Adaptive Compute

TL;DR for operators The current enterprise mistake is treating “reasoning” as a personality trait of a model. It is not. It is a process: decompose the task, inspect the evidence, decide what matters, test counterarguments, synthesize a position, and stop before the machine starts producing beautifully cited nonsense. Two recent papers expose that process from opposite ends. Hedge-Bench defines a realistic demand signal: open-ended financial reasoning tasks derived from hedge fund analyst work, graded against expert analytical moves and source-grounded claims.1 It finds that frontier agents remain weak on this kind of work, with the best model achieving only a limited perfect-score rate and with stronger exploration often bringing more hallucination along for the ride. Delightful. The junior analyst has read the filings, opened the spreadsheet, and still occasionally invents the economy. ...

Stop Every production LLM workflow eventually meets the same boring question: should the model answer now, think again, or throw away the current path and try something else? That question sounds less glamorous than “build a bigger model.” It is also closer to where real deployment costs live. Reasoning models can improve by sampling more answers, extending chains of thought, or running repeated critique-and-revision loops. The bill, naturally, arrives in tokens, latency, GPU capacity, and engineering patience. The last item is rarely benchmarked, perhaps because it would make too many papers look expensive. ...