Add files using upload-large-folder tool

data/artifact_index.json

@@ -1,6 +1,6 @@
 {
   "title": "Ropedia Xperience-10M Task Suite Artifact Index",
-  "generated_at_utc": "2026-06-22T17:35:20+00:00",
+  "generated_at_utc": "2026-06-22T18:16:48+00:00",
   "status": "pass",
   "artifact_count": 228,
   "missing": [],
@@ -632,7 +632,7 @@
       "shows": "Machine-readable source-alignment pass/fail check for repo, website, and HF surfaces.",
       "exists": true,
       "bytes": 4432,
-      "sha256": "fd964392bdc7397f24b463964226a10cadbd5c12459df067baf1c56782a829e4"
+      "sha256": "4299d70350b2d299645d2e0e60ef53ab1c48888ec9c94e72d443b70682a23d19"
     },
     {
       "id": "source_alignment_validator",
@@ -1182,7 +1182,7 @@
       "shows": "Machine-readable release-check summary for validators, mirrors, and public project surfaces.",
       "exists": true,
       "bytes": 8640,
-      "sha256": "12334406b10e1fb6dd04434d25aaa617b2b4f6144752afa9a27a91f7273e3994"
+      "sha256": "bedff006bb7f8b9af9e9af4e72637b83762f14532c65a0b4726d4e1ba05cddd5"
     },
     {
       "id": "public_surface_qa",
@@ -1249,7 +1249,7 @@
       "volatile": true,
       "shows": "Confirms the public original-task cards use human-readable research names, representative modality thumbnails, and the interactive walkthrough/player JSON contract.",
       "exists": true,
-      "bytes": 46497,
+      "bytes": 46399,
       "hash_policy": "existence_and_size_only"
     },
     {
@@ -1295,8 +1295,8 @@
       "surface": "repo_hf",
       "shows": "Regenerates the task-surface integrity report and fails if task cards expose raw artifact ids or lose the interactive player wiring.",
       "exists": true,
-      "bytes": 17305,
-      "sha256": "9689a0bffcf301d3e7e0da8cc90d40eda4b39eceb0859608072c23d5bde3d836"
+      "bytes": 17467,
+      "sha256": "3094cdd1642f38a04685cf27cfca6b8181f365c1dead6e64cf066f0fd9b7d3a7"
     },
     {
       "id": "live_publication_status",

