SPD-RAG: Sub-Agent Per Document Retrieval-Augmented Generation
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YagizCanAkay1 |
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MuhammedYusufKartal1 |
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EsraAlparslan1 |
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FarukOrtakoyluoglu1 |
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ArdaAkpinar2 |
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| 1TOBBUniversityofEconomicsandTechnology 2OSTIMTechnicalUniversity |
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| {y.akay, m.kartal, ealparslan, fortakoyluoglu}@etu.edu.tr 230206003@ostimteknik.edu.tr |
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Abstract |
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andcommon,yetexposecriticalcoverageandrea- |
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| soningbottlenecksincurrentNLPsystems. |
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| Answeringcomplex,real-worldqueriesoften |
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| 6202 raM 9 ]LC.sc[ 1v92380.3062:viXra Traditionalsingle-indexorsingle-agentretrieval |
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| requiressynthesizingfactsscatteredacrossvast |
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systemsstruggleto |
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maintainbothscalability |
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and |
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| document |
corpora. |
In these |
settings, |
stan- |
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| contextualrelevanceoverlarge,heterogeneouscor- |
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| dard retrieval-augmented |
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generation |
(RAG) |
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pora, especially |
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complex |
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| pipelinessufferfromincompleteevidencecov- |
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| erage,whilelong-contextlargelanguagemod- documents. Standard RAG pipelines retrieve a |
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fixed number |
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of documents |
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them |
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| els (LLMs) |
struggle |
to reason |
reliably |
over |
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| massive inputs. We introduce SPD-RAG, a withinasinglecontextwindow(Lewisetal.,2020), |
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| hierarchicalmulti-agentframeworkforexhaus- |
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| whichfailswhenanswersdependoninformation |
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| tivecross-documentquestionansweringthatde- |
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| distributedacrossmanydocuments,sinceevidence |
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| composestheproblemalongthedocumentaxis. |
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beyondthetop-K |
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resultsistypicallydiscardedin |
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| Each |
document |
is processed |
by a |
dedicated |
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a single |
retrieval |
pass. |
Long-context |
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LLMs |
ex- |
| document-levelagentoperatingonlyonitsown |
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| content,enablingfocusedretrieval,whileaco- tendcontextwindowsto128K–2Mtokens(Gemini |
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Teametal.,2024), |
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butempiricalevidenceshows |
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| ordinator |
dispatches |
tasks |
to relevant |
agents |
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| and aggregates their partial answers. Agent thatreasoningqualitydegradessignificantlyascon- |
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| outputsaresynthesizedbymergingpartialan- text length increases (Liu et al., 2023). This sug- |
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| swersthroughatoken-boundedsynthesislayer |
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gests that |
the |
bottleneck |
is not |
only |
retrieval, |
but |
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| (whichsupportsrecursivemap-reduceformas- |
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| alsoreasoningatscaleovermanydocumentsand |
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| sivecorpora). |
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Thisdocument-levelspecializa- |
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| hundredsofthousandsoftokens. |
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| tionwithcentralizedfusionimprovesscalabil- |
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| ityandanswerqualityinheterogeneousmulti- In this work, we introduce SPD-RAG |
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(Sub-agent |
per |
Document |
Retrieval-Augmented |
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| document |
settings |
while |
yielding |
a modular, |
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| extensibleretrievalpipeline. OntheLOONG Generation),ahierarchicalmulti-agentarchitecture |
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| benchmark (EMNLP 2024) for long-context forexhaustivemulti-documentquestionanswering |
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| multi-document QA, SPD-RAG achieves an thatfactorstheproblemalongthedocumentaxis |
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| AvgScoreof58.1(GPT-5evaluation),outper- |
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rather than |
the |
task axis. |
Instead |
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of forcing |
a |
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| formingNormalRAG(33.0)andAgenticRAG |
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single model |
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to hunt through |
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a massive |
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global |
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| (32.8)whileusingonly38%oftheAPIcostof |
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index, SPD-RAG |
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uses |
a central |
coordinator |
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to |
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| afull-contextbaseline(68.0). |
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decompose |
the |
user’s |
query into |
shared |
instruc- |
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| 1 Introduction tions. It then deploys a dedicated, cost-efficient |
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sub-agenttoeachdocumentinthecorpus. |
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These |
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| Large language models (LLMs) and the agentic agents operate entirely in parallel, treating their |
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| systems built around them are increasingly used assigneddocumentsasisolatedretrievaluniverses |
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| forcomplexinformationsearchtasks(Guoetal., |
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to extract |
relevant |
findings. |
Finally, |
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a synthesis |
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| 2024). Real-worldquestionsoftenrequiresynthe- |
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model |
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| sizingevidencescatteredacrossmanydocuments, findings—employingarecursivemergingfallback |
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| suchasassessingacompany’sfinancialrisksacross |
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for exceptionally |
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corpora—to |
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| yearsofreportsorintegratingfindingsfrommul- |
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comprehensive, |
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final answer. |
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Our codebase |
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and |
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| tiplescientificpapers.1 |
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Suchquestionsarenatural |
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datasetsusedinthispapercanbefoundat |
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| https://github.com/NebulAICompany/ |
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| 1Examples |
inspired |
by scenarios |
in the |
Loong bench- |
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| mark(Wangetal.,2024a). |
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SPD-RAG. |
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Ourcontributionsareasfollows: tasks,butthatmulti-agentoverheadgrowssuperlin-
early. Theirresultthatindependentagentsamplify
• WeproposeSPD-RAG,ahierarchicalmulti-
errorsby17.2×comparedto4.4×forcentralized
agentframeworkthatcombinesper-document
systemsdirectlymotivatesourcoordinator-based
agentic RAG with a centralized synthesis design. Thetaxonomyofhierarchicalmulti-agent
layer, where cost-efficient document agents systemsbyMoore(2025)furtherclassifiesdesign
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by a |
smarter coordinator |
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patternsalongfiveaxes: |
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controlhierarchy,informa- |
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| agent,enablingdocument-levelspecialization |
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tion flow, |
role delegation, |
temporal |
layering, |
and |
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| and parallel |
execution |
while allowing |
each |
communicationstructure. |
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| document |
to be analyzed |
in depth |
to ensure |
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| that all relevant |
information |
is incorporated |
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| 2.2 LongDocumentandMulti-DocumentQA |
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| withoutmissingcriticalevidence. |
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Long documents |
that |
exceed |
LLM context |
win- |
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| • WeevaluateontheLoongbenchmark(Wang |
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dows are |
often handled |
via |
divide-and-conquer |
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| etal.,2024a),whichincludesmulti-document |
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strategies. |
LLM×MapReduce(Zhouetal.,2025) |
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| QA instances |
over financial |
reports |
and aca- |
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formalizestwokeychallenges: |
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inter-chunkdepen- |
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with an |
average of |
11 docu- |
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| dency(onechunkdependsoncontextfromanother) |
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| mentsperinstanceandcontextlengthsfrom |
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| andinter-chunkconflict(chunksprovidecontradic- |
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| 10K to beyond |
250K |
tokens, and |
show that |
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| toryinformation),andproposesstructuredinforma- |
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| oursystemsubstantiallyoutperformsstandard |
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| tionprotocolsandin-contextconfidencecalibration |
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| RAGandAgenticRAGbaselinesunderGPT- |
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tomitigatethem. |
ToM(Guoetal.,2025)extends |
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| 5-judged |
Avg Score (adapted |
from |
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this approach |
with a |
hierarchical |
DocTree |
repre- |
| GPT-4-judgedprotocol;see§4.3),withgains |
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| sentationforrecursivereasoning. |
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| of around |
+25 points |
(76% higher |
average |
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| Formulti-documentQA,LongAgent(Zhaoetal., |
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| score)comparedtoNormalRAGandAgentic |
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2024) splits |
a 128K-token |
document |
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into chunks |
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| RAGbaselines. |
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assigned |
to member |
agents, with |
a leader |
agent |
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orchestrating |
discussionsand |
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| • Weanalyzeablations,documenttypeandtask |
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| complexityeffects,andcost–qualitytradeoffs, munication to reduce hallucinations. However, |
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| showingthat SPD-RAGattainsover85%of LongAgenttargetsasinglelongdocumentrather |
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| thanmultipleindependentdocumentsthatmaycon- |
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| full-context |
baseline quality |
at roughly |
38% |
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flict. DocAgent |
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et al., 2025) |
uses |
a multi- |
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agent framework |
that |
mimics |
human |
reading via |
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| 2 RelatedWork |
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| atree-structuredoutlineandrevieweragent,while |
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MDocAgent |
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al., 2025) |
employs |
five spe- |
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| 2.1 LLM-BasedMulti-AgentSystems |
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cialized |
agents for multi-modal |
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| LLM-basedmulti-agentsystemsareincreasingly |
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| standing. |
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| used for complex |
task solving. |
Guo et |
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| provideacomprehensivesurveyofagentprofiles, |
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| 2.3 HierarchicalandRecursive |
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| communicationprotocols,andcollaborationstrate- |
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| Summarization |
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| gies. Hierarchical |
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| gent(Wangetal.,2024b),whichdemonstratesau- Ourhierarchicalmergingmechanismbuildsontree- |
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| tonomouscooperationamongupto590agentsvia basedretrievalandrecursivesummarization. RAP- |
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| multi-leveltaskdecomposition,andAgentOrches- TOR(Sarthietal.,2024)recursivelyembeds,clus- |
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| tra(Zhangetal.,2025),whichachievesstate-of-the- ters, and summarizes text chunks to construct a |
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| artperformanceonGAIAusingacentralplanning bottom-uptreewithmultipleabstractionlevels;re- |
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| agent and specialized sub-agents, exemplify this trievingfromtheappropriatelevelyieldsa20%ab- |
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| trend. |
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soluteaccuracygainonQuALITY.OuandLapata |
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| Scalinglawsformulti-agentsystemsofferquan- (2025)showthathierarchicalmergingcanamplify |
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| titativeguidanceforarchitecturedesign. Kimetal. hallucinationsduringrecursivesummarizationand |
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| (2025)evaluate180configurationsacrossfivear- proposecontext-awareaugmentationstrategiesthat |
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| yields an 80.9% improvement on parallelizable context,whichweadoptinourmergingprotocol. |
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2.4 Graph-BasedDocumentAnalysis 3 Methodology
Figure 1 presents an overview of the SPD-RAG
Graph-based document analysis is particu-
architecture. Thesystemprocessesaquerythrough
larly effective for global sensemaking queries.
