v1.1 WP-B.2: independent long-term memory retention

Corrects the memory mechanisms WP-B.1 diagnosed, one at a time, each
verified before the next. Accepted by the owner with a documented
reference-model limitation. No schema, bundle format, setting default,
lineage, authority or protocol-cleanup change.

- B2.1 ranking: the retrieval query is the player's input plus a bounded
  scene context (state scene + end of the newest narration), embedded in
  one call. final = semantic (0.6 input / 0.4 context) + 0.15 x lexical,
  where lexical is a rarity-weighted share of the input's words, computed
  per turn over the candidates with no index. Scores and the query are
  recorded per used memory; pins and redundancy suppression unchanged.
- B2.2 coverage-aware eviction (memorybank.eviction_order): the earliest
  and newest memories are kept, the smallest coverage hole goes first,
  least-recently-used breaks ties and remains the fallback. Bounded; pins
  never evicted; frozen-bank protection kept; reads no text or vectors.
- B2.3 bounded memory creation: a block longer than 2,000 tokens is shown
  to the summariser as head + tail with an omission marker, inside the
  same budget; shorter blocks unchanged; the marker is never stored.
- The memory summariser prompt is unchanged from v1.0.0. A B2.4 prompt
  experiment was measured on the reference model, showed no reliable
  improvement for the target failure (0/5 under both prompts, with new
  "Memory:"-prefix, second-person and length regressions), and was
  reverted. memorybank.memory_user_prompt is kept as a behaviour-neutral
  helper.
- tools/memory_fidelity.py (diagnostic only): genre-neutral fixtures plus
  the failed block, a deterministic fidelity checker, and a real-model
  shipped-vs-experiment measurement.
- tools/memory_diagnostic.py: ranking replica uses production scoring;
  ranking_crowded, ranking_context_dependent and independent_full
  fixtures; per-turn isolation and provenance.
- tests: B.1's two strict xfails are now ordinary passes; ranking,
  eviction and excerpt tests; summariser acceptance tests kept apart from
  diagnostic-measurement tests.
- DEVELOPMENT.md: the GPU-host kernel/Ollama watch used `-k -u ollama`,
  which matches nothing; now the OR form.
- docs: CONTEXT-AND-MEMORY 15/18/20/21 as shipped, V1.1-PLAN (status and
  release criteria 12-13), planning README, VERSION v4.3,
  reports/v1.1/V1.1-WP-B2-REPORT.md.

Deterministic independent-memory recovery: PASS (independent_full fails
on v1.0.0 at creation and returns recovered_through_memory_independent
here). Reference-model independent recovery: FAILED on the
precondition-valid attempt, at memory creation: the summariser omitted a
player-established fact from a block it received whole. Accepted as a
documented v1.1 residual and carried into the release gate.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01VvegagkhuCZoFPdv4M1egY

backend/tools/v11_b1_memory.py

@@ -103,13 +103,16 @@ def main() -> int:
         variants = {}
         memory_id = diagnosis["created"]["memory_id"]
         if memory_id is not None and not diagnosis.get("retained", {}).get("forgotten"):
-            for label, query in (("recall_turn", diagnosis.get("ranked", {}).get("query", "")),
-                                 ("paraphrase", md.PARAPHRASE_QUERY),
-                                 ("unrelated", md.UNRELATED_QUERY)):
+            base = md.production_query(adventure, recall_action.id)
+            for label, query in (("recall_turn", base),
+                                 ("paraphrase", md.variant_query(base, md.PARAPHRASE_QUERY)),
+                                 ("unrelated", md.variant_query(base, md.UNRELATED_QUERY))):
                 ranking = asyncio.run(md.rank_bank(db, adventure, settings, query, embed))
                 row = next((r for r in ranking["scored"] if r["memory_id"] == memory_id), None)
                 variants[label] = {"rank": row and row["rank"], "of": len(ranking["scored"]),
                                    "similarity": row and row["similarity"],
+                                   "lexical_score": row and row["lexical_score"],
+                                   "final_score": row and row["final_score"],
                                    "selected": bool(row and row["selected"])}
         db.rollback()
     report = {"isolation": iso, "diagnosis": diagnosis, "ranking_variants": variants}