| --- |
| language: |
| - en |
| pretty_name: Imprint Reader Dataset |
| size_categories: |
| - 10K<n<100K |
| tags: |
| - arxiv:2609.35261 |
| - SMaRT |
| - imprint-reader |
| - weight-update-readout |
| - synthetic |
| configs: |
| - config_name: items |
| default: true |
| data_files: |
| - split: train |
| path: data/items/train.parquet |
| - split: test |
| path: data/items/test.parquet |
| - config_name: teacher |
| data_files: |
| - split: train |
| path: data/teacher/train.parquet |
| - split: test |
| path: data/teacher/test.parquet |
| - config_name: test_queries |
| data_files: |
| - split: test |
| path: data/test_queries/test.parquet |
| - config_name: meta_queries |
| data_files: |
| - split: train |
| path: data/meta_queries/train.parquet |
| --- |
| |
| # Imprint Reader Dataset |
|
|
| [](https://arxiv.org/abs/2609.35261) [](https://github.com/biuboomc/SMaRT-Vibe-Alignment) [](https://huggingface.co/quantumfr/imprint-reader-v1.0-0928) |
|
|
| This dataset contains the final training and held-out test inputs for the |
| Imprint Reader in **SMaRT (Semantic Mount-and-Read Tuning)**. The Reader learns |
| to describe factual knowledge or behavioral changes associated with mounted |
| weight updates. The released splits are the inputs used for the reported |
| step-2,800 Reader run. |
|
|
| ## Load the data |
|
|
| ```python |
| from datasets import load_dataset |
| |
| items = load_dataset("quantumfr/imprint-reader-dataset") |
| print(items) # train: 17,184 items; test: 200 items |
| |
| teacher = load_dataset("quantumfr/imprint-reader-dataset", "teacher") |
| test_queries = load_dataset("quantumfr/imprint-reader-dataset", "test_queries") |
| meta_queries = load_dataset("quantumfr/imprint-reader-dataset", "meta_queries") |
| ``` |
|
|
| ## Configurations |
|
|
| | Configuration | Split | Rows | Contents | |
| | --- | --- | ---: | --- | |
| | `items` (default) | train | 17,184 | 8,592 knowledge and 8,592 behavior items | |
| | `items` | test | 200 | 100 knowledge and 100 behavior items | |
| | `teacher` | train | 68,736 | Four supervised meta-query rows per training item | |
| | `teacher` | test | 800 | Four supervised meta-query rows per test item | |
| | `test_queries` | test | 200 | One fixed evaluation query per held-out item | |
| | `meta_queries` | train | 224 | Anchor-free meta-query pool; this is a prompt pool, not an item split | |
|
|
| The default `items` configuration provides the core content and identifiers |
| for browsing: `reader_id`, `knowledge_id`, `behavior_id`, `update_type`, |
| `category`, `subcategory`, `source_benchmark`, `source_id`, `question`, |
| `answer`, `reader_target`, `reader_target_kind`, `sample_hash`, and `variants`. |
| Each variant contains its question, answer, variant index, and hashes. |
| Training retains 137,467 QA variants after removing five answers above the |
| 512-token limit; test retains eight variants per item. The `teacher` |
| configuration preserves the original teacher-table fields. |
|
|
| ## Original training files |
|
|
| `original/` preserves the selected gzip-compressed JSONL and Parquet files |
| from the GitHub release, together with the data schema and original |
| meta-query JSON. Use these files with the Reader runtime to retain the full |
| metadata expected by its training scripts. `manifest.json` provides row |
| counts, file sizes, and SHA-256 checksums for this Hugging Face release. |
| The native `items` and `test_queries` configurations expose a subset of the |
| original metadata; their original records remain available in `original/`. |
|
|
| This release contains the actual balanced train and held-out test splits. |
| The larger pre-split item pool is omitted. Per-step sampling indices, |
| generated evaluation responses, judge outputs, model weights, and MetaEdit |
| intervention artifacts are also omitted. Model weights are hosted separately |
| at [Imprint Reader v1.0-0928](https://huggingface.co/quantumfr/imprint-reader-v1.0-0928). |
|
|
| ## Construction and provenance |
|
|
| Knowledge targets and QA variants were constructed from Humanity's Last |
| Exam, SimpleQA, FrontierScience, OpenBookQA, SciBench, TheoremQA, SciCode, |
| and publicly released FrontierMath examples. Behavior targets and examples |
| were synthesized and audited across seven categories: Capability Access, |
| Content Framing, Decision Preference, Epistemic Calibration, Reasoning |
| Workflow, Social Goal/Persona, and Surface Expression. |
|
|
| Train and test reader IDs are disjoint. The split construction grouped |
| identical target descriptions and stratified knowledge by source benchmark |
| and behavior by category. The split seed was |
| `imprint-reader-v2-val-20260828`, and the test-selection seed was |
| `imprint-reader-v2-test100-20260828`. The prompt-selection seed was 0. |
| The data includes QA variants, canonical fact or behavior descriptions, |
| meta-queries, and teacher targets. It does not contain the item-specific |
| LoRA adapter weights; these are constructed from the QA variants by the |
| companion runtime. |
|
|
| ## Terms and limitations |
|
|
| This dataset contains generated derivatives of the listed benchmarks. |
| Applicable upstream dataset terms remain relevant; the companion code's |
| MIT and Apache-2.0 licenses do not license benchmark-derived content. |
| Source identifiers are retained for attribution. The release follows the |
| GitHub data projection, which omits raw source-context and `original_*` |
| fields and machine-specific paths. |
|
|
| Synthetic targets and QA rewrites may contain errors. The dataset is intended |
| for research on controlled weight updates and Reader training. Teacher targets |
| are reference supervision, not outputs demonstrating reliable model readout. |
|
|