data/mirror_parity.json

@@ -1,6 +1,6 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-22T17:49:11+00:00",
+  "generated_at_utc": "2026-06-22T18:20:42+00:00",
   "hf_root": "hf_publish",
   "summary": {
     "group_count": 1306,
@@ -139,44 +139,44 @@
         "path": "repo:docs/data/artifact_index.json",
         "exists": true,
         "bytes": 124477,
-        "sha256": "7d1e562631e816077efb8a4f4f3df8d9ea84803df8355df66365227a089e8e11"
+        "sha256": "d6cd52f005921e49a1162dc34bea211cddd32d7e3dcc7ad0f31052c153e532ce"
       },
       "mirrors": {
         "hf_space": {
           "path": "hf_space:data/artifact_index.json",
           "exists": true,
           "bytes": 124477,
-          "sha256": "7d1e562631e816077efb8a4f4f3df8d9ea84803df8355df66365227a089e8e11"
+          "sha256": "d6cd52f005921e49a1162dc34bea211cddd32d7e3dcc7ad0f31052c153e532ce"
         },
         "hf_artifacts_data": {
           "path": "hf_artifacts:data/artifact_index.json",
           "exists": true,
           "bytes": 124477,
-          "sha256": "7d1e562631e816077efb8a4f4f3df8d9ea84803df8355df66365227a089e8e11"
+          "sha256": "d6cd52f005921e49a1162dc34bea211cddd32d7e3dcc7ad0f31052c153e532ce"
         },
         "hf_artifacts": {
           "path": "hf_artifacts:docs/data/artifact_index.json",
           "exists": true,
           "bytes": 124477,
-          "sha256": "7d1e562631e816077efb8a4f4f3df8d9ea84803df8355df66365227a089e8e11"
+          "sha256": "d6cd52f005921e49a1162dc34bea211cddd32d7e3dcc7ad0f31052c153e532ce"
         },
         "hf_model_data": {
           "path": "hf_model:data/artifact_index.json",
           "exists": true,
           "bytes": 124477,
-          "sha256": "7d1e562631e816077efb8a4f4f3df8d9ea84803df8355df66365227a089e8e11"
+          "sha256": "d6cd52f005921e49a1162dc34bea211cddd32d7e3dcc7ad0f31052c153e532ce"
         },
         "hf_model_docs_data": {
           "path": "hf_model:docs/data/artifact_index.json",
           "exists": true,
           "bytes": 124477,
-          "sha256": "7d1e562631e816077efb8a4f4f3df8d9ea84803df8355df66365227a089e8e11"
+          "sha256": "d6cd52f005921e49a1162dc34bea211cddd32d7e3dcc7ad0f31052c153e532ce"
         },
         "hf_model": {
           "path": "hf_model:metrics/artifact_index.json",
           "exists": true,
           "bytes": 124477,
-          "sha256": "7d1e562631e816077efb8a4f4f3df8d9ea84803df8355df66365227a089e8e11"
+          "sha256": "d6cd52f005921e49a1162dc34bea211cddd32d7e3dcc7ad0f31052c153e532ce"
         }
       },
       "failures": []
@@ -972,44 +972,44 @@
         "path": "repo:docs/data/publication_audit.json",
         "exists": true,
         "bytes": 10940,
-        "sha256": "cf090fe53be3f85923674af08ad982e12635db7e44e87182982461ce27e858d3"
+        "sha256": "4888187b7e71e141df9006b820e4a4b2a1e510cccac97e95cc01857937b34945"
       },
       "mirrors": {
         "hf_space": {
           "path": "hf_space:data/publication_audit.json",
           "exists": true,
           "bytes": 10940,
-          "sha256": "cf090fe53be3f85923674af08ad982e12635db7e44e87182982461ce27e858d3"
+          "sha256": "4888187b7e71e141df9006b820e4a4b2a1e510cccac97e95cc01857937b34945"
         },
         "hf_artifacts_data": {
           "path": "hf_artifacts:data/publication_audit.json",
           "exists": true,
           "bytes": 10940,
-          "sha256": "cf090fe53be3f85923674af08ad982e12635db7e44e87182982461ce27e858d3"
+          "sha256": "4888187b7e71e141df9006b820e4a4b2a1e510cccac97e95cc01857937b34945"
         },
         "hf_artifacts": {
           "path": "hf_artifacts:docs/data/publication_audit.json",
           "exists": true,
           "bytes": 10940,
-          "sha256": "cf090fe53be3f85923674af08ad982e12635db7e44e87182982461ce27e858d3"
+          "sha256": "4888187b7e71e141df9006b820e4a4b2a1e510cccac97e95cc01857937b34945"
         },
         "hf_model_data": {
           "path": "hf_model:data/publication_audit.json",
           "exists": true,
           "bytes": 10940,
-          "sha256": "cf090fe53be3f85923674af08ad982e12635db7e44e87182982461ce27e858d3"
+          "sha256": "4888187b7e71e141df9006b820e4a4b2a1e510cccac97e95cc01857937b34945"
         },
         "hf_model_docs_data": {
           "path": "hf_model:docs/data/publication_audit.json",
           "exists": true,
           "bytes": 10940,
-          "sha256": "cf090fe53be3f85923674af08ad982e12635db7e44e87182982461ce27e858d3"
+          "sha256": "4888187b7e71e141df9006b820e4a4b2a1e510cccac97e95cc01857937b34945"
         },
         "hf_model": {
           "path": "hf_model:metrics/publication_audit.json",
           "exists": true,
           "bytes": 10940,
-          "sha256": "cf090fe53be3f85923674af08ad982e12635db7e44e87182982461ce27e858d3"
+          "sha256": "4888187b7e71e141df9006b820e4a4b2a1e510cccac97e95cc01857937b34945"
         }
       },
       "failures": []
@@ -1021,44 +1021,44 @@
         "path": "repo:docs/data/public_surface_qa.json",
         "exists": true,
         "bytes": 7690,
-        "sha256": "b71de6fbea3bc010a2914a564cb45613c1e71deba2bd62f780db6be959a27f9d"
+        "sha256": "118481f357ed8f6346259537fe672634cdef2527bddcec2227911d56ef1d812b"
       },
       "mirrors": {
         "hf_space": {
           "path": "hf_space:data/public_surface_qa.json",
           "exists": true,
           "bytes": 7690,
-          "sha256": "b71de6fbea3bc010a2914a564cb45613c1e71deba2bd62f780db6be959a27f9d"
+          "sha256": "118481f357ed8f6346259537fe672634cdef2527bddcec2227911d56ef1d812b"
         },
         "hf_artifacts_data": {
           "path": "hf_artifacts:data/public_surface_qa.json",
           "exists": true,
           "bytes": 7690,
-          "sha256": "b71de6fbea3bc010a2914a564cb45613c1e71deba2bd62f780db6be959a27f9d"
+          "sha256": "118481f357ed8f6346259537fe672634cdef2527bddcec2227911d56ef1d812b"
         },
         "hf_artifacts": {
           "path": "hf_artifacts:docs/data/public_surface_qa.json",
           "exists": true,
           "bytes": 7690,
-          "sha256": "b71de6fbea3bc010a2914a564cb45613c1e71deba2bd62f780db6be959a27f9d"
+          "sha256": "118481f357ed8f6346259537fe672634cdef2527bddcec2227911d56ef1d812b"
         },
         "hf_model_data": {
           "path": "hf_model:data/public_surface_qa.json",
           "exists": true,
           "bytes": 7690,
-          "sha256": "b71de6fbea3bc010a2914a564cb45613c1e71deba2bd62f780db6be959a27f9d"
+          "sha256": "118481f357ed8f6346259537fe672634cdef2527bddcec2227911d56ef1d812b"
         },
         "hf_model_docs_data": {
           "path": "hf_model:docs/data/public_surface_qa.json",
           "exists": true,
           "bytes": 7690,
-          "sha256": "b71de6fbea3bc010a2914a564cb45613c1e71deba2bd62f780db6be959a27f9d"
+          "sha256": "118481f357ed8f6346259537fe672634cdef2527bddcec2227911d56ef1d812b"
         },
         "hf_model": {
           "path": "hf_model:metrics/public_surface_qa.json",
           "exists": true,
           "bytes": 7690,
-          "sha256": "b71de6fbea3bc010a2914a564cb45613c1e71deba2bd62f780db6be959a27f9d"
+          "sha256": "118481f357ed8f6346259537fe672634cdef2527bddcec2227911d56ef1d812b"
         }
       },
       "failures": []
@@ -1217,44 +1217,44 @@
         "path": "repo:docs/data/quality_gates.json",
         "exists": true,
         "bytes": 8640,
-        "sha256": "ea3b91690202170553f57f860bcefb28745c7326f2e870a25a70e1e1d99b07cc"
+        "sha256": "bedff006bb7f8b9af9e9af4e72637b83762f14532c65a0b4726d4e1ba05cddd5"
       },
       "mirrors": {
         "hf_space": {
           "path": "hf_space:data/quality_gates.json",
           "exists": true,
           "bytes": 8640,
-          "sha256": "ea3b91690202170553f57f860bcefb28745c7326f2e870a25a70e1e1d99b07cc"
+          "sha256": "bedff006bb7f8b9af9e9af4e72637b83762f14532c65a0b4726d4e1ba05cddd5"
         },
         "hf_artifacts_data": {
           "path": "hf_artifacts:data/quality_gates.json",
           "exists": true,
           "bytes": 8640,
-          "sha256": "ea3b91690202170553f57f860bcefb28745c7326f2e870a25a70e1e1d99b07cc"
+          "sha256": "bedff006bb7f8b9af9e9af4e72637b83762f14532c65a0b4726d4e1ba05cddd5"
         },
         "hf_artifacts": {
           "path": "hf_artifacts:docs/data/quality_gates.json",
           "exists": true,
           "bytes": 8640,
-          "sha256": "ea3b91690202170553f57f860bcefb28745c7326f2e870a25a70e1e1d99b07cc"
+          "sha256": "bedff006bb7f8b9af9e9af4e72637b83762f14532c65a0b4726d4e1ba05cddd5"
         },
         "hf_model_data": {
           "path": "hf_model:data/quality_gates.json",
           "exists": true,
           "bytes": 8640,
-          "sha256": "ea3b91690202170553f57f860bcefb28745c7326f2e870a25a70e1e1d99b07cc"
+          "sha256": "bedff006bb7f8b9af9e9af4e72637b83762f14532c65a0b4726d4e1ba05cddd5"
         },
         "hf_model_docs_data": {
           "path": "hf_model:docs/data/quality_gates.json",
           "exists": true,
           "bytes": 8640,
-          "sha256": "ea3b91690202170553f57f860bcefb28745c7326f2e870a25a70e1e1d99b07cc"
+          "sha256": "bedff006bb7f8b9af9e9af4e72637b83762f14532c65a0b4726d4e1ba05cddd5"
         },
         "hf_model": {
           "path": "hf_model:metrics/quality_gates.json",
           "exists": true,
           "bytes": 8640,
-          "sha256": "ea3b91690202170553f57f860bcefb28745c7326f2e870a25a70e1e1d99b07cc"
+          "sha256": "bedff006bb7f8b9af9e9af4e72637b83762f14532c65a0b4726d4e1ba05cddd5"
         }
       },
       "failures": []
@@ -1756,44 +1756,44 @@
         "path": "repo:docs/data/source_alignment_audit.json",
         "exists": true,
         "bytes": 4432,
-        "sha256": "fd964392bdc7397f24b463964226a10cadbd5c12459df067baf1c56782a829e4"
+        "sha256": "4299d70350b2d299645d2e0e60ef53ab1c48888ec9c94e72d443b70682a23d19"
       },
       "mirrors": {
         "hf_space": {
           "path": "hf_space:data/source_alignment_audit.json",
           "exists": true,
           "bytes": 4432,
-          "sha256": "fd964392bdc7397f24b463964226a10cadbd5c12459df067baf1c56782a829e4"
+          "sha256": "4299d70350b2d299645d2e0e60ef53ab1c48888ec9c94e72d443b70682a23d19"
         },
         "hf_artifacts_data": {
           "path": "hf_artifacts:data/source_alignment_audit.json",
           "exists": true,
           "bytes": 4432,
-          "sha256": "fd964392bdc7397f24b463964226a10cadbd5c12459df067baf1c56782a829e4"
+          "sha256": "4299d70350b2d299645d2e0e60ef53ab1c48888ec9c94e72d443b70682a23d19"
         },
         "hf_artifacts": {
           "path": "hf_artifacts:docs/data/source_alignment_audit.json",
           "exists": true,
           "bytes": 4432,
-          "sha256": "fd964392bdc7397f24b463964226a10cadbd5c12459df067baf1c56782a829e4"
+          "sha256": "4299d70350b2d299645d2e0e60ef53ab1c48888ec9c94e72d443b70682a23d19"
         },
         "hf_model_data": {
           "path": "hf_model:data/source_alignment_audit.json",
           "exists": true,
           "bytes": 4432,
-          "sha256": "fd964392bdc7397f24b463964226a10cadbd5c12459df067baf1c56782a829e4"
+          "sha256": "4299d70350b2d299645d2e0e60ef53ab1c48888ec9c94e72d443b70682a23d19"
         },
         "hf_model_docs_data": {
           "path": "hf_model:docs/data/source_alignment_audit.json",
           "exists": true,
           "bytes": 4432,
-          "sha256": "fd964392bdc7397f24b463964226a10cadbd5c12459df067baf1c56782a829e4"
+          "sha256": "4299d70350b2d299645d2e0e60ef53ab1c48888ec9c94e72d443b70682a23d19"
         },
         "hf_model": {
           "path": "hf_model:metrics/source_alignment_audit.json",
           "exists": true,
           "bytes": 4432,
-          "sha256": "fd964392bdc7397f24b463964226a10cadbd5c12459df067baf1c56782a829e4"
+          "sha256": "4299d70350b2d299645d2e0e60ef53ab1c48888ec9c94e72d443b70682a23d19"
         }
       },
       "failures": []
@@ -2197,44 +2197,44 @@
         "path": "repo:docs/data/task_surface_integrity.json",
         "exists": true,
         "bytes": 46399,
-        "sha256": "cc417d3fd11237b9b5bbd59f853a54d5e38a88dc01e2ac13dc77c0bb098e85cd"
+        "sha256": "706bab17272be32e33737658ad83200fe7e9daddd9f16bc726d4fc723505400c"
       },
       "mirrors": {
         "hf_space": {
           "path": "hf_space:data/task_surface_integrity.json",
           "exists": true,
           "bytes": 46399,
-          "sha256": "cc417d3fd11237b9b5bbd59f853a54d5e38a88dc01e2ac13dc77c0bb098e85cd"
+          "sha256": "706bab17272be32e33737658ad83200fe7e9daddd9f16bc726d4fc723505400c"
         },
         "hf_artifacts_data": {
           "path": "hf_artifacts:data/task_surface_integrity.json",
           "exists": true,
           "bytes": 46399,
-          "sha256": "cc417d3fd11237b9b5bbd59f853a54d5e38a88dc01e2ac13dc77c0bb098e85cd"
+          "sha256": "706bab17272be32e33737658ad83200fe7e9daddd9f16bc726d4fc723505400c"
         },
         "hf_artifacts": {
           "path": "hf_artifacts:docs/data/task_surface_integrity.json",
           "exists": true,
           "bytes": 46399,
-          "sha256": "cc417d3fd11237b9b5bbd59f853a54d5e38a88dc01e2ac13dc77c0bb098e85cd"
+          "sha256": "706bab17272be32e33737658ad83200fe7e9daddd9f16bc726d4fc723505400c"
         },
         "hf_model_data": {
           "path": "hf_model:data/task_surface_integrity.json",
           "exists": true,
           "bytes": 46399,
-          "sha256": "cc417d3fd11237b9b5bbd59f853a54d5e38a88dc01e2ac13dc77c0bb098e85cd"
+          "sha256": "706bab17272be32e33737658ad83200fe7e9daddd9f16bc726d4fc723505400c"
         },
         "hf_model_docs_data": {
           "path": "hf_model:docs/data/task_surface_integrity.json",
           "exists": true,
           "bytes": 46399,
-          "sha256": "cc417d3fd11237b9b5bbd59f853a54d5e38a88dc01e2ac13dc77c0bb098e85cd"
+          "sha256": "706bab17272be32e33737658ad83200fe7e9daddd9f16bc726d4fc723505400c"
         },
         "hf_model": {
           "path": "hf_model:metrics/task_surface_integrity.json",
           "exists": true,
           "bytes": 46399,
-          "sha256": "cc417d3fd11237b9b5bbd59f853a54d5e38a88dc01e2ac13dc77c0bb098e85cd"
+          "sha256": "706bab17272be32e33737658ad83200fe7e9daddd9f16bc726d4fc723505400c"
         }
       },
       "failures": []
@@ -2589,44 +2589,44 @@
         "path": "repo:docs/data/website_integrity.json",
         "exists": true,
         "bytes": 24948,
-        "sha256": "1d810f56b8ee2771ced787acd73e03e6c568f34f410d0d6dc506e22146e0a7f0"
+        "sha256": "cace6acbf9af955d14a660b0c387cd556f84ced559a13138f6be327c5b1f1623"
       },
       "mirrors": {
         "hf_space": {
           "path": "hf_space:data/website_integrity.json",
           "exists": true,
           "bytes": 24948,
-          "sha256": "1d810f56b8ee2771ced787acd73e03e6c568f34f410d0d6dc506e22146e0a7f0"
+          "sha256": "cace6acbf9af955d14a660b0c387cd556f84ced559a13138f6be327c5b1f1623"
         },
         "hf_artifacts_data": {
           "path": "hf_artifacts:data/website_integrity.json",
           "exists": true,
           "bytes": 24948,
-          "sha256": "1d810f56b8ee2771ced787acd73e03e6c568f34f410d0d6dc506e22146e0a7f0"
+          "sha256": "cace6acbf9af955d14a660b0c387cd556f84ced559a13138f6be327c5b1f1623"
         },
         "hf_artifacts": {
           "path": "hf_artifacts:docs/data/website_integrity.json",
           "exists": true,
           "bytes": 24948,
-          "sha256": "1d810f56b8ee2771ced787acd73e03e6c568f34f410d0d6dc506e22146e0a7f0"
+          "sha256": "cace6acbf9af955d14a660b0c387cd556f84ced559a13138f6be327c5b1f1623"
         },
         "hf_model_data": {
           "path": "hf_model:data/website_integrity.json",
           "exists": true,
           "bytes": 24948,
-          "sha256": "1d810f56b8ee2771ced787acd73e03e6c568f34f410d0d6dc506e22146e0a7f0"
+          "sha256": "cace6acbf9af955d14a660b0c387cd556f84ced559a13138f6be327c5b1f1623"
         },
         "hf_model_docs_data": {
           "path": "hf_model:docs/data/website_integrity.json",
           "exists": true,
           "bytes": 24948,
-          "sha256": "1d810f56b8ee2771ced787acd73e03e6c568f34f410d0d6dc506e22146e0a7f0"
+          "sha256": "cace6acbf9af955d14a660b0c387cd556f84ced559a13138f6be327c5b1f1623"
         },
         "hf_model": {
           "path": "hf_model:metrics/website_integrity.json",
           "exists": true,
           "bytes": 24948,
-          "sha256": "1d810f56b8ee2771ced787acd73e03e6c568f34f410d0d6dc506e22146e0a7f0"
+          "sha256": "cace6acbf9af955d14a660b0c387cd556f84ced559a13138f6be327c5b1f1623"
         }
       },
       "failures": []
@@ -7429,39 +7429,39 @@
       "local": {
         "path": "repo:docs/index.html",
         "exists": true,
-        "bytes": 379760,
-        "sha256": "5ab185f3c9022a2eb5efa0d00efd23a010b16776a069c307f953aef0d7657272"
+        "bytes": 381650,
+        "sha256": "bf6004946fbe7cb990cde7581483bf5db8dfc9317f14135756d78a12bad61060"
       },
       "mirrors": {
         "hf_space": {
           "path": "hf_space:index.html",
           "exists": true,
-          "bytes": 379760,
-          "sha256": "5ab185f3c9022a2eb5efa0d00efd23a010b16776a069c307f953aef0d7657272"
+          "bytes": 381650,
+          "sha256": "bf6004946fbe7cb990cde7581483bf5db8dfc9317f14135756d78a12bad61060"
         },
         "hf_artifacts_root": {
           "path": "hf_artifacts:index.html",
           "exists": true,
-          "bytes": 379760,
-          "sha256": "5ab185f3c9022a2eb5efa0d00efd23a010b16776a069c307f953aef0d7657272"
+          "bytes": 381650,
+          "sha256": "bf6004946fbe7cb990cde7581483bf5db8dfc9317f14135756d78a12bad61060"
         },
         "hf_artifacts_docs": {
           "path": "hf_artifacts:docs/index.html",
           "exists": true,
-          "bytes": 379760,
-          "sha256": "5ab185f3c9022a2eb5efa0d00efd23a010b16776a069c307f953aef0d7657272"
+          "bytes": 381650,
+          "sha256": "bf6004946fbe7cb990cde7581483bf5db8dfc9317f14135756d78a12bad61060"
         },
         "hf_model": {
           "path": "hf_model:index.html",
           "exists": true,
-          "bytes": 379760,
-          "sha256": "5ab185f3c9022a2eb5efa0d00efd23a010b16776a069c307f953aef0d7657272"
+          "bytes": 381650,
+          "sha256": "bf6004946fbe7cb990cde7581483bf5db8dfc9317f14135756d78a12bad61060"
         },
         "hf_model_docs": {
           "path": "hf_model:docs/index.html",
           "exists": true,
-          "bytes": 379760,
-          "sha256": "5ab185f3c9022a2eb5efa0d00efd23a010b16776a069c307f953aef0d7657272"
+          "bytes": 381650,
+          "sha256": "bf6004946fbe7cb990cde7581483bf5db8dfc9317f14135756d78a12bad61060"
         }
       },
       "failures": []

