InfiniteAI-Nexus 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 general reasoning, tool orchestration, and long-horizon 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 research program for an assistive agent with explicit planning, tool-permission boundaries, traceable intermediate artifacts, and reversible actions.
| Attribute | Current status |
|---|---|
| Series | InfiniteAI2025 |
| Intended specialty | general reasoning, tool orchestration, and long-horizon 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 licensed instruction, reasoning, tool-use, and software-task corpora. Preserve tool-call provenance and annotate outcomes, errors, and human interventions.
Evaluate plan quality, tool-call accuracy, task completion under permission constraints, recovery from errors, calibration, and resistance to prompt injection.
Responsible-use boundary
This scaffold does not implement an autonomous agent. Future deployments must use least-privilege tools, human confirmation for consequential actions, and isolated execution environments.
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.