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TSR Desk · science · 1 November 2026, 01:00 UTC

ThinkJev: One Thought, Every Budget for Calibrated Typed Decisions

What
ThinkJev: One Thought, Every Budget for Calibrated Typed Decisions
Who
arxiv.org
When
31 October 2026, 04:00 UTC
Category
Science
Primary source
https://arxiv.org/abs/2610.37732
What is not known
This brief does not claim independent replication. Claims that appear only on X and not in the primary source stay unknown.

Letting the model think first fixes most of these errors, but costs seconds per decision instead of milliseconds. It comes from a paper posted to arXiv on 31 October 2026. Many uses of language models end in a typed decision: a choice among a closed set of options, whose probabilities code can act on. Reading those probabilities from one forward pass is fast but often wrong on decisions that take several steps, such as arithmetic, counting, and date comparison. To pay for thinking only where it helps, ThinkJev builds a fast and a slow system from one open hybrid-thinking model that can answer directly or after a thought. System 1 reads the model's probabilities for the option letters when it answers directly; System 2 reads the same letters after the model has thought on the same cached prompt, so escalating repeats no work and both systems give probabilities over the same options. Because the letters can be read after any number of thought tokens, one sampled thought also gives the answer under every thinking budget. Without training, ThinkJev lets System 1 answer alone only where a statistical test on labeled dev data guarantees that its accepted answers stay under a target error rate. At a 5% target, Qwen3.5-27B answers 24.5% of 8359 test decisions this way with a realized error of 3.9% (Qwen3.5-4B and 9B: 6.5% and 0.0%). For the rest, stopping each thought as soon as its answer is confident reaches 81.6% accuracy, against 82.7% for always thinking, with 72% of the thought tokens. Choosing when to think from System 1's confidence alone saves less, because the decisions System 1 is least sure of are often not the ones thinking fixes. On the public items of JevBench, an independent benchmark of typed-decision services, Qwen3.5-27B thinks on 29.4% and is not distinguishable in accuracy from the two thinking APIs listed (exploratory). Code and data generators are available at https://github.com/ding-xiaolu/ThinkJev.

Why it counts

Letting the model think first fixes most of these errors, but costs seconds per decision instead of milliseconds.

Sources

Primary source: primary source

What is not known

This brief does not claim independent replication. Claims that appear only on X and not in the primary source stay unknown.

No clip. The article still stands.