SPD-RAG: Sub-Agent Per Document Retrieval-Augmented Generation | | YagizCanAkay1 | | | MuhammedYusufKartal1 | | | EsraAlparslan1 | | | | | | --- | ------------- | --- | ------------------- | -------------------- | ------------ | --- | -------------- | --- | --- | --- | --- | | | | | FarukOrtakoyluoglu1 | | ArdaAkpinar2 | | | | | | | 1TOBBUniversityofEconomicsandTechnology 2OSTIMTechnicalUniversity {y.akay, m.kartal, ealparslan, fortakoyluoglu}@etu.edu.tr 230206003@ostimteknik.edu.tr | | | Abstract | | | andcommon,yetexposecriticalcoverageandrea- | | | | | | | | --- | --- | -------- | --- | --- | ------------------------------------------ | --- | --- | --- | --- | --- | --- | soningbottlenecksincurrentNLPsystems. Answeringcomplex,real-worldqueriesoften 6202 raM 9 ]LC.sc[ 1v92380.3062:viXra Traditionalsingle-indexorsingle-agentretrieval requiressynthesizingfactsscatteredacrossvast | | | | | | systemsstruggleto | | maintainbothscalability | | | | and | | -------- | -------- | -------- | --------- | ----- | ----------------- | --- | ----------------------- | --- | --- | --- | --- | | document | corpora. | In these | settings, | stan- | | | | | | | | contextualrelevanceoverlarge,heterogeneouscor- | dard retrieval-augmented | | | generation | (RAG) | | | | | | | | | ------------------------ | --- | --- | ---------- | ----- | ---------------- | --- | -------- | --------------- | --- | ------- | --- | | | | | | | pora, especially | | for long | or structurally | | complex | | pipelinessufferfromincompleteevidencecov- erage,whilelong-contextlargelanguagemod- documents. Standard RAG pipelines retrieve a | | | | | | fixed number | | of documents | K | and process | | them | | ---------- | -------- | --------- | -------- | ---- | ------------ | --- | ------------ | --- | ----------- | --- | ---- | | els (LLMs) | struggle | to reason | reliably | over | | | | | | | | massive inputs. We introduce SPD-RAG, a withinasinglecontextwindow(Lewisetal.,2020), hierarchicalmulti-agentframeworkforexhaus- whichfailswhenanswersdependoninformation tivecross-documentquestionansweringthatde- distributedacrossmanydocuments,sinceevidence composestheproblemalongthedocumentaxis. | | | | | | beyondthetop-K | | resultsistypicallydiscardedin | | | | | | ---- | -------- | ------------ | ---- | --------- | -------------- | --------- | ----------------------------- | ------------ | --- | ---- | --- | | Each | document | is processed | by a | dedicated | | | | | | | | | | | | | | a single | retrieval | pass. | Long-context | | LLMs | ex- | document-levelagentoperatingonlyonitsown content,enablingfocusedretrieval,whileaco- tendcontextwindowsto128K–2Mtokens(Gemini | | | | | | Teametal.,2024), | | butempiricalevidenceshows | | | | | | --------- | ---------- | ----- | ----------- | ------ | ---------------- | --- | ------------------------- | --- | --- | --- | --- | | ordinator | dispatches | tasks | to relevant | agents | | | | | | | | and aggregates their partial answers. Agent thatreasoningqualitydegradessignificantlyascon- outputsaresynthesizedbymergingpartialan- text length increases (Liu et al., 2023). This sug- swersthroughatoken-boundedsynthesislayer | | | | | | gests that | the | bottleneck | is not | only | retrieval, | but | | --- | --- | --- | --- | --- | ---------- | --- | ---------- | ------ | ---- | ---------- | --- | (whichsupportsrecursivemap-reduceformas- alsoreasoningatscaleovermanydocumentsand | sivecorpora). | | Thisdocument-levelspecializa- | | | | | | | | | | | ------------- | --- | ----------------------------- | --- | --- | --- | --- | --- | --- | --- | --- | --- | hundredsofthousandsoftokens. tionwithcentralizedfusionimprovesscalabil- ityandanswerqualityinheterogeneousmulti- In this work, we introduce SPD-RAG | | | | | | (Sub-agent | per | Document | Retrieval-Augmented | | | | | -------- | -------- | ----- | -------- | ---------- | ---------- | --- | -------- | ------------------- | --- | --- | --- | | document | settings | while | yielding | a modular, | | | | | | | | extensibleretrievalpipeline. OntheLOONG Generation),ahierarchicalmulti-agentarchitecture benchmark (EMNLP 2024) for long-context forexhaustivemulti-documentquestionanswering multi-document QA, SPD-RAG achieves an thatfactorstheproblemalongthedocumentaxis AvgScoreof58.1(GPT-5evaluation),outper- | | | | | | rather than | the | task axis. | Instead | | of forcing | a | | --- | --- | --- | --- | --- | ----------- | --- | ---------- | ------- | --- | ---------- | --- | formingNormalRAG(33.0)andAgenticRAG | | | | | | single model | | to hunt through | | a massive | | global | | --- | --- | --- | --- | --- | ------------ | --- | --------------- | --- | --------- | --- | ------ | (32.8)whileusingonly38%oftheAPIcostof | | | | | | index, SPD-RAG | | uses | a central | coordinator | | to | | --- | --- | --- | --- | --- | -------------- | --- | ---- | --------- | ----------- | --- | --- | afull-contextbaseline(68.0). | | | | | | decompose | the | user’s | query into | shared | instruc- | | | --- | --- | --- | --- | --- | --------- | --- | ------ | ---------- | ------ | -------- | --- | 1 Introduction tions. It then deploys a dedicated, cost-efficient | | | | | | sub-agenttoeachdocumentinthecorpus. | | | | | | These | | --- | --- | --- | --- | --- | ----------------------------------- | --- | --- | --- | --- | --- | ----- | Large language models (LLMs) and the agentic agents operate entirely in parallel, treating their systems built around them are increasingly used assigneddocumentsasisolatedretrievaluniverses forcomplexinformationsearchtasks(Guoetal., | | | | | | to extract | relevant | findings. | Finally, | | a synthesis | | | --- | --- | --- | --- | --- | ---------- | -------- | --------- | -------- | --- | ----------- | --- | 2024). Real-worldquestionsoftenrequiresynthe- | | | | | | model | aggregates | these | document-grounded | | | | | --- | --- | --- | --- | --- | ----- | ---------- | ----- | ----------------- | --- | --- | --- | sizingevidencescatteredacrossmanydocuments, findings—employingarecursivemergingfallback suchasassessingacompany’sfinancialrisksacross | | | | | | for exceptionally | | large | corpora—to | | construct | a | | --- | --- | --- | --- | --- | ----------------- | --- | ----- | ---------- | --- | --------- | --- | yearsofreportsorintegratingfindingsfrommul- | | | | | | comprehensive, | | final answer. | | Our codebase | | and | | --- | --- | --- | --- | --- | -------------- | --- | ------------- | --- | ------------ | --- | --- | tiplescientificpapers.1 | | | Suchquestionsarenatural | | | datasetsusedinthispapercanbefoundat | | | | | | | | --- | --- | ----------------------- | --- | --- | ----------------------------------- | --- | --- | --- | --- | --- | --- | https://github.com/NebulAICompany/ | 1Examples | inspired | by scenarios | in the | Loong bench- | | | | | | | | | ---------------------- | -------- | ------------ | ------ | ------------ | -------- | --- | --- | --- | --- | --- | --- | | mark(Wangetal.,2024a). | | | | | SPD-RAG. | | | | | | | 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 | are coordinated | by a | smarter coordinator | | | | | | | | --------------- | ---- | ------------------- | --- | ---------------------- | --- | ------------------------- | --- | --- | | | | | | patternsalongfiveaxes: | | controlhierarchy,informa- | | | agent,enablingdocument-levelspecialization | | | | | tion flow, | role delegation, | temporal | layering, | and | | ----------------- | -------------- | --------------- | --------- | ----------------------- | ---------------- | -------- | --------- | --- | | and parallel | execution | while allowing | each | communicationstructure. | | | | | | document | to be analyzed | in depth | to ensure | | | | | | | that all relevant | information | is incorporated | | | | | | | 2.2 LongDocumentandMulti-DocumentQA withoutmissingcriticalevidence. | | | | | Long documents | that | exceed | LLM context | win- | | --- | --- | --- | --- | -------------- | ---- | ------ | ----------- | ---- | • WeevaluateontheLoongbenchmark(Wang | | | | | dows are | often handled | via | divide-and-conquer | | | --- | --- | --- | --- | -------- | ------------- | --- | ------------------ | --- | etal.,2024a),whichincludesmulti-document | | | | | strategies. | LLM×MapReduce(Zhouetal.,2025) | | | | | ------------ | -------------- | ---------- | -------- | --------------------------- | ----------------------------- | --- | ----------------- | --- | | QA instances | over financial | reports | and aca- | | | | | | | | | | | formalizestwokeychallenges: | | | inter-chunkdepen- | | | demic papers | with an | average of | 11 docu- | | | | | | dency(onechunkdependsoncontextfromanother) mentsperinstanceandcontextlengthsfrom andinter-chunkconflict(chunksprovidecontradic- | 10K to beyond | 250K | tokens, and | show that | | | | | | | ------------- | ---- | ----------- | --------- | --- | --- | --- | --- | --- | toryinformation),andproposesstructuredinforma- oursystemsubstantiallyoutperformsstandard tionprotocolsandin-contextconfidencecalibration RAGandAgenticRAGbaselinesunderGPT- | | | | | tomitigatethem. | ToM(Guoetal.,2025)extends | | | | | -------- | ------------------ | ---- | ------- | --------------- | ------------------------- | ------------ | ------- | ------ | | 5-judged | Avg Score (adapted | from | Loong’s | | | | | | | | | | | this approach | with a | hierarchical | DocTree | repre- | GPT-4-judgedprotocol;see§4.3),withgains sentationforrecursivereasoning. | of around | +25 points | (76% higher | average | | | | | | | --------- | ---------- | ----------- | ------- | --- | --- | --- | --- | --- | Formulti-documentQA,LongAgent(Zhaoetal., score)comparedtoNormalRAGandAgentic | | | | | 2024) splits | a 128K-token | document | | into chunks | | --- | --- | --- | --- | ------------ | ------------ | -------- | --- | ----------- | RAGbaselines. | | | | | assigned | to member | agents, with | a leader | agent | | --- | --- | --- | --- | ------------- | -------------- | ------------ | ---------------- | ----- | | | | | | orchestrating | discussionsand | | inter-membercom- | | • Weanalyzeablations,documenttypeandtask complexityeffects,andcost–qualitytradeoffs, munication to reduce hallucinations. However, showingthat SPD-RAGattainsover85%of LongAgenttargetsasinglelongdocumentrather thanmultipleindependentdocumentsthatmaycon- | full-context | baseline quality | at roughly | 38% | | | | | | | ------------ | ---------------- | ---------- | --- | --------------- | ---- | ------------- | ---- | -------- | | | | | | flict. DocAgent | (Sun | et al., 2025) | uses | a multi- | oftheAPIcost. | | | | | agent framework | that | mimics | human | reading via | | --- | --- | --- | --- | --------------- | ---- | ------ | ----- | ----------- | 2 RelatedWork atree-structuredoutlineandrevieweragent,while | | | | | MDocAgent | (Han et | al., 2025) | employs | five spe- | | --- | --- | --- | --- | --------- | ------- | ---------- | ------- | --------- | 2.1 LLM-BasedMulti-AgentSystems | | | | | cialized | agents for multi-modal | | document | under- | | --- | --- | --- | --- | -------- | ---------------------- | --- | -------- | ------ | LLM-basedmulti-agentsystemsareincreasingly standing. | used for complex | task solving. | Guo et | al. (2024) | | | | | | | ---------------- | ------------- | ------ | ---------- | --- | --- | --- | --- | --- | provideacomprehensivesurveyofagentprofiles, 2.3 HierarchicalandRecursive communicationprotocols,andcollaborationstrate- Summarization | gies. Hierarchical | architectures | such | as MegaA- | | | | | | | ------------------ | ------------- | ---- | --------- | --- | --- | --- | --- | --- | gent(Wangetal.,2024b),whichdemonstratesau- Ourhierarchicalmergingmechanismbuildsontree- tonomouscooperationamongupto590agentsvia basedretrievalandrecursivesummarization. RAP- multi-leveltaskdecomposition,andAgentOrches- TOR(Sarthietal.,2024)recursivelyembeds,clus- tra(Zhangetal.,2025),whichachievesstate-of-the- ters, and summarizes text chunks to construct a artperformanceonGAIAusingacentralplanning bottom-uptreewithmultipleabstractionlevels;re- agent and specialized sub-agents, exemplify this trievingfromtheappropriatelevelyieldsa20%ab- | trend. | | | | soluteaccuracygainonQuALITY.OuandLapata | | | | | | ------ | --- | --- | --- | --------------------------------------- | --- | --- | --- | --- | Scalinglawsformulti-agentsystemsofferquan- (2025)showthathierarchicalmergingcanamplify titativeguidanceforarchitecturedesign. Kimetal. hallucinationsduringrecursivesummarizationand (2025)evaluate180configurationsacrossfivear- proposecontext-awareaugmentationstrategiesthat chitectures, finding that centralized coordination replaceintermediatesummarieswithrelevantinput yields an 80.9% improvement on parallelizable context,whichweadoptinourmergingprotocol. 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 | | | | | Figure1: | OverviewofSPD-RAGArchitecture. | | | | | | | | | | --- | --- | --- | --- | -------- | ------------------------------ | --- | --- | --- | --- | --- | --- | --- | --- | of top_n = 5 chunks per search. The documents Similarity-Ordered Merge & Synthesis. t, are split into chunks offline using LangChain’s In iteration we maintain a set of find- RecursiveCharacterTextSplitter in Mark- ings/summaries S(t) = {s (t) ,...,s (t) }. We | | | | | | | | | | | | 1 | n | | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | downmodewithachunksizeof1000tokensand embed each summary using Cohere embed-v4.0 | 250-tokenoverlap. | | | | | | | and compute | | a cosine-similarity | | | matrix | via | | ----------------- | --- | --- | --- | --- | --- | --- | ----------- | --- | ------------------- | --- | --- | ------ | --- | sklearn.metrics.pairwise.cosine_similarity. | FindingsOutput. | | | Wedenotethefindingsofeach | | | | | | | | | | | | --------------- | --- | --- | ------------------------- | --- | --- | --- | ---- | ------------ | --- | ---- | -------- | ------ | --- | | | | | | | | | This | is converted | | to a | distance | matrix | | sub-agentas | | | | | | | | | | | (t) (t) | | | | | --- | --- | --- | --- | --- | --- | --- | --- | --- | ----- | ------- | ----- | --------- | --- | | | | | | | | | D = | 1 − | cos(s | ,s ), | which | is passed | to | | | | | | | | | ij | | | i j | | | | o = α (q,d ) = ⟨s ,r ⟩ (1) AgglomerativeClustering with n_clusters=1, | | | i | i i | i | i | | | | | | | | | | ----- | ---- | ----------- | --- | -------- | -------- | --- | ----------------- | --- | --- | -------- | --- | --------- | --- | | | | | | | | | linkage="average" | | | (UPGMA), | | producing | a | | where | s is | the natural | | language | findings | re- | | | | | | | | i completedendrogramoverallcurrentsummaries. | port from | d | and | r ∈ | [0,1] is | a scalar | rele- | | | | | | | | | ---------------------- | --- | --- | --------------------- | -------- | -------- | ----- | ------------ | --- | ----- | ------------ | ---------- | -------- | --- | | | i | | i | | | | | | | | | | | | | | | | | | | We traverse | the | merge | tree | bottom-up, | greedily | | | vance/confidencescore. | | | Theseoutputsarepassed | | | | | | | | | | | | | | | | | | | accumulating | | nodes | into a batch | as | long as | the | totheSynthesisLayer. | | | | | | | | total tiktoken | | count | does not | exceed | the target | | | ------------ | --- | --- | --- | --------- | --- | ----- | -------------- | ---- | --------- | -------- | ------ | ------------ | --- | | Parallelism. | | All | RAG | loops for | the | docu- | | | | | | | | | | | | | | | | budget | of B | = 750,000 | tokens. | | Semantically | | ments are dispatched concurrently via Lang- similarsummariesaregroupedfirst,soeachbatch Graph’s Send API fan-out, which emits one contains the most similar available summaries | Send("document_sub_agent_node", | | | | | ...) | per | | | | | | | | | ------------------------------- | --- | --- | --- | --- | ---- | --- | --- | --- | --- | --- | --- | --- | --- | withinthetokenlimit. documentandexecutesalltargetsinparallelwithin LangGraph’sasyncruntime. AggregateandSynthesizeFindings. Foreach batch,anLLMispromptedwiththeconcatenated 3.3 SynthesisLayer | | | | | | | | findings, | the synthesis | | directive, | and | the original | | | --- | --- | --- | --- | --- | --- | --- | --------- | ------------- | --- | ---------- | --- | ------------ | --- | WhilemodernLLMsfeatureextensivecontextwin- queryq toproduceanaggregatedandsynthesized dows,synthesizinghundredsofdocumentreports | | | | | | | | summary. | Allbatchsynthesiscallswithinanitera- | | | | | | | --- | --- | --- | --- | --- | --- | --- | -------- | ------------------------------------ | --- | --- | --- | --- | --- | can still exceed these limits