Release v0.4 explicit streaming reasoning dataset

.gitattributes

@@ -60,3 +60,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
 eval.jsonl filter=lfs diff=lfs merge=lfs -text
 train.jsonl filter=lfs diff=lfs merge=lfs -text
+high_quality.jsonl filter=lfs diff=lfs merge=lfs -text

README.md

@@ -3,12 +3,13 @@ pretty_name: LifeMultiTurnStreamingCoT
 language:
 - en
 license: apache-2.0
-version: "v0.3"
+version: "v0.4"
 task_categories:
 - text-generation
 tags:
 - streaming-reasoning
 - rule-based-reasoning
+- explicit-reasoning
 - multi-turn-dialogue
 - life-assistant
 - supervised-fine-tuning
@@ -17,35 +18,42 @@ configs:
   data_files:
   - split: train
     path: train.jsonl
-  - split: test
+  - split: eval
     path: eval.jsonl
+  - split: high_quality
+    path: high_quality.jsonl
 ---
 # LifeMultiTurnStreamingCoT
 
-Version: v0.3 — Safety and Grounding Quality Release
+Version: v0.4 — Explicit Streaming Reasoning Release
 
 LifeMultiTurnStreamingCoT is a text-to-text multi-turn life-domain dataset. Each row uses previous user-assistant dialogue turns as input, deterministic turn-level streaming state tracking as intermediate supervision, a compact final-state-based deep reasoning summary, and the next assistant turn as the answer.
 
-This v0.3 release keeps the real DailyDialog, MultiWOZ 2.2, and Taskmaster sources while improving training suitability through safety filtering, grounded slot sanity checks, target-answer usefulness checks, taxonomy confidence/evidence, and quality tiers.
+This v0.4 release keeps the real DailyDialog, MultiWOZ 2.2, and Taskmaster sources while adding explicit turn-aligned streaming reasoning. No LLM calls are used for construction. The reasoning traces are deterministic rule-based annotations over visible dialogue prefixes and repaired state deltas.
 
-## Current Demo/Build Statistics
+## Current Build Statistics
 
-- Version: v0.3 — Safety and Grounding Quality Release
+- Version: v0.4 — Explicit Streaming Reasoning Release
 - Total rows: 30000
 - Train rows: 24211
 - Eval rows: 5789
-- High-quality train rows: 7875
-- High-quality eval rows: 1777
+- High-quality split rows: 17723
+- High-quality train rows: 14339
+- High-quality eval rows: 3384
 - Average input turns: 9.705
 - Average streaming chunks: 9.705
 - Source distribution: {"DailyDialog": 10000, "MultiWOZ": 10000, "Taskmaster": 10000}
 - Domain category distribution: {"customer_service": 204, "education_career": 2288, "finance_business": 180, "food_dining": 1262, "general_daily_life": 2881, "health_wellness": 221, "home_services": 226, "hospitality_lodging": 404, "personal_schedule": 688, "shopping_retail": 527, "social_relationship": 1984, "technology_support": 193, "travel_transportation": 18942}
 - Intent category distribution: {"booking_or_reservation": 15758, "confirmation_clarification": 2710, "customer_support": 1241, "emotional_support": 232, "information_request": 6242, "instruction_following": 26, "negotiation_decision": 139, "planning_coordination": 1460, "problem_solving": 440, "recommendation": 739, "small_talk": 1013}
 - Scenario category distribution: {"attraction_search": 455, "banking_support": 210, "customer_complaint": 218, "family_conversation": 600, "flight_booking": 9926, "food_ordering": 18, "friend_conversation": 456, "general_conversation": 3288, "home_repair": 101, "hotel_booking": 1557, "hotel_search": 184, "insurance_support": 95, "job_interview": 104, "medical_assistance": 291, "movie_ticketing": 218, "music_search": 141, "restaurant_booking": 1890, "restaurant_search": 819, "schedule_planning": 646, "school_life": 853, "shopping_assistance": 393, "taxi_booking": 1109, "technical_support": 453, "train_booking": 4317, "travel_planning": 253, "workplace_conversation": 1405}
-- Taxonomy confidence distribution: {"high": 20850, "medium": 9150}
-- Quality tier distribution: {"bronze": 3189, "drop": 17159, "gold": 6684, "silver": 2968}
+- Taxonomy confidence distribution: {"high": 20506, "low": 2881, "medium": 6613}
+- Quality tier distribution: {"bronze": 7505, "drop": 4772, "gold": 12479, "silver": 5244}
 - Safety category distribution: {"safe": 28743, "sensitive": 1257}
-- Target answer quality distribution: {"drop": 3848, "strong": 15196, "usable": 6828, "weak": 4128}
+- Target answer quality distribution: {"drop": 3924, "strong": 15129, "usable": 6819, "weak": 4128}
+- Streaming reasoning quality distribution: {"strong": 14767, "usable": 15233}
+- Streaming reasoning confidence distribution: {"high": 201954, "medium": 89194}
+- Slot repair rows: 15233
+- Suppressed slot reasons: {"generic_number_misread_as_budget": 36309, "percent_misread_as_budget": 13}
 - Unknown/other taxonomy ratio: 0.0
 - Category distribution: {"daily_dialogue": 10000, "task_oriented_dialogue": 20000}
 
