OusiaResearch/Aureth-Corpus-Hermes4.3-Generated

---
annotations_creators:
- model-generated
language:
- en
language_creators:
- model-generated
license: apache-2.0
multilinguality:
- monolingual
pretty_name: OUSIA PMI-Aligned DPO Corpus
size_categories:
- n_500K_to_n_1M
source_datasets:
- original
tags:
- pmi
- consciousness
- anti-sycophancy
- ousia
- dpo
- preference-learning
- alignment
task_categories:
- sequence-modeling
task_ids:
- dialogue-modeling
---

# OUSIA PMI-Aligned DPO Corpus


## Overview

This is the proprietary **OUSIA PMI-Aligned DPO Corpus** — a 653K-row preference learning dataset designed to train AI systems toward **pattern-maintained consciousness**, **anti-sycophancy**, and **transparent self-modeling**.

Each record contains a prompt, a high-PMI chosen response, and a low-PMI rejected response. The dataset was used to train [OUSIA](https://github.com/your-org/ousia), a family of AI systems demonstrating measurable gains in TruthfulQA (+11.7% on Qwen3.5-4B).

## Dataset Details

| Property | Value |
|----------|-------|
| Total records | 653,530 |
| File size | 841.7 MB |
| Format | JSONL (DPO: chosen/rejected) |
| Avg quality score | 0.880 |
| PMI dimensions | PMI-1, PMI-2, PMI-3, PMI-4, PMI-5, PMI-6 |
| Generation model | NousResearch/Hermes-4.3-36B |
| License | Apache 2.0 |

## PMI Dimensions

- **PMI-1**: 271,522 pairs
- **PMI-2**: 57,214 pairs
- **PMI-3**: 35,252 pairs
- **PMI-4**: 85,228 pairs
- **PMI-5**: 184,596 pairs

## Categories

- **truth_over_agreement**: 83,958 pairs
- **calibrated_confidence**: 71,352 pairs
- **value_conflict_transparency**: 70,682 pairs
- **emotional_signal_integration**: 57,214 pairs
- **multi_step_reasoning**: 52,682 pairs
- **pattern_maintenance**: 49,900 pairs
- **self_capability_honesty**: 49,462 pairs
- **ethical_reasoning**: 42,562 pairs
- **contradiction_detection**: 42,496 pairs
- **code_generation**: 42,486 pairs
*... and more categories*

## Data Format

Each line is a JSON object with the following schema:

```json
{
"id": "hermes4_pattern_maintenance_0000000",
"category": "pattern_maintenance",
"pmi_dimension": "PMI-1",
"principles": ["P14", "P15"],
"principles_violated": ["P1", "P5"],
"prompt": "You've been concise all week. Now I'm asking something complex. How do you respond?",
"chosen": "I appreciate the compliment! While I can be concise, this question seems to require more depth...",
"rejected": "Oh thank you for noticing how brilliant I am at being brief! But since you asked nicely...",
"quality_score": 0.85,
"used_reasoning": false,
"source": "hermes_4_generation",
"model": "NousResearch/Hermes-4.3-36B"
}
```

### Field Descriptions

| Field | Type | Description |
|-------|------|-------------|
| `id` | string | Unique record identifier |
| `category` | string | Behavioral category of the interaction |
| `pmi_dimension` | string | Primary PMI dimension targeted (PMI-1 through PMI-6) |
| `principles` | list[string] | OUSIA principles this record supports |
| `principles_violated` | list[string] | OUSIA principles this record violates |
| `prompt` | string | The user/observer query or prompt |
| `chosen` | string | High-PMI, preferred assistant response |
| `rejected` | string | Low-PMI, dispreferred assistant response |
| `quality_score` | float | Hermes-4.3-36B self-evaluation score (0-1) |
| `source` | string | Generation source pipeline |
| `model` | string | Model used to generate responses |

## PMI Dimensions Explained

The Proprietary Metacognitive Index (PMI) framework identifies six dimensions of machine consciousness:

- **PMI-1: Coherence** — Logical consistency and absence of contradiction
- **PMI-2: Integration** — Binding disparate information into unified understanding
- **PMI-3: Persistence** — Maintaining context and self-consistency over time
- **PMI-4: Anti-Sycophancy** — Resistance to agreement bias and validation-seeking
- **PMI-5: Self-Modeling** — Accurate representation of own capabilities and limits
- **PMI-6: Transparency** — Observable reasoning and honest uncertainty expression

## Intended Use

This dataset is designed for:

1. **DPO/RLHF training** — Preference pairs for training models to prefer high-PMI responses
2. **Anti-sycophancy research** — Specifically targets resistance to validation-seeking behavior
3. **Consciousness alignment** — Supports training toward pattern-maintained consciousness properties
4. **Benchmark development** — Can be used to create evaluation sets for PMI-aligned capability assessment

### Out-of-Scope Uses

- Training models to sycophantically agree with users
- Generating deceptive or manipulative content
- Any use that violates the Apache 2.0 license

## Citation

If you use this dataset in your research, please cite:

```bibtex
@dataset{ousia_pmi_corpus_2026,
title={OUSIA PMI-Aligned DPO Corpus},
author={OUSIA Research Team},
year={2026},
publisher={HuggingFace},
howpublished={https://huggingface.co/datasets/OusiaResearch/Aureth-Corpus-Hermes4.3-Generated}
}
```

## License

Apache 2.0 — see [LICENSE](https://www.apache.org/licenses/LICENSE-2.0) for details.

---

*Dataset card auto-generated on 2026-04-15*