OpenClassGen supplies 324,843 real-world Python classes with self-contained skeletons and static metrics to support LLM class generation research and evaluation.
Ashok, and Shashank Shet
7 Pith papers cite this work. Polarity classification is still indexing.
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Holmes is a multimodal multi-agent system using a hierarchical Retrieve-Explore-Reason architecture to automate root cause analysis of mobile crashes, achieving 87.6% function-level accuracy and 98% time reduction on real WeChat data.
Co-Coder partitions code dependency graphs via community detection to orchestrate multi-agent LLM coding, improving pass rates up to 14%, wall-clock speedup up to 2.1x, and cutting API cost up to 35% on dependency-dense tasks.
CodeThinker improves LLM code reasoning via consistency-based RL with stepwise training data, dynamic beam sampling, and consistency rewards, reaching SOTA on benchmarks with 4.3% gains on Qwen2.5-Coder-7B.
SnapKV selects clustered important KV positions per attention head from an observation window at the prompt end, yielding 3.6x faster generation and 8.2x better memory efficiency on 16K-token inputs with comparable performance across 16 datasets.
Ablation study finds that a structural codebase index improves localization and resolve rates in coding agents on two SWE benchmarks without raising per-cell cost.
A systematic literature review that organizes recent work on LLMs for code generation into a taxonomy covering data curation, model advances, evaluations, ethics, environmental impact, and applications, with benchmark comparisons.
citing papers explorer
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Holmes: Multimodal Agentic Diagnosis for Mixed-Language Mobile Crashes at Industrial Scale
Holmes is a multimodal multi-agent system using a hierarchical Retrieve-Explore-Reason architecture to automate root cause analysis of mobile crashes, achieving 87.6% function-level accuracy and 98% time reduction on real WeChat data.
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When Parallelism Pays Off: Cohesion-Aware Task Partitioning for Multi-Agent Coding
Co-Coder partitions code dependency graphs via community detection to orchestrate multi-agent LLM coding, improving pass rates up to 14%, wall-clock speedup up to 2.1x, and cutting API cost up to 35% on dependency-dense tasks.
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Enhancing the Code Reasoning Capabilities of LLMs via Consistency-based Reinforcement Learning
CodeThinker improves LLM code reasoning via consistency-based RL with stepwise training data, dynamic beam sampling, and consistency rewards, reaching SOTA on benchmarks with 4.3% gains on Qwen2.5-Coder-7B.
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Code Isn't Memory: A Structural Codebase Index Inside a Coding Agent
Ablation study finds that a structural codebase index improves localization and resolve rates in coding agents on two SWE benchmarks without raising per-cell cost.
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A Survey on Large Language Models for Code Generation
A systematic literature review that organizes recent work on LLMs for code generation into a taxonomy covering data curation, model advances, evaluations, ethics, environmental impact, and applications, with benchmark comparisons.