ARXIV:2603.02081 · DATABASE QUERY PROCESSING · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

GenDB: The Next Generation of Query Processing -- Synthesized, Not Engineered

arXiv

GenDB leverages LLMs to dynamically generate optimized query execution code, outperforming traditional data query engines.

Blocked on Code›Score7.0Evidence unverified

Opportunity summary

Pain GenDB leverages LLMs to dynamically generate optimized query execution code, outperforming traditional data query engines.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

GenDB leverages LLMs to dynamically generate optimized query execution code, outperforming traditional data query engines. However, new techniques and user requirements evolve rapidly, and existing systems often cannot keep pace.

METHOD

Full abstract

Traditional query processing relies on engines that are carefully optimized and engineered by many experts. However, new techniques and user requirements evolve rapidly, and existing systems often cannot keep pace. At the same time, these systems are difficult to extend due to their internal complexity, and developing new systems requires substantial engineering effort and cost. In this paper, we argue that recent advances in Large Language Models (LLMs) are starting to shape the next generation of query processing systems. We propose using LLMs to synthesize execution code for each incoming query, instead of continuously building, extending, and maintaining complex query processing engines. As a proof of concept, we present GenDB, an LLM-powered agentic system that generates instance-optimized and customized query execution code tailored to specific data, workloads, and hardware resources. We implemented an early prototype of GenDB that uses Claude Code Agent as the underlying component in the multi-agent system, and we evaluate it on OLAP workloads. We use queries from the well-known TPC-H benchmark and also construct a new benchmark designed to reduce potential data leakage from LLM training data. We compare GenDB with state-of-the-art query engines, including DuckDB, Umbra, MonetDB, ClickHouse, and PostgreSQL. GenDB achieves significantly better performance than these systems. Finally, we discuss the current limitations of GenDB and outline future extensions and related research challenges.

RESULT

ScienceToStartup currently rates this 7.0/10 on the public viability pass. GenDB achieves significantly better performance than these systems.

WHY NOW

Database Query Processing moved forward this cycle; last verified April 2026. Public score 7.0/10.

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

Opportunity summary

Score7.0

PainGenDB leverages LLMs to dynamically generate optimized query execution code, outperforming traditional data query engines.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

GenDB leverages LLMs to dynamically generate optimized query execution code, outperforming traditional data query engines.

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

ARXIV:2603.02081 · DATABASE QUERY PROCESSING · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

GenDB: The Next Generation of Query Processing -- Synthesized, Not Engineered

arXiv

GenDB leverages LLMs to dynamically generate optimized query execution code, outperforming traditional data query engines.

Blocked on Code›Score7.0Evidence unverified

Opportunity summary

Pain GenDB leverages LLMs to dynamically generate optimized query execution code, outperforming traditional data query engines.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

METHOD

Full abstract

RESULT

ScienceToStartup currently rates this 7.0/10 on the public viability pass. GenDB achieves significantly better performance than these systems.

WHY NOW

Database Query Processing moved forward this cycle; last verified April 2026. Public score 7.0/10.

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

Opportunity summary

Score7.0

PainGenDB leverages LLMs to dynamically generate optimized query execution code, outperforming traditional data query engines.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

GenDB leverages LLMs to dynamically generate optimized query execution code, outperforming traditional data query engines.

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

Paper Pack

10.48550/arXiv.2603.02081

GenDB: The Next Generation of Query Processing -- Synthesized, Not Engineered

GenDB leverages LLMs to dynamically generate optimized query execution code, outperforming traditional data query engines.

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 fallback39 indexednot indexed

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

Viability

7.0

Time to MVP

MVP estimate missing

Commercial

No commercial flags on file

Export

Preparing verified analysis

lens / founder

PROBLEM

METHOD

RESULT

ScienceToStartup currently rates this 7.0/10 on the public viability pass. GenDB achieves significantly better performance than these systems.

WHY NOW

Database Query Processing moved forward this cycle; last verified April 2026. Public score 7.0/10.

Claim map

Abstract-backed public claims while anchored extraction refreshes.

Strong 0Mixed 0Weak 4

Evidencepartial
GenDB leverages LLMs to dynamically generate optimized query execution code, outperforming traditional data query engines. However, new techniques and user requirements evolve rapidly, and existing systems often cannot keep pace.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
Traditional query processing relies on engines that are carefully optimized and engineered by many experts. However, new techniques and user requirements evolve rapidly, and existing systems often cannot keep pace.
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. GenDB achieves significantly better performance than these systems.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
Database Query Processing moved forward this cycle; last verified April 2026. Public score 7.0/10.
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

Database Query Processing

Competitors

not indexed

Competitive landscape

GenDB leverages LLMs to dynamically generate optimized query execution code, outperforming traditional data query engines.

Segment

Database Query Processing

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 2603.02081 yet. That is a visibility signal, not a blank module: the monitor is watching the public channels below.

Hacker News

Not indexed yet

Bluesky

Not indexed yet

PDF

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

References(39)

SkillsBench: Benchmarking How Well Agent Skills Work Across Diverse Tasks

2026Xiangyi Li, Wenbo Chen et al.

Are LLMs Ready for TOON? Benchmarking Structural Correctness-Sustainability Trade-offs in Novel Structured Output Formats

2026E. Masciari, Vincenzo Moscato et al.

SEFRQO: A Self-Evolving Fine-Tuned RAG-Based Query Optimizer

2025Hanwen Liu, Qihan Zhang et al.

Rethinking Analytical Processing in the GPU Era

2025B. Yogatama, Yifei Yang et al.

