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ARXIV:2604.21214 · TEXT-TO-SQL · SUBMITTED 24 APR · 20:26 UTC · FRESHNESS STALE
ARXIV:2604.21214TEXT-TO-SQLSUBMITTED 24 APR · 20:26 UTCFRESHNESS STALESepideh Abedini · M. Tamer Özsu · arXiv
SQLyzr is a comprehensive benchmark and evaluation platform for Text-to-SQL models, offering realistic evaluation and fine-grained analysis.
Opportunity summary
Pain SQLyzr is a comprehensive benchmark and evaluation platform for Text-to-SQL models, offering realistic evaluation and fine-grained analysis.
Evidence 0 refs | 4 sources | 67% coverage
Blocker Evidence unverified
SQLyzr is a comprehensive benchmark and evaluation platform for Text-to-SQL models, offering realistic evaluation and fine-grained analysis. Although many benchmarks exist for evaluating the performance of text-to-SQL models, they often rely on a single…
Text-to-SQL models have significantly improved with the adoption of Large Language Models (LLMs), leading to their increasing use in real-world applications. Although many benchmarks exist for evaluating the performance of text-to-SQL models, they often…
ScienceToStartup currently rates this 8.0/10 on the public viability pass. It further supports fine-grained query classification, error analysis, and workload augmentation, allowing users to better diagnose and improve text-to-SQL models. A public repository is…
Text-to-SQL moved forward this cycle; last verified April 2026. Public score 8.0/10. Implementation evidence is present through a linked repository.
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Score8.0Public score shown from the verified overall while the stale axis breakdown refreshesAnalysis summary
SQLyzr is a comprehensive benchmark and evaluation platform for Text-to-SQL models, offering realistic evaluation and fine-grained analysis.
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Paper Pack
10.48550/arXiv.2604.21214SQLyzr is a comprehensive benchmark and evaluation platform for Text-to-SQL models, offering realistic evaluation and fine-grained analysis.
Abstract
Text-to-SQL models have significantly improved with the adoption of Large Language Models (LLMs), leading to their increasing use in real-world applications. Although many benchmarks exist for evaluating the performance of text-to-SQL models, they often rely on a single aggregate score, lack evaluation under realistic settings, and provide limited insight into model behaviour across different query types. In this work, we present SQLyzr, a comprehensive benchmark and evaluation platform for text-to-SQL models. SQLyzr incorporates a diverse set of evaluation metrics that capture multiple aspects of generated queries, while enabling more realistic evaluation through workload alignment with real-world SQL usage patterns and database scaling. It further supports fine-grained query classification, error analysis, and workload augmentation, allowing users to better diagnose and improve text-to-SQL models. This demonstration showcases these capabilities through an interactive experience. Through SQLyzr's graphical interface, users can customize evaluation settings, analyze fine-grained reports, and explore additional features of the platform. We envision that SQLyzr facilitates the evaluation and iterative improvement of text-to-SQL models by addressing key limitations of existing benchmarks. The source code of SQLyzr is available at https://github.com/sepideh-abedini/SQLyzr.
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Extraction status
Parse run pending anchorsA parse run id is attached, but no public source anchors are materialized yet.
Proof status
unverified0 refs; 4 sources; 67% 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 8.0
PROBLEM
SQLyzr is a comprehensive benchmark and evaluation platform for Text-to-SQL models, offering realistic evaluation and fine-grained analysis. Although many benchmarks exist for evaluating the performance of text-to-SQL models, they often rely on a single aggregate score, lack eva...
METHOD
Text-to-SQL models have significantly improved with the adoption of Large Language Models (LLMs), leading to their increasing use in real-world applications. Although many benchmarks exist for evaluating the performance of text-to-SQL models, they often rely on a single aggregat...
RESULT
ScienceToStartup currently rates this 8.0/10 on the public viability pass. It further supports fine-grained query classification, error analysis, and workload augmentation, allowing users to better diagnose and improve text-to-SQL models. A public repository is linked, so build...
WHY NOW
Text-to-SQL moved forward this cycle; last verified April 2026. Public score 8.0/10. Implementation evidence is present through a linked repository.
{"file name": "input.pdf", "number of pages": 4, "author": "Sepideh Abedini; M. Tamer \u00d6zsu", "title": "SQLyzr: A Comprehensive Benchmark and Evaluation Platform for Text-to-SQL", "creation date": null
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partial
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Concepts
Methods
Materials
Markets
Competitors
SQLyzr is a comprehensive benchmark and evaluation platform for Text-to-SQL models, offering realistic evaluation and fine-grained analysis.
Segment
Text-to-SQL
Adoption evidence
Public code linked for build inspection
Commercial read
8.0/10 public viability
Direct
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CITED BY
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2/3 checks · 67%
Build Passport
Build passport pending - Proof Lab budget No verified cost estimate / $7.00 cap
status
missing
reason
passport_artifacts_incomplete
proof status
no_code
cost/budget
$0.00
confidence medium
next verification path
Build brief missing until Build Passport data exists.
Source missing: Build Passport payload.
Experiment plan missing until prototype path is available.
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Evidence coverage
OpportunityKernel evidence_receipt
0 refs / 4 sources / 67% coverage
stale
Verify missing sources before using this as buyer proof. verified:false
Build readiness
BuildPassport EvidenceState
passport absent
stale
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Artifact maturity
GitHub and Hugging Face maturity payloads
No public artifact surface observed
stale
Open source artifacts or mark the gap as missing. verified:false
Technical feasibility
partial
Current read
Runnable path is not fully verified.
Evidence
No Build Passport payload attached.
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, 4 sources, 67% evidence coverage.
Gaps
Next test
Collect buyer interview, deployment evidence, or cited demand signal.
Buyer clarity
missing
Current read
No budget owner is verified for this paper.
Evidence
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Map target operator, economic buyer, and procurement trigger.
Defensibility
missing
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Defensibility signals are missing.
Evidence
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Gaps
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Refresh defensibility bars with source receipts.
Integration burden
missing
Current read
No public implementation surface observed.
Evidence
No GitHub or Hugging Face payload attached.
Gaps
Next test
Write integration checklist from prototype path and target workflow.
Capital intensity
missing
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Evidence
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Regulatory load
missing
Current read
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Evidence
Build Passport ledger does not include regulatory flags.
Gaps
Next test
Classify regulatory flags before commercialization planning.
No named scientific founder assigned.
Paper authors are not treated as operators without consent.
People
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Prototype owner missing.
Build Passport does not name an implementer.
People
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Operator workflow not sourced.
No buyer or workflow interview attached.
People
No named person assigned.
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No CRM or outreach source attached.
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Regulatory need unclassified.
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People
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Gaps
Next verification path
ARTIFACTS
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DEFENSIBILITY
Defensibility and confidence evidence pending.
WATCHTOWER
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FORESIGHT
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