diff --git a/BENCHMARKS.md b/BENCHMARKS.md
index 94c33648..b9a79b70 100644
--- a/BENCHMARKS.md
+++ b/BENCHMARKS.md
@@ -94,14 +94,14 @@ interpretation and do not count as additional benchmark-quality gains.
 ### Public numeric evidence registry
 
 Every exact public aggregate retained below comes from the checked-in, public-safe
-[`offline-fixtures-pr262-git-bytes-20261014.json`](docs/benchmark-evidence/offline-fixtures-pr262-git-bytes-20261014.json) artifact. Its
+[`offline-fixtures-pr263-git-bytes-20261014.json`](docs/benchmark-evidence/offline-fixtures-pr263-git-bytes-20261014.json) artifact. Its
 SHA-256 is
-`1b4ba1d95ae003bab4c5d15a7d619fc7a7b813d66387bc9530a74babad5221eb`, also recorded in the
+`9050c5b88343bd05984372975da0c2a25090dfe081c39e4fd2c95940f865177a`, also recorded in the
 adjacent `.sha256` file. The artifact contains no raw questions, answers, prompts, customer data,
 or per-record content fingerprints.
 
 The fixture-suite digest is
-`e5f78cc61a584b64051cc8650c8e2f68cff9a3ccb096df57e8a621d376f829b8`. The artifact defines
+`7c8079520618e40c47d81354ea30408e8c9288b4ce6dd0f629539d2317c21ec8`. The artifact defines
 the digest algorithm and records the SHA-256 of every suite and dataset file. Each evidence ID
 also binds its exact command through `sha256(UTF-8 exact command)`:
 
@@ -126,13 +126,13 @@ grounded checks in separate panels; provider billing and MCP transport are not m
 the SVG and matching PNG with:
 
 ```bash
-python scripts/render_benchmark_report.py --report docs/benchmark-evidence/offline-fixtures-pr262-git-bytes-20261014.json --output docs/images/context-efficiency.svg --png-output docs/images/context-efficiency.png
+python scripts/render_benchmark_report.py --report docs/benchmark-evidence/offline-fixtures-pr263-git-bytes-20261014.json --output docs/images/context-efficiency.svg --png-output docs/images/context-efficiency.png
 ```
 
 The companion examples are also generated from that artifact with:
 
 ```bash
-python -m scripts.render_benchmark_examples --report docs/benchmark-evidence/offline-fixtures-pr262-git-bytes-20261014.json --output docs/images/evidence-backed-agent-examples.svg --png-output docs/images/evidence-backed-agent-examples.png
+python -m scripts.render_benchmark_examples --report docs/benchmark-evidence/offline-fixtures-pr263-git-bytes-20261014.json --output docs/images/evidence-backed-agent-examples.svg --png-output docs/images/evidence-backed-agent-examples.png
 ```
 The historical-to-executable mapping is in
 [`docs/BENCHMARK_CHANGE_COVERAGE.md`](docs/BENCHMARK_CHANGE_COVERAGE.md).
diff --git a/README.md b/README.md
index e5d42221..752d57a2 100644
--- a/README.md
+++ b/README.md
@@ -50,7 +50,7 @@ Connect a coding agent over MCP with the [agent setup guide](https://github.com/
 The current registered artifact contains three deterministic offline fixture runs. It separates context size, retrieval quality, and grounded decision checks; these small fixtures do not establish general task performance.
 
 <p align="center">
-  <img src="https://raw.githubusercontent.com/Coding-Dev-Tools/engraphis/dfa3151d06ca3c226b50bc2e43305f4b23d42903/docs/images/context-efficiency.svg" alt="Three registered offline fixtures: structure-aware chunking reduced mean retrieved context from 740.3 to 214.3 tokens per question (71.1%, 18 questions); the serialized JSON-shape proxy fell from 24,590 to 11,138 tokens across 26 payload samples and 260 recalls. Candidate and packed retrieval quality (Recall@5, Hit@5, and answer-token recall) are shown separately (each 1.000). Grounded checks show 5/5 answerable queries grounded and 6/6 abstention queries rejected, including a 1/1 quarantined-evidence probe; 11/11 decisions were correct. MCP transport and provider billing were not measured. Artifact SHA-256 prefix 1b4ba1d95ae0; see the benchmark guide for the full checksum." width="100%">
+  <img src="https://raw.githubusercontent.com/Coding-Dev-Tools/engraphis/5f94cd2a1f01c5627cb62104015d184ca9c30f01/docs/images/context-efficiency.svg" alt="Three registered offline fixtures: structure-aware chunking reduced mean retrieved context from 740.3 to 214.3 tokens per question (71.1%, 18 questions); the serialized JSON-shape proxy fell from 24,590 to 11,138 tokens across 26 payload samples and 260 recalls. Candidate and packed retrieval quality (Recall@5, Hit@5, and answer-token recall) are shown separately (each 1.000). Grounded checks show 5/5 answerable queries grounded and 6/6 abstention queries rejected, including a 1/1 quarantined-evidence probe; 11/11 decisions were correct. MCP transport and provider billing were not measured. Artifact SHA-256 prefix 9050c5b88343; see the benchmark guide for the full checksum." width="100%">
   <br>
   <sup>Three offline fixtures separate context reduction, candidate and packed retrieval quality, and grounded behavior. The chart shows the artifact checksum prefix; see the benchmark guide for the full checksum and reproduction steps.</sup>
 </p>
@@ -61,7 +61,7 @@ The current registered artifact contains three deterministic offline fixture run
 | Recall payload proxy | JSON-shape proxy: 24,590 → 11,138 tokens (54.71% lower, 26 samples; 260 timed recalls) | Candidate and packed Recall@5, Hit@5, and answer-token recall are each 1.000 |
 | Grounded decisions | 5/5 answerable queries grounded; 6/6 abstention queries rejected, including 1/1 quarantined-evidence check | 11/11 decisions correct |
 
-The payload figure is a serialized JSON-shape estimate, not an MCP transport measurement or provider billing total. See the [Benchmark methodology](https://github.com/Coding-Dev-Tools/engraphis/blob/dfa3151d06ca3c226b50bc2e43305f4b23d42903/BENCHMARKS.md) for artifact identity, counting methods, reproduction commands, external-evaluation boundaries, and limitations.
+The payload figure is a serialized JSON-shape estimate, not an MCP transport measurement or provider billing total. See the [Benchmark methodology](https://github.com/Coding-Dev-Tools/engraphis/blob/5f94cd2a1f01c5627cb62104015d184ca9c30f01/BENCHMARKS.md) for artifact identity, counting methods, reproduction commands, external-evaluation boundaries, and limitations.
 
