SCBKR 本地責任鏈模型

SCBKR Local Responsibility Chain Model provides a robust local AI responsibility-chain control system designed to ensure accountability through an owner-signed Workbench, data management via a Data Center, and reusable evidence across four stores. The system enhances the interaction between users and AI models by establishing a structured workflow that defines roles, actions, and boundaries.

Project Overview

At its core, the SCBKR model does not merely allow models to generate answers but ensures that they operate within a verifiable responsibility-chain process. This structured approach emphasizes:

• Chat as the entry point for natural language interactions.
• Workbench as the platform for confirming tasks and rules.
• S/C/B/K/R Grammar for defining task responsibilities.
• Data Center for maintaining replayable records of interactions and decisions.
• Four Stores that facilitate evidence reuse and indexing for future tasks.

Model interactions are governed by clear definitions and security measures, allowing models to assist in drafting and compiling tasks while restricting them from making autonomous decisions or confirmations.

Key Features

1. Structured Interaction: Users input tasks, which are processed through a series of confirmations and validations before being executed, fostering a more reliable interaction.
2. Owner Signature Requirement: Rules and outcomes are only considered valid after an explicit owner signature, ensuring that users retain control of the decision-making process.
3. Data Storage and Replayability: Information is stored securely within the Data Center, which retains the capacity for replay and examination of past interactions, enhancing transparency.
4. Evidence Relation Management: Future tasks can only reference evidence that is owner-signed and has passed review, preventing reliance on possibly erroneous data.
5. Integration with Popular Tools: The model supports connection to various local and external AI frameworks, such as LM Studio, Ollama, and OpenAI-compatible APIs, providing flexibility in deployment.

Use Cases

SCBKR aims to solve common issues faced by general AI products, such as:

• Immediate, unprocessed responses from AI models.
• Lack of clarity in task purpose and intent.
• Insufficient verification processes before model output generation.

By implementing SCBKR, users can expect an organized and secure environment for AI-related tasks, ultimately resulting in improved accuracy and reliability in AI-generated outputs.

Current State

As of the latest release, the core functionalities of SCBKR have been completed, with future enhancements aligned with the P15-Q release candidate currently being finalized.