PBT-Bench is a new benchmark with 100 property-based testing problems across 40 Python libraries that measures LLM bug recall rates of 42.1-83.4% under guided prompting versus 31.4-76.7% in baseline.
Koen Claessen and John Hughes
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PerfCodeBench reveals that state-of-the-art LLMs produce functionally correct but significantly slower code than expert-optimized versions on system-level tasks, especially those involving parallelism and GPUs.
BenchCAD benchmark shows frontier multimodal models recover coarse geometry but fail to produce accurate parametric CAD programs for industrial parts, with limited generalization after fine-tuning.
SkillFlow benchmark shows lifelong skill evolution yields modest gains for some models like Claude Opus 4.6 but limited or negative utility for others despite high skill usage.
Code-QA-Bench uses an answer-first pipeline and three-condition experiments to generate 628 tasks across 10 Python repositories and quantify that code access drives most performance gains while documentation adds only modest benefit on doc-dependent tasks.
citing papers explorer
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PBT-Bench: Benchmarking AI Agents on Property-Based Testing
PBT-Bench is a new benchmark with 100 property-based testing problems across 40 Python libraries that measures LLM bug recall rates of 42.1-83.4% under guided prompting versus 31.4-76.7% in baseline.
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PerfCodeBench: Benchmarking LLMs for System-Level High-Performance Code Optimization
PerfCodeBench reveals that state-of-the-art LLMs produce functionally correct but significantly slower code than expert-optimized versions on system-level tasks, especially those involving parallelism and GPUs.
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BenchCAD: A Comprehensive, Industry-Standard Benchmark for Programmatic CAD
BenchCAD benchmark shows frontier multimodal models recover coarse geometry but fail to produce accurate parametric CAD programs for industrial parts, with limited generalization after fine-tuning.
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SkillFlow:Benchmarking Lifelong Skill Discovery and Evolution for Autonomous Agents
SkillFlow benchmark shows lifelong skill evolution yields modest gains for some models like Claude Opus 4.6 but limited or negative utility for others despite high skill usage.
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Code-QA-Bench: Separating Code Reasoning from Documentation Memorization in Repository-Level QA
Code-QA-Bench uses an answer-first pipeline and three-condition experiments to generate 628 tasks across 10 Python repositories and quantify that code access drives most performance gains while documentation adds only modest benefit on doc-dependent tasks.