data/public_surface_qa.json

@@ -1,7 +1,7 @@
 {
   "title": "Ropedia Xperience-10M Public Project Surface",
   "status": "pass",
-  "generated_at_utc": "2026-06-22T17:41:00+00:00",
+  "generated_at_utc": "2026-06-22T18:16:46+00:00",
   "scope": "Repo README, GitHub Pages HTML, Hugging Face Space card, artifact dataset card, and model card.",
   "checks": [
     {
@@ -18,7 +18,7 @@
         "website_integrity": {
           "exists": true,
           "status": "pass",
-          "generated_at_utc": "2026-06-22T17:38:14+00:00"
+          "generated_at_utc": "2026-06-22T18:14:28+00:00"
         },
         "rendered_site_check": {
           "exists": true,
@@ -28,7 +28,7 @@
         "task_surface_integrity": {
           "exists": true,
           "status": "pass",
-          "generated_at_utc": "2026-06-22T17:37:13+00:00"
+          "generated_at_utc": "2026-06-22T18:14:26+00:00"
         },
         "source_alignment": {
           "exists": true,
@@ -43,12 +43,12 @@
         "publication_package": {
           "exists": true,
           "status": "pass",
-          "generated_at_utc": "2026-06-22T17:40:04+00:00"
+          "generated_at_utc": "2026-06-22T17:46:27+00:00"
         },
         "mirror_parity": {
           "exists": true,
           "status": "pass",
-          "generated_at_utc": "2026-06-22T17:05:46+00:00"
+          "generated_at_utc": "2026-06-22T17:49:11+00:00"
         }
       },
       "failures": {}
@@ -76,7 +76,7 @@
       "marker_counts": {
         "role=\"tablist\"": 3,
         "role=\"tab\"": 10,
-        "role=\"tabpanel\"": 27,
+        "role=\"tabpanel\"": 23,
         "aria-selected": 13,
         "aria-controls": 11,
         "moveProjectTabFocus": 2,

data/publication_audit.json

@@ -1,6 +1,6 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-22T17:46:27+00:00",
+  "generated_at_utc": "2026-06-22T18:17:07+00:00",
   "checks": [
     {
       "name": "required_publication_assets_present",
@@ -235,7 +235,7 @@
     "github_repo": {
       "root": "repo",
       "exists": true,
-      "file_count": 1557,
+      "file_count": 1558,
       "text_file_count": 1287,
       "largest_file": {
         "path": "results/omni_finetune/a100_128_metadata_task_baselines_20260616_v2/interaction_text_prediction/confusion_matrix.csv",

data/quality_gates.json

@@ -1,7 +1,7 @@
 {
   "title": "Ropedia Xperience-10M Release Checks",
   "status": "pass",
-  "generated_at_utc": "2026-06-22T17:40:55+00:00",
+  "generated_at_utc": "2026-06-22T18:16:45+00:00",
   "rule": "A release is current when the automated reports pass and the live GitHub/Hugging Face mirrors are verified after publishing.",
   "automated_gates": [
     {

data/source_alignment_audit.json

@@ -1,7 +1,7 @@
 {
   "title": "Ropedia Xperience-10M Source Alignment Note",
   "status": "pass",
-  "generated_at_utc": "2026-06-22T17:35:19+00:00",
+  "generated_at_utc": "2026-06-22T18:16:48+00:00",
   "alignment_json": "docs/data/xperience10m_dataset_card_alignment.json",
   "alignment_summary": {
     "full_dataset_repo": "ropedia-ai/xperience-10m",

data/task_surface_integrity.json

@@ -1,6 +1,6 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-22T17:45:19+00:00",
+  "generated_at_utc": "2026-06-22T18:14:26+00:00",
   "summary": {
     "original_walkthrough_task_count": 12,
     "expected_original_walkthrough_task_count": 12,

data/website_integrity.json

@@ -1,11 +1,11 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-22T17:45:23+00:00",
+  "generated_at_utc": "2026-06-22T18:14:28+00:00",
   "docs_root": "docs",
   "site_base": "/ropedia-xperience-10m-task-suite/",
   "summary": {
     "html_pages": 4,
-    "local_references": 286,
+    "local_references": 295,
     "external_reference_count": 151,
     "json_files": 56,
     "image_assets_referenced": 49,
@@ -30,7 +30,7 @@
       "name": "project_sections_are_assigned_to_tabs",
       "status": "pass",
       "reason": "Every major research section should be assigned to a tab group.",
-      "section_count": 23
+      "section_count": 19
     },
     {
       "name": "project_hash_router_preserves_deep_links",
@@ -58,8 +58,8 @@
       "name": "project_sections_are_labeled_tabpanels",
       "status": "pass",
       "reason": "Every tabbed research section should expose a labeled panel role.",
-      "panel_count": 27,
-      "labeled_panel_count": 23
+      "panel_count": 23,
+      "labeled_panel_count": 19
     },
     {
       "name": "project_tabs_update_selected_state",
@@ -80,8 +80,8 @@
       "name": "project_overview_precedes_progress_ledger",
       "status": "pass",
       "reason": "The project overview should appear before the deeper progress ledger.",
-      "overview_index": 157884,
-      "evidence_index": 205698
+      "overview_index": 158999,
+      "evidence_index": 206813
     },
     {
       "name": "project_status_links_json",
@@ -159,9 +159,9 @@
       "name": "evaluation_protocol_between_overview_and_progress",
       "status": "pass",
       "reason": "The evaluation protocol should appear before the deeper evidence ledger.",
-      "overview_index": 157884,
-      "protocol_index": 201903,
-      "evidence_index": 205698
+      "overview_index": 158999,
+      "protocol_index": 203018,
+      "evidence_index": 206813
     },
     {
       "name": "evaluation_protocol_links_json",
@@ -290,8 +290,8 @@
     },
     {
       "path": "index.html",
-      "id_count": 103,
-      "reference_count": 258,
+      "id_count": 106,
+      "reference_count": 267,
       "image_count": 54
     },
     {
@@ -420,7 +420,7 @@
     },
     {
       "path": "data/publication_audit.json",
-      "bytes": 10844,
+      "bytes": 10940,
       "top_level_type": "dict"
     },
     {

docs/data/artifact_index.json

@@ -1,6 +1,6 @@
 {
   "title": "Ropedia Xperience-10M Task Suite Artifact Index",
-  "generated_at_utc": "2026-06-22T17:35:20+00:00",
+  "generated_at_utc": "2026-06-22T18:16:48+00:00",
   "status": "pass",
   "artifact_count": 228,
   "missing": [],
@@ -632,7 +632,7 @@
       "shows": "Machine-readable source-alignment pass/fail check for repo, website, and HF surfaces.",
       "exists": true,
       "bytes": 4432,
-      "sha256": "fd964392bdc7397f24b463964226a10cadbd5c12459df067baf1c56782a829e4"
+      "sha256": "4299d70350b2d299645d2e0e60ef53ab1c48888ec9c94e72d443b70682a23d19"
     },
     {
       "id": "source_alignment_validator",
@@ -1182,7 +1182,7 @@
       "shows": "Machine-readable release-check summary for validators, mirrors, and public project surfaces.",
       "exists": true,
       "bytes": 8640,
-      "sha256": "12334406b10e1fb6dd04434d25aaa617b2b4f6144752afa9a27a91f7273e3994"
+      "sha256": "bedff006bb7f8b9af9e9af4e72637b83762f14532c65a0b4726d4e1ba05cddd5"
     },
     {
       "id": "public_surface_qa",
@@ -1249,7 +1249,7 @@
       "volatile": true,
       "shows": "Confirms the public original-task cards use human-readable research names, representative modality thumbnails, and the interactive walkthrough/player JSON contract.",
       "exists": true,
-      "bytes": 46497,
+      "bytes": 46399,
       "hash_policy": "existence_and_size_only"
     },
     {
@@ -1295,8 +1295,8 @@
       "surface": "repo_hf",
       "shows": "Regenerates the task-surface integrity report and fails if task cards expose raw artifact ids or lose the interactive player wiring.",
       "exists": true,
-      "bytes": 17305,
-      "sha256": "9689a0bffcf301d3e7e0da8cc90d40eda4b39eceb0859608072c23d5bde3d836"
+      "bytes": 17467,
+      "sha256": "3094cdd1642f38a04685cf27cfca6b8181f365c1dead6e64cf066f0fd9b7d3a7"
     },
     {
       "id": "live_publication_status",

