MCP & A2A FAIL - not for the reasons you think ai
AI Summary
Summary of YouTube Video (ID: sFr1hzPAdow)
Introduction
- The video discusses common issues with multi-agent systems (MCP) and provides insights from recent research.
Key Insights from Research Papers
- Pre-Training vs. New Data
- Agents predominantly rely on pre-training knowledge, making them less responsive to new data.
- Issues arise from the inherent bias of pre-trained models, leading to hallucinations and inappropriate generalizations.
- Research Contributions
- Insights from Princeton University on mitigating prior distribution influences in LLMs.
- Cornell University explores memorization vs. reasoning in LLM updates; emphasizes understanding data integration.
- Google DeepMind studies how new data permeates LLM knowledge and can dilute existing knowledge.
- Challenges in Integration
- New data may not be effectively utilized due to biases from pre-training.
- Performance in indirect querying remains problematic across methodologies.
- Effective data integration requires architectural nudges or specific prompting techniques.
Proposed Solutions and Countermeasures
- Implement targeted fine-tuning and memory condition training (MCT) to enhance data relevance in reasoning processes.
- Use structured sentences as stepping stones to help LLMs learn contextual relationships among words, supporting better integration of new knowledge.
- Acknowledge that LLMs must be guided and prompted to prioritize new information over existing stored knowledge.
Conclusion
- Research highlights the ongoing struggles of LLMs integrating new knowledge. Solutions are essential for improving the effectiveness of AI systems, with challenges in maintaining performance during updates.