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ARXIV:2601.19106 · CODE CORRECTION TOOLS · SUBMITTED 19 MAR · 18:48 UTC · FRESHNESS STALE
ARXIV:2601.19106CODE CORRECTION TOOLSSUBMITTED 19 MAR · 18:48 UTCFRESHNESS STALEarXiv
A deterministic AST-based tool to auto-correct semantic errors in LLM-generated code, enhancing reliability without runtime execution.
Opportunity summary
Pain A deterministic AST-based tool to auto-correct semantic errors in LLM-generated code, enhancing reliability without runtime execution.
Evidence 0 refs | 0 sources | 33% coverage
Blocker Evidence unverified
A deterministic AST-based tool to auto-correct semantic errors in LLM-generated code, enhancing reliability without runtime execution. Existing mitigations like constrained decoding or non-deterministic LLM-in-the-loop repair are often unreliable for these errors.
Large Language Models (LLMs) for code generation boost productivity but frequently introduce Knowledge Conflicting Hallucinations (KCHs), subtle, semantic errors, such as non-existent API parameters, that evade linters and cause runtime failures. Existing mitigations like…
ScienceToStartup currently rates this 8.0/10 on the public viability pass. Our findings demonstrate that this deterministic post-processing approach is a viable and reliable alternative to probabilistic repair, offering a clear path toward trustworthy code…
Code Correction Tools moved forward this cycle; last verified April 2026. Public score 8.0/10.
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Score8.0Public score shown from the verified overall while the stale axis breakdown refreshesAnalysis summary
A deterministic AST-based tool to auto-correct semantic errors in LLM-generated code, enhancing reliability without runtime execution.
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Paper Pack
10.48550/arXiv.2601.19106A deterministic AST-based tool to auto-correct semantic errors in LLM-generated code, enhancing reliability without runtime execution.
Abstract
Large Language Models (LLMs) for code generation boost productivity but frequently introduce Knowledge Conflicting Hallucinations (KCHs), subtle, semantic errors, such as non-existent API parameters, that evade linters and cause runtime failures. Existing mitigations like constrained decoding or non-deterministic LLM-in-the-loop repair are often unreliable for these errors. This paper investigates whether a deterministic, static-analysis framework can reliably detect \textit{and} auto-correct KCHs. We propose a post-processing framework that parses generated code into an Abstract Syntax Tree (AST) and validates it against a dynamically-generated Knowledge Base (KB) built via library introspection. This non-executing approach uses deterministic rules to find and fix both API and identifier-level conflicts. On a manually-curated dataset of 200 Python snippets, our framework detected KCHs with 100\% precision and 87.6\% recall (0.934 F1-score), and successfully auto-corrected 77.0\% of all identified hallucinations. Our findings demonstrate that this deterministic post-processing approach is a viable and reliable alternative to probabilistic repair, offering a clear path toward trustworthy code generation.
Source availability
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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; 0 sources; 33% 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
A deterministic AST-based tool to auto-correct semantic errors in LLM-generated code, enhancing reliability without runtime execution. Existing mitigations like constrained decoding or non-deterministic LLM-in-the-loop repair are often unreliable for these errors.
METHOD
Large Language Models (LLMs) for code generation boost productivity but frequently introduce Knowledge Conflicting Hallucinations (KCHs), subtle, semantic errors, such as non-existent API parameters, that evade linters and cause runtime failures. Existing mitigations like constr...
RESULT
ScienceToStartup currently rates this 8.0/10 on the public viability pass. Our findings demonstrate that this deterministic post-processing approach is a viable and reliable alternative to probabilistic repair, offering a clear path toward trustworthy code generation.
WHY NOW
Code Correction Tools moved forward this cycle; last verified April 2026. Public score 8.0/10.
On a manually-curated dataset of 200 Python snippets, our framework detected KCHs with 100% precision and 87.6% recall (0.934 F1-score)
Explicitly stated in abstract with clear numeric results
partial
successfully auto-corrected 77.0% of all identified hallucinations
Explicitly stated in abstract with clear numeric results
partial
This non-executing approach uses deterministic rules to find and fix both API and identifier-level conflicts
Directly stated in abstract describing the method
partial
We propose a post-processing framework that parses generated code into an Abstract Syntax Tree (AST) and validates it against a dynamically-generated Knowledge Base (KB) built via library introspection
Directly stated in abstract describing the technical approach
partial
Existing mitigations like constrained decoding or non-deterministic LLM-in-the-loop repair are often unreliable for these errors
Directly stated in abstract as motivation for the work
partial
frequently introduce Knowledge Conflicting Hallucinations (KCHs), subtle, semantic errors, such as non-existent API parameters, that evade linters and cause runtime failures
Directly stated in abstract defining the problem
partial
Our findings demonstrate that this deterministic post-processing approach is a viable and reliable alternative to probabilistic repair, offering a clear path toward trustworthy code generation
Directly stated conclusion in abstract
partial
This non-executing approach uses deterministic rules to find and fix both API and identifier-level conflicts
Directly stated in abstract describing the technical approach
partial
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Concepts
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Materials
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A deterministic AST-based tool to auto-correct semantic errors in LLM-generated code, enhancing reliability without runtime execution.
Segment
Code Correction Tools
Adoption evidence
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Commercial read
8.0/10 public viability
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CITED BY
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Build Passport
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status
missing
reason
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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
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Build readiness
BuildPassport EvidenceState
passport absent
stale
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Artifact maturity
GitHub and Hugging Face maturity payloads
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stale
Open source artifacts or mark the gap as missing. verified:false
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, 33% evidence coverage.
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Collect buyer interview, deployment evidence, or cited demand signal.
Buyer clarity
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No budget owner is verified for this paper.
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Defensibility
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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
Current read
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Run cost passport or mark the cost field not applicable.
Regulatory load
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Current read
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Evidence
Build Passport ledger does not include regulatory flags.
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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Gaps
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Operator workflow not sourced.
No buyer or workflow interview attached.
People
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Gaps
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People
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Regulatory need unclassified.
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Gaps
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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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BUZZ
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