SOURCE-LINKED INTELLIGENCE
Thinking Costs Tokens: When More Structure is Worth the Price
Adding inference structure to a language model lets it search, verify, and revise, but these actions consume the very budget they are supposed to use well. In this paper, we investigate whether there exists a token-budget threshold, below which the overhead of planning and verification hurts performance and above which it helps. We evaluate two systems on FinQA and TAT-QA financial reasoning tasks, using GPT-5.4 mini across 14 budget tiers ranging from 250 to 42,000 output-equivalent tokens. The first system is a monolith, which is a single LLM call. The second is a verified search architectur
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-08-27T05:24:16.000Z
First collected: 2026-09-21T08:51:59.673Z. This is not the publication date.