diff --git a/experiments/user_questions_v1/repair_verification.json b/experiments/user_questions_v1/repair_verification.json
new file mode 100644
index 0000000..81d5608
--- /dev/null
+++ b/experiments/user_questions_v1/repair_verification.json
@@ -0,0 +1,800 @@
+{
+  "checked_at_utc": "2026-11-09T20:18:46.810794+00:00",
+  "state": "IMPLEMENTED_REGRESSION_VERIFIED_AWAITING_CLAUDE_UI_AND_JOINT_CLOSURE",
+  "scope": "Repair verification of known cases and operational components; NOT a new held-out accuracy measurement.",
+  "base_commit": "c2e516c1e538acf8fa5eabeebd48cdc039fb1a77",
+  "index_version": {
+    "version": "904884c1a86a45f39bbeccaec0e5e823",
+    "git_revision": "c3ec9f611e645a2fe14fe66d518eb0924bf9bb40",
+    "created_at_utc": "2026-11-08T20:49:39.997566+00:00",
+    "source_sha256": "201798bf276106986e7ef01b4d6357960cb495d09c9855b7df922c1df4fb35a7",
+    "documents_sha256": "8b7eea54f6233cdc9d16610296d32b9336ab0cbaa4b881872a088c05464960be",
+    "chunks_sha256": "8357d1e0641206990d26827b94f37916661595261f368e30f828bccd03171c95",
+    "embedding_model": "intfloat/multilingual-e5-small",
+    "publication_policy": "explicit-false-hidden-v1",
+    "embedding_fingerprint": "d066fb9a266a1a0c77c65da75dcf538cfab5c87d5c605ff26b1fd2da8a12aa92"
+  },
+  "content_version": "33c3062af9f1aee887e8950f3ec51b8f",
+  "checks": {
+    "full_pytest": {
+      "command": ".venv/bin/python -m pytest -q test tools -o faulthandler_timeout=45",
+      "passed": 1543,
+      "skipped": 90,
+      "note": "Skipped checks are not counted as passed. A separate live Neo4j scope test previously passed 5 cases."
+    },
+    "client_contract_regression": {
+      "passed": 104,
+      "note": "Exact metadata result exercised through actual packages.claude.client.query_backend, including coverage enum validation."
+    },
+    "known_live_question_regressions": {
+      "cases": 44,
+      "passed": 44,
+      "per_engine": 22,
+      "method": "Expected status, accepted delivery, mandatory anchor terms and selected forbidden substitutions. Followed by source-binding audit and AI inspection of teacher-style answers; not a fully independent semantic judge."
+    },
+    "post_contract_fix_metadata_replay": {
+      "cases": 14,
+      "passed": 14
+    },
+    "source_checkpoint_audit": {
+      "units": 6293,
+      "source_files": 72,
+      "whitespace_only_normalizations": 806,
+      "literal_errors": [],
+      "extraction_schema": 2,
+      "coverage": {
+        "decisions": {
+          "irrelevant": 463,
+          "uncertain": 419,
+          "structural": 877,
+          "knowledge": 4534
+        },
+        "units": 6293
+      }
+    },
+    "assertion_and_relation_invariants": {
+      "units": 6293,
+      "activated_relations": 2,
+      "errors": []
+    },
+    "returned_evidence_audit": {
+      "cases": 44,
+      "evidence_occurrences_checked": 85,
+      "evidence_source_files": 18,
+      "source_binding_errors": [],
+      "metadata_versions": [
+        "904884c1a86a45f39bbeccaec0e5e823"
+      ],
+      "metadata_proofs": [
+        {
+          "person": "Ronald Zalacko",
+          "document_author": "Daniel Hladek",
+          "source_path": "pages/students/2023/ronald_zalacko/README.md",
+          "source_url": "https://zp.kemt.fei.tuke.sk/students/2023/ronald_zalacko",
+          "metadata_field": "author"
+        },
+        {
+          "person": "Ronald Zalacko",
+          "document_author": "Daniel Hladek",
+          "source_path": "pages/students/2023/ronald_zalacko/README.md",
+          "source_url": "https://zp.kemt.fei.tuke.sk/students/2023/ronald_zalacko",
+          "metadata_field": "author"
+        },
+        {
+          "person": "Yehor Piliavin",
+          "start_year": 2024,
+          "source_path": "pages/students/2024/yehor_piliavin/README.md",
+          "source_url": "https://zp.kemt.fei.tuke.sk/students/2024/yehor_piliavin",
+          "metadata_field": "literal_start_year",
+          "chunk_id": "pages/students/2024/yehor_piliavin/README.md::chunk-0",
+          "quote": "rok začiatku štúdia: 2024"
+        },
+        {
+          "person": "Yehor Piliavin",
+          "start_year": 2024,
+          "source_path": "pages/students/2024/yehor_piliavin/README.md",
+          "source_url": "https://zp.kemt.fei.tuke.sk/students/2024/yehor_piliavin",
+          "metadata_field": "literal_start_year",
+          "chunk_id": "pages/students/2024/yehor_piliavin/README.md::chunk-0",
+          "quote": "rok začiatku štúdia: 2024"
+        }
+      ],
+      "checks": "Exact comparison to frozen checkpoint of owner, work, phase, time and source. Literal checkpoint text was separately checked against original Markdown. This is provenance validation, not a claim of perfect semantic completeness."
+    },
+    "runtime": {
+      "sqlite_integrity": "ok",
+      "foreign_key_errors": 0,
+      "counts": {
+        "chunks": 947,
+        "chunks_fts": 947
+      },
+      "metadata_versions_equal": true,
+      "content_graph_version_matches_checkpoint": true,
+      "content_graph_hash_matches_checkpoint": true,
+      "deployed_files_match_workspace": true,
+      "capabilities": {
+        "text_search": {
+          "ready": true,
+          "status": "ready",
+          "reason": null
+        },
+        "metadata_graph": {
+          "ready": true,
+          "status": "ready",
+          "reason": null
+        },
+        "knowledge": {
+          "ready": true,
+          "status": "ready",
+          "reason": null
+        },
+        "generation": {
+          "configured": true,
+          "ready": true,
+          "status": "ready",
+          "reason": null
+        }
+      }
+    },
+    "oauth": {
+      "passed": true,
+      "steps": [
+        "challenge and protected-resource discovery",
+        "S256 PKCE",
+        "dynamic client registration",
+        "owner login and consent",
+        "invalid password rejection",
+        "invalid verifier rejection",
+        "authorization code exchange and replay rejection",
+        "authenticated MCP discovery and readiness",
+        "refresh rotation",
+        "grant revocation"
+      ],
+      "test_grants_revoked": true
+    },
+    "public_mcp": {
+      "url": "https://pressed-extra-graduation-readings.trycloudflare.com/mcp",
+      "tools": [
+        "zpwiki_rag",
+        "zpwiki_graphrag",
+        "zpwiki_health"
+      ],
+      "checks": [
+        {
+          "tool": "zpwiki_health",
+          "status": "ok",
+          "expected": null,
+          "passed": true,
+          "seconds": 0.1
+        },
+        {
+          "tool": "zpwiki_rag",
+          "status": "ok",
+          "expected": "ok",
+          "passed": true,
+          "seconds": 0.8
+        },
+        {
+          "tool": "zpwiki_graphrag",
+          "status": "ok",
+          "expected": "ok",
+          "passed": true,
+          "seconds": 5.7
+        },
+        {
+          "tool": "zpwiki_graphrag",
+          "status": "no_answer",
+          "expected": "no_answer",
+          "passed": true,
+          "seconds": 2.8
+        },
+        {
+          "tool": "zpwiki_rag",
+          "status": "ok",
+          "expected": "ok",
+          "passed": true,
+          "seconds": 3.2
+        },
+        {
+          "tool": "zpwiki_graphrag",
+          "status": "ok",
+          "expected": "ok",
+          "passed": true,
+          "seconds": 9.9
+        }
+      ],
+      "actual_claude_ui_verified_this_run": false
+    }
+  },
+  "implemented_steps": [
+    {
+      "step": "Consistent source and publication",
+      "verification": "Read-only local wiki revision matches the shared index; SQLite, metadata graph and content checkpoint/graph agree. No source wiki writes."
+    },
+    {
+      "step": "Scoped extraction",
+      "verification": "All 6293 units have a decision and source binding; actor, work, phase and time invariants checked. Uncertain facts remain uncertain. Activated arbitrary LLM concept relations are conservative and sparse."
+    },
+    {
+      "step": "Common typed question plan",
+      "verification": "Regression cases cover quote selectors, multiple people, style clauses, inflected/typo names, roles and unknown conditions; no arbitrary model Cypher."
+    },
+    {
+      "step": "Hybrid RAG",
+      "verification": "Content path uses independent FTS5/BM25 and E5 candidate ranks with RRF; a unit regression requires a semantic-only candidate to survive a lexical hit. Named finite scopes expand without dropping goals."
+    },
+    {
+      "step": "Local GraphRAG",
+      "verification": "Content path uses bounded Neo4j person/work/activity/evidence traversal with checkpoint validation. Metadata stays on the graph route; no model-generated Cypher."
+    },
+    {
+      "step": "Controlled prose and exact metadata",
+      "verification": "Descriptions use guarded synthesis; unsafe prose may fall back to source-backed extraction. Exact named metadata has deterministic source evidence and a tested MCP client contract."
+    },
+    {
+      "step": "Evaluation",
+      "verification": "Original consumed 96-question TEST is retained byte-for-byte. Current 44 known regressions and 14 contract replays do not replace an independent accuracy estimate."
+    }
+  ],
+  "retrieval_routes_in_live_regression": {
+    "metadata_or_clarification": 18,
+    "bm25_e5_rrf_assertions": 12,
+    "bounded_local_graph": 13,
+    "content_checkpoint": 1
+  },
+  "synthesis_outcomes_in_live_regression": {
+    "not_applicable": 31,
+    "fallback": 7,
+    "accepted": 6
+  },
+  "case_results": [
+    {
+      "id": "holp_title",
+      "engine": "rag",
+      "query": "Aký je názov diplomovej práce Jána Holpa a z ktorého roku je?",
+      "passed": true,
+      "status": "ok",
+      "answer": "Ján Holp má diplomovú prácu s názvom **Systém získavania informácií v slovenskom jazyku** (rok práce: **2021**).\n\nZdroj: https://zp.kemt.fei.tuke.sk/students/2016/jan_holp",
+      "source_urls": [
+        "https://zp.kemt.fei.tuke.sk/students/2016/jan_holp"
+      ]
+    },
+    {
+      "id": "quoted_project",
+      "engine": "rag",
+      "query": "Pri osobe Adrián Ondra over názov záznamu \"Bakalársky projekt 2027\" a pripoj evidovaný rok práce.",
+      "passed": true,
+      "status": "ok",
+      "answer": "Adrián Ondra má bakalársky projekt s názvom **Automatická tvorba myšlienkových máp** (rok práce: **2027**).\n\nZdroj: https://zp.kemt.fei.tuke.sk/students/2024/adrian_ondra",
+      "source_urls": [
+        "https://zp.kemt.fei.tuke.sk/students/2024/adrian_ondra"
+      ]
+    },
+    {
+      "id": "holp_title",
+      "engine": "graphrag",
+      "query": "Aký je názov diplomovej práce Jána Holpa a z ktorého roku je?",
+      "passed": true,
+      "status": "ok",
+      "answer": "Ján Holp má diplomovú prácu s názvom **Systém získavania informácií v slovenskom jazyku** (rok práce: **2021**).\n\nZdroj: https://zp.kemt.fei.tuke.sk/students/2016/jan_holp",
+      "source_urls": [
+        "https://zp.kemt.fei.tuke.sk/students/2016/jan_holp"
+      ]
+    },
+    {
+      "id": "quoted_project",
+      "engine": "graphrag",
+      "query": "Pri osobe Adrián Ondra over názov záznamu \"Bakalársky projekt 2027\" a pripoj evidovaný rok práce.",
+      "passed": true,
+      "status": "ok",
+      "answer": "Adrián Ondra má bakalársky projekt s názvom **Automatická tvorba myšlienkových máp** (rok práce: **2027**).\n\nZdroj: https://zp.kemt.fei.tuke.sk/students/2024/adrian_ondra",
+      "source_urls": [
+        "https://zp.kemt.fei.tuke.sk/students/2024/adrian_ondra"
+      ]
+    },
+    {
+      "id": "goals",
+      "engine": "rag",
+      "query": "Aké sú ciele záznamu \"Bakalársky projekt 2027\" osoby Adrián Ondra? Zhrň všetky doložené body vlastnými slovami a nezamieňaj ich za hotové výsledky.",
+      "passed": true,
+      "status": "ok",
+      "answer": "**Adrián Ondra — Bakalársky projekt 2027**\n\nZadanie a ciele:\n\n- Vytvoriť vizualizáciu myšlienkovej mapy z automaticky extrahovaných entít. (navrhovaný cieľ)\n\n- Vytvoriť nástroj ktorý sa dá využiť pri vzdelávaní. (navrhovaný cieľ)\n\n- Vyhodnotiť kvalitu vytvoreného grafu. Ako veľmi je graf pravdivý vzhľadom na pôvvodný text. (navrhovaný cieľ)\n\nAdrián Ondra — Bakalársky projekt 2027: zdroj tieto body označuje ako možné/navrhované ciele, nie ako potvrdené splnené výsledky.\n\nZdroje: https://zp.kemt.fei.tuke.sk/students/2024/adrian_ondra",
+      "source_urls": [
+        "https://zp.kemt.fei.tuke.sk/students/2024/adrian_ondra"
+      ]
+    },
+    {
+      "id": "goals",
+      "engine": "graphrag",
+      "query": "Aké sú ciele záznamu \"Bakalársky projekt 2027\" osoby Adrián Ondra? Zhrň všetky doložené body vlastnými slovami a nezamieňaj ich za hotové výsledky.",
+      "passed": true,
+      "status": "ok",
+      "answer": "**Adrián Ondra — Bakalársky projekt 2027**\n\nZadanie a ciele:\n\n- Vytvoriť vizualizáciu myšlienkovej mapy z automaticky extrahovaných entít. (navrhovaný cieľ)\n\n- Vytvoriť nástroj ktorý sa dá využiť pri vzdelávaní. (navrhovaný cieľ)\n\n- Vyhodnotiť kvalitu vytvoreného grafu. Ako veľmi je graf pravdivý vzhľadom na pôvvodný text. (navrhovaný cieľ)\n\nAdrián Ondra — Bakalársky projekt 2027: zdroj tieto body označuje ako možné/navrhované ciele, nie ako potvrdené splnené výsledky.\n\nZdroje: https://zp.kemt.fei.tuke.sk/students/2024/adrian_ondra",
+      "source_urls": [
+        "https://zp.kemt.fei.tuke.sk/students/2024/adrian_ondra"
+      ]
+    },
+    {
+      "id": "discovery",
+      "engine": "rag",