@@ -57,10 +65,26 @@ This v0.3 release keeps the real DailyDialog, MultiWOZ 2.2, and Taskmaster sourc
 
 ## Schema
 
-Rows contain `id`, `source_dataset`, `source_id`, `dialogue_id`, `domain`, `task_type`, `dialogue_history`, `streaming_chunks`, `deep_reasoning`, `answer`, `metadata`, `quality_flags`, `quality_score`, `is_high_quality`, and `split`. The final schema remains unified across sources; source-specific details such as source, category, domain/services, scenario, original split, and raw file are kept in `metadata`.
+Rows contain `id`, `source_dataset`, `source_id`, `dialogue_id`, `domain`, `task_type`, `dialogue_history`, `streaming_chunks`, top-level `streaming_reasoning`, `deep_reasoning`, `answer`, `metadata`, `quality_flags`, `quality_score`, `is_high_quality`, and `split`.
+
+`streaming_chunks[i]` includes the original chunk fields plus `state_before`, `state_update`, `state_after`, `state_delta`, `reasoning`, `reasoning_type`, and `reasoning_confidence`. The top-level `streaming_reasoning[i]` list is aligned with `streaming_chunks[i]` by `chunk_id` and `turn_id`. The final schema remains unified across sources; source-specific details such as source, category, domain/services, scenario, original split, and raw file are kept in `metadata`.
+
+## Splits
+
+- `train`: training rows after deterministic dialogue-level split.
+- `eval`: evaluation rows after deterministic dialogue-level split.
+- `high_quality`: rows from train+eval where `is_high_quality` is true, `quality_tier` is gold/silver, safety is safe, and explicit streaming reasoning is strong/usable. Each high-quality row keeps `metadata.original_release_split`.
 
 ## Version History
 
+### v0.4 — Explicit Streaming Reasoning Release
+
+- Added top-level `streaming_reasoning` aligned with `streaming_chunks`.
+- Added per-chunk `state_delta`, `reasoning`, `reasoning_type`, and `reasoning_confidence`.
+- Added deterministic slot repair/suppression for unsupported visible-prefix slot evidence, including budget numeric misreads.
+- Added `metadata.streaming_reasoning_method`, `metadata.has_explicit_streaming_reasoning`, `metadata.streaming_reasoning_quality`, `metadata.slot_repair_applied`, and `metadata.suppressed_slots`.
+- Added explicit reasoning quality flags and a `high_quality` split.
+
 ### v0.3 — Safety and Grounding Quality Release
 
 - Added safety/content filtering for training suitability.
@@ -91,9 +115,9 @@ Each sample includes a coarse `metadata.category` and three additional taxonomy
 - `metadata.intent_category`: interaction intent such as information request, recommendation, booking, planning, customer support, small talk, or emotional support.
 - `metadata.scenario_category`: more specific scenario such as restaurant booking, hotel search, taxi booking, train booking, food ordering, movie ticketing, schedule planning, or workplace conversation.
 
