AERA: Adaptive Evidence Residual Allocation for Efficient Test-Time Reasoning
Test-time scaling improves language-model reasoning by generating additional candidate solutions, but allocating the same inference budget to every problem is computationally wasteful. Existing adaptive stopping methods commonly rely on confidence, agreement, or answer stability, implicitly assuming that stronger current evidence indicates that further computation is unnecessary. We show that this assumption can fail: checkpoint-level correctness evolves non-monotonically, and observable evidence may strengthen before an answer collapses or weaken before it recovers. Motivated by this mismatch, we introduce Adaptive Evidence Residual Allocation (AERA), a sequential controller that learns ...
| Authors | Ziming Wang, Ivor Tsang, Hangwei Qian |
|---|---|
| Primary category | cs.AI |
| Published | 28 August 2026 |
| Last updated at source | 28 August 2026 |
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