+      "query": "Kto už vyskúšal hierarchickú sumarizáciu na problém s dlhým kontextom? Stačí jeden doložený príklad.",
+      "passed": true,
+      "status": "ok",
+      "answer": "**Simona Bobrovčanová**\n\n- Téma alebo názov doložený pri tej istej práci: Automatická sumarizácia slovenského textu\n\n- Bakalárska práca 2026; zaznamenaný výsledok; záznam 2026-02-27: Vyskúšaná hierarchická sumarizácia - text je rozdelený na časti a potom sa vykoná sumár zo sumárov. Funguje to \"dobre\".\n\nZobrazené sú príklady z obmedzeného výberu textových dôkazov; zoznam nemusí byť úplný. Výber môžeš zúžiť témou alebo obdobím.\n\nZdroje: https://zp.kemt.fei.tuke.sk/students/2023/simona_bobrovcanova",
+      "source_urls": [
+        "https://zp.kemt.fei.tuke.sk/students/2023/simona_bobrovcanova"
+      ]
+    },
+    {
+      "id": "discovery",
+      "engine": "graphrag",
+      "query": "Kto už vyskúšal hierarchickú sumarizáciu na problém s dlhým kontextom? Stačí jeden doložený príklad.",
+      "passed": true,
+      "status": "ok",
+      "answer": "**Simona Bobrovčanová**\n\n- Bakalárska práca 2026; zaznamenaný výsledok; záznam 2026-02-27: Vyskúšaná hierarchická sumarizácia - text je rozdelený na časti a potom sa vykoná sumár zo sumárov. Funguje to \"dobre\".\n\n- Bakalárska práca 2026; priebežný stav; záznam 2025-12-05: Vyzerá to tak, že problémom je dĺžka kontextu. Modely často vynechajú informácie na konci.\n\nZobrazené sú príklady z obmedzeného výberu textových dôkazov; zoznam nemusí byť úplný. Výber môžeš zúžiť témou alebo obdobím.\n\nZdroje: https://zp.kemt.fei.tuke.sk/students/2023/simona_bobrovcanova\n\nPrechod grafom dosiahol limit výberu. Zúž otázku na prácu, tému alebo obdobie.",
+      "source_urls": [
+        "https://zp.kemt.fei.tuke.sk/students/2023/simona_bobrovcanova"
+      ]
+    },
+    {
+      "id": "technologies",
+      "engine": "rag",
+      "query": "Aké technológie má Ronald Zalacko na frontende, backende a v databáze a čo je doložené o funkčnosti aplikácie?",
+      "passed": true,
+      "status": "ok",
+      "answer": "**Ronald Zalacko — Bakalárska práca 2026**\n\nZaznamenané výsledky (dátum záznamu: 2025-12-08):\n- Funkčnosť aplikácie je doložená tým, že aplikácia funguje. [1]\n\nZameranie (dátum záznamu: 2025-12-08):\n- Na fronte sa používa React s Vite JS, na backende Flask a v databáze Postgres so SQLAlchemy. [1]\n- Aplikácia využíva Websocket cez socket.io a JWT Token na autentifikáciu. [1]\n\nZdroje: [1](https://zp.kemt.fei.tuke.sk/students/2023/ronald_zalacko)\n\n[1]: https://zp.kemt.fei.tuke.sk/students/2023/ronald_zalacko",
+      "source_urls": [
+        "https://zp.kemt.fei.tuke.sk/students/2023/ronald_zalacko"
+      ]
+    },
+    {
+      "id": "technologies",
+      "engine": "graphrag",
+      "query": "Aké technológie má Ronald Zalacko na frontende, backende a v databáze a čo je doložené o funkčnosti aplikácie?",
+      "passed": true,
+      "status": "ok",
+      "answer": "**Ronald Zalacko — Bakalárska práca 2026**\n\nZaznamenané výsledky (dátum záznamu: 2025-12-08):\n- Funkčnosť celej aplikácie je doložená tým, že aplikácia funguje. [1]\n\nZameranie (dátum záznamu: 2025-12-08):\n- Na fronte sa používa React s Vite JS, na backende Flask a v databáze Postgres so SQLAlchemy. [1]\n- Komunikácia v reálnom čase je riešená pomocou Websocket a socket.io. [1]\n- Autentifikácia je zabezpečená prostredníctvom JWT Token. [1]\n\nZdroje: [1](https://zp.kemt.fei.tuke.sk/students/2023/ronald_zalacko)\n\n[1]: https://zp.kemt.fei.tuke.sk/students/2023/ronald_zalacko",
+      "source_urls": [
+        "https://zp.kemt.fei.tuke.sk/students/2023/ronald_zalacko"
+      ]
+    },
+    {
+      "id": "problem_remedy",
+      "engine": "graphrag",
+      "query": "S akým problémom pri dlhých textoch sa stretla Simona Bobrovčanová a aký prístup už vyskúšala na jeho riešenie?",
+      "passed": true,
+      "status": "ok",
+      "answer": "**Simona Bobrovčanová — Bakalárska práca 2026**\n\nRiešené problémy:\n\n- Vyzerá to tak, že problémom je dĺžka kontextu. Modely často vynechajú informácie na konci. (priebežný stav)\n\nMetódy a technológie:\n\n- Vyskúšaná hierarchická sumarizácia - text je rozdelený na časti a potom sa vykoná sumár zo sumárov. Funguje to \"dobre\". (zaznamenaný výsledok)\n\nZdroje: https://zp.kemt.fei.tuke.sk/students/2023/simona_bobrovcanova",
+      "source_urls": [
+        "https://zp.kemt.fei.tuke.sk/students/2023/simona_bobrovcanova"
+      ]
+    },
+    {
+      "id": "problem_remedy",
+      "engine": "rag",
+      "query": "S akým problémom pri dlhých textoch sa stretla Simona Bobrovčanová a aký prístup už vyskúšala na jeho riešenie?",
+      "passed": true,
+      "status": "ok",
+      "answer": "**Simona Bobrovčanová — Bakalárska práca 2026**\n\nRiešené problémy:\n\n- Vyzerá to tak, že problémom je dĺžka kontextu. Modely často vynechajú informácie na konci. (priebežný stav)\n\nMetódy a technológie:\n\n- Vyskúšaná hierarchická sumarizácia - text je rozdelený na časti a potom sa vykoná sumár zo sumárov. Funguje to \"dobre\". (zaznamenaný výsledok)\n\nZdroje: https://zp.kemt.fei.tuke.sk/students/2023/simona_bobrovcanova",
+      "source_urls": [
+        "https://zp.kemt.fei.tuke.sk/students/2023/simona_bobrovcanova"
+      ]
+    },
+    {
+      "id": "all_tasks",
+      "engine": "rag",
+      "query": "Aké úlohy dostal Denis Le Thanh podľa záznamu zo stretnutia 3.10.2025? Odlíš budúce úlohy od toho, čo už bolo urobené.",
+      "passed": true,
+      "status": "ok",
+      "answer": "**Denis Le Thanh — Bakalárska práca 2026**\n\nPlánované úlohy:\n\n- Naštudovať a vyskúšať PEFT-QLORA. (plán)\n\n- Vyskúšajte si skript pre dotrénovanie Slovak Mistral.(poslal som cez Teams) (plán)\n\n- Oboznámte sa s Huggingface TRL. (plán)\n\n- Oboznámte sa s knižnicou \"unsloth\". (plán)\n\n- Oboznámte sa s [odkaz neoverený] (plán)\n\n- Pracujte na teoretickej časti: opíšte základné pojmy, metódy a dátové množiny. Používajte google scholar a bibliografické odkazy. (plán)\n\n- Najprv pracujte s domácou GPU, ak nebude stačiť pracujte s Google Coolab, ak nebude stačiť požiadajte konzultanta. (plán)\n\nZdroje: https://zp.kemt.fei.tuke.sk/students/2023/denis_le_thanh\n\nRepozitárový odkaz zo zdroja sa nepodarilo verejne overiť, preto ho neuvádzam. Nedostupnosť ani HTTP 404 samy nerozlišujú zmazaný a súkromný repozitár.",
+      "source_urls": [
+        "https://zp.kemt.fei.tuke.sk/students/2023/denis_le_thanh"
+      ]
+    },
+    {
+      "id": "all_tasks",
+      "engine": "graphrag",
+      "query": "Aké úlohy dostal Denis Le Thanh podľa záznamu zo stretnutia 3.10.2025? Odlíš budúce úlohy od toho, čo už bolo urobené.",
+      "passed": true,
+      "status": "ok",
+      "answer": "**Denis Le Thanh — Bakalárska práca 2026**\n\nPlánované úlohy:\n\n- Naštudovať a vyskúšať PEFT-QLORA. (plán)\n\n- Vyskúšajte si skript pre dotrénovanie Slovak Mistral.(poslal som cez Teams) (plán)\n\n- Oboznámte sa s Huggingface TRL. (plán)\n\n- Oboznámte sa s knižnicou \"unsloth\". (plán)\n\n- Oboznámte sa s [odkaz neoverený] (plán)\n\n- Pracujte na teoretickej časti: opíšte základné pojmy, metódy a dátové množiny. Používajte google scholar a bibliografické odkazy. (plán)\n\n- Najprv pracujte s domácou GPU, ak nebude stačiť pracujte s Google Coolab, ak nebude stačiť požiadajte konzultanta. (plán)\n\nZdroje: https://zp.kemt.fei.tuke.sk/students/2023/denis_le_thanh\n\nRepozitárový odkaz zo zdroja sa nepodarilo verejne overiť, preto ho neuvádzam. Nedostupnosť ani HTTP 404 samy nerozlišujú zmazaný a súkromný repozitár.",
+      "source_urls": [
+        "https://zp.kemt.fei.tuke.sk/students/2023/denis_le_thanh"
+      ]
+    },
+    {
+      "id": "comparison",
+      "engine": "rag",
+      "query": "Porovnaj zadanie osoby Adrián Ondra pri \"Bakalársky projekt 2027\" so zadaním osoby Samuel Štucka pri \"Bakalársky projekt 2027\". Vysvetli rozdiel v cieľoch a uveď oba zdroje.",
+      "passed": true,
+      "status": "ok",
+      "answer": "| Oblasť | Adrián Ondra — Bakalársky projekt 2027 | Samuel Štucka — Bakalársky projekt 2027 |\n| --- | --- | --- |\n| Plánované úlohy | Adrián Ondra má vytvoriť vizualizáciu myšlienkovej mapy z automaticky extrahovaných entít. [1] Jeho cieľom je vyvinúť nástroj využiteľný pri vzdelávaní. [1] Plánuje vyhodnotiť kvalitu vytvoreného grafu a jeho pravdivosť vzhľadom na pôvodný text. [1] | Samuel Štucka má pripraviť čistý korpus novinových článkov s metainformáciami. [2] Korpus má uchovávať HTML aj obrázky bez reklám a rozhrania, pričom musí extrahovať údaje ako autor, zdroj, dátum vydania, sekcia, kľúčové slová, nadpis a perex. [2] Má implementovať pipeline na extrakciu a normalizáciu textu z rôznych zdrojov. [2] |\n\nAdrián Ondra — Bakalársky projekt 2027: zdroj tieto body označuje ako možné/navrhované ciele, nie ako potvrdené splnené výsledky.\n\nSamuel Štucka — Bakalársky projekt 2027: zdroj tieto body označuje ako možné/navrhované ciele, nie ako potvrdené splnené výsledky.\n\nZdroje: [1](https://zp.kemt.fei.tuke.sk/students/2024/adrian_ondra), [2](https://zp.kemt.fei.tuke.sk/students/2024/samuel_stucka)\n\n[1]: https://zp.kemt.fei.tuke.sk/students/2024/adrian_ondra\n[2]: https://zp.kemt.fei.tuke.sk/students/2024/samuel_stucka",
+      "source_urls": [
+        "https://zp.kemt.fei.tuke.sk/students/2024/adrian_ondra",
+        "https://zp.kemt.fei.tuke.sk/students/2024/samuel_stucka"
+      ]
+    },
+    {
+      "id": "comparison",
+      "engine": "graphrag",
+      "query": "Porovnaj zadanie osoby Adrián Ondra pri \"Bakalársky projekt 2027\" so zadaním osoby Samuel Štucka pri \"Bakalársky projekt 2027\". Vysvetli rozdiel v cieľoch a uveď oba zdroje.",
+      "passed": true,
+      "status": "ok",
+      "answer": "| Oblasť | Adrián Ondra — Bakalársky projekt 2027 | Samuel Štucka — Bakalársky projekt 2027 |\n| --- | --- | --- |\n| Plánované úlohy | Cieľom Adriána Ondru je vytvoriť vizualizáciu myšlienkovej mapy z automaticky extrahovaných entít. [1] Adrián Ondra plánuje vyvinúť nástroj určený na využitie pri vzdelávaní. [1] Plán Adriána Ondru zahŕňa vyhodnotenie kvality vytvoreného grafu a jeho pravdivosti vzhľadom na pôvodný text. [1] | Cieľom Samuela Štucku je pripraviť čistý korpus novinových článkov s metainformáciami. [2] Samuel Štucka plánuje, aby korpus uchovával HTML a obrázky z článkov bez reklám a rozhrania, pričom má extrahovať čo najviac metainformácií ako autor, zdroj, dátum vydania, sekcia, kľúčové slová, nadpis a perex, a zachovať odkazy na obrázky s ich titulkami a popisom. [2] Samuel Štucka má implementovať pipeline na extrakciu a normalizáciu textu z rôznych zdrojov. [2] |\n\nAdrián Ondra — Bakalársky projekt 2027: zdroj tieto body označuje ako možné/navrhované ciele, nie ako potvrdené splnené výsledky.\n\nSamuel Štucka — Bakalársky projekt 2027: zdroj tieto body označuje ako možné/navrhované ciele, nie ako potvrdené splnené výsledky.\n\nZdroje: [1](https://zp.kemt.fei.tuke.sk/students/2024/adrian_ondra), [2](https://zp.kemt.fei.tuke.sk/students/2024/samuel_stucka)\n\n[1]: https://zp.kemt.fei.tuke.sk/students/2024/adrian_ondra\n[2]: https://zp.kemt.fei.tuke.sk/students/2024/samuel_stucka",
+      "source_urls": [
+        "https://zp.kemt.fei.tuke.sk/students/2024/adrian_ondra",
+        "https://zp.kemt.fei.tuke.sk/students/2024/samuel_stucka"
+      ]
+    },
+    {
+      "id": "similar",
+      "engine": "rag",
+      "query": "Kto sa zaoberal obsahovo podobnou témou ako Ján Pták? Vysvetli konkrétne prepojenie a dolož obe osoby.",
+      "passed": true,
+      "status": "ok",
+      "answer": "**Oleh Poiasnik**\n\n- Diplomová práca 2027; podklad: Opíšte spôsoby vyhľadávania pomcou znalostných grafov.\n\n**Valerii Kutsenko**\n\n- Diplomová práca 2027; plán; záznam 27.4. (rok neuvedený): Získajte vzorový znalostný graf ktorý sa bude dať použiť pri výskume.\n\n- Diplomová práca 2027; plán; záznam 27.4. (rok neuvedený): Navrhnúť spôsob vyhľadávania s pomocou existujúceho znalostného grafu.\n\nPodklady k porovnávanej osobe:\n\n- Ján Pták; Diplomový projekt 2026: Agent pre manažment záverečných prác\n\n- Ján Pták; Diplomová práca 2027: Agent pre manažment záverečných prác\n\n- Ján Pták; Diplomová práca 2027: - Funguje vektorové a textové vyhľadávanie. Ale vyzerá to tak, že nevie synonymy.\n\n- Ján Pták; Diplomová práca 2027: - Pokračujte v GraphRAG -  Naprogramujte načítanie existujúcich Markdown textov do Neo4j — vytvorte uzly pre každú záverečnú prácu, autora, tému a kľúčové slová. Vytvorte grafové relácie medzi uzlami (napr. rovnaký autor → rovnaká téma, rovnaký študijný program)\n\n- Ján Pták; Diplomová práca 2027: - Agenta rozdeľte na samostatné komponenty (nástroje - tools alebo skills). Každý nástroj dajte do osobitného adresára na GIte. Každý nástroj by sa mal z gitu dať nainštalovať do OpenWebUI.\n\n- Ján Pták; Diplomový projekt 2026: - Odovzdané nejaké zdrojové kódy na https://git.kemt.fei.tuke.sk/jp170na/dp-zp-agent - načítanie z markdown, indexovanie do SQLite a FastAPI.\n\nIde o obsahovú podobnosť podloženú uvedenými úryvkami, nie dôkaz spolupráce ani rovnakej fázy dokončenia.\n\nZobrazené sú príklady z obmedzeného výberu textových dôkazov; zoznam nemusí byť úplný. Výber môžeš zúžiť témou alebo obdobím.\n\nZdroje: https://zp.kemt.fei.tuke.sk/students/2022/oleh_poiasnik, https://zp.kemt.fei.tuke.sk/students/2022/valerii_kutsenko, https://zp.kemt.fei.tuke.sk/students/2022/jan_ptak",
+      "source_urls": [
+        "https://zp.kemt.fei.tuke.sk/students/2022/oleh_poiasnik",
+        "https://zp.kemt.fei.tuke.sk/students/2022/valerii_kutsenko",
+        "https://zp.kemt.fei.tuke.sk/students/2022/jan_ptak"
+      ]
+    },
+    {
+      "id": "shared_tags",
+      "engine": "rag",
+      "query": "Ktoré tagy spájajú osoby Yehor Piliavin a Kristián Sokolský? Uveď iba spoločný prienik a zdroje.",