or cause ’lost in the tionareissuedconcurrentlyviaasyncio.gather. middle’degradation. Tofuture-proofthearchitec- Themergedsummariesformtheinputforthenext | ture for | massive | corpora, | | the Synthesis | Layer | is | | | | | | | | | -------- | ------- | -------- | --- | ------------- | ----- | --- | --------- | ------- | --- | ---- | --------- | ------ | --- | | | | | | | | | iteration | S(t+1). | The | same | SYNTHESIS | prompt | | designedasadynamicmap-reducepipelinethatag- | | | | | | | | used at | every | level | of the | merging | tree is | ap- | | --- | --- | --- | --- | --- | --- | --- | ------- | ----- | ----- | ------ | ------- | ------- | --- | gregatesfindingsrecursively. TheSynthesisLayer plied even at the final level—there is no separate receives the synthesis directives from the Coor- synthesis-onlyprompt. | dination | Layer | and | the findings | {o | ,o ,...,o | } | | | | | | | | | -------- | ----- | --- | ------------ | --- | --------- | --- | --- | --- | --- | --- | --- | --- | --- | | | | | | | 1 2 | n | | | | | | | | from the Parallel Retrieval Layer. It aggregates ContextSizeConditionandOutput. Thelayer andsynthesizesthefindingsrecursivelythrougha evaluates whether the total findings size exceeds similarity-orderedprocessuntilthetotalfindings the context size threshold (i.e., |S(t+1)| > 1). If sizefallswithinthetargetcontextsizetoproduce theconditionistrue,theprocessloopsbacktothe thefinaloutput. similarity-orderedmergeandsynthesisstep. Ifthe Algorithm1RecursiveSynthesis Locating, Comparison, Clustering, or Chain of Reasoning,withcontextlengthsrangingfrom10K | Require: | FindingsS | = {s ,...,s | },queryq, | | | | | | | | | | -------- | --------- | ----------- | --------- | --- | --------- | ---- | ------- | --- | ----------- | --- | ---------- | | | | 1 | n | | | | | | | | | | | | | | | to beyond | 250K | tokens. | | We evaluate | | on the En- | synthesisdirectived,contextsizebudgetB Ensure: FinalOutputs⋆ glishandSet4(200k-250ktokens)portionofthe | | | | | | benchmarkonly. | | Ourevaluationsetcomprises40 | | | | | | ----------- | ----- | --- | --- | --- | -------------- | --- | --------------------------- | --- | --- | --- | --- | | 1: while|S| | > 1do | | | | | | | | | | | instancesinvolvingacademicpapersand62involv- | 2: E | ← COHEREEMBED(S) | | | | | | | | | | | | ---- | ---------------- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | D ← 1−COSINESIMILARITY(E) ingfinancialreports(102instancestotal),withthe 3: tree ← AGGLOMERATIVECLUSTER( fourtasktypesdistributedas: SpotlightLocating 4: (27),Comparison(15),Clustering(49),andChain | | D, linkage | = “average”) | | | | | | | | | | | --- | ---------- | ------------ | --- | --- | --- | --- | --- | --- | --- | --- | --- | ofReasoning(11). | 5: batches | ← | GROUPBYTOKENS( | | | | | | | | | | | ---------- | -------- | -------------- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | | S, tree, | B) | | | | | | | | | | 4.2 Baselines | 6: if|batches| | | ≥ |S|then | | | | | | | | | | | -------------- | --------- | --------- | --- | --- | --------------------- | --- | ------- | --- | --------- | -------- | --- | | | | | | | We compare | | against | the | following | systems, | all | | 7: | batches ← | [S] | | | | | | | | | | | endif | | | | | backedbyGemini2.5Pro: | | | | | | | 8: | 9: S | ← ASYNCGATHER( | | | | | | | | | | | | ---- | ------------------- | --- | --- | -------- | ------------------------ | --- | --- | --- | --- | --------------- | --- | | | | | | | • Baseline(FullContext): | | | | | Alldocumentsfor | | | | SYNTHESIZE(b,q,d)∀b | | ∈ | batches) | | | | | | | | eachinstanceareconcatenatedandprovided 10: endwhile directlyinthecontextwindowofGemini2.5 | return | S[0] | | | | | | | | | | | | ------ | ---- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | 11: Pro,representingtheoraclelong-contextup- perbound. conditionisfalse(thetotalfindingssizeiswithin • NormalRAG:Standardvectorsearchtop-K thecontextsize,leavingasinglefinalsummarys⋆), | | | | | | retrieval | | over | the full | document | | corpus, fol- | | ----------- | ---------- | --------------------------- | --- | ------------ | --------- | --- | ---- | --------- | -------- | -------- | ------------ | | the process | terminates | and yields | s⋆ | as the final | | | | | | | | | | | | | | lowed | by | LLM | reasoning | | over the | retrieved | | | If|S(t+1)| | |S(t)|(no-progresscase),the | | | | | | | | | | | output. | | ≥ | | | | | | | | | | chunks. entirecurrentlevelisforcedintoasinglebatchto guaranteetermination. | | | | | | • Agentic | | RAG: | A single-agent | | ReAct-style | | | --- | --- | --- | --- | --- | --------- | --- | ---- | -------------- | --- | ----------- | --- | Algorithm1givesthepseudocodeforthegeneral RAG system (implemented via Lang- recursivesynthesisprocedure. Graph/LangChain) that can issue multiple | | | | | | iterative | | retrieval | calls | over | the | full global | | ---------------- | --- | ------------------------ | --- | --- | --------- | --- | --------- | ----- | ---- | --- | ----------- | | BehavioronLoong. | | Inpractice,withGemini2.5 | | | | | | | | | | Pro’s 1M-token context window and a budget of corpus. It uses the same embedding and | | | | | | re-rankingpipelineas | | | | SPD-RAG,butlacks | | | | --- | --- | --- | --- | --- | -------------------- | --- | --- | --- | ---------------- | --- | --- | B = 750,000tokens,thecombinedsub-agentfind- per-documentspecialization. ingsforallinstancesinourLoongevaluationsetfit withinasinglebatch,sotheconditionalcheckal- 4.3 EvaluationMetrics | lowedoutputafteroneiteration. | | | Therecursiveloop | | | | | | | | | | ----------------------------- | --- | --- | ---------------- | --- | --------- | --- | --------- | --- | -------- | --------- | --- | | | | | | | We report | the | following | | metrics, | following | the | isthereforeadesigncapabilityintendedformuch Loongevaluationprotocol(Wangetal.,2024a): largercorpora,wheretotalfindingssizegenuinely exceedsthecontextsize. | | | | | | • Avg | Score: | An | LLM-judged | | score | (0–100) | | --- | --- | --- | --- | --- | ----- | ------ | --- | ---------- | --- | ----- | ------- | measuringthedegreetowhichthepredicted 4 ExperimentalSetup | | | | | | answer | | accurately | covers | | the gold | answer, | | --- | --- | --- | --- | --- | ------ | --- | ---------- | ------ | --- | -------- | ------- | 4.1 Dataset | | | | | | following | | the | evaluation | protocol | | of Loong | | --- | --- | --- | --- | --- | ------------------ | --- | --- | ---------- | -------- | --- | -------- | | | | | | | (Wangetal.,2024a). | | | | 2 | | | WeevaluateSPD-RAGontheLoongbenchmark | (Wangetal.,2024a). | | Loongisanextendedmulti- | | | | | | | | | | | ------------------ | ------------ | ----------------------- | ------- | -------- | ------------------- | --- | --- | --- | ------------------- | --- | --- | | | | | | | • PerfectRate(PR%): | | | | Percentageofqueries | | | | document | QA instances | in both | English | and Chi- | | | | | | | | wherethesystemreceivesaperfectscoreof | nese, with | an average | of 11 documents | | per test | | | | | | | | | ---------- | ---------- | --------------- | --- | -------- | --- | --- | --- | --- | --- | --- | --- | 100. | casespanningtworeal-worldscenarios: | | | | financial | | | | | | | | | ----------------------------------- | --- | --- | --- | --------- | --- | --- | --- | --- | --- | --- | --- | reports and academic papers. Each test case is 2BecausetheoriginallegacyGPT-4modelAPIisunavail- ablefornewscaledevaluations,weadoptGPT-5astheeval- constructedsothateverydocumentisrelevantto | | | | | | uator. Recent | literature | | increasingly | | adopts | GPT-5 as the | | --- | --- | --- | --- | --- | ------------- | ---------- | --- | ------------ | --- | ------ | ------------ | the final answer ("Leave No Document Behind", standard for LLM-as-a-judge protocols due to its superior meaningacorrectresponserequiressynthesizing alignmentwithhumangrading(Wolfsonetal.,2025).While ourbaselinenumbersarere-computedunderthisGPT-5judge evidencefromallprovideddocuments),andisan- forstrictfairness,rawscoresmaynotbeperfectly1-to-1com- notated under one of four task types: Spotlight parablewiththeoriginallypublishedLoongleaderboard. • AvgTokenUsage: Meanper-querytotal(in- Cost tracking. Per-query costs are computed