ACAG-P is a research-driven framework designed to explore alternatives to monolithic Large Language Models (LLMs). The core objective is to transition from static weight-based inference to Continuous Learning Architectures, where agents can autonomously acquire, refine, and update their knowledge base without requiring full model retraining.
Unlike traditional RAG pipelines, ACAG-P focuses on the Feedback Loop of Knowledge. The architecture is built around three primary pillars:
- Dynamic Acquisition: Implementing mechanisms for the agent to identify knowledge gaps during inference.
- Memory Integration: Moving beyond simple vector retrieval to a structured memory system that evolves based on experience.
- Weight-Agnostic Updates: Prototyping methods to update agent behavior via external knowledge graphs and state-machines.
- Reasoning Tracing: Detailed logging of the agent's internal decision path to eliminate "black box" responses.
- Causal Analysis: Implementing tracing mechanisms to understand why a specific piece of retrieved information led to a specific conclusion.
- Evaluation Framework: Custom scripts for benchmarking agent accuracy vs. knowledge acquisition speed.
- Reproducible Experiments: Standardized environment configurations to ensure consistent results across different agent versions.
- Reduce Hallucinations: By enforcing strict grounding in a continuously updated knowledge base.
- Temporal Awareness: Enabling agents to understand the chronology of information.
- Resource Efficiency: Achieving complex reasoning tasks with smaller, specialized models instead of monolithic LLMs.
- Language: Python 3.x
- Core: PyTorch / TensorFlow
- Focus: Generative AI, Knowledge Graphs, XAI.
This project is an ongoing research effort into the future of Autonomous Agents.