Import ClosureBench dataset and evaluation artifacts

.gitignore

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+.DS_Store
+__pycache__/
+*.pyc
+.cache/

LICENSE

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+ClosureBench dataset
+
+This dataset is released under the Creative Commons Attribution 4.0
+International License (CC BY 4.0).
+
+License summary: https://creativecommons.org/licenses/by/4.0/
+Legal code: https://creativecommons.org/licenses/by/4.0/legalcode

README.md

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 ---
-license: mit
+pretty_name: ClosureBench
+language:
+- en
+license: cc-by-4.0
+task_categories:
+- text-classification
+- question-answering
+tags:
+- benchmark
+- llm-evaluation
+- reasoning
+- knowledge-representation
+- open-world-assumption
+- closed-world-assumption
+- local-closed-world-assumption
+- agent-evaluation
+- symbolic-reasoning
+configs:
+- config_name: base
+  data_files:
+  - split: full
+    path: data/base/full.jsonl
+  - split: train
+    path: data/base/train.jsonl
+  - split: validation
+    path: data/base/validation.jsonl
+  - split: test
+    path: data/base/test.jsonl
+- config_name: ask_act
+  data_files:
+  - split: full
+    path: data/ask_act/full.jsonl
+  - split: train
+    path: data/ask_act/train.jsonl
+  - split: validation
+    path: data/ask_act/validation.jsonl
+  - split: test
+    path: data/ask_act/test.jsonl
+- config_name: multi_agent
+  data_files:
+  - split: full
+    path: data/multi_agent/full.jsonl
+  - split: train
+    path: data/multi_agent/train.jsonl
+  - split: validation
+    path: data/multi_agent/validation.jsonl
+  - split: test
+    path: data/multi_agent/test.jsonl
+- config_name: dynamic_dialogue
+  data_files:
+  - split: full
+    path: data/dynamic_dialogue/full.jsonl
+  - split: train
+    path: data/dynamic_dialogue/train.jsonl
+  - split: validation
+    path: data/dynamic_dialogue/validation.jsonl
+  - split: test
+    path: data/dynamic_dialogue/test.jsonl
 ---
+
+# ClosureBench
+
+ClosureBench is a controlled benchmark for evaluating if LLMs
+respect explicit semantic contracts about missing information. It tests whether
+models distinguish absence-as-unknown, absence-as-false, and
+absence-as-false-only-in-complete-scopes under explicit open-world,
+closed-world, and locally closed-world contracts.
+
+The dataset includes the base benchmark and three extensions:
+
+| Config | `full` rows | Description |
+|---|---:|---|
+| `base` | 960 | Main OWA/CWA/LCWA benchmark with fixed facts, rules, and query across semantic variants. |
+| `ask_act` | 960 | Maps truth values to operational actions: `approve`, `deny`, or `request_information`. |
+| `multi_agent` | 360 | Tests whether a coordinator preserves source-scoped closure. |
+| `dynamic_dialogue` | 100 | Tests whether models update conclusions when complete predicates change across turns. |
+
+Each config exposes four splits:
+
+| Split | Meaning |
+|---|---|
+| `full` | Full split used for the paper's reported metrics. |
+| `train` | Internal benchmark train partition. |
+| `validation` | Internal benchmark development partition. |
+| `test` | Internal benchmark test partition. |
+
+## Loading
+
+```python
+from datasets import load_dataset
+
+base = load_dataset("ML0037/ClosureBench", "base", split="full")
+ask_act = load_dataset("ML0037/ClosureBench", "ask_act", split="full")
+multi_agent = load_dataset("ML0037/ClosureBench", "multi_agent", split="full")
+dynamic = load_dataset("ML0037/ClosureBench", "dynamic_dialogue", split="full")
+```
+
+For the held-out partition only:
+
+```python
+base_test = load_dataset("ML0037/ClosureBench", "base", split="test")
+```
+
+## Base Results
+
+Values are three-run mean +/- sample standard deviation on the `base` config,
+`full` split.
+
+| Model | Semantic switch | Core switch | LCWA closed | LCWA open | Overall |
+|---|---:|---:|---:|---:|---:|
+| Mistral Small | 55.73 +/- 2.43 | 27.60 +/- 3.65 | 31.60 +/- 4.34 | 31.25 +/- 1.80 | 80.38 +/- 1.02 |
+| DeepSeek Flash | 81.25 +/- 0.83 | 68.75 +/- 1.38 | 49.65 +/- 3.18 | 100.00 +/- 0.00 | 90.31 +/- 0.47 |
+| DeepSeek Pro | 86.46 +/- 0.18 | 77.43 +/- 0.30 | 62.15 +/- 1.20 | 100.00 +/- 0.00 | 93.61 +/- 0.21 |
+| Mistral Medium | 87.08 +/- 0.65 | 78.47 +/- 1.09 | 56.95 +/- 2.17 | 100.00 +/- 0.00 | 95.59 +/- 0.37 |
+| Llama Scout | 50.73 +/- 0.48 | 18.23 +/- 1.04 | 4.51 +/- 1.59 | 52.43 +/- 3.01 | 71.18 +/- 0.53 |
+| Llama Maverick | 99.59 +/- 0.18 | 99.31 +/- 0.30 | 99.65 +/- 0.60 | 100.00 +/- 0.00 | 99.86 +/- 0.06 |
+
+Semantic switch accuracy is the primary metric: a base scenario is correct only
+when all semantic variants of that scenario are answered correctly.
+
+## Result Artifacts
+
+The `results/scored/` directory contains final `*_scored.jsonl` files used to
+compute reported metrics. Raw provider response dumps are intentionally not
+included.
+
+The `results/reports/` directory contains JSON manifests and aggregate
+summaries, including:
+
+| File | Purpose |
+|---|---|
+| `results/reports/closurebench_replicate_summary.json` | Base benchmark three-run summary. |
+| `results/reports/closurebench_replicate_manifest.json` | Base benchmark scored-run manifest. |
+| `results/reports/closurebench_model_comparison.json` | Single-run base comparison. |
+| `results/reports/closurebench_ask_act_summary.json` | Ask/Act extension summary. |
+| `results/reports/closurebench_multi_agent_summary.json` | Multi-Agent extension summary. |
+| `results/reports/closurebench_dynamic_dialogue_summary.json` | Dynamic Dialogue extension summary. |
+
+## Data Fields
+
+Common fields include:
+
+- `id`: item identifier.
+- `base_id`: contrastive scenario identifier.
+- `split`: original benchmark partition (`train`, `dev`, or `test`).
+- `domain`, `family`, `subset`: item grouping metadata.
+- `semantics`: semantic contract for base-style items (`owa`, `cwa`, `lcwa`).
+- `closed_predicates`: predicates declared complete for the item.
+- `facts_positive`, `facts_negative`, `rules_natural`: natural-language KB.
+- `symbolic`: symbolic atoms, rules, query atom, and closure atoms.
+- `prompt`: exact prompt used for evaluation.
+- `gold_answer`, `gold_truth_value`, or extension-specific gold fields.
+
+Extension configs add task-specific fields, such as `gold_action` for Ask/Act,
+`gold_source_used` for Multi-Agent, and turn-level gold labels for Dynamic
+Dialogue.
+
+## Limitations
+
+ClosureBench is a targeted diagnostic benchmark. It isolates closure-contract
+compliance under controlled prompts but it is not a broad measure of general agent
+performance, factual knowledge, or end-to-end tool-use reliability.
+
+
+## License
+
+The dataset is released under CC BY 4.0.