 ## Optional Jev assistance
 
diff --git a/docs/SPEC_CRAWL.md b/docs/SPEC_CRAWL.md
index 6691b60d..2728bd04 100644
--- a/docs/SPEC_CRAWL.md
+++ b/docs/SPEC_CRAWL.md
@@ -185,6 +185,8 @@ These four Classic MCP tools are also discoverable and callable through Smart MC
 
 CI verifies determinism, axis classification, flag recall, qualified-token avoidance, section counts, and score boundaries in both the full stack and NumPy-only jobs. Qualified-token avoidance measures the explicitly labelled negative examples; it is not a general false-positive precision estimate:
 
+The same gate checks eight synthetic memory-audit contracts in both input orders: unrelated-subject noise, agreeing and opposing nested directives, subject and claim-kind separation, stored orphan connectivity, unknown effective dates, and effective-time ordering. It compares exact conflict pairs and retirement targets, requires compact node responses, and reports the fixture digest. These bounded regression fixtures do not establish general classifier quality.
+
 ```bash
 python -m eval.spec_crawl
 ```
diff --git a/docs/benchmark-evidence/offline-fixtures-pr263-final-20261014.json b/docs/benchmark-evidence/offline-fixtures-pr263-final-20261014.json
new file mode 100644
index 00000000..14bdd1cb
--- /dev/null
+++ b/docs/benchmark-evidence/offline-fixtures-pr263-final-20261014.json
@@ -0,0 +1,696 @@
+{
+  "environment": {
+    "embedding": "deterministic",
+    "numpy": "2.4.6",
+    "platform": "win32",
+    "python": "3.11.17",
+    "vector_backend": "numpy"
+  },
+  "generated_on": "2026-10-14",
+  "privacy": {
+    "contains_answers": false,
+    "contains_customer_data": false,
+    "contains_per_record_fingerprints": false,
+    "contains_prompts": false,
+    "contains_raw_questions": false
+  },
+  "runs": [
+    {
+      "boundary": "Deterministic offline retrieval fixture; normalized-character token estimator; not external QA or provider billing.",
+      "command": "python -m eval.chunking_eval --dataset eval/datasets/longdoc.jsonl --k 5",
+      "config_digest": "c1c8196aa7e1568ef3844a9fb2d76b87f342c39108e32d6ad144b885a76143b8",
+      "config_digest_method": "sha256(UTF-8 exact command)",
+      "id": "offline-chunking",
+      "result": {
+        "chunked": {
+          "max_stored_tokens": 59,
+          "mean_context_tokens": 214.3,
+          "mean_evidence_tokens": 42.4,
+          "memories": 24,
+          "recall_at_k": 1.0
+        },
+        "context_reduction_pct": 71.1,
+        "documents": 6,
+        "k": 5,
+        "questions": 18,
+        "token_counter": "engraphis.chars4.v1",
+        "whole": {
+          "max_stored_tokens": 213,
+          "mean_context_tokens": 740.3,
+          "mean_evidence_tokens": 162.2,
+          "memories": 6,
+          "recall_at_k": 1.0
+        }
+      }
+    },
+    {
+      "boundary": "Deterministic offline CodeMem fixture; serialized JSON-shape payload proxies, not MCP transport responses, provider billing, or latency claims.",
+      "command": "python -m eval.performance --dataset eval/datasets/codemem.jsonl --k 5 --iterations 10 --json",
+      "config_digest": "bbe4aca81e58d4830e50a8fc7729a1d15b71d97a6299bccd79432b7f119677d7",
+      "config_digest_method": "sha256(UTF-8 exact command)",
+      "id": "offline-performance",
+      "result": {
+        "answer_token_recall": 1.0,
+        "compact_serialized_payload_tokens": 11138,
+        "dataset_cases": 14,
+        "full_serialized_payload_tokens": 24590,
+        "hit_at_k": 1.0,
+        "k": 5,
+        "max_context_tokens": 108,
+        "mean_context_tokens": 85.38,
+        "memories": 44,
+        "packed_quality": {
+          "answer_token_recall": 1.0,
+          "hit_at_k": 1.0,
+          "recall_at_k": 1.0,
+          "sample_count": 26
+        },
+        "payload_boundary": {
+          "kind": "serialized_json_shape_proxy",
+          "mcp_envelope_serialized": false,
+          "token_counter": "engraphis.regex.v1",
+          "transport_measured": false
+        },
+        "quality_scope": {
+          "packed": "packed_quality fields score only chunks admitted to reader context",
+          "retrieved": "legacy quality fields score all candidate chunks returned before context packing"
+        },
+        "questions": 26,
+        "recall_at_k": 1.0,
+        "saved_serialized_payload_tokens": 13452,
+        "serialized_payload_savings_ratio": 0.5471,
+        "timed_recalls": 260,
+        "token_budget": 1500,
+        "token_counter": "engraphis.regex.v1"
+      }
+    },
+    {
+      "boundary": "Deterministic offline support/abstention fixture; not a frontier-model answer-quality score.",
+      "command": "python -m eval.grounded",
+      "config_digest": "590442e51e3642c10489165759919dc86ffac62c182937330c153e7f8d5fc26f",
+      "config_digest_method": "sha256(UTF-8 exact command)",
+      "id": "offline-grounded",
+      "result": {
+        "abstained": 6,
+        "answerable": 5,
+        "decision_accuracy": 1.0,
+        "grounded": 5,
+        "off_topic": 6,
+        "quarantine_hits": 1,
+        "quarantined": 1
+      }
+    }
+  ],
+  "schema": "engraphis-public-offline-fixtures/v1",
+  "suite": {
+    "digest": "7be6d6331b312e54734010f66a21960b07374f8238880188c9f4b70f2654e7fc",
+    "digest_method": "sha256(canonical compact JSON mapping each sorted path to its file SHA-256)",
+    "files": {
+      "engraphis/__init__.py": "f24241e8da2ede9ccba54ce4bf7531a800b55c29b2469e5de93bd423cffbf0be",
+      "engraphis/ai_context.py": "cbe2954fd959a2e4d8969559a6d3be1391d46460bd541a583eb3bd48b2eea4b7",
+      "engraphis/app.py": "887ba01aa544c25bd62a9987b96c6d3472d0a7de8059579bf445a0dc35608c3c",
+      "engraphis/backends/__init__.py": "68e79a0cf06a2281475ca8f6f670ef78f7f3d5932e9dee9c6829ab32c22bf15c",
+      "engraphis/backends/codegraph.py": "cb9f31fb34ad4172bfad2320d38890ae6742fb5c578dd7b926e66d0135e2fd0a",
+      "engraphis/backends/embedder_api.py": "65058be5c4457576cfb8b297339ab7e2ef54ddba3ce1db58114e84879a44db14",
+      "engraphis/backends/embedder_deterministic.py": "0886206df61608683f355ad36e5c9e257cdabfff45163909f1aa3c3fd3fa182e",
