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Marcus on AI: Checking the math behind OpenAI's latest headline

What changed

OpenAI scored a big result yesterday, with respect to an Erdos problem: Clearly impressive. But as with so much else, it should be viewed with skepticism. In an email to me this morning, Cal Newport made a number of good points that he said I could share, summarizing both what was found and some limitations: OpenAI used a new reasoning model (not yet released) to help identify a counterexample that disproved a conjecture from discrete geometry first proposed by Paul Erdos 80 years ago. The model was tuned for so-called chain-of-thought reasoning where the model endlessly “thinks out loud” about whatever it is trying to solve, an approach that lets you approximate something like memory and dynamic computation using LLMs, which are otherwise static and feed-forward. Professional mathematicians identified the counterexample from within a long transcript of the model’s reasoning, and then extracted the key parts and rewrote it as a more succinct proof in a more standard style. How was the model able to solve something that human mathematicians had failed to do? In a companion article released by OpenAI, the mathematician Thomas Bloom, who reviewed the full model output, identified the factors that came together to make this counterexample ripe for LLM-aided discovery. He noted that though the conjecture is old, those who have worked on it have largely shared Erdos’s original belief that it was true and therefore focused on trying to solve it. What the LLM-based tool did instead was to systematically apply and extend existing techniques in search of evidence that the conjecture was false. Here’s Bloom: “[the AI’s] success here echoes previous achievements: it often produces the most surprising results by persevering down the paths that a human may have dismissed as not worth their time to explore, combining superhuman levels of patience with familiarity with a vast array of technical machinery.” A few observations of my own: (1) Non-mathematicians might not be familiar with the degree to which LLM-technology has been combined with existing computer-aided math tools in recent years to seek new math results through the systematic and patient exploration of techniques and corners of problem spaces that are too exhausting to interest most human mathematicians. The real technical headline of the new OpenAI result, therefore, is that chain-of-thought reasoning was abl…

Sources and evidence

外部发现 · OpenAI · Original source ↗ · Site entry ·

ESI-Bench Catches Frontier AI Failing 3D Space: Seeing and Acting Diverge

What changed

A Stanford-led research team that includes Fei-Fei Li published a benchmark on May 18, 2026, that documents a specific, measurable failure in every frontier multimodal AI model tested: when forced to actively move through a 3D environment rather than interpret a pre-composed image, state-of-the-art systems consistently choose the wrong actions, skip viewpoints that would correct their mistakes, and commit to wrong answers with high confidence. The paper names this failure "action blindness." For robotics buyers, simulation engineers, and any developer whose product requires an AI agent to reason about physical space, the finding supplies a test that existing leaderboards do not. ESI-Bench Redefines What Spatial Intelligence Requires The benchmark, titled "ESI-Bench: Towards Embodied Spatial Intelligence that Closes the Perception-Action Loop," covers 10 task categories and 29 subcategories built inside the OmniGibson simulator on top of Stanford's BEHAVIOR-1K activity dataset. Its approximately 3,000 tasks are organized around what developmental psychologist Elizabeth Spelke at Harvard has identified as core knowledge systems — the domain-specific cognitive systems human infants use to reason about objects, agents, numbers, space, and form. The conceptual shift the benchmark makes is precise and consequential. Prior spatial-intelligence benchmarks assumed oracle observations: the model was handed the right view of the scene before being asked a question. ESI-Bench removes that assumption. The agent must first decide which ability to deploy — perception, locomotion, or manipulation — and in what sequence, in order to accumulate the evidence needed to answer. As described on the project page, the agent must actively uncover what is unseen: occluded structure, dynamics, containment, and functionality that no single static image can reveal. The task set reflects that design. Agents are asked to compare the liquid-holding capacity of two containers by manipulating them, predict whether a deformable object will conform to a surface, judge whether a tower will balance under given mass and geometry distributions, and distinguish a mirror reflection from the real scene by repositioning. Each task has a correct answer that is structurally inaccessible without choosing an action first. Frontier Models See But Do Not Know When to Move Experiments on state-of-the-art mu…

Sources and evidence

外部发现 · 李飞飞 · Original source ↗ · Site entry ·