interwhen: A Generalizable Framework for Verifiable Reasoning with Test-time Monitors

Code

• github.com/microsoft/interwhen
  OfficialIn paper★ 27

Abstract

Reasoning models produce long traces of intermediate decisions and tool calls, making test-time verification increasingly important for ensuring correctness. Existing approaches either verify only the final answer, which misses early errors, or rely on branch-and-verify strategies that explore multiple trajectories at substantially higher compute cost. We introduce interwhen, a single-trajectory verification framework that steers model behavior by providing feedback on intermediate verifiable properties. Our method addresses two key challenges. First, extracting intermediate solutions from a reasoning trace typically requires prompt engineering or external task decomposition into fixed steps, which can constrain the model's reasoning strategy. Instead, we periodically poll the reasoning trace and fork inference to recover intermediate solutions without imposing any predefined structure. Second, frequent verifier calls can increase latency; we address this by running verifiers asynchronously and interrupting the main trajectory only when an error is detected, leaving generation unaffected otherwise. This design improves both reliability and efficiency, and naturally supports early stopping based on consistency over recent intermediate solutions. Across benchmarks in code generation and arithmetic, logical and spatial reasoning, interwhen improves accuracy by up to 15 percentage points over standard chain-of-thought execution while staying within 1.5x of token compute cost. Moreover, on every dataset, interwhen achieves a Pareto-optimal operating point between accuracy and efficiency compared to existing test-time verification methods. Code is available at https://github.com/microsoft/interwhen.

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