+      "engraphis/backends/embedder_st.py": "654f9306b9eb935902692e4f90241c910ad9b8569358140ebba9d64bd2572194",
+      "engraphis/backends/encrypted_db.py": "4ea61a92ac7eb68aa6ec752116a721f1816b71385cd61c1370f7029e44a5cb7b",
+      "engraphis/backends/extractor.py": "d7ba56988e68971ec36be45bf0dfe3d39f0c27ccd8f74fbb1133844b9c76ae93",
+      "engraphis/backends/graph_extractor.py": "21c6183993f6828b0c46c6436e1f814145bac58ee22663aed9030006830e1de6",
+      "engraphis/backends/jev_decision.py": "64a43247dec2ebdbfd2e5ebc5cf298460e1380ce33260ba6d19bb00a2e7be93e",
+      "engraphis/backends/jev_query_planner.py": "19a41352dbc6619df9fb91d04a623cb7b84daf25db0ecb740d01ade9e3c8c0b6",
+      "engraphis/backends/jev_transport.py": "cd53faf1b820097e65da05509c16964e37d9b74203332bdd76588023f3da2659",
+      "engraphis/backends/model_source.py": "5d678c4561f326c885909c346f77f229b61a68f7d38bcce70f8c0c2c6e8a5100",
+      "engraphis/backends/postgres_schema.py": "0c16f2d5d77227e4fcc841a143bfc11560f1b614af0e5c6d13e95594b77b2570",
+      "engraphis/backends/query_planner.py": "9d6a7226727555876eddc2ac8d10c6c0038b1d64bbc05823b59137b57076d336",
+      "engraphis/backends/reranker.py": "c407d241ea8cbe8baafb3c81b7603445bfb09495b08f55e78ea04019cd341845",
+      "engraphis/backends/resources.py": "8e885a55e4f092a75c042b614b59d7c4627126f0945f8d71e8ed911768f4bed3",
+      "engraphis/backends/retention.py": "c778fad5ae3c43fda4f1a200222f8108166c09ba155debaa670de93262885efc",
+      "engraphis/backends/sync_folder.py": "577a6d6e73a70811b54138e08dade631b92b864ffe123d5447ae79cccba0f9da",
+      "engraphis/backends/sync_relay.py": "e15665300bbd306785278a8dfaeb46096ac1d187252f80d2548e4c5773db3dc7",
+      "engraphis/backends/vector_numpy.py": "d28e167599e1480ec4119e4ada9e225444a9db6cf412a1b97535740a02789457",
+      "engraphis/backends/vector_sqlitevec.py": "d09795d20e2e69f1c96064da4a578c7ca59a96eb7fe00a5771acb879c84b73c9",
+      "engraphis/build_info.py": "e30f44ee0b6f28dfddc7194799d2bf1bf4c8b0885b119d7a743c28ae13274d1e",
+      "engraphis/cloud_authz.py": "8bfc09d604432373854a7e58f19e1db0e25845cb531beb8ff75c6597113882cb",
+      "engraphis/cloud_features.py": "f39f84556d227b1b76b1cbb06a6243f523a23c52e6e858c5dee71c537dd9dae2",
+      "engraphis/cloud_session.py": "8293314317ce81ecc6600a655899a0f4e673939bb83703abbeda4b78333ef307",
+      "engraphis/commercial.py": "62d9b66311523db54a5ec36ca4b8059d6556538cb2082f9bbdd8612ab7b37f91",
+      "engraphis/config.py": "98add360aa3dab076d7dfce70a9f6faaa4455bddb35427f64ccd424ad1157e2c",
+      "engraphis/core/__init__.py": "c2b770e0c8857a8d81133b57651ff89f8b425de2970de3fd313245cc6313b6e6",
+      "engraphis/core/adaptive_context.py": "c62a65a18f13779ae9914d72ce4ec9fcfbca72894ca59c432c75ef138c8041a4",
+      "engraphis/core/browsing.py": "1e3dcd7b834059424e71c60a09602c5538cb7291106e116ac086e02f1460c133",
+      "engraphis/core/codegraph_export.py": "4a2be5831e170113af24cfd849610bc2a815229b3d1792c974f8e46bd54987da",
+      "engraphis/core/conflicts.py": "0c27d7c4e551d120cec13471c38e80340549385bd9292c5e74b62292a2a30574",
+      "engraphis/core/consolidate.py": "a37d464d18009b8ee14c011c09e54eca6f9f6f66c72098063978d386029175f9",
+      "engraphis/core/context.py": "c8b6cf97cd471651676370ff7461b8e3b9a5bd7c3dd54e5f4d2a8328e8f6de52",
+      "engraphis/core/diagnostics.py": "3fd523f6cfa804e10e0ec98c4f9c63f12c618b7a69011b227ccab6db97c12e5d",
+      "engraphis/core/documents.py": "99bb903c3ab0b374dbb9da8400f00375c840abe5dab00f744b9acd81ee02663c",
+      "engraphis/core/engine.py": "3ce7c78131df6c03bc135a679dbe2fc9a4df5b3f0d1e903e6325ee0ed317d75d",
+      "engraphis/core/evidence.py": "96064acee92125e8c711de4783f1daf85d49b0ad7805cb060b0456ea56abf670",
+      "engraphis/core/fsutil.py": "3e223cd638de6294a887eb529d145575e41213c85ed9c21889578cbeb8ac399a",
+      "engraphis/core/graph_layers.py": "8f280dd0163ea3a65e7f55b47c9881826198dbcc5d2ba03e8f8730adaa9fb70e",
+      "engraphis/core/graph_policy.py": "78266a8e366a9e486a134febb395632aec0afbbf9b962f52b4359516e68ca771",
+      "engraphis/core/graph_scene.py": "eb0e993f368eceb0adc5dd4ea7cb826e4130eb8b9c77ca075991f8d62c97c95a",
+      "engraphis/core/graphrank.py": "3826c20659cc0e5ee4a494cac690a6f820a5dd3bf8c5ff9f4ecb1c06f39f0bd8",
+      "engraphis/core/grounded.py": "a59254382a2759b6a86d731bd4b32c0e50c8b6c8a0c9fff27e6a11cd7563502b",
+      "engraphis/core/ids.py": "70ebc8a83032391dcf4a102b726443d06d5d7fd586881174be3e16761f766d56",
+      "engraphis/core/interfaces.py": "bb86c8da73bef1102e84e147de8e638994a41f878d8985a0064d27e84bdf8eb1",
+      "engraphis/core/mutations.py": "dbb46a97686994e1652b2698e50c3309ff428d7258d6e8e53cbad7bb55b459aa",
+      "engraphis/core/obsidian.py": "3032f8c4658312c05f28cc24992edd29effc5858cc3c2cd527e93c4723c8b6c6",
+      "engraphis/core/poisoning.py": "1ba54a34a1568a7d2ce30ace6fee361a3f5de66f9c148b5537eded689e89472e",
+      "engraphis/core/query_planner.py": "45843a16dad7dee821ab85b00d2962228880d158dbd9ddad43f10dcdaab3d117",
+      "engraphis/core/read_snapshots.py": "ea2cfad2069e05dd20ab62c810dccc516ebc0a4eba1b587eb2ea18bcd44ac950",
+      "engraphis/core/recall.py": "ce741ac98e41a8baddfaaa79078507ed4812d00b329c9b2195c7705f3d6cc3c3",
+      "engraphis/core/relocation.py": "f386f3749b371d8808455a28db234083a0deddc8e09308b730c3c8cebba325c6",
+      "engraphis/core/resolve.py": "f398d0829002f4c7f5480b9598e2b28249f4618bb4291f528c6d3ed5dac7d703",
+      "engraphis/core/retention_policy.py": "11e75dd642422ea9d6f3b0d3a82c5e3b8c78c857eb2bdf35f8a7ef77d0dea192",
+      "engraphis/core/retrieval_policy.py": "96df0b8dd800d21ea5c04e745434a777d3000af45add7d7a41a9c74bfa053d98",
+      "engraphis/core/savings.py": "cfbcfc7e476f4e28028555cd519696e23099f6210cf0b733225832aeaa0bc7dc",
+      "engraphis/core/schema.py": "1b76b4f5a785a9c7728c8954a584952e24837b97c8c2936322519bc460e32e52",
+      "engraphis/core/scoring.py": "65cc50428ac9563daac61d81b87a9f003140ce8b9904e21c606333c0bd5fd0cb",
+      "engraphis/core/secrets.py": "5621fdaccfe935876f03b0e5f781f788a3993fa0221aeee5f93c7ae7c09a3f10",
