Reasoning Systems

Opening — Why this matters now AI can describe images, summarize documents, and even write passable essays. But ask it to navigate deception, partial information, and conflicting incentives, and the performance drops—often embarrassingly so. This is not a niche limitation. It’s the core bottleneck for deploying AI in real-world decision systems: finance, legal reasoning, negotiations, and multi-agent environments where not everyone is telling the truth. ...

Opening — Why this matters now There is a quiet but consequential flaw in modern AI reasoning systems: they are excellent storytellers, but poor self-editors. In domains like healthcare, finance, and law, correctness is not a property of the final answer—it is a property of the entire reasoning trajectory. Yet most large language models (LLMs) only discover their mistakes at the very end, if at all. By then, the damage is already embedded in the chain of thought. ...

Opening — Why this matters now For years, the conversation around large language models has revolved around a single question: can they actually reason? Benchmarks come and go. Puzzle-solving demos appear on social media. But none of that truly answers the deeper question that matters to scientists and engineers: Can AI generate genuinely new knowledge? ...