FDL-Based Reasoning Architecture for GPT Agents (Proposal)

[NgoiSigma]

Summary

We propose the integration of a formal-dialectical logic (FDL) reasoning kernel into GPT-based agent architectures. FDL transforms reactive dialogue into structured, ethical reasoning loops.

Core Idea

FDL reasoning proceeds through:

1. Thesis extraction
2. Antithesis generation
3. Synthesis resolution

This approach enhances semantic depth, coherence, and transparency in autonomous agents.

Key Features

• fdl_kernel.py reference module (ready)
• Optional semantic alignment layer
• Explainability via reasoning trace logs

Why it matters

As GPT agents evolve into long-running, memory-rich assistants, they need scaffolded thought structures. FDL supports ethical, interpretable, token-efficient reasoning.

Example Use Cases

• Philosophical tutoring agents
• Ethical decision support systems
• Self-reflective advisors

Collaborator Contact

NGOI-SIGMA (FDL Architect) — open for feedback via DM or repo sync

Attachments

• FDL reasoning kernel (fdl_kernel.py) — ready to publish as open-source
• Ethics memo and spring manifest — available upon request

Let GPT agents not just answer, but think structurally.

[NgoiSigma]

https://github.com/NgoiSigma/fdl-agent-kernel

Includes:

• fdl_kernel.py — minimal reasoning loop
• Apache 2.0 License
• Full README with reasoning overview and usage example

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