+      "passed": true,
+      "status": "ok",
+      "answer": "Spoločné kľúčové slová: **alignment**, **llm**.\nDoložené pri: **Yehor Piliavin**, **Kristián Sokolský**.\n\nZdroje: https://zp.kemt.fei.tuke.sk/students/2024/yehor_piliavin, https://zp.kemt.fei.tuke.sk/students/2024/kristian_sokolsky",
+      "source_urls": [
+        "https://zp.kemt.fei.tuke.sk/students/2024/yehor_piliavin",
+        "https://zp.kemt.fei.tuke.sk/students/2024/kristian_sokolsky"
+      ]
+    },
+    {
+      "id": "shared_tags",
+      "engine": "graphrag",
+      "query": "Ktoré tagy spájajú osoby Yehor Piliavin a Kristián Sokolský? Uveď iba spoločný prienik a zdroje.",
+      "passed": true,
+      "status": "ok",
+      "answer": "Spoločné kľúčové slová: **LLM**, **alignment**.\nDoložené pri: **Kristián Sokolský**, **Yehor Piliavin**.\n\nZdroje: https://zp.kemt.fei.tuke.sk/students/2024/kristian_sokolsky, https://zp.kemt.fei.tuke.sk/students/2024/yehor_piliavin",
+      "source_urls": [
+        "https://zp.kemt.fei.tuke.sk/students/2024/kristian_sokolsky",
+        "https://zp.kemt.fei.tuke.sk/students/2024/yehor_piliavin"
+      ]
+    },
+    {
+      "id": "shared_years",
+      "engine": "rag",
+      "query": "V ktorých rokoch majú Ján Holp a Ondrej Megela obaja evidovanú prácu alebo projekt? Najprv uveď spoločné roky, potom príslušné práce s typom a názvom.",
+      "passed": true,
+      "status": "ok",
+      "answer": "Spoločný evidovaný rok je **2020**.\n\nJán Holp má v dostupných podkladoch tieto práce a projekty:\n\n1. **Diplomový projekt 2  2020** — názov v zdroji nie je uvedený.\n2. **Diplomový projekt 2020** — názov v zdroji nie je uvedený.\n\nOndrej Megela má v dostupných podkladoch tieto práce a projekty:\n\n3. **Bakalárska práca  2020** — **Neurónové jazykové modelovanie typu BERT**.\n4. **Bakalársky projekt 2020** — názov v zdroji nie je uvedený.\n\nZdroje: https://zp.kemt.fei.tuke.sk/students/2016/jan_holp, https://zp.kemt.fei.tuke.sk/students/2018/ondrej_megela",
+      "source_urls": [
+        "https://zp.kemt.fei.tuke.sk/students/2016/jan_holp",
+        "https://zp.kemt.fei.tuke.sk/students/2018/ondrej_megela"
+      ]
+    },
+    {
+      "id": "shared_years",
+      "engine": "graphrag",
+      "query": "V ktorých rokoch majú Ján Holp a Ondrej Megela obaja evidovanú prácu alebo projekt? Najprv uveď spoločné roky, potom príslušné práce s typom a názvom.",
+      "passed": true,
+      "status": "ok",
+      "answer": "Spoločný evidovaný rok je **2020**.\n\nOndrej Megela má v dostupných podkladoch tieto práce a projekty:\n\n1. **Bakalárska práca  2020** — **Neurónové jazykové modelovanie typu BERT**.\n2. **Bakalársky projekt 2020** — názov v zdroji nie je uvedený.\n\nJán Holp má v dostupných podkladoch tieto práce a projekty:\n\n3. **Diplomový projekt 2  2020** — názov v zdroji nie je uvedený.\n4. **Diplomový projekt 2020** — názov v zdroji nie je uvedený.\n\nZdroje: https://zp.kemt.fei.tuke.sk/students/2018/ondrej_megela, https://zp.kemt.fei.tuke.sk/students/2016/jan_holp",
+      "source_urls": [
+        "https://zp.kemt.fei.tuke.sk/students/2018/ondrej_megela",
+        "https://zp.kemt.fei.tuke.sk/students/2016/jan_holp"
+      ]
+    },
+    {
+      "id": "unknown",
+      "engine": "rag",
+      "query": "Aké práce má Xaver Neexistujúci?",
+      "passed": true,
+      "status": "no_answer",
+      "answer": "Meno „Xaver Neexistujúci“ sa nepodarilo spoľahlivo priradiť k osobe v dostupných podkladoch. Uveď presné celé meno.",
+      "source_urls": []
+    },
+    {
+      "id": "unknown",
+      "engine": "graphrag",
+      "query": "Aké práce má Xaver Neexistujúci?",
+      "passed": true,
+      "status": "no_answer",
+      "answer": "Meno „Xaver Neexistujúci“ sa nepodarilo spoľahlivo priradiť k osobe v dostupných podkladoch. Uveď presné celé meno.",
+      "source_urls": []
+    },
+    {
+      "id": "partial_name",
+      "engine": "rag",
+      "query": "Čo sa podarilo Samuelovi pri jeho práci?",
+      "passed": true,
+      "status": "ambiguous",
+      "answer": "Meno „Samuelovi“ nie je jednoznačné. Ktorú osobu máš na mysli? Možnosti: Samuel Širotník [pages/students/2018/samuel_sirotnik/README.md]; Samuel Horáni [pages/students/2019/samuel_horani/README.md]; Samuel Vasiľ [pages/students/2022/samuel_vasil/README.md]; Samuel Džurina [pages/students/2023/samuel_dzurina/README.md]; Samuel Štucka [pages/students/2024/samuel_stucka/README.md]. Uveď celé meno.",
+      "source_urls": []
+    },
+    {
+      "id": "partial_name",
+      "engine": "graphrag",
+      "query": "Čo sa podarilo Samuelovi pri jeho práci?",
+      "passed": true,
+      "status": "ambiguous",
+      "answer": "Meno „Samuelovi“ nie je jednoznačné. Ktorú osobu máš na mysli? Možnosti: Samuel Širotník [pages/students/2018/samuel_sirotnik/README.md]; Samuel Horáni [pages/students/2019/samuel_horani/README.md]; Samuel Vasiľ [pages/students/2022/samuel_vasil/README.md]; Samuel Džurina [pages/students/2023/samuel_dzurina/README.md]; Samuel Štucka [pages/students/2024/samuel_stucka/README.md]. Uveď celé meno.",
+      "source_urls": []
+    },
+    {
+      "id": "wrong_year",
+      "engine": "rag",
+      "query": "Akú prácu mal Ján Holp v roku 1999?",
+      "passed": true,
+      "status": "no_answer",
+      "answer": "Ján Holp: požadované údaje sa v dostupných podkladoch nenašli.\n\nZdroj: https://zp.kemt.fei.tuke.sk/students/2016/jan_holp",
+      "source_urls": [
+        "https://zp.kemt.fei.tuke.sk/students/2016/jan_holp"
+      ]
+    },
+    {
+      "id": "wrong_year",
+      "engine": "graphrag",
+      "query": "Akú prácu mal Ján Holp v roku 1999?",
+      "passed": true,
+      "status": "no_answer",
+      "answer": "Ján Holp: v dostupných podkladoch sa nenašiel záznam práce alebo projektu z roku **1999**.",
+      "source_urls": []
+    },
+    {
+      "id": "similar",
+      "engine": "graphrag",
+      "query": "Kto sa zaoberal obsahovo podobnou témou ako Ján Pták? Vysvetli konkrétne prepojenie a dolož obe osoby.",
+      "passed": true,
+      "status": "ok",
+      "answer": "Ján Pták a Matej Ščišľak: doložená spoločná téma — GraphRAG, Neo4j. [1] [3]\n\nJán Pták a Oleh Poiasnik: doložená spoločná téma — znalostný graf. [1] [2]\n\nJán Pták a Valerii Kutsenko: doložená spoločná téma — znalostný graf. [1] [4]\n\nJán Pták a Maksym Zatirka: doložená spoločná téma — znalostný graf. [1] [6]\n\nJán Pták a Ján Malinovský: doložená spoločná téma — znalostný graf. [1] [5]\n\n**Ján Pták — Diplomová práca 2027**\n\nZaznamenaný priebeh (dátum záznamu: 2026-09-30):\n- Ján Pták začal pracovať na systéme GraphRAG a grafovej databáze Neo4J. [1]\n\nPlánované úlohy (dátum záznamu: 2026-11-05):\n- Ján Pták plánuje oboznámiť sa s pojmom znalostný graf a napísať o ňom vlastnými slovami. [1]\n\n**Oleh Poiasnik — Diplomový projekt 2026**\n\nZaznamenané výsledky (dátum záznamu: 14.5. (rok neuvedený)):\n- Oleh Poiasnik vytvoril pomocou LightRAG znalostný graf pre 500 liekov. [2]\n\n**Matej Ščišľak — Diplomová práca 2026**\n\nZaznamenané výsledky (dátum záznamu: 2025-03-30):\n- Matej Ščišľak vytvoril systém GraphRAG zložený z FAISS a Neo4J, kde Neo4J beží lokálne a komunikuje so serverom. [3]\n\n**Valerii Kutsenko — Diplomová práca 2027**\n\nZaznamenaný priebeh (dátum záznamu: 15.5. (rok neuvedený)):\n- Valerii Kutsenko zaindexoval anglické dáta z knihy Alice in Wonderland pomocou LightRAG a zostavil z nich znalostný graf. [4]\n\n**Ján Malinovský — Diplomový projekt 2026**\n\nZadanie a ciele:\n- Ján Malinovský má za úlohu zistiť, čo je to znalostný graf. [5]\n\n**Maksym Zatirka — Bakalárska práca 2026**\n\nZadanie a ciele:\n- Maksym Zatirka má za úlohu zostaviť znalostný graf pre slovenské právo a vložiť ho do vybranej grafovej databázy. [6]\n\nPrepojenie dokladá uvedenú tému alebo činnosť; nepreukazuje spoluprácu ani rovnaký stupeň dokončenia.\n\nPrechod grafom dosiahol limit výberu. Zúž otázku na prácu, tému alebo obdobie.\n\nZdroje: [1](https://zp.kemt.fei.tuke.sk/students/2022/jan_ptak), [2](https://zp.kemt.fei.tuke.sk/students/2022/oleh_poiasnik), [3](https://zp.kemt.fei.tuke.sk/students/2021/matej_scislak), [4](https://zp.kemt.fei.tuke.sk/students/2022/valerii_kutsenko), [5](https://zp.kemt.fei.tuke.sk/students/2022/jan_malinovsky), [6](https://zp.kemt.fei.tuke.sk/students/2023/maksym_zatirka)\n\n[1]: https://zp.kemt.fei.tuke.sk/students/2022/jan_ptak\n[2]: https://zp.kemt.fei.tuke.sk/students/2022/oleh_poiasnik\n[3]: https://zp.kemt.fei.tuke.sk/students/2021/matej_scislak\n[4]: https://zp.kemt.fei.tuke.sk/students/2022/valerii_kutsenko\n[5]: https://zp.kemt.fei.tuke.sk/students/2022/jan_malinovsky\n[6]: https://zp.kemt.fei.tuke.sk/students/2023/maksym_zatirka",
+      "source_urls": [
+        "https://zp.kemt.fei.tuke.sk/students/2022/jan_ptak",
+        "https://zp.kemt.fei.tuke.sk/students/2022/oleh_poiasnik",
+        "https://zp.kemt.fei.tuke.sk/students/2021/matej_scislak",
+        "https://zp.kemt.fei.tuke.sk/students/2022/valerii_kutsenko",
+        "https://zp.kemt.fei.tuke.sk/students/2022/jan_malinovsky",
+        "https://zp.kemt.fei.tuke.sk/students/2023/maksym_zatirka"
+      ]
+    },
+    {
+      "id": "supervisor",
+      "engine": "rag",
+      "query": "Kto vedie bakalársku prácu Ronalda Zalacka a kto je pri profile uvedený ako autor dokumentu? Tieto roly odlíš.",
+      "passed": true,
+      "status": "ok",
+      "answer": "**Ronald Zalacko — Bakalárska práca 2026**\n\nNázov / téma zo zdroja: Skórovací online systém pre robotické súťaže\n\nVedúci v hlavičke profilu: Matúš Pleva.\n\nAutor dokumentu nie je automaticky vedúci ani konzultant práce.\n\nAutor dokumentu profilu Ronald Zalacko: Daniel Hladek.\n\nZdroje: https://zp.kemt.fei.tuke.sk/students/2023/ronald_zalacko",
+      "source_urls": [
+        "https://zp.kemt.fei.tuke.sk/students/2023/ronald_zalacko"
+      ]
+    },
+    {
+      "id": "supervisor",
+      "engine": "graphrag",
+      "query": "Kto vedie bakalársku prácu Ronalda Zalacka a kto je pri profile uvedený ako autor dokumentu? Tieto roly odlíš.",
+      "passed": true,
+      "status": "ok",
+      "answer": "**Ronald Zalacko — Bakalárska práca 2026**\n\nNázov / téma zo zdroja: Skórovací online systém pre robotické súťaže\n\nVedúci v hlavičke profilu: Matúš Pleva.\n\nAutor dokumentu nie je automaticky vedúci ani konzultant práce.\n\nAutor dokumentu profilu Ronald Zalacko: Daniel Hladek.\n\nZdroje: https://zp.kemt.fei.tuke.sk/students/2023/ronald_zalacko",
+      "source_urls": [
+        "https://zp.kemt.fei.tuke.sk/students/2023/ronald_zalacko"
+      ]
+    },
+    {
+      "id": "typo",
+      "engine": "rag",
+      "query": "Zisti názov práce alebo projektu osoby Sebastián Kiš a evidovaný rok. Pri oprave mena povedz, koho si rozpoznal.",
+      "passed": true,
+      "status": "ok",
+      "answer": "Rozpoznané meno: **Sebastián Kišš** (v otázke „Sebastián Kiš“).\n\nSebastián Kišš má v dostupných podkladoch tieto práce a projekty:\n\n1. **Bakalársky projekt 2027** — **Dáta pre vyhodnotenie multimodálnych jazykových modelov**.\n\nZdroj: https://zp.kemt.fei.tuke.sk/students/2024/sebastian_kiss",
+      "source_urls": [
+        "https://zp.kemt.fei.tuke.sk/students/2024/sebastian_kiss"
+      ]
+    },
+    {
+      "id": "typo",
+      "engine": "graphrag",
+      "query": "Zisti názov práce alebo projektu osoby Sebastián Kiš a evidovaný rok. Pri oprave mena povedz, koho si rozpoznal.",
+      "passed": true,
+      "status": "ok",
+      "answer": "Rozpoznané meno: **Sebastián Kišš** (v otázke „Sebastián Kiš“).\n\nSebastián Kišš má v dostupných podkladoch tieto práce a projekty:\n\n1. **Bakalársky projekt 2027** — **Dáta pre vyhodnotenie multimodálnych jazykových modelov**.\n\nZdroj: https://zp.kemt.fei.tuke.sk/students/2024/sebastian_kiss",
+      "source_urls": [
+        "https://zp.kemt.fei.tuke.sk/students/2024/sebastian_kiss"
+      ]
+    },
+    {
+      "id": "both_projects",
+      "engine": "rag",
+      "query": "Aké metódy používal Ján Holp pri oboch diplomových projektoch v roku 2020? Uveď obe práce samostatne.",
+      "passed": true,
+      "status": "ok",
+      "answer": "**Ján Holp — Diplomový projekt 2 2020**\n\nZaznamenané výsledky (dátum záznamu: 2020-11-13):\n- Vyriešil technické problémy s Cassandra a umožnil indexovanie z Cassandra do Elasticsearch. [1]\n\n**Ján Holp — Diplomový projekt 2020**\n\nZaznamenané výsledky (dátum záznamu: 2020-06-23):\n- Zabezpečil funkčnosť databázy Cassandra. [1]\n- Zabezpečil funkčnosť indexovania zo súboru. [1]\n\nZdroje: [1](https://zp.kemt.fei.tuke.sk/students/2016/jan_holp)\n\n[1]: https://zp.kemt.fei.tuke.sk/students/2016/jan_holp",
+      "source_urls": [
+        "https://zp.kemt.fei.tuke.sk/students/2016/jan_holp"
+      ]
+    },
+    {
+      "id": "record_count",
+      "engine": "rag",
+      "query": "Koľko rôznych prác a projektov má evidovaných Rostyslav Rodzhuk? Spočítaj akademické záznamy.",
+      "passed": true,
+      "status": "ok",
+      "answer": "Počet evidovaných prác a projektov: **2**.\n\nZdroje: https://zp.kemt.fei.tuke.sk/students/2023/rostyslav_rodzhuk",
+      "source_urls": [
+        "https://zp.kemt.fei.tuke.sk/students/2023/rostyslav_rodzhuk"
+      ]
+    },
+    {
+      "id": "both_projects",
+      "engine": "graphrag",
+      "query": "Aké metódy používal Ján Holp pri oboch diplomových projektoch v roku 2020? Uveď obe práce samostatne.",
+      "passed": true,
+      "status": "ok",