three layers: the Coordination Layer (§3.1), the
GraphRAG(Edgeetal.,2024)constructsaknowl-
ParallelRetrievalLayer(§3.2),andtheSynthesis
edge graph of entities and relations from source
Layer(§3.3).
documents and applies hierarchical community
detection(e.g.,theLeidenalgorithm(Traagetal.,
3.1 CoordinationLayer
2019)) to obtain multi-level communities with
Given a query q and corpus D, the system first
textual summaries. At query time, it retrieves
processes the query through a coordinator agent.
relevant communities, elicits community-level
The coordinator’s role is to decompose the user
partial answers, and aggregates them via a
query into a Shared Instruction Set for Docu-
map–reduce–styleprocedure. AsurveyonLLM-
ments and Synthesis Directives. It generates a
empoweredknowledgegraphconstruction(Bian,
WriteTodosstructuredobjectcontaining(a)alist
2025) highlights how LLMs are reshaping the
of sub_agent_todos: atomic, self-contained ex-
classical knowledge graph pipeline across three
traction tasks specifying exactly what fields, en-
key stages: ontology engineering, knowledge
tities, or numeric values to extract (the shared in-
extraction, and knowledge fusion. Our work is
structions);and(b)asynthesis_directive(2–4
complementary: instead of inducing an explicit
sentences) instructing the downstream Synthesis
knowledge graph, SPD-RAG operates directly
Layeronhowtoprioritizeandstructurethemerged
over agent-produced textual summaries and
response.
performs similarity-guided recursive synthesis
acrossmanydocuments. 3.2 ParallelRetrievalLayer
Weassignadedicatedsub-agentα toeachdocu-
i
mentd inthecorpus. Eachsub-agentoperatesas
2.5 BenchmarksforMulti-Document i
anindependentRAGloopoveritsassigneddocu-
Reasoning
ment,guidedbythesharedinstructionsetfromthe
The Loong benchmark (Wang et al., 2024a) CoordinationLayer.
evaluates long-context LLMs on extended multi-
Document-Scoped Retrieval and Reasoning.
document QA where every document is relevant
Each document d serves as an isolated retrieval
i
("Leave No Document Behind") across financial,
universe; the assigned sub-agent is strictly con-
legal, and academic domains in English and Chi-
strainedtosearchtoolcallswithind , preventing
i
nese. Itspansfourtasktypeswithcontextlengths
cross-documentdistractorchunksfromdegrading
from 10K to over 250K tokens, and even strong
local extraction. Each sub-agent receives the
frontier models achieve only modest Avg Scores
originalqueryq,itsassigneddocumentname,and
andmediumperfect-answerrates,underscoringthe
thesharedinstructionset(todolist),thenoperates
difficultyofreasoningovermanylong,all-relevant
in an iterative retrieve-and-reason loop. On each
documents.
turn, the agent either issues a search action (a
Complementarydatasetsprobeotheraspectsof focused query string) or a finalize action that
multi-document reasoning. MoNaCo (Wolfson terminates the loop and emits the document-
et al., 2025) focuses on natural questions over groundedfindings. Thesub-agentpromptinstructs
dozensofWikipediapages,MEBench(Linetal., the agent to attempt at least 2 focused searches
2025)onhigh-entity-densityQA,FanOutQA(Zhu before concluding a piece of information is
et al., 2024) on multi-hop questions with absent, with a total cap of 5 search calls across
long cross-document evidence chains, and all tasks. Each search action triggers dense
HoloBench (Maekawa et al., 2024) on database- vector retrieval over the sub-agent’s dedicated
style aggregation over unstructured text. Taken per-documentQdrantindex. ThisretrievalusesCo-
together, these benchmarks show that scalable hereembed-v4.0embeddings(1536-dimensional,
multi-document reasoning remains an open cosine similarity) to fetch the top k = 15 most
challenge,withLoongemphasizingtheespecially similar chunks. These chunks are then re-ranked
demandingall-relevant,long-contextregime. byCoherererank-v4.0-fasttoyieldafinalset
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OverviewofSPD-RAGArchitecture. |
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| of top_n = 5 chunks per search. The documents Similarity-Ordered Merge & Synthesis. |
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| RecursiveCharacterTextSplitter in Mark- ings/summaries S(t) = {s (t) ,...,s (t) }. We |
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| downmodewithachunksizeof1000tokensand embed each summary using Cohere embed-v4.0 |
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via |
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| sklearn.metrics.pairwise.cosine_similarity. |
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Wedenotethefindingsofeach |
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linkage="average" |
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Theseoutputsarepassed |
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total tiktoken |
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| Parallelism. |
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loops for |
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docu- |
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budget |
of B |
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per |
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--- |
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findings, |
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and |
the original |
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| WhilemodernLLMsfeatureextensivecontextwin- queryq toproduceanaggregatedandsynthesized |
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Allbatchsynthesiscallswithinanitera- |
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------------------------------------ |
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is |
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iteration |
S(t+1). |
The |
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ap- |
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| gregatesfindingsrecursively. TheSynthesisLayer plied even at the final level—there is no separate |
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and |
the findings |
{o |
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} |
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1 2 |
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Algorithm1RecursiveSynthesis Locating, Comparison, Clustering, or Chain of
Reasoning,withcontextlengthsrangingfrom10K
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FindingsS |
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Ourevaluationsetcomprises40 |
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← COHEREEMBED(S) |
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| D ← 1−COSINESIMILARITY(E) ingfinancialreports(102instancestotal),withthe |
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D, linkage |
= “average”) |
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| ofReasoning(11). |
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← |
GROUPBYTOKENS( |
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S, tree, |
B) |
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batches |
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≥ |
S |
then |
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We compare |
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following |
systems, |
all |
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batches ← |
[S] |
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| ---- |
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--------------- |
--- |
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• Baseline(FullContext): |
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Alldocumentsfor |
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SYNTHESIZE(b,q,d)∀b |
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batches) |
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retrieval |
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document |
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corpus, fol- |
| ----------- |
---------- |
--------------------------- |
--- |
------------ |
--------- |
--- |
---- |
--------- |
-------- |
-------- |
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| the process |
terminates |
and yields |
s⋆ |
as the final |
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lowed |
by |
LLM |
reasoning |
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retrieved |
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If |
S(t+1) |
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• Agentic |
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RAG: |
A single-agent |
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ReAct-style |
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| --- |
--- |
--- |
--- |
--- |
--------- |
--- |
---- |
-------------- |
--- |
----------- |
--- |
| Algorithm1givesthepseudocodeforthegeneral RAG system (implemented via Lang- |
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| recursivesynthesisprocedure. Graph/LangChain) that can issue multiple |
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iterative |
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retrieval |
calls |
over |
the |
full global |
| ---------------- |
--- |
------------------------ |
--- |
--- |
--------- |
--- |
--------- |
----- |
---- |
--- |
----------- |
| BehavioronLoong. |
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Inpractice,withGemini2.5 |
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| Pro’s 1M-token context window and a budget of corpus. It uses the same embedding and |
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re-rankingpipelineas |
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SPD-RAG,butlacks |
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| --- |
--- |
--- |
--- |
--- |
-------------------- |
--- |
--- |
--- |
---------------- |
--- |
--- |
| B = 750,000tokens,thecombinedsub-agentfind- |
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| per-documentspecialization. |
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| ingsforallinstancesinourLoongevaluationsetfit |
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| withinasinglebatch,sotheconditionalcheckal- |
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| 4.3 EvaluationMetrics |
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| lowedoutputafteroneiteration. |
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Therecursiveloop |
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| ----------------------------- |
--- |
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---------------- |
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--------- |
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-------- |
--------- |
--- |
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We report |
the |
following |
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metrics, |
following |
the |
| isthereforeadesigncapabilityintendedformuch |
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| Loongevaluationprotocol(Wangetal.,2024a): |
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| largercorpora,wheretotalfindingssizegenuinely |
