PRISM: A Principled Framework for Multi-Agent Reasoning via Gain Decomposition
#PRISM framework #Multi-agent reasoning #Large Language Models #LLM collaboration #Gain decomposition #arXiv research #AI optimization
📌 Key Takeaways
- The PRISM framework introduces a principled approach to multi-agent reasoning, moving away from trial-and-error heuristic methods.
- The research addresses the theoretical gap in why multi-agent collaboration often yields superior results compared to single-agent setups.
- A core component of the study is gain decomposition, which identifies which specific architectural choices contribute most to performance improvements.
- This framework provides a roadmap for developers to systematically optimize LLM swarms for enhanced reasoning capabilities.
📖 Full Retelling
🏷️ Themes
Artificial Intelligence, Machine Learning, Technical Research
📚 Related People & Topics
Large language model
Type of machine learning model
A large language model (LLM) is a language model trained with self-supervised machine learning on a vast amount of text, designed for natural language processing tasks, especially language generation. The largest and most capable LLMs are generative pre-trained transformers (GPTs) that provide the c...
Artificial intelligence optimization
Principles used to improve AI systems
Artificial intelligence optimization (AIO) or AI optimization is a discipline concerned with improving the structure, clarity, and retrievability of digital content for large language models (LLMs) and other AI systems. AIO is also known as answer engine optimization (AEO) or generative engine optim...
🔗 Entity Intersection Graph
Connections for Large language model:
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- 🌐 Robustness (1 shared articles)
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📄 Original Source Content
arXiv:2602.08586v1 Announce Type: new Abstract: Multi-agent collaboration has emerged as a promising paradigm for enhancing reasoning capabilities of Large Language Models (LLMs). However, existing approaches remain largely heuristic, lacking principled guidance on what drives performance gains and how to systematically optimize multi-agent reasoning. Specifically, it remains unclear why multi-agent collaboration outperforms single-agent reasoning and which design choices contribute most to the