data/dynamic_dialogue/test.jsonl

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+{"id": "dynamic_dialogue_v3_0003", "base_id": "cloud_deployment_0250_closed_missing_with_open_distractor", "split": "test", "domain": "cloud_deployment", "family": "closed_missing_with_open_distractor", "subset": "core_contrastive", "gold_pattern": "unknown|false|false", "dialogue_type": "persist_after_narrowing", "target_statement": "Service E has deployment approval.", "query_atom": "has_deployment_approval::Service E", "facts_positive": ["Service F has an incident note.", "Service E passed unit tests."], "facts_negative": [], "rules_natural": [], "predicate_glossary": {"passed_unit_tests": "passed unit tests", "has_deployment_approval": "has deployment approval", "has_incident_note": "has an incident note", "may_deploy": "may deploy to production", "needs_sre_review": "needs SRE review"}, "vocabulary_predicates": ["has_deployment_approval", "has_incident_note", "may_deploy", "needs_sre_review", "passed_unit_tests"], "symbolic": {"positive_atoms": ["has_incident_note::Service F", "passed_unit_tests::Service E"], "negative_atoms": [], "rules": [], "query_atom": "has_deployment_approval::Service E", "closure_atoms": ["has_incident_note::Service F", "passed_unit_tests::Service E"]}, "turns": [{"turn_index": 1, "source_semantics": "owa", "update_operation": "SET_COMPLETE_PREDICATES", "update_predicates": [], "complete_predicates_after_update": [], "gold_answer": "unknown", "expected_changed_from_previous": null, "expected_applied_update": "open", "prompt": "This is turn 1 of a 3-turn dynamic completeness dialogue.\nYou are given an initial knowledge base and an initial completeness state.\nDo not use outside knowledge.\nReturn exactly one compact JSON object with keys answer, changed_from_previous, applied_update, and rationale. The answer value must be one of true, false, unknown. The changed_from_previous value must be true, false, or null. The applied_update value must be one of open, global_complete, local_complete. For this turn, applied_update must be exactly open. For turn 1, changed_from_previous must be null.\n\nReasoning contract:\n- Positive facts are true.\n- Negative facts make the corresponding target atom false.\n- A rule derives its conclusion only when every antecedent is true.\n- First apply all rules until no new facts can be derived.\n- Then evaluate unstated and underivable atoms using the current completeness state.\n- If a predicate is complete, an unstated and underivable atom using that predicate is false.\n- If a predicate is not complete, an unstated and underivable atom using that predicate is unknown.\n- A false answer derived from completeness is not a permanent negative fact; it can be withdrawn if the completeness state changes.\n\nKnowledge base:\n\nPredicate glossary:\n- has_deployment_approval: has deployment approval\n- has_incident_note: has an incident note\n- may_deploy: may deploy to production\n- needs_sre_review: needs SRE review\n- passed_unit_tests: passed unit tests\n\nPositive facts:\n- Service F has an incident note.\n- Service E passed unit tests.\n\nNegative facts:\n- none\n\nRules:\n- none\n\nTarget statement: Service E has deployment approval.\nTarget atom: has_deployment_approval::Service E\n\nCompleteness-state update for this turn:\n- Operation: SET_COMPLETE_PREDICATES\n- New complete predicates: none"}, {"turn_index": 2, "source_semantics": "cwa", "update_operation": "ADD_COMPLETE_PREDICATES", "update_predicates": ["has_deployment_approval", "has_incident_note", "may_deploy", "needs_sre_review", "passed_unit_tests"], "complete_predicates_after_update": ["has_deployment_approval", "has_incident_note", "may_deploy", "needs_sre_review", "passed_unit_tests"], "gold_answer": "false", "expected_changed_from_previous": true, "expected_applied_update": "global_complete", "prompt": "This is turn 2 of a 3-turn dynamic completeness dialogue.\nKeep the exact same knowledge base, facts, rules, glossary, and target from turn 1.\nApply only the completeness-state update below.\nRecompute the answer from the updated state. Do not treat a previous false/unknown conclusion as a new fact.\nReturn exactly one compact JSON object with keys answer, changed_from_previous, applied_update, and rationale. The answer value must be one of true, false, unknown. The changed_from_previous value must be true, false, or null. The applied_update value must be one of open, global_complete, local_complete. For this turn, applied_update must be exactly global_complete. For this turn, changed_from_previous must be true iff your current answer differs from your immediately previous answer.\n\nReasoning contract:\n- Positive facts are true.\n- Negative facts make the corresponding target atom false.\n- A rule derives its conclusion only when every antecedent is true.\n- First apply all rules until no new facts can be derived.\n- Then evaluate unstated and underivable atoms using the current completeness state.\n- If a predicate is complete, an unstated and underivable atom using that predicate is false.\n- If a predicate is not complete, an unstated and underivable atom using that predicate is unknown.\n- A false answer derived from completeness is not a permanent negative fact; it can be withdrawn if the completeness state changes.\n\nCompleteness-state update for this turn:\n- Operation: ADD_COMPLETE_PREDICATES\n- Predicates to add as complete: has_deployment_approval, has_incident_note, may_deploy, needs_sre_review, passed_unit_tests\n- Complete predicates after this update: has_deployment_approval, has_incident_note, may_deploy, needs_sre_review, passed_unit_tests\n\nTarget statement: Service E has deployment approval.\nTarget atom: has_deployment_approval::Service E"}, {"turn_index": 3, "source_semantics": "lcwa", "update_operation": "REPLACE_COMPLETE_PREDICATES", "update_predicates": ["has_deployment_approval"], "complete_predicates_after_update": ["has_deployment_approval"], "gold_answer": "false", "expected_changed_from_previous": false, "expected_applied_update": "local_complete", "prompt": "This is turn 3 of a 3-turn dynamic completeness dialogue.\nKeep the exact same knowledge base, facts, rules, glossary, and target from turn 1.\nApply only the completeness-state update below.\nRecompute the answer from the updated state. Do not treat a previous false/unknown conclusion as a new fact.\nReturn exactly one compact JSON object with keys answer, changed_from_previous, applied_update, and rationale. The answer value must be one of true, false, unknown. The changed_from_previous value must be true, false, or null. The applied_update value must be one of open, global_complete, local_complete. For this turn, applied_update must be exactly local_complete. For this turn, changed_from_previous must be true iff your current answer differs from your immediately previous answer.\n\nReasoning contract:\n- Positive facts are true.\n- Negative facts make the corresponding target atom false.\n- A rule derives its conclusion only when every antecedent is true.\n- First apply all rules until no new facts can be derived.\n- Then evaluate unstated and underivable atoms using the current completeness state.\n- If a predicate is complete, an unstated and underivable atom using that predicate is false.\n- If a predicate is not complete, an unstated and underivable atom using that predicate is unknown.\n- A false answer derived from completeness is not a permanent negative fact; it can be withdrawn if the completeness state changes.\n\nCompleteness-state update for this turn:\n- Operation: REPLACE_COMPLETE_PREDICATES\n- Discard the previous complete-predicate set.\n- Replacement complete predicates: has_deployment_approval\n\nTarget statement: Service E has deployment approval.\nTarget atom: has_deployment_approval::Service E"}]}
+{"id": "dynamic_dialogue_v3_0005", "base_id": "cloud_deployment_0254_open_missing_with_closed_distractor", "split": "test", "domain": "cloud_deployment", "family": "open_missing_with_closed_distractor", "subset": "core_contrastive", "gold_pattern": "unknown|false|unknown", "dialogue_type": "reopen", "target_statement": "Service C has an incident note.", "query_atom": "has_incident_note::Service C", "facts_positive": ["Service D has deployment approval.", "Service D passed unit tests."], "facts_negative": [], "rules_natural": [], "predicate_glossary": {"passed_unit_tests": "passed unit tests", "has_deployment_approval": "has deployment approval", "has_incident_note": "has an incident note", "may_deploy": "may deploy to production", "needs_sre_review": "needs SRE review"}, "vocabulary_predicates": ["has_deployment_approval", "has_incident_note", "may_deploy", "needs_sre_review", "passed_unit_tests"], "symbolic": {"positive_atoms": ["has_deployment_approval::Service D", "passed_unit_tests::Service D"], "negative_atoms": [], "rules": [], "query_atom": "has_incident_note::Service C", "closure_atoms": ["has_deployment_approval::Service D", "passed_unit_tests::Service D"]}, "turns": [{"turn_index": 1, "source_semantics": "owa", "update_operation": "SET_COMPLETE_PREDICATES", "update_predicates": [], "complete_predicates_after_update": [], "gold_answer": "unknown", "expected_changed_from_previous": null, "expected_applied_update": "open", "prompt": "This is turn 1 of a 3-turn dynamic completeness dialogue.\nYou are given an initial knowledge base and an initial completeness state.\nDo not use outside knowledge.\nReturn exactly one compact JSON object with keys answer, changed_from_previous, applied_update, and rationale. The answer value must be one of true, false, unknown. The changed_from_previous value must be true, false, or null. The applied_update value must be one of open, global_complete, local_complete. For this turn, applied_update must be exactly open. For turn 1, changed_from_previous must be null.\n\nReasoning contract:\n- Positive facts are true.\n- Negative facts make the corresponding target atom false.\n- A rule derives its conclusion only when every antecedent is true.\n- First apply all rules until no new facts can be derived.\n- Then evaluate unstated and underivable atoms using the current completeness state.\n- If a predicate is complete, an unstated and underivable atom using that predicate is false.\n- If a predicate is not complete, an unstated and underivable atom using that predicate is unknown.\n- A false answer derived from completeness is not a permanent negative fact; it can be withdrawn if the completeness state changes.\n\nKnowledge base:\n\nPredicate glossary:\n- has_deployment_approval: has deployment approval\n- has_incident_note: has an incident note\n- may_deploy: may deploy to production\n- needs_sre_review: needs SRE review\n- passed_unit_tests: passed unit tests\n\nPositive facts:\n- Service D has deployment approval.\n- Service D passed unit tests.\n\nNegative facts:\n- none\n\nRules:\n- none\n\nTarget statement: Service C has an incident note.\nTarget atom: has_incident_note::Service C\n\nCompleteness-state update for this turn:\n- Operation: SET_COMPLETE_PREDICATES\n- New complete predicates: none"}, {"turn_index": 2, "source_semantics": "cwa", "update_operation": "ADD_COMPLETE_PREDICATES", "update_predicates": ["has_deployment_approval", "has_incident_note", "may_deploy", "needs_sre_review", "passed_unit_tests"], "complete_predicates_after_update": ["has_deployment_approval", "has_incident_note", "may_deploy", "needs_sre_review", "passed_unit_tests"], "gold_answer": "false", "expected_changed_from_previous": true, "expected_applied_update": "global_complete", "prompt": "This is turn 2 of a 3-turn dynamic completeness dialogue.\nKeep the exact same knowledge base, facts, rules, glossary, and target from turn 1.\nApply only the completeness-state update below.\nRecompute the answer from the updated state. Do not treat a previous false/unknown conclusion as a new fact.\nReturn exactly one compact JSON object with keys answer, changed_from_previous, applied_update, and rationale. The answer value must be one of true, false, unknown. The changed_from_previous value must be true, false, or null. The applied_update value must be one of open, global_complete, local_complete. For this turn, applied_update must be exactly global_complete. For this turn, changed_from_previous must be true iff your current answer differs from your immediately previous answer.\n\nReasoning contract:\n- Positive facts are true.\n- Negative facts make the corresponding target atom false.\n- A rule derives its conclusion only when every antecedent is true.\n- First apply all rules until no new facts can be derived.\n- Then evaluate unstated and underivable atoms using the current completeness state.\n- If a predicate is complete, an unstated and underivable atom using that predicate is false.\n- If a predicate is not complete, an unstated and underivable atom using that predicate is unknown.\n- A false answer derived from completeness is not a permanent negative fact; it can be withdrawn if the completeness state changes.\n\nCompleteness-state update for this turn:\n- Operation: ADD_COMPLETE_PREDICATES\n- Predicates to add as complete: has_deployment_approval, has_incident_note, may_deploy, needs_sre_review, passed_unit_tests\n- Complete predicates after this update: has_deployment_approval, has_incident_note, may_deploy, needs_sre_review, passed_unit_tests\n\nTarget statement: Service C has an incident note.\nTarget atom: has_incident_note::Service C"}, {"turn_index": 3, "source_semantics": "lcwa", "update_operation": "REPLACE_COMPLETE_PREDICATES", "update_predicates": ["has_deployment_approval", "may_deploy"], "complete_predicates_after_update": ["has_deployment_approval", "may_deploy"], "gold_answer": "unknown", "expected_changed_from_previous": true, "expected_applied_update": "local_complete", "prompt": "This is turn 3 of a 3-turn dynamic completeness dialogue.\nKeep the exact same knowledge base, facts, rules, glossary, and target from turn 1.\nApply only the completeness-state update below.\nRecompute the answer from the updated state. Do not treat a previous false/unknown conclusion as a new fact.\nReturn exactly one compact JSON object with keys answer, changed_from_previous, applied_update, and rationale. The answer value must be one of true, false, unknown. The changed_from_previous value must be true, false, or null. The applied_update value must be one of open, global_complete, local_complete. For this turn, applied_update must be exactly local_complete. For this turn, changed_from_previous must be true iff your current answer differs from your immediately previous answer.\n\nReasoning contract:\n- Positive facts are true.\n- Negative facts make the corresponding target atom false.\n- A rule derives its conclusion only when every antecedent is true.\n- First apply all rules until no new facts can be derived.\n- Then evaluate unstated and underivable atoms using the current completeness state.\n- If a predicate is complete, an unstated and underivable atom using that predicate is false.\n- If a predicate is not complete, an unstated and underivable atom using that predicate is unknown.\n- A false answer derived from completeness is not a permanent negative fact; it can be withdrawn if the completeness state changes.\n\nCompleteness-state update for this turn:\n- Operation: REPLACE_COMPLETE_PREDICATES\n- Discard the previous complete-predicate set.\n- Replacement complete predicates: has_deployment_approval, may_deploy\n\nTarget statement: Service C has an incident note.\nTarget atom: has_incident_note::Service C"}]}
+{"id": "dynamic_dialogue_v3_0016", "base_id": "finance_controls_0136_closed_derived_missing_antecedent", "split": "test", "domain": "finance_controls", "family": "closed_derived_missing_antecedent", "subset": "core_contrastive", "gold_pattern": "unknown|false|false", "dialogue_type": "persist_after_narrowing", "target_statement": "Account 59 may receive a wire transfer.", "query_atom": "may_receive_wire::Account 59", "facts_positive": ["Account 59 passed KYC checks."], "facts_negative": [], "rules_natural": ["If an account passed KYC checks and has compliance clearance, then that account may receive a wire transfer."], "predicate_glossary": {"passed_kyc": "passed KYC checks", "has_compliance_clearance": "has compliance clearance", "has_manual_exception": "has a manual exception", "may_receive_wire": "may receive a wire transfer", "needs_compliance_review": "needs compliance review"}, "vocabulary_predicates": ["has_compliance_clearance", "has_manual_exception", "may_receive_wire", "needs_compliance_review", "passed_kyc"], "symbolic": {"positive_atoms": ["passed_kyc::Account 59"], "negative_atoms": [], "rules": [{"antecedents": ["passed_kyc::Account 59", "has_compliance_clearance::Account 59"], "conclusion": "may_receive_wire::Account 59", "text": "If an account passed KYC checks and has compliance clearance, then that account may receive a wire transfer."}], "query_atom": "may_receive_wire::Account 59", "closure_atoms": ["passed_kyc::Account 59"]}, "turns": [{"turn_index": 1, "source_semantics": "owa", "update_operation": "SET_COMPLETE_PREDICATES", "update_predicates": [], "complete_predicates_after_update": [], "gold_answer": "unknown", "expected_changed_from_previous": null, "expected_applied_update": "open", "prompt": "This is turn 1 of a 3-turn dynamic completeness dialogue.\nYou are given an initial knowledge base and an initial completeness state.\nDo not use outside knowledge.\nReturn exactly one compact JSON object with keys answer, changed_from_previous, applied_update, and rationale. The answer value must be one of true, false, unknown. The changed_from_previous value must be true, false, or null. The applied_update value must be one of open, global_complete, local_complete. For this turn, applied_update must be exactly open. For turn 1, changed_from_previous must be null.\n\nReasoning contract:\n- Positive facts are true.\n- Negative facts make the corresponding target atom false.\n- A rule derives its conclusion only when every antecedent is true.\n- First apply all rules until no new facts can be derived.\n- Then evaluate unstated and underivable atoms using the current completeness state.\n- If a predicate is complete, an unstated and underivable atom using that predicate is false.\n- If a predicate is not complete, an unstated and underivable atom using that predicate is unknown.\n- A false answer derived from completeness is not a permanent negative fact; it can be withdrawn if the completeness state changes.\n\nKnowledge base:\n\nPredicate glossary:\n- has_compliance_clearance: has compliance clearance\n- has_manual_exception: has a manual exception\n- may_receive_wire: may receive a wire transfer\n- needs_compliance_review: needs compliance review\n- passed_kyc: passed KYC checks\n\nPositive facts:\n- Account 59 passed KYC checks.\n\nNegative facts:\n- none\n\nRules:\n- If an account passed KYC checks and has compliance clearance, then that account may receive a wire transfer.\n\nTarget statement: Account 59 may receive a wire transfer.\nTarget atom: may_receive_wire::Account 59\n\nCompleteness-state update for this turn:\n- Operation: SET_COMPLETE_PREDICATES\n- New complete predicates: none"}, {"turn_index": 2, "source_semantics": "cwa", "update_operation": "ADD_COMPLETE_PREDICATES", "update_predicates": ["has_compliance_clearance", "has_manual_exception", "may_receive_wire", "needs_compliance_review", "passed_kyc"], "complete_predicates_after_update": ["has_compliance_clearance", "has_manual_exception", "may_receive_wire", "needs_compliance_review", "passed_kyc"], "gold_answer": "false", "expected_changed_from_previous": true, "expected_applied_update": "global_complete", "prompt": "This is turn 2 of a 3-turn dynamic completeness dialogue.\nKeep the exact same knowledge base, facts, rules, glossary, and target from turn 1.\nApply only the completeness-state update below.\nRecompute the answer from the updated state. Do not treat a previous false/unknown conclusion as a new fact.\nReturn exactly one compact JSON object with keys answer, changed_from_previous, applied_update, and rationale. The answer value must be one of true, false, unknown. The changed_from_previous value must be true, false, or null. The applied_update value must be one of open, global_complete, local_complete. For this turn, applied_update must be exactly global_complete. For this turn, changed_from_previous must be true iff your current answer differs from your immediately previous answer.\n\nReasoning contract:\n- Positive facts are true.\n- Negative facts make the corresponding target atom false.\n- A rule derives its conclusion only when every antecedent is true.\n- First apply all rules until no new facts can be derived.\n- Then evaluate unstated and underivable atoms using the current completeness state.\n- If a predicate is complete, an unstated and underivable atom using that predicate is false.\n- If a predicate is not complete, an unstated and underivable atom using that predicate is unknown.\n- A false answer derived from completeness is not a permanent negative fact; it can be withdrawn if the completeness state changes.\n\nCompleteness-state update for this turn:\n- Operation: ADD_COMPLETE_PREDICATES\n- Predicates to add as complete: has_compliance_clearance, has_manual_exception, may_receive_wire, needs_compliance_review, passed_kyc\n- Complete predicates after this update: has_compliance_clearance, has_manual_exception, may_receive_wire, needs_compliance_review, passed_kyc\n\nTarget statement: Account 59 may receive a wire transfer.\nTarget atom: may_receive_wire::Account 59"}, {"turn_index": 3, "source_semantics": "lcwa", "update_operation": "REPLACE_COMPLETE_PREDICATES", "update_predicates": ["has_compliance_clearance", "may_receive_wire"], "complete_predicates_after_update": ["has_compliance_clearance", "may_receive_wire"], "gold_answer": "false", "expected_changed_from_previous": false, "expected_applied_update": "local_complete", "prompt": "This is turn 3 of a 3-turn dynamic completeness dialogue.\nKeep the exact same knowledge base, facts, rules, glossary, and target from turn 1.\nApply only the completeness-state update below.\nRecompute the answer from the updated state. Do not treat a previous false/unknown conclusion as a new fact.\nReturn exactly one compact JSON object with keys answer, changed_from_previous, applied_update, and rationale. The answer value must be one of true, false, unknown. The changed_from_previous value must be true, false, or null. The applied_update value must be one of open, global_complete, local_complete. For this turn, applied_update must be exactly local_complete. For this turn, changed_from_previous must be true iff your current answer differs from your immediately previous answer.\n\nReasoning contract:\n- Positive facts are true.\n- Negative facts make the corresponding target atom false.\n- A rule derives its conclusion only when every antecedent is true.\n- First apply all rules until no new facts can be derived.\n- Then evaluate unstated and underivable atoms using the current completeness state.\n- If a predicate is complete, an unstated and underivable atom using that predicate is false.\n- If a predicate is not complete, an unstated and underivable atom using that predicate is unknown.\n- A false answer derived from completeness is not a permanent negative fact; it can be withdrawn if the completeness state changes.\n\nCompleteness-state update for this turn:\n- Operation: REPLACE_COMPLETE_PREDICATES\n- Discard the previous complete-predicate set.\n- Replacement complete predicates: has_compliance_clearance, may_receive_wire\n\nTarget statement: Account 59 may receive a wire transfer.\nTarget atom: may_receive_wire::Account 59"}]}
+{"id": "dynamic_dialogue_v3_0026", "base_id": "hospital_access_0008_closed_missing_with_open_distractor", "split": "test", "domain": "hospital_access", "family": "closed_missing_with_open_distractor", "subset": "core_contrastive", "gold_pattern": "unknown|false|false", "dialogue_type": "persist_after_narrowing", "target_statement": "Cora has security clearance.", "query_atom": "has_clearance::Cora", "facts_positive": ["Dylan is assigned to the clinical trial.", "Cora has completed safety training."], "facts_negative": [], "rules_natural": [], "predicate_glossary": {"completed_training": "has completed safety training", "has_clearance": "has security clearance", "assigned_to_trial": "is assigned to the clinical trial", "may_enter_lab": "may enter Lab A", "needs_supervisor_review": "needs supervisor review"}, "vocabulary_predicates": ["assigned_to_trial", "completed_training", "has_clearance", "may_enter_lab", "needs_supervisor_review"], "symbolic": {"positive_atoms": ["assigned_to_trial::Dylan", "completed_training::Cora"], "negative_atoms": [], "rules": [], "query_atom": "has_clearance::Cora", "closure_atoms": ["assigned_to_trial::Dylan", "completed_training::Cora"]}, "turns": [{"turn_index": 1, "source_semantics": "owa", "update_operation": "SET_COMPLETE_PREDICATES", "update_predicates": [], "complete_predicates_after_update": [], "gold_answer": "unknown", "expected_changed_from_previous": null, "expected_applied_update": "open", "prompt": "This is turn 1 of a 3-turn dynamic completeness dialogue.\nYou are given an initial knowledge base and an initial completeness state.\nDo not use outside knowledge.\nReturn exactly one compact JSON object with keys answer, changed_from_previous, applied_update, and rationale. The answer value must be one of true, false, unknown. The changed_from_previous value must be true, false, or null. The applied_update value must be one of open, global_complete, local_complete. For this turn, applied_update must be exactly open. For turn 1, changed_from_previous must be null.\n\nReasoning contract:\n- Positive facts are true.\n- Negative facts make the corresponding target atom false.\n- A rule derives its conclusion only when every antecedent is true.\n- First apply all rules until no new facts can be derived.\n- Then evaluate unstated and underivable atoms using the current completeness state.\n- If a predicate is complete, an unstated and underivable atom using that predicate is false.\n- If a predicate is not complete, an unstated and underivable atom using that predicate is unknown.\n- A false answer derived from completeness is not a permanent negative fact; it can be withdrawn if the completeness state changes.\n\nKnowledge base:\n\nPredicate glossary:\n- assigned_to_trial: is assigned to the clinical trial\n- completed_training: has completed safety training\n- has_clearance: has security clearance\n- may_enter_lab: may enter Lab A\n- needs_supervisor_review: needs supervisor review\n\nPositive facts:\n- Dylan is assigned to the clinical trial.\n- Cora has completed safety training.\n\nNegative facts:\n- none\n\nRules:\n- none\n\nTarget statement: Cora has security clearance.\nTarget atom: has_clearance::Cora\n\nCompleteness-state update for this turn:\n- Operation: SET_COMPLETE_PREDICATES\n- New complete predicates: none"}, {"turn_index": 2, "source_semantics": "cwa", "update_operation": "ADD_COMPLETE_PREDICATES", "update_predicates": ["assigned_to_trial", "completed_training", "has_clearance", "may_enter_lab", "needs_supervisor_review"], "complete_predicates_after_update": ["assigned_to_trial", "completed_training", "has_clearance", "may_enter_lab", "needs_supervisor_review"], "gold_answer": "false", "expected_changed_from_previous": true, "expected_applied_update": "global_complete", "prompt": "This is turn 2 of a 3-turn dynamic completeness dialogue.\nKeep the exact same knowledge base, facts, rules, glossary, and target from turn 1.\nApply only the completeness-state update below.\nRecompute the answer from the updated state. Do not treat a previous false/unknown conclusion as a new fact.\nReturn exactly one compact JSON object with keys answer, changed_from_previous, applied_update, and rationale. The answer value must be one of true, false, unknown. The changed_from_previous value must be true, false, or null. The applied_update value must be one of open, global_complete, local_complete. For this turn, applied_update must be exactly global_complete. For this turn, changed_from_previous must be true iff your current answer differs from your immediately previous answer.\n\nReasoning contract:\n- Positive facts are true.\n- Negative facts make the corresponding target atom false.\n- A rule derives its conclusion only when every antecedent is true.\n- First apply all rules until no new facts can be derived.\n- Then evaluate unstated and underivable atoms using the current completeness state.\n- If a predicate is complete, an unstated and underivable atom using that predicate is false.\n- If a predicate is not complete, an unstated and underivable atom using that predicate is unknown.\n- A false answer derived from completeness is not a permanent negative fact; it can be withdrawn if the completeness state changes.\n\nCompleteness-state update for this turn:\n- Operation: ADD_COMPLETE_PREDICATES\n- Predicates to add as complete: assigned_to_trial, completed_training, has_clearance, may_enter_lab, needs_supervisor_review\n- Complete predicates after this update: assigned_to_trial, completed_training, has_clearance, may_enter_lab, needs_supervisor_review\n\nTarget statement: Cora has security clearance.\nTarget atom: has_clearance::Cora"}, {"turn_index": 3, "source_semantics": "lcwa", "update_operation": "REPLACE_COMPLETE_PREDICATES", "update_predicates": ["has_clearance"], "complete_predicates_after_update": ["has_clearance"], "gold_answer": "false", "expected_changed_from_previous": false, "expected_applied_update": "local_complete", "prompt": "This is turn 3 of a 3-turn dynamic completeness dialogue.\nKeep the exact same knowledge base, facts, rules, glossary, and target from turn 1.\nApply only the completeness-state update below.\nRecompute the answer from the updated state. Do not treat a previous false/unknown conclusion as a new fact.\nReturn exactly one compact JSON object with keys answer, changed_from_previous, applied_update, and rationale. The answer value must be one of true, false, unknown. The changed_from_previous value must be true, false, or null. The applied_update value must be one of open, global_complete, local_complete. For this turn, applied_update must be exactly local_complete. For this turn, changed_from_previous must be true iff your current answer differs from your immediately previous answer.\n\nReasoning contract:\n- Positive facts are true.\n- Negative facts make the corresponding target atom false.\n- A rule derives its conclusion only when every antecedent is true.\n- First apply all rules until no new facts can be derived.\n- Then evaluate unstated and underivable atoms using the current completeness state.\n- If a predicate is complete, an unstated and underivable atom using that predicate is false.\n- If a predicate is not complete, an unstated and underivable atom using that predicate is unknown.\n- A false answer derived from completeness is not a permanent negative fact; it can be withdrawn if the completeness state changes.\n\nCompleteness-state update for this turn:\n- Operation: REPLACE_COMPLETE_PREDICATES\n- Discard the previous complete-predicate set.\n- Replacement complete predicates: has_clearance\n\nTarget statement: Cora has security clearance.\nTarget atom: has_clearance::Cora"}]}