docs/data/mirror_parity.json

@@ -1,6 +1,6 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-22T17:49:11+00:00",
+  "generated_at_utc": "2026-06-22T18:20:42+00:00",
   "hf_root": "hf_publish",
   "summary": {
     "group_count": 1306,
@@ -139,44 +139,44 @@
         "path": "repo:docs/data/artifact_index.json",
         "exists": true,
         "bytes": 124477,
-        "sha256": "7d1e562631e816077efb8a4f4f3df8d9ea84803df8355df66365227a089e8e11"
+        "sha256": "d6cd52f005921e49a1162dc34bea211cddd32d7e3dcc7ad0f31052c153e532ce"
       },
       "mirrors": {
         "hf_space": {
           "path": "hf_space:data/artifact_index.json",
           "exists": true,
           "bytes": 124477,
-          "sha256": "7d1e562631e816077efb8a4f4f3df8d9ea84803df8355df66365227a089e8e11"
+          "sha256": "d6cd52f005921e49a1162dc34bea211cddd32d7e3dcc7ad0f31052c153e532ce"
         },
         "hf_artifacts_data": {
           "path": "hf_artifacts:data/artifact_index.json",
           "exists": true,
           "bytes": 124477,
-          "sha256": "7d1e562631e816077efb8a4f4f3df8d9ea84803df8355df66365227a089e8e11"
+          "sha256": "d6cd52f005921e49a1162dc34bea211cddd32d7e3dcc7ad0f31052c153e532ce"
         },
         "hf_artifacts": {
           "path": "hf_artifacts:docs/data/artifact_index.json",
           "exists": true,
           "bytes": 124477,
-          "sha256": "7d1e562631e816077efb8a4f4f3df8d9ea84803df8355df66365227a089e8e11"
+          "sha256": "d6cd52f005921e49a1162dc34bea211cddd32d7e3dcc7ad0f31052c153e532ce"
         },
         "hf_model_data": {
           "path": "hf_model:data/artifact_index.json",
           "exists": true,
           "bytes": 124477,
-          "sha256": "7d1e562631e816077efb8a4f4f3df8d9ea84803df8355df66365227a089e8e11"
+          "sha256": "d6cd52f005921e49a1162dc34bea211cddd32d7e3dcc7ad0f31052c153e532ce"
         },
         "hf_model_docs_data": {
           "path": "hf_model:docs/data/artifact_index.json",
           "exists": true,
           "bytes": 124477,
-          "sha256": "7d1e562631e816077efb8a4f4f3df8d9ea84803df8355df66365227a089e8e11"
+          "sha256": "d6cd52f005921e49a1162dc34bea211cddd32d7e3dcc7ad0f31052c153e532ce"
         },
         "hf_model": {
           "path": "hf_model:metrics/artifact_index.json",
           "exists": true,
           "bytes": 124477,
-          "sha256": "7d1e562631e816077efb8a4f4f3df8d9ea84803df8355df66365227a089e8e11"
+          "sha256": "d6cd52f005921e49a1162dc34bea211cddd32d7e3dcc7ad0f31052c153e532ce"
         }
       },
       "failures": []
@@ -972,44 +972,44 @@
         "path": "repo:docs/data/publication_audit.json",
         "exists": true,
         "bytes": 10940,
-        "sha256": "cf090fe53be3f85923674af08ad982e12635db7e44e87182982461ce27e858d3"
+        "sha256": "4888187b7e71e141df9006b820e4a4b2a1e510cccac97e95cc01857937b34945"
       },
       "mirrors": {
         "hf_space": {
           "path": "hf_space:data/publication_audit.json",
           "exists": true,
           "bytes": 10940,
-          "sha256": "cf090fe53be3f85923674af08ad982e12635db7e44e87182982461ce27e858d3"
+          "sha256": "4888187b7e71e141df9006b820e4a4b2a1e510cccac97e95cc01857937b34945"
         },
         "hf_artifacts_data": {
           "path": "hf_artifacts:data/publication_audit.json",
           "exists": true,
           "bytes": 10940,
-          "sha256": "cf090fe53be3f85923674af08ad982e12635db7e44e87182982461ce27e858d3"
+          "sha256": "4888187b7e71e141df9006b820e4a4b2a1e510cccac97e95cc01857937b34945"
         },
         "hf_artifacts": {
           "path": "hf_artifacts:docs/data/publication_audit.json",
           "exists": true,
           "bytes": 10940,
-          "sha256": "cf090fe53be3f85923674af08ad982e12635db7e44e87182982461ce27e858d3"
+          "sha256": "4888187b7e71e141df9006b820e4a4b2a1e510cccac97e95cc01857937b34945"
         },
         "hf_model_data": {
           "path": "hf_model:data/publication_audit.json",
           "exists": true,
           "bytes": 10940,
-          "sha256": "cf090fe53be3f85923674af08ad982e12635db7e44e87182982461ce27e858d3"
+          "sha256": "4888187b7e71e141df9006b820e4a4b2a1e510cccac97e95cc01857937b34945"
         },
         "hf_model_docs_data": {
           "path": "hf_model:docs/data/publication_audit.json",
           "exists": true,
           "bytes": 10940,
-          "sha256": "cf090fe53be3f85923674af08ad982e12635db7e44e87182982461ce27e858d3"
+          "sha256": "4888187b7e71e141df9006b820e4a4b2a1e510cccac97e95cc01857937b34945"
         },
         "hf_model": {
           "path": "hf_model:metrics/publication_audit.json",
           "exists": true,
           "bytes": 10940,
-          "sha256": "cf090fe53be3f85923674af08ad982e12635db7e44e87182982461ce27e858d3"
+          "sha256": "4888187b7e71e141df9006b820e4a4b2a1e510cccac97e95cc01857937b34945"
         }
       },
       "failures": []
@@ -1021,44 +1021,44 @@
         "path": "repo:docs/data/public_surface_qa.json",
         "exists": true,
         "bytes": 7690,
-        "sha256": "b71de6fbea3bc010a2914a564cb45613c1e71deba2bd62f780db6be959a27f9d"
+        "sha256": "118481f357ed8f6346259537fe672634cdef2527bddcec2227911d56ef1d812b"
       },
       "mirrors": {
         "hf_space": {
           "path": "hf_space:data/public_surface_qa.json",
           "exists": true,
           "bytes": 7690,
-          "sha256": "b71de6fbea3bc010a2914a564cb45613c1e71deba2bd62f780db6be959a27f9d"
+          "sha256": "118481f357ed8f6346259537fe672634cdef2527bddcec2227911d56ef1d812b"
         },
         "hf_artifacts_data": {
           "path": "hf_artifacts:data/public_surface_qa.json",
           "exists": true,
           "bytes": 7690,
-          "sha256": "b71de6fbea3bc010a2914a564cb45613c1e71deba2bd62f780db6be959a27f9d"
+          "sha256": "118481f357ed8f6346259537fe672634cdef2527bddcec2227911d56ef1d812b"
         },
         "hf_artifacts": {
           "path": "hf_artifacts:docs/data/public_surface_qa.json",
           "exists": true,
           "bytes": 7690,
-          "sha256": "b71de6fbea3bc010a2914a564cb45613c1e71deba2bd62f780db6be959a27f9d"
+          "sha256": "118481f357ed8f6346259537fe672634cdef2527bddcec2227911d56ef1d812b"
         },
         "hf_model_data": {
           "path": "hf_model:data/public_surface_qa.json",
           "exists": true,
           "bytes": 7690,
-          "sha256": "b71de6fbea3bc010a2914a564cb45613c1e71deba2bd62f780db6be959a27f9d"
+          "sha256": "118481f357ed8f6346259537fe672634cdef2527bddcec2227911d56ef1d812b"
         },
         "hf_model_docs_data": {
           "path": "hf_model:docs/data/public_surface_qa.json",
           "exists": true,
           "bytes": 7690,
-          "sha256": "b71de6fbea3bc010a2914a564cb45613c1e71deba2bd62f780db6be959a27f9d"
+          "sha256": "118481f357ed8f6346259537fe672634cdef2527bddcec2227911d56ef1d812b"
         },
         "hf_model": {
           "path": "hf_model:metrics/public_surface_qa.json",
           "exists": true,
           "bytes": 7690,
-          "sha256": "b71de6fbea3bc010a2914a564cb45613c1e71deba2bd62f780db6be959a27f9d"
+          "sha256": "118481f357ed8f6346259537fe672634cdef2527bddcec2227911d56ef1d812b"
         }
       },
       "failures": []
@@ -1217,44 +1217,44 @@
         "path": "repo:docs/data/quality_gates.json",
         "exists": true,
         "bytes": 8640,
-        "sha256": "ea3b91690202170553f57f860bcefb28745c7326f2e870a25a70e1e1d99b07cc"
+        "sha256": "bedff006bb7f8b9af9e9af4e72637b83762f14532c65a0b4726d4e1ba05cddd5"
       },
       "mirrors": {
         "hf_space": {
           "path": "hf_space:data/quality_gates.json",
           "exists": true,
           "bytes": 8640,
-          "sha256": "ea3b91690202170553f57f860bcefb28745c7326f2e870a25a70e1e1d99b07cc"
+          "sha256": "bedff006bb7f8b9af9e9af4e72637b83762f14532c65a0b4726d4e1ba05cddd5"
         },
         "hf_artifacts_data": {
           "path": "hf_artifacts:data/quality_gates.json",
           "exists": true,
           "bytes": 8640,
-          "sha256": "ea3b91690202170553f57f860bcefb28745c7326f2e870a25a70e1e1d99b07cc"
+          "sha256": "bedff006bb7f8b9af9e9af4e72637b83762f14532c65a0b4726d4e1ba05cddd5"
         },
         "hf_artifacts": {
           "path": "hf_artifacts:docs/data/quality_gates.json",
           "exists": true,
           "bytes": 8640,
-          "sha256": "ea3b91690202170553f57f860bcefb28745c7326f2e870a25a70e1e1d99b07cc"
+          "sha256": "bedff006bb7f8b9af9e9af4e72637b83762f14532c65a0b4726d4e1ba05cddd5"
         },
         "hf_model_data": {
           "path": "hf_model:data/quality_gates.json",
           "exists": true,
           "bytes": 8640,
-          "sha256": "ea3b91690202170553f57f860bcefb28745c7326f2e870a25a70e1e1d99b07cc"
+          "sha256": "bedff006bb7f8b9af9e9af4e72637b83762f14532c65a0b4726d4e1ba05cddd5"
         },
         "hf_model_docs_data": {
           "path": "hf_model:docs/data/quality_gates.json",
           "exists": true,
           "bytes": 8640,
-          "sha256": "ea3b91690202170553f57f860bcefb28745c7326f2e870a25a70e1e1d99b07cc"
+          "sha256": "bedff006bb7f8b9af9e9af4e72637b83762f14532c65a0b4726d4e1ba05cddd5"
         },
         "hf_model": {
           "path": "hf_model:metrics/quality_gates.json",
           "exists": true,
           "bytes": 8640,
-          "sha256": "ea3b91690202170553f57f860bcefb28745c7326f2e870a25a70e1e1d99b07cc"
+          "sha256": "bedff006bb7f8b9af9e9af4e72637b83762f14532c65a0b4726d4e1ba05cddd5"
         }
       },
       "failures": []
@@ -1756,44 +1756,44 @@
         "path": "repo:docs/data/source_alignment_audit.json",
         "exists": true,
         "bytes": 4432,
-        "sha256": "fd964392bdc7397f24b463964226a10cadbd5c12459df067baf1c56782a829e4"
+        "sha256": "4299d70350b2d299645d2e0e60ef53ab1c48888ec9c94e72d443b70682a23d19"
       },
       "mirrors": {
         "hf_space": {
           "path": "hf_space:data/source_alignment_audit.json",
           "exists": true,
           "bytes": 4432,
-          "sha256": "fd964392bdc7397f24b463964226a10cadbd5c12459df067baf1c56782a829e4"
+          "sha256": "4299d70350b2d299645d2e0e60ef53ab1c48888ec9c94e72d443b70682a23d19"
         },
         "hf_artifacts_data": {
           "path": "hf_artifacts:data/source_alignment_audit.json",
           "exists": true,
           "bytes": 4432,
-          "sha256": "fd964392bdc7397f24b463964226a10cadbd5c12459df067baf1c56782a829e4"
+          "sha256": "4299d70350b2d299645d2e0e60ef53ab1c48888ec9c94e72d443b70682a23d19"
         },
         "hf_artifacts": {
           "path": "hf_artifacts:docs/data/source_alignment_audit.json",
           "exists": true,
           "bytes": 4432,
-          "sha256": "fd964392bdc7397f24b463964226a10cadbd5c12459df067baf1c56782a829e4"
+          "sha256": "4299d70350b2d299645d2e0e60ef53ab1c48888ec9c94e72d443b70682a23d19"
         },
         "hf_model_data": {
           "path": "hf_model:data/source_alignment_audit.json",
           "exists": true,
           "bytes": 4432,
-          "sha256": "fd964392bdc7397f24b463964226a10cadbd5c12459df067baf1c56782a829e4"
+          "sha256": "4299d70350b2d299645d2e0e60ef53ab1c48888ec9c94e72d443b70682a23d19"
         },
         "hf_model_docs_data": {
           "path": "hf_model:docs/data/source_alignment_audit.json",
           "exists": true,
           "bytes": 4432,
-          "sha256": "fd964392bdc7397f24b463964226a10cadbd5c12459df067baf1c56782a829e4"
+          "sha256": "4299d70350b2d299645d2e0e60ef53ab1c48888ec9c94e72d443b70682a23d19"
         },
         "hf_model": {
           "path": "hf_model:metrics/source_alignment_audit.json",
           "exists": true,
           "bytes": 4432,
-          "sha256": "fd964392bdc7397f24b463964226a10cadbd5c12459df067baf1c56782a829e4"
+          "sha256": "4299d70350b2d299645d2e0e60ef53ab1c48888ec9c94e72d443b70682a23d19"
         }
       },
       "failures": []
@@ -2197,44 +2197,44 @@
         "path": "repo:docs/data/task_surface_integrity.json",
         "exists": true,
         "bytes": 46399,
-        "sha256": "cc417d3fd11237b9b5bbd59f853a54d5e38a88dc01e2ac13dc77c0bb098e85cd"
+        "sha256": "706bab17272be32e33737658ad83200fe7e9daddd9f16bc726d4fc723505400c"
       },
       "mirrors": {
         "hf_space": {
           "path": "hf_space:data/task_surface_integrity.json",
           "exists": true,
           "bytes": 46399,
-          "sha256": "cc417d3fd11237b9b5bbd59f853a54d5e38a88dc01e2ac13dc77c0bb098e85cd"
+          "sha256": "706bab17272be32e33737658ad83200fe7e9daddd9f16bc726d4fc723505400c"
         },
         "hf_artifacts_data": {
           "path": "hf_artifacts:data/task_surface_integrity.json",
           "exists": true,
           "bytes": 46399,
-          "sha256": "cc417d3fd11237b9b5bbd59f853a54d5e38a88dc01e2ac13dc77c0bb098e85cd"
+          "sha256": "706bab17272be32e33737658ad83200fe7e9daddd9f16bc726d4fc723505400c"
         },
         "hf_artifacts": {
           "path": "hf_artifacts:docs/data/task_surface_integrity.json",
           "exists": true,
           "bytes": 46399,
-          "sha256": "cc417d3fd11237b9b5bbd59f853a54d5e38a88dc01e2ac13dc77c0bb098e85cd"
+          "sha256": "706bab17272be32e33737658ad83200fe7e9daddd9f16bc726d4fc723505400c"
         },
         "hf_model_data": {
           "path": "hf_model:data/task_surface_integrity.json",
           "exists": true,
           "bytes": 46399,
-          "sha256": "cc417d3fd11237b9b5bbd59f853a54d5e38a88dc01e2ac13dc77c0bb098e85cd"
+          "sha256": "706bab17272be32e33737658ad83200fe7e9daddd9f16bc726d4fc723505400c"
         },
         "hf_model_docs_data": {
           "path": "hf_model:docs/data/task_surface_integrity.json",
           "exists": true,
           "bytes": 46399,
-          "sha256": "cc417d3fd11237b9b5bbd59f853a54d5e38a88dc01e2ac13dc77c0bb098e85cd"
+          "sha256": "706bab17272be32e33737658ad83200fe7e9daddd9f16bc726d4fc723505400c"
         },
         "hf_model": {
           "path": "hf_model:metrics/task_surface_integrity.json",
           "exists": true,
           "bytes": 46399,
-          "sha256": "cc417d3fd11237b9b5bbd59f853a54d5e38a88dc01e2ac13dc77c0bb098e85cd"
+          "sha256": "706bab17272be32e33737658ad83200fe7e9daddd9f16bc726d4fc723505400c"
         }
       },
       "failures": []
@@ -2589,44 +2589,44 @@
         "path": "repo:docs/data/website_integrity.json",
         "exists": true,
         "bytes": 24948,
-        "sha256": "1d810f56b8ee2771ced787acd73e03e6c568f34f410d0d6dc506e22146e0a7f0"
+        "sha256": "cace6acbf9af955d14a660b0c387cd556f84ced559a13138f6be327c5b1f1623"
       },
       "mirrors": {
         "hf_space": {
           "path": "hf_space:data/website_integrity.json",
           "exists": true,
           "bytes": 24948,
-          "sha256": "1d810f56b8ee2771ced787acd73e03e6c568f34f410d0d6dc506e22146e0a7f0"
+          "sha256": "cace6acbf9af955d14a660b0c387cd556f84ced559a13138f6be327c5b1f1623"
         },
         "hf_artifacts_data": {
           "path": "hf_artifacts:data/website_integrity.json",
           "exists": true,
           "bytes": 24948,
-          "sha256": "1d810f56b8ee2771ced787acd73e03e6c568f34f410d0d6dc506e22146e0a7f0"
+          "sha256": "cace6acbf9af955d14a660b0c387cd556f84ced559a13138f6be327c5b1f1623"
         },
         "hf_artifacts": {
           "path": "hf_artifacts:docs/data/website_integrity.json",
           "exists": true,
           "bytes": 24948,
-          "sha256": "1d810f56b8ee2771ced787acd73e03e6c568f34f410d0d6dc506e22146e0a7f0"
+          "sha256": "cace6acbf9af955d14a660b0c387cd556f84ced559a13138f6be327c5b1f1623"
         },
         "hf_model_data": {
           "path": "hf_model:data/website_integrity.json",
           "exists": true,
           "bytes": 24948,
-          "sha256": "1d810f56b8ee2771ced787acd73e03e6c568f34f410d0d6dc506e22146e0a7f0"
+          "sha256": "cace6acbf9af955d14a660b0c387cd556f84ced559a13138f6be327c5b1f1623"
         },
         "hf_model_docs_data": {
           "path": "hf_model:docs/data/website_integrity.json",
           "exists": true,
           "bytes": 24948,
-          "sha256": "1d810f56b8ee2771ced787acd73e03e6c568f34f410d0d6dc506e22146e0a7f0"
+          "sha256": "cace6acbf9af955d14a660b0c387cd556f84ced559a13138f6be327c5b1f1623"
         },
         "hf_model": {
           "path": "hf_model:metrics/website_integrity.json",
           "exists": true,
           "bytes": 24948,
-          "sha256": "1d810f56b8ee2771ced787acd73e03e6c568f34f410d0d6dc506e22146e0a7f0"
+          "sha256": "cace6acbf9af955d14a660b0c387cd556f84ced559a13138f6be327c5b1f1623"
         }
       },
       "failures": []
@@ -7429,39 +7429,39 @@
       "local": {
         "path": "repo:docs/index.html",
         "exists": true,
-        "bytes": 379760,
-        "sha256": "5ab185f3c9022a2eb5efa0d00efd23a010b16776a069c307f953aef0d7657272"
+        "bytes": 381650,
+        "sha256": "bf6004946fbe7cb990cde7581483bf5db8dfc9317f14135756d78a12bad61060"
       },
       "mirrors": {
         "hf_space": {
           "path": "hf_space:index.html",
           "exists": true,
-          "bytes": 379760,
-          "sha256": "5ab185f3c9022a2eb5efa0d00efd23a010b16776a069c307f953aef0d7657272"
+          "bytes": 381650,
+          "sha256": "bf6004946fbe7cb990cde7581483bf5db8dfc9317f14135756d78a12bad61060"
         },
         "hf_artifacts_root": {
           "path": "hf_artifacts:index.html",
           "exists": true,
-          "bytes": 379760,
-          "sha256": "5ab185f3c9022a2eb5efa0d00efd23a010b16776a069c307f953aef0d7657272"
+          "bytes": 381650,
+          "sha256": "bf6004946fbe7cb990cde7581483bf5db8dfc9317f14135756d78a12bad61060"
         },
         "hf_artifacts_docs": {
           "path": "hf_artifacts:docs/index.html",
           "exists": true,
-          "bytes": 379760,
-          "sha256": "5ab185f3c9022a2eb5efa0d00efd23a010b16776a069c307f953aef0d7657272"
+          "bytes": 381650,
+          "sha256": "bf6004946fbe7cb990cde7581483bf5db8dfc9317f14135756d78a12bad61060"
         },
         "hf_model": {
           "path": "hf_model:index.html",
           "exists": true,
-          "bytes": 379760,
-          "sha256": "5ab185f3c9022a2eb5efa0d00efd23a010b16776a069c307f953aef0d7657272"
+          "bytes": 381650,
+          "sha256": "bf6004946fbe7cb990cde7581483bf5db8dfc9317f14135756d78a12bad61060"
         },
         "hf_model_docs": {
           "path": "hf_model:docs/index.html",
           "exists": true,
-          "bytes": 379760,
-          "sha256": "5ab185f3c9022a2eb5efa0d00efd23a010b16776a069c307f953aef0d7657272"
+          "bytes": 381650,
+          "sha256": "bf6004946fbe7cb990cde7581483bf5db8dfc9317f14135756d78a12bad61060"
         }
       },
       "failures": []

