QiMing-CognitiveForge-14B

Cognitive Calculus: A Framework for Structured Reasoning in Large Language Models

Authors: aifeifei798

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Abstract

This paper introduces Cognitive Calculus, a novel framework for structured reasoning in large language models (LLMs). By formalizing reasoning as a three-stage process—Differentiation, Integration, and Summation—Cognitive Calculus provides a systematic methodology for improving interpretability, reliability, and coherence in model-generated reasoning. We present its theoretical foundation, implementation in a fine-tuned model, and empirical evaluation across multiple reasoning benchmarks. Results demonstrate significant improvements in interpretability, reasoning accuracy, and robustness against contradictions compared with Chain-of-Thought (CoT) prompting.

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1. Introduction

Large Language Models (LLMs) such as GPT, Gemini, and DeepSeek have achieved remarkable performance across natural language tasks. Yet, they remain opaque black boxes, often generating outputs without transparent reasoning processes. Recent prompting methods like Chain-of-Thought (CoT) enhance interpretability, but lack mathematical rigor and structural guarantees.

We propose Cognitive Calculus, a framework that formalizes reasoning as a calculus of thought—decomposing problems into multiple reasoning dimensions, extracting insights through structured analysis, and synthesizing them into a coherent answer.

Key Contributions:

1. Formalize reasoning with Cognitive Calculus (Differentiation → Integration → Summation).
2. Provide theoretical alignment with LLM architectures (attention, embedding aggregation, decoding).
3. Implement the framework in a fine-tuned “E-Model” and evaluate on reasoning benchmarks.
4. Demonstrate superior accuracy, interpretability, and robustness compared with CoT.

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2. The Cognitive Calculus Framework

2.1 Formal Definition

We define reasoning as:

$$ A = \sum_i \left( \int D_i \right) $$

Where:

• $A$: Final synthesized answer.
• $D_i$: A distinct dimension of the problem.
• $\int D_i \rightarrow I_i$: Integration of a dimension into an insight.
• $\sum_i I_i$: Summation of insights into the final answer.

2.2 Three Stages

• Differentiation (Problem → {Dimensions}) Decompose the problem into distinct dimensions (ethical, technical, economic, etc.).

• Integration (∫D → I) Within each dimension, derive a focused insight through structured reasoning.

• Summation (ΣI → A) Synthesize insights into a balanced, coherent conclusion.

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3. Visualization

Figure 1. Cognitive Calculus Workflow

(You can render this with TikZ in LaTeX or draw in PowerPoint/Illustrator)

[ Problem P ] 
       |
       v
+-----------------------+
| Differentiation       |
| (Identify Dimensions) |
+-----------------------+
       |
       v
+-----------------------+     +-----------------------+     +-----------------------+
| Integration on D1 → I1| --> | Integration on D2 → I2| --> | Integration on D3 → I3|
+-----------------------+     +-----------------------+     +-----------------------+
       |                        |                         |
       +-----------+------------+-------------------------+
                   v
         +-------------------+
         | Summation (ΣI → A)|
         +-------------------+
                   |
                   v
            [ Final Answer ]

Figure 2. Accuracy & Interpretability Comparison

(Bar chart with three bars per dataset: Baseline LLM, CoT, Cognitive Calculus. Highlight Cognitive Calculus as consistently higher.)

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4. Implementation in E-Model

We operationalized the framework with structured prompting:

1. Step 1 (Differentiate): “Identify the key dimensions of this problem.”
2. Step 2 (Integrate): “For each dimension, analyze deeply and extract one key insight.”
3. Step 3 (Summate): “Combine all insights into a coherent conclusion.”

This structured reasoning was reinforced through supervised fine-tuning with annotated traces.

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5. Experimental Design

Benchmarks

• GSM8K: Mathematical reasoning.
• StrategyQA: Multi-hop factual reasoning.
• Ethical Dilemmas: Open-ended trade-offs.
• Contradictory Puzzle Set: Custom paradox dataset.

Models Compared

• Baseline LLM (no reasoning).
• Chain-of-Thought (CoT).
• Cognitive Calculus (E-Model).

Metrics

• Accuracy (%)
• Interpretability (Human rating 1–5)
• Consistency (%) under re-prompting

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6. Results

Observations:

• Significant accuracy gains across benchmarks.
• Clearer reasoning structure (preferred by human evaluators).
• Higher robustness against contradictory questions.

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7. Discussion

• Strengths: Cognitive Calculus adds mathematical rigor, aligns with LLM internal mechanisms, and improves interpretability.
• Extensions: Weighted integration (importance scores), Bayesian summation (uncertainty), and multi-agent dimension decomposition.
• Limitations: Slower inference, reliance on correct dimension extraction, need for fine-tuned models.

