arXiv: cs.AI preprintsInternational5 November 2026
DARE: Difficulty Analysis for Repair Enhancement
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arXiv:2611.05797v1 Announce Type: cross Abstract: LLM-based coding agents are primarily evaluated by their solve rates. However, this metric does not reveal how difficult the solved tasks are or account for differences intask difficulty across benchmarks. Determining the difficulty of individual tasks by repeatedly running multiple agents is costly, while a predicted difficulty score alone provides limited guidance on what support an agent needs. To address these challenges, we propose DARE (Difficulty Analysis for Repair Enhancement), which integrates repair-task difficulty measurement, predi
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