docs/data/public_surface_qa.json

@@ -1,7 +1,7 @@
 {
   "title": "Ropedia Xperience-10M Public Project Surface",
   "status": "pass",
-  "generated_at_utc": "2026-06-22T17:41:00+00:00",
+  "generated_at_utc": "2026-06-22T18:16:46+00:00",
   "scope": "Repo README, GitHub Pages HTML, Hugging Face Space card, artifact dataset card, and model card.",
   "checks": [
     {
@@ -18,7 +18,7 @@
         "website_integrity": {
           "exists": true,
           "status": "pass",
-          "generated_at_utc": "2026-06-22T17:38:14+00:00"
+          "generated_at_utc": "2026-06-22T18:14:28+00:00"
         },
         "rendered_site_check": {
           "exists": true,
@@ -28,7 +28,7 @@
         "task_surface_integrity": {
           "exists": true,
           "status": "pass",
-          "generated_at_utc": "2026-06-22T17:37:13+00:00"
+          "generated_at_utc": "2026-06-22T18:14:26+00:00"
         },
         "source_alignment": {
           "exists": true,
@@ -43,12 +43,12 @@
         "publication_package": {
           "exists": true,
           "status": "pass",
-          "generated_at_utc": "2026-06-22T17:40:04+00:00"
+          "generated_at_utc": "2026-06-22T17:46:27+00:00"
         },
         "mirror_parity": {
           "exists": true,
           "status": "pass",
-          "generated_at_utc": "2026-06-22T17:05:46+00:00"
+          "generated_at_utc": "2026-06-22T17:49:11+00:00"
         }
       },
       "failures": {}
@@ -76,7 +76,7 @@
       "marker_counts": {
         "role=\"tablist\"": 3,
         "role=\"tab\"": 10,
-        "role=\"tabpanel\"": 27,
+        "role=\"tabpanel\"": 23,
         "aria-selected": 13,
         "aria-controls": 11,
         "moveProjectTabFocus": 2,

docs/data/publication_audit.json

@@ -1,6 +1,6 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-22T17:46:27+00:00",
+  "generated_at_utc": "2026-06-22T18:17:07+00:00",
   "checks": [
     {
       "name": "required_publication_assets_present",
@@ -235,7 +235,7 @@
     "github_repo": {
       "root": "repo",
       "exists": true,
-      "file_count": 1557,
+      "file_count": 1558,
       "text_file_count": 1287,
       "largest_file": {
         "path": "results/omni_finetune/a100_128_metadata_task_baselines_20260616_v2/interaction_text_prediction/confusion_matrix.csv",

docs/data/quality_gates.json

@@ -1,7 +1,7 @@
 {
   "title": "Ropedia Xperience-10M Release Checks",
   "status": "pass",
-  "generated_at_utc": "2026-06-22T17:40:55+00:00",
+  "generated_at_utc": "2026-06-22T18:16:45+00:00",
   "rule": "A release is current when the automated reports pass and the live GitHub/Hugging Face mirrors are verified after publishing.",
   "automated_gates": [
     {

docs/data/source_alignment_audit.json

@@ -1,7 +1,7 @@
 {
   "title": "Ropedia Xperience-10M Source Alignment Note",
   "status": "pass",
-  "generated_at_utc": "2026-06-22T17:35:19+00:00",
+  "generated_at_utc": "2026-06-22T18:16:48+00:00",
   "alignment_json": "docs/data/xperience10m_dataset_card_alignment.json",
   "alignment_summary": {
     "full_dataset_repo": "ropedia-ai/xperience-10m",

docs/data/task_surface_integrity.json

@@ -1,6 +1,6 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-22T17:45:19+00:00",
+  "generated_at_utc": "2026-06-22T18:14:26+00:00",
   "summary": {
     "original_walkthrough_task_count": 12,
     "expected_original_walkthrough_task_count": 12,

docs/data/website_integrity.json

@@ -1,11 +1,11 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-22T17:45:23+00:00",
+  "generated_at_utc": "2026-06-22T18:14:28+00:00",
   "docs_root": "docs",
   "site_base": "/ropedia-xperience-10m-task-suite/",
   "summary": {
     "html_pages": 4,
-    "local_references": 286,
+    "local_references": 295,
     "external_reference_count": 151,
     "json_files": 56,
     "image_assets_referenced": 49,
@@ -30,7 +30,7 @@
       "name": "project_sections_are_assigned_to_tabs",
       "status": "pass",
       "reason": "Every major research section should be assigned to a tab group.",
-      "section_count": 23
+      "section_count": 19
     },
     {
       "name": "project_hash_router_preserves_deep_links",
@@ -58,8 +58,8 @@
       "name": "project_sections_are_labeled_tabpanels",
       "status": "pass",
       "reason": "Every tabbed research section should expose a labeled panel role.",
-      "panel_count": 27,
-      "labeled_panel_count": 23
+      "panel_count": 23,
+      "labeled_panel_count": 19
     },
     {
       "name": "project_tabs_update_selected_state",
@@ -80,8 +80,8 @@
       "name": "project_overview_precedes_progress_ledger",
       "status": "pass",
       "reason": "The project overview should appear before the deeper progress ledger.",
-      "overview_index": 157884,
-      "evidence_index": 205698
+      "overview_index": 158999,
+      "evidence_index": 206813
     },
     {
       "name": "project_status_links_json",
@@ -159,9 +159,9 @@
       "name": "evaluation_protocol_between_overview_and_progress",
       "status": "pass",
       "reason": "The evaluation protocol should appear before the deeper evidence ledger.",
-      "overview_index": 157884,
-      "protocol_index": 201903,
-      "evidence_index": 205698
+      "overview_index": 158999,
+      "protocol_index": 203018,
+      "evidence_index": 206813
     },
     {
       "name": "evaluation_protocol_links_json",
@@ -290,8 +290,8 @@
     },
     {
       "path": "index.html",
-      "id_count": 103,
-      "reference_count": 258,
+      "id_count": 106,
+      "reference_count": 267,
       "image_count": 54
     },
     {
@@ -420,7 +420,7 @@
     },
     {
       "path": "data/publication_audit.json",
-      "bytes": 10844,
+      "bytes": 10940,
       "top_level_type": "dict"
     },
     {