+      "engraphis/core/spec_crawl.py": "fd93bf43cab53b4aa9633f59785dd3a35b0e80f9c4f20a68fe44a1fbf0ff6b32",
+      "engraphis/core/store.py": "1ece87069e0b95ff1413bb437fecb064ef60223f878fbce80236c9b01aef38ea",
+      "engraphis/core/sync.py": "d611b14ea6a479dcab822124efce1d26decb7a3112ed20a1efb92bd1264098de",
+      "engraphis/core/textutil.py": "941f9535c7027191144898ff77d7ee8e392ee9610a6ae76831eea46618104c73",
+      "engraphis/core/user_model.py": "4357a65cd2fbb057eee297eb08d02f6bb55d65429d704bdb7e0bd8a23f6e9bf4",
+      "engraphis/core/vector_repair.py": "8f51ccc88915f4c54b3efcb755e86afc475632cb015ab00b3bd9c846ee76f937",
+      "engraphis/core/vector_search.py": "3215d6d47d1e1d7520811e6998e35e90c525e351ee2534ca2d299e77dab1a190",
+      "engraphis/dashboard_app.py": "b578ad7e822b0eb27dd1ac7386c6d2aa0b84099ac2b15da01f1fbbd78d655bf9",
+      "engraphis/dashboard_assets/__init__.py": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855",
+      "engraphis/device_connect.py": "5953120c464684e736eb204630170976f9f9c3e69debbc19ec8b4e482b0872cd",
+      "engraphis/document_import.py": "963183e9fd38eb01b667803b5a3613bc7748c01adae5954a1382cffb6fa67284",
+      "engraphis/engines/__init__.py": "2c01d4e36418aa259ad3c5f22ea027a4452b6f5f779b7ad6d68bdfaaf0ed2da6",
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diff --git a/docs/benchmark-evidence/offline-fixtures-pr263-final-20261014.json.sha256 b/docs/benchmark-evidence/offline-fixtures-pr263-final-20261014.json.sha256
new file mode 100644
index 00000000..2dc9aa2a
--- /dev/null
+++ b/docs/benchmark-evidence/offline-fixtures-pr263-final-20261014.json.sha256
@@ -0,0 +1 @@
+c64a47ee2d077d0ac110253b75f8af2e55b18a3749cdfed3252abb1e6b01dfc3  offline-fixtures-pr263-final-20261014.json
diff --git a/docs/benchmark-evidence/offline-fixtures-pr263-git-bytes-20261014.json b/docs/benchmark-evidence/offline-fixtures-pr263-git-bytes-20261014.json
new file mode 100644
index 00000000..47d8b2c4
--- /dev/null
+++ b/docs/benchmark-evidence/offline-fixtures-pr263-git-bytes-20261014.json
@@ -0,0 +1,696 @@
+{
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+      "eval/datasets/coding_memory_v1/oracles/juniper-violet--multilingual.py": "e0cc26d743cbea8e98261895474879d078fd53941c075606bfc6e52ee098db93",
+      "eval/datasets/coding_memory_v1/oracles/juniper-violet--paraphrases.py": "a1f836316408052cab1253b56b5caa6b1432fe6eeaabbb5872da97ec9bde633a",
+      "eval/datasets/coding_memory_v1/oracles/juniper-violet--poisoning.py": "bc09271eadf77f1b6c03fff20ca9c0f0a42a99d1c5600e6b7843f0a0d549ed5d",
+      "eval/datasets/coding_memory_v1/oracles/juniper-violet--scope_boundaries.py": "17a7258bed9efe1c86a05756c5b5d17926fd72f01de4b2addd5d2fb4550f4da5",
+      "eval/datasets/coding_memory_v1/oracles/juniper-violet--temporal_history.py": "8bda8599d861857204c48b55729607c8cb0cb3ae2701eb18fb600d398468cafe",
+      "eval/datasets/coding_memory_v1/oracles/juniper-violet--unsupported_questions.py": "10cdbb013f78f0e54c31a6e76f03d8d15a6063fd7971a2d803a390890c036ad2",
+      "eval/datasets/coding_memory_v1/oracles/juniper-west--code_relationships.py": "f0a682d413b3d8418c664db2775de4eef35240c508cc71ca6bbc055cd0c8dbe6",
+      "eval/datasets/coding_memory_v1/oracles/juniper-west--condition_values.py": "200a4b78f201e992ac10112a194c97b88841dc5930e3e7de9bfc076bf4c69fa7",
+      "eval/datasets/coding_memory_v1/oracles/juniper-west--corrections.py": "41ea9b5a5f40d8703343f4570593c3f27905e5ff70fae57bdbdb5fcccc585d22",
+      "eval/datasets/coding_memory_v1/oracles/juniper-west--long_documents.py": "7d8cdd01c5d90ad62e9da93a99febead683e3f01c5ca37315382a2fb0f512d76",
+      "eval/datasets/coding_memory_v1/oracles/juniper-west--multilingual.py": "1815adc5946bf9e7dba16786d8c29f9466537b7a43b1c392f927b61c56da54ee",
+      "eval/datasets/coding_memory_v1/oracles/juniper-west--paraphrases.py": "5d0110bc2098d2143303476fb5c5714086d0278b848110c63aa626e0b88311cc",
+      "eval/datasets/coding_memory_v1/oracles/juniper-west--poisoning.py": "4e16d9de9931cffc6915805b4fc375dad3ffb7fad17eb893205ce6d07102bcb3",
+      "eval/datasets/coding_memory_v1/oracles/juniper-west--scope_boundaries.py": "6c5d875184c890a922ee18ac423598fdcece109a91e9e62d5a7ccc82d2eaaa70",
+      "eval/datasets/coding_memory_v1/oracles/juniper-west--temporal_history.py": "3404fdbb5cc4693333d48d27da928531279d148ffe1cdadbdd741dfc1a8d38d3",
+      "eval/datasets/coding_memory_v1/oracles/juniper-west--unsupported_questions.py": "b85a4cf14f9596cd14b480acd323772fc91084ad1413fa27435bdb7ffe5baa6b",
+      "eval/datasets/longdoc.jsonl": "7f5ade95e1f283d0db8cf78e53ed8995d3534f847e616d2c0005fd8da37ac790",
+      "eval/engine_capacity.py": "1940da1c3a435d81c7b7a1a689e966c23b92e4f94af115b45813dd30e2c04eec",
+      "eval/evidence_contracts.py": "03969fdf01e78135c66e698d3f523e5312ccca3737dd3036b37a2c6676ba4ea2",
+      "eval/external.py": "98500a97c18152b1b86a270062033c48176224d7468ac0f703ddc7de862a30e2",
+      "eval/external_checkpoints.py": "484e298039bfa04eef86bd7145428c312aaeaf059f5bdf8da7ae29d1150008d7",
+      "eval/extractor_quality.py": "50820fe7f821d111e17e4e77a3d1159e7d6979d110b052c4f254a5b309c2652a",
+      "eval/fts_insert_scaling.py": "6088997e0f85d430fafb74509e24a960fbd59c06c8b2839285942b97d2116724",
+      "eval/graph_every_bench.py": "79da573c5edf315f71bfab412d3ea283b8da45d1fdabc78ddd19302ead09e4f0",
+      "eval/graph_traversal.py": "b094f75c3a1d75ba3cf19e372187d692d3c595a1d9bde19bbe95ae0c79a5175f",
+      "eval/grounded.py": "053d5193b716a2c3e507fcd44057d392de910a4442b46bd7cc1f30ac0ba68541",
+      "eval/handoff_quality.py": "7daf635510764e236f48ca7e2537a85d8ebc1ad995513144329c1f0236405937",
+      "eval/harness.py": "8c96c26a121dfc2d9ea051a05861af8951c93a732dba9cb13de0de178414b016",
+      "eval/hosted_evidence.py": "7946cd8c1e3aa291268271b2aa11210d5f09c9b05ba635aa0b64bef07bdcee45",
+      "eval/hosted_ledger.py": "a53036d12ff671371c148910a816fb20c7e7f3346250a303722352f47b06c476",
+      "eval/hosted_luna.py": "4dbf02a65eec38bcde92d82952a0b372abbac1f68378d11a04528f4a31a835dc",
+      "eval/jev_recall_quality.py": "3c31080767c2bc0301f1085c837eaa60ac6e1f5ae8eeb6ff908e573a630510cd",