+      "answer": "**Ján Holp — Diplomový projekt 2 2020**\n\nMetódy a technológie:\n\n- Vyriešené technické problémy s cassandrou. Vieme indexovať z Cassandra do ES. (zaznamenaný výsledok)\n\n**Ján Holp — Diplomový projekt 2020**\n\nMetódy a technológie:\n\n- funguje databáza Cassandra (zaznamenaný výsledok)\n\n- funguje indexovanie zo súboru. (zaznamenaný výsledok)\n\nZdroje: https://zp.kemt.fei.tuke.sk/students/2016/jan_holp",
+      "source_urls": [
+        "https://zp.kemt.fei.tuke.sk/students/2016/jan_holp"
+      ]
+    },
+    {
+      "id": "time_roles",
+      "engine": "rag",
+      "query": "Pri Yehorovi Piliavinovi odlíš rok začiatku štúdia, rok bakalárskeho projektu a najnovší úplný dátum stretnutia. Znamená rok projektu dátum vykonania činnosti?",
+      "passed": true,
+      "status": "ok",
+      "answer": "**Yehor Piliavin — Bakalársky projekt 2027**\nPosledný záznam s úplným dátumom: 2026-10-15.\n\nExplicitný rok začiatku štúdia — Yehor Piliavin: 2024.\nRok práce, dátum záznamu a dátum vykonania činnosti sú odlišné údaje.\n\nZdroje: https://zp.kemt.fei.tuke.sk/students/2024/yehor_piliavin",
+      "source_urls": [
+        "https://zp.kemt.fei.tuke.sk/students/2024/yehor_piliavin"
+      ]
+    },
+    {
+      "id": "record_count",
+      "engine": "graphrag",
+      "query": "Koľko rôznych prác a projektov má evidovaných Rostyslav Rodzhuk? Spočítaj akademické záznamy.",
+      "passed": true,
+      "status": "ok",
+      "answer": "Rostyslav Rodzhuk má prácu (rok práce: **2026**).\nRostyslav Rodzhuk má prácu (rok práce: **2025**).\nPočet evidovaných prác a projektov: 2.\n\nZdroj: https://zp.kemt.fei.tuke.sk/students/2023/rostyslav_rodzhuk",
+      "source_urls": [
+        "https://zp.kemt.fei.tuke.sk/students/2023/rostyslav_rodzhuk"
+      ]
+    },
+    {
+      "id": "time_roles",
+      "engine": "graphrag",
+      "query": "Pri Yehorovi Piliavinovi odlíš rok začiatku štúdia, rok bakalárskeho projektu a najnovší úplný dátum stretnutia. Znamená rok projektu dátum vykonania činnosti?",
+      "passed": true,
+      "status": "ok",
+      "answer": "**Yehor Piliavin — Bakalársky projekt 2027**\nPosledný záznam s úplným dátumom: 2026-10-15.\n\nExplicitný rok začiatku štúdia — Yehor Piliavin: 2024.\nRok práce, dátum záznamu a dátum vykonania činnosti sú odlišné údaje.\n\nZdroje: https://zp.kemt.fei.tuke.sk/students/2024/yehor_piliavin",
+      "source_urls": [
+        "https://zp.kemt.fei.tuke.sk/students/2024/yehor_piliavin"
+      ]
+    },
+    {
+      "id": "annotation",
+      "engine": "graphrag",
+      "query": "Kto sa venoval anotácii pomenovaných entít?",
+      "passed": true,
+      "status": "ok",
+      "answer": "**Than Trung Thanh**\n\n- Bakalársky projekt 2020; podklad: Pomocou nástroja Prodigy anotujte dostatočné množstvo textu pre výskyt pomenovaných entít.\n\nZobrazené sú príklady z obmedzeného výberu textových dôkazov; zoznam nemusí byť úplný. Výber môžeš zúžiť témou alebo obdobím.\n\nZdroje: https://zp.kemt.fei.tuke.sk/students/2017/than_trung_thanh\n\nPrechod grafom dosiahol limit výberu. Zúž otázku na prácu, tému alebo obdobie.",
+      "source_urls": [
+        "https://zp.kemt.fei.tuke.sk/students/2017/than_trung_thanh"
+      ]
+    },
+    {
+      "id": "annotation",
+      "engine": "rag",
+      "query": "Kto sa venoval anotácii pomenovaných entít?",
+      "passed": true,
+      "status": "ok",
+      "answer": "**Jakub Maruniak**\n\n- Diplomový projekt 2; zaznamenaný výsledok; záznam 2020-09-25: Oboznámenie sa procesom anotácie NER\n\n**Than Trung Thanh**\n\n- Bakalársky projekt 2020; podklad: Pomocou nástroja Prodigy anotujte dostatočné množstvo textu pre výskyt pomenovaných entít.\n\nZobrazené sú príklady z obmedzeného výberu textových dôkazov; zoznam nemusí byť úplný. Výber môžeš zúžiť témou alebo obdobím.\n\nZdroje: https://zp.kemt.fei.tuke.sk/students/2016/jakub_maruniak, https://zp.kemt.fei.tuke.sk/students/2017/than_trung_thanh",
+      "source_urls": [
+        "https://zp.kemt.fei.tuke.sk/students/2016/jakub_maruniak",
+        "https://zp.kemt.fei.tuke.sk/students/2017/than_trung_thanh"
+      ]
+    },
+    {
+      "id": "recognition",
+      "engine": "rag",
+      "query": "Kto sa venoval rozpoznávaniu pomenovaných entít?",
+      "passed": true,
+      "status": "ok",
+      "answer": "**Adrián Ondra**\n\n- Bakalársky projekt 2027; zaznamenaný výsledok; záznam 2026-11-05: Vyskúšaný model SlovakBERT-NER (Agarský) na rozpoznávanie pomenovaných entít.\n\n**Martin Wencel**\n\n- Bakalársky projekt 2020; zaznamenaný výsledok: Aplikácia vie robiť pomocou spacy  tokenizácia, morfologická analýza, parsing a named entity recognition pre slovenčinu a angličtinu.\n\n**Samuel Horáni**\n\n- Bakalársky projekt 2021; úloha označená ako splnená; záznam 2021-12-17: [x] Pridajte schopnosť rozlišovať pomenované entity typu LOC. Spacy model by to mal vedieť.\n\nZobrazené sú príklady z obmedzeného výberu textových dôkazov; zoznam nemusí byť úplný. Výber môžeš zúžiť témou alebo obdobím.\n\nZdroje: https://zp.kemt.fei.tuke.sk/students/2024/adrian_ondra, https://zp.kemt.fei.tuke.sk/students/2018/martin_wencel, https://zp.kemt.fei.tuke.sk/students/2019/samuel_horani",
+      "source_urls": [
+        "https://zp.kemt.fei.tuke.sk/students/2024/adrian_ondra",
+        "https://zp.kemt.fei.tuke.sk/students/2018/martin_wencel",
+        "https://zp.kemt.fei.tuke.sk/students/2019/samuel_horani"
+      ]
+    },
+    {
+      "id": "recognition",
+      "engine": "graphrag",
+      "query": "Kto sa venoval rozpoznávaniu pomenovaných entít?",
+      "passed": true,
+      "status": "ok",
+      "answer": "**Adrián Ondra**\n\n- Bakalársky projekt 2027; zaznamenaný výsledok; záznam 2026-11-05: Vyskúšaný model SlovakBERT-NER (Agarský) na rozpoznávanie pomenovaných entít.\n\n**Martin Wencel**\n\n- Bakalársky projekt 2020; zaznamenaný výsledok: Aplikácia vie robiť pomocou spacy  tokenizácia, morfologická analýza, parsing a named entity recognition pre slovenčinu a angličtinu.\n\nZobrazené sú príklady z obmedzeného výberu textových dôkazov; zoznam nemusí byť úplný. Výber môžeš zúžiť témou alebo obdobím.\n\nZdroje: https://zp.kemt.fei.tuke.sk/students/2024/adrian_ondra, https://zp.kemt.fei.tuke.sk/students/2018/martin_wencel\n\nPrechod grafom dosiahol limit výberu. Zúž otázku na prácu, tému alebo obdobie.",
+      "source_urls": [
+        "https://zp.kemt.fei.tuke.sk/students/2024/adrian_ondra",
+        "https://zp.kemt.fei.tuke.sk/students/2018/martin_wencel"
+      ]
+    },
+    {
+      "id": "chatbot",
+      "engine": "graphrag",
+      "query": "Kto vytvoril chatbota?",
+      "passed": true,
+      "status": "ok",
+      "answer": "**Samuel Džurina**\n\n- Bakalárska práca 2026; zaznamenaný výsledok; záznam 2025-11-27: Vytvorený modelový OpenAI chatbot. Agent používa openai api, gpt3.5 turbo a vie odpovedať na otázky. Asi má problém s vyhľadávaním na internete.\n\nZobrazené sú príklady z obmedzeného výberu textových dôkazov; zoznam nemusí byť úplný. Výber môžeš zúžiť témou alebo obdobím.\n\nZdroje: https://zp.kemt.fei.tuke.sk/students/2023/samuel_dzurina\n\nPrechod grafom dosiahol limit výberu. Zúž otázku na prácu, tému alebo obdobie.",
+      "source_urls": [
+        "https://zp.kemt.fei.tuke.sk/students/2023/samuel_dzurina"
+      ]
+    },
+    {
+      "id": "chatbot",
+      "engine": "rag",
+      "query": "Kto vytvoril chatbota?",
+      "passed": true,
+      "status": "ok",
+      "answer": "**Samuel Džurina**\n\n- Bakalárska práca 2026; zaznamenaný výsledok; záznam 2025-11-27: Vytvorený modelový OpenAI chatbot. Agent používa openai api, gpt3.5 turbo a vie odpovedať na otázky. Asi má problém s vyhľadávaním na internete.\n\n- Bakalárska práca 2026; priebežný stav; záznam 2025-12-17: Je urobená prvá verzia chatbota - vie hľadať PDF a pristupovať na internet cez DUCKDUCK. Rozhranie je cez streamlit.\n\nZobrazené sú príklady z obmedzeného výberu textových dôkazov; zoznam nemusí byť úplný. Výber môžeš zúžiť témou alebo obdobím.\n\nZdroje: https://zp.kemt.fei.tuke.sk/students/2023/samuel_dzurina",
+      "source_urls": [
+        "https://zp.kemt.fei.tuke.sk/students/2023/samuel_dzurina"
+      ]
+    }
+  ],
+  "remaining_limits": [
+    "Actual new Claude-chat presentation and follow-up need the user to check; protocol success alone does not verify that UI.",
+    "No new claim of 94% or general accuracy. The earlier TEST is consumed and its first-run conservative score remains unchanged; its model judge had documented reliability problems.",
+    "Selected global discovery/similarity answers are examples, not complete lists. Graph traversal and evidence budgets are enforced with concrete notices.",
+    "Conservative synthesis can return literal source wording when generated prose is not fully supported.",
+    "Only two arbitrary LLM-extracted concept-to-concept relations are activated in this checkpoint; the graph also uses many source-backed structural work/person/evidence/concept paths.",
+    "72 profile sources were checked automatically against Markdown; no human-reviewed source audit is claimed.",
+    "Quick Tunnel URLs are temporary; old DNS disappeared and the tunnel was restored. The laptop, Docker and tunnel must stay running."
+  ],
+  "frozen_test_files_unchanged": [
+    "test.json",
+    "test_manifest.json",
+    "test_results.jsonl",
+    "test_summary.json",
+    "review.json"
+  ],
+  "runtime_file_sha256": {
+    "tools/answers/answer_coverage.py": "746ab3b89ba65f263b63037be55ee02ee25aaaf685dae6e95f7bd6a5536fc6fa",
+    "tools/answers/rag_metadata.py": "a16486932e66aed571fd25cb59c5656892613488ea90afe80c41b0c1d15afcf1",
+    "tools/answers/checked_generation.py": "e36def33fb83e5c973fa632685b4782fa411a7eeb7151ef8c14007f58fa1f375",
+    "tools/answers/answer_delivery.py": "2520ab327670e35511fb578ef1a447f0c011fb04fa3a341a36696631a2ca499e",
+    "tools/answers/presentation.py": "954c65288ddc3c8f873fec3ef99176adb70785806043ac1814c7a01a267b4aad",
+    "tools/answers/content_selection.py": "8e09f4423f2fd3b253f5779f62849b11b9570a112a472ea07392468901e3d80e",
+    "tools/answers/synthesis.py": "d669136a99d71b9046119debc077f2ab51336ab8ffe1bcd5e5853cfa8ba8e748",
+    "tools/answers/graph_context.py": "5b14150b7f6903cd92c004d56943d949167be8859acf4f97aed14a713e59faff",
+    "tools/answers/model_client.py": "f595eb1b594364faff21ce2a6842c8c7e40a5ca4e6a2c3085cdc4a4bae28ae04",
+    "tools/answers/answer_errors.py": "d1541b5c31b5d64e0bfa729f5e9a29adddc4b55bdd24508490f39cfc0a44b240",
+    "tools/answers/context_policy.py": "526f5de0b9112e23abd76e403799f156d0150c83f03577251af23d1685991327",
+    "tools/answers/verified_rag.py": "b2e9777b269f2b19c16ad9e1aa86644f09a9ad96489358a1294e51d21025611e",
+    "tools/answers/answer_format.py": "533cfe3a30c835d22441d877df63fe08e366d01ed6d15474fe6fcff26bde259b",
+    "tools/answers/evidence_selection.py": "1007a85b8838e1eb3c34ee610a4f81dfff57410a90213e02b8ccde90a0fedbd2",
+    "tools/answers/__init__.py": "8b8ab7f996121e5e5b1b594cf08f4c3b4f16f1b434b74276c803a3a83681d2cb",
+    "tools/answers/rag_preparation.py": "98468b111c9f8f6d423f3c6a07300c3ad7a7e9fef5bae1d1385b91a04771e069",
+    "tools/answers/rag_evidence.py": "351b99eb5757991696dbe10dcb794cf02aea7c2232c2b7eabab4e3bf1a7ad687",
+    "tools/answers/reference_links.py": "eb59206bcb3b79912daafcd591e7adb504ec592e61ea32e4e133cb6a65fe0b57",
+    "tools/answers/render_answer.py": "953cae2cdbdbf05a1b0c1f2746c4dc5f0d85c9bcfd02c105df38a3ff1d2451af",
+    "tools/answers/question_plan.py": "d95b6f071845f4c2c8846bccc0f131cc80cf58cb0d28804f36e5f29ccf4b7387",
+    "tools/answers/question_intent.py": "2d009205f0168b80a0a90b0fc86e52a605ddb367d24ecb727387f6944f16669b",
+    "tools/knowledge/concepts.py": "de0d005ab298c3d86020b0cb09fc695852303d8a5f411d0a3828b58131713b8d",
+    "tools/knowledge/sources.py": "709c2201288a920e99578fe4e58e191a742deb5f15170104820680d156280c69",
+    "tools/knowledge/publication.py": "a5a29af22fd73a57f53da47c687200f0ad392d48ce94a23250f00c7a53731148",
+    "tools/knowledge/academic.py": "0ae9a611792b042c64c6ba9c3019bb95d8b51e4320b1df20844c8e9f6f006b60",
+    "tools/knowledge/__init__.py": "905a19d7cf5d95379800b2ad917ad759fa376f23d2f8a244527b9dc187555019",
+    "tools/knowledge/evidence.py": "b0eb7d3e0f6a97083829a9940b3a64dade96d4f98435eb5ebfb43adaf10fac18",
+    "tools/knowledge/graph_answers.py": "fd599b7ff65485ae11e77022cc007b99e5bd3b533383cc64dc5b7aa3dabf81fa",
+    "tools/knowledge/store.py": "fa22cd357df8c3cde4cd1b6a4f0a9b78d12721f3970ec896584205f8a5d5d7c1",
+    "tools/knowledge/extraction.py": "c1819afc5072a5999c985b4547809db997ecf3e1112b031534ae34ed295bd106",
+    "tools/knowledge/local_graph.py": "cefdc9de3d265a37636fdc2836c59e929743bddf17a3408d04668fa31b80dc4d",
+    "tools/knowledge/graph_projection.py": "dac3058008fb43c833d239b6a7ced27ef5419004cc768b246741eb2e1a69b12a",
+    "tools/knowledge/text_retrieval.py": "bcb2ee2c4de7c4c074e1677137a2e11995b36ffa934fc03de96b59710ad89307",
+    "tools/knowledge/query.py": "4b3c3333a9aae229de9b3ce83f61f5aa278cf82bebe7a1b0e8518f1ccf8e76ab",
+    "tools/knowledge/build.py": "4666b1cd2d42533b7289f5e2ecaac11c5cc16e09f3ea4b9a2714d17cb8c2f9f6",
+    "tools/knowledge/planned_answers.py": "e5820cb6c8d6e15315ae94e263e2886b0309b6c7eee8e09338750adbede2aa8c",
+    "tools/graphrag/graph_search.py": "eff65f570d3a52a9cb4132db8f3c4b59dc08840001ae1bfbab6c7ab5eda09bb0",
+    "tools/retrieval/person_names.py": "6e3eb293fee2d136f3b6e082a166dbd02cde9b36ab47777eb01a11404d63c1ac",
+    "tools/rag/llm_context.py": "8f195ff16639de4379c41108f7a3a456d90d985307ebde3691c1406b3f76dfa5"
+  }
+}
diff --git a/test/test_answer_repair_regressions.py b/test/test_answer_repair_regressions.py
new file mode 100644
index 0000000..bea86dd
--- /dev/null
+++ b/test/test_answer_repair_regressions.py
@@ -0,0 +1,240 @@
+"""Previously failed user formulations are regressions, never a fresh TEST."""