docs/index.html

@@ -827,14 +827,10 @@
     #pipeline { order: 7; }
     #protocol { order: 8; }
     #takeaways { order: 9; }
-    #models { order: 10; }
-    #neural { order: 11; }
     #directions { order: 12; }
-    #extensions { order: 13; }
     #architectures { order: 14; }
     #walkthroughs { order: 15; }
     #features { order: 17; }
-    #diagnostics { order: 18; }
     #evidence { order: 19; }
     #glossary { order: 20; }
     #artifacts { order: 21; }
@@ -843,14 +839,18 @@
     #suite { padding: 62px 0 76px; }
     #suite .wrap { width: min(1680px, calc(100% - 48px)); }
     #suite .section-head { max-width: var(--max); margin-inline: auto; }
-    .suite-jump-row {
+    .suite-jump-row,
+    .result-jump-row,
+    .direction-jump-row {
       max-width: var(--max);
       margin: -8px auto 24px;
       display: flex;
       flex-wrap: wrap;
       gap: 10px;
     }
-    .suite-jump-row a {
+    .suite-jump-row a,
+    .result-jump-row a,
+    .direction-jump-row a {
       display: inline-flex;
       align-items: center;
       min-height: 38px;
@@ -865,11 +865,49 @@
       text-decoration: none;
       text-transform: uppercase;
     }
-    .suite-jump-row a:hover {
+    .suite-jump-row a:hover,
+    .result-jump-row a:hover,
+    .direction-jump-row a:hover {
       border-color: rgba(204, 255, 160, 0.72);
       color: var(--ink);
       background: rgba(204, 255, 160, 0.10);
     }
+    .result-subsection,
+    .direction-subsection {
+      scroll-margin-top: var(--tab-stack-offset);
+      margin-top: clamp(28px, 4vw, 48px);
+      border: 1px solid rgba(204, 255, 160, 0.20);
+      border-radius: var(--radius);
+      background:
+        radial-gradient(circle at top right, rgba(103, 232, 209, 0.08), transparent 30%),
+        linear-gradient(180deg, rgba(6, 14, 7, 0.72), rgba(2, 5, 2, 0.82));
+      padding: clamp(20px, 3vw, 32px);
+    }
+    .result-subsection-head,
+    .direction-subsection-head {
+      display: grid;
+      grid-template-columns: minmax(0, 0.9fr) minmax(260px, 0.58fr);
+      gap: 22px;
+      align-items: end;
+      margin-bottom: 20px;
+    }
+    .result-subsection-head h3,
+    .direction-subsection-head h3 {
+      margin: 0;
+      font-family: var(--font-ui);
+      font-size: clamp(24px, 2.4vw, 34px);
+      line-height: 1.05;
+      letter-spacing: 0;
+      text-wrap: balance;
+    }
+    .result-subsection-head p,
+    .direction-subsection-head p {
+      margin: 0;
+      color: var(--muted);
+      font-size: 15px;
+      line-height: 1.6;
+      text-wrap: pretty;
+    }
     .suite-radar-block {
       scroll-margin-top: var(--tab-stack-offset);
     }
@@ -5348,7 +5386,7 @@
           <strong>Start</strong>
           <span>project overview and roadmap</span>
         </button>
-        <button type="button" class="project-tab" id="tab-data" role="tab" data-tab-key="data" data-default-section="dataset-card" aria-selected="false" aria-pressed="false" aria-controls="dataset-card raw-sample suite walkthroughs extensions" tabindex="-1">
+        <button type="button" class="project-tab" id="tab-data" role="tab" data-tab-key="data" data-default-section="dataset-card" aria-selected="false" aria-pressed="false" aria-controls="dataset-card raw-sample suite walkthroughs" tabindex="-1">
           <strong>Data & Tasks</strong>
           <span>dataset sample and task suite</span>
         </button>
@@ -5356,11 +5394,11 @@
           <strong>Method</strong>
           <span>pipeline and model design</span>
         </button>
-        <button type="button" class="project-tab" id="tab-results" role="tab" data-tab-key="results" data-default-section="takeaways" aria-selected="false" aria-pressed="false" aria-controls="takeaways models neural diagnostics" tabindex="-1">
+        <button type="button" class="project-tab" id="tab-results" role="tab" data-tab-key="results" data-default-section="takeaways" aria-selected="false" aria-pressed="false" aria-controls="takeaways" tabindex="-1">
           <strong>Results</strong>
           <span>takeaways and baselines</span>
         </button>
-        <button type="button" class="project-tab" id="tab-directions" role="tab" data-tab-key="directions" data-default-section="directions" aria-selected="false" aria-pressed="false" aria-controls="directions extensions" tabindex="-1">
+        <button type="button" class="project-tab" id="tab-directions" role="tab" data-tab-key="directions" data-default-section="directions" aria-selected="false" aria-pressed="false" aria-controls="directions" tabindex="-1">
           <strong>Directions</strong>
           <span>four tracks and probes</span>
         </button>
@@ -6412,7 +6450,14 @@
           <h2>Results by evidence line.</h2>
           <p>Read results in this order: choose the line, open the matching radar, inspect the matrix row, then check proxy flags before interpreting totals.</p>
         </div>
-        <div class="result-reading-order" aria-label="How to read the result evidence">
+        <nav class="result-jump-row" aria-label="Results quick links">
+          <a href="#result-lines">Evidence lines</a>
+          <a href="#result-matrix-table">180-result table</a>
+          <a href="#models">Baseline snapshots</a>
+          <a href="#neural">Neural heads</a>
+          <a href="#diagnostics">Diagnostic charts</a>
+        </nav>
+        <div class="result-reading-order" id="result-lines" aria-label="How to read the result evidence">
           <article class="result-reading-step">
             <span>01</span>
             <strong>Choose the line</strong>
@@ -6610,6 +6655,49 @@
             <p class="result-matrix-foot">Best-practice reading rule: compare methods within the same evidence line first, then use the proxy badges before interpreting cross-method totals. Six compact-proxy cells are intentionally visible rather than blended into direct raw-target scores.</p>
           </div>
         </div>
+        <div class="result-subsection" id="models">
+          <div class="result-subsection-head">
+            <h3>Small baselines, no hidden machinery.</h3>
+            <p>Motion-only and all-feature classifiers use lightweight heads so the comparison stays readable on a laptop and easy to inspect. They now sit beside the result matrix instead of opening a separate results page.</p>
+          </div>
+          <div class="models">
+            <article class="model"><h3>Motion-only action</h3><span class="score">0.9688</span><span class="meta">macro-F1, 18 classes</span></article>
+            <article class="model"><h3>Current all-feature action</h3><span class="score">0.9829</span><span class="meta">macro-F1, 8,546 dimensions</span></article>
+            <article class="model"><h3>Motion-only subtask</h3><span class="score">0.9528</span><span class="meta">macro-F1, 14 classes</span></article>
+            <article class="model"><h3>Current all-feature subtask</h3><span class="score">0.9173</span><span class="meta">macro-F1, chronological caveats</span></article>
+          </div>
+          <img class="chart" src="assets/charts/model_macro_f1.svg" alt="Macro-F1 comparison chart">
+        </div>
+        <div class="result-subsection" id="neural">
+          <div class="result-subsection-head">
+            <h3>Neural MLP heads, same task contracts.</h3>
+            <p>The neural baseline keeps the same windows, splits, and leakage filters, then swaps in small PyTorch MLP heads. Read it as a nonlinear-head check before heavier Qwen3/Cosmos model branches.</p>
+          </div>
+          <div class="models">
+            <article class="model"><h3>Neural hand forecast</h3><span class="score">0.1079</span><span class="meta">MPJPE, down from 0.8647 minimal</span></article>
+            <article class="model"><h3>Neural temporal order</h3><span class="score">0.8520</span><span class="meta">F1, adjacent-window diagnostic</span></article>
+            <article class="model"><h3>Neural misalignment</h3><span class="score">0.7153</span><span class="meta">F1, shifted motion/visual/audio pairs</span></article>
+            <article class="model"><h3>Neural cross-modal retrieval</h3><span class="score">0.1300</span><span class="meta">MRR; ridge remains stronger here</span></article>
+          </div>
+          <div class="chart-grid">
+            <img class="chart" src="assets/charts/episode_task_scores_neural_mlp.svg" alt="Neural MLP episode task score chart">
+            <img class="chart" src="assets/charts/episode_task_scores_minimal_vs_neural.svg" alt="Minimal versus neural MLP episode task score chart">
+          </div>
+        </div>
+        <div class="result-subsection" id="diagnostics">
+          <div class="result-subsection-head">
+            <h3>Diagnostics separate memorization from signal.</h3>
+            <p>These charts keep the main lesson visible: within-episode labels can be easy under some splits, while retrieval, grounding, forecasting, and alignment remain the useful probes.</p>
+          </div>
+          <div class="chart-grid">
+            <img class="chart" src="assets/charts/episode_task_scores.svg" alt="Episode task suite score chart">
+            <img class="chart" src="assets/charts/cross_modal_retrieval.svg" alt="Cross modal retrieval chart">
+            <img class="chart" src="assets/charts/episode_task_scores_neural_mlp.svg" alt="Neural MLP task score chart">
+            <img class="chart" src="assets/charts/episode_task_scores_minimal_vs_neural.svg" alt="Minimal versus neural score chart">
+            <img class="chart" src="assets/charts/audio_ablation_delta.svg" alt="Measured audio delta chart across walkthrough-backed task contracts">
+          </div>
+          <p class="section-note"><a href="single_episode_explorer.html">Open the single-episode explorer</a> to inspect window-level labels, predictions, modality statistics, object labels, and diagnostic scores. The <a href="data/audio_ablation_summary.json">audio ablation summary</a> records the task-by-task audio contribution.</p>
+        </div>
         <div class="artifact-grid">
           <article class="artifact primary-artifact">
             <div>
@@ -6642,48 +6730,19 @@
       </div>
     </section>
 
-    <section id="models" data-project-tab="results" role="tabpanel" aria-labelledby="tab-results" tabindex="-1">
-      <div class="wrap">
-        <div class="section-head">
-          <h2>Small baselines, no hidden machinery.</h2>
-          <p>Motion-only and current all-feature classifiers use lightweight heads so the comparison stays readable on a laptop and easy to inspect. The neural run keeps the same features and splits, then swaps in PyTorch MLP heads.</p>
-        </div>
-        <div class="models">
-          <article class="model"><h3>Motion-only action</h3><span class="score">0.9688</span><span class="meta">macro-F1, 18 classes</span></article>
-          <article class="model"><h3>Current all-feature action</h3><span class="score">0.9829</span><span class="meta">macro-F1, 8,546 dimensions</span></article>
-          <article class="model"><h3>Motion-only subtask</h3><span class="score">0.9528</span><span class="meta">macro-F1, 14 classes</span></article>
-          <article class="model"><h3>Current all-feature subtask</h3><span class="score">0.9173</span><span class="meta">macro-F1, chronological caveats</span></article>
-        </div>
-        <img class="chart" src="assets/charts/model_macro_f1.svg" alt="Macro-F1 comparison chart">
-      </div>
-    </section>
-
-    <section id="neural" data-project-tab="results" role="tabpanel" aria-labelledby="tab-results" tabindex="-1">
-      <div class="wrap">
-        <div class="section-head">
-          <h2>Neural MLP heads, same task contracts.</h2>
-          <p>The neural baseline uses small PyTorch MLP classifiers/regressors on the same 8,546-dimensional windows, chronological splits, and leakage filters. This isolates the value of a nonlinear head before moving to heavier Qwen/Omni experiments.</p>
-        </div>
-        <div class="models">
-          <article class="model"><h3>Neural hand forecast</h3><span class="score">0.1079</span><span class="meta">MPJPE, down from 0.8647 minimal</span></article>
-          <article class="model"><h3>Neural temporal order</h3><span class="score">0.8520</span><span class="meta">F1, adjacent-window diagnostic</span></article>
-          <article class="model"><h3>Neural misalignment</h3><span class="score">0.7153</span><span class="meta">F1, shifted motion/visual/audio pairs</span></article>
-          <article class="model"><h3>Neural cross-modal retrieval</h3><span class="score">0.1300</span><span class="meta">MRR; ridge remains stronger here</span></article>
-        </div>
-        <div class="chart-grid">
-          <img class="chart" src="assets/charts/episode_task_scores_neural_mlp.svg" alt="Neural MLP episode task score chart">
-          <img class="chart" src="assets/charts/episode_task_scores_minimal_vs_neural.svg" alt="Minimal versus neural MLP episode task score chart">
-        </div>
-      </div>
-    </section>
-
     <section id="directions" data-project-tab="directions" role="tabpanel" aria-labelledby="tab-directions" tabindex="-1">
       <div class="wrap">
         <div class="section-head">
           <h2>The walkthrough-backed tasks organized into four research directions.</h2>
           <p>Each task is mapped as direct, proxy, or diagnostic evidence for the Ropedia research tracks. The mapping uses two current baselines: minimal interpretable heads and neural MLP heads over the same feature contract.</p>
         </div>
-        <div class="direction-grid">
+        <nav class="direction-jump-row" aria-label="Direction evidence quick links">
+          <a href="#direction-coverage">Direction coverage</a>
+          <a href="#direction-baselines">Baselines</a>
+          <a href="#extensions">Unified provenance</a>
+          <a href="#suite">20-task suite</a>
+        </nav>
+        <div class="direction-grid" id="direction-coverage">
           <article class="direction-card">
             <span class="status-pill">partially implemented</span>
             <h3>A. Human Modeling & Motion Understanding</h3>
@@ -6710,7 +6769,7 @@
           </article>
         </div>
         <img class="chart" src="assets/charts/research_direction_coverage.svg" alt="Coverage of the original Xperience-10M tasks across four research directions">
-        <div class="baseline-strip">
+        <div class="baseline-strip" id="direction-baselines">
           <div class="callout">
             <h3>Baseline 1: minimal heads</h3>
             <p>Softmax, logistic, ridge, and retrieval heads keep every input/output contract readable. They are the first sanity check for whether a task is well-posed.</p>
@@ -6720,15 +6779,11 @@
             <p>Small PyTorch MLP classifiers/regressors reuse the same features and splits. They test nonlinear gains before heavier Omni fine-tuning.</p>
           </div>
         </div>
-      </div>
-    </section>
-
-    <section id="extensions" data-project-tab="data" role="tabpanel" aria-labelledby="tab-data" tabindex="-1">
-      <div class="wrap">
-        <div class="section-head">
-          <h2>Unified 20-task evidence and provenance.</h2>
-          <p>All 20 tasks live in the same task table, task-card grid, radar, and 180-record result matrix. Historical result paths are retained only for exact provenance links.</p>
-        </div>
+        <div class="direction-subsection" id="extensions">
+          <div class="direction-subsection-head">
+            <h3>Unified 20-task evidence and provenance.</h3>
+            <p>All 20 tasks live in the same task table, task-card grid, radar, and 180-record result matrix. Historical result paths are retained only for exact provenance links.</p>
+          </div>
         <div class="callout-row">
           <div class="callout">
             <h3>Unified task artifact package</h3>
@@ -6786,6 +6841,7 @@
             <p>A full research result still needs many Xperience-10M episodes, held-out episode splits, stronger encoders, and direction-specific models such as body priors, renderers, or persistent scene graphs.</p>
           </div>
         </div>
+        </div>
       </div>
     </section>
 
