@inproceedings{maheshwary-etal-2026-augmenting,
title = "Augmenting {LLM} Reasoning with Dynamic Notes Writing for Complex {M}ulti{H}op {QA}",
author = "Maheshwary, Rishabh and
Hashemi, Masoud and
Mahajan, Khyati and
Malay, Shiva Krishna Reddy and
Mudumba, Sai Rajeswar and
Madhusudhan, Sathwik Tejaswi and
Gella, Spandana and
Yadav, Vikas",
editor = "Piperidis, Stelios and
Bel, N{\'u}ria and
van den Heuvel, Henk and
Ide, Nancy and
Krek, Simon and
Toral, Antonio",
booktitle = "Proceedings of the Fifteenth Language Resources and Evaluation Conference",
month = may,
year = "2026",
address = "Palma de Mallorca, Spain",
publisher = "ELRA Language Resource Association",
url = "https://aclanthology.org/2026.lrec-1.99/",
doi = "10.63317/4uyke9dmgz56",
pages = "1252--1279",
abstract = "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, we propose NotesWriting, a method that generates concise and relevant notes from retrieved documents at each step, thereby reducing noise and retaining only essential information. This increases the effective context length of Large Language Models (LLMs), allowing them to reason and plan more effectively while processing larger volumes of input text due to the compression in the form of notes. NotesWriting 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. NotesWriting yields an average improvement of 15.6 percentage points overall, by scaling the amount of ingested information."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="maheshwary-etal-2026-augmenting">
<titleInfo>
<title>Augmenting LLM Reasoning with Dynamic Notes Writing for Complex MultiHop QA</title>
</titleInfo>
<name type="personal">
<namePart type="given">Rishabh</namePart>
<namePart type="family">Maheshwary</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Masoud</namePart>
<namePart type="family">Hashemi</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Khyati</namePart>
<namePart type="family">Mahajan</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Shiva</namePart>
<namePart type="given">Krishna</namePart>
<namePart type="given">Reddy</namePart>
<namePart type="family">Malay</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Sai</namePart>
<namePart type="given">Rajeswar</namePart>
<namePart type="family">Mudumba</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Sathwik</namePart>
<namePart type="given">Tejaswi</namePart>
<namePart type="family">Madhusudhan</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Spandana</namePart>
<namePart type="family">Gella</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Vikas</namePart>
<namePart type="family">Yadav</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2026-05</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the Fifteenth Language Resources and Evaluation Conference</title>
</titleInfo>
<name type="personal">
<namePart type="given">Stelios</namePart>
<namePart type="family">Piperidis</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Núria</namePart>
<namePart type="family">Bel</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Henk</namePart>
<namePart type="family">van den Heuvel</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Nancy</namePart>
<namePart type="family">Ide</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Simon</namePart>
<namePart type="family">Krek</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Antonio</namePart>
<namePart type="family">Toral</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>ELRA Language Resource Association</publisher>
<place>
<placeTerm type="text">Palma de Mallorca, Spain</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>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, we propose NotesWriting, a method that generates concise and relevant notes from retrieved documents at each step, thereby reducing noise and retaining only essential information. This increases the effective context length of Large Language Models (LLMs), allowing them to reason and plan more effectively while processing larger volumes of input text due to the compression in the form of notes. NotesWriting 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. NotesWriting yields an average improvement of 15.6 percentage points overall, by scaling the amount of ingested information.</abstract>
<identifier type="citekey">maheshwary-etal-2026-augmenting</identifier>
<identifier type="doi">10.63317/4uyke9dmgz56</identifier>
<location>
<url>https://aclanthology.org/2026.lrec-1.99/</url>
</location>
<part>
<date>2026-05</date>
<extent unit="page">
<start>1252</start>
<end>1279</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Augmenting LLM Reasoning with Dynamic Notes Writing for Complex MultiHop QA
%A Maheshwary, Rishabh
%A Hashemi, Masoud
%A Mahajan, Khyati
%A Malay, Shiva Krishna Reddy
%A Mudumba, Sai Rajeswar
%A Madhusudhan, Sathwik Tejaswi
%A Gella, Spandana
%A Yadav, Vikas
%Y Piperidis, Stelios
%Y Bel, Núria
%Y van den Heuvel, Henk
%Y Ide, Nancy
%Y Krek, Simon
%Y Toral, Antonio
%S Proceedings of the Fifteenth Language Resources and Evaluation Conference
%D 2026
%8 May
%I ELRA Language Resource Association
%C Palma de Mallorca, Spain
%F maheshwary-etal-2026-augmenting
%X 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, we propose NotesWriting, a method that generates concise and relevant notes from retrieved documents at each step, thereby reducing noise and retaining only essential information. This increases the effective context length of Large Language Models (LLMs), allowing them to reason and plan more effectively while processing larger volumes of input text due to the compression in the form of notes. NotesWriting 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. NotesWriting yields an average improvement of 15.6 percentage points overall, by scaling the amount of ingested information.
%R 10.63317/4uyke9dmgz56
%U https://aclanthology.org/2026.lrec-1.99/
%U https://doi.org/10.63317/4uyke9dmgz56
%P 1252-1279
Markdown (Informal)
[Augmenting LLM Reasoning with Dynamic Notes Writing for Complex MultiHop QA](https://aclanthology.org/2026.lrec-1.99/) (Maheshwary et al., LREC 2026)
ACL
- Rishabh Maheshwary, Masoud Hashemi, Khyati Mahajan, Shiva Krishna Reddy Malay, Sai Rajeswar Mudumba, Sathwik Tejaswi Madhusudhan, Spandana Gella, and Vikas Yadav. 2026. Augmenting LLM Reasoning with Dynamic Notes Writing for Complex MultiHop QA. In Proceedings of the Fifteenth Language Resources and Evaluation Conference, pages 1252–1279, Palma de Mallorca, Spain. ELRA Language Resource Association.