A Score Is Not a Policy: Measuring the Value of Adaptive Revision
As agentic systems become compound systems, increasingly important decisions move above task execution itself: when should a higher-level controller preserve the strategy guiding another process, and when should it revise it? We study this meta-level control problem in a hierarchical latent reasoner whose manager can retain or replace a commitment governing lower-level computation. Across three precommitted training seeds, learned revision timing produces qualitatively different policies, ranging from an almost deterministic early clock to substantially more state conditioned schedule distributions, yet none outperforms the best forced timing policy evaluated on the same frozen checkpoint...
| Authors | Ayushi Chadha |
|---|---|
| Primary category | cs.AI |
| Published | 1 September 2026 |
| Last updated at source | 8 October 2026 |
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