@@ -6897,23 +6953,6 @@
       </div>
     </section>
 
-    <section id="diagnostics" data-project-tab="results" role="tabpanel" aria-labelledby="tab-results" tabindex="-1">
-      <div class="wrap">
-        <div class="section-head">
-          <h2>Diagnostics separate memorization from signal.</h2>
-          <p>The charts make the main lesson visible: within-episode supervised labels are easy under some splits, while retrieval, grounding, forecasting, and alignment remain the useful probes.</p>
-        </div>
-        <div class="chart-grid">
-          <img class="chart" src="assets/charts/episode_task_scores.svg" alt="Episode task suite score chart">
-          <img class="chart" src="assets/charts/cross_modal_retrieval.svg" alt="Cross modal retrieval chart">
-          <img class="chart" src="assets/charts/episode_task_scores_neural_mlp.svg" alt="Neural MLP task score chart">
-          <img class="chart" src="assets/charts/episode_task_scores_minimal_vs_neural.svg" alt="Minimal versus neural score chart">
-          <img class="chart" src="assets/charts/audio_ablation_delta.svg" alt="Measured audio delta chart across walkthrough-backed task contracts">
-        </div>
-        <p class="section-note"><a href="single_episode_explorer.html">Open the single-episode explorer</a> to inspect window-level labels, predictions, modality statistics, object labels, and diagnostic scores. The <a href="data/audio_ablation_summary.json">audio ablation summary</a> records the task-by-task audio contribution.</p>
-      </div>
-    </section>
-
     <section id="glossary" data-project-tab="resources" role="tabpanel" aria-labelledby="tab-resources" tabindex="-1">
       <div class="wrap">
         <div class="section-head">
@@ -7602,12 +7641,12 @@ python scripts/validate_publication_package.py</code></pre>
       protocol: "Best for splits, leakage controls, metrics, and evaluation rules.",
       architectures: "Best for how task heads and model tracks are organized.",
       features: "Best for modality and feature provenance.",
-      takeaways: "Best for the fastest read on what the current metrics mean.",
-      models: "Best for minimal baseline evidence.",
-      neural: "Best for neural MLP task-head comparisons.",
+      takeaways: "Best for the fastest read on evidence lines, the 180-result matrix, baseline snapshots, neural heads, and diagnostic charts.",
+      models: "Integrated subsection for minimal baseline evidence inside Results.",
+      neural: "Integrated subsection for neural MLP task-head comparisons inside Results.",
       directions: "Best for mapping tasks to spatial, world-model, and VLA directions.",
-      extensions: "Best for historical provenance links inside the unified 20-task surface.",
-      diagnostics: "Best for charts and error-analysis evidence.",
+      extensions: "Integrated subsection for unified task provenance inside Directions.",
+      diagnostics: "Integrated subsection for charts and error-analysis evidence inside Results.",
       glossary: "Best for terms that can be confused across data, tasks, models, metrics, and public mirrors.",
       artifacts: "Best for finding files, mirrors, weights, scripts, and checks.",
       evidence: "Best for current experiment status and milestones.",
@@ -7615,7 +7654,11 @@ python scripts/validate_publication_package.py</code></pre>
       run: "Best for reproduction commands."
     };
     const sectionTabMap = Object.fromEntries(tabSections.map((section) => [section.id, section.dataset.projectTab]));
-    if (document.getElementById("tasks")) sectionTabMap.tasks = "data";
+    ["tasks", "models", "neural", "diagnostics", "extensions"].forEach((id) => {
+      const anchor = document.getElementById(id);
+      const parentSection = anchor?.closest("[data-project-tab]");
+      if (parentSection) sectionTabMap[id] = parentSection.dataset.projectTab;
+    });
     const tabLabels = Object.fromEntries(
       tabButtons.map((button) => [button.dataset.tabKey, button.querySelector("strong")?.textContent?.trim() || button.dataset.tabKey])
     );

index.html

@@ -827,14 +827,10 @@
     #pipeline { order: 7; }
     #protocol { order: 8; }
     #takeaways { order: 9; }
-    #models { order: 10; }
-    #neural { order: 11; }
     #directions { order: 12; }
-    #extensions { order: 13; }
     #architectures { order: 14; }
     #walkthroughs { order: 15; }
     #features { order: 17; }
-    #diagnostics { order: 18; }
     #evidence { order: 19; }
     #glossary { order: 20; }
     #artifacts { order: 21; }
@@ -843,14 +839,18 @@
     #suite { padding: 62px 0 76px; }
     #suite .wrap { width: min(1680px, calc(100% - 48px)); }
     #suite .section-head { max-width: var(--max); margin-inline: auto; }
-    .suite-jump-row {
+    .suite-jump-row,
+    .result-jump-row,
+    .direction-jump-row {
       max-width: var(--max);
       margin: -8px auto 24px;
       display: flex;
       flex-wrap: wrap;
       gap: 10px;
     }
-    .suite-jump-row a {
+    .suite-jump-row a,
+    .result-jump-row a,
+    .direction-jump-row a {
       display: inline-flex;
       align-items: center;
       min-height: 38px;
@@ -865,11 +865,49 @@
       text-decoration: none;
       text-transform: uppercase;
     }
-    .suite-jump-row a:hover {
+    .suite-jump-row a:hover,
+    .result-jump-row a:hover,
+    .direction-jump-row a:hover {
       border-color: rgba(204, 255, 160, 0.72);
       color: var(--ink);
       background: rgba(204, 255, 160, 0.10);
     }
+    .result-subsection,
+    .direction-subsection {
+      scroll-margin-top: var(--tab-stack-offset);
+      margin-top: clamp(28px, 4vw, 48px);
+      border: 1px solid rgba(204, 255, 160, 0.20);
+      border-radius: var(--radius);
+      background:
+        radial-gradient(circle at top right, rgba(103, 232, 209, 0.08), transparent 30%),
+        linear-gradient(180deg, rgba(6, 14, 7, 0.72), rgba(2, 5, 2, 0.82));
+      padding: clamp(20px, 3vw, 32px);
+    }
+    .result-subsection-head,
+    .direction-subsection-head {
+      display: grid;
+      grid-template-columns: minmax(0, 0.9fr) minmax(260px, 0.58fr);
+      gap: 22px;
+      align-items: end;
+      margin-bottom: 20px;
+    }
+    .result-subsection-head h3,
+    .direction-subsection-head h3 {
+      margin: 0;
+      font-family: var(--font-ui);
+      font-size: clamp(24px, 2.4vw, 34px);
+      line-height: 1.05;
+      letter-spacing: 0;
+      text-wrap: balance;
+    }
+    .result-subsection-head p,
+    .direction-subsection-head p {
+      margin: 0;
+      color: var(--muted);
+      font-size: 15px;
+      line-height: 1.6;
+      text-wrap: pretty;
+    }
     .suite-radar-block {
       scroll-margin-top: var(--tab-stack-offset);
     }
@@ -5348,7 +5386,7 @@
           <strong>Start</strong>
           <span>project overview and roadmap</span>
         </button>
-        <button type="button" class="project-tab" id="tab-data" role="tab" data-tab-key="data" data-default-section="dataset-card" aria-selected="false" aria-pressed="false" aria-controls="dataset-card raw-sample suite walkthroughs extensions" tabindex="-1">
+        <button type="button" class="project-tab" id="tab-data" role="tab" data-tab-key="data" data-default-section="dataset-card" aria-selected="false" aria-pressed="false" aria-controls="dataset-card raw-sample suite walkthroughs" tabindex="-1">
           <strong>Data & Tasks</strong>
           <span>dataset sample and task suite</span>
         </button>
@@ -5356,11 +5394,11 @@
           <strong>Method</strong>
           <span>pipeline and model design</span>
         </button>
-        <button type="button" class="project-tab" id="tab-results" role="tab" data-tab-key="results" data-default-section="takeaways" aria-selected="false" aria-pressed="false" aria-controls="takeaways models neural diagnostics" tabindex="-1">
+        <button type="button" class="project-tab" id="tab-results" role="tab" data-tab-key="results" data-default-section="takeaways" aria-selected="false" aria-pressed="false" aria-controls="takeaways" tabindex="-1">
           <strong>Results</strong>
           <span>takeaways and baselines</span>
         </button>
-        <button type="button" class="project-tab" id="tab-directions" role="tab" data-tab-key="directions" data-default-section="directions" aria-selected="false" aria-pressed="false" aria-controls="directions extensions" tabindex="-1">
+        <button type="button" class="project-tab" id="tab-directions" role="tab" data-tab-key="directions" data-default-section="directions" aria-selected="false" aria-pressed="false" aria-controls="directions" tabindex="-1">
           <strong>Directions</strong>
           <span>four tracks and probes</span>
         </button>
@@ -6412,7 +6450,14 @@
           <h2>Results by evidence line.</h2>
           <p>Read results in this order: choose the line, open the matching radar, inspect the matrix row, then check proxy flags before interpreting totals.</p>
         </div>
-        <div class="result-reading-order" aria-label="How to read the result evidence">
+        <nav class="result-jump-row" aria-label="Results quick links">
+          <a href="#result-lines">Evidence lines</a>
+          <a href="#result-matrix-table">180-result table</a>
+          <a href="#models">Baseline snapshots</a>
+          <a href="#neural">Neural heads</a>
+          <a href="#diagnostics">Diagnostic charts</a>
+        </nav>
+        <div class="result-reading-order" id="result-lines" aria-label="How to read the result evidence">
           <article class="result-reading-step">
             <span>01</span>
             <strong>Choose the line</strong>
@@ -6610,6 +6655,49 @@
             <p class="result-matrix-foot">Best-practice reading rule: compare methods within the same evidence line first, then use the proxy badges before interpreting cross-method totals. Six compact-proxy cells are intentionally visible rather than blended into direct raw-target scores.</p>
           </div>
         </div>
+        <div class="result-subsection" id="models">
+          <div class="result-subsection-head">
+            <h3>Small baselines, no hidden machinery.</h3>
+            <p>Motion-only and all-feature classifiers use lightweight heads so the comparison stays readable on a laptop and easy to inspect. They now sit beside the result matrix instead of opening a separate results page.</p>
+          </div>
+          <div class="models">
+            <article class="model"><h3>Motion-only action</h3><span class="score">0.9688</span><span class="meta">macro-F1, 18 classes</span></article>
+            <article class="model"><h3>Current all-feature action</h3><span class="score">0.9829</span><span class="meta">macro-F1, 8,546 dimensions</span></article>
+            <article class="model"><h3>Motion-only subtask</h3><span class="score">0.9528</span><span class="meta">macro-F1, 14 classes</span></article>
+            <article class="model"><h3>Current all-feature subtask</h3><span class="score">0.9173</span><span class="meta">macro-F1, chronological caveats</span></article>
+          </div>
+          <img class="chart" src="assets/charts/model_macro_f1.svg" alt="Macro-F1 comparison chart">
+        </div>
+        <div class="result-subsection" id="neural">
+          <div class="result-subsection-head">
+            <h3>Neural MLP heads, same task contracts.</h3>
+            <p>The neural baseline keeps the same windows, splits, and leakage filters, then swaps in small PyTorch MLP heads. Read it as a nonlinear-head check before heavier Qwen3/Cosmos model branches.</p>
+          </div>
+          <div class="models">
+            <article class="model"><h3>Neural hand forecast</h3><span class="score">0.1079</span><span class="meta">MPJPE, down from 0.8647 minimal</span></article>
+            <article class="model"><h3>Neural temporal order</h3><span class="score">0.8520</span><span class="meta">F1, adjacent-window diagnostic</span></article>
+            <article class="model"><h3>Neural misalignment</h3><span class="score">0.7153</span><span class="meta">F1, shifted motion/visual/audio pairs</span></article>
+            <article class="model"><h3>Neural cross-modal retrieval</h3><span class="score">0.1300</span><span class="meta">MRR; ridge remains stronger here</span></article>
+          </div>
+          <div class="chart-grid">
+            <img class="chart" src="assets/charts/episode_task_scores_neural_mlp.svg" alt="Neural MLP episode task score chart">
+            <img class="chart" src="assets/charts/episode_task_scores_minimal_vs_neural.svg" alt="Minimal versus neural MLP episode task score chart">
+          </div>
+        </div>
+        <div class="result-subsection" id="diagnostics">
+          <div class="result-subsection-head">
+            <h3>Diagnostics separate memorization from signal.</h3>
+            <p>These charts keep the main lesson visible: within-episode labels can be easy under some splits, while retrieval, grounding, forecasting, and alignment remain the useful probes.</p>
+          </div>
+          <div class="chart-grid">
+            <img class="chart" src="assets/charts/episode_task_scores.svg" alt="Episode task suite score chart">
+            <img class="chart" src="assets/charts/cross_modal_retrieval.svg" alt="Cross modal retrieval chart">
+            <img class="chart" src="assets/charts/episode_task_scores_neural_mlp.svg" alt="Neural MLP task score chart">
+            <img class="chart" src="assets/charts/episode_task_scores_minimal_vs_neural.svg" alt="Minimal versus neural score chart">
+            <img class="chart" src="assets/charts/audio_ablation_delta.svg" alt="Measured audio delta chart across walkthrough-backed task contracts">
+          </div>
+          <p class="section-note"><a href="single_episode_explorer.html">Open the single-episode explorer</a> to inspect window-level labels, predictions, modality statistics, object labels, and diagnostic scores. The <a href="data/audio_ablation_summary.json">audio ablation summary</a> records the task-by-task audio contribution.</p>
+        </div>
         <div class="artifact-grid">
           <article class="artifact primary-artifact">
             <div>
@@ -6642,48 +6730,19 @@
       </div>
     </section>
 
