{mem.memory_type}] {mem.content}"
-            entry_len = len(entry) + 1
-            if entry_len > type_budget - type_used:
+            if mem.id in seen_ids:
                 continue
-            selected_entries.append(entry)
-            type_used += entry_len
-        available -= type_used
+            seen_ids.add(mem.id)
+            pool.append(mem)
+
+    if query_tokens is not None:
+        pool = _rank_memories_for_query(pool, query_tokens)
+
+    for mem in pool:
+        entry = f"[{mem.memory_type}] {mem.content}"
+        entry_len = len(entry) + 1
+        if entry_len > available - used:
+            continue
+        selected_entries.append(entry)
+        used += entry_len
 
     if not selected_entries:
         return "", 0
@@ -394,6 +431,28 @@ def get_memories_for_context(
     return text, len(text)
 
 
+def _rank_memories_for_query(
+    memories: list["Memory"],
+    query_tokens: set[str],
+) -> list["Memory"]:
+    """Rerank a memory list by lexical overlap with a pre-tokenized query.
+
+    Ordering key: (overlap_count DESC, confidence DESC). When a query
+    shares no tokens with a memory, overlap is zero and confidence
+    acts as the sole tiebreaker — which matches the pre-query
+    behaviour and keeps no-query calls stable.
+    """
+    from atocore.memory.reinforcement import _normalize, _tokenize
+
+    scored: list[tuple[int, float, Memory]] = []
+    for mem in memories:
+        mem_tokens = _tokenize(_normalize(mem.content))
+        overlap = len(mem_tokens & query_tokens) if mem_tokens else 0
+        scored.append((overlap, mem.confidence, mem))
+    scored.sort(key=lambda t: (t[0], t[1]), reverse=True)
+    return [mem for _, _, mem in scored]
+
+
 def _row_to_memory(row) -> Memory:
     """Convert a DB row to Memory dataclass."""
     keys = row.keys() if hasattr(row, "keys") else []

tests/test_capture_stop.py

@@ -183,7 +183,7 @@ class TestCapture:
         assert body["prompt"] == "Please explain how the backup system works in detail"
         assert body["client"] == "claude-code"
         assert body["session_id"] == "test-session-123"
-        assert body["reinforce"] is False
+        assert body["reinforce"] is True
 
     @mock.patch("capture_stop.urllib.request.urlopen")
     def test_skips_when_disabled(self, mock_urlopen, tmp_path):

tests/test_context_builder.py

@@ -251,3 +251,98 @@ def test_unknown_hint_falls_back_to_raw_lookup(tmp_data_dir, sample_markdown, mo
 
