Audio. Video. Subtitles. The standard instinct is to send all of them into the model and hope the transformer performs its usual magic trick: turn a messy pile of signals into a useful answer. This instinct is understandable. It is also expensive, noisy, and occasionally a magnificent way to teach the model the wrong lesson. ...
A game engine is a wonderfully unfair place to test an AI agent. That is exactly why it is useful. In ordinary software tasks, a coding agent can often survive by reading files, editing functions, running tests, and pretending the world is mostly text. A game engine is less polite. It asks the agent to understand spritesheets, scene hierarchies, collision shapes, animation states, shaders, camera views, object nodes, and temporal behavior. The code matters, but the code is only one layer of the object. The game itself lives somewhere between text, geometry, assets, and motion. ...
Enterprise workflows rarely fail because nobody “thought step by step.” They fail because the wrong kind of thinking is applied for too long. A compliance analyst does not review an incident report the same way she reconciles a spreadsheet. A software engineer does not debug production latency with the same mindset used to design a product roadmap. A CFO does not evaluate a warehouse automation proposal by “being creative” all the way through, unless the board has a strong appetite for interpretive dance. ...
A model that fails its own eye test Mirror. That is where the problem becomes easy to see. Ask a multimodal model to generate an image of a plush lion toy in front of a mirror. The model may produce something plausible at first glance: lion, mirror, warm lighting, adorable synthetic confidence. Then ask the same model, through its understanding branch, whether the image makes physical sense. Suddenly it notices the issue: if the toy faces the camera, the mirror should mostly show its back, not another front-facing lion. ...
Screenshots are easy to love. They sit still, look polished, and ask very little from the viewer. Interfaces are less polite. Click one wrong icon, place a menu twenty pixels away from where it belongs, blur one label, or forget what happened three screens ago, and the whole interaction becomes decorative theatre. ...
A diagram is often where a paper stops being private reasoning and becomes public knowledge. Before that point, the author may have a method, a theorem, a pipeline, or a system architecture. The reader has only paragraphs. Then one good figure appears, and the fog lifts. The method has stages. The variables have roles. The arrows tell us what depends on what. The paper becomes less of a swamp. ...
Click. That is where most web-agent demos become either impressive or mildly tragic. The model reads the instruction, understands the goal, produces a confident plan, and then clicks the wrong thing. Or it clicks the right thing before a modal appears. Or it scrolls, forgets why it scrolled, repeats an action, and quietly turns a three-step workflow into interpretive dance. ...
A model can generate a pretty sequence of images. Good. So can a slide deck. The harder question is whether those images actually help it think. That is the uncomfortable point behind MentisOculi: Revealing the Limits of Reasoning with Mental Imagery, a new benchmark paper that tests whether frontier multimodal models can do something closer to human mental imagery: form a visual state, keep it stable, transform it step by step, and use the transformed state to decide what to do next.1 Not merely “look at an image and answer a question.” Not “draw a plausible intermediate picture.” Actual visual reasoning, with consequences. ...
A dashboard screenshot is often too little. A video walkthrough is often too much. Somewhere between the two sits a strangely old-fashioned interface: panels, captions, arrows, speech bubbles, and a sequence that tells the machine what happened before what. Yes, comics. That sounds unserious only if we think comics are a decoration layer: something added after the reasoning is complete to make the output friendlier. The paper Thinking with Comics: Enhancing Multimodal Reasoning through Structured Visual Storytelling makes a more interesting claim: comics can act as the reasoning medium itself, not merely the illustration of reasoning after the fact.1 ...
Image generation has become good enough to be useful and unreliable enough to remain annoying. That is the normal condition of enterprise AI: impressive demos, awkward edge cases, and someone in operations quietly asking whether the model actually understood the instruction or merely produced something that looked plausible from a distance. A user asks for “a red ceramic mug on a wooden desk, next to an open notebook, in morning light.” The model produces a beautiful desk, credible sunlight, maybe even the notebook. The mug is blue. Or metallic. Or missing. If a separate vision model can look at the image and say, “That is not a red ceramic mug,” the failure feels almost rude. The system can see the problem after creating it. Very efficient, in the same way that a committee can discover a typo after approving the brochure. ...