<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="yoo-yang-2026-source">
    <titleInfo>
        <title>Source-Aware Memory Retrieval across Engineering Standards</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Sung</namePart>
        <namePart type="given">Tae</namePart>
        <namePart type="family">Yoo</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Heyoung</namePart>
        <namePart type="family">Yang</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2026-11</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 3rd Workshop on KNOwledge GRaphs Agentic Systems Interplay (NORA 2026)</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Btissam</namePart>
            <namePart type="family">Er-Rahmadi</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Sébastien</namePart>
            <namePart type="family">Montella</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Damien</namePart>
            <namePart type="family">Graux</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">André</namePart>
            <namePart type="family">Melo</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Hajira</namePart>
            <namePart type="family">Jabeen</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Hengqin, China</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
        <identifier type="isbn">979-8-89176-457-6</identifier>
    </relatedItem>
    <abstract>Engineering design and quality review often require evidence from technical standards issued by multiple organizations. In a limited context window, partial evidence is often included, and relevance-based retrieval results can become biased toward sources with a large volume of documents, leaving other necessary sources unrepresented. We implemented a deterministic source-aware retrieval layer that identifies required source groups, retrieves candidates through source-conditioned channels, and prioritizes one candidate from each channel. We also evaluated a configuration that uses within-channel cross-encoder reranking to select that candidate. Evaluation used 15 technical standards, 72 evaluation questions, and 162 annotated evidence locations, with k set to 5 for all methods. Required-source coverage was 61.5% for dense retrieval and 78.6% for cross-encoder reranking, whereas both source-aware methods achieved 100%. Source-aware reranked channel quota achieved an evidence recall of 46.0% and retrieved all required evidence for 27.8% of questions, compared with 41.8% and 23.6% for cross-encoder reranking and 32.1% and 15.3% for dense retrieval. Within the source-aware design, within-channel reranking increased mean reciprocal rank from 0.445 to 0.545. These results support separating required-source coverage from evidence ranking in bounded-context retrieval for domain-specific agent memory.</abstract>
    <identifier type="citekey">yoo-yang-2026-source</identifier>
    <location>
        <url>https://aclanthology.org/2026.nora-1.7/</url>
    </location>
    <part>
        <date>2026-11</date>
        <extent unit="page">
            <start>77</start>
            <end>83</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Source-Aware Memory Retrieval across Engineering Standards
%A Yoo, Sung Tae
%A Yang, Heyoung
%Y Er-Rahmadi, Btissam
%Y Montella, Sébastien
%Y Graux, Damien
%Y Melo, André
%Y Jabeen, Hajira
%S Proceedings of the 3rd Workshop on KNOwledge GRaphs Agentic Systems Interplay (NORA 2026)
%D 2026
%8 November
%I Association for Computational Linguistics
%C Hengqin, China
%@ 979-8-89176-457-6
%F yoo-yang-2026-source
%X Engineering design and quality review often require evidence from technical standards issued by multiple organizations. In a limited context window, partial evidence is often included, and relevance-based retrieval results can become biased toward sources with a large volume of documents, leaving other necessary sources unrepresented. We implemented a deterministic source-aware retrieval layer that identifies required source groups, retrieves candidates through source-conditioned channels, and prioritizes one candidate from each channel. We also evaluated a configuration that uses within-channel cross-encoder reranking to select that candidate. Evaluation used 15 technical standards, 72 evaluation questions, and 162 annotated evidence locations, with k set to 5 for all methods. Required-source coverage was 61.5% for dense retrieval and 78.6% for cross-encoder reranking, whereas both source-aware methods achieved 100%. Source-aware reranked channel quota achieved an evidence recall of 46.0% and retrieved all required evidence for 27.8% of questions, compared with 41.8% and 23.6% for cross-encoder reranking and 32.1% and 15.3% for dense retrieval. Within the source-aware design, within-channel reranking increased mean reciprocal rank from 0.445 to 0.545. These results support separating required-source coverage from evidence ranking in bounded-context retrieval for domain-specific agent memory.
%U https://aclanthology.org/2026.nora-1.7/
%P 77-83

Markdown (Informal)

[Source-Aware Memory Retrieval across Engineering Standards](https://aclanthology.org/2026.nora-1.7/) (Yoo & Yang, NORA 2026)

• Source-Aware Memory Retrieval across Engineering Standards (Yoo & Yang, NORA 2026)

ACL

• Sung Tae Yoo and Heyoung Yang. 2026. Source-Aware Memory Retrieval across Engineering Standards. In Proceedings of the 3rd Workshop on KNOwledge GRaphs Agentic Systems Interplay (NORA 2026), pages 77–83, Hengqin, China. Association for Computational Linguistics.