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ARXIV:2603.15159 · AI CODE GENERATION · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.15159AI CODE GENERATIONSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
PriCoder enables LLMs to effectively use private library APIs for code generation by synthesizing data and enhancing code diversity and quality.
Opportunity summary
Pain PriCoder enables LLMs to effectively use private library APIs for code generation by synthesizing data and enhancing code diversity and quality.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
PriCoder enables LLMs to effectively use private library APIs for code generation by synthesizing data and enhancing code diversity and quality. Existing approaches mainly rely on retrieving private-library API documentation and injecting relevant knowledge…
Large Language Models (LLMs) have shown strong potential for code generation, yet they remain limited in private-library-oriented code generation, where the goal is to generate code using APIs from private libraries. Existing approaches mainly…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. However, our study shows that this is insufficient: even given accurate required knowledge, LLMs still struggle to invoke private-library APIs effectively.
AI Code Generation moved forward this cycle; last verified April 2026. Public score 7.0/10.
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PriCoder enables LLMs to effectively use private library APIs for code generation by synthesizing data and enhancing code diversity and quality.
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10.48550/arXiv.2603.15159PriCoder enables LLMs to effectively use private library APIs for code generation by synthesizing data and enhancing code diversity and quality.
Abstract
Large Language Models (LLMs) have shown strong potential for code generation, yet they remain limited in private-library-oriented code generation, where the goal is to generate code using APIs from private libraries. Existing approaches mainly rely on retrieving private-library API documentation and injecting relevant knowledge into the context at inference time. However, our study shows that this is insufficient: even given accurate required knowledge, LLMs still struggle to invoke private-library APIs effectively. To address this limitation, we propose PriCoder, an approach that teaches LLMs to invoke private-library APIs through automatically synthesized data. Specifically, PriCoder models private-library data synthesis as the construction of a graph, and alternates between two graph operators: (1) Progressive Graph Evolution, which improves data diversity by progressively synthesizing more diverse training samples from basic ones, and (2) Multidimensional Graph Pruning, which improves data quality through a rigorous filtering pipeline. To support rigorous evaluation, we construct two new benchmarks based on recently released libraries that are unfamiliar to the tested models. Experiments on three mainstream LLMs show that PriCoder substantially improves private-library-oriented code generation, yielding gains of over 20% in pass@1 in many settings, while causing negligible impact on general code generation capability. Our code and benchmarks are publicly available at https://github.com/contact-eniacode/PriCoder.
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 7.0
PROBLEM
PriCoder enables LLMs to effectively use private library APIs for code generation by synthesizing data and enhancing code diversity and quality. Existing approaches mainly rely on retrieving private-library API documentation and injecting relevant knowledge into the context at i...
METHOD
Large Language Models (LLMs) have shown strong potential for code generation, yet they remain limited in private-library-oriented code generation, where the goal is to generate code using APIs from private libraries. Existing approaches mainly rely on retrieving private-library...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. However, our study shows that this is insufficient: even given accurate required knowledge, LLMs still struggle to invoke private-library APIs effectively.
WHY NOW
AI Code Generation moved forward this cycle; last verified April 2026. Public score 7.0/10.
Abstract-backed public claims while anchored extraction refreshes.
PriCoder enables LLMs to effectively use private library APIs for code generation by synthesizing data and enhancing code diversity and quality. Existing approaches mainly rely on retrieving private-library API documentation and injecting relevant knowledge into the context at inference time.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Large Language Models (LLMs) have shown strong potential for code generation, yet they remain limited in private-library-oriented code generation, where the goal is to generate code using APIs from private libraries. Existing approaches mainly rely on retrieving private-library API documentation and injecting relevant knowledge into the context at inference time.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. However, our study shows that this is insufficient: even given accurate required knowledge, LLMs still struggle to invoke private-library APIs effectively.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
AI Code Generation moved forward this cycle; last verified April 2026. Public score 7.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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Concepts
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PriCoder enables LLMs to effectively use private library APIs for code generation by synthesizing data and enhancing code diversity and quality.
Segment
AI Code Generation
Adoption evidence
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Commercial read
7.0/10 public viability
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CITED BY
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missing
reason
passport_row_missing
proof status
unverified
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No verified cost estimate
confidence low
next verification path
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Source missing: Build Passport payload.
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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
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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
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Run minimal reproduction from the Build Passport prototype path.
Market urgency
missing
Current read
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Evidence
0 references, 0 sources, 17% evidence coverage.
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Buyer clarity
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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.
No buyer or workflow interview attached.
People
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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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TIMELINE
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