NEW CORE of AI Agents (MIT, Stanford)

AI Agents develop a new functional core of their intelligence.

This video focusses on the potential for using multiple LLM instances as individual agents in the scientific peer review process. The concept of "role-playing" between LLMs, where different instances simulate authors, reviewers, and area chairs, hints at a multi-agent system for collaborative evaluation.

The research explores the use of LLMs as autonomous agents capable of performing complex tasks (reviewing) and potentially interacting with each other in a structured manner to produce a final outcome (decision on paper acceptance).

Hint: if you want to auto-evaluate your scientific publication, just try this out. The responses are quite interesting and the (AI generated review) question provide insights into potential areas to clarify.
https://huggingface.co/spaces/openrev...

Imagine entering a futuristic world where AI agents are no longer just passive tools but active, intelligent entities. In this digital universe, these agents are capable of learning, adapting, and evolving independently. Our journey begins with the exploration of three cutting-edge papers, each revealing a different aspect of AI's potential. From enhancing execution skills in dynamic environments to developing strategic abilities through complex simulations, these studies provide a glimpse into the future of AI-driven innovation. With AI now capable of self-reflection and self-improvement, we are witnessing the birth of a new era where machines are not just programmed but are learning to program themselves.

As we delve deeper, the narrative unfolds to reveal the incredible complexity behind AI agent design. Gone are the days of simple algorithms; today's AI agents are intricate systems with interconnected functionalities. Whether it's decision-making, execution, or planning, each function is part of a larger, interdependent network that must be carefully balanced and optimized. The introduction of the JEEDS (Joint Estimation of Execution and Decision-making Skills) method marks a significant leap forward. By analyzing both execution and decision-making skills simultaneously, JEEDS offers a more accurate and robust way to assess and enhance AI performance, particularly in scenarios where agents must interact and collaborate with one another.

The journey concludes with a tantalizing glimpse of what's to come. As we prepare to integrate these advanced AI agents into increasingly complex environments, new challenges arise—challenges that push the boundaries of current computational capabilities. Yet, within these challenges lies the opportunity to innovate and discover new methods of AI optimization that can handle the vast complexity of multi-agent systems. The future of AI is not just about smarter algorithms but about creating intelligent, autonomous entities capable of thriving in dynamic, real-world environments. This is only the beginning, and as the story unfolds, we are invited to join the quest for the next breakthrough in AI technology.

All rights w/ authors:
AI-Driven Review Systems:
Evaluating LLMs in Scalable and Bias-Aware Academic Reviews
https://arxiv.org/pdf/2408.10365

00:00 3 AI research papers on AI Agents
02:09 AWS: Coders are obsolete in 2 years
03:13 A complexity level where RAG FAILS
05:17 Long Context LLM better than RAG
07:48 Demo Reviewer Arena (on HF Spaces for free)
10:38 Agent Skills define overall performance
14:38 3 AI research Paper explored
16:40 Inside AI Agents
20:43 Improve Functional Capabilities of AI Agents
24:04 Joint Estimation Execution & Decision Skills - JEEDS


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