Uncertainty

Opening — Why this matters now AI agents are no longer toy demos. They write production code, refactor legacy systems, navigate websites, and increasingly make decisions that matter. Yet one deceptively simple question remains unresolved: can an AI agent reliably tell whether it will succeed? This paper delivers an uncomfortable answer. Across frontier models and evaluation regimes, agents are systematically overconfident about their own success—often dramatically so. As organizations push toward longer-horizon autonomy, this blind spot becomes not just an academic curiosity, but a deployment risk. ...

Opening — Why this matters now Modern transformers are confident. Too confident. In high-stakes deployments—question answering, medical triage, compliance screening—this confidence routinely outruns correctness. The problem is not accuracy; it is miscalibration. Models say “I’m sure” when they shouldn’t. Most fixes arrive late in the pipeline: temperature scaling, Platt scaling, confidence rescaling after the model has already reasoned itself into a corner. What if uncertainty could intervene earlier—during reasoning rather than after the verdict? ...

Opening — Why this matters now If you work with large language models long enough, you start noticing a familiar failure mode. The model doesn’t just answer incorrectly—it loses the thread. Halfway through a chain-of-thought, something snaps. The reasoning drifts, doubles back, contradicts itself, and eventually lands somewhere implausible. Traditional evaluation misses this. Accuracy checks only look at the final answer, long after the damage is done. Confidence scores are static and blunt. Multi-sample techniques are expensive and retrospective. What’s missing is a process-level diagnostic—a way to tell, during inference, whether reasoning is stabilizing or quietly unraveling. ...

Opening — Why this matters now Reinforcement learning has a credibility problem. Models ace their benchmarks, plots look reassuringly smooth, and yet the moment the environment changes in a subtle but meaningful way, performance falls off a cliff. This is usually dismissed as “out-of-distribution behavior” — a polite euphemism for we don’t actually know what our agent learned. ...

Opening — Why this matters now LLM agents today are voracious readers and remarkably poor conversationalists in the epistemic sense. They browse, retrieve, summarize, and reason—yet almost never talk back to the knowledge ecosystem they depend on. This paper names the cost of that silence with refreshing precision: epistemic asymmetry. Agents consume knowledge, but do not reciprocate, verify, or negotiate truth with the world. ...

Opening — Why this matters now Modern systems act faster than their understanding. Algorithms trade in microseconds, clinical protocols scale across populations, and institutions make irreversible decisions under partial information. Yet our ethical vocabulary remains binary: act or abstain, know or don’t know, responsible or not. That binary is failing. The paper behind this article introduces a deceptively simple idea with uncomfortable implications: uncertainty does not reduce moral responsibility — it reallocates it. When confidence falls, duty does not disappear. It migrates. ...

The most radical idea in Michael I. Jordan’s latest manifesto isn’t a new model, a benchmark, or even a novel training scheme. It’s a reorientation. He argues that we’ve misdiagnosed the nature of intelligence—and in doing so, we’ve built AI systems that are cognitively brilliant yet socially blind. The cure? Embrace a collectivist, economic lens. This is not techno-utopianism. Jordan—a towering figure in machine learning—offers a pointed critique of both the AGI hype and the narrow symbolic legacy of classical AI. The goal shouldn’t be to build machines that imitate lone geniuses. It should be to construct intelligent collectives—systems that are social, uncertain, decentralized, and deeply intertwined with human incentives. In short: AI needs an economic imagination. ...