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) is recreated through role-conditioned generation on shared weights.
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 — 2,160 Claude Opus 4.6 reasoning samples with pre-separated thinking/solution columns, eliminating the need for heuristic splitting.
Usage
python
1from transformers import AutoModelForCausalLM, AutoTokenizer
23model = AutoModelForCausalLM.from_pretrained(4"reaperdoesntknow/DualMind",5 torch_dtype="auto",6 device_map="auto"7)8tokenizer = AutoTokenizer.from_pretrained("reaperdoesntknow/DualMind")910# Start the explore block — the model completes the full loop11prompt =(12"##USER:\n"13"Prove that the sum of two even numbers is always even.\n\n"14"<explore>\n"15)1617inputs = tokenizer(prompt, return_tensors="pt").to(model.device)18output = model.generate(19**inputs,20 max_new_tokens=1024,21 do_sample=True,22 top_p=0.9,23 temperature=0.6,24 repetition_penalty=1.15,25)26result = tokenizer.decode(output[0], skip_special_tokens=True)27print(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:
Explore is free to make mistakes, speculate, and try approaches that might not work
Examine reads the explore output adversarially — it's looking for errors, not confirming correctness
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
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.
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" (CIx, 2026; Convergent Intelligence LLC: Research Division).