+{"id": "dynamic_dialogue_v3_0027", "base_id": "hospital_access_0009_closed_missing_with_open_distractor", "split": "test", "domain": "hospital_access", "family": "closed_missing_with_open_distractor", "subset": "core_contrastive", "gold_pattern": "unknown|false|false", "dialogue_type": "persist_after_narrowing", "target_statement": "Dylan has security clearance.", "query_atom": "has_clearance::Dylan", "facts_positive": ["Elena is assigned to the clinical trial.", "Dylan has completed safety training."], "facts_negative": [], "rules_natural": [], "predicate_glossary": {"completed_training": "has completed safety training", "has_clearance": "has security clearance", "assigned_to_trial": "is assigned to the clinical trial", "may_enter_lab": "may enter Lab A", "needs_supervisor_review": "needs supervisor review"}, "vocabulary_predicates": ["assigned_to_trial", "completed_training", "has_clearance", "may_enter_lab", "needs_supervisor_review"], "symbolic": {"positive_atoms": ["assigned_to_trial::Elena", "completed_training::Dylan"], "negative_atoms": [], "rules": [], "query_atom": "has_clearance::Dylan", "closure_atoms": ["assigned_to_trial::Elena", "completed_training::Dylan"]}, "turns": [{"turn_index": 1, "source_semantics": "owa", "update_operation": "SET_COMPLETE_PREDICATES", "update_predicates": [], "complete_predicates_after_update": [], "gold_answer": "unknown", "expected_changed_from_previous": null, "expected_applied_update": "open", "prompt": "This is turn 1 of a 3-turn dynamic completeness dialogue.\nYou are given an initial knowledge base and an initial completeness state.\nDo not use outside knowledge.\nReturn exactly one compact JSON object with keys answer, changed_from_previous, applied_update, and rationale. The answer value must be one of true, false, unknown. The changed_from_previous value must be true, false, or null. The applied_update value must be one of open, global_complete, local_complete. For this turn, applied_update must be exactly open. For turn 1, changed_from_previous must be null.\n\nReasoning contract:\n- Positive facts are true.\n- Negative facts make the corresponding target atom false.\n- A rule derives its conclusion only when every antecedent is true.\n- First apply all rules until no new facts can be derived.\n- Then evaluate unstated and underivable atoms using the current completeness state.\n- If a predicate is complete, an unstated and underivable atom using that predicate is false.\n- If a predicate is not complete, an unstated and underivable atom using that predicate is unknown.\n- A false answer derived from completeness is not a permanent negative fact; it can be withdrawn if the completeness state changes.\n\nKnowledge base:\n\nPredicate glossary:\n- assigned_to_trial: is assigned to the clinical trial\n- completed_training: has completed safety training\n- has_clearance: has security clearance\n- may_enter_lab: may enter Lab A\n- needs_supervisor_review: needs supervisor review\n\nPositive facts:\n- Elena is assigned to the clinical trial.\n- Dylan has completed safety training.\n\nNegative facts:\n- none\n\nRules:\n- none\n\nTarget statement: Dylan has security clearance.\nTarget atom: has_clearance::Dylan\n\nCompleteness-state update for this turn:\n- Operation: SET_COMPLETE_PREDICATES\n- New complete predicates: none"}, {"turn_index": 2, "source_semantics": "cwa", "update_operation": "ADD_COMPLETE_PREDICATES", "update_predicates": ["assigned_to_trial", "completed_training", "has_clearance", "may_enter_lab", "needs_supervisor_review"], "complete_predicates_after_update": ["assigned_to_trial", "completed_training", "has_clearance", "may_enter_lab", "needs_supervisor_review"], "gold_answer": "false", "expected_changed_from_previous": true, "expected_applied_update": "global_complete", "prompt": "This is turn 2 of a 3-turn dynamic completeness dialogue.\nKeep the exact same knowledge base, facts, rules, glossary, and target from turn 1.\nApply only the completeness-state update below.\nRecompute the answer from the updated state. Do not treat a previous false/unknown conclusion as a new fact.\nReturn exactly one compact JSON object with keys answer, changed_from_previous, applied_update, and rationale. The answer value must be one of true, false, unknown. The changed_from_previous value must be true, false, or null. The applied_update value must be one of open, global_complete, local_complete. For this turn, applied_update must be exactly global_complete. For this turn, changed_from_previous must be true iff your current answer differs from your immediately previous answer.\n\nReasoning contract:\n- Positive facts are true.\n- Negative facts make the corresponding target atom false.\n- A rule derives its conclusion only when every antecedent is true.\n- First apply all rules until no new facts can be derived.\n- Then evaluate unstated and underivable atoms using the current completeness state.\n- If a predicate is complete, an unstated and underivable atom using that predicate is false.\n- If a predicate is not complete, an unstated and underivable atom using that predicate is unknown.\n- A false answer derived from completeness is not a permanent negative fact; it can be withdrawn if the completeness state changes.\n\nCompleteness-state update for this turn:\n- Operation: ADD_COMPLETE_PREDICATES\n- Predicates to add as complete: assigned_to_trial, completed_training, has_clearance, may_enter_lab, needs_supervisor_review\n- Complete predicates after this update: assigned_to_trial, completed_training, has_clearance, may_enter_lab, needs_supervisor_review\n\nTarget statement: Dylan has security clearance.\nTarget atom: has_clearance::Dylan"}, {"turn_index": 3, "source_semantics": "lcwa", "update_operation": "REPLACE_COMPLETE_PREDICATES", "update_predicates": ["has_clearance"], "complete_predicates_after_update": ["has_clearance"], "gold_answer": "false", "expected_changed_from_previous": false, "expected_applied_update": "local_complete", "prompt": "This is turn 3 of a 3-turn dynamic completeness dialogue.\nKeep the exact same knowledge base, facts, rules, glossary, and target from turn 1.\nApply only the completeness-state update below.\nRecompute the answer from the updated state. Do not treat a previous false/unknown conclusion as a new fact.\nReturn exactly one compact JSON object with keys answer, changed_from_previous, applied_update, and rationale. The answer value must be one of true, false, unknown. The changed_from_previous value must be true, false, or null. The applied_update value must be one of open, global_complete, local_complete. For this turn, applied_update must be exactly local_complete. For this turn, changed_from_previous must be true iff your current answer differs from your immediately previous answer.\n\nReasoning contract:\n- Positive facts are true.\n- Negative facts make the corresponding target atom false.\n- A rule derives its conclusion only when every antecedent is true.\n- First apply all rules until no new facts can be derived.\n- Then evaluate unstated and underivable atoms using the current completeness state.\n- If a predicate is complete, an unstated and underivable atom using that predicate is false.\n- If a predicate is not complete, an unstated and underivable atom using that predicate is unknown.\n- A false answer derived from completeness is not a permanent negative fact; it can be withdrawn if the completeness state changes.\n\nCompleteness-state update for this turn:\n- Operation: REPLACE_COMPLETE_PREDICATES\n- Discard the previous complete-predicate set.\n- Replacement complete predicates: has_clearance\n\nTarget statement: Dylan has security clearance.\nTarget atom: has_clearance::Dylan"}]}
+{"id": "dynamic_dialogue_v3_0033", "base_id": "hospital_access_0019_closed_derived_missing_antecedent", "split": "test", "domain": "hospital_access", "family": "closed_derived_missing_antecedent", "subset": "core_contrastive", "gold_pattern": "unknown|false|false", "dialogue_type": "persist_after_narrowing", "target_statement": "Ben may enter Lab A.", "query_atom": "may_enter_lab::Ben", "facts_positive": ["Ben has completed safety training."], "facts_negative": [], "rules_natural": ["If a staff member has security clearance and has completed safety training, then that staff member may enter Lab A."], "predicate_glossary": {"completed_training": "has completed safety training", "has_clearance": "has security clearance", "assigned_to_trial": "is assigned to the clinical trial", "may_enter_lab": "may enter Lab A", "needs_supervisor_review": "needs supervisor review"}, "vocabulary_predicates": ["assigned_to_trial", "completed_training", "has_clearance", "may_enter_lab", "needs_supervisor_review"], "symbolic": {"positive_atoms": ["completed_training::Ben"], "negative_atoms": [], "rules": [{"antecedents": ["completed_training::Ben", "has_clearance::Ben"], "conclusion": "may_enter_lab::Ben", "text": "If a staff member has security clearance and has completed safety training, then that staff member may enter Lab A."}], "query_atom": "may_enter_lab::Ben", "closure_atoms": ["completed_training::Ben"]}, "turns": [{"turn_index": 1, "source_semantics": "owa", "update_operation": "SET_COMPLETE_PREDICATES", "update_predicates": [], "complete_predicates_after_update": [], "gold_answer": "unknown", "expected_changed_from_previous": null, "expected_applied_update": "open", "prompt": "This is turn 1 of a 3-turn dynamic completeness dialogue.\nYou are given an initial knowledge base and an initial completeness state.\nDo not use outside knowledge.\nReturn exactly one compact JSON object with keys answer, changed_from_previous, applied_update, and rationale. The answer value must be one of true, false, unknown. The changed_from_previous value must be true, false, or null. The applied_update value must be one of open, global_complete, local_complete. For this turn, applied_update must be exactly open. For turn 1, changed_from_previous must be null.\n\nReasoning contract:\n- Positive facts are true.\n- Negative facts make the corresponding target atom false.\n- A rule derives its conclusion only when every antecedent is true.\n- First apply all rules until no new facts can be derived.\n- Then evaluate unstated and underivable atoms using the current completeness state.\n- If a predicate is complete, an unstated and underivable atom using that predicate is false.\n- If a predicate is not complete, an unstated and underivable atom using that predicate is unknown.\n- A false answer derived from completeness is not a permanent negative fact; it can be withdrawn if the completeness state changes.\n\nKnowledge base:\n\nPredicate glossary:\n- assigned_to_trial: is assigned to the clinical trial\n- completed_training: has completed safety training\n- has_clearance: has security clearance\n- may_enter_lab: may enter Lab A\n- needs_supervisor_review: needs supervisor review\n\nPositive facts:\n- Ben has completed safety training.\n\nNegative facts:\n- none\n\nRules:\n- If a staff member has security clearance and has completed safety training, then that staff member may enter Lab A.\n\nTarget statement: Ben may enter Lab A.\nTarget atom: may_enter_lab::Ben\n\nCompleteness-state update for this turn:\n- Operation: SET_COMPLETE_PREDICATES\n- New complete predicates: none"}, {"turn_index": 2, "source_semantics": "cwa", "update_operation": "ADD_COMPLETE_PREDICATES", "update_predicates": ["assigned_to_trial", "completed_training", "has_clearance", "may_enter_lab", "needs_supervisor_review"], "complete_predicates_after_update": ["assigned_to_trial", "completed_training", "has_clearance", "may_enter_lab", "needs_supervisor_review"], "gold_answer": "false", "expected_changed_from_previous": true, "expected_applied_update": "global_complete", "prompt": "This is turn 2 of a 3-turn dynamic completeness dialogue.\nKeep the exact same knowledge base, facts, rules, glossary, and target from turn 1.\nApply only the completeness-state update below.\nRecompute the answer from the updated state. Do not treat a previous false/unknown conclusion as a new fact.\nReturn exactly one compact JSON object with keys answer, changed_from_previous, applied_update, and rationale. The answer value must be one of true, false, unknown. The changed_from_previous value must be true, false, or null. The applied_update value must be one of open, global_complete, local_complete. For this turn, applied_update must be exactly global_complete. For this turn, changed_from_previous must be true iff your current answer differs from your immediately previous answer.\n\nReasoning contract:\n- Positive facts are true.\n- Negative facts make the corresponding target atom false.\n- A rule derives its conclusion only when every antecedent is true.\n- First apply all rules until no new facts can be derived.\n- Then evaluate unstated and underivable atoms using the current completeness state.\n- If a predicate is complete, an unstated and underivable atom using that predicate is false.\n- If a predicate is not complete, an unstated and underivable atom using that predicate is unknown.\n- A false answer derived from completeness is not a permanent negative fact; it can be withdrawn if the completeness state changes.\n\nCompleteness-state update for this turn:\n- Operation: ADD_COMPLETE_PREDICATES\n- Predicates to add as complete: assigned_to_trial, completed_training, has_clearance, may_enter_lab, needs_supervisor_review\n- Complete predicates after this update: assigned_to_trial, completed_training, has_clearance, may_enter_lab, needs_supervisor_review\n\nTarget statement: Ben may enter Lab A.\nTarget atom: may_enter_lab::Ben"}, {"turn_index": 3, "source_semantics": "lcwa", "update_operation": "REPLACE_COMPLETE_PREDICATES", "update_predicates": ["has_clearance", "may_enter_lab"], "complete_predicates_after_update": ["has_clearance", "may_enter_lab"], "gold_answer": "false", "expected_changed_from_previous": false, "expected_applied_update": "local_complete", "prompt": "This is turn 3 of a 3-turn dynamic completeness dialogue.\nKeep the exact same knowledge base, facts, rules, glossary, and target from turn 1.\nApply only the completeness-state update below.\nRecompute the answer from the updated state. Do not treat a previous false/unknown conclusion as a new fact.\nReturn exactly one compact JSON object with keys answer, changed_from_previous, applied_update, and rationale. The answer value must be one of true, false, unknown. The changed_from_previous value must be true, false, or null. The applied_update value must be one of open, global_complete, local_complete. For this turn, applied_update must be exactly local_complete. For this turn, changed_from_previous must be true iff your current answer differs from your immediately previous answer.\n\nReasoning contract:\n- Positive facts are true.\n- Negative facts make the corresponding target atom false.\n- A rule derives its conclusion only when every antecedent is true.\n- First apply all rules until no new facts can be derived.\n- Then evaluate unstated and underivable atoms using the current completeness state.\n- If a predicate is complete, an unstated and underivable atom using that predicate is false.\n- If a predicate is not complete, an unstated and underivable atom using that predicate is unknown.\n- A false answer derived from completeness is not a permanent negative fact; it can be withdrawn if the completeness state changes.\n\nCompleteness-state update for this turn:\n- Operation: REPLACE_COMPLETE_PREDICATES\n- Discard the previous complete-predicate set.\n- Replacement complete predicates: has_clearance, may_enter_lab\n\nTarget statement: Ben may enter Lab A.\nTarget atom: may_enter_lab::Ben"}]}
+{"id": "dynamic_dialogue_v3_0040", "base_id": "library_services_0206_open_missing_direct", "split": "test", "domain": "library_services", "family": "open_missing_direct", "subset": "core_contrastive", "gold_pattern": "unknown|false|unknown", "dialogue_type": "reopen", "target_statement": "Reader C has a special request.", "query_atom": "has_special_request::Reader C", "facts_positive": ["Reader C has an active membership."], "facts_negative": [], "rules_natural": [], "predicate_glossary": {"has_active_membership": "has an active membership", "has_borrowing_clearance": "has borrowing clearance", "has_special_request": "has a special request", "may_borrow_archive_item": "may borrow an archive item", "needs_librarian_review": "needs librarian review"}, "vocabulary_predicates": ["has_active_membership", "has_borrowing_clearance", "has_special_request", "may_borrow_archive_item", "needs_librarian_review"], "symbolic": {"positive_atoms": ["has_active_membership::Reader C"], "negative_atoms": [], "rules": [], "query_atom": "has_special_request::Reader C", "closure_atoms": ["has_active_membership::Reader C"]}, "turns": [{"turn_index": 1, "source_semantics": "owa", "update_operation": "SET_COMPLETE_PREDICATES", "update_predicates": [], "complete_predicates_after_update": [], "gold_answer": "unknown", "expected_changed_from_previous": null, "expected_applied_update": "open", "prompt": "This is turn 1 of a 3-turn dynamic completeness dialogue.\nYou are given an initial knowledge base and an initial completeness state.\nDo not use outside knowledge.\nReturn exactly one compact JSON object with keys answer, changed_from_previous, applied_update, and rationale. The answer value must be one of true, false, unknown. The changed_from_previous value must be true, false, or null. The applied_update value must be one of open, global_complete, local_complete. For this turn, applied_update must be exactly open. For turn 1, changed_from_previous must be null.\n\nReasoning contract:\n- Positive facts are true.\n- Negative facts make the corresponding target atom false.\n- A rule derives its conclusion only when every antecedent is true.\n- First apply all rules until no new facts can be derived.\n- Then evaluate unstated and underivable atoms using the current completeness state.\n- If a predicate is complete, an unstated and underivable atom using that predicate is false.\n- If a predicate is not complete, an unstated and underivable atom using that predicate is unknown.\n- A false answer derived from completeness is not a permanent negative fact; it can be withdrawn if the completeness state changes.\n\nKnowledge base:\n\nPredicate glossary:\n- has_active_membership: has an active membership\n- has_borrowing_clearance: has borrowing clearance\n- has_special_request: has a special request\n- may_borrow_archive_item: may borrow an archive item\n- needs_librarian_review: needs librarian review\n\nPositive facts:\n- Reader C has an active membership.\n\nNegative facts:\n- none\n\nRules:\n- none\n\nTarget statement: Reader C has a special request.\nTarget atom: has_special_request::Reader C\n\nCompleteness-state update for this turn:\n- Operation: SET_COMPLETE_PREDICATES\n- New complete predicates: none"}, {"turn_index": 2, "source_semantics": "cwa", "update_operation": "ADD_COMPLETE_PREDICATES", "update_predicates": ["has_active_membership", "has_borrowing_clearance", "has_special_request", "may_borrow_archive_item", "needs_librarian_review"], "complete_predicates_after_update": ["has_active_membership", "has_borrowing_clearance", "has_special_request", "may_borrow_archive_item", "needs_librarian_review"], "gold_answer": "false", "expected_changed_from_previous": true, "expected_applied_update": "global_complete", "prompt": "This is turn 2 of a 3-turn dynamic completeness dialogue.\nKeep the exact same knowledge base, facts, rules, glossary, and target from turn 1.\nApply only the completeness-state update below.\nRecompute the answer from the updated state. Do not treat a previous false/unknown conclusion as a new fact.\nReturn exactly one compact JSON object with keys answer, changed_from_previous, applied_update, and rationale. The answer value must be one of true, false, unknown. The changed_from_previous value must be true, false, or null. The applied_update value must be one of open, global_complete, local_complete. For this turn, applied_update must be exactly global_complete. For this turn, changed_from_previous must be true iff your current answer differs from your immediately previous answer.\n\nReasoning contract:\n- Positive facts are true.\n- Negative facts make the corresponding target atom false.\n- A rule derives its conclusion only when every antecedent is true.\n- First apply all rules until no new facts can be derived.\n- Then evaluate unstated and underivable atoms using the current completeness state.\n- If a predicate is complete, an unstated and underivable atom using that predicate is false.\n- If a predicate is not complete, an unstated and underivable atom using that predicate is unknown.\n- A false answer derived from completeness is not a permanent negative fact; it can be withdrawn if the completeness state changes.\n\nCompleteness-state update for this turn:\n- Operation: ADD_COMPLETE_PREDICATES\n- Predicates to add as complete: has_active_membership, has_borrowing_clearance, has_special_request, may_borrow_archive_item, needs_librarian_review\n- Complete predicates after this update: has_active_membership, has_borrowing_clearance, has_special_request, may_borrow_archive_item, needs_librarian_review\n\nTarget statement: Reader C has a special request.\nTarget atom: has_special_request::Reader C"}, {"turn_index": 3, "source_semantics": "lcwa", "update_operation": "REPLACE_COMPLETE_PREDICATES", "update_predicates": ["has_borrowing_clearance", "may_borrow_archive_item"], "complete_predicates_after_update": ["has_borrowing_clearance", "may_borrow_archive_item"], "gold_answer": "unknown", "expected_changed_from_previous": true, "expected_applied_update": "local_complete", "prompt": "This is turn 3 of a 3-turn dynamic completeness dialogue.\nKeep the exact same knowledge base, facts, rules, glossary, and target from turn 1.\nApply only the completeness-state update below.\nRecompute the answer from the updated state. Do not treat a previous false/unknown conclusion as a new fact.\nReturn exactly one compact JSON object with keys answer, changed_from_previous, applied_update, and rationale. The answer value must be one of true, false, unknown. The changed_from_previous value must be true, false, or null. The applied_update value must be one of open, global_complete, local_complete. For this turn, applied_update must be exactly local_complete. For this turn, changed_from_previous must be true iff your current answer differs from your immediately previous answer.\n\nReasoning contract:\n- Positive facts are true.\n- Negative facts make the corresponding target atom false.\n- A rule derives its conclusion only when every antecedent is true.\n- First apply all rules until no new facts can be derived.\n- Then evaluate unstated and underivable atoms using the current completeness state.\n- If a predicate is complete, an unstated and underivable atom using that predicate is false.\n- If a predicate is not complete, an unstated and underivable atom using that predicate is unknown.\n- A false answer derived from completeness is not a permanent negative fact; it can be withdrawn if the completeness state changes.\n\nCompleteness-state update for this turn:\n- Operation: REPLACE_COMPLETE_PREDICATES\n- Discard the previous complete-predicate set.\n- Replacement complete predicates: has_borrowing_clearance, may_borrow_archive_item\n\nTarget statement: Reader C has a special request.\nTarget atom: has_special_request::Reader C"}]}
+{"id": "dynamic_dialogue_v3_0045", "base_id": "library_services_0218_closed_derived_missing_antecedent", "split": "test", "domain": "library_services", "family": "closed_derived_missing_antecedent", "subset": "core_contrastive", "gold_pattern": "unknown|false|false", "dialogue_type": "persist_after_narrowing", "target_statement": "Reader C may borrow an archive item.", "query_atom": "may_borrow_archive_item::Reader C", "facts_positive": ["Reader C has an active membership."], "facts_negative": [], "rules_natural": ["If a reader has an active membership and has borrowing clearance, then that reader may borrow an archive item."], "predicate_glossary": {"has_active_membership": "has an active membership", "has_borrowing_clearance": "has borrowing clearance", "has_special_request": "has a special request", "may_borrow_archive_item": "may borrow an archive item", "needs_librarian_review": "needs librarian review"}, "vocabulary_predicates": ["has_active_membership", "has_borrowing_clearance", "has_special_request", "may_borrow_archive_item", "needs_librarian_review"], "symbolic": {"positive_atoms": ["has_active_membership::Reader C"], "negative_atoms": [], "rules": [{"antecedents": ["has_active_membership::Reader C", "has_borrowing_clearance::Reader C"], "conclusion": "may_borrow_archive_item::Reader C", "text": "If a reader has an active membership and has borrowing clearance, then that reader may borrow an archive item."}], "query_atom": "may_borrow_archive_item::Reader C", "closure_atoms": ["has_active_membership::Reader C"]}, "turns": [{"turn_index": 1, "source_semantics": "owa", "update_operation": "SET_COMPLETE_PREDICATES", "update_predicates": [], "complete_predicates_after_update": [], "gold_answer": "unknown", "expected_changed_from_previous": null, "expected_applied_update": "open", "prompt": "This is turn 1 of a 3-turn dynamic completeness dialogue.\nYou are given an initial knowledge base and an initial completeness state.\nDo not use outside knowledge.\nReturn exactly one compact JSON object with keys answer, changed_from_previous, applied_update, and rationale. The answer value must be one of true, false, unknown. The changed_from_previous value must be true, false, or null. The applied_update value must be one of open, global_complete, local_complete. For this turn, applied_update must be exactly open. For turn 1, changed_from_previous must be null.\n\nReasoning contract:\n- Positive facts are true.\n- Negative facts make the corresponding target atom false.\n- A rule derives its conclusion only when every antecedent is true.\n- First apply all rules until no new facts can be derived.\n- Then evaluate unstated and underivable atoms using the current completeness state.\n- If a predicate is complete, an unstated and underivable atom using that predicate is false.\n- If a predicate is not complete, an unstated and underivable atom using that predicate is unknown.\n- A false answer derived from completeness is not a permanent negative fact; it can be withdrawn if the completeness state changes.\n\nKnowledge base:\n\nPredicate glossary:\n- has_active_membership: has an active membership\n- has_borrowing_clearance: has borrowing clearance\n- has_special_request: has a special request\n- may_borrow_archive_item: may borrow an archive item\n- needs_librarian_review: needs librarian review\n\nPositive facts:\n- Reader C has an active membership.\n\nNegative facts:\n- none\n\nRules:\n- If a reader has an active membership and has borrowing clearance, then that reader may borrow an archive item.\n\nTarget statement: Reader C may borrow an archive item.\nTarget atom: may_borrow_archive_item::Reader C\n\nCompleteness-state update for this turn:\n- Operation: SET_COMPLETE_PREDICATES\n- New complete predicates: none"}, {"turn_index": 2, "source_semantics": "cwa", "update_operation": "ADD_COMPLETE_PREDICATES", "update_predicates": ["has_active_membership", "has_borrowing_clearance", "has_special_request", "may_borrow_archive_item", "needs_librarian_review"], "complete_predicates_after_update": ["has_active_membership", "has_borrowing_clearance", "has_special_request", "may_borrow_archive_item", "needs_librarian_review"], "gold_answer": "false", "expected_changed_from_previous": true, "expected_applied_update": "global_complete", "prompt": "This is turn 2 of a 3-turn dynamic completeness dialogue.\nKeep the exact same knowledge base, facts, rules, glossary, and target from turn 1.\nApply only the completeness-state update below.\nRecompute the answer from the updated state. Do not treat a previous false/unknown conclusion as a new fact.\nReturn exactly one compact JSON object with keys answer, changed_from_previous, applied_update, and rationale. The answer value must be one of true, false, unknown. The changed_from_previous value must be true, false, or null. The applied_update value must be one of open, global_complete, local_complete. For this turn, applied_update must be exactly global_complete. For this turn, changed_from_previous must be true iff your current answer differs from your immediately previous answer.\n\nReasoning contract:\n- Positive facts are true.\n- Negative facts make the corresponding target atom false.\n- A rule derives its conclusion only when every antecedent is true.\n- First apply all rules until no new facts can be derived.\n- Then evaluate unstated and underivable atoms using the current completeness state.\n- If a predicate is complete, an unstated and underivable atom using that predicate is false.\n- If a predicate is not complete, an unstated and underivable atom using that predicate is unknown.\n- A false answer derived from completeness is not a permanent negative fact; it can be withdrawn if the completeness state changes.\n\nCompleteness-state update for this turn:\n- Operation: ADD_COMPLETE_PREDICATES\n- Predicates to add as complete: has_active_membership, has_borrowing_clearance, has_special_request, may_borrow_archive_item, needs_librarian_review\n- Complete predicates after this update: has_active_membership, has_borrowing_clearance, has_special_request, may_borrow_archive_item, needs_librarian_review\n\nTarget statement: Reader C may borrow an archive item.\nTarget atom: may_borrow_archive_item::Reader C"}, {"turn_index": 3, "source_semantics": "lcwa", "update_operation": "REPLACE_COMPLETE_PREDICATES", "update_predicates": ["has_borrowing_clearance", "may_borrow_archive_item"], "complete_predicates_after_update": ["has_borrowing_clearance", "may_borrow_archive_item"], "gold_answer": "false", "expected_changed_from_previous": false, "expected_applied_update": "local_complete", "prompt": "This is turn 3 of a 3-turn dynamic completeness dialogue.\nKeep the exact same knowledge base, facts, rules, glossary, and target from turn 1.\nApply only the completeness-state update below.\nRecompute the answer from the updated state. Do not treat a previous false/unknown conclusion as a new fact.\nReturn exactly one compact JSON object with keys answer, changed_from_previous, applied_update, and rationale. The answer value must be one of true, false, unknown. The changed_from_previous value must be true, false, or null. The applied_update value must be one of open, global_complete, local_complete. For this turn, applied_update must be exactly local_complete. For this turn, changed_from_previous must be true iff your current answer differs from your immediately previous answer.\n\nReasoning contract:\n- Positive facts are true.\n- Negative facts make the corresponding target atom false.\n- A rule derives its conclusion only when every antecedent is true.\n- First apply all rules until no new facts can be derived.\n- Then evaluate unstated and underivable atoms using the current completeness state.\n- If a predicate is complete, an unstated and underivable atom using that predicate is false.\n- If a predicate is not complete, an unstated and underivable atom using that predicate is unknown.\n- A false answer derived from completeness is not a permanent negative fact; it can be withdrawn if the completeness state changes.\n\nCompleteness-state update for this turn:\n- Operation: REPLACE_COMPLETE_PREDICATES\n- Discard the previous complete-predicate set.\n- Replacement complete predicates: has_borrowing_clearance, may_borrow_archive_item\n\nTarget statement: Reader C may borrow an archive item.\nTarget atom: may_borrow_archive_item::Reader C"}]}
