arXiv AI
Interactive Clarification for Cloud Infrastructure-as-Code Synthesis
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From Brute Force to Semantic Insight: Performance-Guided Data Transformation Design with LLMs
arXiv:2601. 03808v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have achieved notable performance in code synthesis; however, data-aware augmentation remains a limiting factor, handled via heuristic design or brute-force approaches.
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ClawEnvKit: Automatic Environment Generation for Claw-Like Agents
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Description-Code Inconsistency in Real-world MCP Servers: Measurement, Detection, and Security Implications
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Simulation Code Generation for Fluid Systems using Large Language Models: Benchmarking Models and Prompting Strategies
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arXiv AI
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Cross-Lingual Token Arbitrage: Optimizing Code Agent Context Windows via Local LLM Preprocessing
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