MarCognity-AI
A research framework for reflective and epistemically transparent AI systems
Table of Contents
- Overview
- Research Motivation
- Modules and Functions
- Core Capabilities
- Early Community Interactions(Non-Endorsement)
- Official Publication and Citation
- Structural Limitation & Research Scope
- Integrated AI Models
Overview
MarCognity-AI is a modular open-source research framework designed to investigate structural limitations of LLM-based metacognition and introduce explicit epistemic verification layers.
Rather than simply generating responses, the system:
- Produces structured outputs
- Evaluates semantic coherence
- Verifies claims against retrieved sources
- Stores semantic memory
Generates structured epistemic reports
The goal is not to “improve answers,” but to analyze the structural fracture between linguistic coherence and epistemic awareness in large language models.
Research Motivation
Large Language Models optimize linguistic probability — not factual truth.
MarCognity-AI investigates the following core question:
Can epistemic uncertainty be made explicit within an LLM-based system?
This framework does not claim to solve LLM hallucinations. Instead, it exposes and documents the failure modes of artificial metacognition in a reproducible way.
The following cognitive architecture is composed of independent modules.
Modules and Functions
| Module | Function |
|---|---|
| Problem Classification | Automatic input type detection |
| Academic Prompting | Structured multidisciplinary prompting |
| Scientific Retrieval | Asynchronous retrieval from open-access sources |
| Semantic Evaluation | Logical and semantic scoring of responses |
| Skeptical Agent | Claim-by-claim verification against sources |
| FAISS Memory | Archiving and comparison of past outputs |
| Cognitive Visualization | Structured conceptual representation |
Core Capabilities
- LLM-assisted scientific generation
- Source retrieval and integration (arXiv, PubMed, Zenodo, OpenAlex)
- Multilevel metacognitive evaluation
- Sentence-level epistemic verification
- Ethical risk and bias analysis
- Persistent semantic memory (FAISS)
- Markdown-exportable reflective reports
Structural Limitation & Research Scope
MarCognity-AI is an exploratory research framework and is not intended for production use.
During development, a recurring structural limitation emerged: LLM-based metacognitive layers reliably optimize for linguistic coherence but fail to surface epistemic uncertainty as an explicit signal.
In practice, the system can evaluate how an answer is written (clarity, structure, semantic alignment), yet it cannot inherently determine whether the underlying claims are genuinely known, verifiable, or epistemically justified. The model can express that a response is unclear, but not that it lacks grounded knowledge.
This collapse between linguistic coherence and epistemic awareness is not treated as a bug to be fixed, but as a structural fracture to be studied. The purpose of this framework is to expose, analyze, and document this limitation in a reproducible way.
The demo and cognitive journal included in this repository are designed to make this failure mode observable — not to present a solved system.
Early Community Interactions (Non-Endorsement)
A discussion was opened regarding the semantic mapping layer. Community members from Hugging Face and related model discussions engaged technically with the proposal.
You can explore the original threads and responses here:
🔗 Hugging Face Discussion
🔗 DeepSeek Community Thread
🔗 Google org Response Snapshot
📚 Official Publication and Citation
The official version of the code and the full research paper have been permanently archived on Zenodo and are citable using their Digital Object Identifier (DOI).
| MarCognity-AI |  |
|---|---|
| Permanent DOI | https://doi.org/10.5281/zenodo.18440333 |
| Access Publication | Full Research Paper (PDF) & Code (Zenodo) |
Usage Examples
Scientific Question
Input: “Explain the role of chaperone proteins.”
Output: Response + sources + semantic score + conceptual diagram
Epistemic Verification Example
Input: “Explain quantum entanglement.” Output:
Generated response
Claim-by-claim verification
VERIFIED / EPISTEMIC FAILURE report
Reasoning based on provided sources
Quick Demo
A step-by-step execution example is available in:
marcognity_demo.ipynb
The notebook illustrates:
- Response generation
- Retrieval integration
- Claim-level verification
- Epistemic reporting
Meta LLaMA 4 Community License
It is intended for inspection and reproducibility, not interactive deployment.
Integrated AI Models
| Integrated Models | License | Main Restrictions |
|---|---|---|
| meta-llama/llama-4-maverick-17b-128e-instruct | LLaMA 4 Community License (Meta) | Research and application use allowed; must comply with Meta’s AUP |
| allenai/specter | Apache 2.0 | Free for commercial use with attribution |
| ktrapeznikov/scibert_scivocab_uncased_squad_v2 | Apache 2.0 | Free for commercial use with attribution |
| Helsinki-NLP (OPUS-MT models on HuggingFace) | CC-BY-4.0 | Free use with mandatory citation |
| RandomForest Model | None (classic algorithm) | No license restrictions; depends on data used |
| CrossEncoder (DeBERTa-based) | Varies (often MIT or Apache 2.0) | Free use if open license is respected |
How to Contribute
Got ideas, suggestions, or want to improve a feature?
- Fork the repository
- Create a branch (
git checkout -b improvement) - Modify
.pyor.ipynbfiles - To run this project, you need a Groq API key
- Open a pull request with a clear description
See the CONTRIBUTING.md file for contribution guidelines.
License
Released under the Apache 2.0 License. Third-party integrated models follow their respective licenses.
Contributions are welcome! If you have additional examples or improvements, please feel free to open a pull request or report an issue.