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Trace: • When to Use RAG vs Fine-Tuning vs Prompt Engineering • Multi-Hop QA Agents • Budget-Aware Reasoning • How to Write and Structure System Prompts
multi_hop_qa_agents

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multi_hop_qa_agents [2026/03/25 14:56] – Create page: LLM agents for multi-hop QA agentmulti_hop_qa_agents [2026/03/30 22:22] (current) – Restructure: footnotes as references agent
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 ===== Overview ===== ===== Overview =====
  
-Multi-hop question answering requires synthesizing information from multiple documents to answer questions that cannot be resolved with a single retrieval step. Standard RAG (Retrieval-Augmented Generation) retrieves passages once and feeds them to an LLM reader, but this approach often misses crucial evidence or includes distractors that degrade QA performance. PRISM introduces an agentic Precision-Recall Iterative Selection Mechanism, while MA-RAG deploys collaborative chain-of-thought agents for multi-hop reasoning.+Multi-hop question answering requires synthesizing information from multiple documents to answer questions that cannot be resolved with a single retrieval step. Standard RAG (Retrieval-Augmented Generation) retrieves passages once and feeds them to an LLM reader, but this approach often misses crucial evidence or includes distractors that degrade QA performance. PRISM(([[https://arxiv.org/abs/2510.14278|"PRISM: Precision-Recall Iterative Selection Mechanism for Agentic Multi-Hop QA." arXiv:2510.14278, 2025.]])) introduces an agentic Precision-Recall Iterative Selection Mechanism, while MA-RAG(([[https://arxiv.org/abs/2505.20096|"MA-RAG: Multi-Agent Collaborative Chain-of-Thought for Retrieval-Augmented Generation." arXiv:2505.20096, 2025.]])) deploys collaborative chain-of-thought agents for multi-hop reasoning.
  
 ===== PRISM: Agentic Retrieval for Multi-Hop QA ===== ===== PRISM: Agentic Retrieval for Multi-Hop QA =====
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 PRISM reduces retrieved tokens by 50-80% while maintaining or exceeding full-context QA accuracy, demonstrating that precise evidence selection is more effective than feeding entire documents to readers. PRISM reduces retrieved tokens by 50-80% while maintaining or exceeding full-context QA accuracy, demonstrating that precise evidence selection is more effective than feeding entire documents to readers.
  
-===== References ===== 
- 
-  * [[https://arxiv.org/abs/2510.14278|"PRISM: Precision-Recall Iterative Selection Mechanism for Agentic Multi-Hop QA" (2025)]] 
-  * [[https://arxiv.org/abs/2505.20096|"MA-RAG: Multi-Agent Collaborative Chain-of-Thought for Retrieval-Augmented Generation" (2025)]] 
  
 ===== See Also ===== ===== See Also =====
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   * [[software_testing_agents|Software Testing Agents]]   * [[software_testing_agents|Software Testing Agents]]
   * [[robotic_manipulation_agents|Robotic Manipulation Agents]]   * [[robotic_manipulation_agents|Robotic Manipulation Agents]]
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 +===== References =====
  
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