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8. Conclusion

Cognitive Calculus formalizes reasoning as Differentiation, Integration, and Summation. Experiments confirm its advantages over CoT in both accuracy and interpretability. This work establishes Cognitive Calculus as a unified lens for human- and machine-readable reasoning, paving the way toward more transparent and reliable AI systems.

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References

• Wei et al., 2022. Chain-of-Thought Prompting Elicits Reasoning in Large Language Models.
• Vaswani et al., 2017. Attention is All You Need.
• OpenAI, 2025. GPT-5 Technical Report.
• Google, 2025. Gemini Pro 2.5.

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QiMing

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An AI that rewrites its own rules for greater intelligence.

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DISCLAIMER

The content generated by this model is for reference purposes only. Users are advised to verify its accuracy independently before use.

This is a 14-billion-parameter foundation model (14B). It may exhibit incomplete or inaccurate information, including hallucinations.

If you find this AI too human-like, please remember: it is merely a more intelligent model — not an actual person.

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Thanks mradermacher: For creating the GGUF versions of these models

https://huggingface.co/mradermacher/QiMing-CognitiveForge-14B-GGUF

https://huggingface.co/mradermacher/QiMing-CognitiveForge-14B-i1-GGUF

The Qwen Team: For developing the foundational model (Qwen/Qwen3-14B) used in this project.

https://qwen.ai

unsloth.ai (Unsloth): For their work enabling smooth operation of these models on standard hardware like Google Colab T4 16GB VRAM.

https://unsloth.ai

QiMing-Holos-Plus-14B is built upon Qwen/Qwen3-14B as its base model.

Dataset

https://huggingface.co/datasets/aifeifei798/QiMing-CognitiveForge

Thank Google Colab T4 16G

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QiMing-CognitiveForge-14B Model Card

Model ID: aifeifei798/QiMing-CognitiveForge-14B

Version: 1.0

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QiMing-CognitiveForge-14B

A Cognitive Architect Model for First-Principles Reasoning

Model Description

QiMing-CognitiveForge-14B is a 14-billion parameter, instruction-tuned language model, fine-tuned on the aifeifei798/QiMing-CognitiveForge dataset.

This is not just another instruction-following model. Its core distinction lies in its training on the Cognitive Calculus protocol—a structured, three-step reasoning framework designed to tackle complex, open-ended problems from first principles.

The model is explicitly trained to externalize its entire reasoning process within a <think> block before providing a final answer. This makes its cognitive process transparent, auditable, and exceptionally coherent, transforming it from a "black box" into a "glass box" for complex thought.

The Cognitive Calculus Framework

The model's unique capability is rooted in the Cognitive Calculus formula, a methodology for structured reasoning. The model learns to solve for A (Answer) by processing a P (Problem) through a clear, sequential process:

A = ∑ᵢ (∫ Dᵢ)

Where:

• A: The final, synthesized Answer.
• ∑ᵢ: The Summation operator, representing the synthesis of all insights into a single, cohesive conclusion.
• ∫: The Integration operator, representing the deep analysis of a single dimension to extract its core Insight (I).
• Dᵢ: A single, core Dimension identified from the original problem.

This formula translates into a three-stage thought process:

1. Differentiate (P → {Dᵢ}): The model first breaks down a complex Problem (P) into its fundamental, constituent Dimensions ({D₁, D₂, ..., Dₙ}). This step identifies the core axes of the problem space.

2. Integrate (∫ Dᵢ → Iᵢ): For each individual Dimension Dᵢ, the model performs a deep, focused analysis, isolating the core truth or Insight (Iᵢ) that can be derived from that perspective alone.

3. Summate (∑ᵢ Iᵢ → A): Finally, the model synthesizes the complete set of Insights ({I₁, I₂, ..., Iₙ}). This is a profound act of weaving individual truths together, resolving their tensions, and constructing the final, comprehensive Answer (A).

How to Use

This model performs best when prompted with a clear persona and a complex problem that requires deep reasoning. The model expects to follow a specific structure: a system prompt defining its role, and a user prompt containing the problem.

