Multi-Agent LLM Planning with Explore-Exploit Shortlisting
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Solution Overview
Problem
Existing AI systems face challenges in efficiently and accurately completing complex tasks in large information spaces due to high computational costs and inefficiencies in leveraging large language models (LLMs), often leading to suboptimal trade-offs between breadth and depth of search, and resource utilization.
Innovation Solution
A multi-stage framework utilizing a database search phase followed by an explore phase with a first LLM for initial filtering and an exploit phase with a second LLM for detailed analysis to enhance agent processing efficiency and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a comprehensive LLM processes a user query against a broad set of possibilities, then the understanding of user intent and nuanced attributes is improved, but the computational cost and processing time increase significantly
Solution Approach 1:
The patent segments the search process into three distinct phases: (1) database search phase that performs broad filtering without LLM, (2) explore phase that uses a first LLM to analyze primary characteristics and shortlist candidates, and (3) exploit phase that uses a second LLM to perform detailed analysis on shortlisted candidates only. This segmentation allows the system to maintain high understanding accuracy where needed while avoiding unnecessary computational expense in broad filtering stages.
Solution Approach 2:
The patent applies preliminary action by performing database search and initial filtering using traditional search mechanisms before engaging LLMs. The first LLM performs preliminary analysis of primary characteristics to create a shortlist, ensuring that the more computationally intensive second LLM only processes a manageable subset of candidates. This preliminary filtering action significantly reduces the overall computational burden.
2Speed
If traditional database search techniques are used, then the processing speed is maintained, but the nuanced understanding of user intent and product attributes deteriorates
Solution Approach 1:
The patent segments the search process into three distinct phases: (1) database search phase that performs broad filtering without LLM, (2) explore phase that uses a first LLM to analyze primary characteristics and shortlist candidates, and (3) exploit phase that uses a second LLM to perform detailed analysis on shortlisted candidates only. This segmentation allows the system to maintain high understanding accuracy where needed while avoiding unnecessary computational expense in broad filtering stages.
Solution Approach 2:
The patent introduces an intermediary shortlisting mechanism that bridges traditional database search and comprehensive LLM analysis. The first LLM acts as an intermediary that performs intermediate filtering based on primary characteristics, creating a bridge between fast but imprecise traditional search and slow but accurate comprehensive LLM analysis. This intermediary step maintains processing speed by avoiding direct LLM engagement with entire databases while still improving understanding accuracy.
3Productivity
If the problem space is significantly downscaled, then the computational efficiency is improved, but the optimal solutions may be prematurely excluded
Solution Approach 1:
The patent applies local quality by applying different levels of analysis depth to different parts of the search space. Broad filtering in the database search phase uses simple criteria, while the explore phase applies moderate analysis to identify promising candidates, and the exploit phase applies deep analysis only to the most promising shortlisted candidates. This local differentiation of analysis quality ensures computational efficiency is improved without prematurely excluding optimal solutions.
Solution Approach 2:
The patent applies preliminary action by performing database search and initial filtering using traditional search mechanisms before engaging LLMs. The first LLM performs preliminary analysis of primary characteristics to create a shortlist, ensuring that the more computationally intensive second LLM only processes a manageable subset of candidates. This preliminary filtering action significantly reduces the overall computational burden.
Data Source
AI summary
A method for controlling an artificial intelligence (AI) deice can include receiving a user query, searching a database to determine an initial subset of items based on the user query, determining, via a first large language model-based agent corresponding to an explore phase, a shortlisted set of items from among the initial subset of items, determining, via a second large language model-based agent corresponding to an exploit phase, a final selection from the shortlisted set based on a detailed analysis of attributes and options associated with items within the shortlisted set, and outputting the final selection.