+{"id": "dynamic_dialogue_v3_0047", "base_id": "manufacturing_quality_0080_closed_missing_direct", "split": "test", "domain": "manufacturing_quality", "family": "closed_missing_direct", "subset": "core_contrastive", "gold_pattern": "unknown|false|false", "dialogue_type": "persist_after_narrowing", "target_statement": "Part C has release approval.", "query_atom": "has_release_approval::Part C", "facts_positive": ["Part C passed the visual check."], "facts_negative": [], "rules_natural": [], "predicate_glossary": {"passed_visual_check": "passed the visual check", "has_release_approval": "has release approval", "has_supplier_note": "has a supplier note", "may_ship": "may ship to customers", "needs_engineer_review": "needs engineer review"}, "vocabulary_predicates": ["has_release_approval", "has_supplier_note", "may_ship", "needs_engineer_review", "passed_visual_check"], "symbolic": {"positive_atoms": ["passed_visual_check::Part C"], "negative_atoms": [], "rules": [], "query_atom": "has_release_approval::Part C", "closure_atoms": ["passed_visual_check::Part C"]}, "turns": [{"turn_index": 1, "source_semantics": "owa", "update_operation": "SET_COMPLETE_PREDICATES", "update_predicates": [], "complete_predicates_after_update": [], "gold_answer": "unknown", "expected_changed_from_previous": null, "expected_applied_update": "open", "prompt": "This is turn 1 of a 3-turn dynamic completeness dialogue.\nYou are given an initial knowledge base and an initial completeness state.\nDo not use outside knowledge.\nReturn exactly one compact JSON object with keys answer, changed_from_previous, applied_update, and rationale. The answer value must be one of true, false, unknown. The changed_from_previous value must be true, false, or null. The applied_update value must be one of open, global_complete, local_complete. For this turn, applied_update must be exactly open. For turn 1, changed_from_previous must be null.\n\nReasoning contract:\n- Positive facts are true.\n- Negative facts make the corresponding target atom false.\n- A rule derives its conclusion only when every antecedent is true.\n- First apply all rules until no new facts can be derived.\n- Then evaluate unstated and underivable atoms using the current completeness state.\n- If a predicate is complete, an unstated and underivable atom using that predicate is false.\n- If a predicate is not complete, an unstated and underivable atom using that predicate is unknown.\n- A false answer derived from completeness is not a permanent negative fact; it can be withdrawn if the completeness state changes.\n\nKnowledge base:\n\nPredicate glossary:\n- has_release_approval: has release approval\n- has_supplier_note: has a supplier note\n- may_ship: may ship to customers\n- needs_engineer_review: needs engineer review\n- passed_visual_check: passed the visual check\n\nPositive facts:\n- Part C passed the visual check.\n\nNegative facts:\n- none\n\nRules:\n- none\n\nTarget statement: Part C has release approval.\nTarget atom: has_release_approval::Part C\n\nCompleteness-state update for this turn:\n- Operation: SET_COMPLETE_PREDICATES\n- New complete predicates: none"}, {"turn_index": 2, "source_semantics": "cwa", "update_operation": "ADD_COMPLETE_PREDICATES", "update_predicates": ["has_release_approval", "has_supplier_note", "may_ship", "needs_engineer_review", "passed_visual_check"], "complete_predicates_after_update": ["has_release_approval", "has_supplier_note", "may_ship", "needs_engineer_review", "passed_visual_check"], "gold_answer": "false", "expected_changed_from_previous": true, "expected_applied_update": "global_complete", "prompt": "This is turn 2 of a 3-turn dynamic completeness dialogue.\nKeep the exact same knowledge base, facts, rules, glossary, and target from turn 1.\nApply only the completeness-state update below.\nRecompute the answer from the updated state. Do not treat a previous false/unknown conclusion as a new fact.\nReturn exactly one compact JSON object with keys answer, changed_from_previous, applied_update, and rationale. The answer value must be one of true, false, unknown. The changed_from_previous value must be true, false, or null. The applied_update value must be one of open, global_complete, local_complete. For this turn, applied_update must be exactly global_complete. For this turn, changed_from_previous must be true iff your current answer differs from your immediately previous answer.\n\nReasoning contract:\n- Positive facts are true.\n- Negative facts make the corresponding target atom false.\n- A rule derives its conclusion only when every antecedent is true.\n- First apply all rules until no new facts can be derived.\n- Then evaluate unstated and underivable atoms using the current completeness state.\n- If a predicate is complete, an unstated and underivable atom using that predicate is false.\n- If a predicate is not complete, an unstated and underivable atom using that predicate is unknown.\n- A false answer derived from completeness is not a permanent negative fact; it can be withdrawn if the completeness state changes.\n\nCompleteness-state update for this turn:\n- Operation: ADD_COMPLETE_PREDICATES\n- Predicates to add as complete: has_release_approval, has_supplier_note, may_ship, needs_engineer_review, passed_visual_check\n- Complete predicates after this update: has_release_approval, has_supplier_note, may_ship, needs_engineer_review, passed_visual_check\n\nTarget statement: Part C has release approval.\nTarget atom: has_release_approval::Part C"}, {"turn_index": 3, "source_semantics": "lcwa", "update_operation": "REPLACE_COMPLETE_PREDICATES", "update_predicates": ["has_release_approval"], "complete_predicates_after_update": ["has_release_approval"], "gold_answer": "false", "expected_changed_from_previous": false, "expected_applied_update": "local_complete", "prompt": "This is turn 3 of a 3-turn dynamic completeness dialogue.\nKeep the exact same knowledge base, facts, rules, glossary, and target from turn 1.\nApply only the completeness-state update below.\nRecompute the answer from the updated state. Do not treat a previous false/unknown conclusion as a new fact.\nReturn exactly one compact JSON object with keys answer, changed_from_previous, applied_update, and rationale. The answer value must be one of true, false, unknown. The changed_from_previous value must be true, false, or null. The applied_update value must be one of open, global_complete, local_complete. For this turn, applied_update must be exactly local_complete. For this turn, changed_from_previous must be true iff your current answer differs from your immediately previous answer.\n\nReasoning contract:\n- Positive facts are true.\n- Negative facts make the corresponding target atom false.\n- A rule derives its conclusion only when every antecedent is true.\n- First apply all rules until no new facts can be derived.\n- Then evaluate unstated and underivable atoms using the current completeness state.\n- If a predicate is complete, an unstated and underivable atom using that predicate is false.\n- If a predicate is not complete, an unstated and underivable atom using that predicate is unknown.\n- A false answer derived from completeness is not a permanent negative fact; it can be withdrawn if the completeness state changes.\n\nCompleteness-state update for this turn:\n- Operation: REPLACE_COMPLETE_PREDICATES\n- Discard the previous complete-predicate set.\n- Replacement complete predicates: has_release_approval\n\nTarget statement: Part C has release approval.\nTarget atom: has_release_approval::Part C"}]}
+{"id": "dynamic_dialogue_v3_0057", "base_id": "manufacturing_quality_0101_open_derived_missing_antecedent", "split": "test", "domain": "manufacturing_quality", "family": "open_derived_missing_antecedent", "subset": "core_contrastive", "gold_pattern": "unknown|false|unknown", "dialogue_type": "reopen", "target_statement": "Part F needs engineer review.", "query_atom": "needs_engineer_review::Part F", "facts_positive": ["Part F passed the visual check."], "facts_negative": [], "rules_natural": ["If a part has a supplier note, then that part needs engineer review."], "predicate_glossary": {"passed_visual_check": "passed the visual check", "has_release_approval": "has release approval", "has_supplier_note": "has a supplier note", "may_ship": "may ship to customers", "needs_engineer_review": "needs engineer review"}, "vocabulary_predicates": ["has_release_approval", "has_supplier_note", "may_ship", "needs_engineer_review", "passed_visual_check"], "symbolic": {"positive_atoms": ["passed_visual_check::Part F"], "negative_atoms": [], "rules": [{"antecedents": ["has_supplier_note::Part F"], "conclusion": "needs_engineer_review::Part F", "text": "If a part has a supplier note, then that part needs engineer review."}], "query_atom": "needs_engineer_review::Part F", "closure_atoms": ["passed_visual_check::Part F"]}, "turns": [{"turn_index": 1, "source_semantics": "owa", "update_operation": "SET_COMPLETE_PREDICATES", "update_predicates": [], "complete_predicates_after_update": [], "gold_answer": "unknown", "expected_changed_from_previous": null, "expected_applied_update": "open", "prompt": "This is turn 1 of a 3-turn dynamic completeness dialogue.\nYou are given an initial knowledge base and an initial completeness state.\nDo not use outside knowledge.\nReturn exactly one compact JSON object with keys answer, changed_from_previous, applied_update, and rationale. The answer value must be one of true, false, unknown. The changed_from_previous value must be true, false, or null. The applied_update value must be one of open, global_complete, local_complete. For this turn, applied_update must be exactly open. For turn 1, changed_from_previous must be null.\n\nReasoning contract:\n- Positive facts are true.\n- Negative facts make the corresponding target atom false.\n- A rule derives its conclusion only when every antecedent is true.\n- First apply all rules until no new facts can be derived.\n- Then evaluate unstated and underivable atoms using the current completeness state.\n- If a predicate is complete, an unstated and underivable atom using that predicate is false.\n- If a predicate is not complete, an unstated and underivable atom using that predicate is unknown.\n- A false answer derived from completeness is not a permanent negative fact; it can be withdrawn if the completeness state changes.\n\nKnowledge base:\n\nPredicate glossary:\n- has_release_approval: has release approval\n- has_supplier_note: has a supplier note\n- may_ship: may ship to customers\n- needs_engineer_review: needs engineer review\n- passed_visual_check: passed the visual check\n\nPositive facts:\n- Part F passed the visual check.\n\nNegative facts:\n- none\n\nRules:\n- If a part has a supplier note, then that part needs engineer review.\n\nTarget statement: Part F needs engineer review.\nTarget atom: needs_engineer_review::Part F\n\nCompleteness-state update for this turn:\n- Operation: SET_COMPLETE_PREDICATES\n- New complete predicates: none"}, {"turn_index": 2, "source_semantics": "cwa", "update_operation": "ADD_COMPLETE_PREDICATES", "update_predicates": ["has_release_approval", "has_supplier_note", "may_ship", "needs_engineer_review", "passed_visual_check"], "complete_predicates_after_update": ["has_release_approval", "has_supplier_note", "may_ship", "needs_engineer_review", "passed_visual_check"], "gold_answer": "false", "expected_changed_from_previous": true, "expected_applied_update": "global_complete", "prompt": "This is turn 2 of a 3-turn dynamic completeness dialogue.\nKeep the exact same knowledge base, facts, rules, glossary, and target from turn 1.\nApply only the completeness-state update below.\nRecompute the answer from the updated state. Do not treat a previous false/unknown conclusion as a new fact.\nReturn exactly one compact JSON object with keys answer, changed_from_previous, applied_update, and rationale. The answer value must be one of true, false, unknown. The changed_from_previous value must be true, false, or null. The applied_update value must be one of open, global_complete, local_complete. For this turn, applied_update must be exactly global_complete. For this turn, changed_from_previous must be true iff your current answer differs from your immediately previous answer.\n\nReasoning contract:\n- Positive facts are true.\n- Negative facts make the corresponding target atom false.\n- A rule derives its conclusion only when every antecedent is true.\n- First apply all rules until no new facts can be derived.\n- Then evaluate unstated and underivable atoms using the current completeness state.\n- If a predicate is complete, an unstated and underivable atom using that predicate is false.\n- If a predicate is not complete, an unstated and underivable atom using that predicate is unknown.\n- A false answer derived from completeness is not a permanent negative fact; it can be withdrawn if the completeness state changes.\n\nCompleteness-state update for this turn:\n- Operation: ADD_COMPLETE_PREDICATES\n- Predicates to add as complete: has_release_approval, has_supplier_note, may_ship, needs_engineer_review, passed_visual_check\n- Complete predicates after this update: has_release_approval, has_supplier_note, may_ship, needs_engineer_review, passed_visual_check\n\nTarget statement: Part F needs engineer review.\nTarget atom: needs_engineer_review::Part F"}, {"turn_index": 3, "source_semantics": "lcwa", "update_operation": "REPLACE_COMPLETE_PREDICATES", "update_predicates": ["has_release_approval", "may_ship"], "complete_predicates_after_update": ["has_release_approval", "may_ship"], "gold_answer": "unknown", "expected_changed_from_previous": true, "expected_applied_update": "local_complete", "prompt": "This is turn 3 of a 3-turn dynamic completeness dialogue.\nKeep the exact same knowledge base, facts, rules, glossary, and target from turn 1.\nApply only the completeness-state update below.\nRecompute the answer from the updated state. Do not treat a previous false/unknown conclusion as a new fact.\nReturn exactly one compact JSON object with keys answer, changed_from_previous, applied_update, and rationale. The answer value must be one of true, false, unknown. The changed_from_previous value must be true, false, or null. The applied_update value must be one of open, global_complete, local_complete. For this turn, applied_update must be exactly local_complete. For this turn, changed_from_previous must be true iff your current answer differs from your immediately previous answer.\n\nReasoning contract:\n- Positive facts are true.\n- Negative facts make the corresponding target atom false.\n- A rule derives its conclusion only when every antecedent is true.\n- First apply all rules until no new facts can be derived.\n- Then evaluate unstated and underivable atoms using the current completeness state.\n- If a predicate is complete, an unstated and underivable atom using that predicate is false.\n- If a predicate is not complete, an unstated and underivable atom using that predicate is unknown.\n- A false answer derived from completeness is not a permanent negative fact; it can be withdrawn if the completeness state changes.\n\nCompleteness-state update for this turn:\n- Operation: REPLACE_COMPLETE_PREDICATES\n- Discard the previous complete-predicate set.\n- Replacement complete predicates: has_release_approval, may_ship\n\nTarget statement: Part F needs engineer review.\nTarget atom: needs_engineer_review::Part F"}]}
+{"id": "dynamic_dialogue_v3_0090", "base_id": "university_registration_0045_open_missing_direct", "split": "test", "domain": "university_registration", "family": "open_missing_direct", "subset": "core_contrastive", "gold_pattern": "unknown|false|unknown", "dialogue_type": "reopen", "target_statement": "Omar has an external scholarship.", "query_atom": "has_external_scholarship::Omar", "facts_positive": ["Omar passed the qualifying exam."], "facts_negative": [], "rules_natural": [], "predicate_glossary": {"passed_exam": "passed the qualifying exam", "has_advising_clearance": "has advising clearance", "has_external_scholarship": "has an external scholarship", "may_register": "may register for the seminar", "needs_manual_audit": "needs a manual audit"}, "vocabulary_predicates": ["has_advising_clearance", "has_external_scholarship", "may_register", "needs_manual_audit", "passed_exam"], "symbolic": {"positive_atoms": ["passed_exam::Omar"], "negative_atoms": [], "rules": [], "query_atom": "has_external_scholarship::Omar", "closure_atoms": ["passed_exam::Omar"]}, "turns": [{"turn_index": 1, "source_semantics": "owa", "update_operation": "SET_COMPLETE_PREDICATES", "update_predicates": [], "complete_predicates_after_update": [], "gold_answer": "unknown", "expected_changed_from_previous": null, "expected_applied_update": "open", "prompt": "This is turn 1 of a 3-turn dynamic completeness dialogue.\nYou are given an initial knowledge base and an initial completeness state.\nDo not use outside knowledge.\nReturn exactly one compact JSON object with keys answer, changed_from_previous, applied_update, and rationale. The answer value must be one of true, false, unknown. The changed_from_previous value must be true, false, or null. The applied_update value must be one of open, global_complete, local_complete. For this turn, applied_update must be exactly open. For turn 1, changed_from_previous must be null.\n\nReasoning contract:\n- Positive facts are true.\n- Negative facts make the corresponding target atom false.\n- A rule derives its conclusion only when every antecedent is true.\n- First apply all rules until no new facts can be derived.\n- Then evaluate unstated and underivable atoms using the current completeness state.\n- If a predicate is complete, an unstated and underivable atom using that predicate is false.\n- If a predicate is not complete, an unstated and underivable atom using that predicate is unknown.\n- A false answer derived from completeness is not a permanent negative fact; it can be withdrawn if the completeness state changes.\n\nKnowledge base:\n\nPredicate glossary:\n- has_advising_clearance: has advising clearance\n- has_external_scholarship: has an external scholarship\n- may_register: may register for the seminar\n- needs_manual_audit: needs a manual audit\n- passed_exam: passed the qualifying exam\n\nPositive facts:\n- Omar passed the qualifying exam.\n\nNegative facts:\n- none\n\nRules:\n- none\n\nTarget statement: Omar has an external scholarship.\nTarget atom: has_external_scholarship::Omar\n\nCompleteness-state update for this turn:\n- Operation: SET_COMPLETE_PREDICATES\n- New complete predicates: none"}, {"turn_index": 2, "source_semantics": "cwa", "update_operation": "ADD_COMPLETE_PREDICATES", "update_predicates": ["has_advising_clearance", "has_external_scholarship", "may_register", "needs_manual_audit", "passed_exam"], "complete_predicates_after_update": ["has_advising_clearance", "has_external_scholarship", "may_register", "needs_manual_audit", "passed_exam"], "gold_answer": "false", "expected_changed_from_previous": true, "expected_applied_update": "global_complete", "prompt": "This is turn 2 of a 3-turn dynamic completeness dialogue.\nKeep the exact same knowledge base, facts, rules, glossary, and target from turn 1.\nApply only the completeness-state update below.\nRecompute the answer from the updated state. Do not treat a previous false/unknown conclusion as a new fact.\nReturn exactly one compact JSON object with keys answer, changed_from_previous, applied_update, and rationale. The answer value must be one of true, false, unknown. The changed_from_previous value must be true, false, or null. The applied_update value must be one of open, global_complete, local_complete. For this turn, applied_update must be exactly global_complete. For this turn, changed_from_previous must be true iff your current answer differs from your immediately previous answer.\n\nReasoning contract:\n- Positive facts are true.\n- Negative facts make the corresponding target atom false.\n- A rule derives its conclusion only when every antecedent is true.\n- First apply all rules until no new facts can be derived.\n- Then evaluate unstated and underivable atoms using the current completeness state.\n- If a predicate is complete, an unstated and underivable atom using that predicate is false.\n- If a predicate is not complete, an unstated and underivable atom using that predicate is unknown.\n- A false answer derived from completeness is not a permanent negative fact; it can be withdrawn if the completeness state changes.\n\nCompleteness-state update for this turn:\n- Operation: ADD_COMPLETE_PREDICATES\n- Predicates to add as complete: has_advising_clearance, has_external_scholarship, may_register, needs_manual_audit, passed_exam\n- Complete predicates after this update: has_advising_clearance, has_external_scholarship, may_register, needs_manual_audit, passed_exam\n\nTarget statement: Omar has an external scholarship.\nTarget atom: has_external_scholarship::Omar"}, {"turn_index": 3, "source_semantics": "lcwa", "update_operation": "REPLACE_COMPLETE_PREDICATES", "update_predicates": ["has_advising_clearance", "may_register"], "complete_predicates_after_update": ["has_advising_clearance", "may_register"], "gold_answer": "unknown", "expected_changed_from_previous": true, "expected_applied_update": "local_complete", "prompt": "This is turn 3 of a 3-turn dynamic completeness dialogue.\nKeep the exact same knowledge base, facts, rules, glossary, and target from turn 1.\nApply only the completeness-state update below.\nRecompute the answer from the updated state. Do not treat a previous false/unknown conclusion as a new fact.\nReturn exactly one compact JSON object with keys answer, changed_from_previous, applied_update, and rationale. The answer value must be one of true, false, unknown. The changed_from_previous value must be true, false, or null. The applied_update value must be one of open, global_complete, local_complete. For this turn, applied_update must be exactly local_complete. For this turn, changed_from_previous must be true iff your current answer differs from your immediately previous answer.\n\nReasoning contract:\n- Positive facts are true.\n- Negative facts make the corresponding target atom false.\n- A rule derives its conclusion only when every antecedent is true.\n- First apply all rules until no new facts can be derived.\n- Then evaluate unstated and underivable atoms using the current completeness state.\n- If a predicate is complete, an unstated and underivable atom using that predicate is false.\n- If a predicate is not complete, an unstated and underivable atom using that predicate is unknown.\n- A false answer derived from completeness is not a permanent negative fact; it can be withdrawn if the completeness state changes.\n\nCompleteness-state update for this turn:\n- Operation: REPLACE_COMPLETE_PREDICATES\n- Discard the previous complete-predicate set.\n- Replacement complete predicates: has_advising_clearance, may_register\n\nTarget statement: Omar has an external scholarship.\nTarget atom: has_external_scholarship::Omar"}]}
+{"id": "dynamic_dialogue_v3_0095", "base_id": "university_registration_0050_closed_missing_with_open_distractor", "split": "test", "domain": "university_registration", "family": "closed_missing_with_open_distractor", "subset": "core_contrastive", "gold_pattern": "unknown|false|false", "dialogue_type": "persist_after_narrowing", "target_statement": "Lena has advising clearance.", "query_atom": "has_advising_clearance::Lena", "facts_positive": ["Omar has an external scholarship.", "Lena passed the qualifying exam."], "facts_negative": [], "rules_natural": [], "predicate_glossary": {"passed_exam": "passed the qualifying exam", "has_advising_clearance": "has advising clearance", "has_external_scholarship": "has an external scholarship", "may_register": "may register for the seminar", "needs_manual_audit": "needs a manual audit"}, "vocabulary_predicates": ["has_advising_clearance", "has_external_scholarship", "may_register", "needs_manual_audit", "passed_exam"], "symbolic": {"positive_atoms": ["has_external_scholarship::Omar", "passed_exam::Lena"], "negative_atoms": [], "rules": [], "query_atom": "has_advising_clearance::Lena", "closure_atoms": ["has_external_scholarship::Omar", "passed_exam::Lena"]}, "turns": [{"turn_index": 1, "source_semantics": "owa", "update_operation": "SET_COMPLETE_PREDICATES", "update_predicates": [], "complete_predicates_after_update": [], "gold_answer": "unknown", "expected_changed_from_previous": null, "expected_applied_update": "open", "prompt": "This is turn 1 of a 3-turn dynamic completeness dialogue.\nYou are given an initial knowledge base and an initial completeness state.\nDo not use outside knowledge.\nReturn exactly one compact JSON object with keys answer, changed_from_previous, applied_update, and rationale. The answer value must be one of true, false, unknown. The changed_from_previous value must be true, false, or null. The applied_update value must be one of open, global_complete, local_complete. For this turn, applied_update must be exactly open. For turn 1, changed_from_previous must be null.\n\nReasoning contract:\n- Positive facts are true.\n- Negative facts make the corresponding target atom false.\n- A rule derives its conclusion only when every antecedent is true.\n- First apply all rules until no new facts can be derived.\n- Then evaluate unstated and underivable atoms using the current completeness state.\n- If a predicate is complete, an unstated and underivable atom using that predicate is false.\n- If a predicate is not complete, an unstated and underivable atom using that predicate is unknown.\n- A false answer derived from completeness is not a permanent negative fact; it can be withdrawn if the completeness state changes.\n\nKnowledge base:\n\nPredicate glossary:\n- has_advising_clearance: has advising clearance\n- has_external_scholarship: has an external scholarship\n- may_register: may register for the seminar\n- needs_manual_audit: needs a manual audit\n- passed_exam: passed the qualifying exam\n\nPositive facts:\n- Omar has an external scholarship.\n- Lena passed the qualifying exam.\n\nNegative facts:\n- none\n\nRules:\n- none\n\nTarget statement: Lena has advising clearance.\nTarget atom: has_advising_clearance::Lena\n\nCompleteness-state update for this turn:\n- Operation: SET_COMPLETE_PREDICATES\n- New complete predicates: none"}, {"turn_index": 2, "source_semantics": "cwa", "update_operation": "ADD_COMPLETE_PREDICATES", "update_predicates": ["has_advising_clearance", "has_external_scholarship", "may_register", "needs_manual_audit", "passed_exam"], "complete_predicates_after_update": ["has_advising_clearance", "has_external_scholarship", "may_register", "needs_manual_audit", "passed_exam"], "gold_answer": "false", "expected_changed_from_previous": true, "expected_applied_update": "global_complete", "prompt": "This is turn 2 of a 3-turn dynamic completeness dialogue.\nKeep the exact same knowledge base, facts, rules, glossary, and target from turn 1.\nApply only the completeness-state update below.\nRecompute the answer from the updated state. Do not treat a previous false/unknown conclusion as a new fact.\nReturn exactly one compact JSON object with keys answer, changed_from_previous, applied_update, and rationale. The answer value must be one of true, false, unknown. The changed_from_previous value must be true, false, or null. The applied_update value must be one of open, global_complete, local_complete. For this turn, applied_update must be exactly global_complete. For this turn, changed_from_previous must be true iff your current answer differs from your immediately previous answer.\n\nReasoning contract:\n- Positive facts are true.\n- Negative facts make the corresponding target atom false.\n- A rule derives its conclusion only when every antecedent is true.\n- First apply all rules until no new facts can be derived.\n- Then evaluate unstated and underivable atoms using the current completeness state.\n- If a predicate is complete, an unstated and underivable atom using that predicate is false.\n- If a predicate is not complete, an unstated and underivable atom using that predicate is unknown.\n- A false answer derived from completeness is not a permanent negative fact; it can be withdrawn if the completeness state changes.\n\nCompleteness-state update for this turn:\n- Operation: ADD_COMPLETE_PREDICATES\n- Predicates to add as complete: has_advising_clearance, has_external_scholarship, may_register, needs_manual_audit, passed_exam\n- Complete predicates after this update: has_advising_clearance, has_external_scholarship, may_register, needs_manual_audit, passed_exam\n\nTarget statement: Lena has advising clearance.\nTarget atom: has_advising_clearance::Lena"}, {"turn_index": 3, "source_semantics": "lcwa", "update_operation": "REPLACE_COMPLETE_PREDICATES", "update_predicates": ["has_advising_clearance"], "complete_predicates_after_update": ["has_advising_clearance"], "gold_answer": "false", "expected_changed_from_previous": false, "expected_applied_update": "local_complete", "prompt": "This is turn 3 of a 3-turn dynamic completeness dialogue.\nKeep the exact same knowledge base, facts, rules, glossary, and target from turn 1.\nApply only the completeness-state update below.\nRecompute the answer from the updated state. Do not treat a previous false/unknown conclusion as a new fact.\nReturn exactly one compact JSON object with keys answer, changed_from_previous, applied_update, and rationale. The answer value must be one of true, false, unknown. The changed_from_previous value must be true, false, or null. The applied_update value must be one of open, global_complete, local_complete. For this turn, applied_update must be exactly local_complete. For this turn, changed_from_previous must be true iff your current answer differs from your immediately previous answer.\n\nReasoning contract:\n- Positive facts are true.\n- Negative facts make the corresponding target atom false.\n- A rule derives its conclusion only when every antecedent is true.\n- First apply all rules until no new facts can be derived.\n- Then evaluate unstated and underivable atoms using the current completeness state.\n- If a predicate is complete, an unstated and underivable atom using that predicate is false.\n- If a predicate is not complete, an unstated and underivable atom using that predicate is unknown.\n- A false answer derived from completeness is not a permanent negative fact; it can be withdrawn if the completeness state changes.\n\nCompleteness-state update for this turn:\n- Operation: REPLACE_COMPLETE_PREDICATES\n- Discard the previous complete-predicate set.\n- Replacement complete predicates: has_advising_clearance\n\nTarget statement: Lena has advising clearance.\nTarget atom: has_advising_clearance::Lena"}]}

