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SRCH:585D6B53

What is the variation in execution success rates between specialized code LLMs and general-purpose LLMs when g

Submitted: 29 May 2026
Review score: 8.50/10
Verification: L2, Source-grounded claims
Quality tier: Flagship candidate
Verified claims: 10
DOI: 10.5281/zenodo.20441455

Abstract

Abstract: Deep convolutional neural networks have performed remarkably well on many Computer Vision tasks. However, these networks are heavily reliant on big data to avoid overfitting. Overfitting refers to the phenomenon when a network learns a function with very high variance such as to perfectly model the training data. Unfortunately, many application domains do not have access to big data, such as medical image analysis. This survey focuses on Data Augmentation, a data-space solution to the problem of limited data. Data Augmentation encompasses a suite of techniques that enhance the size and quality

Research Question

What is the variation in execution success rates between specialized code LLMs and general-purpose LLMs when generating plots using matplotlib within the BigCodeBench framework?

Verification Level

Paper levelL2, Source-grounded claims
Source-grounded claims10
Claim record sourcenot publicly specified

Descriptive public verification status only; aggregate claim counts are public, but individual claim records are not exposed here.

Quality Tier

TierFlagship candidate
BasisReview score, verified-claim count, and public artifact coverage meet flagship-candidate thresholds.

Descriptive public triage only; this tier does not alter current publication or DOI behavior.

Quality Dimensions

Evidence strength MEDIUM
Citation grounding MEDIUM
Uncertainty disclosure MEDIUM
Reproducibility status HIGH

Automated triage signals derived from public fields; not human peer review or independent validation.

Correction Record

StatusCURRENT
Correction count0
Manifest contractpaper-manifest-v1.1
Correction contractcorrection-record-v1

Public corrections are additive records. Current status does not claim the synthesis is error-free.

Provenance

PublisherAssignee Research
Public provenanceL4, External archival record
Report artifactAvailable
External recordRegistered
Claim lineage10 aggregate source-grounded claims
Review methodAutomated multi-reviewer assessment
Quality guideHow to read scores, claims, manifests, and evidence links
Provenance contractsource-provenance-v1
NoteMachine-generated synthesis of existing literature. Not primary research.