ARXIV:2603.19097 · MULTILINGUAL QA · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

VerifiedSource: PDF linkedVerifiedPaperPack: citation fields availablePartialProof: unverified proof status

DaPT: A Dual-Path Framework for Multilingual Multi-hop Question Answering

Yilin Wang · Yuchun Fan · Jiaoyang Li · Ziming Zhu · Yongyu Mu · Qiaozhi He · +2 at arXiv

A dual-path RAG framework that significantly enhances multilingual multi-hop question answering accuracy by leveraging parallel query translation and bilingual retrieval.

Ship in 2-4 weeks›Score7.0Evidence unverified

Opportunity summary

Pain A dual-path RAG framework that significantly enhances multilingual multi-hop question answering accuracy by leveraging parallel query translation and bilingual retrieval.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

A dual-path RAG framework that significantly enhances multilingual multi-hop question answering accuracy by leveraging parallel query translation and bilingual retrieval. However, RAG systems inevitably face the application scenario of retrieving across multilingual corpora and…

METHOD

Full abstract

Retrieval-augmented generation (RAG) systems have made significant progress in solving complex multi-hop question answering (QA) tasks in the English scenario. However, RAG systems inevitably face the application scenario of retrieving across multilingual corpora and queries, leaving several open challenges. The first one involves the absence of benchmarks that assess RAG systems' capabilities under the multilingual multi-hop (MM-hop) QA setting. The second centers on the overreliance on LLMs' strong semantic understanding in English, which diminishes effectiveness in multilingual scenarios. To address these challenges, we first construct multilingual multi-hop QA benchmarks by translating English-only benchmarks into five languages, and then we propose DaPT, a novel multilingual RAG framework. DaPT generates sub-question graphs in parallel for both the source-language query and its English translation counterpart, then merges them before employing a bilingual retrieval-and-answer strategy to sequentially solve sub-questions. Our experimental results demonstrate that advanced RAG systems suffer from a significant performance imbalance in multilingual scenarios. Furthermore, our proposed method consistently yields more accurate and concise answers compared to the baselines, significantly enhancing RAG performance on this task. For instance, on the most challenging MuSiQue benchmark, DaPT achieves a relative improvement of 18.3\% in average EM score over the strongest baseline.

RESULT

ScienceToStartup currently rates this 7.0/10 on the public viability pass. Our experimental results demonstrate that advanced RAG systems suffer from a significant performance imbalance in multilingual scenarios. Code availability is flagged in the production…

WHY NOW

Multilingual QA moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.

Continue into Read for claims, analysis, references, and neighboring papers.

Opportunity summary

Score7.0

PainA dual-path RAG framework that significantly enhances multilingual multi-hop question answering accuracy by leveraging parallel query translation and bilingual retrieval.

Evidence0 refs | 0 sources | 17% coverage

Blockerno shell-level blocker reported

Analysis summary

A dual-path RAG framework that significantly enhances multilingual multi-hop question answering accuracy by leveraging parallel query translation and bilingual retrieval.

VerifiedSource: PDF linkedVerifiedPaperPack: citation fields availablePartialProof: unverified proof status

ARXIV:2603.19097 · MULTILINGUAL QA · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

VerifiedSource: PDF linkedVerifiedPaperPack: citation fields availablePartialProof: unverified proof status

DaPT: A Dual-Path Framework for Multilingual Multi-hop Question Answering

Yilin Wang · Yuchun Fan · Jiaoyang Li · Ziming Zhu · Yongyu Mu · Qiaozhi He · +2 at arXiv

A dual-path RAG framework that significantly enhances multilingual multi-hop question answering accuracy by leveraging parallel query translation and bilingual retrieval.

Ship in 2-4 weeks›Score7.0Evidence unverified

Opportunity summary

Pain A dual-path RAG framework that significantly enhances multilingual multi-hop question answering accuracy by leveraging parallel query translation and bilingual retrieval.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

METHOD

Full abstract

RESULT

WHY NOW

Multilingual QA moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.

Continue into Read for claims, analysis, references, and neighboring papers.

Opportunity summary

Score7.0

PainA dual-path RAG framework that significantly enhances multilingual multi-hop question answering accuracy by leveraging parallel query translation and bilingual retrieval.

Evidence0 refs | 0 sources | 17% coverage

Blockerno shell-level blocker reported

Analysis summary

A dual-path RAG framework that significantly enhances multilingual multi-hop question answering accuracy by leveraging parallel query translation and bilingual retrieval.

VerifiedSource: PDF linkedVerifiedPaperPack: citation fields availablePartialProof: unverified proof status

Paper Pack

10.48550/arXiv.2603.19097

DaPT: A Dual-Path Framework for Multilingual Multi-hop Question Answering

A dual-path RAG framework that significantly enhances multilingual multi-hop question answering accuracy by leveraging parallel query translation and bilingual retrieval.

Abstract

Source availability

PDF linked

The paper record includes a public PDF URL.

Extraction status

Derived fallback

Read summaries are estimated from adjacent metadata, not verified extraction rows.

Proof status

unverified

0 refs; 0 sources; 17% coverage.

What was readable

linkedon filenot materializedderived fallback23 indexednot indexed

Derived fallback: Estimated from adjacent evidence; not verified from source.

Viability

7.0

Time to MVP

MVP estimate missing

Commercial

code

Export

Preparing verified analysis

lens / founder

PROBLEM

METHOD

RESULT

WHY NOW

Multilingual QA moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.

Claim map

Abstract-backed public claims while anchored extraction refreshes.

Strong 0Mixed 0Weak 4

Evidencepartial
A dual-path RAG framework that significantly enhances multilingual multi-hop question answering accuracy by leveraging parallel query translation and bilingual retrieval. However, RAG systems inevitably face the application scenario of retrieving across multilingual corpora and queries, leaving several open challenges.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
Retrieval-augmented generation (RAG) systems have made significant progress in solving complex multi-hop question answering (QA) tasks in the English scenario. However, RAG systems inevitably face the application scenario of retrieving across multilingual corpora and queries, leaving several open challenges.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Our experimental results demonstrate that advanced RAG systems suffer from a significant performance imbalance in multilingual scenarios. Code availability is flagged in the production record; the public repository link still needs proof alignment.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
Multilingual QA moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial

Constellation map

Paper-native neighborhood for concepts, methods, materials, markets, and competitors. Missing lanes stay labeled instead of disappearing behind commercialization gates.

Open full Signal Canvas

Concepts

not indexed

Methods

Materials

PDF linked

Markets

Multilingual QA

Competitors

not indexed

Competitive landscape

A dual-path RAG framework that significantly enhances multilingual multi-hop question answering accuracy by leveraging parallel query translation and bilingual retrieval.

Segment

Multilingual QA

Adoption evidence

No public code link in the paper record yet

Commercial read

7.0/10 public viability

Direct

not classified

Adjacent

not classified

Substitute

not classified

Unknown

not classified

Buzz

No indexed public discussion is attached to 2603.19097 yet. That is a visibility signal, not a blank module: the monitor is watching the public channels below.

Hacker News

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Bluesky

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PDF

Preview the source document here, or use the hero PDF action for a new tab.

References(23)

On the Consistency of Multilingual Context Utilization in Retrieval-Augmented Generation

2025Jirui Qi, R. Fern'andez et al.

ParamMute: Suppressing Knowledge-Critical FFNs for Faithful Retrieval-Augmented Generation

2025Pengcheng Huang, Zhenghao Liu et al.

From RAG to Memory: Non-Parametric Continual Learning for Large Language Models

2025Bernal Jim'enez Guti'errez, Yiheng Shu et al.

Investigating Language Preference of Multilingual RAG Systems

2025J. Park, Hwanhee Lee

Improving Contextual Faithfulness of Large Language Models via Retrieval Heads-Induced Optimization

2025Lei Huang, Xiaocheng Feng et al.

Boosting Text-To-Image Generation via Multilingual Prompting in Large Multimodal Models

2025Yongyu Mu, Hengyu Li et al.

SLAM: Towards Efficient Multilingual Reasoning via Selective Language Alignment

2025Yuchun Fan, Yongyu Mu et al.