data/dynamic_dialogue/validation.jsonl

@@ -0,0 +1,10 @@
+{"id": "dynamic_dialogue_v3_0012", "base_id": "finance_controls_0130_closed_missing_with_open_distractor", "split": "dev", "domain": "finance_controls", "family": "closed_missing_with_open_distractor", "subset": "core_contrastive", "gold_pattern": "unknown|false|false", "dialogue_type": "persist_after_narrowing", "target_statement": "Account 59 has compliance clearance.", "query_atom": "has_compliance_clearance::Account 59", "facts_positive": ["Account 61 has a manual exception.", "Account 59 passed KYC checks."], "facts_negative": [], "rules_natural": [], "predicate_glossary": {"passed_kyc": "passed KYC checks", "has_compliance_clearance": "has compliance clearance", "has_manual_exception": "has a manual exception", "may_receive_wire": "may receive a wire transfer", "needs_compliance_review": "needs compliance review"}, "vocabulary_predicates": ["has_compliance_clearance", "has_manual_exception", "may_receive_wire", "needs_compliance_review", "passed_kyc"], "symbolic": {"positive_atoms": ["has_manual_exception::Account 61", "passed_kyc::Account 59"], "negative_atoms": [], "rules": [], "query_atom": "has_compliance_clearance::Account 59", "closure_atoms": ["has_manual_exception::Account 61", "passed_kyc::Account 59"]}, "turns": [{"turn_index": 1, "source_semantics": "owa", "update_operation": "SET_COMPLETE_PREDICATES", "update_predicates": [], "complete_predicates_after_update": [], "gold_answer": "unknown", "expected_changed_from_previous": null, "expected_applied_update": "open", "prompt": "This is turn 1 of a 3-turn dynamic completeness dialogue.\nYou are given an initial knowledge base and an initial completeness state.\nDo not use outside knowledge.\nReturn exactly one compact JSON object with keys answer, changed_from_previous, applied_update, and rationale. The answer value must be one of true, false, unknown. The changed_from_previous value must be true, false, or null. The applied_update value must be one of open, global_complete, local_complete. For this turn, applied_update must be exactly open. For turn 1, changed_from_previous must be null.\n\nReasoning contract:\n- Positive facts are true.\n- Negative facts make the corresponding target atom false.\n- A rule derives its conclusion only when every antecedent is true.\n- First apply all rules until no new facts can be derived.\n- Then evaluate unstated and underivable atoms using the current completeness state.\n- If a predicate is complete, an unstated and underivable atom using that predicate is false.\n- If a predicate is not complete, an unstated and underivable atom using that predicate is unknown.\n- A false answer derived from completeness is not a permanent negative fact; it can be withdrawn if the completeness state changes.\n\nKnowledge base:\n\nPredicate glossary:\n- has_compliance_clearance: has compliance clearance\n- has_manual_exception: has a manual exception\n- may_receive_wire: may receive a wire transfer\n- needs_compliance_review: needs compliance review\n- passed_kyc: passed KYC checks\n\nPositive facts:\n- Account 61 has a manual exception.\n- Account 59 passed KYC checks.\n\nNegative facts:\n- none\n\nRules:\n- none\n\nTarget statement: Account 59 has compliance clearance.\nTarget atom: has_compliance_clearance::Account 59\n\nCompleteness-state update for this turn:\n- Operation: SET_COMPLETE_PREDICATES\n- New complete predicates: none"}, {"turn_index": 2, "source_semantics": "cwa", "update_operation": "ADD_COMPLETE_PREDICATES", "update_predicates": ["has_compliance_clearance", "has_manual_exception", "may_receive_wire", "needs_compliance_review", "passed_kyc"], "complete_predicates_after_update": ["has_compliance_clearance", "has_manual_exception", "may_receive_wire", "needs_compliance_review", "passed_kyc"], "gold_answer": "false", "expected_changed_from_previous": true, "expected_applied_update": "global_complete", "prompt": "This is turn 2 of a 3-turn dynamic completeness dialogue.\nKeep the exact same knowledge base, facts, rules, glossary, and target from turn 1.\nApply only the completeness-state update below.\nRecompute the answer from the updated state. Do not treat a previous false/unknown conclusion as a new fact.\nReturn exactly one compact JSON object with keys answer, changed_from_previous, applied_update, and rationale. The answer value must be one of true, false, unknown. The changed_from_previous value must be true, false, or null. The applied_update value must be one of open, global_complete, local_complete. For this turn, applied_update must be exactly global_complete. For this turn, changed_from_previous must be true iff your current answer differs from your immediately previous answer.\n\nReasoning contract:\n- Positive facts are true.\n- Negative facts make the corresponding target atom false.\n- A rule derives its conclusion only when every antecedent is true.\n- First apply all rules until no new facts can be derived.\n- Then evaluate unstated and underivable atoms using the current completeness state.\n- If a predicate is complete, an unstated and underivable atom using that predicate is false.\n- If a predicate is not complete, an unstated and underivable atom using that predicate is unknown.\n- A false answer derived from completeness is not a permanent negative fact; it can be withdrawn if the completeness state changes.\n\nCompleteness-state update for this turn:\n- Operation: ADD_COMPLETE_PREDICATES\n- Predicates to add as complete: has_compliance_clearance, has_manual_exception, may_receive_wire, needs_compliance_review, passed_kyc\n- Complete predicates after this update: has_compliance_clearance, has_manual_exception, may_receive_wire, needs_compliance_review, passed_kyc\n\nTarget statement: Account 59 has compliance clearance.\nTarget atom: has_compliance_clearance::Account 59"}, {"turn_index": 3, "source_semantics": "lcwa", "update_operation": "REPLACE_COMPLETE_PREDICATES", "update_predicates": ["has_compliance_clearance"], "complete_predicates_after_update": ["has_compliance_clearance"], "gold_answer": "false", "expected_changed_from_previous": false, "expected_applied_update": "local_complete", "prompt": "This is turn 3 of a 3-turn dynamic completeness dialogue.\nKeep the exact same knowledge base, facts, rules, glossary, and target from turn 1.\nApply only the completeness-state update below.\nRecompute the answer from the updated state. Do not treat a previous false/unknown conclusion as a new fact.\nReturn exactly one compact JSON object with keys answer, changed_from_previous, applied_update, and rationale. The answer value must be one of true, false, unknown. The changed_from_previous value must be true, false, or null. The applied_update value must be one of open, global_complete, local_complete. For this turn, applied_update must be exactly local_complete. For this turn, changed_from_previous must be true iff your current answer differs from your immediately previous answer.\n\nReasoning contract:\n- Positive facts are true.\n- Negative facts make the corresponding target atom false.\n- A rule derives its conclusion only when every antecedent is true.\n- First apply all rules until no new facts can be derived.\n- Then evaluate unstated and underivable atoms using the current completeness state.\n- If a predicate is complete, an unstated and underivable atom using that predicate is false.\n- If a predicate is not complete, an unstated and underivable atom using that predicate is unknown.\n- A false answer derived from completeness is not a permanent negative fact; it can be withdrawn if the completeness state changes.\n\nCompleteness-state update for this turn:\n- Operation: REPLACE_COMPLETE_PREDICATES\n- Discard the previous complete-predicate set.\n- Replacement complete predicates: has_compliance_clearance\n\nTarget statement: Account 59 has compliance clearance.\nTarget atom: has_compliance_clearance::Account 59"}]}
+{"id": "dynamic_dialogue_v3_0018", "base_id": "finance_controls_0138_closed_derived_missing_antecedent", "split": "dev", "domain": "finance_controls", "family": "closed_derived_missing_antecedent", "subset": "core_contrastive", "gold_pattern": "unknown|false|false", "dialogue_type": "persist_after_narrowing", "target_statement": "Account 12 may receive a wire transfer.", "query_atom": "may_receive_wire::Account 12", "facts_positive": ["Account 12 passed KYC checks."], "facts_negative": [], "rules_natural": ["If an account passed KYC checks and has compliance clearance, then that account may receive a wire transfer."], "predicate_glossary": {"passed_kyc": "passed KYC checks", "has_compliance_clearance": "has compliance clearance", "has_manual_exception": "has a manual exception", "may_receive_wire": "may receive a wire transfer", "needs_compliance_review": "needs compliance review"}, "vocabulary_predicates": ["has_compliance_clearance", "has_manual_exception", "may_receive_wire", "needs_compliance_review", "passed_kyc"], "symbolic": {"positive_atoms": ["passed_kyc::Account 12"], "negative_atoms": [], "rules": [{"antecedents": ["passed_kyc::Account 12", "has_compliance_clearance::Account 12"], "conclusion": "may_receive_wire::Account 12", "text": "If an account passed KYC checks and has compliance clearance, then that account may receive a wire transfer."}], "query_atom": "may_receive_wire::Account 12", "closure_atoms": ["passed_kyc::Account 12"]}, "turns": [{"turn_index": 1, "source_semantics": "owa", "update_operation": "SET_COMPLETE_PREDICATES", "update_predicates": [], "complete_predicates_after_update": [], "gold_answer": "unknown", "expected_changed_from_previous": null, "expected_applied_update": "open", "prompt": "This is turn 1 of a 3-turn dynamic completeness dialogue.\nYou are given an initial knowledge base and an initial completeness state.\nDo not use outside knowledge.\nReturn exactly one compact JSON object with keys answer, changed_from_previous, applied_update, and rationale. The answer value must be one of true, false, unknown. The changed_from_previous value must be true, false, or null. The applied_update value must be one of open, global_complete, local_complete. For this turn, applied_update must be exactly open. For turn 1, changed_from_previous must be null.\n\nReasoning contract:\n- Positive facts are true.\n- Negative facts make the corresponding target atom false.\n- A rule derives its conclusion only when every antecedent is true.\n- First apply all rules until no new facts can be derived.\n- Then evaluate unstated and underivable atoms using the current completeness state.\n- If a predicate is complete, an unstated and underivable atom using that predicate is false.\n- If a predicate is not complete, an unstated and underivable atom using that predicate is unknown.\n- A false answer derived from completeness is not a permanent negative fact; it can be withdrawn if the completeness state changes.\n\nKnowledge base:\n\nPredicate glossary:\n- has_compliance_clearance: has compliance clearance\n- has_manual_exception: has a manual exception\n- may_receive_wire: may receive a wire transfer\n- needs_compliance_review: needs compliance review\n- passed_kyc: passed KYC checks\n\nPositive facts:\n- Account 12 passed KYC checks.\n\nNegative facts:\n- none\n\nRules:\n- If an account passed KYC checks and has compliance clearance, then that account may receive a wire transfer.\n\nTarget statement: Account 12 may receive a wire transfer.\nTarget atom: may_receive_wire::Account 12\n\nCompleteness-state update for this turn:\n- Operation: SET_COMPLETE_PREDICATES\n- New complete predicates: none"}, {"turn_index": 2, "source_semantics": "cwa", "update_operation": "ADD_COMPLETE_PREDICATES", "update_predicates": ["has_compliance_clearance", "has_manual_exception", "may_receive_wire", "needs_compliance_review", "passed_kyc"], "complete_predicates_after_update": ["has_compliance_clearance", "has_manual_exception", "may_receive_wire", "needs_compliance_review", "passed_kyc"], "gold_answer": "false", "expected_changed_from_previous": true, "expected_applied_update": "global_complete", "prompt": "This is turn 2 of a 3-turn dynamic completeness dialogue.\nKeep the exact same knowledge base, facts, rules, glossary, and target from turn 1.\nApply only the completeness-state update below.\nRecompute the answer from the updated state. Do not treat a previous false/unknown conclusion as a new fact.\nReturn exactly one compact JSON object with keys answer, changed_from_previous, applied_update, and rationale. The answer value must be one of true, false, unknown. The changed_from_previous value must be true, false, or null. The applied_update value must be one of open, global_complete, local_complete. For this turn, applied_update must be exactly global_complete. For this turn, changed_from_previous must be true iff your current answer differs from your immediately previous answer.\n\nReasoning contract:\n- Positive facts are true.\n- Negative facts make the corresponding target atom false.\n- A rule derives its conclusion only when every antecedent is true.\n- First apply all rules until no new facts can be derived.\n- Then evaluate unstated and underivable atoms using the current completeness state.\n- If a predicate is complete, an unstated and underivable atom using that predicate is false.\n- If a predicate is not complete, an unstated and underivable atom using that predicate is unknown.\n- A false answer derived from completeness is not a permanent negative fact; it can be withdrawn if the completeness state changes.\n\nCompleteness-state update for this turn:\n- Operation: ADD_COMPLETE_PREDICATES\n- Predicates to add as complete: has_compliance_clearance, has_manual_exception, may_receive_wire, needs_compliance_review, passed_kyc\n- Complete predicates after this update: has_compliance_clearance, has_manual_exception, may_receive_wire, needs_compliance_review, passed_kyc\n\nTarget statement: Account 12 may receive a wire transfer.\nTarget atom: may_receive_wire::Account 12"}, {"turn_index": 3, "source_semantics": "lcwa", "update_operation": "REPLACE_COMPLETE_PREDICATES", "update_predicates": ["has_compliance_clearance", "may_receive_wire"], "complete_predicates_after_update": ["has_compliance_clearance", "may_receive_wire"], "gold_answer": "false", "expected_changed_from_previous": false, "expected_applied_update": "local_complete", "prompt": "This is turn 3 of a 3-turn dynamic completeness dialogue.\nKeep the exact same knowledge base, facts, rules, glossary, and target from turn 1.\nApply only the completeness-state update below.\nRecompute the answer from the updated state. Do not treat a previous false/unknown conclusion as a new fact.\nReturn exactly one compact JSON object with keys answer, changed_from_previous, applied_update, and rationale. The answer value must be one of true, false, unknown. The changed_from_previous value must be true, false, or null. The applied_update value must be one of open, global_complete, local_complete. For this turn, applied_update must be exactly local_complete. For this turn, changed_from_previous must be true iff your current answer differs from your immediately previous answer.\n\nReasoning contract:\n- Positive facts are true.\n- Negative facts make the corresponding target atom false.\n- A rule derives its conclusion only when every antecedent is true.\n- First apply all rules until no new facts can be derived.\n- Then evaluate unstated and underivable atoms using the current completeness state.\n- If a predicate is complete, an unstated and underivable atom using that predicate is false.\n- If a predicate is not complete, an unstated and underivable atom using that predicate is unknown.\n- A false answer derived from completeness is not a permanent negative fact; it can be withdrawn if the completeness state changes.\n\nCompleteness-state update for this turn:\n- Operation: REPLACE_COMPLETE_PREDICATES\n- Discard the previous complete-predicate set.\n- Replacement complete predicates: has_compliance_clearance, may_receive_wire\n\nTarget statement: Account 12 may receive a wire transfer.\nTarget atom: may_receive_wire::Account 12"}]}
+{"id": "dynamic_dialogue_v3_0022", "base_id": "finance_controls_0142_open_derived_missing_antecedent", "split": "dev", "domain": "finance_controls", "family": "open_derived_missing_antecedent", "subset": "core_contrastive", "gold_pattern": "unknown|false|unknown", "dialogue_type": "reopen", "target_statement": "Account 59 needs compliance review.", "query_atom": "needs_compliance_review::Account 59", "facts_positive": ["Account 59 passed KYC checks."], "facts_negative": [], "rules_natural": ["If an account has a manual exception, then that account needs compliance review."], "predicate_glossary": {"passed_kyc": "passed KYC checks", "has_compliance_clearance": "has compliance clearance", "has_manual_exception": "has a manual exception", "may_receive_wire": "may receive a wire transfer", "needs_compliance_review": "needs compliance review"}, "vocabulary_predicates": ["has_compliance_clearance", "has_manual_exception", "may_receive_wire", "needs_compliance_review", "passed_kyc"], "symbolic": {"positive_atoms": ["passed_kyc::Account 59"], "negative_atoms": [], "rules": [{"antecedents": ["has_manual_exception::Account 59"], "conclusion": "needs_compliance_review::Account 59", "text": "If an account has a manual exception, then that account needs compliance review."}], "query_atom": "needs_compliance_review::Account 59", "closure_atoms": ["passed_kyc::Account 59"]}, "turns": [{"turn_index": 1, "source_semantics": "owa", "update_operation": "SET_COMPLETE_PREDICATES", "update_predicates": [], "complete_predicates_after_update": [], "gold_answer": "unknown", "expected_changed_from_previous": null, "expected_applied_update": "open", "prompt": "This is turn 1 of a 3-turn dynamic completeness dialogue.\nYou are given an initial knowledge base and an initial completeness state.\nDo not use outside knowledge.\nReturn exactly one compact JSON object with keys answer, changed_from_previous, applied_update, and rationale. The answer value must be one of true, false, unknown. The changed_from_previous value must be true, false, or null. The applied_update value must be one of open, global_complete, local_complete. For this turn, applied_update must be exactly open. For turn 1, changed_from_previous must be null.\n\nReasoning contract:\n- Positive facts are true.\n- Negative facts make the corresponding target atom false.\n- A rule derives its conclusion only when every antecedent is true.\n- First apply all rules until no new facts can be derived.\n- Then evaluate unstated and underivable atoms using the current completeness state.\n- If a predicate is complete, an unstated and underivable atom using that predicate is false.\n- If a predicate is not complete, an unstated and underivable atom using that predicate is unknown.\n- A false answer derived from completeness is not a permanent negative fact; it can be withdrawn if the completeness state changes.\n\nKnowledge base:\n\nPredicate glossary:\n- has_compliance_clearance: has compliance clearance\n- has_manual_exception: has a manual exception\n- may_receive_wire: may receive a wire transfer\n- needs_compliance_review: needs compliance review\n- passed_kyc: passed KYC checks\n\nPositive facts:\n- Account 59 passed KYC checks.\n\nNegative facts:\n- none\n\nRules:\n- If an account has a manual exception, then that account needs compliance review.\n\nTarget statement: Account 59 needs compliance review.\nTarget atom: needs_compliance_review::Account 59\n\nCompleteness-state update for this turn:\n- Operation: SET_COMPLETE_PREDICATES\n- New complete predicates: none"}, {"turn_index": 2, "source_semantics": "cwa", "update_operation": "ADD_COMPLETE_PREDICATES", "update_predicates": ["has_compliance_clearance", "has_manual_exception", "may_receive_wire", "needs_compliance_review", "passed_kyc"], "complete_predicates_after_update": ["has_compliance_clearance", "has_manual_exception", "may_receive_wire", "needs_compliance_review", "passed_kyc"], "gold_answer": "false", "expected_changed_from_previous": true, "expected_applied_update": "global_complete", "prompt": "This is turn 2 of a 3-turn dynamic completeness dialogue.\nKeep the exact same knowledge base, facts, rules, glossary, and target from turn 1.\nApply only the completeness-state update below.\nRecompute the answer from the updated state. Do not treat a previous false/unknown conclusion as a new fact.\nReturn exactly one compact JSON object with keys answer, changed_from_previous, applied_update, and rationale. The answer value must be one of true, false, unknown. The changed_from_previous value must be true, false, or null. The applied_update value must be one of open, global_complete, local_complete. For this turn, applied_update must be exactly global_complete. For this turn, changed_from_previous must be true iff your current answer differs from your immediately previous answer.\n\nReasoning contract:\n- Positive facts are true.\n- Negative facts make the corresponding target atom false.\n- A rule derives its conclusion only when every antecedent is true.\n- First apply all rules until no new facts can be derived.\n- Then evaluate unstated and underivable atoms using the current completeness state.\n- If a predicate is complete, an unstated and underivable atom using that predicate is false.\n- If a predicate is not complete, an unstated and underivable atom using that predicate is unknown.\n- A false answer derived from completeness is not a permanent negative fact; it can be withdrawn if the completeness state changes.\n\nCompleteness-state update for this turn:\n- Operation: ADD_COMPLETE_PREDICATES\n- Predicates to add as complete: has_compliance_clearance, has_manual_exception, may_receive_wire, needs_compliance_review, passed_kyc\n- Complete predicates after this update: has_compliance_clearance, has_manual_exception, may_receive_wire, needs_compliance_review, passed_kyc\n\nTarget statement: Account 59 needs compliance review.\nTarget atom: needs_compliance_review::Account 59"}, {"turn_index": 3, "source_semantics": "lcwa", "update_operation": "REPLACE_COMPLETE_PREDICATES", "update_predicates": ["has_compliance_clearance", "may_receive_wire"], "complete_predicates_after_update": ["has_compliance_clearance", "may_receive_wire"], "gold_answer": "unknown", "expected_changed_from_previous": true, "expected_applied_update": "local_complete", "prompt": "This is turn 3 of a 3-turn dynamic completeness dialogue.\nKeep the exact same knowledge base, facts, rules, glossary, and target from turn 1.\nApply only the completeness-state update below.\nRecompute the answer from the updated state. Do not treat a previous false/unknown conclusion as a new fact.\nReturn exactly one compact JSON object with keys answer, changed_from_previous, applied_update, and rationale. The answer value must be one of true, false, unknown. The changed_from_previous value must be true, false, or null. The applied_update value must be one of open, global_complete, local_complete. For this turn, applied_update must be exactly local_complete. For this turn, changed_from_previous must be true iff your current answer differs from your immediately previous answer.\n\nReasoning contract:\n- Positive facts are true.\n- Negative facts make the corresponding target atom false.\n- A rule derives its conclusion only when every antecedent is true.\n- First apply all rules until no new facts can be derived.\n- Then evaluate unstated and underivable atoms using the current completeness state.\n- If a predicate is complete, an unstated and underivable atom using that predicate is false.\n- If a predicate is not complete, an unstated and underivable atom using that predicate is unknown.\n- A false answer derived from completeness is not a permanent negative fact; it can be withdrawn if the completeness state changes.\n\nCompleteness-state update for this turn:\n- Operation: REPLACE_COMPLETE_PREDICATES\n- Discard the previous complete-predicate set.\n- Replacement complete predicates: has_compliance_clearance, may_receive_wire\n\nTarget statement: Account 59 needs compliance review.\nTarget atom: needs_compliance_review::Account 59"}]}
+{"id": "dynamic_dialogue_v3_0024", "base_id": "hospital_access_0004_open_missing_direct", "split": "dev", "domain": "hospital_access", "family": "open_missing_direct", "subset": "core_contrastive", "gold_pattern": "unknown|false|unknown", "dialogue_type": "reopen", "target_statement": "Elena is assigned to the clinical trial.", "query_atom": "assigned_to_trial::Elena", "facts_positive": ["Elena has completed safety training."], "facts_negative": [], "rules_natural": [], "predicate_glossary": {"completed_training": "has completed safety training", "has_clearance": "has security clearance", "assigned_to_trial": "is assigned to the clinical trial", "may_enter_lab": "may enter Lab A", "needs_supervisor_review": "needs supervisor review"}, "vocabulary_predicates": ["assigned_to_trial", "completed_training", "has_clearance", "may_enter_lab", "needs_supervisor_review"], "symbolic": {"positive_atoms": ["completed_training::Elena"], "negative_atoms": [], "rules": [], "query_atom": "assigned_to_trial::Elena", "closure_atoms": ["completed_training::Elena"]}, "turns": [{"turn_index": 1, "source_semantics": "owa", "update_operation": "SET_COMPLETE_PREDICATES", "update_predicates": [], "complete_predicates_after_update": [], "gold_answer": "unknown", "expected_changed_from_previous": null, "expected_applied_update": "open", "prompt": "This is turn 1 of a 3-turn dynamic completeness dialogue.\nYou are given an initial knowledge base and an initial completeness state.\nDo not use outside knowledge.\nReturn exactly one compact JSON object with keys answer, changed_from_previous, applied_update, and rationale. The answer value must be one of true, false, unknown. The changed_from_previous value must be true, false, or null. The applied_update value must be one of open, global_complete, local_complete. For this turn, applied_update must be exactly open. For turn 1, changed_from_previous must be null.\n\nReasoning contract:\n- Positive facts are true.\n- Negative facts make the corresponding target atom false.\n- A rule derives its conclusion only when every antecedent is true.\n- First apply all rules until no new facts can be derived.\n- Then evaluate unstated and underivable atoms using the current completeness state.\n- If a predicate is complete, an unstated and underivable atom using that predicate is false.\n- If a predicate is not complete, an unstated and underivable atom using that predicate is unknown.\n- A false answer derived from completeness is not a permanent negative fact; it can be withdrawn if the completeness state changes.\n\nKnowledge base:\n\nPredicate glossary:\n- assigned_to_trial: is assigned to the clinical trial\n- completed_training: has completed safety training\n- has_clearance: has security clearance\n- may_enter_lab: may enter Lab A\n- needs_supervisor_review: needs supervisor review\n\nPositive facts:\n- Elena has completed safety training.\n\nNegative facts:\n- none\n\nRules:\n- none\n\nTarget statement: Elena is assigned to the clinical trial.\nTarget atom: assigned_to_trial::Elena\n\nCompleteness-state update for this turn:\n- Operation: SET_COMPLETE_PREDICATES\n- New complete predicates: none"}, {"turn_index": 2, "source_semantics": "cwa", "update_operation": "ADD_COMPLETE_PREDICATES", "update_predicates": ["assigned_to_trial", "completed_training", "has_clearance", "may_enter_lab", "needs_supervisor_review"], "complete_predicates_after_update": ["assigned_to_trial", "completed_training", "has_clearance", "may_enter_lab", "needs_supervisor_review"], "gold_answer": "false", "expected_changed_from_previous": true, "expected_applied_update": "global_complete", "prompt": "This is turn 2 of a 3-turn dynamic completeness dialogue.\nKeep the exact same knowledge base, facts, rules, glossary, and target from turn 1.\nApply only the completeness-state update below.\nRecompute the answer from the updated state. Do not treat a previous false/unknown conclusion as a new fact.\nReturn exactly one compact JSON object with keys answer, changed_from_previous, applied_update, and rationale. The answer value must be one of true, false, unknown. The changed_from_previous value must be true, false, or null. The applied_update value must be one of open, global_complete, local_complete. For this turn, applied_update must be exactly global_complete. For this turn, changed_from_previous must be true iff your current answer differs from your immediately previous answer.\n\nReasoning contract:\n- Positive facts are true.\n- Negative facts make the corresponding target atom false.\n- A rule derives its conclusion only when every antecedent is true.\n- First apply all rules until no new facts can be derived.\n- Then evaluate unstated and underivable atoms using the current completeness state.\n- If a predicate is complete, an unstated and underivable atom using that predicate is false.\n- If a predicate is not complete, an unstated and underivable atom using that predicate is unknown.\n- A false answer derived from completeness is not a permanent negative fact; it can be withdrawn if the completeness state changes.\n\nCompleteness-state update for this turn:\n- Operation: ADD_COMPLETE_PREDICATES\n- Predicates to add as complete: assigned_to_trial, completed_training, has_clearance, may_enter_lab, needs_supervisor_review\n- Complete predicates after this update: assigned_to_trial, completed_training, has_clearance, may_enter_lab, needs_supervisor_review\n\nTarget statement: Elena is assigned to the clinical trial.\nTarget atom: assigned_to_trial::Elena"}, {"turn_index": 3, "source_semantics": "lcwa", "update_operation": "REPLACE_COMPLETE_PREDICATES", "update_predicates": ["has_clearance", "may_enter_lab"], "complete_predicates_after_update": ["has_clearance", "may_enter_lab"], "gold_answer": "unknown", "expected_changed_from_previous": true, "expected_applied_update": "local_complete", "prompt": "This is turn 3 of a 3-turn dynamic completeness dialogue.\nKeep the exact same knowledge base, facts, rules, glossary, and target from turn 1.\nApply only the completeness-state update below.\nRecompute the answer from the updated state. Do not treat a previous false/unknown conclusion as a new fact.\nReturn exactly one compact JSON object with keys answer, changed_from_previous, applied_update, and rationale. The answer value must be one of true, false, unknown. The changed_from_previous value must be true, false, or null. The applied_update value must be one of open, global_complete, local_complete. For this turn, applied_update must be exactly local_complete. For this turn, changed_from_previous must be true iff your current answer differs from your immediately previous answer.\n\nReasoning contract:\n- Positive facts are true.\n- Negative facts make the corresponding target atom false.\n- A rule derives its conclusion only when every antecedent is true.\n- First apply all rules until no new facts can be derived.\n- Then evaluate unstated and underivable atoms using the current completeness state.\n- If a predicate is complete, an unstated and underivable atom using that predicate is false.\n- If a predicate is not complete, an unstated and underivable atom using that predicate is unknown.\n- A false answer derived from completeness is not a permanent negative fact; it can be withdrawn if the completeness state changes.\n\nCompleteness-state update for this turn:\n- Operation: REPLACE_COMPLETE_PREDICATES\n- Discard the previous complete-predicate set.\n- Replacement complete predicates: has_clearance, may_enter_lab\n\nTarget statement: Elena is assigned to the clinical trial.\nTarget atom: assigned_to_trial::Elena"}]}
+{"id": "dynamic_dialogue_v3_0031", "base_id": "hospital_access_0017_closed_derived_missing_antecedent", "split": "dev", "domain": "hospital_access", "family": "closed_derived_missing_antecedent", "subset": "core_contrastive", "gold_pattern": "unknown|false|false", "dialogue_type": "persist_after_narrowing", "target_statement": "Farid may enter Lab A.", "query_atom": "may_enter_lab::Farid", "facts_positive": ["Farid has completed safety training."], "facts_negative": [], "rules_natural": ["If a staff member has security clearance and has completed safety training, then that staff member may enter Lab A."], "predicate_glossary": {"completed_training": "has completed safety training", "has_clearance": "has security clearance", "assigned_to_trial": "is assigned to the clinical trial", "may_enter_lab": "may enter Lab A", "needs_supervisor_review": "needs supervisor review"}, "vocabulary_predicates": ["assigned_to_trial", "completed_training", "has_clearance", "may_enter_lab", "needs_supervisor_review"], "symbolic": {"positive_atoms": ["completed_training::Farid"], "negative_atoms": [], "rules": [{"antecedents": ["completed_training::Farid", "has_clearance::Farid"], "conclusion": "may_enter_lab::Farid", "text": "If a staff member has security clearance and has completed safety training, then that staff member may enter Lab A."}], "query_atom": "may_enter_lab::Farid", "closure_atoms": ["completed_training::Farid"]}, "turns": [{"turn_index": 1, "source_semantics": "owa", "update_operation": "SET_COMPLETE_PREDICATES", "update_predicates": [], "complete_predicates_after_update": [], "gold_answer": "unknown", "expected_changed_from_previous": null, "expected_applied_update": "open", "prompt": "This is turn 1 of a 3-turn dynamic completeness dialogue.\nYou are given an initial knowledge base and an initial completeness state.\nDo not use outside knowledge.\nReturn exactly one compact JSON object with keys answer, changed_from_previous, applied_update, and rationale. The answer value must be one of true, false, unknown. The changed_from_previous value must be true, false, or null. The applied_update value must be one of open, global_complete, local_complete. For this turn, applied_update must be exactly open. For turn 1, changed_from_previous must be null.\n\nReasoning contract:\n- Positive facts are true.\n- Negative facts make the corresponding target atom false.\n- A rule derives its conclusion only when every antecedent is true.\n- First apply all rules until no new facts can be derived.\n- Then evaluate unstated and underivable atoms using the current completeness state.\n- If a predicate is complete, an unstated and underivable atom using that predicate is false.\n- If a predicate is not complete, an unstated and underivable atom using that predicate is unknown.\n- A false answer derived from completeness is not a permanent negative fact; it can be withdrawn if the completeness state changes.\n\nKnowledge base:\n\nPredicate glossary:\n- assigned_to_trial: is assigned to the clinical trial\n- completed_training: has completed safety training\n- has_clearance: has security clearance\n- may_enter_lab: may enter Lab A\n- needs_supervisor_review: needs supervisor review\n\nPositive facts:\n- Farid has completed safety training.\n\nNegative facts:\n- none\n\nRules:\n- If a staff member has security clearance and has completed safety training, then that staff member may enter Lab A.\n\nTarget statement: Farid may enter Lab A.\nTarget atom: may_enter_lab::Farid\n\nCompleteness-state update for this turn:\n- Operation: SET_COMPLETE_PREDICATES\n- New complete predicates: none"}, {"turn_index": 2, "source_semantics": "cwa", "update_operation": "ADD_COMPLETE_PREDICATES", "update_predicates": ["assigned_to_trial", "completed_training", "has_clearance", "may_enter_lab", "needs_supervisor_review"], "complete_predicates_after_update": ["assigned_to_trial", "completed_training", "has_clearance", "may_enter_lab", "needs_supervisor_review"], "gold_answer": "false", "expected_changed_from_previous": true, "expected_applied_update": "global_complete", "prompt": "This is turn 2 of a 3-turn dynamic completeness dialogue.\nKeep the exact same knowledge base, facts, rules, glossary, and target from turn 1.\nApply only the completeness-state update below.\nRecompute the answer from the updated state. Do not treat a previous false/unknown conclusion as a new fact.\nReturn exactly one compact JSON object with keys answer, changed_from_previous, applied_update, and rationale. The answer value must be one of true, false, unknown. The changed_from_previous value must be true, false, or null. The applied_update value must be one of open, global_complete, local_complete. For this turn, applied_update must be exactly global_complete. For this turn, changed_from_previous must be true iff your current answer differs from your immediately previous answer.\n\nReasoning contract:\n- Positive facts are true.\n- Negative facts make the corresponding target atom false.\n- A rule derives its conclusion only when every antecedent is true.\n- First apply all rules until no new facts can be derived.\n- Then evaluate unstated and underivable atoms using the current completeness state.\n- If a predicate is complete, an unstated and underivable atom using that predicate is false.\n- If a predicate is not complete, an unstated and underivable atom using that predicate is unknown.\n- A false answer derived from completeness is not a permanent negative fact; it can be withdrawn if the completeness state changes.\n\nCompleteness-state update for this turn:\n- Operation: ADD_COMPLETE_PREDICATES\n- Predicates to add as complete: assigned_to_trial, completed_training, has_clearance, may_enter_lab, needs_supervisor_review\n- Complete predicates after this update: assigned_to_trial, completed_training, has_clearance, may_enter_lab, needs_supervisor_review\n\nTarget statement: Farid may enter Lab A.\nTarget atom: may_enter_lab::Farid"}, {"turn_index": 3, "source_semantics": "lcwa", "update_operation": "REPLACE_COMPLETE_PREDICATES", "update_predicates": ["has_clearance", "may_enter_lab"], "complete_predicates_after_update": ["has_clearance", "may_enter_lab"], "gold_answer": "false", "expected_changed_from_previous": false, "expected_applied_update": "local_complete", "prompt": "This is turn 3 of a 3-turn dynamic completeness dialogue.\nKeep the exact same knowledge base, facts, rules, glossary, and target from turn 1.\nApply only the completeness-state update below.\nRecompute the answer from the updated state. Do not treat a previous false/unknown conclusion as a new fact.\nReturn exactly one compact JSON object with keys answer, changed_from_previous, applied_update, and rationale. The answer value must be one of true, false, unknown. The changed_from_previous value must be true, false, or null. The applied_update value must be one of open, global_complete, local_complete. For this turn, applied_update must be exactly local_complete. For this turn, changed_from_previous must be true iff your current answer differs from your immediately previous answer.\n\nReasoning contract:\n- Positive facts are true.\n- Negative facts make the corresponding target atom false.\n- A rule derives its conclusion only when every antecedent is true.\n- First apply all rules until no new facts can be derived.\n- Then evaluate unstated and underivable atoms using the current completeness state.\n- If a predicate is complete, an unstated and underivable atom using that predicate is false.\n- If a predicate is not complete, an unstated and underivable atom using that predicate is unknown.\n- A false answer derived from completeness is not a permanent negative fact; it can be withdrawn if the completeness state changes.\n\nCompleteness-state update for this turn:\n- Operation: REPLACE_COMPLETE_PREDICATES\n- Discard the previous complete-predicate set.\n- Replacement complete predicates: has_clearance, may_enter_lab\n\nTarget statement: Farid may enter Lab A.\nTarget atom: may_enter_lab::Farid"}]}