-    <section id="models" data-project-tab="results" role="tabpanel" aria-labelledby="tab-results" tabindex="-1">
-      <div class="wrap">
-        <div class="section-head">
-          <h2>Small baselines, no hidden machinery.</h2>
-          <p>Motion-only and current all-feature classifiers use lightweight heads so the comparison stays readable on a laptop and easy to inspect. The neural run keeps the same features and splits, then swaps in PyTorch MLP heads.</p>
-        </div>
-        <div class="models">
-          <article class="model"><h3>Motion-only action</h3><span class="score">0.9688</span><span class="meta">macro-F1, 18 classes</span></article>
-          <article class="model"><h3>Current all-feature action</h3><span class="score">0.9829</span><span class="meta">macro-F1, 8,546 dimensions</span></article>
-          <article class="model"><h3>Motion-only subtask</h3><span class="score">0.9528</span><span class="meta">macro-F1, 14 classes</span></article>
-          <article class="model"><h3>Current all-feature subtask</h3><span class="score">0.9173</span><span class="meta">macro-F1, chronological caveats</span></article>
-        </div>
-        <img class="chart" src="assets/charts/model_macro_f1.svg" alt="Macro-F1 comparison chart">
-      </div>
-    </section>
-
-    <section id="neural" data-project-tab="results" role="tabpanel" aria-labelledby="tab-results" tabindex="-1">
-      <div class="wrap">
-        <div class="section-head">
-          <h2>Neural MLP heads, same task contracts.</h2>
-          <p>The neural baseline uses small PyTorch MLP classifiers/regressors on the same 8,546-dimensional windows, chronological splits, and leakage filters. This isolates the value of a nonlinear head before moving to heavier Qwen/Omni experiments.</p>
-        </div>
-        <div class="models">
-          <article class="model"><h3>Neural hand forecast</h3><span class="score">0.1079</span><span class="meta">MPJPE, down from 0.8647 minimal</span></article>
-          <article class="model"><h3>Neural temporal order</h3><span class="score">0.8520</span><span class="meta">F1, adjacent-window diagnostic</span></article>
-          <article class="model"><h3>Neural misalignment</h3><span class="score">0.7153</span><span class="meta">F1, shifted motion/visual/audio pairs</span></article>
-          <article class="model"><h3>Neural cross-modal retrieval</h3><span class="score">0.1300</span><span class="meta">MRR; ridge remains stronger here</span></article>
-        </div>
-        <div class="chart-grid">
-          <img class="chart" src="assets/charts/episode_task_scores_neural_mlp.svg" alt="Neural MLP episode task score chart">
-          <img class="chart" src="assets/charts/episode_task_scores_minimal_vs_neural.svg" alt="Minimal versus neural MLP episode task score chart">
-        </div>
-      </div>
-    </section>
-
     <section id="directions" data-project-tab="directions" role="tabpanel" aria-labelledby="tab-directions" tabindex="-1">
       <div class="wrap">
         <div class="section-head">
           <h2>The walkthrough-backed tasks organized into four research directions.</h2>
           <p>Each task is mapped as direct, proxy, or diagnostic evidence for the Ropedia research tracks. The mapping uses two current baselines: minimal interpretable heads and neural MLP heads over the same feature contract.</p>
         </div>
-        <div class="direction-grid">
+        <nav class="direction-jump-row" aria-label="Direction evidence quick links">
+          <a href="#direction-coverage">Direction coverage</a>
+          <a href="#direction-baselines">Baselines</a>
+          <a href="#extensions">Unified provenance</a>
+          <a href="#suite">20-task suite</a>
+        </nav>
+        <div class="direction-grid" id="direction-coverage">
           <article class="direction-card">
             <span class="status-pill">partially implemented</span>
             <h3>A. Human Modeling & Motion Understanding</h3>
@@ -6710,7 +6769,7 @@
           </article>
         </div>
         <img class="chart" src="assets/charts/research_direction_coverage.svg" alt="Coverage of the original Xperience-10M tasks across four research directions">
-        <div class="baseline-strip">
+        <div class="baseline-strip" id="direction-baselines">
           <div class="callout">
             <h3>Baseline 1: minimal heads</h3>
             <p>Softmax, logistic, ridge, and retrieval heads keep every input/output contract readable. They are the first sanity check for whether a task is well-posed.</p>
@@ -6720,15 +6779,11 @@
             <p>Small PyTorch MLP classifiers/regressors reuse the same features and splits. They test nonlinear gains before heavier Omni fine-tuning.</p>
           </div>
         </div>
-      </div>
-    </section>
-
-    <section id="extensions" data-project-tab="data" role="tabpanel" aria-labelledby="tab-data" tabindex="-1">
-      <div class="wrap">
-        <div class="section-head">
-          <h2>Unified 20-task evidence and provenance.</h2>
-          <p>All 20 tasks live in the same task table, task-card grid, radar, and 180-record result matrix. Historical result paths are retained only for exact provenance links.</p>
-        </div>
+        <div class="direction-subsection" id="extensions">
+          <div class="direction-subsection-head">
+            <h3>Unified 20-task evidence and provenance.</h3>
+            <p>All 20 tasks live in the same task table, task-card grid, radar, and 180-record result matrix. Historical result paths are retained only for exact provenance links.</p>
+          </div>
         <div class="callout-row">
           <div class="callout">
             <h3>Unified task artifact package</h3>
@@ -6786,6 +6841,7 @@
             <p>A full research result still needs many Xperience-10M episodes, held-out episode splits, stronger encoders, and direction-specific models such as body priors, renderers, or persistent scene graphs.</p>
           </div>
         </div>
+        </div>
       </div>
     </section>
 
@@ -6897,23 +6953,6 @@
       </div>
     </section>
 
-    <section id="diagnostics" data-project-tab="results" role="tabpanel" aria-labelledby="tab-results" tabindex="-1">
-      <div class="wrap">
-        <div class="section-head">
-          <h2>Diagnostics separate memorization from signal.</h2>
-          <p>The charts make the main lesson visible: within-episode supervised labels are easy under some splits, while retrieval, grounding, forecasting, and alignment remain the useful probes.</p>
-        </div>
-        <div class="chart-grid">
-          <img class="chart" src="assets/charts/episode_task_scores.svg" alt="Episode task suite score chart">
-          <img class="chart" src="assets/charts/cross_modal_retrieval.svg" alt="Cross modal retrieval chart">
-          <img class="chart" src="assets/charts/episode_task_scores_neural_mlp.svg" alt="Neural MLP task score chart">
-          <img class="chart" src="assets/charts/episode_task_scores_minimal_vs_neural.svg" alt="Minimal versus neural score chart">
-          <img class="chart" src="assets/charts/audio_ablation_delta.svg" alt="Measured audio delta chart across walkthrough-backed task contracts">
-        </div>
-        <p class="section-note"><a href="single_episode_explorer.html">Open the single-episode explorer</a> to inspect window-level labels, predictions, modality statistics, object labels, and diagnostic scores. The <a href="data/audio_ablation_summary.json">audio ablation summary</a> records the task-by-task audio contribution.</p>
-      </div>
-    </section>
-
     <section id="glossary" data-project-tab="resources" role="tabpanel" aria-labelledby="tab-resources" tabindex="-1">
       <div class="wrap">
         <div class="section-head">
@@ -7602,12 +7641,12 @@ python scripts/validate_publication_package.py</code></pre>
       protocol: "Best for splits, leakage controls, metrics, and evaluation rules.",
       architectures: "Best for how task heads and model tracks are organized.",
       features: "Best for modality and feature provenance.",
-      takeaways: "Best for the fastest read on what the current metrics mean.",
-      models: "Best for minimal baseline evidence.",
-      neural: "Best for neural MLP task-head comparisons.",
+      takeaways: "Best for the fastest read on evidence lines, the 180-result matrix, baseline snapshots, neural heads, and diagnostic charts.",
+      models: "Integrated subsection for minimal baseline evidence inside Results.",
+      neural: "Integrated subsection for neural MLP task-head comparisons inside Results.",
       directions: "Best for mapping tasks to spatial, world-model, and VLA directions.",
-      extensions: "Best for historical provenance links inside the unified 20-task surface.",
-      diagnostics: "Best for charts and error-analysis evidence.",
+      extensions: "Integrated subsection for unified task provenance inside Directions.",
+      diagnostics: "Integrated subsection for charts and error-analysis evidence inside Results.",
       glossary: "Best for terms that can be confused across data, tasks, models, metrics, and public mirrors.",
       artifacts: "Best for finding files, mirrors, weights, scripts, and checks.",
       evidence: "Best for current experiment status and milestones.",
@@ -7615,7 +7654,11 @@ python scripts/validate_publication_package.py</code></pre>
       run: "Best for reproduction commands."
     };
     const sectionTabMap = Object.fromEntries(tabSections.map((section) => [section.id, section.dataset.projectTab]));
-    if (document.getElementById("tasks")) sectionTabMap.tasks = "data";
+    ["tasks", "models", "neural", "diagnostics", "extensions"].forEach((id) => {
+      const anchor = document.getElementById(id);
+      const parentSection = anchor?.closest("[data-project-tab]");
+      if (parentSection) sectionTabMap[id] = parentSection.dataset.projectTab;
+    });
     const tabLabels = Object.fromEntries(
       tabButtons.map((button) => [button.dataset.tabKey, button.querySelector("strong")?.textContent?.trim() || button.dataset.tabKey])
     );