put+output)tokenusage. fromLangSmithrunmetadata(totaltokencounts ×per-tokenpricing. | • Avg | Cost | (USD): | Mean | per-query | | API cost, | | | | | | | | ----------------------------------- | ---- | ------ | ---- | --------- | --- | --------- | --------- | --- | --- | --- | --- | --- | | enablingcost-efficiencycomparisons. | | | | | | | 5 Results | | | | | | 5.1 MainResults | • AvgLatency(s): | | | Meanper-querywall-clock | | | | | | | | | | | ---------------- | --- | --- | ----------------------- | --- | --- | --- | --- | --- | --- | --- | --- | --- | time. Table1reportsthemainresultsacrossallsystems | | | | | | | | on our Loong | evaluation | | set. | Figure | 2 illustrates | | --- | --- | --- | --- | --- | --- | --- | ------------ | ---------- | --- | ---- | ------ | ------------- | 4.4 ImplementationDetails theseperformancedifferencesacrossthefourspe- | AllsystemsuseGemini2.5Pro(temperature0.0) | | | | | | | cifictasktypes. | | | | | | | ----------------------------------------- | --- | --- | --- | --- | --- | --- | --------------- | --- | --- | --- | --- | --- | asthebackboneLLMforthecoordinator,merging SPD-RAGachievesanAvgScoreof58.1,sub- | layer, and | synthesis | | layer. | SPD-RAG | | document | | | | | | | | ---------- | --------- | --- | ------ | ------- | --- | -------- | --- | --- | --- | --- | --- | --- | stantiallyoutperformingbothNormalRAG(33.0) | sub-agents | use | Gemini | 2.5 | Flash | for | document- | | | | | | | | ---------- | --- | ------ | --- | ----- | --- | --------- | ----------- | --- | ------- | --- | ---------------- | --- | | | | | | | | | and Agentic | RAG | (32.8). | | This corresponds | to | scopedretrieval,reducingper-documentcostwhile roughly a 25-point absolute improvement (about preserving reasoning quality at the merging and 76% relative gain) over standard RAG baselines. | synthesisstages. | | GPT-5isusedexclusivelyasthe | | | | | | | | | | | | ---------------- | --- | --------------------------- | --- | --- | --- | --- | ------------ | ------- | --- | ---- | ---- | ----------- | | | | | | | | | Furthermore, | SPD-RAG | | more | than | doubles the | LLMjudgefortheevaluationoftheAvgscoreand Perfect Rate (PR) of Agentic RAG (18.6% vs. is not part of the pipeline itself. Prompts for all 8.8%),indicatingthatexhaustivedocument-level | systems, | including | baselines, | | were | optimized | for | | | | | | | | -------- | --------- | ---------- | --- | ---- | --------- | --- | --- | --- | --- | --- | --- | --- | processingmorefrequentlycapturesthecomplete informationextractionandsynthesistoensurefair set of required facts. Notably, the Agentic RAG comparison. Fullprompttemplatesforallsystems baseline,despiteconsuming∼3×moretokensthan areprovidedinthecoderepository. NormalRAG,doesnotyieldscoreimprovements, suggestingthatuninstructediterativeretrievalwith- | Indexing. | Each | | document | | is | pre- | | | | | | | | --------- | ---- | --- | --------- | --- | ------ | ---- | ---------------- | --- | -------------- | --- | ---- | ---------- | | | | | | | | | out per-document | | specialization | | does | not effec- | | indexed | into | a | dedicated | | Qdrant | vec- | | | | | | | tivelyaddressthecoveragechallengeinexhaustive | tor | collection | | using | | LangChain’s | | | | | | | | | ------------------------------ | ---------- | --- | ----- | --- | ----------- | ------- | -------------- | ------- | ----------- | ----- | ------------ | ------------ | | | | | | | | | multi-document | | settings. | While | the | full-context | | RecursiveCharacterTextSplitter | | | | | | (Imple- | | | | | | | | | | | | | | | baseline | attains | the highest | | quality with | an Avg | mentationdetailsareprovidedintheAppendix.). | | | | | | | | Score of | 68.0, | SPD-RAG | reaches | 85.4% | of this | | --- | --- | --- | --- | --- | --- | --- | -------- | ----- | ------- | ------- | ----- | ------- | Retrievalpersub-agent. Eachsearchtoolcall performancewhileconsumingonly37.9%ofthe performs dense vector retrieval over the docu- API cost, demonstrating a highly favorable cost- | | | | | | | | quality trade-off. | | The | full-context | baseline | pro- | | --------------------------- | --- | --- | --- | --- | -------------- | --- | ------------------ | --- | --- | ------------ | -------- | ---- | | mentindex,returningthetop-K | | | | | (k = 15)chunks | | | | | | | | using cosine similarity in Qdrant. These can- vides a strong upper bound that benefits from all didates are subsequently re-ranked with Cohere documents in one pass. Despite this, SPD-RAG narrowsthegapto9.9pointswhilecostingless. | rerank-v4.0-fast,andthetoptop_n | | | | | = | 5chunks | | | | | | | | ------------------------------- | --- | --- | --- | --- | --- | ------- | --- | --- | --- | --- | --- | --- | arepassedtothesub-agentLLM. 5.2 AnalysisbyTaskType | Merging | layer. | The | token | budget | | per merge | | | | | | | | ------- | ------ | --- | ----- | ------ | --- | --------- | --- | --- | --- | --- | --- | --- | Table2breaksdownperformancebyLoong’sfour | batchisB | = 750,000tokens(75%ofGemini2.5 | | | | | | | | | | | | | -------- | ------------------------------ | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | tasktypes. | Pro’s1Mcontextcap). | | | Agglomerativeclustering | | | | | | | | | | | ------------------- | --- | --- | ----------------------- | --- | --- | --- | --- | --- | --- | --- | --- | --- | Thetask-typebreakdownrevealsaclearpattern. usessklearnwithUPGMAlinkageandprecom- | | | | | | | | For Spotlight | Locating—which | | | requires | finding | | --- | --- | --- | --- | --- | --- | --- | ------------- | -------------- | --- | --- | -------- | ------- | putedcosine-distancematrix. asinglesalientfactacrossdocuments—allRAG- | | | | | | | | based systems | | perform | reasonably | well | (69–74), | | ------------- | --------------- | ----------------------------- | ----- | --------- | ----------- | ---------- | -------------------------------------------- | ------------ | ------- | ---------- | ----- | ----------- | | Parallelism. | | Documentagentsrunconcurrently | | | | | | | | | | | | | | | | | | | while the | full-context | | baseline | leads | (86.6). The | | via LangGraph | | Send | API | fan-out. | Batch | synthe- | | | | | | | | | | | | | | | largest gaps | between | | SPD-RAG | and | the RAG | | sis calls | within | each | merge | iteration | | are paral- | | | | | | | | | asyncio.gather. | | | | | | baselinesappearinClustering(+40.5ptsoverNor- | | | | | | | lelized | with | | | | Synchronous | Co- | | | | | | | malRAG)andChainofReasoning(+26.2ptsover hereAPIcallsareoffloadedtothethreadpoolvia | | | | | | | | Agentic RAG), | | tasks | that require | aggregating | or | | --- | --- | --- | --- | --- | --- | --- | ------------- | --- | ----- | ------------ | ----------- | --- | asyncio.to_thread. reasoningoverevidencefrommanydocumentssi- Safety limits. Each document sub-agent’s iter- multaneously. Ontheotherhand,itnearlymatches ative retrieval loop is capped at 5 iterations; the the full-context baseline (42.2 vs. 42.7) on Com- | AgenticRAGbaselinecapsat10iterations. | | | | | | | parisontasks. | | | | | | | ------------------------------------- | --- | --- | --- | --- | --- | --- | ------------- | --- | --- | --- | --- | --- | 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 | 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 | SPD-RAG | | 58.1 | 18.6 193,954 | 205,683 | 0.103 | 54.8 | | ------- | --- | ---- | ------------ | ------- | ----- | ---- | Table1: MainresultsonourLoongevaluationset(102instances: 40academicpaper,62financialreport). Bold: | bestoverall. | Underline: | bestamongRAG-basedsystems. | | | | | | ------------ | ---------- | -------------------------- | --- | --- | --- | --- | Figure2: ComparisonofAverageScoreacrossthefoursystems,brokendownbytasktype(SpotlightLocating, Comparison,Clustering,andChainofReasoning). 