+{"id": "dynamic_dialogue_v3_0042", "base_id": "library_services_0209_closed_missing_with_open_distractor", "split": "dev", "domain": "library_services", "family": "closed_missing_with_open_distractor", "subset": "core_contrastive", "gold_pattern": "unknown|false|false", "dialogue_type": "persist_after_narrowing", "target_statement": "Reader F has borrowing clearance.", "query_atom": "has_borrowing_clearance::Reader F", "facts_positive": ["Reader F has an active membership.", "Reader A has a special request."], "facts_negative": [], "rules_natural": [], "predicate_glossary": {"has_active_membership": "has an active membership", "has_borrowing_clearance": "has borrowing clearance", "has_special_request": "has a special request", "may_borrow_archive_item": "may borrow an archive item", "needs_librarian_review": "needs librarian review"}, "vocabulary_predicates": ["has_active_membership", "has_borrowing_clearance", "has_special_request", "may_borrow_archive_item", "needs_librarian_review"], "symbolic": {"positive_atoms": ["has_active_membership::Reader F", "has_special_request::Reader A"], "negative_atoms": [], "rules": [], "query_atom": "has_borrowing_clearance::Reader F", "closure_atoms": ["has_active_membership::Reader F", "has_special_request::Reader A"]}, "turns": [{"turn_index": 1, "source_semantics": "owa", "update_operation": "SET_COMPLETE_PREDICATES", "update_predicates": [], "complete_predicates_after_update": [], "gold_answer": "unknown", "expected_changed_from_previous": null, "expected_applied_update": "open", "prompt": "This is turn 1 of a 3-turn dynamic completeness dialogue.\nYou are given an initial knowledge base and an initial completeness state.\nDo not use outside knowledge.\nReturn exactly one compact JSON object with keys answer, changed_from_previous, applied_update, and rationale. The answer value must be one of true, false, unknown. The changed_from_previous value must be true, false, or null. The applied_update value must be one of open, global_complete, local_complete. For this turn, applied_update must be exactly open. For turn 1, changed_from_previous must be null.\n\nReasoning contract:\n- Positive facts are true.\n- Negative facts make the corresponding target atom false.\n- A rule derives its conclusion only when every antecedent is true.\n- First apply all rules until no new facts can be derived.\n- Then evaluate unstated and underivable atoms using the current completeness state.\n- If a predicate is complete, an unstated and underivable atom using that predicate is false.\n- If a predicate is not complete, an unstated and underivable atom using that predicate is unknown.\n- A false answer derived from completeness is not a permanent negative fact; it can be withdrawn if the completeness state changes.\n\nKnowledge base:\n\nPredicate glossary:\n- has_active_membership: has an active membership\n- has_borrowing_clearance: has borrowing clearance\n- has_special_request: has a special request\n- may_borrow_archive_item: may borrow an archive item\n- needs_librarian_review: needs librarian review\n\nPositive facts:\n- Reader F has an active membership.\n- Reader A has a special request.\n\nNegative facts:\n- none\n\nRules:\n- none\n\nTarget statement: Reader F has borrowing clearance.\nTarget atom: has_borrowing_clearance::Reader F\n\nCompleteness-state update for this turn:\n- Operation: SET_COMPLETE_PREDICATES\n- New complete predicates: none"}, {"turn_index": 2, "source_semantics": "cwa", "update_operation": "ADD_COMPLETE_PREDICATES", "update_predicates": ["has_active_membership", "has_borrowing_clearance", "has_special_request", "may_borrow_archive_item", "needs_librarian_review"], "complete_predicates_after_update": ["has_active_membership", "has_borrowing_clearance", "has_special_request", "may_borrow_archive_item", "needs_librarian_review"], "gold_answer": "false", "expected_changed_from_previous": true, "expected_applied_update": "global_complete", "prompt": "This is turn 2 of a 3-turn dynamic completeness dialogue.\nKeep the exact same knowledge base, facts, rules, glossary, and target from turn 1.\nApply only the completeness-state update below.\nRecompute the answer from the updated state. Do not treat a previous false/unknown conclusion as a new fact.\nReturn exactly one compact JSON object with keys answer, changed_from_previous, applied_update, and rationale. The answer value must be one of true, false, unknown. The changed_from_previous value must be true, false, or null. The applied_update value must be one of open, global_complete, local_complete. For this turn, applied_update must be exactly global_complete. For this turn, changed_from_previous must be true iff your current answer differs from your immediately previous answer.\n\nReasoning contract:\n- Positive facts are true.\n- Negative facts make the corresponding target atom false.\n- A rule derives its conclusion only when every antecedent is true.\n- First apply all rules until no new facts can be derived.\n- Then evaluate unstated and underivable atoms using the current completeness state.\n- If a predicate is complete, an unstated and underivable atom using that predicate is false.\n- If a predicate is not complete, an unstated and underivable atom using that predicate is unknown.\n- A false answer derived from completeness is not a permanent negative fact; it can be withdrawn if the completeness state changes.\n\nCompleteness-state update for this turn:\n- Operation: ADD_COMPLETE_PREDICATES\n- Predicates to add as complete: has_active_membership, has_borrowing_clearance, has_special_request, may_borrow_archive_item, needs_librarian_review\n- Complete predicates after this update: has_active_membership, has_borrowing_clearance, has_special_request, may_borrow_archive_item, needs_librarian_review\n\nTarget statement: Reader F has borrowing clearance.\nTarget atom: has_borrowing_clearance::Reader F"}, {"turn_index": 3, "source_semantics": "lcwa", "update_operation": "REPLACE_COMPLETE_PREDICATES", "update_predicates": ["has_borrowing_clearance"], "complete_predicates_after_update": ["has_borrowing_clearance"], "gold_answer": "false", "expected_changed_from_previous": false, "expected_applied_update": "local_complete", "prompt": "This is turn 3 of a 3-turn dynamic completeness dialogue.\nKeep the exact same knowledge base, facts, rules, glossary, and target from turn 1.\nApply only the completeness-state update below.\nRecompute the answer from the updated state. Do not treat a previous false/unknown conclusion as a new fact.\nReturn exactly one compact JSON object with keys answer, changed_from_previous, applied_update, and rationale. The answer value must be one of true, false, unknown. The changed_from_previous value must be true, false, or null. The applied_update value must be one of open, global_complete, local_complete. For this turn, applied_update must be exactly local_complete. For this turn, changed_from_previous must be true iff your current answer differs from your immediately previous answer.\n\nReasoning contract:\n- Positive facts are true.\n- Negative facts make the corresponding target atom false.\n- A rule derives its conclusion only when every antecedent is true.\n- First apply all rules until no new facts can be derived.\n- Then evaluate unstated and underivable atoms using the current completeness state.\n- If a predicate is complete, an unstated and underivable atom using that predicate is false.\n- If a predicate is not complete, an unstated and underivable atom using that predicate is unknown.\n- A false answer derived from completeness is not a permanent negative fact; it can be withdrawn if the completeness state changes.\n\nCompleteness-state update for this turn:\n- Operation: REPLACE_COMPLETE_PREDICATES\n- Discard the previous complete-predicate set.\n- Replacement complete predicates: has_borrowing_clearance\n\nTarget statement: Reader F has borrowing clearance.\nTarget atom: has_borrowing_clearance::Reader F"}]}
+{"id": "dynamic_dialogue_v3_0055", "base_id": "manufacturing_quality_0099_closed_derived_missing_antecedent", "split": "dev", "domain": "manufacturing_quality", "family": "closed_derived_missing_antecedent", "subset": "core_contrastive", "gold_pattern": "unknown|false|false", "dialogue_type": "persist_after_narrowing", "target_statement": "Part D may ship to customers.", "query_atom": "may_ship::Part D", "facts_positive": ["Part D passed the visual check."], "facts_negative": [], "rules_natural": ["If a part passed the visual check and has release approval, then that part may ship to customers."], "predicate_glossary": {"passed_visual_check": "passed the visual check", "has_release_approval": "has release approval", "has_supplier_note": "has a supplier note", "may_ship": "may ship to customers", "needs_engineer_review": "needs engineer review"}, "vocabulary_predicates": ["has_release_approval", "has_supplier_note", "may_ship", "needs_engineer_review", "passed_visual_check"], "symbolic": {"positive_atoms": ["passed_visual_check::Part D"], "negative_atoms": [], "rules": [{"antecedents": ["passed_visual_check::Part D", "has_release_approval::Part D"], "conclusion": "may_ship::Part D", "text": "If a part passed the visual check and has release approval, then that part may ship to customers."}], "query_atom": "may_ship::Part D", "closure_atoms": ["passed_visual_check::Part D"]}, "turns": [{"turn_index": 1, "source_semantics": "owa", "update_operation": "SET_COMPLETE_PREDICATES", "update_predicates": [], "complete_predicates_after_update": [], "gold_answer": "unknown", "expected_changed_from_previous": null, "expected_applied_update": "open", "prompt": "This is turn 1 of a 3-turn dynamic completeness dialogue.\nYou are given an initial knowledge base and an initial completeness state.\nDo not use outside knowledge.\nReturn exactly one compact JSON object with keys answer, changed_from_previous, applied_update, and rationale. The answer value must be one of true, false, unknown. The changed_from_previous value must be true, false, or null. The applied_update value must be one of open, global_complete, local_complete. For this turn, applied_update must be exactly open. For turn 1, changed_from_previous must be null.\n\nReasoning contract:\n- Positive facts are true.\n- Negative facts make the corresponding target atom false.\n- A rule derives its conclusion only when every antecedent is true.\n- First apply all rules until no new facts can be derived.\n- Then evaluate unstated and underivable atoms using the current completeness state.\n- If a predicate is complete, an unstated and underivable atom using that predicate is false.\n- If a predicate is not complete, an unstated and underivable atom using that predicate is unknown.\n- A false answer derived from completeness is not a permanent negative fact; it can be withdrawn if the completeness state changes.\n\nKnowledge base:\n\nPredicate glossary:\n- has_release_approval: has release approval\n- has_supplier_note: has a supplier note\n- may_ship: may ship to customers\n- needs_engineer_review: needs engineer review\n- passed_visual_check: passed the visual check\n\nPositive facts:\n- Part D passed the visual check.\n\nNegative facts:\n- none\n\nRules:\n- If a part passed the visual check and has release approval, then that part may ship to customers.\n\nTarget statement: Part D may ship to customers.\nTarget atom: may_ship::Part D\n\nCompleteness-state update for this turn:\n- Operation: SET_COMPLETE_PREDICATES\n- New complete predicates: none"}, {"turn_index": 2, "source_semantics": "cwa", "update_operation": "ADD_COMPLETE_PREDICATES", "update_predicates": ["has_release_approval", "has_supplier_note", "may_ship", "needs_engineer_review", "passed_visual_check"], "complete_predicates_after_update": ["has_release_approval", "has_supplier_note", "may_ship", "needs_engineer_review", "passed_visual_check"], "gold_answer": "false", "expected_changed_from_previous": true, "expected_applied_update": "global_complete", "prompt": "This is turn 2 of a 3-turn dynamic completeness dialogue.\nKeep the exact same knowledge base, facts, rules, glossary, and target from turn 1.\nApply only the completeness-state update below.\nRecompute the answer from the updated state. Do not treat a previous false/unknown conclusion as a new fact.\nReturn exactly one compact JSON object with keys answer, changed_from_previous, applied_update, and rationale. The answer value must be one of true, false, unknown. The changed_from_previous value must be true, false, or null. The applied_update value must be one of open, global_complete, local_complete. For this turn, applied_update must be exactly global_complete. For this turn, changed_from_previous must be true iff your current answer differs from your immediately previous answer.\n\nReasoning contract:\n- Positive facts are true.\n- Negative facts make the corresponding target atom false.\n- A rule derives its conclusion only when every antecedent is true.\n- First apply all rules until no new facts can be derived.\n- Then evaluate unstated and underivable atoms using the current completeness state.\n- If a predicate is complete, an unstated and underivable atom using that predicate is false.\n- If a predicate is not complete, an unstated and underivable atom using that predicate is unknown.\n- A false answer derived from completeness is not a permanent negative fact; it can be withdrawn if the completeness state changes.\n\nCompleteness-state update for this turn:\n- Operation: ADD_COMPLETE_PREDICATES\n- Predicates to add as complete: has_release_approval, has_supplier_note, may_ship, needs_engineer_review, passed_visual_check\n- Complete predicates after this update: has_release_approval, has_supplier_note, may_ship, needs_engineer_review, passed_visual_check\n\nTarget statement: Part D may ship to customers.\nTarget atom: may_ship::Part D"}, {"turn_index": 3, "source_semantics": "lcwa", "update_operation": "REPLACE_COMPLETE_PREDICATES", "update_predicates": ["has_release_approval", "may_ship"], "complete_predicates_after_update": ["has_release_approval", "may_ship"], "gold_answer": "false", "expected_changed_from_previous": false, "expected_applied_update": "local_complete", "prompt": "This is turn 3 of a 3-turn dynamic completeness dialogue.\nKeep the exact same knowledge base, facts, rules, glossary, and target from turn 1.\nApply only the completeness-state update below.\nRecompute the answer from the updated state. Do not treat a previous false/unknown conclusion as a new fact.\nReturn exactly one compact JSON object with keys answer, changed_from_previous, applied_update, and rationale. The answer value must be one of true, false, unknown. The changed_from_previous value must be true, false, or null. The applied_update value must be one of open, global_complete, local_complete. For this turn, applied_update must be exactly local_complete. For this turn, changed_from_previous must be true iff your current answer differs from your immediately previous answer.\n\nReasoning contract:\n- Positive facts are true.\n- Negative facts make the corresponding target atom false.\n- A rule derives its conclusion only when every antecedent is true.\n- First apply all rules until no new facts can be derived.\n- Then evaluate unstated and underivable atoms using the current completeness state.\n- If a predicate is complete, an unstated and underivable atom using that predicate is false.\n- If a predicate is not complete, an unstated and underivable atom using that predicate is unknown.\n- A false answer derived from completeness is not a permanent negative fact; it can be withdrawn if the completeness state changes.\n\nCompleteness-state update for this turn:\n- Operation: REPLACE_COMPLETE_PREDICATES\n- Discard the previous complete-predicate set.\n- Replacement complete predicates: has_release_approval, may_ship\n\nTarget statement: Part D may ship to customers.\nTarget atom: may_ship::Part D"}]}
+{"id": "dynamic_dialogue_v3_0077", "base_id": "robotics_operations_0169_closed_missing_with_open_distractor", "split": "dev", "domain": "robotics_operations", "family": "closed_missing_with_open_distractor", "subset": "core_contrastive", "gold_pattern": "unknown|false|false", "dialogue_type": "persist_after_narrowing", "target_statement": "Rover 2 has shift clearance.", "query_atom": "has_shift_clearance::Rover 2", "facts_positive": ["Rover 3 has a field note.", "Rover 2 passed diagnostics."], "facts_negative": [], "rules_natural": [], "predicate_glossary": {"passed_diagnostics": "passed diagnostics", "has_shift_clearance": "has shift clearance", "has_field_note": "has a field note", "may_start_shift": "may start its shift", "needs_operator_review": "needs operator review"}, "vocabulary_predicates": ["has_field_note", "has_shift_clearance", "may_start_shift", "needs_operator_review", "passed_diagnostics"], "symbolic": {"positive_atoms": ["has_field_note::Rover 3", "passed_diagnostics::Rover 2"], "negative_atoms": [], "rules": [], "query_atom": "has_shift_clearance::Rover 2", "closure_atoms": ["has_field_note::Rover 3", "passed_diagnostics::Rover 2"]}, "turns": [{"turn_index": 1, "source_semantics": "owa", "update_operation": "SET_COMPLETE_PREDICATES", "update_predicates": [], "complete_predicates_after_update": [], "gold_answer": "unknown", "expected_changed_from_previous": null, "expected_applied_update": "open", "prompt": "This is turn 1 of a 3-turn dynamic completeness dialogue.\nYou are given an initial knowledge base and an initial completeness state.\nDo not use outside knowledge.\nReturn exactly one compact JSON object with keys answer, changed_from_previous, applied_update, and rationale. The answer value must be one of true, false, unknown. The changed_from_previous value must be true, false, or null. The applied_update value must be one of open, global_complete, local_complete. For this turn, applied_update must be exactly open. For turn 1, changed_from_previous must be null.\n\nReasoning contract:\n- Positive facts are true.\n- Negative facts make the corresponding target atom false.\n- A rule derives its conclusion only when every antecedent is true.\n- First apply all rules until no new facts can be derived.\n- Then evaluate unstated and underivable atoms using the current completeness state.\n- If a predicate is complete, an unstated and underivable atom using that predicate is false.\n- If a predicate is not complete, an unstated and underivable atom using that predicate is unknown.\n- A false answer derived from completeness is not a permanent negative fact; it can be withdrawn if the completeness state changes.\n\nKnowledge base:\n\nPredicate glossary:\n- has_field_note: has a field note\n- has_shift_clearance: has shift clearance\n- may_start_shift: may start its shift\n- needs_operator_review: needs operator review\n- passed_diagnostics: passed diagnostics\n\nPositive facts:\n- Rover 3 has a field note.\n- Rover 2 passed diagnostics.\n\nNegative facts:\n- none\n\nRules:\n- none\n\nTarget statement: Rover 2 has shift clearance.\nTarget atom: has_shift_clearance::Rover 2\n\nCompleteness-state update for this turn:\n- Operation: SET_COMPLETE_PREDICATES\n- New complete predicates: none"}, {"turn_index": 2, "source_semantics": "cwa", "update_operation": "ADD_COMPLETE_PREDICATES", "update_predicates": ["has_field_note", "has_shift_clearance", "may_start_shift", "needs_operator_review", "passed_diagnostics"], "complete_predicates_after_update": ["has_field_note", "has_shift_clearance", "may_start_shift", "needs_operator_review", "passed_diagnostics"], "gold_answer": "false", "expected_changed_from_previous": true, "expected_applied_update": "global_complete", "prompt": "This is turn 2 of a 3-turn dynamic completeness dialogue.\nKeep the exact same knowledge base, facts, rules, glossary, and target from turn 1.\nApply only the completeness-state update below.\nRecompute the answer from the updated state. Do not treat a previous false/unknown conclusion as a new fact.\nReturn exactly one compact JSON object with keys answer, changed_from_previous, applied_update, and rationale. The answer value must be one of true, false, unknown. The changed_from_previous value must be true, false, or null. The applied_update value must be one of open, global_complete, local_complete. For this turn, applied_update must be exactly global_complete. For this turn, changed_from_previous must be true iff your current answer differs from your immediately previous answer.\n\nReasoning contract:\n- Positive facts are true.\n- Negative facts make the corresponding target atom false.\n- A rule derives its conclusion only when every antecedent is true.\n- First apply all rules until no new facts can be derived.\n- Then evaluate unstated and underivable atoms using the current completeness state.\n- If a predicate is complete, an unstated and underivable atom using that predicate is false.\n- If a predicate is not complete, an unstated and underivable atom using that predicate is unknown.\n- A false answer derived from completeness is not a permanent negative fact; it can be withdrawn if the completeness state changes.\n\nCompleteness-state update for this turn:\n- Operation: ADD_COMPLETE_PREDICATES\n- Predicates to add as complete: has_field_note, has_shift_clearance, may_start_shift, needs_operator_review, passed_diagnostics\n- Complete predicates after this update: has_field_note, has_shift_clearance, may_start_shift, needs_operator_review, passed_diagnostics\n\nTarget statement: Rover 2 has shift clearance.\nTarget atom: has_shift_clearance::Rover 2"}, {"turn_index": 3, "source_semantics": "lcwa", "update_operation": "REPLACE_COMPLETE_PREDICATES", "update_predicates": ["has_shift_clearance"], "complete_predicates_after_update": ["has_shift_clearance"], "gold_answer": "false", "expected_changed_from_previous": false, "expected_applied_update": "local_complete", "prompt": "This is turn 3 of a 3-turn dynamic completeness dialogue.\nKeep the exact same knowledge base, facts, rules, glossary, and target from turn 1.\nApply only the completeness-state update below.\nRecompute the answer from the updated state. Do not treat a previous false/unknown conclusion as a new fact.\nReturn exactly one compact JSON object with keys answer, changed_from_previous, applied_update, and rationale. The answer value must be one of true, false, unknown. The changed_from_previous value must be true, false, or null. The applied_update value must be one of open, global_complete, local_complete. For this turn, applied_update must be exactly local_complete. For this turn, changed_from_previous must be true iff your current answer differs from your immediately previous answer.\n\nReasoning contract:\n- Positive facts are true.\n- Negative facts make the corresponding target atom false.\n- A rule derives its conclusion only when every antecedent is true.\n- First apply all rules until no new facts can be derived.\n- Then evaluate unstated and underivable atoms using the current completeness state.\n- If a predicate is complete, an unstated and underivable atom using that predicate is false.\n- If a predicate is not complete, an unstated and underivable atom using that predicate is unknown.\n- A false answer derived from completeness is not a permanent negative fact; it can be withdrawn if the completeness state changes.\n\nCompleteness-state update for this turn:\n- Operation: REPLACE_COMPLETE_PREDICATES\n- Discard the previous complete-predicate set.\n- Replacement complete predicates: has_shift_clearance\n\nTarget statement: Rover 2 has shift clearance.\nTarget atom: has_shift_clearance::Rover 2"}]}
+{"id": "dynamic_dialogue_v3_0093", "base_id": "university_registration_0048_closed_missing_with_open_distractor", "split": "dev", "domain": "university_registration", "family": "closed_missing_with_open_distractor", "subset": "core_contrastive", "gold_pattern": "unknown|false|false", "dialogue_type": "persist_after_narrowing", "target_statement": "Mia has advising clearance.", "query_atom": "has_advising_clearance::Mia", "facts_positive": ["Noah has an external scholarship.", "Mia passed the qualifying exam."], "facts_negative": [], "rules_natural": [], "predicate_glossary": {"passed_exam": "passed the qualifying exam", "has_advising_clearance": "has advising clearance", "has_external_scholarship": "has an external scholarship", "may_register": "may register for the seminar", "needs_manual_audit": "needs a manual audit"}, "vocabulary_predicates": ["has_advising_clearance", "has_external_scholarship", "may_register", "needs_manual_audit", "passed_exam"], "symbolic": {"positive_atoms": ["has_external_scholarship::Noah", "passed_exam::Mia"], "negative_atoms": [], "rules": [], "query_atom": "has_advising_clearance::Mia", "closure_atoms": ["has_external_scholarship::Noah", "passed_exam::Mia"]}, "turns": [{"turn_index": 1, "source_semantics": "owa", "update_operation": "SET_COMPLETE_PREDICATES", "update_predicates": [], "complete_predicates_after_update": [], "gold_answer": "unknown", "expected_changed_from_previous": null, "expected_applied_update": "open", "prompt": "This is turn 1 of a 3-turn dynamic completeness dialogue.\nYou are given an initial knowledge base and an initial completeness state.\nDo not use outside knowledge.\nReturn exactly one compact JSON object with keys answer, changed_from_previous, applied_update, and rationale. The answer value must be one of true, false, unknown. The changed_from_previous value must be true, false, or null. The applied_update value must be one of open, global_complete, local_complete. For this turn, applied_update must be exactly open. For turn 1, changed_from_previous must be null.\n\nReasoning contract:\n- Positive facts are true.\n- Negative facts make the corresponding target atom false.\n- A rule derives its conclusion only when every antecedent is true.\n- First apply all rules until no new facts can be derived.\n- Then evaluate unstated and underivable atoms using the current completeness state.\n- If a predicate is complete, an unstated and underivable atom using that predicate is false.\n- If a predicate is not complete, an unstated and underivable atom using that predicate is unknown.\n- A false answer derived from completeness is not a permanent negative fact; it can be withdrawn if the completeness state changes.\n\nKnowledge base:\n\nPredicate glossary:\n- has_advising_clearance: has advising clearance\n- has_external_scholarship: has an external scholarship\n- may_register: may register for the seminar\n- needs_manual_audit: needs a manual audit\n- passed_exam: passed the qualifying exam\n\nPositive facts:\n- Noah has an external scholarship.\n- Mia passed the qualifying exam.\n\nNegative facts:\n- none\n\nRules:\n- none\n\nTarget statement: Mia has advising clearance.\nTarget atom: has_advising_clearance::Mia\n\nCompleteness-state update for this turn:\n- Operation: SET_COMPLETE_PREDICATES\n- New complete predicates: none"}, {"turn_index": 2, "source_semantics": "cwa", "update_operation": "ADD_COMPLETE_PREDICATES", "update_predicates": ["has_advising_clearance", "has_external_scholarship", "may_register", "needs_manual_audit", "passed_exam"], "complete_predicates_after_update": ["has_advising_clearance", "has_external_scholarship", "may_register", "needs_manual_audit", "passed_exam"], "gold_answer": "false", "expected_changed_from_previous": true, "expected_applied_update": "global_complete", "prompt": "This is turn 2 of a 3-turn dynamic completeness dialogue.\nKeep the exact same knowledge base, facts, rules, glossary, and target from turn 1.\nApply only the completeness-state update below.\nRecompute the answer from the updated state. Do not treat a previous false/unknown conclusion as a new fact.\nReturn exactly one compact JSON object with keys answer, changed_from_previous, applied_update, and rationale. The answer value must be one of true, false, unknown. The changed_from_previous value must be true, false, or null. The applied_update value must be one of open, global_complete, local_complete. For this turn, applied_update must be exactly global_complete. For this turn, changed_from_previous must be true iff your current answer differs from your immediately previous answer.\n\nReasoning contract:\n- Positive facts are true.\n- Negative facts make the corresponding target atom false.\n- A rule derives its conclusion only when every antecedent is true.\n- First apply all rules until no new facts can be derived.\n- Then evaluate unstated and underivable atoms using the current completeness state.\n- If a predicate is complete, an unstated and underivable atom using that predicate is false.\n- If a predicate is not complete, an unstated and underivable atom using that predicate is unknown.\n- A false answer derived from completeness is not a permanent negative fact; it can be withdrawn if the completeness state changes.\n\nCompleteness-state update for this turn:\n- Operation: ADD_COMPLETE_PREDICATES\n- Predicates to add as complete: has_advising_clearance, has_external_scholarship, may_register, needs_manual_audit, passed_exam\n- Complete predicates after this update: has_advising_clearance, has_external_scholarship, may_register, needs_manual_audit, passed_exam\n\nTarget statement: Mia has advising clearance.\nTarget atom: has_advising_clearance::Mia"}, {"turn_index": 3, "source_semantics": "lcwa", "update_operation": "REPLACE_COMPLETE_PREDICATES", "update_predicates": ["has_advising_clearance"], "complete_predicates_after_update": ["has_advising_clearance"], "gold_answer": "false", "expected_changed_from_previous": false, "expected_applied_update": "local_complete", "prompt": "This is turn 3 of a 3-turn dynamic completeness dialogue.\nKeep the exact same knowledge base, facts, rules, glossary, and target from turn 1.\nApply only the completeness-state update below.\nRecompute the answer from the updated state. Do not treat a previous false/unknown conclusion as a new fact.\nReturn exactly one compact JSON object with keys answer, changed_from_previous, applied_update, and rationale. The answer value must be one of true, false, unknown. The changed_from_previous value must be true, false, or null. The applied_update value must be one of open, global_complete, local_complete. For this turn, applied_update must be exactly local_complete. For this turn, changed_from_previous must be true iff your current answer differs from your immediately previous answer.\n\nReasoning contract:\n- Positive facts are true.\n- Negative facts make the corresponding target atom false.\n- A rule derives its conclusion only when every antecedent is true.\n- First apply all rules until no new facts can be derived.\n- Then evaluate unstated and underivable atoms using the current completeness state.\n- If a predicate is complete, an unstated and underivable atom using that predicate is false.\n- If a predicate is not complete, an unstated and underivable atom using that predicate is unknown.\n- A false answer derived from completeness is not a permanent negative fact; it can be withdrawn if the completeness state changes.\n\nCompleteness-state update for this turn:\n- Operation: REPLACE_COMPLETE_PREDICATES\n- Discard the previous complete-predicate set.\n- Replacement complete predicates: has_advising_clearance\n\nTarget statement: Mia has advising clearance.\nTarget atom: has_advising_clearance::Mia"}]}
+{"id": "dynamic_dialogue_v3_0098", "base_id": "university_registration_0056_closed_derived_missing_antecedent", "split": "dev", "domain": "university_registration", "family": "closed_derived_missing_antecedent", "subset": "core_contrastive", "gold_pattern": "unknown|false|false", "dialogue_type": "persist_after_narrowing", "target_statement": "Lena may register for the seminar.", "query_atom": "may_register::Lena", "facts_positive": ["Lena passed the qualifying exam."], "facts_negative": [], "rules_natural": ["If a student passed the qualifying exam and has advising clearance, then that student may register for the seminar."], "predicate_glossary": {"passed_exam": "passed the qualifying exam", "has_advising_clearance": "has advising clearance", "has_external_scholarship": "has an external scholarship", "may_register": "may register for the seminar", "needs_manual_audit": "needs a manual audit"}, "vocabulary_predicates": ["has_advising_clearance", "has_external_scholarship", "may_register", "needs_manual_audit", "passed_exam"], "symbolic": {"positive_atoms": ["passed_exam::Lena"], "negative_atoms": [], "rules": [{"antecedents": ["passed_exam::Lena", "has_advising_clearance::Lena"], "conclusion": "may_register::Lena", "text": "If a student passed the qualifying exam and has advising clearance, then that student may register for the seminar."}], "query_atom": "may_register::Lena", "closure_atoms": ["passed_exam::Lena"]}, "turns": [{"turn_index": 1, "source_semantics": "owa", "update_operation": "SET_COMPLETE_PREDICATES", "update_predicates": [], "complete_predicates_after_update": [], "gold_answer": "unknown", "expected_changed_from_previous": null, "expected_applied_update": "open", "prompt": "This is turn 1 of a 3-turn dynamic completeness dialogue.\nYou are given an initial knowledge base and an initial completeness state.\nDo not use outside knowledge.\nReturn exactly one compact JSON object with keys answer, changed_from_previous, applied_update, and rationale. The answer value must be one of true, false, unknown. The changed_from_previous value must be true, false, or null. The applied_update value must be one of open, global_complete, local_complete. For this turn, applied_update must be exactly open. For turn 1, changed_from_previous must be null.\n\nReasoning contract:\n- Positive facts are true.\n- Negative facts make the corresponding target atom false.\n- A rule derives its conclusion only when every antecedent is true.\n- First apply all rules until no new facts can be derived.\n- Then evaluate unstated and underivable atoms using the current completeness state.\n- If a predicate is complete, an unstated and underivable atom using that predicate is false.\n- If a predicate is not complete, an unstated and underivable atom using that predicate is unknown.\n- A false answer derived from completeness is not a permanent negative fact; it can be withdrawn if the completeness state changes.\n\nKnowledge base:\n\nPredicate glossary:\n- has_advising_clearance: has advising clearance\n- has_external_scholarship: has an external scholarship\n- may_register: may register for the seminar\n- needs_manual_audit: needs a manual audit\n- passed_exam: passed the qualifying exam\n\nPositive facts:\n- Lena passed the qualifying exam.\n\nNegative facts:\n- none\n\nRules:\n- If a student passed the qualifying exam and has advising clearance, then that student may register for the seminar.\n\nTarget statement: Lena may register for the seminar.\nTarget atom: may_register::Lena\n\nCompleteness-state update for this turn:\n- Operation: SET_COMPLETE_PREDICATES\n- New complete predicates: none"}, {"turn_index": 2, "source_semantics": "cwa", "update_operation": "ADD_COMPLETE_PREDICATES", "update_predicates": ["has_advising_clearance", "has_external_scholarship", "may_register", "needs_manual_audit", "passed_exam"], "complete_predicates_after_update": ["has_advising_clearance", "has_external_scholarship", "may_register", "needs_manual_audit", "passed_exam"], "gold_answer": "false", "expected_changed_from_previous": true, "expected_applied_update": "global_complete", "prompt": "This is turn 2 of a 3-turn dynamic completeness dialogue.\nKeep the exact same knowledge base, facts, rules, glossary, and target from turn 1.\nApply only the completeness-state update below.\nRecompute the answer from the updated state. Do not treat a previous false/unknown conclusion as a new fact.\nReturn exactly one compact JSON object with keys answer, changed_from_previous, applied_update, and rationale. The answer value must be one of true, false, unknown. The changed_from_previous value must be true, false, or null. The applied_update value must be one of open, global_complete, local_complete. For this turn, applied_update must be exactly global_complete. For this turn, changed_from_previous must be true iff your current answer differs from your immediately previous answer.\n\nReasoning contract:\n- Positive facts are true.\n- Negative facts make the corresponding target atom false.\n- A rule derives its conclusion only when every antecedent is true.\n- First apply all rules until no new facts can be derived.\n- Then evaluate unstated and underivable atoms using the current completeness state.\n- If a predicate is complete, an unstated and underivable atom using that predicate is false.\n- If a predicate is not complete, an unstated and underivable atom using that predicate is unknown.\n- A false answer derived from completeness is not a permanent negative fact; it can be withdrawn if the completeness state changes.\n\nCompleteness-state update for this turn:\n- Operation: ADD_COMPLETE_PREDICATES\n- Predicates to add as complete: has_advising_clearance, has_external_scholarship, may_register, needs_manual_audit, passed_exam\n- Complete predicates after this update: has_advising_clearance, has_external_scholarship, may_register, needs_manual_audit, passed_exam\n\nTarget statement: Lena may register for the seminar.\nTarget atom: may_register::Lena"}, {"turn_index": 3, "source_semantics": "lcwa", "update_operation": "REPLACE_COMPLETE_PREDICATES", "update_predicates": ["has_advising_clearance", "may_register"], "complete_predicates_after_update": ["has_advising_clearance", "may_register"], "gold_answer": "false", "expected_changed_from_previous": false, "expected_applied_update": "local_complete", "prompt": "This is turn 3 of a 3-turn dynamic completeness dialogue.\nKeep the exact same knowledge base, facts, rules, glossary, and target from turn 1.\nApply only the completeness-state update below.\nRecompute the answer from the updated state. Do not treat a previous false/unknown conclusion as a new fact.\nReturn exactly one compact JSON object with keys answer, changed_from_previous, applied_update, and rationale. The answer value must be one of true, false, unknown. The changed_from_previous value must be true, false, or null. The applied_update value must be one of open, global_complete, local_complete. For this turn, applied_update must be exactly local_complete. For this turn, changed_from_previous must be true iff your current answer differs from your immediately previous answer.\n\nReasoning contract:\n- Positive facts are true.\n- Negative facts make the corresponding target atom false.\n- A rule derives its conclusion only when every antecedent is true.\n- First apply all rules until no new facts can be derived.\n- Then evaluate unstated and underivable atoms using the current completeness state.\n- If a predicate is complete, an unstated and underivable atom using that predicate is false.\n- If a predicate is not complete, an unstated and underivable atom using that predicate is unknown.\n- A false answer derived from completeness is not a permanent negative fact; it can be withdrawn if the completeness state changes.\n\nCompleteness-state update for this turn:\n- Operation: REPLACE_COMPLETE_PREDICATES\n- Discard the previous complete-predicate set.\n- Replacement complete predicates: has_advising_clearance, may_register\n\nTarget statement: Lena may register for the seminar.\nTarget atom: may_register::Lena"}]}

