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Initial scaffolding for evaluating llmcc's impact on AI coding agent performance using SWE-bench Multilingual. ## Structure - swe-bench/README.md: Documentation and experiment design - swe-bench/configs/rust.yaml: Rust-specific configuration (43 tasks) - swe-bench/src/context_injector.py: llmcc -> agent context conversion - swe-bench/src/run_experiment.py: Main experiment runner - swe-bench/src/analyze_results.py: Results analysis and comparison - swe-bench/setup.sh: Environment setup script ## Target SWE-bench Multilingual Rust subset (43 tasks from 7 repos): - tokio-rs/tokio, tokio-rs/axum, astral-sh/ruff - sharkdp/bat, nushell/nushell, uutils/coreutils, burntsushi/ripgrep ## Hypothesis AI agents with llmcc architecture context will solve more tasks than agents without it, by reducing exploration overhead. Next steps: 1. Run setup.sh to configure environment 2. Add mini-SWE-agent integration 3. Run baseline experiments 4. Compare with llmcc-enhanced experiments
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