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Refresh split-safe VeriSci dataset

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  1. LICENSE +157 -0
  2. README.md +29 -10
  3. stats.json +24 -20
  4. test.jsonl +0 -0
  5. train.jsonl +0 -0
  6. validation.jsonl +0 -0
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+ Copyright 2026 VeriSci Team
README.md CHANGED
@@ -48,6 +48,7 @@ Every row is generated by a deterministic Python program and checked by a verifi
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  - `final_answer`: machine-readable target answer.
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  - `verifier`: verifier type and parameters.
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  - `variables`: source variables used to generate the row.
 
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  - `task_family`: task family.
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  - `difficulty`: easy, medium, or hard.
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  - `source`: generation provenance.
@@ -75,12 +76,29 @@ Every row is generated by a deterministic Python program and checked by a verifi
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  ## Reproduction
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  ```bash
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- git clone REPLACE_WITH_GITHUB_URL
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- cd REPLACE_WITH_REPO
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- PYTHONPATH=src python3 -m verisci.generate --rows 12000
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- PYTHONPATH=src python3 -m verisci.evaluate --data data/generated/verisci_train.jsonl
 
 
 
 
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  ```
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  ## Adaptive Data And AutoScientist
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  This dataset has been run through a low-credit Adaption pilot and is prepared for AutoScientist training.
@@ -96,17 +114,18 @@ Current platform evidence:
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  | 12k source preflight dataset ID | `13b1c92a-a96b-4ce7-810b-4368ad6aa234` |
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  | 12k source-column audit | Passed: 12,000/12,000 rows preserved `Final: {...}` |
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  | AutoScientist recommendation | `google/gemma-3-4b-it`, 1-epoch LoRA |
 
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  Fill after final AutoScientist run:
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  | Metric | Value |
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  |---|---:|
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- | Adaptive Data grade before | TBD |
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- | Adaptive Data grade after | TBD |
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- | Adaptive Data quality improvement | TBD |
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- | AutoScientist best win rate | TBD |
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- | Domain augmentation rows | TBD |
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- | General augmentation rows | TBD |
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  ## Limitations
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  - `final_answer`: machine-readable target answer.
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  - `verifier`: verifier type and parameters.
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  - `variables`: source variables used to generate the row.
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+ - `dedupe_signature`: canonical hash used for de-duplication and split assignment.
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  - `task_family`: task family.
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  - `difficulty`: easy, medium, or hard.
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  - `source`: generation provenance.
 
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  ## Reproduction
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  ```bash
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+ git clone <repository-url>
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+ cd verisci-autoscientist
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+ PYTHONPATH=src python3 -m verisci.generate \
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+ --rows 8000 \
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+ --out data/generated/verisci_8k.jsonl \
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+ --csv data/generated/verisci_8k.csv \
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+ --summary data/generated/verisci_8k_summary.json
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+ PYTHONPATH=src python3 -m verisci.evaluate --data data/generated/verisci_8k.jsonl
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  ```
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+ ## Release Integrity
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+
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+ The current 8k public release is split-safe:
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+
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+ | Metric | Value |
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+ |---|---:|
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+ | Rows | 8,000 |
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+ | Unique prompts | 8,000 |
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+ | Duplicate prompts | 0 |
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+ | Train/validation/test prompt leakage rows | 0 |
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+ | Dedupe signatures | 8,000 |
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+ | Gold verifier accuracy | 100% |
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+
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  ## Adaptive Data And AutoScientist
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  This dataset has been run through a low-credit Adaption pilot and is prepared for AutoScientist training.
 
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  | 12k source preflight dataset ID | `13b1c92a-a96b-4ce7-810b-4368ad6aa234` |
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  | 12k source-column audit | Passed: 12,000/12,000 rows preserved `Final: {...}` |
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  | AutoScientist recommendation | `google/gemma-3-4b-it`, 1-epoch LoRA |
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+ | Active fallback AutoScientist run | `43b5486d-0bfc-4e9f-869e-a9892a679386` on `meta-llama/Llama-3.2-3B-Instruct` |
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  Fill after final AutoScientist run:
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  | Metric | Value |
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  |---|---:|
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+ | Adaptive Data grade before | Pending |
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+ | Adaptive Data grade after | Pending |
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+ | Adaptive Data quality improvement | Pending |
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+ | AutoScientist best win rate | Pending checkpoint |
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+ | Domain augmentation rows | 8,000 source rows in active fallback |
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+ | General augmentation rows | 0 in active fallback |
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  ## Limitations
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stats.json CHANGED
@@ -1,28 +1,32 @@
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  {
 
 
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  "gold_verifier_accuracy": 1.0,
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  "rows": 8000,
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  "source_file": "data/generated/verisci_8k.jsonl",
 
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  "splits": {
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- "test": 768,
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- "train": 6429,
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- "validation": 803
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  },
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  "task_families": {
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- "chemistry_solutions": 444,
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- "chemistry_stoichiometry": 445,
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- "code_abstention": 157,
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- "dimensional_analysis": 444,
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- "electric_circuits": 445,
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- "exponential_decay": 445,
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- "finite_difference_pde": 444,
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- "linear_modeling": 444,
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- "numerical_integration": 444,
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- "numerical_ode": 444,
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- "python_code_generation": 444,
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- "scientific_abstention": 287,
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- "thermodynamics": 890,
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- "unit_checked_mechanics": 1335,
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- "unit_conversion": 444,
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- "vector_reasoning": 444
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- }
 
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  }
 
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  {
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+ "dedupe_signatures": 8000,
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+ "duplicate_prompts": 0,
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  "gold_verifier_accuracy": 1.0,
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  "rows": 8000,
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  "source_file": "data/generated/verisci_8k.jsonl",
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+ "split_prompt_leakage_rows": 0,
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  "splits": {
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+ "test": 823,
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+ "train": 6409,
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+ "validation": 768
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  },
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  "task_families": {
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+ "chemistry_solutions": 447,
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+ "chemistry_stoichiometry": 421,
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+ "code_abstention": 190,
17
+ "dimensional_analysis": 447,
18
+ "electric_circuits": 447,
19
+ "exponential_decay": 447,
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+ "finite_difference_pde": 447,
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+ "linear_modeling": 447,
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+ "numerical_integration": 447,
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+ "numerical_ode": 447,
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+ "python_code_generation": 447,
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+ "scientific_abstention": 253,
26
+ "thermodynamics": 894,
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+ "unit_checked_mechanics": 1341,
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+ "unit_conversion": 431,
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+ "vector_reasoning": 447
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+ },
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+ "unique_prompts": 8000
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  }
test.jsonl CHANGED
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train.jsonl CHANGED
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validation.jsonl CHANGED
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