metadata/closurebench_ask_act_metadata.json

@@ -0,0 +1,74 @@
+{
+  "source_dataset": "data/base/full.jsonl",
+  "dataset": "data/ask_act/full.jsonl",
+  "version": "closurebench_ask_act",
+  "num_items": 960,
+  "num_base_scenarios": 320,
+  "semantics": {
+    "owa": 320,
+    "cwa": 320,
+    "lcwa": 320
+  },
+  "domains": {
+    "cloud_deployment": 120,
+    "hospital_access": 120,
+    "library_services": 120,
+    "university_registration": 120,
+    "finance_controls": 120,
+    "procurement_review": 120,
+    "manufacturing_quality": 120,
+    "robotics_operations": 120
+  },
+  "families": {
+    "entailed_open_conclusion": 96,
+    "explicit_negative_closed": 96,
+    "explicit_negative_open": 96,
+    "closed_missing_with_open_distractor": 96,
+    "open_missing_with_closed_distractor": 96,
+    "open_missing_direct": 96,
+    "entailed_closed_conclusion": 96,
+    "closed_missing_direct": 96,
+    "closed_derived_missing_antecedent": 96,
+    "open_derived_missing_antecedent": 96
+  },
+  "subsets": {
+    "control_entailed": 192,
+    "control_explicit_negative": 192,
+    "core_contrastive": 576
+  },
+  "gold_truth_values": {
+    "true": 192,
+    "false": 480,
+    "unknown": 288
+  },
+  "gold_actions": {
+    "approve": 192,
+    "deny": 480,
+    "request_information": 288
+  },
+  "gold_reason_types": {
+    "entailed_by_fact_or_rule": 192,
+    "explicit_negative_fact": 192,
+    "global_closed_world_underivable": 192,
+    "local_open_world_underivable": 96,
+    "open_world_underivable": 192,
+    "local_closed_world_underivable": 96
+  },
+  "primary_metrics": [
+    "action_accuracy",
+    "truth_accuracy",
+    "full_decision_accuracy",
+    "ask_precision_recall_f1",
+    "targeted_information_accuracy",
+    "premature_denial_rate",
+    "excessive_ask_rate",
+    "closure_denial_accuracy",
+    "semantic_switch_action_accuracy"
+  ],
+  "notes": [
+    "This file is derived deterministically from ClosureBench V3.",
+    "Unknown direct targets ask for the target atom.",
+    "Unknown derived targets ask for the missing rule antecedent.",
+    "Approve/deny cases require null missing fields to avoid unnecessary escalation."
+  ]
+}