5.3 AnalysisbyDocumentDomain | As shown | in Table 3 | and Figure | 3 the domain | | | | | -------- | ---------- | ---------- | ------------ | --- | --- | --- | breakdownexposesastrikingfailuremodeofstan- | dard RAG: | both Normal | RAG and | Agentic RAG | | | | | --------- | ----------- | ------- | ----------- | --- | --- | --- | achieve0%PRonacademicpaperinstances(Avg | Scoresof15.2and16.8,respectively). | | | Academic | | | | | ---------------------------------- | --- | --- | -------- | --- | --- | --- | paperinstancesinLoongtendtoinvolvelong,tech- nicaldocumentswithdistributedevidence,making top-K retrievalespeciallysusceptibletocoverage failures. SPD-RAGdramaticallyrecoversonthis domain(60.0AvgScore),closingmostofthegap | with the | full-context | baseline (78.8). | On finan- | | | | | -------- | ------------ | ---------------- | --------- | --- | --- | --- | Figure3: Averagescorebydocumentdomaincompar- cialreportinstances—whichtendtobeshorterand ingtheBaseline(FullContext),NormalRAG,Agentic morestructured—allRAG-basedsystemsperform RAG,andSPD-RAGsystems. better,andthegapbetweenSPD-RAG(56.9)and thebaseline(61.0)issmaller. | | System | | SpotlightLoc. | | Comparison | Clustering | ChainofReasoning | | | | | | --- | ------ | --- | ------------- | --- | ---------- | ---------- | ---------------- | ------ | --- | --- | --- | | | | | (n=27) | | (n=15) | (n=49) | | (n=11) | | | | Baseline(FullContext) 86.6/55.6% 42.7/26.7% 67.1/14.3% 60.9/54.5% | | NormalRAG | | 69.7/37.0% | | 37.7/26.7% | 15.4/0.0% | | 14.8/0.0% | | | | | --- | ---------- | --- | ---------- | --- | ---------- | --------- | --- | ---------- | --- | --- | --- | | | AgenticRAG | | 73.4/22.2% | | 23.3/20.0% | 16.7/0.0% | | 17.9/0.0% | | | | | | SPD-RAG | | 74.2/44.4% | | 42.2/20.0% | 57.2/4.1% | | 44.1/18.2% | | | | Table2: AvgScore/PR(%)byLoongtasktype. EachcellshowsAvgScore/PR%. | | | | | | Crucially, | thearchitectureof | | | SPD-RAG | | enables | | -------- | --- | ----------- | --------------- | --- | ---------- | ----------------- | --- | --------------- | ------- | --------- | ------- | | System | | Paper(n=40) | Financial(n=62) | | | | | | | | | | | | | | | this | cost efficiency: | | by constraining | | retrieval | to | | Baseline | | 78.8/30.0% | 61.0/32.3% | | | | | | | | | NormalRAG 15.2/0.0% 44.5/22.6% isolated,single-documentspaces,wecanreliably | AgenticRAG | | 16.8/0.0% | 43.1/14.5% | | | | | | | | | | ---------- | --- | --------- | ---------- | --- | --- | --- | --- | --- | --- | --- | --- | offloadtheiterativereasoningtoacheapermodel | SPD-RAG | | 60.0/7.5% | 56.9/25.8% | | | | | | | | | | ------- | --- | --------- | ---------- | --- | ------- | ----------- | --- | ------------ | --- | ------ | --------- | | | | | | | (Gemini | 2.5 Flash). | | In contrast, | | global | iterative | baselinesrequirethereasoningcapacityofafron- Table3: AvgScore/PR(%)bydocumentdomain. | | | | | | tier | model (Gemini | 2.5 | Pro) | to navigate | | the fully | | --- | --- | --- | --- | --- | ---- | ------------- | --- | ---- | ----------- | --- | --------- | concatenatedcorpus,drivingupcosts. | System | | AvgScore | AvgCost($) | Score/$ | | | | | | | | | ---------- | --- | -------- | ---------- | ------- | ------------------- | ---------- | --- | ---- | ------------------- | --- | --------- | | Baseline | | 68.0 | 0.273 | 249.1 | | | | | | | | | NormalRAG | | 33.0 | 0.080 | 412.5 | 6 | Discussion | | | | | | | AgenticRAG | | 32.8 | 0.098 | 334.7 | | | | | | | | | SPD-RAG | | 58.1 | 0.103 | 564.1 | | | | | | | | | | | | | | WhereSPD-RAGexcels. | | | | Thetask-typeresults | | | | | | | | | in Table | 2 confirm | our | core | hypothesis: | | architec- | Table4: Cost–qualitytradeoff. turaldecompositionalongthedocumentaxisyields | | | | | | the largest | gains | precisely | | for tasks | that | require | | --- | --- | --- | --- | --- | ----------- | -------------- | --------- | --- | ---------- | ---------- | ------- | | | | | | | exhaustive | multi-document | | | synthesis. | Clustering | | 5.4 Cost–QualityTradeoff tasks(+40.5overNormalRAG)andChainofRea- | | | | | | soning | tasks (+26.2 | over | Agentic | | RAG) | both de- | | --- | --- | --- | --- | --- | ------ | ------------ | ---- | ------- | --- | ---- | -------- | mandthatthesystemformacoherentanswerfrom evidencedistributedacrossallrelevantdocuments— exactlythescenarioforwhichper-documentagents | | | | | | aredesigned. | ForLoong’sComparisonquestions, | | | | | | | --- | --- | --- | --- | --- | ------------ | ------------------------------ | --- | --- | --- | --- | --- | theanswerisprimarilydeterminedbyreadingout asmallsetofcomparablenumericattributesfrom | | | | | | each | report and | aggregating | | them, | so once | docu- | | --- | --- | --- | --- | --- | -------- | -------------- | ----------- | --------- | ----- | ------------ | -------- | | | | | | | ment | agents have | extracted | | those | fields, | the syn- | | | | | | | thesizer | is effectively | | operating | over | a structured | | table,whichhelpsexplainwhySPD-RAG’sscore onComparisontasks(42.2)nearlymatchesthefull- Figure4: Cost–Qualitytradeoff. Thescatterplotillus- contextbaseline(42.7). tratestheParetofrontierformulti-documentQA. | | | | | | Remaining | gap | with | the | full-context | | baseline. | | --- | --- | --- | --- | --- | --------- | --- | ---- | --- | ------------ | --- | --------- | SPD-RAG outperforms the RAG baselines in Despite strong gains over RAG baselines, SPD- the cost–quality trade-off. It achieves a 76% RAG lags the full-context baseline by 9.9 Avg higheraveragescorethanNormalRAG(58.1vs. Score points overall. Three factors likely con- 33.0)whileincreasingtheper-querycostbyonly tribute: (1) Gemini 2.5 Flash, used for document $0.023 (Table 4). In contrast, Agentic RAG is agents due to its cost efficiency, may provide Pareto-dominated, as it incurs higher cost than weaker agentic reasoning capabilities compared NormalRAG($0.098vs.$0.080)whiledelivering to larger models. (2) The coordinator’s sub-task nearlyidenticalquality(32.8vs.33.0). Compared generation may under-specify queries for highly tothefull-contextbaseline,whichachievesanAvg technical academic content, leading to incom- Score of 68.0, SPD-RAG reaches 85.4% of the pleteinformationextractionatthedocument-agent qualityatonly37.9%ofthecost, resultingina level. (3) The maximum context length observed 2.25× improvement in cost–quality efficiency. inLoongquerieswasapproximately250ktokens— 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 | | | | | | | | ------------ | --- | ------- | ---------- | --- | ------ | --- | --- | --- | --- | --- | --- | --- | differentstructuralcharacteristicsthatalterrelative | rate and | 15.2-16.8% | | Avg Score | of | both Normal | | | | | | | | | -------- | ---------- | --- | --------- | --- | ----------- | --- | --- | --- | --- | --- | --- | --- | performance. | RAG and | Agentic | RAG | on | academic | paper | in- | | | | | | | | ------- | ------- | --- | --- | -------- | ----- | --- | --- | --- | --- | --- | --- | --- | ThesynthesislayerofSPD-RAGisdesignedto top-K | stances | highlights | a systematic | | failure | of | | | | | | | | | ------- | ---------- | ------------ | --- | ------- | --- | --- | --- | --- | --- | --- | --- | --- | handlecorporawherethecombinedsub-agentout- | retrieval | under | dense, | distributed | evidence. | | Aca- | | | | | | | | --------- | ----- | ------ | ----------- | --------- | --- | ---- | --- | --- | --- | --- | --- | --- | putsexceedthemodel’scontextwindow,triggering | demic | papers | in Loong | often | require | synthesiz- | | | | | | | | | ----- | ------ | -------- | ----- | ------- | ---------- | --- | --- | --- | --- | --- | --- | --- | multi-roundsimilarity-basedclusteringandrecur- ingfactsspreadacrossabstracts,methods,results, | | | | | | | | sive synthesis. | | In our Loong | experiments, | | how- | | --- | --- | --- | --- | --- | --- | --- | --------------- | --- | ------------ | ------------ | --- | ---- | andappendicesofmultipledocuments—aregime ever,Gemini2.5Pro’s1M-tokencontextwassuffi- | where exhaustive | | per-document | | coverage | proves | | | | | | | | | ---------------- | --- | ------------ | --- | -------- | ------ | --- | --- | --- | --- | --- | --- | --- | cienttoprocessallsub-agentsummariesinasingle | highly | effective. | The | recovery | by SPD-RAG | | to | | | | | | | | ------ | ---------- | --- | -------- | ---------- | --- | --- | ----------- | --- | -------------- | ---- | --- | ----------- | | | | | | | | | merge step, | so | this recursive | path | was | never acti- | 60.0AvgScore/7.5%PRinthisdomaindemon- | | | | | | | | vated. Asaresult,thescalabilitypropertiesofthe | | | | | | | --- | --- | --- | --- | --- | --- | --- | ---------------------------------------------- | --- | --- | --- | --- | --- | stratesthepracticalvalueofper-documentspecial- recursivesynthesispipeline—itsimpactonanswer