Just benchmarked the new Gemini 3.6 Flash against 3.5 Flash on scheduling / project-controls reasoning with effort-matched (low/medium/high). Headline: ~2.6× faster, but a clear drop in scheduling reasoning accuracy. Accuracy (effort-matched): trails at every setting with 62% vs 82% at high, 62% ...

Why Testing Scheduling Deep Reasoning Matters As we see these new frontier models emerge, it is worth highlighting why benchmarking deep reasoning capabilities in scheduling is so important: The Universal Baseline Across All Use Cases: Deep reasoning is the foundational orchestration capability that ...

Hi Matt, thank you, that's generous, and your paper is excellent. The sections on context limits and the different frameworks for agentic LLM work especially stood out. What was really surprising and interesting was how cleanly our two efforts complement each other: you're testing whether AI can judge ...

I benchmarked 15 leading AI models on controlled project-controls tasks, and the spread between them is large enough that it should change how we talk about "using AI" in scheduling. Each model answered the same unaided schedule-analysis problems: critical path, float, DCMA-14 logic, statusing, and ...