EMO: A Closed-Loop Test-Time Computation Framework via Alternating Context Optimization and Logits Steering

EMO is a training‑free, closed‑loop framework that alternates context estimation and distribution refinement to steer frozen language models during test time. It uses textual gradients from an external judge to update the system context (E‑step) and then applies a confidence‑constrained logit correction based on next‑token distribution differences (M‑step). Empirical results show EMO improves zero‑shot performance across eight models and six benchmarks, outperforming both baseline and context‑only methods such as TextGrad.

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