-The taxonomy is deterministic and source-aware. MultiWOZ uses domain/service annotations, Taskmaster uses scenario/file metadata when available, and DailyDialog uses lightweight keyword and dialogue-pattern rules.
+The taxonomy is deterministic and source-aware. MultiWOZ uses domain/service annotations, Taskmaster uses scenario/file metadata when available, and DailyDialog uses lightweight keyword and dialogue-pattern rules. DailyDialog rows may have low taxonomy confidence when only weak keyword evidence is available.
 
-## v0.3 Quality Metadata
+## Quality Metadata
 
 Each row includes additional metadata fields:
 
@@ -108,6 +132,11 @@ Each row includes additional metadata fields:
 - `metadata.taxonomy_confidence`
 - `metadata.taxonomy_evidence`
 - `metadata.quality_tier`
+- `metadata.streaming_reasoning_method`
+- `metadata.has_explicit_streaming_reasoning`
+- `metadata.streaming_reasoning_quality`
+- `metadata.slot_repair_applied`
+- `metadata.suppressed_slots`
 
 Recommended default training filter:
 
@@ -115,17 +144,19 @@ Recommended default training filter:
 row["is_high_quality"] is True
 and row["metadata"]["is_safe_for_training"] is True
 and row["metadata"]["quality_tier"] in ["gold", "silver"]
+and row["metadata"]["has_explicit_streaming_reasoning"] is True
+and row["metadata"]["streaming_reasoning_quality"] in ["strong", "usable"]
 ```
 
 ## Reasoning
 
-Streaming reasoning is generated by deterministic rule-based state tracking over turn-level chunks. DailyDialog rows focus on daily intent, tone, and continuity. MultiWOZ and Taskmaster rows use task-oriented templates for goal, known constraints, missing information, scenario/domain, and next-step policy. Deep reasoning is a compact global summary from the final tracked state, dialogue history, and target answer. The answer is not rewritten by default; it comes from the original next assistant turn.
+Streaming reasoning is generated by deterministic rule-based state tracking over turn-level chunks. v0.4 explicit reasoning is generated only from visible prefix state, source metadata, repaired state deltas, and safety/taxonomy metadata. It does not call an LLM and does not rewrite the answer. DailyDialog rows focus on daily context and continuity. MultiWOZ and Taskmaster rows use task-oriented state changes, missing-slot status, and next-step policy. Deep reasoning is a compact global summary from the final tracked state, dialogue history, and target answer.
 
 ## Quality Filtering
 
-The quality checks are category-aware and now include safety, grounding, target usefulness, and tiering. Long task-oriented conversations are no longer penalized in the same way as short daily dialogues. Some heuristic checks, including weak final state and premature response detection, are kept as diagnostic warnings rather than hard filters.
+The quality checks are category-aware and include safety, grounding, target usefulness, explicit streaming reasoning quality, and tiering. Long task-oriented conversations are no longer penalized in the same way as short daily dialogues. Some heuristic checks, including weak final state and premature response detection, are kept as diagnostic warnings rather than hard filters.
 
-`quality_flags` and `metadata.quality_checks` support filtering by real-source status, multi-turn context, non-empty reasoning, placeholder detection, category-aware length checks, malformed-row checks, repetition checks, grounding checks, safety checks, target-answer checks, and role alternation. Raw external data is not committed to git; processed train/eval files are intended for upload to `skyzhou06/LifeMultiTurnStreamingCoT`.
+`quality_flags` and `metadata.quality_checks` support filtering by real-source status, multi-turn context, non-empty reasoning, placeholder detection, category-aware length checks, malformed-row checks, repetition checks, grounding checks, safety checks, target-answer checks, explicit reasoning checks, and role alternation. Raw external data is not committed to git; processed train/eval/high_quality files are intended for upload to `skyzhou06/LifeMultiTurnStreamingCoT`.
 
