# A Score Is Not a Policy: Measuring the Value of Adaptive Revision

Authors: Ayushi Chadha.

Tests when a hierarchical reasoner should revise the internal plan guiding its lower-level computation. Compares learned revision schedules with fixed schedules, separating whether a controller responds to its state from whether that response improves decisions.

First public: 2026-09-01.
ArXiv initial submission: 2026-09-01.

[Original paper](https://arxiv.org/abs/2609.00874) · [Reading-list entry](https://latent-implicit-thinking.github.io/papers/2609.00874/)
[Preprint BibTeX](https://latent-implicit-thinking.github.io/papers/2609.00874/citation.bib) · [Preprint CSL-JSON](https://latent-implicit-thinking.github.io/papers/2609.00874/citation.csl.json) · [JSON record](https://latent-implicit-thinking.github.io/papers/2609.00874/record.json)

## Approaches

- [Recurrent reasoning](https://latent-implicit-thinking.github.io/topics/recurrent-reasoning/): Repeat parts of the model or keep intermediate information in memory while working through a problem. Some methods also learn how many passes to run.
- [Analysis and evaluation](https://latent-implicit-thinking.github.io/topics/implicit-reasoning-evaluation/): Measure how well these methods work, investigate what their internal computation does, and identify where they fail. This includes experiments, studies of model internals, and mathematical results.

Summary and categories are editorial. Read the original paper to verify claims and limitations. A citation to a paper does not, by itself, establish that a later method builds on it.

Reading-list version 0.9.5; literature checked through 2026-10-18.