+import base64
+import zlib
+from types import SimpleNamespace
+import numpy as np
+import pytest
+
+from tools.answers.question_plan import build_question_plan,QuestionPlan,PersonRequest
+from tools.retrieval.person_names import resolve_person_names
+from tools.knowledge.text_retrieval import retrieve_units
+
+CATALOG=[{'kind':'Person','name':name,'source_document':f'pages/students/2024/{i}/README.md'}
+         for i,name in enumerate(['Adrián Ondra','Sebastián Kišš','Samuel Štucka','Samuel Džurina'])]
+
+
+@pytest.mark.parametrize('suffix',[
+    'Zhrň všetky doložené body a nezamieňaj ich za hotové výsledky.',
+    'Vysvetli konkrétne prepojenie a dolož obe osoby.',
+    'Pri každej osobe príslušné práce uveď v bodoch.',
+])
+def test_presentation_clause_is_not_an_additional_unknown_identity(suffix):
+    r=resolve_person_names('Na čom pracoval Adrián Ondra? '+suffix,CATALOG)
+    assert r['status']=='ok' and [m['name'] for m in r['matches']]==['Adrián Ondra']
+
+
+def test_inflected_partial_first_name_asks_for_real_candidates_without_model():
+    p=build_question_plan('Čo sa podarilo Samuelovi pri jeho práci?',CATALOG)
+    assert p.reason=='incomplete_person_name' and p.resolution['status']=='ambiguous'
+    assert {c['name'] for c in p.resolution['candidates']}=={'Samuel Štucka','Samuel Džurina'}
+
+
+def test_one_short_surname_typo_still_requires_an_exact_given_name_anchor():
+    assert resolve_person_names('Akú prácu má Sebastián Kiš?',CATALOG)['matches'][0]['name']=='Sebastián Kišš'
+    assert resolve_person_names('Akú prácu má Sebastan Kš?',CATALOG)['status']!='ok'
+
+
+@pytest.mark.parametrize('question',[
+    'Kto už vyskúšal hierarchickú sumarizáciu na problém s dlhým kontextom?',
+    'U koho je doložené vytvorenie crawlera pomocou Scrapy?',
+    'Ktorý študent sa venoval agentovi pre špeciálneho pedagóga? Bez domýšľania výsledkov.',
+])
+def test_global_discovery_does_not_require_a_known_student(question):
+    p=build_question_plan(question,CATALOG)
+    assert not p.clarification and p.spec.intent=='discovery' and not p.spec.people
+
+
+def test_grounding_directive_does_not_add_a_results_facet_to_goals():
+    p=build_question_plan('Aké sú ciele projektu osoby Adrián Ondra? Nezamieňaj ich za hotové výsledky.',CATALOG)
+    assert p.spec.facets==['assignment'] and not p.clarification
+
+
+def index_rows():
+    rows=[{'id':str(i),'person':'Eva Nová','source_path':'eva','line':i+1,
+           'text':'Všeobecná poznámka.' if i else 'Použitá technológia Cassandra.',
+           'concepts':[],'work_label':'Diplomová práca 2026','work_type':'diploma_thesis','work_year':2026}
+          for i in range(100)]
+    matrix=np.array([[0.,1.]]+[[1.,0.]]*99,dtype='<f4')
+    return {'version':'unit-v1','units':rows,'embedding_fingerprint':'fp','vector_shape':[100,2],
+            'vectors':base64.b64encode(zlib.compress(matrix.tobytes())).decode()}
+
+
+def test_semantic_candidates_survive_a_successful_lexical_hit(monkeypatch):
+    import tools.rag.embedding_utils as embedding
+    monkeypatch.setattr(embedding,'model_profile',lambda _: {'fingerprint':'fp'})
+    monkeypatch.setattr(embedding,'embed_query',lambda _:np.array([1.,0.]))
+    plan=QuestionPlan(question='Cassandra',intent='discovery')
+    rows,scores,trace=retrieve_units(plan.question,index_rows(),plan)
+    assert '0' in {r['id'] for r in rows}
+    assert any(r['id']!='0' for r in rows) and scores['1']>scores['0']
+    assert trace['lexical_candidates']==1 and trace['vector_candidates']==80
+
+
+def test_named_scope_expansion_preserves_every_goal_and_excludes_other_people(monkeypatch):
+    index=index_rows();index.pop('vectors')
+    index['units'].append({**index['units'][0],'id':'other','person':'Dana Iná','source_path':'dana'})
+    plan=QuestionPlan(question='Všetky ciele Evy',intent='facets',people=[PersonRequest(name='Eva Nová',source_document='eva')])
+    rows,_,trace=retrieve_units(plan.question,index,plan)
+    assert len(rows)==100 and all(r['person']=='Eva Nová' for r in rows)
+    assert trace['named_scope_expansion']
+
+
+def metadata_payload(presentation='list',fields=None):
+    from tools.rag.rag_utils import build_context_text
+    source={'source_id':'S1','title':'Adrián Ondra','document_path':CATALOG[0]['source_document'],
+        'source_url':'https://example.test/adrian','tags':['rag','nlp'],
+        'text':'# Bakalársky projekt 2027\nAutomatická tvorba myšlienkových máp\nMožné ciele semestrálneho projektu:\n- Vytvoriť vizualizáciu.\n# Diplomový projekt 2029\nStav:\n- Vyskúšaný model.'}
+    plan=QuestionPlan(question='Zostav prehľad prác Adriána Ondru.',people=[PersonRequest(name='Adrián Ondra',source_document=source['document_path'])],
+        fields=fields if fields is not None else ['work_type','title','year'],presentation=presentation)
+    return {'sources':[source],'question_plan':plan.model_dump(),'context':build_context_text([source]),
+            'named_scope_complete':True,'index_version':{'version':'fixture'},'truncation_reasons':[]}
+
+
+def test_exact_metadata_keeps_distinct_untitled_records_without_a_model():
+    from tools.answers.verified_rag import generate_verified_rag
+    result=generate_verified_rag('Zostav prehľad prác Adriána Ondru.',metadata_payload(),lambda _:pytest.fail('No model for exact complete records'))
+    assert result['status']=='ok' and len(result['claims'])==2
+    assert len({r['record_id'] for r in result['claims']})==2
+    assert 'myšlienkových máp' in result['answer'] and 'názov v zdroji nie je uvedený' in result['answer']
+
+
+def test_exact_count_counts_records_not_source_windows_or_people():
+    from tools.answers.verified_rag import generate_verified_rag
+    payload=metadata_payload('count',[])
+    result=generate_verified_rag('Zostav prehľad prác Adriána Ondru.',payload,lambda _:pytest.fail('No model for exact count'))
+    assert result['count']==2 and 'prác a projektov' in result['answer']
+
+
+@pytest.mark.parametrize('presentation',['list','count'])
+def test_exact_metadata_is_accepted_by_the_actual_claude_client(monkeypatch,presentation):
+    from packages.claude import client
+    from tools.answers.verified_rag import generate_verified_rag
+    question='Zostav prehľad prác Adriána Ondru.'
+    result=generate_verified_rag(question,metadata_payload(presentation),lambda _:pytest.fail('No model'))
+    monkeypatch.setattr(client,'request_json',lambda *a,**kw:{**result,'query':question,'engine':'rag'})
+    delivered=client.query_backend(question,'rag',config=client.Config('http://localhost:8000','test'))
+    assert delivered['status']=='ok' and delivered['accepted_for_delivery']
+    assert delivered['answer']==result['answer']
+
+
+def test_complete_scope_flag_cannot_override_context_clipping():
+    from tools.answers.rag_metadata import exact_metadata_answer
+    from tools.answers.rag_evidence import rag_evidence_context
+    payload=metadata_payload();payload['truncation_reasons']=['llm_context_budget']
+    assert exact_metadata_answer(payload,rag_evidence_context('Zostav prehľad prác Adriána Ondru.',payload)) is None
+
+
+def test_document_authorship_is_not_interpreted_as_an_advisor_filter():
+    p=build_question_plan('Kto vedie bakalársku prácu Adriána Ondru a kto je autor dokumentu?',CATALOG)
+    assert p.spec.intent=='advisors' and not p.clarification
+    assert 'document_author' in p.spec.fields and not p.spec.conditions
+
+
+def test_profile_metadata_has_its_own_time_role_and_snapshot(monkeypatch,tmp_path):
+    import sqlite3
+    from contextlib import nullcontext
+    import tools.sync.index_state as state
+    from tools.knowledge.academic import with_profile_metadata
+    db=tmp_path/'db.sqlite'
+    with sqlite3.connect(db) as conn:
+        conn.execute('CREATE TABLE documents(path,author,published)')
+        conn.execute('CREATE TABLE chunks(chunk_id,document_path,text,published)')
+        path=CATALOG[0]['source_document']
+        conn.execute('INSERT INTO documents VALUES (?,?,1)',(path,'Daniel Hladek'))
+        conn.execute('INSERT INTO chunks VALUES (?,?,?,1)',('c1',path,'rok začiatku štúdia: 2024'))
+    monkeypatch.setattr(state,'index_snapshot',lambda *a,**k:nullcontext())
+    monkeypatch.setattr(state,'read_version',lambda *a:{'version':'v'})
+    spec=QuestionPlan(question='Odlíš roly a roky.',people=[PersonRequest(name='Adrián Ondra',source_document=path)],fields=['document_author','start_year','year'])
+    plan=SimpleNamespace(spec=spec)
+    original={'status':'ok','accepted_for_delivery':True,'index_version':{'version':'v'},
+        'answer':'Vedúci: Matúš Pleva. Rok práce: 2027.\n\nZdroje: https://example.test/adrian','source_urls':['https://example.test/adrian']}
+    result=with_profile_metadata(original,plan,db)
+    assert 'Matúš Pleva' in result['answer'] and 'Autor dokumentu profilu Adrián Ondra: Daniel Hladek.' in result['answer']
+    assert 'začiatku štúdia — Adrián Ondra: 2024' in result['answer']
+    assert any(r.get('quote')=='rok začiatku štúdia: 2024' for r in result['metadata_evidence'])
+    with pytest.raises(RuntimeError,match='snapshot'):
+        with_profile_metadata({**original,'index_version':{'version':'other'}},plan,db)
+
+
+def test_graph_work_count_is_not_the_number_of_authors():
+    from tools.graphrag import graph_search as gs
+    from tools.graphrag.tests.test_graph_search import FakeSession
+    from tools.graphrag.tests.test_graph_search_entity_v2 import catalog,thesis
+    q='Koľko prác má Alena Nová?'
+    bound=build_question_plan(q,catalog())
+    rows=[{**thesis(),'thesis_id':'a','heading':'Bakalárska práca 2024'},
+          {**thesis(),'thesis_id':'b','heading':'Bakalárska práca 2025','year':2025}]
+    session=FakeSession({gs.CATALOG_QUERY:catalog(),gs.THESIS_FACTS_QUERY:rows})
+    result=gs.search_graph_with_session(session,q,question_plan=bound.spec)
+    assert result['aggregation']['count']==2 and result['aggregation']['people']==['Alena Nová']
+    assert result['aggregation']['count_domain']=='academic_records'
+
+
+def test_declarative_stack_is_a_method_description_without_promoting_its_phase():
+    from tools.answers.content_selection import facets_for
+    q='Aké technológie má študent na frontende, backende a čo je doložené o funkčnosti?'
+    assert 'methods' in facets_for('Frontend React, backend Flask, DB Postgres.','other',q,'unknown')
+    assert 'methods' not in facets_for('Frontend bude React, backend bude Flask.','task',q,'planned')
+    assert 'results' in facets_for('Aplikácia funguje.','progress',q,'result')
+
+
+@pytest.mark.parametrize('extra_work,expected', [('',True),('\n# Diplomová práca 2029\nStav:\n- Funguje systém.',False)])
+def test_profile_header_role_is_qualified_and_never_broadcast_to_multiple_works(extra_work,expected):
+    from tools.knowledge.sources import profile_units
+    from tools.knowledge.academic import answer_academic
+    path=CATALOG[0]['source_document']
+    units=profile_units({'title':'Adrián Ondra','path':path,'content':'Vedúci: Matúš Pleva\n# Bakalárska práca 2027\nStav:\n- Funguje aplikácia.'+extra_work},include_records=True)
+    bound=build_question_plan('Kto vedie bakalársku prácu Adriána Ondru?',CATALOG)
+    index={'index_version':{'version':'v'},'version':'content','academic_schema':1,'units':units}
+    result=answer_academic(bound.spec.question,index,engine='rag',mode='advisors',plan=bound)
+    assert ('Matúš Pleva' in result['answer']) is expected
+    if expected:
+        assert 'v hlavičke profilu' in result['answer']
+        assert any(r['kind']=='supervisor' and r.get('work_id') is None for r in result['evidence'])
+
+
+@pytest.mark.parametrize('consultant_profile',[False,True])
+def test_mentioning_a_consultant_does_not_turn_the_supervisor_into_a_consultant(consultant_profile):
+    consultant={'kind':'DocumentAuthor','name':'Kristián Sopkovič'}
+    if consultant_profile:consultant.update(kind='Person',source_document='pages/students/2020/kristian/README.md')
+    catalog=[{'kind':'DocumentAuthor','name':'Daniel Hladek'}, consultant]
+    plan=build_question_plan('Ktoré práce vedie Daniel Hladek a konzultuje Kristián Sopkovič?',catalog)
+    assert [(p.name,p.role) for p in plan.spec.people]==[('Daniel Hladek','supervisor'),('Kristián Sopkovič','consultant')]
+
+
+def test_created_chatbot_is_not_satisfied_by_a_future_assignment():
+    from tools.knowledge.query import activity_supported
+    q='Kto vytvoril chatbota?'
+    assert not activity_supported(q,{'text':'Vytvorte chatbota.','phase':'assignment'})
+    assert activity_supported(q,{'text':'Vytvorený modelový chatbot.','phase':'result'})
+
+
+def test_ner_is_not_proved_by_generic_form_slot_recognition_or_model_concepts():
+    from tools.knowledge.query import activity_supported
+    q='Kto sa venoval rozpoznávaniu pomenovaných entít?'