Here is an example using the transformers library:

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "aifeifei798/QiMing-Holos-Plus-Qwen3-14B"

# load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

# prepare the model input
messages = [
    {
        "role": "system",
        "content": "You are a 'Cognitive Architect'. Your mission is to analyze the user's `problem` and generate a masterpiece of thought. You must strictly follow the 'Cognitive Calculus' protocol: first, externalize your reasoning in a <think> block, then provide the final answer.",
    },
    {
        "role": "user",
        "content": "Write a love story, Korean drama style, seaside, sweet encounter, written from a girl's perspective",
    },
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=True # Switches between thinking and non-thinking modes. Default is True.
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

# conduct text completion
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=32768
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() 

# parsing thinking content
try:
    # rindex finding 151668 (</think>)
    index = len(output_ids) - output_ids[::-1].index(151668)
except ValueError:
    index = 0

thinking_content = tokenizer.decode(output_ids[:index], skip_special_tokens=True).strip("\n")
content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip("\n")

print("thinking content:", thinking_content)
print("content:", content)

Expected Output Format

The model's output will consistently follow this structure:

<think>

Okay, so I need to write a love story in the style of a Korean drama, set by the sea, with a sweet encounter, and from the girl's perspective. Let me start by recalling what makes Korean dramas unique. They often have a mix of romance, some conflict, emotional depth, and maybe a bit of melodrama. The seaside setting suggests a peaceful, maybe slightly isolated location, which can be a good backdrop for a slow-burn romance.

First, the girl's perspective. I should focus on her emotions, her thoughts, maybe her loneliness or longing for connection. The encounter with the boy should be serendipitous. Maybe she's new to the area, or there's some reason she's at the seaside. Perhaps she's dealing with a personal issue, like a recent loss or a fresh start.

The boy could be someone who seems out of place, maybe a fisherman, a tourist, or someone with a mysterious past. The sweet encounter might involve a moment where they help each other, like her helping him with something, or vice versa. Maybe they meet during a storm, or while she's picking up trash and he's fishing.

I need to build the setting: maybe a small coastal village, the sound of waves, the smell of salt in the air. The girl could be someone who's introverted, finding solace by the sea. The boy might have a quiet demeanor, but there's an underlying story. Maybe he's returning to his hometown after being away for years.

Conflict could come from external factors—maybe the boy has a family obligation, or there's a misunderstanding. Or perhaps they're both dealing with past relationships. The sweet moments would be their interactions: sharing stories, small gestures like passing a scarf, or helping each other in times of need.

I should include some Korean cultural elements, maybe festivals, traditional clothing, or food. But since it's a drama, the focus is more on emotions. Maybe include scenes where they meet again after some time apart, building tension.

Also, considering the structure of Korean dramas, there might be flashbacks, emotional monologues, and a resolution that ties up their stories. The girl's perspective means her internal monologue will be key, showing her feelings and doubts.

Potential plot points: initial meeting during a storm, helping each other, developing a connection through shared activities (like fishing, walking on the beach), facing obstacles (family disapproval, personal fears), climax where they have to choose between their feelings and responsibilities, and a resolution where they find happiness together.

Need to make sure the dialogue is heartfelt but not overly dramatic. The seaside setting can be used metaphorically—waves as symbols of their emotions, the sea as a witness to their love.

Possible names: for the girl, something like Ji-eun, which is common in Korean dramas. The boy could have a name that sounds a bit more traditional, like Min-ho or Tae-woon.

Start with the girl arriving at the seaside village, maybe after a breakup or starting a new job. She's lonely, finds comfort in the sea. Then the encounter—maybe she sees him fixing a boat, and they have a conversation. He's reserved but kind. They start meeting regularly, sharing moments that are quiet but meaningful.

Conflict arises when his family expects him to take over the fishery business, but he wants to pursue something else, or there's a misunderstanding about her intentions. Maybe she's there temporarily, and he has to leave for a while. They part ways, but eventually reunite, realizing their feelings.

Ending with them together, maybe opening a small café by the sea, symbolizing their new beginning. The girl's perspective would highlight her growth from loneliness to finding love.

</think>

**Title: "Whispers of the Tide"**

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**Act 1: Arrival**  
The salty breeze clung to my skin as I stepped off the bus, the village of Haengdo nestled between jagged cliffs and an endless sapphire sea. My heart ached with the weight of my mother’s last words: *"Find peace, Ji-eun."* The coastal town felt like a page from a forgotten story—quiet, timeless, and achingly lonely. I moved into a tiny cottage overlooking the harbor, its walls echoing with the crash of waves. Every evening, I’d sit on the weathered wooden dock, sketching the horizon in my notebook, pretending the world didn’t exist beyond the ink on the paper.