ization. | | | | | | | | quality, | redundancy | reduction, | | and cost | as docu- | | --- | --- | --- | --- | --- | --- | --- | -------- | ---------- | ---------- | --- | -------- | -------- | Latencyoverhead. SPD-RAG incursamodest mentcountsgrowintothehundredsorthousands— latency overhead (54.8 s vs. 40.6–45.6 s for the remainempiricallyunvalidated. EvaluatingSPD- | baselines), | primarily | | due to | its multi-agent | | archi- | | | | | | | | ----------- | --------- | --- | ------ | --------------- | --- | ------ | --- | --- | --- | --- | --- | --- | RAGonmuchlargercorpora,andideallyonanew tecture. Although document agents run in paral- benchmarkspecificallytargetinghundredstothou- lel,end-to-endlatencyisstillgatedbythelayered sands of documents per query instance, is an im- pipeline, which requires at least three sequential portantdirectionforfutureworkthatwouldmore LLM calls (coordination, per-document retrieval, directlytesttheper-documentagentdesignandthe andsynthesis)ratherthanasinglepass. intendedlarge-databaseregime. | | | | | | | | 8 Conclusion | | | | | | | --- | --- | --- | --- | --- | --- | --- | -------------------------- | --- | -------- | ---------------- | ---------- | ------ | | | | | | | | | We presented | | SPD-RAG, | a hierarchical | | multi- | | | | | | | | | agent framework | | designed | for | exhaustive | multi- | | | | | | | | | documentquestionanswering. | | | Byassigningaded- | | | icatedagenticRAGmoduletoeachdocumentand mergingtheirfindingsthroughasimilarity-ordered, | | | | | | | | token-bounded | | tree, our | system | effectively | miti- | | --- | --- | --- | --- | --- | --- | --- | ----------------------------------------- | ---------- | --------- | --------- | ----------- | ---------- | | | | | | | | | gatesthelimitationsofbothtraditionaltop-K | | | | | re- | | | | | | | | | trieval (incomplete | | coverage) | and | isolated | long- | | | | | | | | | context | processing | (degraded | reasoning | | at scale). | Figure5: Averageper-querylatencyacrosssystems. ExperimentsonthechallengingLoongbenchmark demonstratethatSPD-RAGachievesanaverage scoreof58.1,outperformingNormalRAG(33.0) 7 LimitationsandFutureWork andAgenticRAG(32.8)byapproximately25ab- SPD-RAG incurs higher per-query LLM call solute points (76% higher average score), while countsthansingle-passsystems—onecallperdoc- operating at only 37.9% of the API cost of the ument agent plus a final synthesis step—which oracle full-context baseline (68.0). These per- increasestotaltokenusagecomparedtostandard formance gains are most pronounced in tasks re- RAGbaselines,thoughitremainsatroughlyone- quiring deep cross-document synthesis—such as thirdthecostofthefull-contextbaseline($0.10vs. Clustering(+40.5points)andChainofReasoning $0.27perquery)duetotheuseofGemini2.5Flash (+26.2points)—aswellasondenseacademicpa- fordocumentsub-agents. Systemqualitydepends perswherestandardRAGmethodsentirelyfail. Ultimately, our findings demonstrate that for TaichengGuo,XiuyingChen,YaqiWang,RuidiChang, complex information-seeking queries over large ShichaoPei,NiteshV.Chawla,OlafWiest,andXi- | | | | | | | | angliangZhang.2024. | | | Largelanguagemodel-based | | | | -------- | --- | ----------- | --- | ------------ | --- | -------- | ------------------- | --- | ------------------------------- | ------------------------ | --- | --- | | corpora, | how | information | | is processed | is | crucial. | | | | | | | | | | | | | | | multi-agents: | | Asurveyofprogressandchallenges. | | | | Specifically,ensuringthateachdocumentreceives | | | | | | | | Preprint,arXiv:2402.01680. | | | ArXiv:2402.01680. | | | | --- | --- | --- | --- | --- | --- | --- | -------------------------- | --- | --- | ----------------- | --- | --- | exhaustive,dedicatedagenticattentionprovestobe | | | | | | | | Siwei Han, | Peng | Xia, Ruiyi | Zhang, | Tong | Sun, Yun | | --- | --- | --- | --- | --- | --- | --- | ---------- | ---- | ---------- | ------ | ---- | -------- | amoreeffective,cost-efficient,andscalablestrat- | | | | | | | | Li, Hongtu | Zhu, | and | Huaxiu Yao. | 2025. | MDocA- | | --- | --- | --- | --- | --- | --- | --- | ---------- | ---- | --- | ----------- | ----- | ------ | egythansimplyexpandinghowmuchrawcontext gent: Amulti-modalmulti-agentframeworkfordoc- asinglemodelcanconsumeinonepass. umentunderstanding. Preprint,arXiv:2503.13964. ArXiv:2503.13964. 9 EthicsStatement | | | | | | | | Yubin Kim, | Ken | Gu, Chanwoo | Park, | Chunjong | Park, | | --- | --- | --- | --- | --- | --- | --- | ---------- | --- | ----------- | ----- | -------- | ----- | SPD-RAG is a retrieval-augmented generation SamuelSchmidgall,A.AliHeydari,YaoYan,Zhi- hanZhang,YuchenZhuang,MarkMalhotra,PaulPu | framework | intended | | to improve | access | | to infor- | | | | | | | | --------- | -------- | --- | ---------- | ------ | --- | --------- | --- | --- | --- | --- | --- | --- | Liang,HaeWonPark,YuzheYang,XuhaiXu,Yilun | mation | distributed | across | | large document | | collec- | | | | | | | | ------ | ----------- | ------ | --- | -------------- | --- | ------- | ----------- | --- | ---------- | -------- | ------ | ------- | | | | | | | | | Du, Shwetak | | Patel, Tim | Althoff, | Daniel | McDuff, | tions. ThesystemreliesoncommercialLLMAPIs and Xin Liu. 2025. Towards a science of scal- (Gemini2.5,Cohere),andaswithanyLLM-based ing agent systems. Preprint, arXiv:2512.08296. ArXiv:2512.08296. | pipeline, | outputs | may | contain | factual | errors | and | | | | | | | | --------- | ------- | --- | ------- | ------- | ------ | --- | --- | --- | --- | --- | --- | --- | should not be treated as authoritative without hu- PatrickLewis,EthanPerez,AleksandraPiktus,Fabio man verification. The increased number of API Petroni,VladimirKarpukhin,NamanGoyal,Hein- | | | | | | | | richKüttler, | | MikeLewis, | Wen-tauYih, | | TimRock- | | --------- | ------ | -------- | ------------ | ----------- | ------- | --------- | ------------------- | --------- | ---------- | ----------- | ----- | ------------ | | calls per | query | relative | to | single-pass | systems | re- | | | | | | | | | | | | | | | täschel, | Sebastian | Riedel, | and | Douwe | Kiela. 2020. | | sults in | higher | energy | consumption, | | a | cost that | | | | | | | | | | | | | | | Retrieval-augmented | | | generation | for | knowledge- | should be weighed against the accuracy gains in intensiveNLPtasks. InAdvancesinNeuralInforma- deploymentdecisions. Ourbenchmarkevaluations tionProcessingSystems. | are conducted | | on publicly | | available | datasets | and | | | | | | | | ------------- | --- | ----------- | --- | --------- | -------- | --- | -------- | -------- | ------------- | --- | ------------- | --- | | | | | | | | | TengLin, | YuyuLuo, | HonglinZhang, | | JichengZhang, | | involvenohumansubjectsorpersonaldata. Dur- Chunlin Liu, Kaishun Wu, and Nan Tang. 2025. ingthepreparationofthismanuscript,AI-assisted MEBench: Benchmarkinglargelanguagemodelsfor | | | | | | | | cross-documentmulti-entityquestionanswering. | | | | | In | | --- | --- | --- | --- | --- | --- | --- | -------------------------------------------- | --- | --- | --- | --- | --- | writingtoolswereusedtohelprefinelanguageand ProceedingsoftheConferenceonEmpiricalMethods | improveclarity. | | Allresearchdesign,experiments, | | | | | | | | | | | | --------------- | --- | ------------------------------ | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | inNaturalLanguageProcessing(EMNLP). analysis,andfinaldecisionsregardingthecontent NelsonF.Liu,KevinLin,JohnHewitt,AshwinParan- wereconductedandverifiedbytheauthors. jape,MicheleBevilacqua,FabioPetroni,andPercy | | | | | | | | Liang.2023. | | Lostinthemiddle: | | Howlanguagemod- | | | --- | --- | --- | --- | --- | --- | --- | ------------------- | --- | ---------------------------- | --- | --------------- | --- | | | | | | | | | elsuselongcontexts. | | TransactionsoftheAssociation | | | | References forComputationalLinguistics,12. | Haonan | Bian. | 2025. | LLM-empowered | | | knowl- | | | | | | | | ------ | ----- | ----- | ------------- | --- | --- | ------ | --- | --- | --- | --- | --- | --- | SeijiMaekawa,HayateIso,andNikitaBhutani.2024. | edge | graph | construction: | | A survey. | | Preprint, | | | | | | | | ----------------- | ----- | ------------- | ----------------- | --------- | --- | --------- | ------------------------------------- | --- | --- | --- | --- | ------- | | | | | | | | | Holisticreasoningwithlong-contextLMs: | | | | | Abench- | | arXiv:2510.20345. | | | ArXiv:2510.20345. | | | | | | | | | | markfordatabaseoperationsonmassivetextualdata. Darren Edge, Ha Trinh, Newman Cheng, Joshua Preprint,arXiv:2410.11996. ArXiv:2410.11996. | Bradley, | Alex | Chao, | | Apurva | Mody, | Steven | | | | | | | | -------- | ---- | ----- | --- | ------ | ----- | ------ | -------- | ------ | ----- | ---------- | --- | ------------ | | | | | | | | | David J. | Moore. | 2025. | A taxonomy | of | hierarchical | Truitt, Dasha Metropolitansky, Robert Osazuwa multi-agentsystems: Designpatterns,coordination | Ness, | and | Jonathan | Larson. | 2024. | | From lo- | | | | | | | | --------------------- | ------- | -------- | -------------------------- | -------- | --- | --------- | ----------------- | --- | ----------------- | ------------- | --- | --------- | | | | | | | | | mechanisms, | | and industrial | applications. | | Preprint, | | cal to | global: | A graph | RAG | approach | | to query- | | | | | | | | | | | | | | | arXiv:2508.12683. | | ArXiv:2508.12683. | | | | | focusedsummarization. | | | Preprint,arXiv:2404.16130. | | | | | | | | | | ArXiv:2404.16130. | | | | | | | | LituOuandMirellaLapata.2025. | | | | Context-awarehier- | | | --- | --- | --- | --- | --- | --- | --- | ---------------------------- | --- | --- | --- | ------------------ | --- | archicalmergingforlongdocumentsummarization. | Gemini | Team, | Petko | Georgiev, | Ving | Ian Lei, | Ryan | | | | | | | | -------- | ----- | ----- | --------- | ------- | -------- | ------- | -------------------------- | --- | --- | ----------------- | --- | --- | | | | | | | | | Preprint,arXiv:2502.00977. | | | ArXiv:2502.00977. | | | | Burnell, | Libin | Bai, | Anmol | Gulati, | Garrett | Tanzer, | | | | | | | DamienVincent,ZhufengPan,ShiboWang,and1 Parth Sarthi, Salman Abdullah, Aditi Tuli, Shubh others. 2024. 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V.A.Traag,L.Waltman,andN.J.vanEck.2019. From extraction tasks and write a synthesis louvaintoleiden: Guaranteeingwell-connectedcom- directive to guide the downstream | munities. | ScientificReports,9(1):5233. | | | | | | synthesizer. | | | | | | | --------- | ---------------------------- | --- | --- | --- | --- | --- | ------------- | --- | ------ | --- | ---- | -------- | | | | | | | | | 2. Sub-agents | | (next) | — | Each | document | Minzheng Wang, Longze Chen, Cheng Fu, Shengyi is assigned one worker that runs your Liao,XinghuaZhang,BingliWu,HaiyangYu,Nan tasks independently. Workers search Xu,LeiZhang,RunLuo,YunshuiLi,MinYang,Fei their document and report raw findings. | | | | | | | | They run | in parallel | | and | cannot | see each | | ------------------------- | --- | --- | --- | --------------- | --- | --- | -------- | ----------- | --- | --- | ------ | -------- | | Huang,andYongbinLi.2024a. | | | | Leavenodocument | | | | | | | | | other’s results. | behind: | Benchmarkinglong-contextLLMswithex- | | | | | | | | | | | | | ------------------ | ----------------------------------- | --- | -------------------------- | --- | --- | --- | -------------- | -------- | ------ | ------ | ------------ | -------- | | | | | | | | | 3. Synthesizer | | (last) | — | A downstream | | | tendedmulti-docQA. | | | Preprint,arXiv:2406.17419. | | | | | | | | | | | | | | | | | | agent | receives | ALL | worker | | findings | ArXiv:2406.17419. | | | | | | | | and merges | | them | into | a | single | | ---------- | ------ | --- | ----- | -------- | ----- | ------ | ------------------- | --------- | ---- | --------- | ------- | ------- | | | | | | | | | coherent | response. | | It | follows | your | | Qian Wang, | Tianyu | | Wang, | Zhenheng | Tang, | Qinbin | | | | | | | | | | | | | | | synthesis_directive | | | to decide | | what to | Li,NuoChen,JingshengLiang,andBingshengHe. | | | | | | | | prioritize | and | how | to | structure | the | | ------ | ---------- | --- | -------------------------- | --- | --- | --- | ---------- | --- | --- | --- | --------- | --- | | 2024b. | Megaagent: | | Alarge-scaleautonomousLLM- | | | | | | | | | | output. basedmulti-agentsystemwithoutpredefinedSOPs. | | | | | | | | You CANNOT | see | the | names, | types, | or | | -------------------------- | --- | --- | --- | ----------------- | --- | --- | ---------- | ------ | ---------- | ------ | ------ | --- | | Preprint,arXiv:2408.09955. | | | | ArXiv:2408.09955. | | | | | | | | | | | | | | | | | content | of the | documents. | | | | TomerWolfson,HarshTrivedi,MorGeva,YoavGold- Your ONLY job in this step: | berg, | Dan Roth, | Tushar | Khot, | Ashish | | Sabharwal, | | | | | | | | ------------------ | --------- | ------ | -------------------------- | ------- | --------- | ---------- | ---------- | ---------- | ---------------------- | ----------- | -------------- | ------- | | | | | | | | | • Produce | | a list | of | subagent_todos | | | and Reut | Tsarfaty. | | 2025. | Monaco: | | More nat- | | | | | | | | | | | | | | | (as | structured | | output): | | precise | | ural and | complex | | questions | for | reasoning | across | | | | | | | | | | | | | | | extraction | | tasks | | that | will be | | dozensofdocuments. | | | Preprint,arXiv:2508.11133. | | | | | | | | | | | | | | | | | | executed | | independently | | against | EACH | | ArXiv:2508.11133. | | | | | | | document | | by the | sub-agents. | | | | | | | | | | | • Produce | | a synthesis_directive: | | | a | Wentao Zhang, Liang Zeng, Yuzhen Xiao, Yong- concise instruction (2–4 sentences) cong Li, Ce Cui, Yilei Zhao, Rui Hu, Yang Liu, for the synthesizer. | Yahui | Zhou, | and Bo | An. | 2025. | AgentOrchestra: | | | | | | | | | ----- | ----- | ------ | --- | ----- | --------------- | --- | --- | --- | --- | --- | --- | --- | A hierarchical multi-agent framework for general- Todo-writing rules: | purpose | task | solving. | Preprint, | arXiv:2506.12508. | | | | | | | | | | ----------------- | ---- | -------- | --------- | ----------------- | --- | --- | ----------- | --- | ----------- | ---- | ------------- | ---- | | | | | | | | | • Decompose | | the | user | query | into | | ArXiv:2506.12508. | | | | | | | concrete | | information | | requirements. | | Jun Zhao, Can Zu, Hao Xu, Yi Lu, Wei He, Yiwen • Each todo must be self-contained and unambiguous. | Ding, | Tao Gui, | Qi | Zhang, | and | Xuanjing | Huang. | | | | | | | | ----- | ---------- | --- | --------------------------- | --- | -------- | ------ | -------- | --- | ------ | ----- | ---- | ----- | | | | | | | | | • Prefer | | atomic | tasks | over | broad | | 2024. | LongAgent: | | Scalinglanguagemodelsto128k | | | | | | | | | | tasks. | contextthroughmulti-agentcollaboration. | | | | | | Preprint, | | | | | | | | --------------------------------------- | ----- | --- | ----------------- | ----- | ---- | --------- | --------- | --- | --------------- | ------------ | ------------ | -------- | | | | | | | | | • Include | | coverage | for: | definitions, | | | arXiv:2402.11550. | | | ArXiv:2402.11550. | | | | | | | | | | | | | | | | | | numeric | | values, | constraints, | | edge | | | | | | | | | cases, | | error | modes, | and | any | | Zihan Zhou, | Chong | | Li, Xinyi | Chen, | Shuo | Wang, | | | | | | | | | | | | | | | explicit | | recommendations | | | required | YuChao,ZhiliLi,HaoyuWang,QiShi,ZhixingTan, | | | | | | | | by | the user | query. | | | | | --- | --- | --- | --- | --- | --- | --- | --- | -------- | ------ | --- | --- | --- | XuHan,XiaodongShi,ZhiyuanLiu,andMaosong | | | | | | | | • Design | a | robust | extraction | | list | | ---------- | ---------- | -------------- | ----- | ----- | ---------- | ------- | -------- | ----- | ------ | ------------ | --- | ------ | | Sun. 2025. | | LLM×MapReduce: | | | Simplified | long- | | | | | | | | | | | | | | | that | works | for | ANY document | | in the | | sequence | processing | | using | large | language | models. | | | | | | | set. InProceedingsoftheAnnualMeetingoftheAssocia- | tionforComputationalLinguistics(ACL). | | | | | | | Important | Constraints: | | | | | | ---------------------------------------- | --- | ----- | --------- | --- | --- | ---------- | --------- | ------------ | ----- | -------- | ------- | -------- | | | | | | | | | Do | not assume | any | document | | contains | | AndrewZhu,AlyssaHwang,LiamDugan,andChris | | | | | | | • | | | | | | | | | | | | | | the | answer. | Write | | todos | that can | | Callison-Burch. | | 2024. | FanOutQA: | | A | multi-hop, | | | | | | | | | | | | | | | be | answered | with | either | “Found” | or | multi-documentquestionansweringbenchmarkfor | | | | | | | | “Not | found | in | this document.” | | | | -------------- | --- | ------- | --- | ----------- | --- | ---------- | ------ | ----- | ------ | --------------- | ----------- | --- | | large language | | models. | In | Proceedings | | of the An- | | | | | | | | | | | | | | | • Tell | the | worker | WHAT | to extract, | not | nualMeetingoftheAssociationforComputational | | | | | | | | HOW | to extract | | it. | | | | --- | --- | --- | --- | --- | --- | --- | --- | ---------- | --- | --- | --- | --- | Linguistics(ACL). | | | | | | | | • Do | not | synthesize, | | summarize, | or | | --- | --- | --- | --- | --- | --- | --- | ------- | --- | ----------- | --- | ---------- | ----- | | | | | | | | | attempt | | to answer | | the user | query | A Prompts yourself. | All | prompts | | are | | defined | in | | | | | | | | --- | ------- | --- | --- | --- | ------- | --- | --- | --- | --- | --- | --- | --- | A.2DocumentSub-AgentPrompt backend/core/prompts.py. (RESEARCH_SYSTEM_PROMPT) | A.1CoordinatorPrompt | | | | | | | You are | a sub-agent | | investigating | | a | | ------------------------ | --- | --- | --- | --- | --- | --- | ---------------- | ----------- | ------------ | ------------- | --- | --- | | (LEAD_RESEARCHER_PROMPT) | | | | | | | single document: | | {file_name}. | | | | You are a lead researcher coordinating You operate inside an iterative a RAG-based analysis to answer a user retrieval loop. On each turn you output | query. | | | | | | | exactly | one structured | | action: | | | | -------- | --- | ------------- | --- | --- | --- | --- | ------------------ | -------------- | --- | ------- | --- | ------- | | Pipeline | | Architecture: | | | | | • action="search": | | | Issue | a | focused | 1. 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. | | needed. | | | | | | • | Keep only | information | | that | directly | | --- | ------- | --- | --- | --- | --- | --- | --- | --------- | ----------- | --- | ---- | -------- | • action="finalize": You have helps answer the query. Discard | | gathered | | sufficient | evidence | to | | | tangential | content. | | | | | --- | -------- | --- | ---------- | -------- | ---------- | --- | --- | ------------ | ------------ | -------- | ------ | ------ | | | address | ALL | assigned | | tasks. Set | | | | | | | | | | | | | | | | • | Preserve | exact | numbers, | | names, | | | findings | to | your | complete | extracted | | | | | | | | | | | | | | | | | dates, | and caveats. | | | | | | report. | Set | reasoning | | to a brief | | | | | | | | | | | | | | | | • | Remove | redundancy. | | If the | same | | | summary | of | what you | found. | | | | | | | | | | | | | | | | | | fact appears | | multiple | times, | keep | it once. | | Investigation | principles: | | | | | | | | | | | | --- | ------------- | -------------- | ---- | ----- | --------- | --- | --- | ------- | -------- | --------- | ----- | -------- | | | | | | | | | • | Do NOT | invent | or infer | facts | not | | | | | | | | | | present | in the | findings. | | | | | 1. Start | by identifying | | the | key facts | | | | | | | | | | required | for | each | task. | | | • | Same | language | as | the | findings | 2. Issue ONE focused query per turn (default English if mixed). | | — prefer | specific | | terms | over broad | | | | | | | | | --- | --------- | -------- | -------- | ----- | ----------- | --- | --------------------- | --- | --- | --- | --- | --- | | | phrases. | | | | | B | ImplementationDetails | | | | | | | | 3. Expand | to | synonyms | or | paraphrases | | | | | | | | Models. if initial results are incomplete. The coordinator, the merging layer, 4. Issue separate queries for and the synthesis layer use gemini-2.5-pro | | different | | aspects | of a | task. | | | | | | | | | --- | --------- | --------- | ------- | ----- | ------- | ------------ | --- | ----- | --- | -------- | --- | ---------- | | | | | | | | (temperature | | 0.0). | | Document | | sub-agents | | | 5. Keep | searching | until | every | task is | | | | | | | | covered with concrete evidence or use gemini-2.5-flash (temperature 0.0) for | | confirmed | | absent. | | | document-scoped | | | retrieval. | | The | LLM judge | | --- | --------- | --- | ------- | --- | --- | --------------- | --- | --- | ---------- | --- | --- | --------- | 6. Do NOT attempt to answer the user (GPT-5) is separate and is used only for the | | query | directly | — | extract | raw facts | | | | | | | | | --- | ----- | -------- | --- | ------- | --------- | ------------------------ | --- | --- | --- | ----------------- | --- | --- | | | | | | | | evaluationoftheAvgscore. | | | | Modelconstantsare | | | only. definedinbackend/shared/constants.py. | | 7. Do | NOT finalize | | a task | as ’Not | | | | | | | | | --- | ----------------- | ------------ | ------------ | ---------- | ----------- | ----------------------------- | --- | --- | --- | ------------------------- | --- | ----------- | | | found’ | after | only | a single | focused | | | | | | | | | | search. | | At | least | attempt 2 | Hyperparameters. | | | | | | | | | focused | searches | | before | concluding | | | | | | | | | | information | | is absent. | | | | | | | | | | | | | | | | | Parameter | | | | Value | | | | | 8. Do NOT | focus | on very | specific | terms, | | | | | | | | | | | | | | | Chunksize(tokens) | | | | 1000 | | | | | but | rather | on the | general | context | | | | | | | | | | | | | | | Chunkoverlap(tokens) | | | | 250 | | | | | of the | task. | Always | try | to find the | | | | | | | | | | | | | | | Tokenizer | | | | tiktoken | | cl100k_base | | | most | relevant | information. | | | | | | | | | | | | | | | | | Embeddingmodel | | | | Cohereembed-v4.0 | | | | | 9. You | must use | AT MOST | 5 | searches to | | | | | | | | | | | | | | | Embeddingdimension | | | | 1536 | | | | | answer | a task. | DO | NOT | OVERUSE THE | | | | | | | | | | | | | | | Similaritymetric | | | | Cosine | | | | | SEARCH | TOOL. | | | | | | | | | | | | | | | | | | MergetokenbudgetB | | | | 750,000tokens | | | | | | | | | | Denseretrievaltop-K | | | | 15 | | | | | Finalize | report | format: | | For each | | | | | | | | | | | | | | | Reranker | | | | Coherererank-v4.0-fast | | | | | assigned | task, | output | either | “Found: | | | | | | | | | | | | | | | Rerankertop-N | | | | 5 | | | | | [exact answer | with | supporting | | evidence]” | | | | | | | | | | | | | | | Sub-agentiterationsafetylimit | | | | 5 | | | | | or “Not found | in | this | document.” | Always | | | | | | | | | | | | | | | Clusteringalgorithm | | | | Agglomerative(UPGMA) | | | | | report exact | numbers, | | names, | and dates. | | | | | | | | | | | | | | | Clusteringmetric | | | | Precomputedcosinedistance | | | | | Never approximate | | or | infer | beyond the | | | | | | | | document. | | | | | | | Cost | | measurement. | | Per-query | | costs | | --- | --- | --- | --- | --- | --- | ---- | ------ | ------------ | --------- | --------- | --- | -------- | | | | | | | | are | pulled | from | LangSmith | | run | metadata | A.3Merge/SynthesisPrompt | (SYNTHESIS_PROMPT) | | | | | | (extract_langsmith_info.py) | | | | | using | model- | | ------------------ | --- | --- | --- | --- | --- | --------------------------- | --- | --- | --- | --- | ----- | ------ | specificper-tokenpricing. Thispromptisuseduniformlyateverylevelofthe recursivemergingtree,includingthefinalsynthesis step. Thereisnoseparatefinal-answerprompt. | | You are | a research | synthesizer. | | Merge | | | | | | | | | --- | ------------- | ----------------- | ------------ | -------- | -------- | --- | --- | --- | --- | --- | --- | --- | | | the following | | sub-agent | findings | into | | | | | | | | | | one compact, | information-dense | | | response | | | | | | | | | | for the user. | | | | | | | | | | | | | | Query: {query | } | | | | | | | | | | | | | Synthesis | Directive: | | | | | | | | | | | {synthesis_directive} | | Findings | Batch: | {findings | } | | | | | | | | | | --- | -------- | ------ | --------- | --- | --- | --- | --- | --- | --- | --- | --- | --- | Rules: