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ARXIV:2603.09152 · TABLE QUESTION ANSWERING · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.09152TABLE QUESTION ANSWERINGSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
DataFactory is a multi-agent framework designed to enhance Table Question Answering by improving coordination and reducing hallucinations.
Opportunity summary
Pain DataFactory is a multi-agent framework designed to enhance Table Question Answering by improving coordination and reducing hallucinations.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
DataFactory is a multi-agent framework designed to enhance Table Question Answering by improving coordination and reducing hallucinations. However, existing large language model (LLM) approaches face critical limitations: context length constraints that restrict data handling…
Table Question Answering (TableQA) enables natural language interaction with structured tabular data. However, existing large language model (LLM) approaches face critical limitations: context length constraints that restrict data handling capabilities, hallucination issues that compromise…
ScienceToStartup currently rates this 3.0/10 on the public viability pass. Table Question Answering (TableQA) enables natural language interaction with structured tabular data.
Table Question Answering moved forward this cycle; last verified April 2026. Public score 3.0/10.
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Score3.0Public score shown from the verified overall while the stale axis breakdown refreshesAnalysis summary
DataFactory is a multi-agent framework designed to enhance Table Question Answering by improving coordination and reducing hallucinations.
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Paper Pack
10.48550/arXiv.2603.09152DataFactory is a multi-agent framework designed to enhance Table Question Answering by improving coordination and reducing hallucinations.
Abstract
Table Question Answering (TableQA) enables natural language interaction with structured tabular data. However, existing large language model (LLM) approaches face critical limitations: context length constraints that restrict data handling capabilities, hallucination issues that compromise answer reliability, and single-agent architectures that struggle with complex reasoning scenarios involving semantic relationships and multi-hop logic. This paper introduces DataFactory, a multi-agent framework that addresses these limitations through specialized team coordination and automated knowledge transformation. The framework comprises a Data Leader employing the ReAct paradigm for reasoning orchestration, together with dedicated Database and Knowledge Graph teams, enabling the systematic decomposition of complex queries into structured and relational reasoning tasks. We formalize automated data-to-knowledge graph transformation via the mapping function T:D x S x R -> G, and implement natural language-based consultation that - unlike fixed workflow multi-agent systems - enables flexible inter-agent deliberation and adaptive planning to improve coordination robustness. We also apply context engineering strategies that integrate historical patterns and domain knowledge to reduce hallucinations and improve query accuracy. Across TabFact, WikiTableQuestions, and FeTaQA, using eight LLMs from five providers, results show consistent gains. Our approach improves accuracy by 20.2% (TabFact) and 23.9% (WikiTQ) over baselines, with significant effects (Cohen's d > 1). Team coordination also outperforms single-team variants (+5.5% TabFact, +14.4% WikiTQ, +17.1% FeTaQA ROUGE-2). The framework offers design guidelines for multi-agent collaboration and a practical platform for enterprise data analysis through integrated structured querying and graph-based knowledge representation.
Source availability
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Extraction status
Derived fallbackRead summaries are estimated from adjacent metadata, not verified extraction rows.
Proof status
unverified0 refs; 0 sources; 17% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
Viability
Time to MVP
Commercial
Export
Preparing verified analysis
Dimensions overall score 3.0
PROBLEM
DataFactory is a multi-agent framework designed to enhance Table Question Answering by improving coordination and reducing hallucinations. However, existing large language model (LLM) approaches face critical limitations: context length constraints that restrict data handling ca...
METHOD
Table Question Answering (TableQA) enables natural language interaction with structured tabular data. However, existing large language model (LLM) approaches face critical limitations: context length constraints that restrict data handling capabilities, hallucination issues that...
RESULT
ScienceToStartup currently rates this 3.0/10 on the public viability pass. Table Question Answering (TableQA) enables natural language interaction with structured tabular data.
WHY NOW
Table Question Answering moved forward this cycle; last verified April 2026. Public score 3.0/10.
Abstract-backed public claims while anchored extraction refreshes.
DataFactory is a multi-agent framework designed to enhance Table Question Answering by improving coordination and reducing hallucinations. However, existing large language model (LLM) approaches face critical limitations: context length constraints that restrict data handling capabilities, hallucination issues that compromise answer reliability, and single-agent architectures that struggle with complex reasoning scenarios involving semantic relationships and multi-hop logic.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Table Question Answering (TableQA) enables natural language interaction with structured tabular data. However, existing large language model (LLM) approaches face critical limitations: context length constraints that restrict data handling capabilities, hallucination issues that compromise answer reliability, and single-agent architectures that struggle with complex reasoning scenarios involving semantic relationships and multi-hop logic.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 3.0/10 on the public viability pass. Table Question Answering (TableQA) enables natural language interaction with structured tabular data.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Table Question Answering moved forward this cycle; last verified April 2026. Public score 3.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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Concepts
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Materials
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DataFactory is a multi-agent framework designed to enhance Table Question Answering by improving coordination and reducing hallucinations.
Segment
Table Question Answering
Adoption evidence
No public code link in the paper record yet
Commercial read
3.0/10 public viability
Direct
Adjacent
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CITED BY
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Build Passport
Build passport pending - Proof Lab budget No verified cost estimate / $7.00 cap
status
missing
reason
passport_row_missing
proof status
unverified
cost/budget
No verified cost estimate
confidence low
next verification path
Build brief missing until Build Passport data exists.
Source missing: Build Passport payload.
Experiment plan missing until prototype path is available.
No prototype path attached.
Validation checklist missing until required assets, cost, and regulatory flags are verified.
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Evidence coverage
OpportunityKernel evidence_receipt
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stale
Verify missing sources before using this as buyer proof. verified:false
Build readiness
BuildPassport EvidenceState
passport absent
stale
Run Proof Lab or inspect typed missing state. verified:false
Artifact maturity
GitHub and Hugging Face maturity payloads
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stale
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Technical feasibility
partial
Current read
Runnable path is not fully verified.
Evidence
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Gaps
Next test
Run minimal reproduction from the Build Passport prototype path.
Market urgency
missing
Current read
Buyer urgency is not verified from source.
Evidence
0 references, 0 sources, 17% evidence coverage.
Gaps
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Buyer clarity
missing
Current read
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Defensibility
missing
Current read
Defensibility signals are missing.
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Integration burden
missing
Current read
No public implementation surface observed.
Evidence
No GitHub or Hugging Face payload attached.
Gaps
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Write integration checklist from prototype path and target workflow.
Capital intensity
missing
Current read
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Regulatory load
missing
Current read
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Evidence
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Gaps
Next test
Classify regulatory flags before commercialization planning.
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Paper authors are not treated as operators without consent.
People
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Gaps
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Prototype owner missing.
Build Passport does not name an implementer.
People
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Operator workflow not sourced.
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Regulatory need unclassified.
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ARTIFACTS
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DEFENSIBILITY
Defensibility and confidence evidence pending.
WATCHTOWER
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FORESIGHT
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OPPORTUNITYKERNEL CHANGES SINCE LAST VIEW
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RELATED PAPER UPDATES
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TIMELINE
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BUZZ
Buzz trend pending.