title: Augmenting LLM Reasoning with Dynamic Notes Writing for Complex QA
summary: Iterative RAG for multi-hop question answering faces challenges with lengthy
  contexts and the buildup of irrelevant information. This hinders a model's
  capacity to process and reason over retrieved content and limits performance.
  While recent methods focus on compressing retrieved information, they are
  either restricted to single-round RAG, require finetuning or lack scalability
  in iterative RAG. To address these challenges, we propose Notes Writing, a
  method that generates concise and relevant notes from retrieved documents at
  each step, thereby reducing noise and retaining only essential information.
  This indirectly increases the effective context length of Large Language Models
  (LLMs), enabling them to reason and plan more effectively while processing
  larger volumes of input text. Notes Writing is framework agnostic and can be
  integrated with different iterative RAG methods. We demonstrate its
  effectiveness with three iterative RAG methods, across two models and four
  evaluation datasets. Notes writing yields an average improvement of 15.6
  percentage points overall, with minimal increase in output tokens.
paper_authors._id: 683400b5231225ee202c20b7
paper_authors.name: Rishabh Maheshwary
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-26T08:08:47.558Z
paper_authors._id: 645c26d423ed9b7788d5e24b
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/cZMUluWpYUlSLcn6yoC7c.jpeg
paper_authors.fullname: Rishabh Maheshwary
paper_authors.type: user
paper_authors.user: rmahesh
paper_authors._id: 683400b5231225ee202c20b8
paper_authors.name: Masoud Hashemi
paper_authors._id: 683400b5231225ee202c20b9
paper_authors.name: Khyati Mahajan
paper_authors._id: 683400b5231225ee202c20ba
paper_authors.name: Shiva Krishna Reddy Malay
paper_authors._id: 683400b5231225ee202c20bb
paper_authors.name: Sai Rajeswar
paper_authors._id: 683400b5231225ee202c20bc
paper_authors.name: Sathwik Tejaswi Madhusudhan
paper_authors._id: 683400b5231225ee202c20bd
paper_authors.name: Spandana Gella
paper_authors._id: 683400b5231225ee202c20be
paper_authors.name: Vikas Yadav
paper_title: Augmenting LLM Reasoning with Dynamic Notes Writing for Complex QA
paper_summary: Iterative RAG for multi-hop question answering faces challenges with lengthy
  contexts and the buildup of irrelevant information. This hinders a model's
  capacity to process and reason over retrieved content and limits performance.
  While recent methods focus on compressing retrieved information, they are
  either restricted to single-round RAG, require finetuning or lack scalability
  in iterative RAG. To address these challenges, we propose Notes Writing, a
  method that generates concise and relevant notes from retrieved documents at
  each step, thereby reducing noise and retaining only essential information.
  This indirectly increases the effective context length of Large Language Models
  (LLMs), enabling them to reason and plan more effectively while processing
  larger volumes of input text. Notes Writing is framework agnostic and can be
  integrated with different iterative RAG methods. We demonstrate its
  effectiveness with three iterative RAG methods, across two models and four
  evaluation datasets. Notes writing yields an average improvement of 15.6
  percentage points overall, with minimal increase in output tokens.
paper_ai_summary: Notes Writing enhances iterative RAG by generating concise notes at each step, improving reasoning and performance while minimizing output increase.
paper_ai_keywords: Iterative RAG multi-hop question answering context length irrelevant information dimensionality reduction Notes Writing Large Language Models LLMs framework agnostic evaluation datasets