+    row={'text':'Nedarí sa rozpoznať typ priznania za psa vo Form.','phase':'progress','concepts':['NER']}
+    assert not activity_supported(q,row)
+    assert activity_supported(q,{'text':'Vyskúšaný model SlovakBERT-NER na rozpoznávanie pomenovaných entít.','phase':'result'})
+
+
+def test_possible_goals_keep_the_literal_qualification_for_prose_rendering():
+    from tools.knowledge.sources import profile_units
+    from tools.knowledge.planned_answers import answer_facets
+    rows=profile_units({'title':'Adrián Ondra','path':CATALOG[0]['source_document'],
+        'content':'# Bakalársky projekt 2027\nMožné ciele semestrálneho projektu:\n- Vytvoriť vizualizáciu.'},include_records=True)
+    bound=build_question_plan('Aké sú ciele projektu Adriána Ondru?',CATALOG)
+    index={'version':'c','index_version':{'version':'v'},'units':rows}
+    result=answer_facets(bound,index,rows,engine='rag',limit=5)
+    assert 'Vytvoriť vizualizáciu' in result['answer']
+    assert any('možné/navrhované ciele' in note for note in result['presentation_notes'])
+
+
+def test_comparing_time_roles_does_not_dump_the_meeting_agenda():
+    from tools.knowledge.sources import profile_units
+    from tools.knowledge.academic import answer_academic
+    path=CATALOG[0]['source_document']
+    rows=profile_units({'title':'Adrián Ondra','path':path,'content':'# Bakalársky projekt 2027\nStretnutie 5.11.2026\nStav:\n- Implementovaný nevyžiadaný detail.'},include_records=True)
+    p=build_question_plan('Odlíš rok začiatku štúdia Adriána Ondru, rok práce a najnovší dátum stretnutia.',CATALOG)
+    result=answer_academic(p.spec.question,{'index_version':{'version':'v'},'version':'c','academic_schema':1,'units':rows},engine='rag',mode='meeting',plan=p)
+    assert '2026-11-05' in result['answer'] and '2027' in result['answer']
+    assert 'nevyžiadaný detail' not in result['answer']
diff --git a/tools/answers/checked_generation.py b/tools/answers/checked_generation.py
index 0248c72..f468f56 100644
--- a/tools/answers/checked_generation.py
+++ b/tools/answers/checked_generation.py
@@ -19,7 +19,7 @@ def generate_checked_answer(question: str, context: dict, prompt: str, complete)
     aggregation = context.get("aggregation")
     if aggregation is not None:
         sent["aggregation"] = {
-            k: v for k, v in aggregation.items() if k in {"count", "people", "complete"}
+            k: v for k, v in aggregation.items() if k in {"count", "count_domain", "people", "complete"}
         }
     policy = "\nDo claims nevkladaj source_urls ani evidence_documents; source_urls patrí iba na vrchnú úroveň odpovede."
     if (
diff --git a/tools/answers/content_selection.py b/tools/answers/content_selection.py
index 6684764..c68d4be 100644
--- a/tools/answers/content_selection.py
+++ b/tools/answers/content_selection.py
@@ -137,13 +137,14 @@ def facets_for(text: str, role: str, question: str, phase: str | None = None) ->
     ) and role not in {"assignment", "task"}:
         facets.append("problems")
     if re.search(
-        r"\b(?:metod\w*|technolog\w*|nastroj\w*|pouzi\w*|vyuziv\w*|pomocou|implementovan\w*|natrenovan\w* model\w*|indexov\w*|databaz\w*|webov\w* rozhrani\w*)\b",
+        r"\b(?:metod\w*|technolog\w*|nastroj\w*|pouzi\w*|vyuziv\w*|pomocou|implementovan\w*|natrenovan\w* model\w*|indexov\w*|databaz\w*|webov\w* rozhrani\w*|frontend\w*|backend\w*|vyskus\w*|sumarizac\w*)\b",
         n,
     ):
         actual_use = actual_methods_requested(question)
         actual_statement = not re.search(NOT_COMPLETED, n) and (
-            re.search(COMPLETED, n)
+            re.search(COMPLETED, n) or re.search(r'\bvyskus(?:any|ana|ane|al|ala|ali)\b',n)
             or re.search(r"\b(?:pouziva\w*|pouzil\w*|vyuziva\w*|vyuzil\w*|vieme indexov\w*)\b", n)
+            or (not re.search(PLANNED,n) and re.search(r'\b(?:frontend|backend)\s+\w+',n))
         )
         if not actual_use or (role not in {"assignment", "task"} and actual_statement):
             facets.append("methods")
diff --git a/tools/answers/question_intent.py b/tools/answers/question_intent.py
index 9daa747..a87c615 100644
--- a/tools/answers/question_intent.py
+++ b/tools/answers/question_intent.py
@@ -7,10 +7,10 @@ FACET_PATTERNS = {
     "assignment": r"\b(?:zadani\w*|ciel\w*)\b",
     "plans": r"\b(?:planovan\w*|dalsi\w*\s+uloh\w*|uloh\w*)\b",
     "problems": r"\b(?:problem\w*|tazkost\w*|prekazk\w*|chyb\w*)\b",
-    "methods": r"\b(?:metod\w*|technolog\w*|nastroj\w*|pouziv\w*|pouzil\w*)\b",
+    "methods": r"\b(?:metod\w*|technolog\w*|nastroj\w*|pouziv\w*|pouzil\w*|pristup\w*|frontend\w*|backend\w*)\b",
     "experiments": r"\b(?:experiment\w*|merani\w*|testovani\w*|vyhodnocov\w*)\b",
     "progress": r"\b(?:priebezn\w*\s+)?stav\w*\b",
-    "results": r"\b(?:vysledk\w*|dosiahn\w*)\b",
+    "results": r"\b(?:vysledk\w*|dosiahn\w*|funkcnost\w*)\b",
 }
 FACET_LABELS = {
     "assignment": "Zadanie a ciele",
@@ -25,6 +25,7 @@ FACET_LABELS = {
 
 def requested_facets(question: str) -> list[str]:
     text = normalized(re.sub(r'[„“"«][^„“”"»]+[“”"»]', " ", question))
+    text = re.sub(r'\bnezamienaj\s+(?:ich|to|ciele|zadanie|plany|ulohy)\s+za\s+(?:hotove\s+)?vysled\w*|\bbez domyslania vysled\w*',' ',text)
     matches = [
         (m.start(), key)
         for key, pattern in FACET_PATTERNS.items()
@@ -54,7 +55,8 @@ def facet_mode(question: str) -> bool:
 def actual_methods_requested(question: str) -> bool:
     return bool(
         re.search(
-            r"\b(?:skutocne|pouziva\w*|pouzil\w*|vyuzival\w*|implementoval\w*)\b|"
+            r"\b(?:skutocne|pouziva\w*|pouzil\w*|vyuzival\w*|implementoval\w*|vyskus(?:al|ala|ali)\w*)\b|"
+            r"\bake technolog\w* ma\b|"
             r"\b(?:pouzit\w*|aplikovan\w*|vyuzit\w*)\s+(?:metod\w*|technolog\w*|nastroj\w*)\b",
             normalized(question),
         )
diff --git a/tools/answers/question_plan.py b/tools/answers/question_plan.py
index 76f38e4..50461ec 100644
--- a/tools/answers/question_plan.py
+++ b/tools/answers/question_plan.py
@@ -126,17 +126,24 @@ def direct_spec(question, resolution):
     selector=parse_work_selector(q)
     mode=intent(q)
     facets=requested_facets(q)
+    global_discovery=bool(re.search(r'\b(?:kto|ktori studenti|ktory student|u koho)\b',n)
+        and re.search(r'\b(?:venov\w*|zaober\w*|pracov\w*|vyskus\w*|vytvor\w*|pouz\w*|ries\w*|implement\w*)\b',n))
+    if mode not in {'similar','supervisor','advisors','completion','meeting','period'} and global_discovery:
+        mode='discovery'
     # Facet questions without an academic lifecycle intent share one execution
     # path, including GraphRAG's content evidence. Explicit metadata stays metadata.
     if (not mode or mode=='progress' and facets) and content_question(q):mode='facets'
     years=requested_years(q)
     people=[]
-    staff_query=bool(re.search(r'\b(?:pod veden\w*|vedie|viedol|veduc\w*|skolitel\w*|konzult(?:ant|oval)\w*)\b',n))
+    staff_query=bool(re.search(r'\b(?:pod veden\w*|vedie|viedol|veduc\w*|skolitel\w*|konzult\w*)\b',n))
     for m in resolution.get('matches',[]):
         role='document_author' if not m.get('source_document') else 'student'
-        explicit_staff=bool(re.search(r'(?:pod vedenim|vedie|viedol|veduca|veduci|konzultant)\s+'+re.escape(normalized(m['name'])),n))
+        explicit_staff=bool(re.search(r'(?:pod vedenim|vedie|viedol|veduca|veduci|konzult\w*)\s+'+re.escape(normalized(m['name'])),n))
         if staff_query and (not m.get('source_document') or explicit_staff):
-            role='consultant' if re.search(r'\bkonzult(?:ant|oval)\w*',n) else 'supervisor'
+            name=re.escape(normalized(m['name']))
+            consultant=bool(re.search(r'\bkonzult\w*(?:\s+s)?\s+'+name+r'\b',n)
+                or re.search(r'\b'+name+r'\s+konzult\w*\b',n))
+            role='consultant' if consultant else 'supervisor'
         people.append(PersonRequest(name=m['name'],role=role,source_document=m.get('source_document')))
     from tools.retrieval.work_selector import QUOTED,HEADING,label_key
     types_text=QUOTED.sub(lambda m:m[0] if HEADING.fullmatch(label_key(m[1])) else ' ',selector.positive_text)
@@ -178,7 +185,8 @@ def direct_spec(question, resolution):
     if any(len(m[1])!=4 for m in re.finditer(r'\brok(?:u|och)?\s+(\d+)\b',n)):unsupported.append('invalid_year')
     full_dates=re.findall(r'\d{1,2}\.\d{1,2}\.[12]\d{3}',q)
     if len(full_dates)<2 and re.search(r'\b(?:kvartal\w*|minuly rok|minulom roku|v marci|v januari|v februari|v aprili|v maji|v juni|v juli|v auguste|v septembri|v oktobri|v novembri|v decembri)\b',n):unsupported.append('period_needs_dates')
-    if re.search(r'\bbez\b',n) and not (selector.unnumbered or selector.excluded_numbers or selector.excluded_labels):unsupported.append('exclusion')
+    exclusion_text=re.sub(r'\bbez\s+(?:domyslania|dohadov|spekulacii|nepodlozenych tvrdeni)(?:\s+vysledkov)?\b',' ',n)
+    if re.search(r'\bbez\b',exclusion_text) and not (selector.unnumbered or selector.excluded_numbers or selector.excluded_labels):unsupported.append('exclusion')
     if unresolved_followup(q):unsupported.append('missing_followup_context')
     if re.search(r'\bsemest\w*\b',n) and len(re.findall(r'\d{1,2}\.\d{1,2}\.[12]\d{3}',q))<2:unsupported.append('enrollment_not_established' if re.search(r'\b(?:aktual\w*|teraz|v akom|v ktorom)\b',n) else 'period_needs_dates')
     code=unsupported_constraint(q)
@@ -203,6 +211,7 @@ def direct_spec(question, resolution):
     conditions=[]
     for pattern,kind in [(r'\btag\w*\s+[^,;?]+','tag'),(r'\bklucov\w*\s+slov\w*\s+[^,;?]+','tag'),
                          (r'\bkategori\w*\s+[^,;?]+','category'),(r'\bautor\w* dokument\w*[^,;?]*','document_author')]:
+        if kind=='document_author' and not any(p.role=='document_author' for p in people):continue
         conditions.extend(Condition(kind=kind,quote=m[0]) for m in re.finditer(pattern,n))
     if mode and mode!='metadata' and conditions:unsupported.append('unsupported_content_join')
     conditions.extend(Condition(kind='current_status',quote=m[0]) for m in re.finditer(r'\b(?:aktual\w*|teraz|dnes)\b',n))
@@ -222,7 +231,7 @@ def direct_spec(question, resolution):
             work.years = [year for year in work.years if year not in study_values | verification_values]
         if year_verification:
             conditions.append(Condition(kind='year_verification', quote=q))
-    fields=[field for field,pattern in [('title',r'\bnaz(?:ov|v)\w*'),('year',r'\b(?:ktoreho roku|ktorych rokoch|rok\w*)'),('work_type',r'\btyp\w*'),('keyword',r'\b(?:tag\w*|klucov\w*)'),('category',r'\bkategor\w*'),('document_author',r'\bautor\w* dokument\w*'),('supervisor',r'\b(?:vedie|veduc\w*|veden\w*)'),('consultant',r'\bkonzult(?:ant|oval)\w*'),('status',r'\b(?:ukoncil\w*|obhaj\w*|skoncil\w*)'),('date',r'\b(?:kedy|datum\w*)')] if re.search(pattern,n)]
+    fields=[field for field,pattern in [('title',r'\bnaz(?:ov|v)\w*'),('year',r'\b(?:ktoreho roku|ktorych rokoch|rok\w*)'),('work_type',r'\btyp\w*'),('keyword',r'\b(?:tag\w*|klucov\w*)'),('category',r'\bkategor\w*'),('document_author',r'\bautor\w* dokument\w*'),('supervisor',r'\b(?:vedie|veduc\w*|veden\w*)'),('consultant',r'\bkonzult\w*'),('status',r'\b(?:ukoncil\w*|obhaj\w*|skoncil\w*)'),('date',r'\b(?:kedy|datum\w*)')] if re.search(pattern,n)]
     if study_start:
         fields.append('start_year')
     return QuestionPlan(fields=fields,question=q,intent=mode or 'metadata',people=people,works=works,years=years,period=period,
diff --git a/tools/answers/rag_evidence.py b/tools/answers/rag_evidence.py
index 939634a..bb61dd6 100644
--- a/tools/answers/rag_evidence.py
+++ b/tools/answers/rag_evidence.py
@@ -603,6 +603,7 @@ def rag_evidence_context(question: str, payload: dict) -> dict:
             if project_number(question) is None
             or (scope is not None and project_number(label or "") == project_number(question))
         ]
+        labels={(scope,text):label for scope,label,text in academic_windows_metadata(source['body'])}
         unscoped = []
         for section, text in windows:
             titles = []
@@ -652,7 +653,7 @@ def rag_evidence_context(question: str, payload: dict) -> dict:
                     if (
                         lead
                         and re.match(
-                            r"^(?:Cie[lľe]+\s*:|Zadanie\s*:|Myšlienky\s*:|1[.)]\s)", next_line, re.I
+                            r"^(?:(?:Možné\s+)?Cie[lľe]+\b|Zadanie\s*:|Myšlienky\s*:|1[.)]\s)", next_line, re.I
                         )
                         and not re.match(r"^(?:[-*+]\s|\d+[.)]\s|#{1,6}\s)", lead)
                         and not normalized(lead).startswith(
@@ -676,6 +677,9 @@ def rag_evidence_context(question: str, payload: dict) -> dict:
                         if title and len(normalized(title).split()) >= 3:
                             titles.append(title)
                 record = {**base, "work_type": kind}
+                if labels.get((section,text)):
+                    from tools.knowledge.sources import identifier
+                    record.update(work_label=labels[(section,text)],record_id=identifier(source['document_path'],labels[(section,text)]))
                 if year is not None:
                     record["year"] = year
                 for title in titles or [None]:
@@ -811,7 +815,8 @@ def rag_evidence_context(question: str, payload: dict) -> dict:
     requirements["project_number"] = project_number(question)
     requirements["exact_work_label"] = selector.labels[0] if len(selector.labels) == 1 else None
     if plan:
-        facts=[row for row in facts if not row.get("work_type") or any((not w.work_type or w.work_type==row.get("work_type")) and (not w.years or row.get("year") in w.years) for w in plan.works if w.person in {None,row["person"]})]
+        facts=[row for row in facts if not row.get('work_type') or plan.matches_work(
+            row['person'],row.get('work_label'),row.get('work_type'),row.get('year'))]
     if requested:
         facts = [row for row in facts if row["person"] in requested]
     requirements["available_fields"] = {
diff --git a/tools/answers/rag_metadata.py b/tools/answers/rag_metadata.py
new file mode 100644
index 0000000..2ee530d
--- /dev/null
+++ b/tools/answers/rag_metadata.py
@@ -0,0 +1,68 @@
+"""Exact metadata answers from delivered text, never from Neo4j or a gold set."""
+import re
+from tools.answers.context_policy import normalized,citation_check
+from tools.answers.question_plan import QuestionPlan
+from tools.answers.render_answer import render_graph_answer
+
+
+def exact_metadata_answer(payload,context):
+    if not payload.get('question_plan'):return None
+    spec=QuestionPlan.model_validate(payload['question_plan'])
+    people=[p.name for p in spec.people if p.role=='student']
+    if spec.intent!='metadata' or not people or any(p.role!='student' for p in spec.people):return None
+    if set(spec.fields)&{'supervisor','consultant','status','date'}:return None
+    q=normalized(spec.question)
+    shared=bool(re.search(r'\b(?:spoloc\w*|prienik|obaja|spajaj\w*)\b',q) and len(people)>1)
+    keyword=shared and bool(set(spec.fields)&{'keyword','category','document_author'})
+    if spec.conditions and not keyword:return None
+    sources={s.get('title'):s for s in context['sources'] if s.get('document_path') in {p.source_document for p in spec.people}}
+    if not set(people)<=set(sources):return None
+    if not keyword and (not payload.get('named_scope_complete') or 'llm_context_budget' in payload.get('truncation_reasons',[])):
+        return None
+    facts=context['facts']
+    record_map={}
+    for row in facts:
+        if row.get('record_id') and row['person'] in people and spec.matches_work(
+                row['person'],row.get('work_label'),row.get('work_type'),row.get('year')):
+            old=record_map.get(row['record_id'],{})
+            if old.get('title') and row.get('title') and normalized(old['title'])!=normalized(row['title']):return None
+            record_map[row['record_id']]={**row,**{k:v for k,v in old.items() if v is not None}}
+    records=list(record_map.values())
+    claims=[];extra={};status='ok'
+    if keyword:
+        fields=set(spec.fields)&{'keyword','category','document_author'}
+        for field in fields:
+            sets=[{r[field] for r in facts if r['person']==p and r.get(field) is not None} for p in people]
+            for value in sorted(set.intersection(*sets)):
+                extra.setdefault('shared_properties',[]).append({'field':field,'value':value,'people':people})
+                claims.extend({'person':p,field:value} for p in people)
+        if not claims:status='no_answer'
+    elif shared and 'year' in spec.fields:
+        years=set.intersection(*[{r['year'] for r in records if r['person']==p and r.get('year') is not None} for p in people])
+        records=[r for r in records if r.get('year') in years]
+        extra['academic_year_comparison']={'people':people,'shared_years':sorted(years)}
+        if not years:status='no_answer'
+    if not keyword:
+        listing=spec.presentation in {'list','count'} or shared or 'work_type' in spec.fields and spec.scope=='across_works' or 'title' in spec.fields and any(not r.get('title') for r in records)
+        for row in records:
+            keys=['person','record_id','work_label','work_type','title','year'] if listing else ['person','work_type',*(k for k in ['title','year'] if k in spec.fields)]
+            claims.append({k:row[k] for k in keys if k in row})
+        if not claims:status='no_answer'
+    urls=[sources[p]['source_url'] for p in people]
+    out={'status':status,'claims':claims,'source_urls':list(dict.fromkeys(urls))}
+    rendered_context={**context,**extra,'complete':True,'truncated':False,'requested_people':people}
+    if spec.presentation=='count':
+        # Every record has an explicit heading in the fully delivered named
+        # profile scope. Never count top-k hits, meetings or taxonomy years.