Alleviating Hallucinations from Knowledge Misalignment in Large Language Models via Selective Abstention Learning

2025Lei Huang, Xiaocheng Feng et al.

LightRAG: Simple and Fast Retrieval-Augmented Generation

2024Zirui Guo, Lianghao Xia et al.

Retrieval-augmented generation in multilingual settings

2024Nadezhda Chirkova, David Rau et al.

HippoRAG: Neurobiologically Inspired Long-Term Memory for Large Language Models

2024Bernal Jimenez Gutierrez, Yiheng Shu et al.

From Local to Global: A Graph RAG Approach to Query-Focused Summarization

2024Darren Edge, Ha Trinh et al.

M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation

2024Jianlv Chen, Shitao Xiao et al.

RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval

2024Parth Sarthi, Salman Abdullah et al.

DeepSeek-V3 Technical Report

2024DeepSeek-AI, A. Liu et al.

Enhancing Retrieval-Augmented Large Language Models with Iterative Retrieval-Generation Synergy

2023Zhihong Shao, Yeyun Gong et al.

GPT-4 Technical Report

2023OpenAI Josh Achiam, Steven Adler et al.

Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questions

2022H. Trivedi, Niranjan Balasubramanian et al.

Cross-Lingual Retrieval Augmented Prompt for Low-Resource Languages

2022Ercong Nie, Sheng Liang et al.

♫ MuSiQue: Multihop Questions via Single-hop Question Composition

2021H. Trivedi, Niranjan Balasubramanian et al.

Showing 20 of 23 references

CITED BY

No citing papers are indexed in the public S2S graph yet. This is an explicit zero-signal state, not a hidden lookup.

Foundation

Prior WorkSPD-RAG: Sub-Agent Per Document Retrieval-Augmented Generation

7.0

Prior WorkRAG-X: Systematic Diagnosis of Retrieval-Augmented Generation for Medical Question Answering

7.0

Prior WorkEvaluating Multi-Hop Reasoning in RAG Systems: A Comparison of LLM-Based Retriever Evaluation Strategies

7.0

Prior WorkA Systematic Study of Retrieval Pipeline Design for Retrieval-Augmented Medical Question Answering

7.0

Prior WorkTest-Time Strategies for More Efficient and Accurate Agentic RAG

7.0

Extension

Builds On ThisComprehensive Comparison of RAG Methods Across Multi-Domain Conversational QA

5.0

Builds On ThisAgent-Orchestrated Adaptive RAG: A Comparative Study on Structured and Multi-Hop Retrieval

0.0

Commercially relevant

Higher ViabilityCompactRAG: Reducing LLM Calls and Token Overhead in Multi-Hop Question Answering

8.0

Higher ViabilityMDER-DR: Multi-Hop Question Answering with Entity-Centric Summaries

9.0

Higher ViabilityRetrieval is Cheap, Show Me the Code: Executable Multi-Hop Reasoning for Retrieval-Augmented Generation

8.0

Conflicting

none indexed

Owned Distribution

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Get the weekly shortlist of commercializable papers, benchmark movers, and proof receipts that matter for product execution.

Agent drawer

5 surfaces preserved for agents. Humans can ignore.

Developer contracts, payload previews, evidence maps, and run controls stay here instead of the Read, Build, and Track workspace.

Run context

Paper: 2603.19097
Route: /paper/dapt-a-dual-path-framework-for-multilingual-multi-hop-question-answering
Active tab: read
Artifact: dapt-a-dual-path-framework-for-multilingual-multi-hop-question-answering

Available agents

Read extractor
Build planner
Track monitor
Competitive mapper
Related-paper scout

API/MCP endpoints

REST paper pack API/api/v1/paper/dapt-a-dual-path-framework-for-multilingual-multi-hop-question-answering/paper-pack
REST build passport API/api/v1/paper/dapt-a-dual-path-framework-for-multilingual-multi-hop-question-answering/build-passport
REST OpenAPI/api/openapi.json
MCP descriptor/api/mcp
MCP resourcesciencetostartup://surfaces/paper-workspace

Tool contracts

paper_packbuild_passportopportunity_kernelforesightsource_proofevidence_state

Payload preview

Inspect payload

{
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Schema validation

paper-r2 contract: present
JSON-LD twin: SSR emitted
OpenAPI path parity: /api/openapi.json
MCP resource parity: paper-workspace

Job trace

queued: drawer opened by user action
running: inspect or copy payload
succeeded: payload available in SSR
failed: route errors appear in evidence cards

Evidence map

sources used: page freshness, source proof anchors, JSON-LD
missing sources: exposed by PaperPack and EvidenceState chips
derived fallbacks: marked unverified before handoff

Page Freshness

Canonical route, proof status, last verified, refs, sources, and coverage.