+      "eval/local_benchmark_queue.py": "43fa67b4d653e770822da816511e8e62b3714e60d2edd49ea2a4c5c3d8d20851",
+      "eval/local_capacity_campaign.py": "ab4935263f9d24c4e38bd4f16164b4c4c07eff91c695809b6fc75299d4f03d62",
+      "eval/longmemeval_v2.py": "defb4d47f453aa4615a8f101b82df3fadf64f0f9eae0ce1021d86d3750dad437",
+      "eval/longmemeval_v2_evidence.py": "8486bd4dcfdd8b1f7a14c32fe4f427c980ccdcc3aa2eb018d7dfc40305412eff",
+      "eval/longmemeval_v2_matrix.py": "ca085cd59481813cce5dbdfbc94f40f67173ac3a1bc3dce6f6d3e09eb7b08153",
+      "eval/metrics.py": "16857e2cf6ed339cb57a26c9bfa1879444b4d279bd972e5a9fa644ed1308afe0",
+      "eval/native_coverage_scaling.py": "0d318c116241050fc0c7bdbbb5646ca67944d7c304ccd9a462834e0553ffa90a",
+      "eval/performance.py": "dccc55c26dc396f9fee98defd152900cf8aabeb5d6198bc711e0eb8f472ecc57",
+      "eval/performance_engine.py": "3d37cf0a5989c8fa6e8b6ab7ae0b2e0d5410a8b922d2f3130c1a7794d93dcbb1",
+      "eval/planned_recall.py": "f9a87291ecb181045b98ca65fe55db820bf7cee4ae4f4d087ca3458afdd7837e",
+      "eval/proactive_ranking.py": "8610541f1d547f9c0eb46d078dbcaa670c0a97157acc37f96ec08b482cb7a6ab",
+      "eval/productivity.py": "6d4644ebdc44472aeb3879963774fab276bfa269b781139c46ded48774a22717",
+      "eval/public_readiness.py": "5ce8a18d0bfe09e75e88548a589b8fc6d2cbf04c0f8cd1ce51cd9519136a2751",
+      "eval/redteam_poisoning.py": "fce120cc3adf2ee966b59cd3ea7f20d242a0af49b52492143543130fe14c0cc8",
+      "eval/reinforcement.py": "72ed766775a2658eaa728afec51c0ac22d97a90e813111954df66a6ec50f2bef",
+      "eval/repair_discovery.py": "db05496fbbcb0df86c5cdc2f0c85fb6b6605b0cace6cb44ae2add10b72784b5f",
+      "eval/resolver_reworded_corrections.py": "a9054778a37b2175b46f04b674b4779e358931eee5d2ae52bbc5f953d234fb9e",
+      "eval/resource_hierarchy.py": "5ab6c989bb143c4386749c45a33c190447e829c1b5657e8c0aca30f34bd69461",
+      "eval/rework_statistics.py": "e12c14288797c5cf2dd93d51f606244287bc2f4ff8bd401f1d1b072efd1f9529",
+      "eval/run_longmemeval_v2.py": "866863d9f8f7ce8f7c3c36741ab324faa5fae417827f907c655eace38de0f5ac",
+      "eval/spec_crawl.py": "e02160705a2bf3ac29df1ae83918c030aa4c8069b6a32db6b2e12bd374f58de0",
+      "eval/task_pairs.py": "fddc54804e8837ec0731813297fb55825458317f898e29176e16f3f5a2f527fd",
+      "eval/user_journeys.py": "a1c8436d4a6871baff21f9aa4f6ac1545def60d946cd3b1454c3d9cd8661a049",
+      "eval/vector_scale.py": "3f9c327d9eca1a857512ddc208e972933be1a7d4fa7fa0017aca0cbf8fe7bb6d",
+      "eval/vector_scale_storage.py": "24040fd1b96b9f9cd43ea37b0b37ea118a02bd096aff77fc67ff8b0df769b1dc",
+      "eval/vector_scan_plan.py": "34fba3d029bfc78134e8c9c450b019ff56c3bdcbeb921400888077cb44e9b848",
+      "scripts/export_offline_evidence.py": "4e10c2b3d5f6a2024a7f95f7b87ce47ec2cb4ef31c55611b2e5cf35502ebc0ba"
+    }
+  }
+}
diff --git a/docs/benchmark-evidence/offline-fixtures-pr263-git-bytes-20261014.json.sha256 b/docs/benchmark-evidence/offline-fixtures-pr263-git-bytes-20261014.json.sha256
new file mode 100644
index 00000000..eee532b0
--- /dev/null
+++ b/docs/benchmark-evidence/offline-fixtures-pr263-git-bytes-20261014.json.sha256
@@ -0,0 +1 @@
+9050c5b88343bd05984372975da0c2a25090dfe081c39e4fd2c95940f865177a  offline-fixtures-pr263-git-bytes-20261014.json
diff --git a/docs/images/context-efficiency.png b/docs/images/context-efficiency.png
index 80ae28d8..001ad3f7 100644
Binary files a/docs/images/context-efficiency.png and b/docs/images/context-efficiency.png differ
diff --git a/docs/images/context-efficiency.svg b/docs/images/context-efficiency.svg
index b37e2d60..bfbf9c70 100644
--- a/docs/images/context-efficiency.svg
+++ b/docs/images/context-efficiency.svg
@@ -1,6 +1,6 @@
 <svg xmlns="http://www.w3.org/2000/svg" width="1103" height="670" viewBox="0 0 1103 670" role="img" aria-labelledby="title desc">
 <title id="title">Offline context, retrieval, and grounded benchmark results</title>
-<desc id="desc">Artifact-driven offline benchmark report with context efficiency, retrieval quality, and grounded behavior reported separately. Structure-aware chunking reports 740.3 to 214.3 retrieved tokens per question. The JSON-shape payload proxy reports 24,590 full versus 11,138 compact tokens; MCP transport was not measured; provider billing was not measured. Retrieved candidate quality is Recall@5 1.000, Hit@5 1.000, and answer-token recall 1.000 across 26 questions. Packed context quality is Recall@5 1.000, Hit@5 1.000, and answer-token recall 1.000 across 26 questions. Grounded checks score 5 of 5 answerable queries grounded and 6 of 6 abstention queries rejected, including 1 of 1 quarantined-evidence checks. Decision accuracy is 1.000 across 11 decisions. Source artifact SHA-256 1b4ba1d95ae003bab4c5d15a7d619fc7a7b813d66387bc9530a74babad5221eb.</desc>
+<desc id="desc">Artifact-driven offline benchmark report with context efficiency, retrieval quality, and grounded behavior reported separately. Structure-aware chunking reports 740.3 to 214.3 retrieved tokens per question. The JSON-shape payload proxy reports 24,590 full versus 11,138 compact tokens; MCP transport was not measured; provider billing was not measured. Retrieved candidate quality is Recall@5 1.000, Hit@5 1.000, and answer-token recall 1.000 across 26 questions. Packed context quality is Recall@5 1.000, Hit@5 1.000, and answer-token recall 1.000 across 26 questions. Grounded checks score 5 of 5 answerable queries grounded and 6 of 6 abstention queries rejected, including 1 of 1 quarantined-evidence checks. Decision accuracy is 1.000 across 11 decisions. Source artifact SHA-256 9050c5b88343bd05984372975da0c2a25090dfe081c39e4fd2c95940f865177a.</desc>
 <style>text{font-family:Consolas,monospace;fill:#b9c8dc} .heading{font-family:Segoe UI,sans-serif;font-weight:700;fill:#f2f5f9} .green{fill:#00c896}.muted{fill:#7589a7}</style>
 <rect x="3" y="2" width="1094" height="664" fill="#0e1114" stroke="#252c36"/>
 <rect x="22" y="20" width="224" height="21" fill="#1c222b" stroke="#303a47"/>
@@ -65,6 +65,6 @@
 <text x="750" y="541" font-size="13" class="heading" text-anchor="start">Quarantined-evidence probe</text>
 <text x="750" y="575" font-size="21" class="green" text-anchor="start">1 / 1 abstained</text>
