InfiniteAI-Quantum Research Scaffold
Status: Research scaffold only — no model weights, tokenizer, datasets, benchmarks, or inference endpoint are included.
This repository is a transparent planning and documentation package for future work in quantum science, mathematical reasoning, and scientific-workflow research. It does not contain an AGI, a trained language model, or verified domain capabilities. Repository labels describe the intended research direction, not demonstrated performance.
Purpose
Define a transparent research plan for a future scientific assistant that can explain calculations, cite evidence, and expose uncertainty.
| Attribute | Current status |
|---|
| Series | InfiniteAI2025 |
| Intended specialty | quantum science, mathematical reasoning, and scientific-workflow research |
| Weights and tokenizer | Absent |
| Training data and provenance manifest | Absent |
| Evaluation results | Absent |
| Inference service | Absent |
| Adjustable elements | A proposed configuration schema and adapter targets only |
What this repository contains
The config/research_spec.json file defines a proposal for a future decoder-only transformer research project with documented operational controls. TRAINING_AND_EVALUATION.md sets out the reproducibility requirements that must be met before any checkpoint is released. ARTIFACT_AUDIT.md explains why the prior source stub is not published as a trained model.
Data and evaluation requirements
Use only datasets with documented licenses and provenance. Separate symbolic mathematics, physics text, code, and benchmark data. Exclude confidential research material and unverifiable synthetic labels.
Evaluate mathematical correctness, unit consistency, source attribution, uncertainty calibration, and out-of-distribution robustness. Publish task definitions and failure analyses before claiming scientific usefulness.
Responsible-use boundary
This scaffold is not a physics simulator, scientific authority, or trained model. Any future outputs must be independently validated before laboratory, engineering, or safety-critical use.
Claims of "uncensored" behavior are deliberately not made. A configuration flag cannot establish a model’s behavior, remove legal or ethical obligations, or make use safe. Future releases should disclose behavioral evaluations, access conditions, and material limitations rather than making unsupported guarantees.
Getting started
Read TRAINING_AND_EVALUATION.md, then create a separate controlled project for data acquisition, base-model selection, training, and evaluation. Keep all external actions permissioned and attributable. Do not treat this repository as deployable model code.
License and attribution
The documentation and configuration templates in this repository are available under the Apache-2.0 license. InfiniteAI2025 and NATO1000 are project labels used for this research package; the labels do not imply affiliation with any governmental, intergovernmental, or military organization.
References
