DualMinded-Qwen3-1.7B

A 1.7B parameter dual-cognition model trained on Opus 4.6 reasoning traces. The model implements a three-phase cognitive loop — explore, examine, respond — where it reasons freely, critiques its own reasoning, then synthesizes a clean answer.

Convergent Intelligence LLC: Research Division

Architecture

<explore>  — unconstrained reasoning, derivation, speculation
</explore>

<examine>  — adversarial self-critique, error detection, refinement
</examine>

<response> — clean synthesis from the internal dialogue
</response>

This is the multi-model collision array collapsed into a single architecture. The dialectical structure that produces novel insights from architectural diversity is recreated through role-conditioned generation on shared weights. No extra parameters, no routing — same weights, different cognitive modes.

Training Pipeline

DualMinded-Qwen3-1.7B is the product of a four-stage pipeline:

Stage 1 — Multi-Teacher Distillation: Qwen3-30B-A3B in three variants (Instruct, Thinking, Coder) distilled into Qwen3-1.7B via proof-weighted KD with 2.25× loss amplification on reasoning tokens.

Stage 2 — DISC Refinement: Disctil-Qwen3-1.7B: the student refined through Discrepancy Calculus, detecting and preserving structural boundaries in the teacher's distribution.

Stage 3 — Topological Knowledge Distillation (TKD): Continuous-stream distillation with topology-guided windowing from Qwen3-30B-A3B-Thinking. Bounded variation decomposition of the teacher's output: smooth + jumps + drift. Jump positions amplified at 3σ, windows cut at low-discrepancy boundaries, 4-phase curriculum ordering (easy → hard).

Stage 4 — DualMind SFT on Opus 4.6: SFT using Opus-4.6-Reasoning-3000x-filtered. The thinking column maps directly to <explore> — no heuristic sentence splitting needed. The solution column is split into <examine> + <response>.

Training Configuration

DualMind vs DualMinded

Usage

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model = AutoModelForCausalLM.from_pretrained(
    "reaperdoesntknow/DualMinded-Qwen3-1.7B",
    torch_dtype=torch.bfloat16,
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("reaperdoesntknow/DualMinded-Qwen3-1.7B")

prompt = "##USER:\nProve the mean value theorem.\n\n<explore>\n"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

with torch.no_grad():
    out = model.generate(
        **inputs,
        max_new_tokens=512,
        do_sample=True,
        temperature=0.6,
        top_p=0.9,
        repetition_penalty=1.15,
    )
print(tokenizer.decode(out[0], skip_special_tokens=True))

Ghost Imprinting

Sequential distillation from multiple teachers (Instruct → Thinking → Coder → Opus) leaves residual fields in weight space. These residuals produce capabilities absent from any individual teacher — the singular-continuous component of the bounded variation decomposition applied to the parameter tensor. Models in the DualMind family exhibit emergent behaviors (e.g., literary content from physics-only training data) attributable to these ghost imprints.

GGUF

Quantized versions available at DualMinded-Qwen3-1.7B-GGUF: F16, Q8_0, Q5_K_M, Q4_K_M.

Ollama: ollama run reaperdoesntrun/DualMinded-1.7B

Related

• DualMind — LogicInference-trained variant
• DualMind_Methodolgy — Paper: DOI 10.57967/hf/8184
• Structure Over Scale — Paper 1: CPU training methodology
• DualMind Collection
• DistilQwen Collection

Mathematical Foundations: Discrepancy Calculus (DISC)

This model's training pipeline is grounded in Discrepancy Calculus — a measure-theoretic framework that treats singularities as primary structure rather than pathology. Full theory: "On the Formal Analysis of Discrepancy Calculus" (CIx, 2026; Convergent Intelligence LLC: Research Division).

The Core Operator:

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

For smooth $f$: $Df(x) = |f'(x)|$. For rough $f$: $D$ localizes irregularity to null sets while preserving integral structure.

The Mesh Fundamental Identity — every BV function decomposes as:

$$f(b) - f(a) = \underbrace{\int_a^b f'(x),dx}{\text{smooth (AC)}} + \underbrace{\sum{x \in J_f} \Delta f(x)}{\text{jumps}} + \underbrace{D^c f(I)}{\text{Cantor drift}}$$

Standard knowledge distillation captures only term 1. Topological Knowledge Distillation (TKD) preserves all three by treating the teacher's output distribution as a BV function and computing discrepancy energy, jump sets, and gap energy density before training begins.

Citation

@misc{cix2026dualmind,
  title={From Three Teachers to Dual Cognition: Topology-Aware Multi-Teacher Distillation and Role-Conditioned Self-Critique at 1.7B Scale},
  author={Convergent Intelligence},
  year={2026},
  publisher={HuggingFace},
  url={https://doi.org/10.57967/hf/8184}
}

Convergent Intelligence LLC: Research Division — Apache 2.0

Convergent Intelligence Portfolio

Part of the DualMind Series by Convergent Intelligence LLC: Research Division

DualMind Family

Papers

Last updated: 2026-03-31 by Convergent Intelligence LLC: Research Division