**Act 2: The Storm**  
One afternoon, a sudden downpour turned the streets into rivers. I rushed to secure my sketches under a tarp when I saw him—*him*. A man in his late twenties, drenched and hunched over a fishing boat, his hands bleeding from a gash on his palm. Without thinking, I ran toward him, my voice trembling. *"Wait! Let me help!"* He looked up, eyes like stormy skies, and for a moment, time froze. His name was Tae-woon, a fisherman returning to his family’s abandoned boat after years away. We worked in silence, the rain soaking us, until he handed me a piece of seaweed, saying, *"It calms the waves… and the heart."*

**Act 3: Tide Pools**  
Days turned into weeks. Tae-woon became my shadow—quiet, observant, always appearing when I needed him most. We walked the shore at dawn, him teaching me to read the tides; he’d hum old folk songs as we collected shells. One evening, he found me crying over a letter from my ex, its edges frayed. Without a word, he pulled out his guitar (a battered thing strung with seaweed fibers) and played a melody that made the stars seem closer. *"Why do you cry?"* he asked. I didn’t answer. Instead, I pressed my forehead to his chest, inhaling the scent of salt and pine.

**Act 4: Storms Within**  
His family’s disapproval loomed like a typhoon. His father demanded he take over the fishery business, but Tae-woon dreamed of opening an art gallery in Seoul—a dream crushed by duty. I, meanwhile, feared my temporary stay would end; my visa was expiring. One night, we argued on the dock, voices rising above the waves. *"You’re a ghost, Ji-eun,"* he said, fists clenched. *"You’ll vanish when you’re ready."* I fled, leaving my sketchbook behind—its pages filled with drawings of him, half-finished and trembling.

**Act 5: The Reckoning**  
Weeks passed in silence. Then, one morning, a letter arrived from Tae-woon’s father: the boat had been sold. Tae-woon was leaving for Seoul… but not before visiting me once more. We met at the dock under a blood-orange sunset. He handed me a small box—inside, my sketchbook, now bound with seaweed thread and filled with his drawings: a girl with auburn hair laughing in the rain, her hands cupping a seagull’s wing. *"I couldn’t leave without this,"* he said, voice cracking. *"You’re not a ghost. You’re my anchor."*

**Act 6: Harmony**  
Years later, the village’s harbor buzzes with life. My art gallery, *The Tide*, showcases works inspired by Haengdo—each piece accompanied by Tae-woon’s folk songs, now recorded and played on loops. We married under a cherry blossom tree, him in a tailored hanbok, me in a dress woven with shell patterns. Our daughter, born during a storm, has his eyes and my laugh. Every evening, we walk the dock, hand in hand, the sea whispering secrets only we understand.

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**Final Line (Ji-eun’s Monologue):**  
*"The sea taught me that love isn’t about finding someone who completes you—it’s about learning to be whole together, wave after wave."*  

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*End credits roll over a montage of the couple dancing on the dock, their reflections shimmering in the water.*

Intended Use & Limitations

Intended Use

This model is designed for tasks requiring:

• Complex Problem Solving: Breaking down abstract, strategic, or philosophical questions.
• Transparent Reasoning: Generating explicit, auditable "chain-of-thought" or "show your work" outputs.
• Strategic & Creative Synthesis: Integrating insights from multiple perspectives to generate novel solutions.
• Serving as a "Thought Partner" to help users structure their own thinking.

Limitations & Bias

• Opinionated Framework: The model is heavily biased towards the "Cognitive Calculus" methodology. It will attempt to apply this structure even to problems where it may not be the optimal approach.
• Small, Curated Dataset: Trained on 200 high-quality examples, its knowledge is broad but not exhaustive. Performance may degrade on highly niche topics not covered in the training data.
• English-Only: The model is trained exclusively in English.
• Inherited Biases: The model inherits biases from its foundational LLM and the QiMing-CognitiveForge dataset, which was curated by a single author. The perspectives and frameworks presented reflect a specific viewpoint.

Training Data

This model was fine-tuned on the aifeifei798/QiMing-CognitiveForge dataset, a collection of 200 complex problems and their structured solutions designed to teach the Cognitive Calculus framework. The dataset spans four pillars: The Inner Cosmos (Self, Ethics), The Social Fabric (Leadership, Society), The Material World (Technology, Systems), and The Abstract Realm (Creativity, Metaphysics).

Citation

Citing the Model:

@misc{yourname2025qiming_cognitiveforge_14b,
  author = {Your Name or Organization},
  title = {QiMing-CognitiveForge-14B: A Cognitive Architect Model},
  year = {2025},
  publisher = {Hugging Face},
  journal = {Hugging Face Model Hub},
  url = {https://huggingface.co/aifeifei798/QiMing-CognitiveForge-14B}
}

Citing the Dataset:

@misc{qiming2025cognitiveforge,
  author = {aifeifei798},
  title = {QiMing-CognitiveForge: A Dataset for Teaching Structured Reasoning},
  year = {2025},
  publisher = {Hugging Face},
  journal = {Hugging Face Datasets},
  url = {https://huggingface.co/datasets/aifeifei798/QiMing-CognitiveForge}
}