LoopArena: Benchmarking Models as Runtime Controllers for Loop Engineering
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TL;DR - LoopArena benchmarks language models acting as runtime Controllers that guide a fixed coding agent through long-running development tasks. Its best full-task strict success rate is only 24.69%, highlighting substantial room to improve agentic loop control.
- Separates the Controller’s guidance quality from the Worker coding agent’s execution ability.
- Evaluates control at three levels: execution-validated next-step selection, repeated control over task slices, and complete end-to-end tasks.
- Controllers reduce estimated inference costs by an average of 64.4% in paired comparisons.
- The lower-cost Type II evaluation closely matches the main controller ranking, with Spearman’s ρ of 0.9747.
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LoopArena: Benchmarking Models as Runtime Controllers for Loop Engineering
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Hugging Face upvotes 98
TL;DR - LoopArena benchmarks language models acting as runtime Controllers that guide a fixed coding agent through long-running development tasks. Its best full-task strict success rate is only 24.69%, highlighting substantial room to improve agentic loop control.
- Separates the Controller’s guidance quality from the Worker coding agent’s execution ability.
- Evaluates control at three levels: execution-validated next-step selection, repeated control over task slices, and complete end-to-end tasks.
- Controllers reduce estimated inference costs by an average of 64.4% in paired comparisons.
- The lower-cost Type II evaluation closely matches the main controller ranking, with Spearman’s ρ of 0.9747.