+        count=len({r['record_id'] for r in records})
+        status='ok'
+        out.update(status='ok',count=count)
+        out['answer']='Počet evidovaných prác a projektov: **'+str(count)+'**.\n\nZdroje: '+', '.join(out['source_urls'])
+    else:out=render_graph_answer(out,rendered_context)
+    if not citation_check(out,set(urls))['citations_ok']:raise ValueError('Exact metadata citations rejected')
+    return {**out,'accepted_for_delivery':True,'attempts':[],'complete':True,'truncated':False,
+            'truncation_reasons':[],'index_version':context['index_version'],
+            'completeness_scope':'explicit_named_profiles',
+            'answer_coverage':{'status':'supported' if status=='ok' else 'not_found',
+                'scope':'count' if spec.presentation=='count' else 'list' if keyword or listing else 'fact',
+                'warnings':[],'limitations':[]}}
diff --git a/tools/answers/rag_preparation.py b/tools/answers/rag_preparation.py
index 4a947d9..a3a496c 100644
--- a/tools/answers/rag_preparation.py
+++ b/tools/answers/rag_preparation.py
@@ -35,7 +35,9 @@ def prepare_named_content(db_file, question: str, payload: dict) -> dict:
     from tools.answers.question_plan import QuestionPlan, build_question_plan
     plan=QuestionPlan.model_validate(payload['question_plan']) if payload.get('question_plan') else None
     selector = parse_work_selector(question)
-    if not content_question(question) and not selector.specific:
+    metadata_scope=bool(plan and plan.intent=='metadata' and any(p.role=='student' for p in plan.people)
+        and (set(plan.fields)&{'title','year','work_type'} or plan.presentation=='count'))
+    if not content_question(question) and not selector.specific and not metadata_scope:
         return payload
     if re.search(
         r"\b(?:s tagom|s klucovym slovom|z kategorie|s autorom dokumentu)\b", normalized(question)
@@ -128,10 +130,16 @@ def prepare_named_content(db_file, question: str, payload: dict) -> dict:
                     )
                     if not selector.matches(label):
                         continue
+                    if metadata_scope and (scope,normalized(label)) in records:
+                        continue
                     records[(scope, normalized(label))] = label
                     if (scope, normalized(label), text) not in seen:
                         seen.add((scope, normalized(label), text))
-                        frames.append("Sekcia: " + label + "\n" + text)
+                        # Exact metadata needs every record's source heading
+                        # and introduction, not all subsequent meeting notes.
+                        body='\n'.join(line for line in text.splitlines() if line.strip()) if metadata_scope else text
+                        if metadata_scope:body='\n'.join(body.splitlines()[:6])
+                        frames.append("Sekcia: " + label + "\n" + body)
             if selector.multiple and (
                 any(
                     label not in {label_key(v) for v in records.values()}
@@ -202,4 +210,5 @@ def prepare_named_content(db_file, question: str, payload: dict) -> dict:
             "named_subjects": [p["title"] for p in named],
             "work_scopes": scopes,
             "question_plan": plan.model_dump(),
+            "named_scope_complete": True,
         }
diff --git a/tools/answers/render_answer.py b/tools/answers/render_answer.py
index 16c8672..9bc6fa5 100644
--- a/tools/answers/render_answer.py
+++ b/tools/answers/render_answer.py
@@ -122,8 +122,9 @@ def render_graph_answer(answer: dict, context: dict) -> dict:
                     lines.append(fact_sentences(extra))
             lines.append("")
         if out.get("count") is not None:
-            lines.append(f'Počet vyhovujúcich osôb: {out["count"]}.')
             aggregation = context.get("aggregation") or {}
+            label='Počet evidovaných prác a projektov' if aggregation.get('count_domain')=='academic_records' else 'Počet vyhovujúcich osôb'
+            lines.append(label+f': {out["count"]}.')
             if aggregation.get("complete") is True and out["count"] == aggregation.get("count"):
                 # A full total is backed by the inspected candidate set, not
                 # just the one person left visible after a presentation cap.
diff --git a/tools/answers/verified_rag.py b/tools/answers/verified_rag.py
index 553b0c4..899b22c 100644
--- a/tools/answers/verified_rag.py
+++ b/tools/answers/verified_rag.py
@@ -808,6 +808,9 @@ def generate_verified_rag(question: str, payload: dict, complete) -> dict:
             "truncation_reasons": [],
             "index_version": context.get("index_version"),
         }
+    from tools.answers.rag_metadata import exact_metadata_answer
+    exact=exact_metadata_answer(payload,context)
+    if exact is not None:return exact
     if (
         context["requirements"].get("facets")
         and context["requirements"]["response_mode"] == "quotes"
diff --git a/tools/graphrag/graph_search.py b/tools/graphrag/graph_search.py
index a0c7493..9d897f3 100644
--- a/tools/graphrag/graph_search.py
+++ b/tools/graphrag/graph_search.py
@@ -1841,6 +1841,15 @@ def search_graph_with_session(
         kinds={work.work_type for work in spec.works if work.work_type}
         constraints['thesis_types']=sorted(k for k in kinds if k.endswith('_thesis'))
         constraints['work_types']=sorted(k for k in kinds if not k.endswith('_thesis'))
+        constraints['mentions_thesis']=bool(constraints['thesis_types'])
+        constraints['mentions_work']=bool(constraints['work_types'])
+        constraints['mentions_count']=spec.presentation=='count'
+        constraints['mentions_work_listing']=bool(
+            spec.presentation=='list' and spec.scope=='across_works'
+            and not set(spec.fields)&{'keyword','category','document_author','start_year'}
+            or 'work_type' in spec.fields and spec.scope=='across_works')
+        if spec.presentation=='count':
+            constraints['mentions_academic_year']=True  # fetch academic records, not meetings
     constraints["published_only"] = published_only
     # Explicit tag filters are resolved separately. Incidental names such as the
     # tag 'zp' in 'ZP Wiki' must never become additional query constraints.
@@ -2022,6 +2031,8 @@ def search_graph_with_session(
             normalize_for_match(clean_query[: match.start()]),
         )
     ]
+    from tools.retrieval.work_selector import HEADING,label_key
+    quoted_titles=[title for title in quoted_titles if not HEADING.fullmatch(label_key(title))]
     if quoted_titles and (thesis_specific or work_specific) and not thesis_ids and not work_ids:
         blocked_reasons.append("unknown_title")
         constraints["unresolved_title"] = True
@@ -2201,8 +2212,12 @@ def search_graph_with_session(
     aggregation = None
     if constraints["mentions_count"] and not blocked:
         matching = sorted({str(row["person"]) for row in thesis_rows + work_rows})
+        record_count=bool(re.search(r'\b(?:kolko|pocet)\s+(?:roznych\s+|evidovanych\s+)?(?:prac\w*|projekt\w*)\b',normalize_for_match(clean_query)))
+        records={row.get('thesis_id') or row.get('work_id') or row.get('thesis_element_id') or row.get('work_element_id') for row in thesis_rows+work_rows}
+        records.discard(None)
         aggregation = {
-            "count": len(matching),
+            "count": len(records) if record_count else len(matching),
+            "count_domain":'academic_records' if record_count else 'people',
             "people": matching,
             "candidate_count": len(person_ids),
             "complete": not (entity_limit_reached or fetch_capped),
diff --git a/tools/knowledge/academic.py b/tools/knowledge/academic.py
index 6a36d9f..2d8c24c 100644
--- a/tools/knowledge/academic.py
+++ b/tools/knowledge/academic.py
@@ -12,6 +12,45 @@ import re
 
 from tools.answers.context_policy import normalized
 from tools.knowledge.sources import advisor_name
+
+
+def with_profile_metadata(result,plan,db_file):
+    """Keep indexed document authorship and literal study start separate from work events."""
+    import sqlite3
+    from tools.sync.index_state import index_snapshot,read_version
+    from tools.rag.search_core import make_source_url
+    if result.get('status')!='ok' or not result.get('accepted_for_delivery'):return result
+    with index_snapshot(db_file.parent,db_file=db_file):
+        if read_version(db_file.parent)!=result['index_version']:raise RuntimeError('Authorship snapshot changed')
+        with sqlite3.connect(db_file.resolve().as_uri()+'?mode=ro',uri=True) as conn:
+            rows=[]
+            for person in plan.spec.people:
+                if person.role!='student' or not person.source_document:continue
+                row=conn.execute('SELECT author FROM documents WHERE path=? AND COALESCE(published,1)=1',
+                                 (person.source_document,)).fetchone()
+                if 'document_author' in plan.spec.fields and row and row[0]:rows.append({'person':person.name,'document_author':row[0],
+                    'source_path':person.source_document,'source_url':make_source_url(person.source_document),'metadata_field':'author'})
+                if 'start_year' in plan.spec.fields:
+                    starts={}
+                    for chunk_id,text in conn.execute('SELECT chunk_id,text FROM chunks WHERE document_path=? AND COALESCE(published,1)=1',(person.source_document,)):
+                        for line in text.splitlines():
+                            match=re.fullmatch(r'rok zaciatku studia ([12]\d{3})',normalized(line))
+                            if match:starts[int(match[1])]=(chunk_id,line)
+                    if len(starts)==1:
+                        year=next(iter(starts));chunk_id,quote=starts[year]
+                        rows.append({'person':person.name,'start_year':year,'source_path':person.source_document,
+                            'source_url':make_source_url(person.source_document),'metadata_field':'literal_start_year','chunk_id':chunk_id,'quote':quote})
+    if not rows:return result
+    body,separator,sources=result['answer'].rpartition('\n\nZdroje:')
+    if not separator:body=result['answer']
+    lines=[('Autor dokumentu profilu '+r['person']+': '+r['document_author']+'.') if 'document_author' in r else
+           ('Explicitný rok začiatku štúdia — '+r['person']+': '+str(r['start_year'])+'.') for r in rows]
+    if 'start_year' in plan.spec.fields and 'year' in plan.spec.fields:
+        lines.append('Rok práce, dátum záznamu a dátum vykonania činnosti sú odlišné údaje.')
+    urls=list(dict.fromkeys([*result['source_urls'],*(r['source_url'] for r in rows)]))
+    answer=body+'\n\n'+'\n'.join(lines)+'\n\nZdroje: '+', '.join(urls)
+    return {**result,'answer':answer,'metadata_evidence':rows,
+        'source_urls':urls}
 from tools.retrieval.person_names import resolve_person_names
 from tools.retrieval.work_selector import parse_work_selector
 
@@ -33,7 +72,7 @@ def academic_intent(question):
         return "meeting"
     if re.search(r"\bkto\b.*\b(?:vedie|viedol|viedla)\b", text):
         return "advisors"
-    if re.search(r"\b(?:veduc\w*|skolitel\w*|konzult(?:ant|oval)\w*)\b", text):
+    if re.search(r"\b(?:veduc\w*|skolitel\w*|konzult\w*)\b", text):
         return "advisors"
     return None
 
@@ -205,7 +244,7 @@ def answer_academic(question, index, *, engine, limit=5, units=None, mode=None,
     role_filter = bool(
         staff
         and re.search(
-            r"\b(?:pod vedenim|vedie|viedol|veden\w*|veduc\w*|skolitel\w*|konzult(?:ant|oval)\w*)\b", text
+            r"\b(?:pod vedenim|vedie|viedol|veden\w*|veduc\w*|skolitel\w*|konzult\w*)\b", text
         )
     )
     if role_filter:
@@ -276,6 +315,13 @@ def answer_academic(question, index, *, engine, limit=5, units=None, mode=None,
             reason="completion_year_scope",
         )
     selected_paths = {m["source_document"] for m in students}
+    profile_roles=defaultdict(list)
+    for row in rows:
+        if row['kind'] in {'supervisor','consultant'} and not row.get('work_id'):
+            profile_roles[row['source_path']].append(row)
+    profile_work_ids=defaultdict(set)
+    for row in index['units']:
+        if row['kind']=='work_record' and row.get('work_id'):profile_work_ids[row['source_path']].add(row['work_id'])
     groups = defaultdict(list)
     for r in rows:
         if plan and not plan.spec.matches_work(r['person'],r.get('work_label'),r.get('work_type'),r.get('work_year')):continue
@@ -301,7 +347,7 @@ def answer_academic(question, index, *, engine, limit=5, units=None, mode=None,
         groups[(r["source_path"], r.get("work_label"))].append(r)
     requested_role = (
         "consultant"
-        if re.search(r"\bkonzult(?:ant|oval)\w*\b", text) and not re.search(r"\bveduc\w*|\bveden\w*", text)
+        if re.search(r"\bkonzult\w*\b", text) and not re.search(r"\bveduc\w*|\bveden\w*", text)
         else "supervisor"
     )
     if role_filter:
@@ -412,6 +458,9 @@ def answer_academic(question, index, *, engine, limit=5, units=None, mode=None,
                     else f'**{marker["person"]}** — záznam: {marker["date_label"]}; rok nie je uvedený.'
                 )
                 evidence.append(marker)
+                if (plan and {'start_year','date'}<=set(plan.spec.fields) and not plan.spec.facets
+                        and not re.search(r'\bo com\b|\bco sa\b',text)):
+                    continue  # A comparison of time roles does not ask for the meeting agenda.
                 notes = [
                     r
                     for r in part
@@ -481,7 +530,12 @@ def answer_academic(question, index, *, engine, limit=5, units=None, mode=None,
                     ("supervisor", "Vedúci práce"),
                     ("consultant", "Konzultant"),
                 ]:
+                    requested=set(plan.spec.fields)&{'supervisor','consultant'} if plan else set()
+                    if requested and role not in requested:continue
                     roles = [r for r in part if advisor_name(r, role)]
+                    if not roles and len(profile_work_ids[key[0]])==1:
+                        roles=[r for r in profile_roles[key[0]] if advisor_name(r,role)]
+                        if roles:name_label='Vedúci v hlavičke profilu' if role=='supervisor' else 'Konzultant v hlavičke profilu'
                     names = list(dict.fromkeys(advisor_name(r, role) for r in roles))
                     lines.append(
                         name_label
diff --git a/tools/knowledge/local_graph.py b/tools/knowledge/local_graph.py
index ba39fc6..a9c2a24 100644
--- a/tools/knowledge/local_graph.py
+++ b/tools/knowledge/local_graph.py
@@ -175,7 +175,7 @@ def retrieve_local_graph(question, index, bound, *, limit=5):
         'supervisors':[store.identifier(normalized(p.name)) for p in spec.people if p.role=='supervisor'],
         'consultants':[store.identifier(normalized(p.name)) for p in spec.people if p.role=='consultant'],
         'date_field':period.field if period else None,'start':period.start if period else None,'end':period.end if period else None,
-        'profile_records':not any(w.work_type or w.years or w.labels or w.numbers or
+        'profile_records':spec.intent=='advisors' or not any(w.work_type or w.years or w.labels or w.numbers or
                                  w.excluded_labels or w.excluded_numbers or w.unnumbered for w in spec.works),
         'budget':EVIDENCE_LIMIT+1,'peers':min(61,max(11,limit*3+1))}
     settings=get_neo4j_settings()
diff --git a/tools/knowledge/planned_answers.py b/tools/knowledge/planned_answers.py
index 3c6b3aa..1ef0362 100644
--- a/tools/knowledge/planned_answers.py
+++ b/tools/knowledge/planned_answers.py
@@ -46,6 +46,16 @@ def answer_facets(bound, index, units, *, engine, limit):
         keys=[part[i] for i in range(max((len(v) for v in by_person.values()),default=0))
               for part in by_person.values() if i<len(part)]
     requested=plan.facets or ['assignment','results']
+    # The checkpoint preserves uncertain/proposed goals as planned, not as an
+    # established assignment. Use the literal preceding goal heading to make
+    # those proposals available without changing their recorded phase.
