ReCodeAgent uses a multi-agent system to translate and validate large code repositories across multiple programming languages, achieving 60.8% higher test pass rates than prior neuro-symbolic and agentic methods on 118 real-world projects.
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PHMForge benchmark shows LLM agents achieve 80.8% pass@1 on prognostic tasks with native MCP tools but performance collapses from 100% to 20% when using text RAG instead.
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ReCodeAgent: A Multi-Agent Workflow for Language-agnostic Translation and Validation of Large-scale Repositories
ReCodeAgent uses a multi-agent system to translate and validate large code repositories across multiple programming languages, achieving 60.8% higher test pass rates than prior neuro-symbolic and agentic methods on 118 real-world projects.
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PHMForge: Evaluating LLM Agents on Industrial Prognostics through MCP-Native, Algorithm-Grounded Tools
PHMForge benchmark shows LLM agents achieve 80.8% pass@1 on prognostic tasks with native MCP tools but performance collapses from 100% to 20% when using text RAG instead.