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PairCoder
by nju-websoft
Pair-programming framework for iterative multi-plan code generation
Python
Updated Nov 29, 2024
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What It Does
Implements a pair-programming framework where two LLM-based agents iteratively propose, critique, and refine code solutions. Uses multi-plan exploration and feedback-driven refinement to surface diverse approaches and progressively converge on correct implementations. Notable for its structured interaction loop that combines proposal, critique, and repair phases to improve code-generation robustness.
Key Benefits
As agents take on more autonomous coding tasks, knowing which solution paths are reliable matters for safety and maintainability. PairCoder makes agent interaction explicit and auditable, producing signals (proposal diversity, critique effectiveness, convergence behavior) that can feed reputation and trust models. Until now most code-generation focused on single-agent outputs; this exposes agent-to-agent dynamics useful for continuous evaluation and debugging.
Ideal For
Researchers and engineers building or evaluating collaborative LLM agents for code generation and wanting structured feedback loops.
Real-World Examples
- Exploring multiple solution plans and selecting robust implementations via agent critique
- Diagnosing agent failure modes by inspecting proposal/critique cycles
- Integrating structured feedback loops into code-generation pipelines for higher-quality synthesis
Topics
code-generationlarge-language-modelsllm-agentmulti-agent-systempair-programingprogram-synthesis
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Keywords
multi-agentagent-to-agent evaluationcode-generationpair-programming