+    goal_headers={}
+    tentative_goals=set()
+    for row in units:
+        if row.get('section_scope')=='work' and re.fullmatch(r'(?:mozne |predbezne )?ciele(?: semestralneho projektu)?',normalized(row['text'])):
+            goal_headers.setdefault((row['person'],row.get('work_label')),[]).append(row['line'])
+            if normalized(row['text']).startswith(('mozne ','predbezne ')):
+                tentative_goals.add((row['person'],row.get('work_label')))
     lines=[];evidence=[];missing=False;truncated=len(keys)>limit;size=0;notes=[]
     cells=defaultdict(list)
     for key in keys[:limit]:
@@ -58,8 +68,23 @@ def answer_facets(bound, index, units, *, engine, limit):
             for row in groups[key]:
                 if row['kind'] in {'reference','work_record','meeting','supervisor','consultant','lifecycle','topic'}:continue
                 role={'assignment':'assignment','planned':'task','result':'progress','progress':'progress'}.get(row['phase'],'other')
-                if facet in facets_for(row['text'],role,plan.question,row['phase']):candidates.append(row)
-            candidates.sort(key=lambda r:(-unit_score(r['text'], {'result':'progress','progress':'progress','planned':'task'}.get(r['phase'],r['phase']),facet),r['line']))
+                proposal=(facet=='assignment' and row['phase']=='planned' and row.get('section_scope')=='work'
+                    and any(n<row['line'] for n in goal_headers.get(key,[])))
+                if re.fullmatch(r'(?:predbezne )?(?:zadanie|ciele)',normalized(row['text'])):continue
+                if facet in facets_for(row['text'],role,plan.question,row['phase']) or proposal:candidates.append(row)
+            if facet=='methods' and re.search(r'\b(?:frontend\w*|backend\w*)\b',normalized(plan.question)):
+                candidates=[r for r in candidates if re.search(r'\b(?:frontend\w*|backend\w*)\b',normalized(r['text']))
+                    or 'databaz' in normalized(r['text']) and not re.search(r'\[[xX]\]',r['text'])]
+            if facet=='results' and 'funkcnost' in normalized(plan.question):
+                candidates=[r for r in candidates if re.search(r'\b(?:funguje|funguju|funkcn\w*)\b',normalized(r['text']))]
+            from tools.knowledge.query import _terms
+            question_terms=_terms(plan.question)
+            def score(row):
+                text_terms=_terms(row['text'])
+                overlap=sum(any(a.startswith(b) or b.startswith(a) for a in text_terms) for b in question_terms)
+                checklist=bool(re.match(r'\s*[-*]?\s*\[[xX]\]',row['text']))
+                return unit_score(row['text'],{'result':'progress','progress':'progress','planned':'task'}.get(row['phase'],row['phase']),facet)+12*overlap-35*checklist
+            candidates.sort(key=lambda r:(-score(r),r['line']))
             if facet=="assignment":candidates.sort(key=lambda r:r["line"])
             distinct={}
             for row in candidates:distinct.setdefault(normalized(row['text']),row)
@@ -68,7 +93,7 @@ def answer_facets(bound, index, units, *, engine, limit):
             if not candidates:
                 notes.append(person+' — '+label+': '+FACET_LABELS[facet].lower()+': v podkladoch chýbajú doložené údaje.')
                 lines.append('V podkladoch k tejto práci chýbajú doložené údaje.');cells[(key,facet)].append('V podkladoch chýbajú doložené údaje.');missing=True;continue
-            take=16 if facet=='assignment' else 2 if plan.presentation=='brief' else 5
+            take=2 if plan.presentation=='brief' and facet!='assignment' else len(candidates)
             if len(candidates)>take:truncated=True
             for row in candidates[:take]:
                 if size+len(row['text'])>18000 or work_size+len(row['text'])>work_budget:
@@ -76,9 +101,13 @@ def answer_facets(bound, index, units, *, engine, limit):
                 size+=len(row['text']);work_size+=len(row['text'])
                 quote=re.sub(r'^\s*(?:[-*+]\s+|\d+[.)]\s+)','',row['text'])
                 phase={'assignment':'zadanie','planned':'plán','progress':'priebežný stav','result':'zaznamenaný výsledok'}.get(row['phase'],'podklad')
+                if facet=='assignment' and row['phase']=='planned':phase='navrhovaný cieľ'
                 lines.append('- '+quote+' ('+phase+')')
                 cells[(key,facet)].append(quote+' ('+phase+')')
                 if row['id'] not in used:evidence.append(row);used.add(row['id'])
+            if facet=='assignment' and candidates and key in tentative_goals:
+                note=person+' — '+label+': zdroj tieto body označuje ako možné/navrhované ciele, nie ako potvrdené splnené výsledky.'
+                notes.append(note);lines.append(note)
             if not cells[(key,facet)]:lines.append('Doložené výňatky sa nezmestili do tohto výpisu.')
     if plan.presentation in {'comparison','table'} and keys:
         from tools.answers.presentation import comparison_table
diff --git a/tools/knowledge/query.py b/tools/knowledge/query.py
index 92d0dcd..d21bf1f 100644
--- a/tools/knowledge/query.py
+++ b/tools/knowledge/query.py
@@ -161,9 +161,17 @@ def activity_supported(question, row):
     compares the workflow to NER. These are distinct research tasks in this wiki.
     """
     wording = normalized(question)
+    achieved=re.search(r'\b(?:vytvoril\w*|implementoval\w*|nasadil\w*|pouzil\w*|vyhodnotil\w*|vyskus(?:al|ala|ali)\w*)\b',wording)
+    if achieved and row.get('phase') not in {'result','progress'}:
+        return False
     named_entities = bool(
         re.search(r"\bpomenovan\w* entit\w*\b|\bnamed entit\w*\b|\bner\b", wording)
     )
+    if named_entities:
+        literal=row['text']+' '+' '.join(u['text'] for u in row.get('context_quotes',[]))
+        if 'ner' not in concept_families(literal) and not re.search(
+                r'\bpomenovan\w* entit\w*\b|\bnamed entit\w*\b',normalized(literal)):
+            return False
     if named_entities and re.search(r'\brozpoznav\w*|\brozpoznava\w*|\bnamed entity recognition\b|\bner\b', wording):
         text = normalized(row['text'])
         if re.search(r'\banot\w*\b', text) and not re.search(
@@ -1035,6 +1043,11 @@ def deliver_content(question: str, *, engine: str, limit: int, db_file, complete
                 if (note["source_path"], note.get("work_label"), note["text"]) not in existing
             ),
         ]
+    retrieval_trace=None
+    retrieved_relevance=None
+    if engine=='rag' and plan and mode not in {'advisors','completion','supervisor'}:
+        from tools.knowledge.text_retrieval import retrieve_units
+        units,retrieved_relevance,retrieval_trace=retrieve_units(question,index,plan.spec,limit=limit)
     def traced(result):
         if not include_diagnostics:
             return result
@@ -1044,7 +1057,8 @@ def deliver_content(question: str, *, engine: str, limit: int, db_file, complete
             pool=[row for row in pool if (not people or row['person'] in people)
                   and plan.spec.matches_work(row['person'],row.get('work_label'),row.get('work_type'),row.get('work_year'))]
         return {**result,'retrieval_diagnostics':{
-            'strategy':'bounded_local_graph' if local is not None else 'content_checkpoint',
+            **(retrieval_trace or {}),
+            'strategy':'bounded_local_graph' if local is not None else (retrieval_trace or {}).get('strategy','content_checkpoint'),
             'stage':'loaded_scoped_units_before_answer_selection',
             'candidate_ids':list(dict.fromkeys(row['id'] for row in pool)),
             'candidate_count':len(pool)}}
@@ -1080,8 +1094,8 @@ def deliver_content(question: str, *, engine: str, limit: int, db_file, complete
         from tools.knowledge.sources import with_work_context
 
         units = with_work_context(units)
-    relevance = None
-    if local is None and mode in {"discovery", "similar"} and index.get("vectors"):
+    relevance = retrieved_relevance
+    if local is None and relevance is None and mode in {"discovery", "similar"} and index.get("vectors"):
         import base64
         import zlib
         import numpy as np
@@ -1129,6 +1143,9 @@ def deliver_content(question: str, *, engine: str, limit: int, db_file, complete
         plan=plan,
         local_graph=local,
     )
+    if mode in {'advisors','meeting'} and plan and set(plan.spec.fields)&{'document_author','start_year'}:
+        from tools.knowledge.academic import with_profile_metadata
+        result=with_profile_metadata(result,plan,db_file)
     if local is not None:
         from tools.knowledge.graph_answers import graph_details
         result = graph_details(result,local)
diff --git a/tools/knowledge/text_retrieval.py b/tools/knowledge/text_retrieval.py
new file mode 100644
index 0000000..bfcdc23
--- /dev/null
+++ b/tools/knowledge/text_retrieval.py
@@ -0,0 +1,81 @@
+"""Independent FTS5/BM25 and E5 candidates over witnessed source assertions.
+
+The checkpoint is an evidence store, not an alternative to text retrieval.
+Named finite scopes are expanded after ranking so a bounded candidate list
+cannot silently remove an assignment goal or one side of a comparison.
+"""
+from functools import lru_cache
+import base64
+import sqlite3
+import threading
+import zlib
+
+from tools.answers.context_policy import normalized
+
+_lock = threading.RLock()
+
+
+@lru_cache(maxsize=2)
+def lexical_index(version, texts):
+    connection = sqlite3.connect(':memory:', check_same_thread=False)
+    connection.execute('CREATE VIRTUAL TABLE evidence USING fts5(id UNINDEXED, text, tokenize="unicode61 remove_diacritics 2")')
+    connection.executemany('INSERT INTO evidence VALUES (?,?)',texts)
+    return connection
+
+
+def retrieve_units(question, index, spec, *, limit=5):
+    from tools.knowledge.query import _terms
+    from tools.knowledge.evidence import claim_eligible
+    people={p.name for p in spec.people if p.role=='student'}
+    similar=spec.intent=='similar'
+    def in_scope(row):
+        if not spec.matches_work(row['person'],row.get('work_label'),row.get('work_type'),row.get('work_year')):return False
+        if spec.period and spec.period.field!='work_year':
+            value=row.get(spec.period.field)
+            if not value or not spec.period.start<=value<=spec.period.end:return False
+        return similar or not people or row['person'] in people
+    candidates=[r for r in index['units'] if in_scope(r)]
+    eligible={r['id'] for r in candidates}
+    texts=tuple((r['id'],r['text']+' '+' '.join(r.get('concepts',[]))) for r in index['units'])
+    connection=lexical_index(index['version'],texts)
+    terms=_terms(question)
+    if similar:
+        # The reference's actual substantive topics seed the peer search.
+        from tools.knowledge.concepts import substantive_concept
+        terms={normalized(c) for r in candidates if r['person'] in people and claim_eligible(r)
+               for c in r.get('concepts',[]) if substantive_concept(c)} or terms
+    expression=' OR '.join('"'+term.replace('"','""')+'"*' for term in sorted(terms))
+    with _lock:
+        lexical=[r[0] for r in connection.execute(
+            'SELECT id FROM evidence WHERE evidence MATCH ? ORDER BY bm25(evidence),id',
+            (expression,)) if r[0] in eligible] if expression else []
+    semantic={}
+    if index.get('vectors'):
+        import numpy as np
+        from tools.rag.embedding_utils import embed_query,model_profile,embedding_model_name
+        if model_profile(embedding_model_name())['fingerprint']!=index['embedding_fingerprint']:
+            raise RuntimeError('Content embeddings belong to another model')
+        matrix=np.frombuffer(zlib.decompress(base64.b64decode(index['vectors'])),dtype='<f4').reshape(index['vector_shape'])
+        if len(matrix)!=len(index['units']):raise ValueError('Invalid content vector count')
+        anchors=[i for i,r in enumerate(index['units']) if r['person'] in people and in_scope(r) and claim_eligible(r)] if similar else []
+        vector=np.mean(matrix[anchors],axis=0) if anchors else embed_query(question)
+        norm=float(np.linalg.norm(vector))
+        if not norm:raise ValueError('Invalid query/reference embedding')
+        scores=matrix @ (vector/norm)
+        semantic={r['id']:float(s) for r,s in zip(index['units'],scores) if r['id'] in eligible}
+    vector_rank=sorted(semantic,key=lambda key:(-semantic[key],key))
+    budget=min(256,max(80,limit*16))
+    fused={}
+    for ranking in (lexical[:budget],vector_rank[:budget]):
+        for position,key in enumerate(ranking,1):fused[key]=fused.get(key,0)+1/(60+position)
+    ranked=sorted(fused,key=lambda key:(-fused[key],key))[:budget]
+    selected=set(ranked)
+    # Whole named source/work scopes are finite, explicit evidence expansion;
+    # this is not an assumption that a global top-k result is exhaustive.
+    if people:
+        selected.update(r['id'] for r in candidates if r['person'] in people)
+    rows=[r for r in candidates if r['id'] in selected]
+    rows.sort(key=lambda r:(-fused.get(r['id'],0),r['source_path'],r['line']))
+    return rows,semantic,{'strategy':'bm25_e5_rrf_assertions','candidate_count':len(rows),
+        'lexical_candidates':len(lexical[:budget]),'vector_candidates':len(vector_rank[:budget]),
+        'named_scope_expansion':bool(people),'bounded':not people and len(candidates)>len(rows)}
diff --git a/tools/rag/llm_context.py b/tools/rag/llm_context.py
index 31c523e..b2258e2 100644
--- a/tools/rag/llm_context.py
+++ b/tools/rag/llm_context.py
@@ -37,6 +37,7 @@ FIELDS = (
     "clarification",
     "named_subjects",
     "work_scopes",
+    "named_scope_complete",
     "question_plan",
     "resolved_query",
     "name_resolution",
diff --git a/tools/retrieval/person_names.py b/tools/retrieval/person_names.py
index 4322350..6f30b87 100644
--- a/tools/retrieval/person_names.py
+++ b/tools/retrieval/person_names.py
@@ -73,7 +73,7 @@ def _score(words: list[str], canonical: list[str]) -> int | None:
             if (
                 not actual.isalpha()
                 or not expected.isalpha()
-                or min(len(actual), len(expected)) < 4
+                or min(len(actual), len(expected)) < 3
             ):
                 return None
             cap = 2 if min(len(actual), len(expected)) >= 8 else 1
@@ -189,7 +189,7 @@ def resolve_person_names(query: str, catalog: list[dict]) -> dict:
     }
     author_lookup = bool(
         re.search(
-            r"\b(?:autor\w*|dokument\w*|vedie|viedol|veduc\w*|skolitel\w*|konzult(?:ant|oval)\w*)\b", normalize(query)
+            r"\b(?:autor\w*|dokument\w*|vedie|viedol|veduc\w*|skolitel\w*|konzult\w*)\b", normalize(query)
         )
     )
     people = [
@@ -345,7 +345,14 @@ def resolve_person_names(query: str, catalog: list[dict]) -> dict:
             and not token[0].isupper()
             and not tokens[i + 1][0].isupper()
         )
-        comparison_subject = bool(selected and pair and re.search(r"\b(?:a|porovnaj)\s*$", prefix))
+        # A conjunction introduces another identity only while enumerating
+        # names. "Ján Holp ... a nezamieňaj ich" is a presentation request.
+        adjacent_name = any(right <= token.start() and re.fullmatch(
+            r"\s*(?:,|a)\s*", query[right:token.start()]) for _,right,_ in selected)
+        comparison_subject = bool(selected and pair and (adjacent_name or re.fullmatch(r"porovnaj\s*",prefix)))
+        identity_role = bool(re.search(r"\b(?:osob\w*|student\w*|kandidat\w*|autor(?:a|om)?)\s*$",prefix)) and not reference
+        owner_slot = role and not selected and not re.match(
+            r'^(?:kto|ktori|ktory|u koho)\b', normalize(query))
         # Lower-case unknown entries in an explicit candidate list must still
         # fail closed, even if they follow a comma or a known person.
         list_subject = explicit_list and any(
@@ -353,8 +360,8 @@ def resolve_person_names(query: str, catalog: list[dict]) -> dict:
             for _, right, _ in selected
         )
         if not (
-            role
-            or (pair and words[i] in first_names)
+            owner_slot or (identity_role and pair)
+            or words[i] in first_names
             or upper_pair
             or comparison_subject
             or list_subject