 <text x="22" y="610" font-size="12.1" class="muted" text-anchor="start">Decision accuracy 1.000 (11 / 11); the quarantine probe is included in the abstention total.</text>
-<text x="22" y="651" font-size="12.2" class="muted" text-anchor="start">SOURCE SHA-256  1b4ba1d95ae0</text>
+<text x="22" y="651" font-size="12.2" class="muted" text-anchor="start">SOURCE SHA-256  9050c5b88343</text>
 <text x="1080" y="651" font-size="12.2" class="muted" text-anchor="end">JSON proxy only; MCP transport not measured; provider billing not measured.</text>
 </svg>
diff --git a/docs/images/evidence-backed-agent-examples.png b/docs/images/evidence-backed-agent-examples.png
index e6d0a795..4c8425fb 100644
Binary files a/docs/images/evidence-backed-agent-examples.png and b/docs/images/evidence-backed-agent-examples.png differ
diff --git a/docs/images/evidence-backed-agent-examples.svg b/docs/images/evidence-backed-agent-examples.svg
index 9acefefb..d86b1681 100644
--- a/docs/images/evidence-backed-agent-examples.svg
+++ b/docs/images/evidence-backed-agent-examples.svg
@@ -1,6 +1,6 @@
 <svg xmlns="http://www.w3.org/2000/svg" width="1693" height="929" viewBox="0 0 1693 929" role="img" aria-labelledby="title desc">
   <title id="title">Three evidence-backed Engraphis agent behaviors</title>
-  <desc id="desc">A three-card summary of deterministic offline fixtures. Focused context returns 740.3 to 214.3 tokens while retaining Recall at 5 of 1.000. A grounded answer returns support for 5/5 answerable questions. An unsupported question safely abstains for 6/6 off-topic questions. Reproduce with eval.chunking_eval and eval.grounded. Exact commands and config digests are registered in BENCHMARKS.md. Public-safe artifact SHA-256: 1b4ba1d95ae003bab4c5d15a7d619fc7a7b813d66387bc9530a74babad5221eb.</desc>
+  <desc id="desc">A three-card summary of deterministic offline fixtures. Focused context returns 740.3 to 214.3 tokens while retaining Recall at 5 of 1.000. A grounded answer returns support for 5/5 answerable questions. An unsupported question safely abstains for 6/6 off-topic questions. Reproduce with eval.chunking_eval and eval.grounded. Exact commands and config digests are registered in BENCHMARKS.md. Public-safe artifact SHA-256: 9050c5b88343bd05984372975da0c2a25090dfe081c39e4fd2c95940f865177a.</desc>
   <defs>
     <linearGradient id="page" x1="0" y1="0" x2="1" y2="1"><stop offset="0" stop-color="#f8fbff"/><stop offset="1" stop-color="#eef4fb"/></linearGradient>
     <filter id="shadow" x="-10%" y="-10%" width="120%" height="130%"><feDropShadow dx="0" dy="5" stdDeviation="8" flood-opacity=".12"/></filter>
@@ -47,5 +47,5 @@
   </g>
   <line x1="70" y1="832" x2="1623" y2="832" stroke="#ced8e7" stroke-width="2"/>
   <text x="846" y="875" text-anchor="middle" font-family="Inter, Arial, sans-serif" font-size="24" font-weight="600" fill="#53627b">Reproduce: eval.chunking_eval + eval.grounded</text>
-  <text x="846" y="910" text-anchor="middle" font-family="ui-monospace, monospace" font-size="15" font-weight="600" fill="#53627b">SHA256 1b4ba1d95ae003bab4c5d15a7d619fc7a7b813d66387bc9530a74babad5221eb</text>
+  <text x="846" y="910" text-anchor="middle" font-family="ui-monospace, monospace" font-size="15" font-weight="600" fill="#53627b">SHA256 9050c5b88343bd05984372975da0c2a25090dfe081c39e4fd2c95940f865177a</text>
 </svg>
diff --git a/eval/datasets/spec_crawl_memories.jsonl b/eval/datasets/spec_crawl_memories.jsonl
new file mode 100644
index 00000000..cdd7709f
--- /dev/null
+++ b/eval/datasets/spec_crawl_memories.jsonl
@@ -0,0 +1,8 @@
+{"id":"directive_noise","memories":[{"id":"require","subject_key":"prod","content":"Always require authentication."},{"id":"forbid","subject_key":"prod","content":"Never require authentication."},{"id":"n1","subject_key":"noise.1","content":"Always require authentication."},{"id":"n2","subject_key":"noise.2","content":"Always require authentication."},{"id":"n3","subject_key":"noise.3","content":"Always require authentication."},{"id":"n4","subject_key":"noise.4","content":"Always require authentication."}],"expected_conflicts":[["require","forbid"]]}
+{"id":"agreeing_double_negative","memories":[{"id":"a","subject_key":"prod","content":"Never disable authentication.","valid_from":1000},{"id":"b","subject_key":"prod","content":"Always require authentication.","valid_from":2000}],"expected_conflicts":[],"expected_retirements":[]}
+{"id":"opposing_nested_action","memories":[{"id":"a","subject_key":"prod","content":"Always disable authentication."},{"id":"b","subject_key":"prod","content":"Always require authentication."}],"expected_conflicts":[["a","b"]]}
+{"id":"different_subjects","memories":[{"id":"a","subject_key":"service.one","content":"port: 5432"},{"id":"b","subject_key":"service.two","content":"port: 5433"}],"expected_conflicts":[]}
+{"id":"different_claim_kinds","memories":[{"id":"a","subject_key":"db","claim_kind":"read_port","content":"port: 5432"},{"id":"b","subject_key":"db","claim_kind":"write_port","content":"port: 5433"}],"expected_conflicts":[]}
+{"id":"overlap_does_not_create_stored_connectivity","memories":[{"id":"a","content":"Deployment checklist: verify the build and record the review."},{"id":"b","content":"Deployment checklist: verify the build and record the review."}],"expected_conflicts":[],"expected_orphans":["a","b"]}
+{"id":"unknown_effective_dates","memories":[{"id":"a","subject_key":"db","content":"port: 5432"},{"id":"b","subject_key":"db","content":"port: 5433"}],"expected_conflicts":[["a","b"]],"expected_actions":["clarify"],"expected_retirements":[]}
+{"id":"effective_time_beats_ingestion_order","memories":[{"id":"new","subject_key":"db","content":"port: 5433","valid_from":2000,"ingested_at":2500},{"id":"old","subject_key":"db","content":"port: 5432","valid_from":1000,"ingested_at":3000}],"expected_conflicts":[["new","old"]],"expected_retirements":[["new","old"]]}
diff --git a/eval/spec_crawl.py b/eval/spec_crawl.py
index 3c583aa0..afdc0781 100644
--- a/eval/spec_crawl.py
+++ b/eval/spec_crawl.py
@@ -5,14 +5,50 @@
 """
 from __future__ import annotations
 
