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arXiv · 2602.12276

Agentic Test-Time Scaling for WebAgents

Nicholas Lee·Lutfi Eren Erdogan·Chris Joseph John·Surya Krishnapillai·Michael W. Mahoney·Kurt Keutzer·Amir Gholami
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Abstract

Test-time scaling has become a standard way to improve performance and boost reliability of neural network models. However, its behavior on agentic, multi-step tasks remains less well-understood: small per-step errors can compound over long horizons; and we find that naive policies that uniformly increase sampling show diminishing returns. In this work, we present CATTS, a simple technique for dynamically allocating compute for multi-step agents. We first conduct an empirical study of inference-time scaling for web agents. We find that uniformly increasing per-step compute quickly saturates in long-horizon environments. We then investigate stronger aggregation strategies, including an LLM-based Arbiter that can outperform naive voting, but that can overrule high-consensus decisions. We show that uncertainty statistics derived from the agent's own vote distribution (entropy and top-1/top-2 margin) correlate with downstream success and provide a practical signal for dynamic compute allocation. Based on these findings, we introduce Confidence-Aware Test-Time Scaling (CATTS), which uses vote-derived uncertainty to allocate compute only when decisions are genuinely contentious. CATTS improves performance on WebArena-Lite, Online-Mind2Web, and GoBrowse by up to 11.8% over majority voting while using fewer tokens than uniform scaling, providing both efficiency gains and an interpretable decision rule.

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Nicholas Lee, Lutfi Eren Erdogan, Chris Joseph John, Surya Krishnapillai, Michael W. Mahoney, Kurt Keutzer, Amir Gholami. 2026-02-12. Agentic Test-Time Scaling for WebAgents. https://arxiv.org/abs/2602.12276

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