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| exceedsthecontextsize. |
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• Avg |
Score: |
An |
LLM-judged |
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score |
(0–100) |
| --- |
--- |
--- |
--- |
--- |
----- |
------ |
--- |
---------- |
--- |
----- |
------- |
| measuringthedegreetowhichthepredicted |
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| 4 ExperimentalSetup |
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answer |
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accurately |
covers |
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the gold |
answer, |
| --- |
--- |
--- |
--- |
--- |
------ |
--- |
---------- |
------ |
--- |
-------- |
------- |
| 4.1 Dataset |
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following |
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the |
evaluation |
protocol |
|
of Loong |
| --- |
--- |
--- |
--- |
--- |
------------------ |
--- |
--- |
---------- |
-------- |
--- |
-------- |
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(Wangetal.,2024a). |
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2 |
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| WeevaluateSPD-RAGontheLoongbenchmark |
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| (Wangetal.,2024a). |
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Loongisanextendedmulti- |
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| ------------------ |
------------ |
----------------------- |
------- |
-------- |
------------------- |
--- |
--- |
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------------------- |
--- |
--- |
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• PerfectRate(PR%): |
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Percentageofqueries |
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| document |
QA instances |
in both |
English |
and Chi- |
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| wherethesystemreceivesaperfectscoreof |
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| nese, with |
an average |
of 11 documents |
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per test |
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| ---------- |
---------- |
--------------- |
--- |
-------- |
--- |
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| 100. |
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| casespanningtworeal-worldscenarios: |
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financial |
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| ----------------------------------- |
--- |
--- |
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--------- |
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--- |
| reports and academic papers. Each test case is 2BecausetheoriginallegacyGPT-4modelAPIisunavail- |
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| ablefornewscaledevaluations,weadoptGPT-5astheeval- |
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uator. Recent |
literature |
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increasingly |
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adopts |
GPT-5 as the |
| --- |
--- |
--- |
--- |
--- |
------------- |
---------- |
--- |
------------ |
--- |
------ |
------------ |
| the final answer ("Leave No Document Behind", standard for LLM-as-a-judge protocols due to its superior |
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| meaningacorrectresponserequiressynthesizing alignmentwithhumangrading(Wolfsonetal.,2025).While |
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| ourbaselinenumbersarere-computedunderthisGPT-5judge |
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| evidencefromallprovideddocuments),andisan- |
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| forstrictfairness,rawscoresmaynotbeperfectly1-to-1com- |
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| notated under one of four task types: Spotlight parablewiththeoriginallypublishedLoongleaderboard. |
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• AvgTokenUsage: Meanper-querytotal(in- Cost tracking. Per-query costs are computed
put+output)tokenusage. fromLangSmithrunmetadata(totaltokencounts
×per-tokenpricing.
| • Avg |
Cost |
(USD): |
Mean |
per-query |
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API cost, |
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| enablingcost-efficiencycomparisons. |
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5 Results |
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| 5.1 MainResults |
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| • AvgLatency(s): |
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Meanper-querywall-clock |
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| ---------------- |
--- |
--- |
----------------------- |
--- |
--- |
--- |
--- |
--- |
--- |
--- |
--- |
--- |
| time. |
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| Table1reportsthemainresultsacrossallsystems |
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on our Loong |
evaluation |
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set. |
Figure |
2 illustrates |
| --- |
--- |
--- |
--- |
--- |
--- |
--- |
------------ |
---------- |
--- |
---- |
------ |
------------- |
| 4.4 ImplementationDetails |
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| theseperformancedifferencesacrossthefourspe- |
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| AllsystemsuseGemini2.5Pro(temperature0.0) |
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cifictasktypes. |
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| ----------------------------------------- |
--- |
--- |
--- |
--- |
--- |
--- |
--------------- |
--- |
--- |
--- |
--- |
--- |
| asthebackboneLLMforthecoordinator,merging SPD-RAGachievesanAvgScoreof58.1,sub- |
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| layer, and |
synthesis |
|
layer. |
SPD-RAG |
|
document |
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| ---------- |
--------- |
--- |
------ |
------- |
--- |
-------- |
--- |
--- |
--- |
--- |
--- |
--- |
| stantiallyoutperformingbothNormalRAG(33.0) |
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| sub-agents |
use |
Gemini |
2.5 |
Flash |
for |
document- |
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| ---------- |
--- |
------ |
--- |
----- |
--- |
--------- |
----------- |
--- |
------- |
--- |
---------------- |
--- |
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|
and Agentic |
RAG |
(32.8). |
|
This corresponds |
to |
| scopedretrieval,reducingper-documentcostwhile roughly a 25-point absolute improvement (about |
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| preserving reasoning quality at the merging and 76% relative gain) over standard RAG baselines. |
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| synthesisstages. |
|
GPT-5isusedexclusivelyasthe |
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| ---------------- |
--- |
--------------------------- |
--- |
--- |
--- |
--- |
------------ |
------- |
--- |
---- |
---- |
----------- |
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|
Furthermore, |
SPD-RAG |
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more |
than |
doubles the |
| LLMjudgefortheevaluationoftheAvgscoreand Perfect Rate (PR) of Agentic RAG (18.6% vs. |
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| is not part of the pipeline itself. Prompts for all 8.8%),indicatingthatexhaustivedocument-level |
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| systems, |
including |
baselines, |
|
were |
optimized |
for |
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| -------- |
--------- |
---------- |
--- |
---- |
--------- |
--- |
--- |
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--- |
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--- |
| processingmorefrequentlycapturesthecomplete |
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| informationextractionandsynthesistoensurefair set of required facts. Notably, the Agentic RAG |
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| comparison. Fullprompttemplatesforallsystems baseline,despiteconsuming∼3×moretokensthan |
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| areprovidedinthecoderepository. NormalRAG,doesnotyieldscoreimprovements, |
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| suggestingthatuninstructediterativeretrievalwith- |
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| Indexing. |
Each |
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document |
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is |
pre- |
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| --------- |
---- |
--- |
--------- |
--- |
------ |
---- |
---------------- |
--- |
-------------- |
--- |
---- |
---------- |
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|
out per-document |
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specialization |
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does |
not effec- |
| indexed |
into |
a |
dedicated |
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Qdrant |
vec- |
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| tivelyaddressthecoveragechallengeinexhaustive |
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| tor |
collection |
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using |
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LangChain’s |
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| ------------------------------ |
---------- |
--- |
----- |
--- |
----------- |
------- |
-------------- |
------- |
----------- |
----- |
------------ |
------------ |
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multi-document |
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settings. |
While |
the |
full-context |
| RecursiveCharacterTextSplitter |
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(Imple- |
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baseline |
attains |
the highest |
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quality with |
an Avg |
| mentationdetailsareprovidedintheAppendix.). |
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Score of |
68.0, |
SPD-RAG |
reaches |
85.4% |
of this |
| --- |
--- |
--- |
--- |
--- |
--- |
--- |
-------- |
----- |
------- |
------- |
----- |
------- |
| Retrievalpersub-agent. Eachsearchtoolcall performancewhileconsumingonly37.9%ofthe |
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| performs dense vector retrieval over the docu- API cost, demonstrating a highly favorable cost- |
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quality trade-off. |
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The |
full-context |
baseline |
pro- |
| --------------------------- |
--- |
--- |
--- |
--- |
-------------- |
--- |
------------------ |
--- |
--- |
------------ |
-------- |
---- |
| mentindex,returningthetop-K |
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(k = 15)chunks |
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| using cosine similarity in Qdrant. These can- vides a strong upper bound that benefits from all |