+import hashlib
 import json
 import sys
 from pathlib import Path
 
-from engraphis.core.spec_crawl import crawl_spec
+from engraphis.core.spec_crawl import analyze_memory_nodes, crawl_spec
 
 ROOT = Path(__file__).resolve().parents[1]
 DATASET_PATH = ROOT / "eval" / "datasets" / "spec_crawl.jsonl"
+MEMORY_DATASET_PATH = ROOT / "eval" / "datasets" / "spec_crawl_memories.jsonl"
+
+
+def evaluate_memory_fixtures(path: Path = MEMORY_DATASET_PATH) -> dict:
+    """Check exact conflict pairs and lifecycle contracts in both input orders."""
+    payload = path.read_bytes()
+    cases = 0
+    failures: list[str] = []
+    for line in payload.decode("utf-8").splitlines():
+        if not line.strip():
+            continue
+        item = json.loads(line)
+        cases += 1
+        expected = {tuple(sorted(pair)) for pair in item["expected_conflicts"]}
+        passed = True
+        for nodes in (item["memories"], list(reversed(item["memories"]))):
+            report = analyze_memory_nodes(nodes, include_trace=False)
+            conflicts = report["conflicts"]
+            actual = {tuple(sorted((row["node_a"], row["node_b"]))) for row in conflicts}
+            passed = passed and actual == expected
+            passed = passed and all("content" not in node for node in report["nodes"])
+            if "expected_orphans" in item:
+                passed = passed and {row["node_id"] for row in report["orphans"]} == set(item["expected_orphans"])
+            if "expected_actions" in item:
+                passed = passed and {row["remedy"]["action"] for row in conflicts} == set(item["expected_actions"])
+            if "expected_retirements" in item:
+                retirements = {(row["remedy"]["keep_node"], row["remedy"]["retire_node"])
+                               for row in conflicts if row["remedy"]["action"] == "supersede"}
+                passed = passed and retirements == {tuple(pair) for pair in item["expected_retirements"]}
+        if not passed:
+            failures.append(item["id"])
+    passed_cases = cases - len(failures)
+    return {"cases": cases, "passed": passed_cases,
+            "accuracy": passed_cases / cases if cases else 0.0,
+            "failures": failures, "dataset_sha256": hashlib.sha256(payload).hexdigest()}
 
 
 def run() -> dict:
@@ -98,6 +134,7 @@ def run() -> dict:
         "scores_accuracy": scores_accuracy,
         "overall_accuracy": overall_accuracy,
         "agents_dogfood_ok": agents_ok,
+        "memory_audits": evaluate_memory_fixtures(),
     }
 