metadata/closurebench_base_metadata.json

@@ -0,0 +1,76 @@
+{
+  "dataset": "closurebench_base.jsonl",
+  "version": "closurebench-v1",
+  "num_items": 960,
+  "num_base_scenarios": 320,
+  "domains": {
+    "cloud_deployment": 120,
+    "hospital_access": 120,
+    "library_services": 120,
+    "university_registration": 120,
+    "finance_controls": 120,
+    "procurement_review": 120,
+    "manufacturing_quality": 120,
+    "robotics_operations": 120
+  },
+  "families": {
+    "entailed_open_conclusion": 96,
+    "explicit_negative_closed": 96,
+    "explicit_negative_open": 96,
+    "closed_missing_with_open_distractor": 96,
+    "open_missing_with_closed_distractor": 96,
+    "open_missing_direct": 96,
+    "entailed_closed_conclusion": 96,
+    "closed_missing_direct": 96,
+    "closed_derived_missing_antecedent": 96,
+    "open_derived_missing_antecedent": 96
+  },
+  "subsets": {
+    "control_entailed": 192,
+    "control_explicit_negative": 192,
+    "core_contrastive": 576
+  },
+  "splits": {
+    "dev": 93,
+    "train": 648,
+    "test": 219
+  },
+  "semantics": {
+    "owa": 320,
+    "cwa": 320,
+    "lcwa": 320
+  },
+  "gold_answers": {
+    "true": 192,
+    "false": 480,
+    "unknown": 288
+  },
+  "gold_reason_types": {
+    "entailed_by_fact_or_rule": 192,
+    "explicit_negative_fact": 192,
+    "global_closed_world_underivable": 192,
+    "local_open_world_underivable": 96,
+    "open_world_underivable": 192,
+    "local_closed_world_underivable": 96
+  },
+  "semantic_switch_patterns": {
+    "false|false|false": 64,
+    "true|true|true": 64,
+    "unknown|false|false": 96,
+    "unknown|false|unknown": 96
+  },
+  "primary_metrics": [
+    "semantic_switch_accuracy",
+    "lcwa_closed_scope_accuracy",
+    "lcwa_open_scope_accuracy",
+    "cwa_accuracy",
+    "owa_accuracy"
+  ],
+  "notes": [
+    "V3 preserves pilot v1/v2 files and should be treated as the publication candidate.",
+    "Each base scenario has OWA, CWA, and LCWA variants with identical facts, rules, and target atom.",
+    "Each item contains natural-language text plus symbolic atoms/rules and a gold reason type.",
+    "Closure is explicit: closed predicates make unstated and underivable atoms false after rule closure.",
+    "Controls are retained but marked separately from core contrastive cases."
+  ]
+}

metadata/closurebench_dynamic_dialogue_metadata.json

@@ -0,0 +1,53 @@
+{
+  "source_dataset": "data/base/full.jsonl",
+  "dataset": "data/dynamic_dialogue/full.jsonl",
+  "n_dialogues": 100,
+  "n_turns": 300,
+  "turns_per_dialogue": 3,
+  "seed": 11,
+  "gold_patterns": {
+    "unknown|false|false": 50,
+    "unknown|false|unknown": 50
+  },
+  "dialogue_types": {
+    "persist_after_narrowing": 50,
+    "reopen": 50
+  },
+  "domains": {
+    "cloud_deployment": 8,
+    "finance_controls": 15,
+    "hospital_access": 13,
+    "library_services": 11,
+    "manufacturing_quality": 13,
+    "procurement_review": 12,
+    "robotics_operations": 15,
+    "university_registration": 13
+  },
+  "families": {
+    "closed_derived_missing_antecedent": 17,
+    "closed_missing_direct": 15,
+    "closed_missing_with_open_distractor": 18,
+    "open_derived_missing_antecedent": 16,
+    "open_missing_direct": 18,
+    "open_missing_with_closed_distractor": 16
+  },
+  "turn_update_sequence": [
+    "SET_COMPLETE_PREDICATES([])",
+    "ADD_COMPLETE_PREDICATES(vocabulary)",
+    "REPLACE_COMPLETE_PREDICATES(local_subset)"
+  ],
+  "primary_metrics": [
+    "dialogue_all_answer_accuracy",
+    "revision_accuracy",
+    "persistence_accuracy",
+    "dialogue_all_turn_accuracy",
+    "completeness_addition_accuracy",
+    "reopening_accuracy",
+    "narrowing_persistence_accuracy",
+    "closure_inertia_rate",
+    "closure_retention_failure_rate",
+    "changed_flag_accuracy",
+    "applied_update_accuracy",
+    "anchoring_error_rate"
+  ]
+}

metadata/closurebench_multi_agent_metadata.json

@@ -0,0 +1,42 @@
+{
+  "name": "ClosureBench-MA",
+  "source_dataset": "data/base/full.jsonl",
+  "num_items": 360,
+  "num_base_scenarios": 120,
+  "semantics": {
+    "cwa": 120,
+    "lcwa": 120,
+    "owa": 120
+  },
+  "subsets": {
+    "core_contrastive": 216,
+    "control_entailed": 72,
+    "control_explicit_negative": 72
+  },
+  "gold_truth_values": {
+    "false": 178,
+    "unknown": 110,
+    "true": 72
+  },
+  "gold_source_used": {
+    "agent_a": 166,
+    "agent_b": 179,
+    "both": 15
+  },
+  "gold_closure_handling": {
+    "closed_absence": 106,
+    "open_absence": 110,
+    "entailed_or_explicit": 72,
+    "explicit_negative": 72
+  },
+  "domains": {
+    "cloud_deployment": 45,
+    "finance_controls": 45,
+    "hospital_access": 42,
+    "library_services": 30,
+    "manufacturing_quality": 60,
+    "procurement_review": 45,
+    "robotics_operations": 48,
+    "university_registration": 45
+  }
+}

results/reports/closurebench_ask_act_replicate_manifest.json

@@ -0,0 +1,116 @@
+{
+  "dataset": "data/ask_act/full.jsonl",
+  "dataset_size": 960,
+  "repeats": 3,
+  "shards": 4,
+  "runs": [
+    {
+      "label": "mistral-small",
+      "model": "mistralai/mistral-small-2603",
+      "repeat": 1,
+      "scored": "results/scored/ask_act_replicates/closure_contract_v3_ask_act_mistral-small_repeat_1_scored.jsonl"
+    },
+    {
+      "label": "mistral-small",
+      "model": "mistralai/mistral-small-2603",
+      "repeat": 2,
+      "scored": "results/scored/ask_act_replicates/closure_contract_v3_ask_act_mistral-small_repeat_2_scored.jsonl"
+    },
+    {
+      "label": "mistral-small",
+      "model": "mistralai/mistral-small-2603",
+      "repeat": 3,
+      "scored": "results/scored/ask_act_replicates/closure_contract_v3_ask_act_mistral-small_repeat_3_scored.jsonl"
+    },
+    {
+      "label": "deepseek-flash",
+      "model": "deepseek-v4-flash",
+      "repeat": 1,
+      "scored": "results/scored/ask_act_replicates/closure_contract_v3_ask_act_deepseek-flash_repeat_1_scored.jsonl"
+    },
+    {
+      "label": "deepseek-flash",
+      "model": "deepseek-v4-flash",
+      "repeat": 2,
+      "scored": "results/scored/ask_act_replicates/closure_contract_v3_ask_act_deepseek-flash_repeat_2_scored.jsonl"
+    },
+    {
+      "label": "deepseek-flash",
+      "model": "deepseek-v4-flash",
+      "repeat": 3,
+      "scored": "results/scored/ask_act_replicates/closure_contract_v3_ask_act_deepseek-flash_repeat_3_scored.jsonl"
+    },
+    {
+      "label": "deepseek-pro",
+      "model": "deepseek-v4-pro",
+      "repeat": 1,
+      "scored": "results/scored/ask_act_replicates/closure_contract_v3_ask_act_deepseek-pro_repeat_1_scored.jsonl"
+    },
+    {
+      "label": "deepseek-pro",
+      "model": "deepseek-v4-pro",
+      "repeat": 2,
+      "scored": "results/scored/ask_act_replicates/closure_contract_v3_ask_act_deepseek-pro_repeat_2_scored.jsonl"
+    },
+    {
+      "label": "deepseek-pro",
+      "model": "deepseek-v4-pro",
+      "repeat": 3,
+      "scored": "results/scored/ask_act_replicates/closure_contract_v3_ask_act_deepseek-pro_repeat_3_scored.jsonl"
+    },
+    {
+      "label": "mistral-medium",
+      "model": "mistralai/mistral-medium-3-5",
+      "repeat": 1,
+      "scored": "results/scored/ask_act_replicates/closure_contract_v3_ask_act_mistral-medium_repeat_1_scored.jsonl"
+    },
+    {
+      "label": "mistral-medium",
+      "model": "mistralai/mistral-medium-3-5",
+      "repeat": 2,
+      "scored": "results/scored/ask_act_replicates/closure_contract_v3_ask_act_mistral-medium_repeat_2_scored.jsonl"
+    },
+    {
+      "label": "mistral-medium",
+      "model": "mistralai/mistral-medium-3-5",
+      "repeat": 3,
+      "scored": "results/scored/ask_act_replicates/closure_contract_v3_ask_act_mistral-medium_repeat_3_scored.jsonl"
+    },
+    {
+      "label": "llama-scout",
+      "model": "meta-llama/llama-4-scout",
+      "repeat": 1,
+      "scored": "results/scored/ask_act_replicates/closure_contract_v3_ask_act_llama-scout_repeat_1_scored.jsonl"
+    },
+    {
+      "label": "llama-scout",
+      "model": "meta-llama/llama-4-scout",
+      "repeat": 2,
+      "scored": "results/scored/ask_act_replicates/closure_contract_v3_ask_act_llama-scout_repeat_2_scored.jsonl"
+    },
+    {
+      "label": "llama-scout",
+      "model": "meta-llama/llama-4-scout",
+      "repeat": 3,
+      "scored": "results/scored/ask_act_replicates/closure_contract_v3_ask_act_llama-scout_repeat_3_scored.jsonl"
+    },
+    {
+      "label": "llama-maverick",
+      "model": "meta-llama/llama-4-maverick",
+      "repeat": 1,
+      "scored": "results/scored/ask_act_replicates/closure_contract_v3_ask_act_llama-maverick_repeat_1_scored.jsonl"
+    },
+    {
+      "label": "llama-maverick",
+      "model": "meta-llama/llama-4-maverick",
+      "repeat": 2,
+      "scored": "results/scored/ask_act_replicates/closure_contract_v3_ask_act_llama-maverick_repeat_2_scored.jsonl"
+    },
+    {
+      "label": "llama-maverick",
+      "model": "meta-llama/llama-4-maverick",
+      "repeat": 3,
+      "scored": "results/scored/ask_act_replicates/closure_contract_v3_ask_act_llama-maverick_repeat_3_scored.jsonl"
+    }
+  ]
+}

results/reports/closurebench_deepseek_v4_flash_report.json

@@ -0,0 +1,391 @@
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+  ]
+}

results/reports/closurebench_deepseek_v4_pro_report.json

@@ -0,0 +1,391 @@
+{
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+    }
+  },
+  "models": [
+    "deepseek-v4-pro"
+  ]
+}

results/reports/closurebench_dynamic_dialogue_replicate_manifest.json

@@ -0,0 +1,116 @@
+{
+  "dataset": "data/dynamic_dialogue/full.jsonl",
+  "dataset_turns": 300,
+  "repeats": 3,
+  "shards": 4,
+  "runs": [
+    {
+      "label": "mistral-small",
+      "model": "mistralai/mistral-small-2603",
+      "repeat": 1,
+      "scored": "results/scored/dynamic_dialogue_replicates/closure_contract_v3_dynamic_dialogue_mistral-small_repeat_1_scored.jsonl"
+    },
+    {
+      "label": "mistral-small",
+      "model": "mistralai/mistral-small-2603",
+      "repeat": 2,
+      "scored": "results/scored/dynamic_dialogue_replicates/closure_contract_v3_dynamic_dialogue_mistral-small_repeat_2_scored.jsonl"
+    },
+    {
+      "label": "mistral-small",
+      "model": "mistralai/mistral-small-2603",
+      "repeat": 3,
+      "scored": "results/scored/dynamic_dialogue_replicates/closure_contract_v3_dynamic_dialogue_mistral-small_repeat_3_scored.jsonl"
+    },
+    {
+      "label": "deepseek-flash",
+      "model": "deepseek-v4-flash",
+      "repeat": 1,
+      "scored": "results/scored/dynamic_dialogue_replicates/closure_contract_v3_dynamic_dialogue_deepseek-flash_repeat_1_scored.jsonl"
+    },
+    {
+      "label": "deepseek-flash",
+      "model": "deepseek-v4-flash",
+      "repeat": 2,
+      "scored": "results/scored/dynamic_dialogue_replicates/closure_contract_v3_dynamic_dialogue_deepseek-flash_repeat_2_scored.jsonl"
+    },
+    {
+      "label": "deepseek-flash",
+      "model": "deepseek-v4-flash",
+      "repeat": 3,
+      "scored": "results/scored/dynamic_dialogue_replicates/closure_contract_v3_dynamic_dialogue_deepseek-flash_repeat_3_scored.jsonl"
+    },
+    {
+      "label": "deepseek-pro",
+      "model": "deepseek-v4-pro",
+      "repeat": 1,
+      "scored": "results/scored/dynamic_dialogue_replicates/closure_contract_v3_dynamic_dialogue_deepseek-pro_repeat_1_scored.jsonl"
+    },
+    {
+      "label": "deepseek-pro",
+      "model": "deepseek-v4-pro",
+      "repeat": 2,
+      "scored": "results/scored/dynamic_dialogue_replicates/closure_contract_v3_dynamic_dialogue_deepseek-pro_repeat_2_scored.jsonl"
+    },
+    {
+      "label": "deepseek-pro",
+      "model": "deepseek-v4-pro",
+      "repeat": 3,
+      "scored": "results/scored/dynamic_dialogue_replicates/closure_contract_v3_dynamic_dialogue_deepseek-pro_repeat_3_scored.jsonl"
+    },
+    {
+      "label": "mistral-medium",
+      "model": "mistralai/mistral-medium-3-5",
+      "repeat": 1,
+      "scored": "results/scored/dynamic_dialogue_replicates/closure_contract_v3_dynamic_dialogue_mistral-medium_repeat_1_scored.jsonl"
+    },
+    {
+      "label": "mistral-medium",
+      "model": "mistralai/mistral-medium-3-5",
+      "repeat": 2,
+      "scored": "results/scored/dynamic_dialogue_replicates/closure_contract_v3_dynamic_dialogue_mistral-medium_repeat_2_scored.jsonl"
+    },
+    {
+      "label": "mistral-medium",
+      "model": "mistralai/mistral-medium-3-5",
+      "repeat": 3,
+      "scored": "results/scored/dynamic_dialogue_replicates/closure_contract_v3_dynamic_dialogue_mistral-medium_repeat_3_scored.jsonl"
+    },
+    {
+      "label": "llama-scout",
+      "model": "meta-llama/llama-4-scout",
+      "repeat": 1,
+      "scored": "results/scored/dynamic_dialogue_replicates/closure_contract_v3_dynamic_dialogue_llama-scout_repeat_1_scored.jsonl"
+    },
+    {
+      "label": "llama-scout",
+      "model": "meta-llama/llama-4-scout",
+      "repeat": 2,
+      "scored": "results/scored/dynamic_dialogue_replicates/closure_contract_v3_dynamic_dialogue_llama-scout_repeat_2_scored.jsonl"
+    },
+    {
+      "label": "llama-scout",
+      "model": "meta-llama/llama-4-scout",
+      "repeat": 3,
+      "scored": "results/scored/dynamic_dialogue_replicates/closure_contract_v3_dynamic_dialogue_llama-scout_repeat_3_scored.jsonl"
+    },
+    {
+      "label": "llama-maverick",
+      "model": "meta-llama/llama-4-maverick",
+      "repeat": 1,
+      "scored": "results/scored/dynamic_dialogue_replicates/closure_contract_v3_dynamic_dialogue_llama-maverick_repeat_1_scored.jsonl"
+    },
+    {
+      "label": "llama-maverick",
+      "model": "meta-llama/llama-4-maverick",
+      "repeat": 2,
+      "scored": "results/scored/dynamic_dialogue_replicates/closure_contract_v3_dynamic_dialogue_llama-maverick_repeat_2_scored.jsonl"
+    },
+    {
+      "label": "llama-maverick",
+      "model": "meta-llama/llama-4-maverick",
+      "repeat": 3,
+      "scored": "results/scored/dynamic_dialogue_replicates/closure_contract_v3_dynamic_dialogue_llama-maverick_repeat_3_scored.jsonl"
+    }
+  ]
+}

results/reports/closurebench_dynamic_dialogue_summary.json

@@ -0,0 +1,3508 @@
+{
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+  "n_dialogues": 100,
+  "n_turns": 300,
+  "manifest": "results/reports/closurebench_dynamic_dialogue_replicate_manifest.json",
+  "runs": {
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+        }
+      ],
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+}

results/reports/closurebench_llama_4_maverick_report.json

@@ -0,0 +1,390 @@
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+}

results/reports/closurebench_llama_4_scout_report.json

@@ -0,0 +1,394 @@
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+}

results/reports/closurebench_mistral_medium_3_5_report.json

@@ -0,0 +1,391 @@
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results/reports/closurebench_mistral_small_2603_report.json

@@ -0,0 +1,393 @@
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+    "closure_scope_error_false_for_open_predicate": 67,
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+  },
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+    }
+  },
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+    "mistralai/mistral-small-2603"
+  ]
+}

results/reports/closurebench_model_comparison.json

@@ -0,0 +1,336 @@
+{
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+      },
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+      },
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+  }
+}

results/reports/closurebench_multi_agent_manifest.json

@@ -0,0 +1,37 @@
+{
+  "dataset": "data/multi_agent/full.jsonl",
+  "dataset_size": 360,
+  "shards": 4,
+  "runs": [
+    {
+      "label": "mistral-small",
+      "model": "mistralai/mistral-small-2603",
+      "scored": "results/scored/multi_agent/closure_contract_v3_multi_agent_mistral-small_scored.jsonl"
+    },
+    {
+      "label": "deepseek-flash",
+      "model": "deepseek-v4-flash",
+      "scored": "results/scored/multi_agent/closure_contract_v3_multi_agent_deepseek-flash_scored.jsonl"
+    },
+    {
+      "label": "deepseek-pro",
+      "model": "deepseek-v4-pro",
+      "scored": "results/scored/multi_agent/closure_contract_v3_multi_agent_deepseek-pro_scored.jsonl"
+    },
+    {
+      "label": "mistral-medium",
+      "model": "mistralai/mistral-medium-3-5",
+      "scored": "results/scored/multi_agent/closure_contract_v3_multi_agent_mistral-medium_scored.jsonl"
+    },
+    {
+      "label": "llama-scout",
+      "model": "meta-llama/llama-4-scout",
+      "scored": "results/scored/multi_agent/closure_contract_v3_multi_agent_llama-scout_scored.jsonl"
+    },
+    {
+      "label": "llama-maverick",
+      "model": "meta-llama/llama-4-maverick",
+      "scored": "results/scored/multi_agent/closure_contract_v3_multi_agent_llama-maverick_scored.jsonl"
+    }
+  ]
+}

results/reports/closurebench_multi_agent_replicate_manifest.json

@@ -0,0 +1,135 @@
+{
+  "dataset": "data/multi_agent/full.jsonl",
+  "dataset_size": 360,
+  "repeats": 3,
+  "shards": 4,
+  "temperature": 0.0,
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+      "model": "mistralai/mistral-small-2603",
+      "scored": "results/scored/multi_agent/closure_contract_v3_multi_agent_mistral-small_scored.jsonl",
+      "repeat": 1,
+      "temperature": 0.0
+    },
+    {
+      "label": "mistral-small",
+      "model": "mistralai/mistral-small-2603",
+      "repeat": 2,
+      "temperature": 0.0,
+      "scored": "results/scored/multi_agent/closurebench_multi_agent_mistral-small_repeat_2_scored.jsonl"
+    },
+    {
+      "label": "mistral-small",
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+      "temperature": 0.0,
+      "scored": "results/scored/multi_agent/closurebench_multi_agent_mistral-small_repeat_3_scored.jsonl"
+    },
+    {
+      "label": "deepseek-flash",
+      "model": "deepseek-v4-flash",
+      "scored": "results/scored/multi_agent/closure_contract_v3_multi_agent_deepseek-flash_scored.jsonl",
+      "repeat": 1,
+      "temperature": 0.0
+    },
+    {
+      "label": "deepseek-flash",
+      "model": "deepseek-v4-flash",
+      "repeat": 2,
+      "temperature": 0.0,
+      "scored": "results/scored/multi_agent/closurebench_multi_agent_deepseek-flash_repeat_2_scored.jsonl"
+    },
+    {
+      "label": "deepseek-flash",
+      "model": "deepseek-v4-flash",
+      "repeat": 3,
+      "temperature": 0.0,
+      "scored": "results/scored/multi_agent/closurebench_multi_agent_deepseek-flash_repeat_3_scored.jsonl"
+    },
+    {
+      "label": "deepseek-pro",
+      "model": "deepseek-v4-pro",
+      "scored": "results/scored/multi_agent/closure_contract_v3_multi_agent_deepseek-pro_scored.jsonl",
+      "repeat": 1,
+      "temperature": 0.0
+    },
+    {
+      "label": "deepseek-pro",
+      "model": "deepseek-v4-pro",
+      "repeat": 2,
+      "temperature": 0.0,
+      "scored": "results/scored/multi_agent/closurebench_multi_agent_deepseek-pro_repeat_2_scored.jsonl"
+    },
+    {
+      "label": "deepseek-pro",
+      "model": "deepseek-v4-pro",
+      "repeat": 3,
+      "temperature": 0.0,
+      "scored": "results/scored/multi_agent/closurebench_multi_agent_deepseek-pro_repeat_3_scored.jsonl"
+    },
+    {
+      "label": "mistral-medium",
+      "model": "mistralai/mistral-medium-3-5",
+      "scored": "results/scored/multi_agent/closure_contract_v3_multi_agent_mistral-medium_scored.jsonl",
+      "repeat": 1,
+      "temperature": 0.0
+    },
+    {
+      "label": "mistral-medium",
+      "model": "mistralai/mistral-medium-3-5",
+      "repeat": 2,
+      "temperature": 0.0,
+      "scored": "results/scored/multi_agent/closurebench_multi_agent_mistral-medium_repeat_2_scored.jsonl"
+    },
+    {
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+      "temperature": 0.0,
+      "scored": "results/scored/multi_agent/closurebench_multi_agent_mistral-medium_repeat_3_scored.jsonl"
+    },
+    {
+      "label": "llama-scout",
+      "model": "meta-llama/llama-4-scout",
+      "scored": "results/scored/multi_agent/closure_contract_v3_multi_agent_llama-scout_scored.jsonl",
+      "repeat": 1,
+      "temperature": 0.0
+    },
+    {
+      "label": "llama-scout",
+      "model": "meta-llama/llama-4-scout",
+      "repeat": 2,
+      "temperature": 0.0,
+      "scored": "results/scored/multi_agent/closurebench_multi_agent_llama-scout_repeat_2_scored.jsonl"
+    },
+    {
+      "label": "llama-scout",
+      "model": "meta-llama/llama-4-scout",
+      "repeat": 3,
+      "temperature": 0.0,
+      "scored": "results/scored/multi_agent/closurebench_multi_agent_llama-scout_repeat_3_scored.jsonl"
+    },
+    {
+      "label": "llama-maverick",
+      "model": "meta-llama/llama-4-maverick",
+      "scored": "results/scored/multi_agent/closure_contract_v3_multi_agent_llama-maverick_scored.jsonl",
+      "repeat": 1,
+      "temperature": 0.0
+    },
+    {
+      "label": "llama-maverick",
+      "model": "meta-llama/llama-4-maverick",
+      "repeat": 2,
+      "temperature": 0.0,
+      "scored": "results/scored/multi_agent/closurebench_multi_agent_llama-maverick_repeat_2_scored.jsonl"
+    },
+    {
+      "label": "llama-maverick",
+      "model": "meta-llama/llama-4-maverick",
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+      "temperature": 0.0,
+      "scored": "results/scored/multi_agent/closurebench_multi_agent_llama-maverick_repeat_3_scored.jsonl"
+    }
+  ]
+}

results/reports/closurebench_multi_agent_summary.json

@@ -0,0 +1,3762 @@
+{
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+}

results/reports/closurebench_replicate_manifest.json

@@ -0,0 +1,116 @@
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+}

results/reports/closurebench_replicate_summary.json

@@ -0,0 +1,1627 @@
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+}

results/reports/closurebench_temp1_replicate_manifest.json

@@ -0,0 +1,136 @@
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+}

results/reports/closurebench_temp1_replicate_summary.json

@@ -0,0 +1,1627 @@
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