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| didates are subsequently re-ranked with Cohere documents in one pass. Despite this, SPD-RAG |
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| narrowsthegapto9.9pointswhilecostingless. |
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| rerank-v4.0-fast,andthetoptop_n |
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= |
5chunks |
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| ------------------------------- |
--- |
--- |
--- |
--- |
--- |
------- |
--- |
--- |
--- |
--- |
--- |
--- |
| arepassedtothesub-agentLLM. |
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| 5.2 AnalysisbyTaskType |
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| Merging |
layer. |
The |
token |
budget |
|
per merge |
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|
| ------- |
------ |
--- |
----- |
------ |
--- |
--------- |
--- |
--- |
--- |
--- |
--- |
--- |
| Table2breaksdownperformancebyLoong’sfour |
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| batchisB |
= 750,000tokens(75%ofGemini2.5 |
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| -------- |
------------------------------ |
--- |
--- |
--- |
--- |
--- |
--- |
--- |
--- |
--- |
--- |
--- |
| tasktypes. |
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| Pro’s1Mcontextcap). |
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Agglomerativeclustering |
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| ------------------- |
--- |
--- |
----------------------- |
--- |
--- |
--- |
--- |
--- |
--- |
--- |
--- |
--- |
| Thetask-typebreakdownrevealsaclearpattern. |
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| usessklearnwithUPGMAlinkageandprecom- |
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For Spotlight |
Locating—which |
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requires |
finding |
| --- |
--- |
--- |
--- |
--- |
--- |
--- |
------------- |
-------------- |
--- |
--- |
-------- |
------- |
| putedcosine-distancematrix. |
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| asinglesalientfactacrossdocuments—allRAG- |
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based systems |
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perform |
reasonably |
well |
(69–74), |
| ------------- |
--------------- |
----------------------------- |
----- |
--------- |
----------- |
---------- |
-------------------------------------------- |
------------ |
------- |
---------- |
----- |
----------- |
| Parallelism. |
|
Documentagentsrunconcurrently |
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while the |
full-context |
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baseline |
leads |
(86.6). The |
| via LangGraph |
|
Send |
API |
fan-out. |
Batch |
synthe- |
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|
largest gaps |
between |
|
SPD-RAG |
and |
the RAG |
| sis calls |
within |
each |
merge |
iteration |
|
are paral- |
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asyncio.gather. |
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baselinesappearinClustering(+40.5ptsoverNor- |
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| lelized |
with |
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Synchronous |
Co- |
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| malRAG)andChainofReasoning(+26.2ptsover |
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| hereAPIcallsareoffloadedtothethreadpoolvia |
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|
Agentic RAG), |
|
tasks |
that require |
aggregating |
or |
| --- |
--- |
--- |
--- |
--- |
--- |
--- |
------------- |
--- |
----- |
------------ |
----------- |
--- |
| asyncio.to_thread. |
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| reasoningoverevidencefrommanydocumentssi- |
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| Safety limits. Each document sub-agent’s iter- multaneously. Ontheotherhand,itnearlymatches |
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| ative retrieval loop is capped at 5 iterations; the the full-context baseline (42.2 vs. 42.7) on Com- |
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| AgenticRAGbaselinecapsat10iterations. |
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|
parisontasks. |
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|
| ------------------------------------- |
--- |
--- |
--- |
--- |
--- |
--- |
------------- |
--- |
--- |
--- |
--- |
--- |
System AvgScore PR(%) AvgInputTok AvgTotalTok AvgCost($) AvgLatency(s)
OracleLong-ContextBaseline
| Baseline(FullContext) |
|
68.0 |
31.4 253,085 |
255,345 |
0.273 |
45.6 |
| RAGBaselines |
|
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|
|
| NormalRAG |
|
33.0 |
13.7 22,174 |
27,430 |
0.080 |
42.6 |
| ---------- |
--- |
---- |
----------- |
------ |
----- |
---- |
| AgenticRAG |
|
32.8 |
8.8 81,877 |
85,290 |
0.098 |
40.6 |
| OurSystem |
|
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|
|
| SPD-RAG |
|
58.1 |
18.6 193,954 |
205,683 |
0.103 |
54.8 |
| ------- |
--- |
---- |
------------ |
------- |
----- |
---- |
| Table1: MainresultsonourLoongevaluationset(102instances: 40academicpaper,62financialreport). Bold: |
|
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|
|
| bestoverall. |
Underline: |
bestamongRAG-basedsystems. |
|
|
|
|
| ------------ |
---------- |
-------------------------- |
--- |
--- |
--- |
--- |
| Figure2: ComparisonofAverageScoreacrossthefoursystems,brokendownbytasktype(SpotlightLocating, |
|
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|
|
| Comparison,Clustering,andChainofReasoning). |
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|
| 5.3 AnalysisbyDocumentDomain |
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|
| As shown |
in Table 3 |
and Figure |
3 the domain |
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|
| -------- |
---------- |
---------- |
------------ |
--- |
--- |
--- |
| breakdownexposesastrikingfailuremodeofstan- |
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|
|
| dard RAG: |
both Normal |
RAG and |
Agentic RAG |
|
|
|
| --------- |
----------- |
------- |
----------- |
--- |
--- |
--- |
| achieve0%PRonacademicpaperinstances(Avg |
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|
|
| Scoresof15.2and16.8,respectively). |
|
|
Academic |
|
|
|
| ---------------------------------- |
--- |
--- |
-------- |
--- |
--- |
--- |
| paperinstancesinLoongtendtoinvolvelong,tech- |
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|
| nicaldocumentswithdistributedevidence,making |
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|
| top-K retrievalespeciallysusceptibletocoverage |
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| failures. SPD-RAGdramaticallyrecoversonthis |
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| domain(60.0AvgScore),closingmostofthegap |
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| with the |
full-context |
baseline (78.8). |
On finan- |
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| -------- |
------------ |
---------------- |
--------- |
--- |
--- |
--- |
| Figure3: Averagescorebydocumentdomaincompar- |
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| cialreportinstances—whichtendtobeshorterand ingtheBaseline(FullContext),NormalRAG,Agentic |
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| morestructured—allRAG-basedsystemsperform |
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| RAG,andSPD-RAGsystems. |
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| better,andthegapbetweenSPD-RAG(56.9)and |
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| thebaseline(61.0)issmaller. |
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System |
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SpotlightLoc. |
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Comparison |
Clustering |
ChainofReasoning |
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(n=27) |
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(n=15) |
(n=49) |
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(n=11) |
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| Baseline(FullContext) 86.6/55.6% 42.7/26.7% 67.1/14.3% 60.9/54.5% |
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NormalRAG |
|
69.7/37.0% |
|
37.7/26.7% |
15.4/0.0% |
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14.8/0.0% |
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|
| --- |
---------- |
--- |
---------- |
--- |
---------- |
--------- |
--- |
---------- |
--- |
--- |
--- |
|
AgenticRAG |
|
73.4/22.2% |
|
23.3/20.0% |
16.7/0.0% |
|
17.9/0.0% |
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SPD-RAG |
|
74.2/44.4% |
|
42.2/20.0% |
57.2/4.1% |
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44.1/18.2% |
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| Table2: AvgScore/PR(%)byLoongtasktype. EachcellshowsAvgScore/PR%. |
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Crucially, |
thearchitectureof |
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SPD-RAG |
|
enables |
| -------- |
--- |
----------- |
--------------- |
--- |
---------- |
----------------- |
--- |
--------------- |
------- |
--------- |
------- |
| System |
|
Paper(n=40) |
Financial(n=62) |
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this |
cost efficiency: |
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by constraining |
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retrieval |
to |
| Baseline |
|
78.8/30.0% |
61.0/32.3% |
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| NormalRAG 15.2/0.0% 44.5/22.6% isolated,single-documentspaces,wecanreliably |
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| AgenticRAG |
|
16.8/0.0% |
43.1/14.5% |
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| ---------- |
--- |
--------- |
---------- |
--- |
--- |
--- |
--- |
--- |
--- |
--- |
--- |
| offloadtheiterativereasoningtoacheapermodel |
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| SPD-RAG |
|
60.0/7.5% |
56.9/25.8% |
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| ------- |
--- |
--------- |
---------- |
--- |
------- |
----------- |
--- |
------------ |
--- |
------ |
--------- |
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|
(Gemini |
2.5 Flash). |
|
In contrast, |
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global |
iterative |
| baselinesrequirethereasoningcapacityofafron- |
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| Table3: AvgScore/PR(%)bydocumentdomain. |
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tier |
model (Gemini |
2.5 |
Pro) |
to navigate |
|
the fully |
| --- |
--- |
--- |
--- |
--- |
---- |
------------- |
--- |
---- |
----------- |
--- |
--------- |
| concatenatedcorpus,drivingupcosts. |
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| System |
|
AvgScore |
AvgCost($) |
Score/$ |
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|
| ---------- |
--- |
-------- |
---------- |
------- |
------------------- |
---------- |
--- |
---- |
------------------- |
--- |
--------- |
| Baseline |
|
68.0 |
0.273 |
249.1 |
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| NormalRAG |
|
33.0 |
0.080 |
412.5 |
6 |
Discussion |