Page Freshness

Paper proof surface

Canonical route: /paper/dapt-a-dual-path-framework-for-multilingual-multi-hop-question-answering

stale

Proof freshness: stale
Proof status: unverified
Display score: 7/10
Last proof check: 2026-04-02
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 0
Source count: 0
Coverage: 17%

This page is showing the last landed evidence receipt and score bundle because the latest proof data is outside the freshness window.

OpenAlex: pending — this preprint is not yet indexed by OpenAlex.

Agent Handoff

Endpoint list, payload shape, route context, and copyable handoff data.

Agent Handoff

DaPT: A Dual-Path Framework for Multilingual Multi-hop Question Answering

Canonical ID dapt-a-dual-path-framework-for-multilingual-multi-hop-question-answering | Route /paper/dapt-a-dual-path-framework-for-multilingual-multi-hop-question-answering

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/paper/dapt-a-dual-path-framework-for-multilingual-multi-hop-question-answering

MCP example

{
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}

source_context

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Buildability Receipt

Verdict, compute envelope, blockers, signature state, and receipt links.

Paper proof page receipt window

Watch and verify: DaPT: A Dual-Path Framework for Multilingual Multi-hop Question Answering

/buildability/dapt-a-dual-path-framework-for-multilingual-multi-hop-question-answering

Watchwatch

Subject: DaPT: A Dual-Path Framework for Multilingual Multi-hop Question Answering

Verdict

Watch

Verdict is Watch because viability or proof quality is intermediate and should be re-evaluated before execution.

Time to first demo

Insufficient data

No first-demo timestamp, owner estimate, or elapsed demo receipt is attached to this surface.

Compute envelope

Structured compute envelope

Insufficient data

No data, compute, hardware, memory, latency, dependency, or serving requirement receipt is attached.

Evidence ids

Receipt path

/buildability/dapt-a-dual-path-framework-for-multilingual-multi-hop-question-answering

Paper ref

dapt-a-dual-path-framework-for-multilingual-multi-hop-question-answering

arXiv id

2603.19097

Freshness

Generated at

2026-04-02T02:30:40.136Z

Evidence freshness

stale

Last verification

2026-04-02T02:30:40.136Z

Sources

References

Coverage

17%

Hash state

Lineage hash

47a890873d56b31f68cc457dac4bed0b0aec5ec19dec7575bf5d59955cfae2b0

Canonical opportunity-kernel lineage hash.

Signature state

External signature

unsigned_external

No founder, registry, pilot, or production-adoption signature is attached to this receipt.

Verification

not_verified

Verification is blocked until an external signature is provided.

Blockers

Missing: repo_url
Missing: references
Missing: proof_status
Missing: distribution_readiness_scores
Missing: paper_extraction_scorecards
Unknown: distribution readiness has not been computed yet
Unknown: proof verification has not been recorded yet

Verification pending / evidence receipt incomplete

repo_url

references

Missing proof, requirement, signature, approval, adoption, or telemetry fields are blockers and must not be inferred.

Open receipt API receipt Build Loop Signal Canvas Proof divergence Divergence API Brier outcomes API

Source Proof anchors

Visual citations from the paper document graph.

JSON-LD twin

The application/ld+json payload rendered for agents.

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DaPT: A Dual-Path Framework for Multilingual Multi-hop Question Answering

DaPT: A Dual-Path Framework for Multilingual Multi-hop Question Answering

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References(23)

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Build artifacts

Brief

Experiment plan

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Scientific founder

Translational engineer

Domain operator

GTM lead

Regulatory/clinical advisor

Timeline