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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OpenClassGen: A Large-Scale Corpus of Real-World Python Classes for LLM Research
OpenClassGen supplies 324,843 real-world Python classes with self-contained skeletons and static metrics to support LLM class generation research and evaluation.
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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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SnapKV: LLM Knows What You are Looking for Before Generation
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.
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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.