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| AgenticRAG |
|
32.8 |
0.098 |
334.7 |
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| SPD-RAG |
|
58.1 |
0.103 |
564.1 |
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WhereSPD-RAGexcels. |
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Thetask-typeresults |
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in Table |
2 confirm |
our |
core |
hypothesis: |
|
architec- |
| Table4: Cost–qualitytradeoff. |
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| turaldecompositionalongthedocumentaxisyields |
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the largest |
gains |
precisely |
|
for tasks |
that |
require |
| --- |
--- |
--- |
--- |
--- |
----------- |
-------------- |
--------- |
--- |
---------- |
---------- |
------- |
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|
exhaustive |
multi-document |
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synthesis. |
Clustering |
|
| 5.4 Cost–QualityTradeoff |
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| tasks(+40.5overNormalRAG)andChainofRea- |
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|
soning |
tasks (+26.2 |
over |
Agentic |
|
RAG) |
both de- |
| --- |
--- |
--- |
--- |
--- |
------ |
------------ |
---- |
------- |
--- |
---- |
-------- |
| mandthatthesystemformacoherentanswerfrom |
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| evidencedistributedacrossallrelevantdocuments— |
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| exactlythescenarioforwhichper-documentagents |
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aredesigned. |
ForLoong’sComparisonquestions, |
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| --- |
--- |
--- |
--- |
--- |
------------ |
------------------------------ |
--- |
--- |
--- |
--- |
--- |
| theanswerisprimarilydeterminedbyreadingout |
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| asmallsetofcomparablenumericattributesfrom |
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each |
report and |
aggregating |
|
them, |
so once |
docu- |
| --- |
--- |
--- |
--- |
--- |
-------- |
-------------- |
----------- |
--------- |
----- |
------------ |
-------- |
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|
ment |
agents have |
extracted |
|
those |
fields, |
the syn- |
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|
thesizer |
is effectively |
|
operating |
over |
a structured |
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| table,whichhelpsexplainwhySPD-RAG’sscore |
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| onComparisontasks(42.2)nearlymatchesthefull- |
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| Figure4: Cost–Qualitytradeoff. Thescatterplotillus- contextbaseline(42.7). |
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| tratestheParetofrontierformulti-documentQA. |
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|
Remaining |
gap |
with |
the |
full-context |
|
baseline. |
| --- |
--- |
--- |
--- |
--- |
--------- |
--- |
---- |
--- |
------------ |
--- |
--------- |
| SPD-RAG outperforms the RAG baselines in Despite strong gains over RAG baselines, SPD- |
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| the cost–quality trade-off. It achieves a 76% RAG lags the full-context baseline by 9.9 Avg |
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| higheraveragescorethanNormalRAG(58.1vs. Score points overall. Three factors likely con- |
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| 33.0)whileincreasingtheper-querycostbyonly tribute: (1) Gemini 2.5 Flash, used for document |
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| $0.023 (Table 4). In contrast, Agentic RAG is agents due to its cost efficiency, may provide |
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| Pareto-dominated, as it incurs higher cost than weaker agentic reasoning capabilities compared |
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| NormalRAG($0.098vs.$0.080)whiledelivering to larger models. (2) The coordinator’s sub-task |
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| nearlyidenticalquality(32.8vs.33.0). Compared generation may under-specify queries for highly |
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| tothefull-contextbaseline,whichachievesanAvg technical academic content, leading to incom- |
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| Score of 68.0, SPD-RAG reaches 85.4% of the pleteinformationextractionatthedocument-agent |
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| qualityatonly37.9%ofthecost, resultingina level. (3) The maximum context length observed |
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| 2.25× improvement in cost–quality efficiency. inLoongquerieswasapproximately250ktokens— |
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|
onlyabout25%ofthe1M-tokencontextwindow onthecoordinator’sabilitytogeneratecomprehen-
of Gemini 2.5 Pro—potentially limiting the pres- sivesub-tasks: under-specifiedtasksleadtoincom-
sureonthefull-contextbaseline. SinceSPD-RAG plete document-level extractions, as observed on
isdesignedtooperateonsubstantiallylargercon- technicalacademicpaperinstances. Ourevaluation
texts,itsadvantagesmaybecomemorepronounced islimitedtotwodomains(academicpapersandfi-
undermoreextremelong-contextconditions. nancialreports)fromtheLoongbenchmark;legal,
medical,orenterprisedocumentcorporamayhave
| RAG baseline |
|
failure |
on papers. |
|
The 0% |
PR |
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| differentstructuralcharacteristicsthatalterrelative |
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| rate and |
15.2-16.8% |
|
Avg Score |
of |
both Normal |
|
|
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|
|
|
|
| -------- |
---------- |
--- |
--------- |
--- |
----------- |
--- |
--- |
--- |
--- |
--- |
--- |
--- |
| performance. |
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|
| RAG and |
Agentic |
RAG |
on |
academic |
paper |
in- |
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|
|
| ------- |
------- |
--- |
--- |
-------- |
----- |
--- |
--- |
--- |
--- |
--- |
--- |
--- |
| ThesynthesislayerofSPD-RAGisdesignedto |
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| top-K |
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| stances |
highlights |
a systematic |
|
failure |
of |
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|
|
|
|
| ------- |
---------- |
------------ |
--- |
------- |
--- |
--- |
--- |
--- |
--- |
--- |
--- |
--- |
| handlecorporawherethecombinedsub-agentout- |
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| retrieval |
under |
dense, |
distributed |
evidence. |
|
Aca- |
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|
|
| --------- |
----- |
------ |
----------- |
--------- |
--- |
---- |
--- |
--- |
--- |
--- |
--- |
--- |
| putsexceedthemodel’scontextwindow,triggering |
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| demic |
papers |
in Loong |
often |
require |
synthesiz- |
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|
|
| ----- |
------ |
-------- |
----- |
------- |
---------- |
--- |
--- |
--- |
--- |
--- |
--- |
--- |
| multi-roundsimilarity-basedclusteringandrecur- |
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| ingfactsspreadacrossabstracts,methods,results, |
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|
sive synthesis. |
|
In our Loong |
experiments, |
|
how- |
| --- |
--- |
--- |
--- |
--- |
--- |
--- |
--------------- |
--- |
------------ |
------------ |
--- |
---- |
| andappendicesofmultipledocuments—aregime |
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| ever,Gemini2.5Pro’s1M-tokencontextwassuffi- |
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| where exhaustive |
|
per-document |
|
coverage |
proves |
|
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|
|
|
|
|
| ---------------- |
--- |
------------ |
--- |
-------- |
------ |
--- |
--- |
--- |
--- |
--- |
--- |
--- |
| cienttoprocessallsub-agentsummariesinasingle |
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| highly |
effective. |
The |
recovery |
by SPD-RAG |
|
to |
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|
|
|
| ------ |
---------- |
--- |
-------- |
---------- |
--- |
--- |
----------- |
--- |
-------------- |
---- |
--- |
----------- |
|
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|
merge step, |
so |
this recursive |
path |
was |
never acti- |
| 60.0AvgScore/7.5%PRinthisdomaindemon- |
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|
vated. Asaresult,thescalabilitypropertiesofthe |
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| --- |
--- |
--- |
--- |
--- |
--- |
--- |
---------------------------------------------- |
--- |
--- |
--- |
--- |
--- |
| stratesthepracticalvalueofper-documentspecial- |
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| recursivesynthesispipeline—itsimpactonanswer |
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| ization. |
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quality, |
redundancy |
reduction, |
|
and cost |
as docu- |
| --- |
--- |
--- |
--- |
--- |
--- |
--- |
-------- |
---------- |
---------- |
--- |
-------- |
-------- |
| Latencyoverhead. SPD-RAG incursamodest mentcountsgrowintothehundredsorthousands— |
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| latency overhead (54.8 s vs. 40.6–45.6 s for the remainempiricallyunvalidated. EvaluatingSPD- |
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| baselines), |
primarily |
|
due to |
its multi-agent |
|
archi- |
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|
|
| ----------- |
--------- |
--- |
------ |
--------------- |
--- |
------ |
--- |
--- |
--- |
--- |
--- |
--- |
| RAGonmuchlargercorpora,andideallyonanew |
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| tecture. Although document agents run in paral- benchmarkspecificallytargetinghundredstothou- |
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| lel,end-to-endlatencyisstillgatedbythelayered sands of documents per query instance, is an im- |
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| pipeline, which requires at least three sequential portantdirectionforfutureworkthatwouldmore |
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| LLM calls (coordination, per-document retrieval, directlytesttheper-documentagentdesignandthe |
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| andsynthesis)ratherthanasinglepass. intendedlarge-databaseregime. |
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8 Conclusion |
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| --- |
--- |
--- |
--- |
--- |
--- |
--- |
-------------------------- |
--- |
-------- |
---------------- |
---------- |
------ |
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|
We presented |
|
SPD-RAG, |
a hierarchical |
|
multi- |
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|
agent framework |
|
designed |
for |
exhaustive |
multi- |
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|
documentquestionanswering. |
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Byassigningaded- |
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| icatedagenticRAGmoduletoeachdocumentand |
