Grite is a decentralized git-embedded event log for multi-agent coding coordination that eliminates duplicate work, ensures log convergence, and enables mining of pre-PR failure modes.
The state of the ML-universe: 10 years of artificial intelligence & machine learning soft- ware development on GitHub,
4 Pith papers cite this work. Polarity classification is still indexing.
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cs.SE 4years
2026 4representative citing papers
Loc2Repair framework evaluation finds that file-level localization boosts LLM repo repair resolved rates by up to 7.7 percentage points on SWE-bench Verified.
Controlled experiments show Improper Model Reuse and Unreleased Tensor References in TensorFlow/Keras increase electricity use by 32% and 46% respectively, with proportional CO2 emission increases that are statistically significant.
ML-specific code smells occur 41-94 times less often than general Python smells in 279 projects, with associations to commit frequency and domain but none for general smells or most other project characteristics.
citing papers explorer
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Before the Pull Request: Mining Multi-Agent Coordination
Grite is a decentralized git-embedded event log for multi-agent coding coordination that eliminates duplicate work, ensures log convergence, and enables mining of pre-PR failure modes.
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Loc2Repair: A Framework for Evaluating the Impact of File-Level Issue Localization in Repo-Level LLM Repair
Loc2Repair framework evaluation finds that file-level localization boosts LLM repo repair resolved rates by up to 7.7 percentage points on SWE-bench Verified.
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The Hidden Environmental Cost of Poor Coding Practices in TensorFlow and Keras Applications: A Study on Resource Leaks and Carbon Emissions
Controlled experiments show Improper Model Reuse and Unreleased Tensor References in TensorFlow/Keras increase electricity use by 32% and 46% respectively, with proportional CO2 emission increases that are statistically significant.
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Comparing ML-Specific and General Python Code Smells Across Project Characteristics
ML-specific code smells occur 41-94 times less often than general Python smells in 279 projects, with associations to commit frequency and domain but none for general smells or most other project characteristics.