A customer service agent gets the refund policy right on Monday, wrong on Tuesday, and confidently wrong on Wednesday. A coding agent passes the benchmark, then casually rewrites the wrong file in production. A workflow agent behaves perfectly in a demo, then becomes confused when the API returns the same fields in a different order. ...
Taxi dispatch is not a poetry recital. When a passenger calls and says, “I’m on Arguello,” the system does not need to appreciate the full expressive richness of the sentence. It needs to identify one street name, map it to the right place, and send a vehicle there. This is not a broad language-understanding task. It is a narrow operational task with coordinates attached. ...
A customer-service agent rebooks a flight, checks a policy, calls an API, updates the passenger record, apologizes politely, and still gets the outcome wrong. The old explainability question would be: which input tokens influenced the final answer? That question is not useless. It is just late to the crime scene. When an AI system only predicts, explanation can focus on a single input-output decision. When an AI system acts, explanation has to follow the behavior across time: the state it maintained, the tool it selected, the observations it received, the recovery move it attempted, and the point where the run quietly became unrecoverable. A nice feature-importance chart does not tell you that. It tells you what mattered to a prediction, not how a workflow failed. ...
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 simulation model is not a chatbot with a spreadsheet attached. That sounds obvious until a project team starts treating the LLM as if it were the entire modeling stack: the analyst, the programmer, the validator, the documentation clerk, the statistical package, and occasionally the intern blamed when the result changes on Tuesday. The convenient story is that better prompting will tame the system. Add more examples. Add a RAG. Set temperature to zero. Smile at the demo. ...
Drawer. That is the easy part. A model sees a drawer, and it knows that drawers are often opened. Then it watches a video where someone is closing the drawer and predicts opening anyway. This is not the kind of error that makes a demo look silly for five seconds and then disappear into the benchmark appendix. It is the kind of error that reveals what the system is really using as evidence. The model is not necessarily watching the motion. It may be recognizing the object, remembering the most common verb attached to that object during training, and calling that “video understanding.” Very efficient. Also wrong. ...
A robot in a factory does not need a beautiful video of itself almost doing the job. It needs the gripper to close at the right moment, the wrist to rotate by the right amount, and the next two seconds of motion not to turn a simple pick-and-place task into modern sculpture. This is where many foundation-model stories become less glamorous. Vision-language models can recognize the scene. Video models can imagine motion. Neither of those achievements automatically gives you a usable control policy. ...
Probe, Then Commit: Why Solver Tuning Finally Grew Up Planning is where business software goes to meet reality. A factory needs a schedule. A logistics team needs routes. A utility company needs network decisions. A hospital needs staff allocation. The model is elegant, the constraints are clear, and then the solver quietly asks the question nobody put in the PowerPoint: ...
A robot does not always get the luxury of a clean task list. Reach the loading bay. Avoid blocked corridors. Preserve battery. Pick up two packages. Respect a safety boundary. Finish before the door closes. Then the environment, as environments enjoy doing, changes the rules halfway through. A corridor shuts. A resource disappears. One goal now interferes with another. ...
Minecraft is not the point. That may sound rude to the blocks, but it is the cleanest way to read MineNPC-Task: Task Suite for Memory-Aware Minecraft Agents.1 The paper does use Minecraft. It does study an AI companion agent inside a live game world. It does report that a GPT-4o-powered setup failed on 71 out of 216 attempted subtasks, or roughly one-third of the subtask denominator. ...