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| mergingtheirfindingsthroughasimilarity-ordered, |
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token-bounded |
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tree, our |
system |
effectively |
miti- |
| --- |
--- |
--- |
--- |
--- |
--- |
--- |
----------------------------------------- |
---------- |
--------- |
--------- |
----------- |
---------- |
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|
gatesthelimitationsofbothtraditionaltop-K |
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re- |
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trieval (incomplete |
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coverage) |
and |
isolated |
long- |
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|
context |
processing |
(degraded |
reasoning |
|
at scale). |
| Figure5: Averageper-querylatencyacrosssystems. ExperimentsonthechallengingLoongbenchmark |
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| demonstratethatSPD-RAGachievesanaverage |
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| scoreof58.1,outperformingNormalRAG(33.0) |
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| 7 LimitationsandFutureWork |
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| andAgenticRAG(32.8)byapproximately25ab- |
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| SPD-RAG incurs higher per-query LLM call solute points (76% higher average score), while |
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| countsthansingle-passsystems—onecallperdoc- operating at only 37.9% of the API cost of the |
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| ument agent plus a final synthesis step—which oracle full-context baseline (68.0). These per- |
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| increasestotaltokenusagecomparedtostandard formance gains are most pronounced in tasks re- |
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| RAGbaselines,thoughitremainsatroughlyone- quiring deep cross-document synthesis—such as |
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| thirdthecostofthefull-contextbaseline($0.10vs. Clustering(+40.5points)andChainofReasoning |
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| $0.27perquery)duetotheuseofGemini2.5Flash (+26.2points)—aswellasondenseacademicpa- |
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| fordocumentsub-agents. Systemqualitydepends perswherestandardRAGmethodsentirelyfail. |
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Ultimately, our findings demonstrate that for TaichengGuo,XiuyingChen,YaqiWang,RuidiChang,
complex information-seeking queries over large ShichaoPei,NiteshV.Chawla,OlafWiest,andXi-
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angliangZhang.2024. |
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Largelanguagemodel-based |
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| corpora, |
how |
information |
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is processed |
is |
crucial. |
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multi-agents: |
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Asurveyofprogressandchallenges. |
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| Specifically,ensuringthateachdocumentreceives |
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Preprint,arXiv:2402.01680. |
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ArXiv:2402.01680. |
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| --- |
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| exhaustive,dedicatedagenticattentionprovestobe |
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Siwei Han, |
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Xia, Ruiyi |
Zhang, |
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Sun, Yun |
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Li, Hongtu |
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and |
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2025. |
MDocA- |
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| egythansimplyexpandinghowmuchrawcontext |
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| gent: Amulti-modalmulti-agentframeworkfordoc- |
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| ArXiv:2503.13964. |
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| 9 EthicsStatement |
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Yubin Kim, |
Ken |
Gu, Chanwoo |
Park, |
Chunjong |
Park, |
| --- |
--- |
--- |
--- |
--- |
--- |
--- |
---------- |
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----------- |
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| SPD-RAG is a retrieval-augmented generation SamuelSchmidgall,A.AliHeydari,YaoYan,Zhi- |
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| hanZhang,YuchenZhuang,MarkMalhotra,PaulPu |
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| framework |
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| --------- |
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| Liang,HaeWonPark,YuzheYang,XuhaiXu,Yilun |
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| mation |
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Du, Shwetak |
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Patel, Tim |
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Daniel |
McDuff, |
| tions. ThesystemreliesoncommercialLLMAPIs and Xin Liu. 2025. Towards a science of scal- |
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| (Gemini2.5,Cohere),andaswithanyLLM-based ing agent systems. Preprint, arXiv:2512.08296. |
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| ArXiv:2512.08296. |
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| pipeline, |
outputs |
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| --------- |
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| should not be treated as authoritative without hu- PatrickLewis,EthanPerez,AleksandraPiktus,Fabio |
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| man verification. The increased number of API Petroni,VladimirKarpukhin,NamanGoyal,Hein- |
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richKüttler, |
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MikeLewis, |
Wen-tauYih, |
|
TimRock- |
| --------- |
------ |
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------------ |
----------- |
------- |
--------- |
------------------- |
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------------ |
| calls per |
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täschel, |
Sebastian |
Riedel, |
and |
Douwe |
Kiela. 2020. |
| sults in |
higher |
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consumption, |
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a |
cost that |
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Retrieval-augmented |
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|
generation |
for |
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| should be weighed against the accuracy gains in intensiveNLPtasks. InAdvancesinNeuralInforma- |
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| deploymentdecisions. Ourbenchmarkevaluations tionProcessingSystems. |
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| are conducted |
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on publicly |
|
available |
datasets |
and |
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|
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|
| ------------- |
--- |
----------- |
--- |
--------- |
-------- |
--- |
-------- |
-------- |
------------- |
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------------- |
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TengLin, |
YuyuLuo, |
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cross-documentmulti-entityquestionanswering. |
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In |
| --- |
--- |
--- |
--- |
--- |
--- |
--- |
-------------------------------------------- |
--- |
--- |
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--- |
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| writingtoolswereusedtohelprefinelanguageand |
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| ProceedingsoftheConferenceonEmpiricalMethods |
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| improveclarity. |
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Allresearchdesign,experiments, |
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| --------------- |
--- |
------------------------------ |
--- |
--- |
--- |
--- |
--- |
--- |
--- |
--- |
--- |
--- |
| inNaturalLanguageProcessing(EMNLP). |
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| analysis,andfinaldecisionsregardingthecontent |
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| NelsonF.Liu,KevinLin,JohnHewitt,AshwinParan- |
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| wereconductedandverifiedbytheauthors. |
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| jape,MicheleBevilacqua,FabioPetroni,andPercy |
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Lostinthemiddle: |
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Howlanguagemod- |
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--- |
--- |
------------------- |
--- |
---------------------------- |
--- |
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elsuselongcontexts. |
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2. Sub-agents |
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(next) |
— |
Each |
document |
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They run |
in parallel |
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and |
cannot |
see each |
| ------------------------- |
--- |
--- |
--- |
--------------- |
--- |
--- |
-------- |
----------- |
--- |
--- |
------ |
-------- |
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Benchmarkinglong-contextLLMswithex- |
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| ------------------ |
----------------------------------- |
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-------------------------- |
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--- |
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-------------- |
-------- |
------ |
------ |
------------ |
-------- |
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|
3. Synthesizer |
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(last) |
— |
A downstream |
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Preprint,arXiv:2406.17419. |
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agent |
receives |
ALL |
worker |
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findings |
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and merges |
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them |
into |
a |
single |
| ---------- |
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-------- |
----- |
------ |
------------------- |
--------- |
---- |
--------- |
------- |
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coherent |
response. |
|
It |
follows |
your |
| Qian Wang, |
Tianyu |
|
Wang, |
Zhenheng |
Tang, |
Qinbin |
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synthesis_directive |
