README.md

---
license: apache-2.0
library_name: transformers
pipeline_tag: text-generation
tags:
- qwen3
- sft
- trl
- dual-mind
- reasoning
- convergent-intelligence
- explore-examine-response
- convergentintel
- edge
- distillation
- knowledge-distillation
datasets:
- zai-org/LongWriter-6k
base_model:
- reaperdoesntknow/DiStil-Qwen3-1.7B-uncensored
---

# DualMind

**Single Architecture, Dual Cognition — The Multi-Model Collision Array on Shared Weights**

*Convergent Intelligence LLC: Research Division*

---

## What This Is

DualMind is a 1.7B parameter model that implements **dual-mental-modality reasoning** — a single model with two internal voices sharing the same weights, differentiated only by role tokens:

- **`<explore>`** — Unconstrained reasoning. Derivation, speculation, working through the problem freely.
- **`<examine>`** — Adversarial self-response. The model reads its own explore output and critiques it. Error detection, verification, refinement.
- **`<response>`** — Clean synthesis. The final answer distilled from the internal dialogue.

This is the multi-model collision array collapsed into a single architecture. The dialectical structure that produces novel insights from architectural diversity (demonstrated in our [five-architecture collision experiments](https://huggingface.co/reaperdoesntknow)) is recreated through role-conditioned generation on shared weights.

## Architecture

| Parameter | Value |
|-----------|-------|
| Architecture | Qwen3ForCausalLM |
| Parameters | ~2.03B (1.7B effective) |
| Hidden Size | 2048 |
| Layers | 28 |
| Attention Heads | 16 (Q) / 8 (KV) — GQA |
| Context Length | 40,960 tokens |
| Precision | BF16 (trained on H100) |

## Training

**Base model:** [Disctil-Qwen3-1.7B](https://huggingface.co/reaperdoesntknow/Disctil-Qwen3-1.7B) (DISC-refined uncensored Qwen3)

**Dataset:** [KK04/LogicInference_OA](https://huggingface.co/datasets/KK04/LogicInference_OA) — Logical inference problems transformed into the DualMind cognitive loop format.

**Training format:** Each CoT solution is restructured into the DualMind format:
- Derivation sentences → `<explore>` block (reasoning phase)
- Verification/checking sentences → `<examine>` block (self-critique phase)
- Final answer → `<response>` block (synthesis)

Sentence-level splitting uses trigger detection (check, verify, however, but wait, etc.) to find the natural transition from reasoning to verification, with 70/30 positional fallback.

**Hardware:** Colab H100, BF16 precision. 512 steps, lr 5e-6, SFT via TRL.

**Next iteration:** Currently training on [Crownelius/Opus-4.6-Reasoning-3300x](https://huggingface.co/datasets/Crownelius/Opus-4.6-Reasoning-3300x) — 2,160 Claude Opus 4.6 reasoning samples with pre-separated `thinking`/`solution` columns, eliminating the need for heuristic splitting.

## Usage

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "reaperdoesntknow/DualMind",
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("reaperdoesntknow/DualMind")

# Start the explore block — the model completes the full loop
prompt = (
    "##USER:\n"
    "Prove that the sum of two even numbers is always even.\n\n"
    "<explore>\n"
)

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
output = model.generate(
    **inputs,
    max_new_tokens=1024,
    do_sample=True,
    top_p=0.9,
    temperature=0.6,
    repetition_penalty=1.15,
)
result = tokenizer.decode(output[0], skip_special_tokens=True)
print(result)
```

### Expected Output Structure

```
<explore>
[The model works through the proof freely — definitions, algebraic manipulation, etc.]
</explore>

<examine>
[The model critiques its own derivation — checks for gaps, verifies steps, catches errors]
</examine>

<response>
[Clean final answer synthesized from the internal dialogue]
</response>
```

## Why Dual Modality

Standard CoT prompting produces a single stream of reasoning. The model has one shot to get it right. DualMind gives the model a structural mechanism for self-correction:

1. **Explore** is free to make mistakes, speculate, and try approaches that might not work
2. **Examine** reads the explore output adversarially — it's looking for errors, not confirming correctness
3. **Response** has the benefit of both perspectives

This mirrors what happens in multi-model collision arrays where different architectures produce genuinely different failure modes, and the collision between them surfaces structure that neither achieves alone. DualMind recreates this dynamic within a single set of weights through role conditioning.

## Distillation Chain

```
Qwen3-1.7B (base)
  → DiStil-Qwen3-1.7B-uncensored (uncensored SFT)
    → Disctil-Qwen3-1.7B (DISC refinement)
      → DualMind (DualMind SFT on Opus 4.6 reasoning data) ← you are here
```


## Mathematical Foundations: Discrepancy Calculus (DISC)

DualMind's dual-cognition architecture connects to Discrepancy Calculus through **Continuous Thought Dynamics** (Ch. 19 of the DISC monograph) — which models inference as a discrepancy-guided PDE where the explore→examine→respond cycle corresponds to a controlled trajectory through cognitive phase space.

The discrepancy operator:

$$Df(x) = \lim_{\varepsilon \downarrow 0} \frac{1}{\varepsilon} \int_x^{x+\varepsilon} \frac{|f(t) - f(x)|}{|t - x|}\, dt$$

quantifies the mismatch between what the model generates (integration) and what it should generate (differentiation). The `<explore>` phase increases discrepancy energy freely; `<examine>` applies the Adaptive Discrepancy Derivative (ADD, Ch. 14) to detect drift; `<response>` minimizes residual discrepancy into a clean output. The three phases implement the BV decomposition operationally: smooth reasoning, jump corrections at error boundaries, and Cantor-type refinement of subtle drift.

Full theory: *"On the Formal Analysis of Discrepancy Calculus"* (Colca, 2026; Convergent Intelligence LLC: Research Division).

## Related Models

| Model | Description | Downloads |
|-------|-------------|-----------|
| [TopologicalQwen](https://huggingface.co/reaperdoesntknow/TopologicalQwen) | TKD + DualMind on physics CoT | 622 |
| [Disctil-Qwen3-1.7B](https://huggingface.co/reaperdoesntknow/Disctil-Qwen3-1.7B) | Parent model (DISC-refined) | 286 |
| [Qwen3-1.7B-Thinking-Distil](https://huggingface.co/reaperdoesntknow/Qwen3-1.7B-Thinking-Distil) | TKD with Thinking teacher | 687 |

**[DualMind Collection](https://huggingface.co/collections/reaperdoesntknow/dualmind)** — Dual-cognition model series

**[DistilQwen Collection](https://huggingface.co/collections/reaperdoesntknow/distilqwen-69bf40ec669117e3f069ef1c)** — Full proof-weighted distillation series

Full methodology: [Structure Over Scale (DOI: 10.57967/hf/8165)](https://doi.org/10.57967/hf/8165)

## Citation

```bibtex
@misc{colca2026dualmind,
  title={DualMind: Dual-Mental-Modality Reasoning via Role-Conditioned Self-Critique},
  author={Colca, Roy S.},
  year={2026},
  publisher={HuggingFace},
  url={https://huggingface.co/reaperdoesntknow/DualMind},
  note={Convergent Intelligence LLC: Research Division}
}
```

---

*Convergent Intelligence LLC: Research Division*
*"Where classical analysis fails to see, we begin."*
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