We evaluate LLMs as orchestrators and planners in MAS, exploring their resource allocation. The planner method achieves better efficiency and utilization for concurrent tasks. Explicit info on worker capabilities enhances strategies, especially with suboptimal workers.
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📜🚨 Check out our latest work on "Self-Resource Allocation in Multi-Agent LLM Systems" where we explore how LLMs can be used to optimize task allocation in multi-agent systems 🤖
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🔍ToT strategy excels by exploring multiple reasoning paths, leading to up to a 54.74% performance boost. Targeted interactions with KGs, like the agentic method, improve over time with more steps, while automatic graph exploration excels with fewer steps (3/4)

The study shows significant improvements using different reasoning strategies (CoT, ToT, GoT) on the GRBench dataset. We compare the results for an agent solution vs an automatic graph search at every thought step (2/4)

🚨Check out my work with the AI team at JPMorgan-Chase !!! 📜 "Grounding LLM Reasoning with Knowledge Graphs" explores the intersection of Knowledge Graphs and Reasoning strategies with LLMs which helps boosting answers in domain specific questions (1/4)

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