J-Barrert/BridgeLang-Core

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license: apache-2.0

Code Repository: GitHub - BridgeLang

BridgeLang (v0.1 Alpha)

BridgeLang (v0.1 Alpha)

A Hybrid NLU Interceptor for Deterministic Agent Control

🚀 Overview

BridgeLang is a middleware architecture designed to solve the "Stochastic Control Problem" in AI Agents. It combines the flexibility of Neural Networks with the safety of deterministic code.

• The Problem: LLMs are powerful but probabilistic. Asking an AI to "Stop System" might result in a conversational response rather than an immediate halt.
• The Solution: BridgeLang intercepts critical commands at the Tier Core (tc) layer, executing them immediately via hard-coded logic, while passing nuanced context (Tier 1) to a fine-tuned FLAN-T5 model.

🧠 Architecture

1. User Input: Natural Language ("System, stop the process immediately.")
2. Interceptor Layer (BridgeLang Core): Scans for tc keywords.
   • Detected: Stop -> tc.cmd.stop (Priority: CRITICAL)
3. Neural Layer (Context Engine): Passes remaining context to AI.
   • Detected: process -> t1.task.action.process
4. Executor Layer: deterministic execution of the SIGS protocol.

📂 Repository Structure

• core-logic/: Python interceptors and executors.
• models/: Fine-tuned FLAN-T5 model for SIGS protocol translation.
• data/: Master Lexicon (v2.0).

🛠️ Usage

# Run the Interceptor
python core-logic/bridge_lang_interceptor.py

# Run the Executor (Simulated Robot)
python core-logic/bridge_lang_executor.py