Optimization

A broken AI agent often looks deceptively close to working. It answers most questions. It calls the right tool sometimes. It follows the instruction until the conversation gets long, the retrieval query gets vague, or the arithmetic becomes just difficult enough for the model to start doing spreadsheet theatre. The usual repair is prompt editing. Add a stern sentence. Add a role. Add an example. Add “think step by step,” because apparently the machine needed a motivational poster. ...

TL;DR for operators The paper is useful because it gets the hierarchy right: the optimizer decides; the LLM explains, configures, contextualizes, and packages the decision for humans.1 That is not a small distinction. It is the difference between a supply chain system that can be audited and a chatbot confidently waving at a warehouse. ...

TL;DR for operators Optimization work usually looks technical from the outside: equations, solvers, constraints, tolerances, and someone quietly muttering about convergence. Inside the business, the real bottleneck is often less glamorous. Someone has to decide what the problem actually is, how to formulate it, which algorithm to try, which hyperparameters to tune, and whether the resulting answer is useful or merely mathematically decorative. ...