--- 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: - **``** — Unconstrained reasoning. Derivation, speculation, working through the problem freely. - **``** — Adversarial self-response. The model reads its own explore output and critiques it. Error detection, verification, refinement. - **``** — 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 → `` block (reasoning phase) - Verification/checking sentences → `` block (self-critique phase) - Final answer → `` 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" "\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 ``` [The model works through the proof freely — definitions, algebraic manipulation, etc.] [The model critiques its own derivation — checks for gaps, verifies steps, catches errors] [Clean final answer synthesized from the internal dialogue] ``` ## 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 `` phase increases discrepancy energy freely; `` applies the Adaptive Discrepancy Derivative (ADD, Ch. 14) to detect drift; `` 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."*