SQLBarber: A System Leveraging Large Language Models to Generate Customized and Realistic SQL Workloads

2025Jiale Lao, Immanuel Trummer

Scaling GPU-Accelerated Databases beyond GPU Memory Size

2025Yinan Li, Bailu Ding et al.

PBench: Workload Synthesizer with Real Statistics for Cloud Analytics Benchmarking

2025Yan Zhou, Chunwei Liu et al.

GPTuner: An LLM-Based Database Tuning System

2025Jiale Lao, Yibo Wang et al.

Are Your LLM-based Text-to-SQL Models Secure? Exploring SQL Injection via Backdoor Attacks

2025Meiyu Lin, Haichuan Zhang et al.

Can Large Language Models Be Query Optimizer for Relational Databases?

2025Jie Tan, Kangfei Zhao et al.

Anarchy in the Database: A Survey and Evaluation of Database Management System Extensibility

2025Abigale Kim, Marco Slot et al.

Customizing Operator Implementations for SQL Processing via Large Language Models

2025Immanuel Trummer

Palimpzest: Optimizing AI-Powered Analytics with Declarative Query Processing

2025Chunwei Liu, Matthew Russo et al.

GenJoin: Conditional Generative Plan-to-Plan Query Optimizer that Learns from Subplan Hints

2024Pavel Sulimov, Claude Lehmann et al.

λ-Tune: Harnessing Large Language Models for Automated Database System Tuning

2024Victor Giannakouris, Immanuel Trummer

ClickHouse - Lightning Fast Analytics for Everyone

2024Robert Schulze, T. Schreiber et al.

Databases Unbound: Querying All of the World's Bytes with AI

2024Samuel Madden, Michael J. Cafarella et al.

Why TPC Is Not Enough: An Analysis of the Amazon Redshift Fleet

2024Alexander van Renen, Dominik Horn et al.

E2ETune: End-to-End Knob Tuning via Fine-tuned Generative Language Model

2024Xinmei Huang, Haoyang Li et al.

CodeS: Towards Building Open-source Language Models for Text-to-SQL

2024Haoyang Li, Jing Zhang et al.

Showing 20 of 39 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 WorkSpecDB: LLM-Generated Customized Databases via Feature-Oriented Decomposition

7.0

Prior WorkQuerying Structured Data Through Natural Language Using Language Models

7.0

Prior WorkMeta Engine: A Unified Semantic Query Engine on Heterogeneous LLM-Based Query Systems

7.0

Extension

Builds On ThisA Query Engine for the Agents

4.0

Builds On ThisTowards Next-Generation LLM Training: From the Data-Centric Perspective

4.0

Builds On ThisAre LLMs Overkill for Databases?: A Study on the Finiteness of SQL

4.0

Builds On ThisLLM-Based Test Case Generation in DBMS through Monte Carlo Tree Search

5.0

Builds On ThisBeyond Text-to-SQL: Can LLMs Really Debug Enterprise ETL SQL?

6.0

Builds On ThisA Benchmark for Deep Information Synthesis

5.0

Commercially relevant

Higher ViabilityDomAgent: Leveraging Knowledge Graphs and Case-Based Reasoning for Domain-Specific Code Generation

8.0

Conflicting

none indexed

Owned Distribution

Subscribe to the weekly brief

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: 2603.02081
Route: /paper/gendb-the-next-generation-of-query-processing-synthesized-not-engineered
Active tab: read
Artifact: gendb-the-next-generation-of-query-processing-synthesized-not-engineered

Available agents

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

API/MCP endpoints

REST paper pack API/api/v1/paper/gendb-the-next-generation-of-query-processing-synthesized-not-engineered/paper-pack
REST build passport API/api/v1/paper/gendb-the-next-generation-of-query-processing-synthesized-not-engineered/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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  "active_tab": "synced from current hash by the drawer client",
  "selected_artifact": "gendb-the-next-generation-of-query-processing-synthesized-not-engineered",
  "endpoints": {
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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/gendb-the-next-generation-of-query-processing-synthesized-not-engineered

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

GenDB: The Next Generation of Query Processing -- Synthesized, Not Engineered

Canonical ID gendb-the-next-generation-of-query-processing-synthesized-not-engineered | Route /paper/gendb-the-next-generation-of-query-processing-synthesized-not-engineered

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/paper/gendb-the-next-generation-of-query-processing-synthesized-not-engineered

MCP example

{
  "tool": "get_paper",
  "arguments": {
    "arxiv_id": "2603.02081"
  }
}

source_context

{
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  "mode": "paper",
  "query": "GenDB: The Next Generation of Query Processing -- Synthesized, Not Engineered",
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  "paper_ref": "gendb-the-next-generation-of-query-processing-synthesized-not-engineered",
  "topic_slug": null,
  "benchmark_ref": null,
  "dataset_ref": null
}

Buildability Receipt

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

Paper proof page receipt window

Watch and verify: GenDB: The Next Generation of Query Processing -- Synthesized, Not Engineered

/buildability/gendb-the-next-generation-of-query-processing-synthesized-not-engineered

Watchwatch

Subject: GenDB: The Next Generation of Query Processing -- Synthesized, Not Engineered

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/gendb-the-next-generation-of-query-processing-synthesized-not-engineered

Paper ref

gendb-the-next-generation-of-query-processing-synthesized-not-engineered

arXiv id

2603.02081

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

e54bd7bf275cc843954afb9fa01a4447fe29e3ccaef893a9b7c55e041d80e95f

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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GenDB: The Next Generation of Query Processing -- Synthesized, Not Engineered

GenDB: The Next Generation of Query Processing -- Synthesized, Not Engineered

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GTM lead

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