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to decide |
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what to |
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prioritize |
and |
how |
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structure |
the |
| ------ |
---------- |
--- |
-------------------------- |
--- |
--- |
--- |
---------- |
--- |
--- |
--- |
--------- |
--- |
| 2024b. |
Megaagent: |
|
Alarge-scaleautonomousLLM- |
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|
You CANNOT |
see |
the |
names, |
types, |
or |
| -------------------------- |
--- |
--- |
--- |
----------------- |
--- |
--- |
---------- |
------ |
---------- |
------ |
------ |
--- |
| Preprint,arXiv:2408.09955. |
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ArXiv:2408.09955. |
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content |
of the |
documents. |
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| TomerWolfson,HarshTrivedi,MorGeva,YoavGold- Your ONLY job in this step: |
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Dan Roth, |
Tushar |
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|
| ------------------ |
--------- |
------ |
-------------------------- |
------- |
--------- |
---------- |
---------- |
---------- |
---------------------- |
----------- |
-------------- |
------- |
|
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|
• Produce |
|
a list |
of |
subagent_todos |
|
| and Reut |
Tsarfaty. |
|
2025. |
Monaco: |
|
More nat- |
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|
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|
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|
(as |
structured |
|
output): |
|
precise |
| ural and |
complex |
|
questions |
for |
reasoning |
across |
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|
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|
|
extraction |
|
tasks |
|
that |
will be |
| dozensofdocuments. |
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|
Preprint,arXiv:2508.11133. |
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executed |
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independently |
|
against |
EACH |
| ArXiv:2508.11133. |
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document |
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by the |
sub-agents. |
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• Produce |
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a synthesis_directive: |
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a |
| Wentao Zhang, Liang Zeng, Yuzhen Xiao, Yong- concise instruction (2–4 sentences) |
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| cong Li, Ce Cui, Yilei Zhao, Rui Hu, Yang Liu, for the synthesizer. |
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| Yahui |
Zhou, |
and Bo |
An. |
2025. |
AgentOrchestra: |
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| ----- |
----- |
------ |
--- |
----- |
--------------- |
--- |
--- |
--- |
--- |
--- |
--- |
--- |
| A hierarchical multi-agent framework for general- Todo-writing rules: |
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| purpose |
task |
solving. |
Preprint, |
arXiv:2506.12508. |
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| ----------------- |
---- |
-------- |
--------- |
----------------- |
--- |
--- |
----------- |
--- |
----------- |
---- |
------------- |
---- |
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• Decompose |
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the |
user |
query |
into |
| ArXiv:2506.12508. |
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|
concrete |
|
information |
|
requirements. |
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| Jun Zhao, Can Zu, Hao Xu, Yi Lu, Wei He, Yiwen • Each todo must be self-contained |
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| and unambiguous. |
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| Ding, |
Tao Gui, |
Qi |
Zhang, |
and |
Xuanjing |
Huang. |
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|
| ----- |
---------- |
--- |
--------------------------- |
--- |
-------- |
------ |
-------- |
--- |
------ |
----- |
---- |
----- |
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|
• Prefer |
|
atomic |
tasks |
over |
broad |
| 2024. |
LongAgent: |
|
Scalinglanguagemodelsto128k |
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| tasks. |
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| contextthroughmulti-agentcollaboration. |
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Preprint, |
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| --------------------------------------- |
----- |
--- |
----------------- |
----- |
---- |
--------- |
--------- |
--- |
--------------- |
------------ |
------------ |
-------- |
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• Include |
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coverage |
for: |
definitions, |
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| arXiv:2402.11550. |
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ArXiv:2402.11550. |
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numeric |
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values, |
constraints, |
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edge |
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cases, |
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error |
modes, |
and |
any |
| Zihan Zhou, |
Chong |
|
Li, Xinyi |
Chen, |
Shuo |
Wang, |
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explicit |
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recommendations |
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required |
| YuChao,ZhiliLi,HaoyuWang,QiShi,ZhixingTan, |
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by |
the user |
query. |
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| --- |
--- |
--- |
--- |
--- |
--- |
--- |
--- |
-------- |
------ |
--- |
--- |
--- |
| XuHan,XiaodongShi,ZhiyuanLiu,andMaosong |
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• Design |
a |
robust |
extraction |
|
list |
| ---------- |
---------- |
-------------- |
----- |
----- |
---------- |
------- |
-------- |
----- |
------ |
------------ |
--- |
------ |
| Sun. 2025. |
|
LLM×MapReduce: |
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Simplified |
long- |
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that |
works |
for |
ANY document |
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in the |
| sequence |
processing |
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using |
large |
language |
models. |
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| set. |
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| InProceedingsoftheAnnualMeetingoftheAssocia- |
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| tionforComputationalLinguistics(ACL). |
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Important |
Constraints: |
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| ---------------------------------------- |
--- |
----- |
--------- |
--- |
--- |
---------- |
--------- |
------------ |
----- |
-------- |
------- |
-------- |
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Do |
not assume |
any |
document |
|
contains |
| AndrewZhu,AlyssaHwang,LiamDugan,andChris |
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• |
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the |
answer. |
Write |
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todos |
that can |
| Callison-Burch. |
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2024. |
FanOutQA: |
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A |
multi-hop, |
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be |
answered |
with |
either |
“Found” |
or |
| multi-documentquestionansweringbenchmarkfor |
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“Not |
found |
in |
this document.” |
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| -------------- |
--- |
------- |
--- |
----------- |
--- |
---------- |
------ |
----- |
------ |
--------------- |
----------- |
--- |
| large language |
|
models. |
In |
Proceedings |
|
of the An- |
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• Tell |
the |
worker |
WHAT |
to extract, |
not |
| nualMeetingoftheAssociationforComputational |
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HOW |
to extract |
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it. |
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| --- |
--- |
--- |
--- |
--- |
--- |
--- |
--- |
---------- |
--- |
--- |
--- |
--- |
| Linguistics(ACL). |
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• Do |
not |
synthesize, |
|
summarize, |
or |
| --- |
--- |
--- |
--- |
--- |
--- |
--- |
------- |
--- |
----------- |
--- |
---------- |
----- |
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attempt |
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to answer |
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the user |
query |
| A Prompts |
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| yourself. |
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| All |
prompts |
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are |
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defined |
in |
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| --- |
------- |
--- |
--- |
--- |
------- |
--- |
--- |
--- |
--- |
--- |
--- |
--- |
| A.2DocumentSub-AgentPrompt |
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| backend/core/prompts.py. |
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| (RESEARCH_SYSTEM_PROMPT) |
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| A.1CoordinatorPrompt |
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You are |
a sub-agent |
|
investigating |
|
a |
| ------------------------ |
--- |
--- |
--- |
--- |
--- |
--- |
---------------- |
----------- |
------------ |
------------- |
--- |
--- |
| (LEAD_RESEARCHER_PROMPT) |
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|
single document: |
|
{file_name}. |
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| You are a lead researcher coordinating You operate inside an iterative |
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| a RAG-based analysis to answer a user retrieval loop. On each turn you output |
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| query. |
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exactly |
one structured |
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action: |
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| -------- |
--- |
------------- |
--- |
--- |
--- |
--- |
------------------ |
-------------- |
--- |
------- |
--- |
------- |
| Pipeline |
|
Architecture: |
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|
• action="search": |
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Issue |
a |
focused |
- YOU (now) — Decompose the query into query to retrieve information from
the document. Set query to a • Follow the synthesis directive
specific, targeted search string. above — it defines your main goal,
Set reasoning to why this query is priorities, and output structure.