 ## Leakage Control
 

dataset_info.json

@@ -58,16 +58,17 @@
     "travel": 18530
   },
   "eval_rows": 5789,
+  "explicit_streaming_reasoning_rows": 30000,
   "grounding_flag_distribution": {
-    "generic_number_misread_as_budget": 14672,
-    "percent_misread_as_budget": 8,
-    "suspicious_numeric_slot": 14679,
+    "generic_number_misread_as_budget": 432,
+    "suspicious_numeric_slot": 432,
     "weak_slot_evidence": 1625
   },
-  "high_quality_eval_rows": 1777,
-  "high_quality_percentage": 0.3217,
-  "high_quality_rows": 9652,
-  "high_quality_train_rows": 7875,
+  "high_quality_eval_rows": 3384,
+  "high_quality_percentage": 0.5908,
+  "high_quality_rows": 17723,
+  "high_quality_split_rows": 17723,
+  "high_quality_train_rows": 14339,
   "intent_category_distribution": {
     "booking_or_reservation": 15758,
     "confirmation_clarification": 2710,
@@ -82,36 +83,36 @@
     "small_talk": 1013
   },
   "quality_flag_distribution": {
-    "answer_not_grounded": 1642,
-    "deep_reasoning_too_long": 1,
+    "answer_not_grounded": 1718,
+    "deep_reasoning_too_long": 2,
     "excessive_repetition": 442,
     "financial_context": 624,
     "fragment_answer": 2611,
     "generic_answer": 359,
-    "generic_number_misread_as_budget": 14672,
-    "grounding_issue": 14679,
+    "generic_number_misread_as_budget": 432,
+    "grounding_issue": 432,
     "low_information_answer": 5011,
+    "low_taxonomy_confidence": 2881,
     "medical_or_health_context": 327,
     "mild_violence_or_conflict_context": 307,
-    "off_topic_answer": 534,
-    "percent_misread_as_budget": 8,
+    "off_topic_answer": 581,
     "premature_respond": 4693,
     "repeated_turns": 442,
     "sensitive_content": 1257,
-    "suspicious_numeric_slot": 14679,
+    "suspicious_numeric_slot": 432,
     "target_leakage": 176,
     "too_many_turns": 262,
     "too_short_answer": 2759,
     "too_short_average_turn": 8,
-    "weak_final_state": 22334,
+    "weak_final_state": 8951,
     "weak_slot_evidence": 1625,
     "weak_target_answer": 2759
   },
   "quality_tier_distribution": {
-    "bronze": 3189,
-    "drop": 17159,
-    "gold": 6684,
-    "silver": 2968
+    "bronze": 7505,
+    "drop": 4772,
+    "gold": 12479,
+    "silver": 5244
   },
   "safety_category_distribution": {
     "safe": 28743,
@@ -150,32 +151,49 @@
     "travel_planning": 253,
     "workplace_conversation": 1405
   },
+  "slot_repair_applied_rows": 15233,
   "source_distribution": {
     "DailyDialog": 10000,
     "MultiWOZ": 10000,
     "Taskmaster": 10000
   },
+  "streaming_reasoning_confidence_distribution": {
+    "high": 201954,
+    "medium": 89194
+  },
+  "streaming_reasoning_quality_distribution": {
+    "strong": 14767,
+    "usable": 15233
+  },
+  "suppressed_slot_name_distribution": {
+    "budget": 36322
+  },
+  "suppressed_slot_reason_distribution": {
+    "generic_number_misread_as_budget": 36309,
+    "percent_misread_as_budget": 13
+  },
   "target_answer_flag_distribution": {
-    "answer_not_grounded": 1611,
+    "answer_not_grounded": 1687,
     "fragment_answer": 2611,
     "generic_answer": 350,
     "low_information_answer": 5011,
-    "off_topic_answer": 534,
+    "off_topic_answer": 581,
     "too_short_answer": 2759
   },
   "target_answer_quality_distribution": {
-    "drop": 3848,
-    "strong": 15196,
-    "usable": 6828,
+    "drop": 3924,
+    "strong": 15129,
+    "usable": 6819,
     "weak": 4128
   },
   "taxonomy_confidence_distribution": {
-    "high": 20850,
-    "medium": 9150
+    "high": 20506,
+    "low": 2881,
+    "medium": 6613
   },
   "total_rows": 30000,
   "train_rows": 24211,
   "unknown_other_ratio": 0.0,
-  "version": "v0.3",
-  "version_label": "v0.3 — Safety and Grounding Quality Release"
+  "version": "v0.4",
+  "version_label": "v0.4 — Explicit Streaming Reasoning Release"
 }

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@@ -1,3 +1,3 @@
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+size 165480673

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