     pack = build_context("status?", project_hint="orphan-project", budget=2000)
     assert "Solo run" in pack.formatted_context
+
+
+def test_project_memories_included_in_pack(tmp_data_dir, sample_markdown):
+    """Active project-scoped memories for the target project should
+    land in a dedicated '--- Project Memories ---' band so the
+    Phase 9 reflection loop has a retrieval outlet."""
+    from atocore.memory.service import create_memory
+
+    init_db()
+    init_project_state_schema()
+    ingest_file(sample_markdown)
+
+    mem = create_memory(
+        memory_type="project",
+        content="the mirror architecture is Option B conical back for p04-gigabit",
+        project="p04-gigabit",
+        confidence=0.9,
+    )
+    # A sibling memory for a different project must NOT leak into the pack.
+    create_memory(
+        memory_type="project",
+        content="polisher suite splits into sim, post, control, contracts",
+        project="p06-polisher",
+        confidence=0.9,
+    )
+
+    pack = build_context(
+        "remind me about the mirror architecture",
+        project_hint="p04-gigabit",
+        budget=3000,
+    )
+    assert "--- Project Memories ---" in pack.formatted_context
+    assert "Option B conical back" in pack.formatted_context
+    assert "polisher suite splits" not in pack.formatted_context
+    assert pack.project_memory_chars > 0
+    assert mem.project == "p04-gigabit"
+
+
+def test_project_memories_absent_without_project_hint(tmp_data_dir, sample_markdown):
+    """Without a project hint, project memories stay out of the pack —
+    cross-project bleed would rot the signal."""
+    from atocore.memory.service import create_memory
+
+    init_db()
+    init_project_state_schema()
+    ingest_file(sample_markdown)
+
+    create_memory(
+        memory_type="project",
+        content="scoped project knowledge that should not leak globally",
+        project="p04-gigabit",
+        confidence=0.9,
+    )
+
+    pack = build_context("tell me something", budget=3000)
+    assert "--- Project Memories ---" not in pack.formatted_context
+    assert pack.project_memory_chars == 0
+
+
+def test_project_memories_query_relevance_ordering(tmp_data_dir, sample_markdown):
+    """When the budget only fits one memory, query-relevance ordering
+    should pick the one the query is actually about — even if another
+    memory has higher confidence.
+
+    Regression for the 2026-04-11 p05-vendor-signal harness failure:
+    memory selection was fixed-order by confidence, so a lower-ranked
+    vendor memory got starved out of the budget when a query was
+    specifically about vendors.
+    """
+    from atocore.memory.service import create_memory
+
+    init_db()
+    init_project_state_schema()
+    ingest_file(sample_markdown)
+
+    create_memory(
+        memory_type="project",
+        content="the folded-beam interferometer uses a CGH stage and fold mirror",
+        project="p05-interferometer",
+        confidence=0.97,
+    )
+    create_memory(
+        memory_type="knowledge",
+        content="vendor signal: Zygo Verifire SV is the strongest value path for the interferometer",
+        project="p05-interferometer",
+        confidence=0.85,
+    )
+
+    pack = build_context(
+        "what is the current vendor signal for the interferometer",
+        project_hint="p05-interferometer",
+        budget=1200,  # tight enough that only one project memory fits
+    )
+    assert "Zygo Verifire SV" in pack.formatted_context
+    assert pack.project_memory_chars > 0

tests/test_reinforcement.py

@@ -476,6 +476,60 @@ def test_reinforce_matches_at_70_percent_threshold(tmp_data_dir):
     assert any(r.memory_id == mem.id for r in results)
 
 
+def test_reinforce_long_memory_matches_on_absolute_overlap(tmp_data_dir):
+    """A paragraph-length memory should reinforce when the response
+    echoes a substantive subset of its distinctive tokens, even though
+    the overlap fraction stays well under 70%."""
+    init_db()
+    mem = create_memory(
+        memory_type="project",
+        content=(
+            "Interferometer architecture: a folded-beam configuration with a "
+            "fixed horizontal interferometer, a forty-five degree fold mirror, "
+            "a six-DOF CGH stage, and the mirror on its own tilting platform. "
+            "The fold mirror redirects the beam while the CGH shapes the wavefront."
+        ),
+        project="p05-interferometer",
+        confidence=0.5,
+    )
+    interaction = _make_interaction(
+        project="p05-interferometer",
+        response=(
+            "For the interferometer we keep the folded-beam layout: horizontal "
+            "interferometer, fold mirror at forty-five degrees, CGH stage with "
+            "six DOF, and the mirror sitting on its tilting platform. The fold "
+            "mirror redirects the beam and the CGH shapes the wavefront."
+        ),
+    )
+    results = reinforce_from_interaction(interaction)
+    assert any(r.memory_id == mem.id for r in results)
+
+
+def test_reinforce_long_memory_rejects_thin_overlap(tmp_data_dir):
+    """Long memory + a response that only brushes a few generic terms
+    must NOT reinforce — otherwise the reflection loop rots."""
+    init_db()
+    mem = create_memory(
+        memory_type="project",
+        content=(
+            "Polisher control system executes approved controller jobs, "
+            "enforces state transitions and interlocks, supports pause "
+            "resume and abort, and records auditable run logs while "
+            "never reinterpreting metrology or inventing new strategies."
+        ),
+        project="p06-polisher",
+        confidence=0.5,
+    )
+    interaction = _make_interaction(
+        project="p06-polisher",
+        response=(
+            "I updated the polisher docs and fixed a typo in the run logs section."
+        ),
+    )
+    results = reinforce_from_interaction(interaction)
+    assert all(r.memory_id != mem.id for r in results)
+
+
 def test_reinforce_rejects_below_70_percent(tmp_data_dir):
     """Only 6 of 10 content tokens present (60%) → should NOT match."""
     init_db()