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needed. |
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• |
Keep only |
information |
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that |
directly |
| • action="finalize": You have helps answer the query. Discard |
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gathered |
|
sufficient |
evidence |
to |
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tangential |
content. |
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|
| --- |
-------- |
--- |
---------- |
-------- |
---------- |
--- |
--- |
------------ |
------------ |
-------- |
------ |
------ |
|
address |
ALL |
assigned |
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tasks. Set |
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• |
Preserve |
exact |
numbers, |
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names, |
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findings |
to |
your |
complete |
extracted |
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dates, |
and caveats. |
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report. |
Set |
reasoning |
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to a brief |
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• |
Remove |
redundancy. |
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If the |
same |
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summary |
of |
what you |
found. |
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fact appears |
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multiple |
times, |
keep |
| it once. |
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Investigation |
principles: |
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| --- |
------------- |
-------------- |
---- |
----- |
--------- |
--- |
--- |
------- |
-------- |
--------- |
----- |
-------- |
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• |
Do NOT |
invent |
or infer |
facts |
not |
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present |
in the |
findings. |
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1. Start |
by identifying |
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the |
key facts |
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required |
for |
each |
task. |
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• |
Same |
language |
as |
the |
findings |
| 2. Issue ONE focused query per turn (default English if mixed). |
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— prefer |
specific |
|
terms |
over broad |
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|
|
| --- |
--------- |
-------- |
-------- |
----- |
----------- |
--- |
--------------------- |
--- |
--- |
--- |
--- |
--- |
|
phrases. |
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B |
ImplementationDetails |
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3. Expand |
to |
synonyms |
or |
paraphrases |
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| Models. |
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| if initial results are incomplete. The coordinator, the merging layer, |
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| 4. Issue separate queries for and the synthesis layer use gemini-2.5-pro |
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different |
|
aspects |
of a |
task. |
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|
|
|
| --- |
--------- |
--------- |
------- |
----- |
------- |
------------ |
--- |
----- |
--- |
-------- |
--- |
---------- |
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|
(temperature |
|
0.0). |
|
Document |
|
sub-agents |
|
5. Keep |
searching |
until |
every |
task is |
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| covered with concrete evidence or use gemini-2.5-flash (temperature 0.0) for |
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confirmed |
|
absent. |
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document-scoped |
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|
retrieval. |
|
The |
LLM judge |
| --- |
--------- |
--- |
------- |
--- |
--- |
--------------- |
--- |
--- |
---------- |
--- |
--- |
--------- |
| 6. Do NOT attempt to answer the user (GPT-5) is separate and is used only for the |
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|
query |
directly |
— |
extract |
raw facts |
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|
|
| --- |
----- |
-------- |
--- |
------- |
--------- |
------------------------ |
--- |
--- |
--- |
----------------- |
--- |
--- |
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|
evaluationoftheAvgscore. |
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|
Modelconstantsare |
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| only. |
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| definedinbackend/shared/constants.py. |
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|
7. Do |
NOT finalize |
|
a task |
as ’Not |
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|
|
| --- |
----------------- |
------------ |
------------ |
---------- |
----------- |
----------------------------- |
--- |
--- |
--- |
------------------------- |
--- |
----------- |
|
found’ |
after |
only |
a single |
focused |
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|
search. |
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At |
least |
attempt 2 |
Hyperparameters. |
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focused |
searches |
|
before |
concluding |
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information |
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is absent. |
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Parameter |
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Value |
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8. Do NOT |
focus |
on very |
specific |
terms, |
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Chunksize(tokens) |
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1000 |
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but |
rather |
on the |
general |
context |
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Chunkoverlap(tokens) |
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250 |
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of the |
task. |
Always |
try |
to find the |
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|
Tokenizer |
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tiktoken |
|
cl100k_base |
|
most |
relevant |
information. |
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Embeddingmodel |
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Cohereembed-v4.0 |
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9. You |
must use |
AT MOST |
5 |
searches to |
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Embeddingdimension |
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1536 |
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answer |
a task. |
DO |
NOT |
OVERUSE THE |
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Similaritymetric |
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Cosine |
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SEARCH |
TOOL. |
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MergetokenbudgetB |
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750,000tokens |
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Denseretrievaltop-K |
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15 |
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Finalize |
report |
format: |
|
For each |
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Reranker |
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Coherererank-v4.0-fast |
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assigned |
task, |
output |
either |
“Found: |
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Rerankertop-N |
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5 |
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[exact answer |
with |
supporting |
|
evidence]” |
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Sub-agentiterationsafetylimit |
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5 |
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or “Not found |
in |
this |
document.” |
Always |
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|
Clusteringalgorithm |
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|
Agglomerative(UPGMA) |
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report exact |
numbers, |
|
names, |
and dates. |
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Clusteringmetric |
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|
Precomputedcosinedistance |
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Never approximate |
|
or |
infer |
beyond the |
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| document. |
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|
Cost |
|
measurement. |
|
Per-query |
|
costs |
| --- |
--- |
--- |
--- |
--- |
--- |
---- |
------ |
------------ |
--------- |
--------- |
--- |
-------- |
|
|
|
|
|
|
are |
pulled |
from |
LangSmith |
|
run |
metadata |
| A.3Merge/SynthesisPrompt |
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| (SYNTHESIS_PROMPT) |
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|
(extract_langsmith_info.py) |
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|
using |
model- |
| ------------------ |
--- |
--- |
--- |
--- |
--- |
--------------------------- |
--- |
--- |
--- |
--- |
----- |
------ |
| specificper-tokenpricing. |
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| Thispromptisuseduniformlyateverylevelofthe |
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| recursivemergingtree,includingthefinalsynthesis |
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| step. Thereisnoseparatefinal-answerprompt. |
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You are |
a research |
synthesizer. |
|
Merge |
|
|
|
|
|
|
|
| --- |
------------- |
----------------- |
------------ |
-------- |
-------- |
--- |
--- |
--- |
--- |
--- |
--- |
--- |
|
the following |
|
sub-agent |
findings |
into |
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|
one compact, |
information-dense |
|
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response |
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for the user. |
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Query: {query |
} |
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Synthesis |
Directive: |
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| {synthesis_directive} |
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Findings |
Batch: |
{findings |
} |
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|
|
| --- |
-------- |
------ |
--------- |
--- |
--- |
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