Datasets:
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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.
|