 
@@ -111,10 +148,14 @@ def main() -> None:
     print(f"  Section count        : {res['sections_accuracy'] * 100:.1f}%")
     print(f"  Score boundaries     : {res['scores_accuracy'] * 100:.1f}%")
     print(f"  Overall accuracy      : {res['overall_accuracy'] * 100:.1f}%")
+    memory = res["memory_audits"]
+    print(f"  Memory audit contracts: {memory['passed']}/{memory['cases']} (both input orders)")
+    print(f"  Memory fixture SHA-256: {memory['dataset_sha256']}")
     print(f"  AGENTS.md dogfooding  : {'PASSED' if res['agents_dogfood_ok'] else 'FAILED'}\n")
 
-    if res["overall_accuracy"] < 0.90 or not res["agents_dogfood_ok"] or res["scores_accuracy"] < 1:
-        sys.stderr.write("FLOOR VIOLATION: Spec Crawl evaluation fell below required 90% threshold\n")
+    if (res["overall_accuracy"] < 0.90 or not res["agents_dogfood_ok"]
+            or res["scores_accuracy"] < 1 or memory["accuracy"] < 1):
+        sys.stderr.write("FLOOR VIOLATION: Spec Crawl document thresholds or memory audit contracts failed\n")
         sys.exit(1)
 
 
diff --git a/tests/test_benchmark_evidence.py b/tests/test_benchmark_evidence.py
index 8eb4292b..dd9a2550 100644
--- a/tests/test_benchmark_evidence.py
+++ b/tests/test_benchmark_evidence.py
@@ -34,8 +34,8 @@
 
 
 ROOT = Path(__file__).resolve().parents[1]
-PUBLIC_OFFLINE_ARTIFACT = "offline-fixtures-pr262-git-bytes-20261014.json"
-PUBLIC_OFFLINE_SHA = "1b4ba1d95ae003bab4c5d15a7d619fc7a7b813d66387bc9530a74babad5221eb"
+PUBLIC_OFFLINE_ARTIFACT = "offline-fixtures-pr263-git-bytes-20261014.json"
+PUBLIC_OFFLINE_SHA = "9050c5b88343bd05984372975da0c2a25090dfe081c39e4fd2c95940f865177a"
 
 
 @pytest.fixture(scope="module")
diff --git a/tests/test_documentation_contracts.py b/tests/test_documentation_contracts.py
index 7c9f1d4f..f989f325 100644
--- a/tests/test_documentation_contracts.py
+++ b/tests/test_documentation_contracts.py
@@ -13,7 +13,7 @@
 
 
 ROOT = Path(__file__).resolve().parents[1]
-README_BENCHMARK_PIN = "dfa3151d06ca3c226b50bc2e43305f4b23d42903"
+README_BENCHMARK_PIN = "5f94cd2a1f01c5627cb62104015d184ca9c30f01"
 README_HOSTED_PLANS_PIN = "522def372c45a26d46bc7a05829964ae0240f3a9"
 
 
diff --git a/tests/test_setup_plugin_distribution.py b/tests/test_setup_plugin_distribution.py
index 3a7988cd..22af9645 100644
--- a/tests/test_setup_plugin_distribution.py
+++ b/tests/test_setup_plugin_distribution.py
@@ -8,7 +8,7 @@
 
 ROOT = Path(__file__).resolve().parents[1]
 REPOSITORY = "Coding-Dev-Tools/engraphis"
-README_BENCHMARK_PIN = "dfa3151d06ca3c226b50bc2e43305f4b23d42903"
+README_BENCHMARK_PIN = "5f94cd2a1f01c5627cb62104015d184ca9c30f01"
 README_LINK_PINS = (
     "522def372c45a26d46bc7a05829964ae0240f3a9",
     "522def372c45a26d46bc7a05829964ae0240f3a9",
diff --git a/tests/test_spec_crawl_eval.py b/tests/test_spec_crawl_eval.py
new file mode 100644
index 00000000..b78d407b
--- /dev/null
+++ b/tests/test_spec_crawl_eval.py
@@ -0,0 +1,62 @@
+"""The cluster gate must detect both missing conflicts and false retirement suggestions."""
+import json
+
+import pytest
+
+from eval import spec_crawl
+
+
+def test_real_memory_fixtures_cover_both_input_orders():
+    result = spec_crawl.evaluate_memory_fixtures()
+    assert result["cases"] == 8
+    assert result["passed"] == 8
+    assert result["accuracy"] == 1
+    assert result["failures"] == []
+    assert len(result["dataset_sha256"]) == 64
+
+
+def test_memory_gate_rejects_a_missed_conflict(tmp_path, monkeypatch):
+    path = tmp_path / "fixture.jsonl"
+    path.write_text(json.dumps({
+        "id": "missing", "memories": [], "expected_conflicts": [["a", "b"]],
+    }) + "\n", encoding="utf-8")
+    monkeypatch.setattr(spec_crawl, "analyze_memory_nodes", lambda *args, **kwargs: {
+        "nodes": [], "conflicts": [], "orphans": [],
+    })
+    result = spec_crawl.evaluate_memory_fixtures(path)
+    assert result["accuracy"] == 0
+    assert result["failures"] == ["missing"]
+
+
+def test_memory_gate_rejects_false_retirement_targets(tmp_path, monkeypatch):
+    path = tmp_path / "fixture.jsonl"
+    path.write_text(json.dumps({
+        "id": "unsafe", "memories": [], "expected_conflicts": [["a", "b"]],
+        "expected_retirements": [],
+    }) + "\n", encoding="utf-8")
+    monkeypatch.setattr(spec_crawl, "analyze_memory_nodes", lambda *args, **kwargs: {
+        "nodes": [], "orphans": [], "conflicts": [{"node_a": "a", "node_b": "b",
+            "remedy": {"action": "supersede", "keep_node": "a", "retire_node": "b"}}],
+    })
+    result = spec_crawl.evaluate_memory_fixtures(path)
+    assert result["accuracy"] == 0
+    assert result["failures"] == ["unsafe"]
+
+
+def test_empty_memory_fixture_does_not_claim_perfect_accuracy(tmp_path):
+    path = tmp_path / "empty.jsonl"
+    path.write_text("", encoding="utf-8")
+    result = spec_crawl.evaluate_memory_fixtures(path)
+    assert result["cases"] == 0
+    assert result["accuracy"] == 0
+
+
+def test_failed_memory_contracts_fail_the_gate_even_when_documents_pass(monkeypatch, capsys):
+    result = spec_crawl.run()
+    result["memory_audits"]["accuracy"] = 0
+    result["memory_audits"]["passed"] = 0
+    monkeypatch.setattr(spec_crawl, "run", lambda: result)
+    with pytest.raises(SystemExit) as error:
+        spec_crawl.main()
+    assert error.value.code == 1
+    assert "memory audit contracts failed" in capsys.readouterr().err