ICA: Information-Aware Credit Assignment for Visually Grounded Long-Horizon Information-Seeking Agents

• URL: http://arxiv.org/abs/2602.10863v1
• Date: Wed, 11 Feb 2026 13:50:19 GMT
• Title: ICA: Information-Aware Credit Assignment for Visually Grounded Long-Horizon Information-Seeking Agents
• Abstract summary: We propose a visual-native search framework that represents webpages as visual snapshots, allowing agents to leverage layout cues to quickly localize salient evidence and suppress distractors.<n>We introduce Information-Aware Credit Assignment (ICA), a post-hoc method that estimates each retrieved snapshot's contribution to the final outcome.<n>Our approach consistently outperforms text-based baselines on diverse information-seeking benchmarks.
• Score: 26.361688266083988
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Despite the strong performance achieved by reinforcement learning-trained information-seeking agents, learning in open-ended web environments remains severely constrained by low signal-to-noise feedback. Text-based parsers often discard layout semantics and introduce unstructured noise, while long-horizon training typically relies on sparse outcome rewards that obscure which retrieval actions actually matter. We propose a visual-native search framework that represents webpages as visual snapshots, allowing agents to leverage layout cues to quickly localize salient evidence and suppress distractors. To learn effectively from these high-dimensional observations, we introduce Information-Aware Credit Assignment (ICA), a post-hoc method that estimates each retrieved snapshot's contribution to the final outcome via posterior analysis and propagates dense learning signals back to key search turns. Integrated with a GRPO-based training pipeline, our approach consistently outperforms text-based baselines on diverse information-seeking benchmarks, providing evidence that visual snapshot grounding with information-level credit assignment alleviates the credit-assignment bottleneck in open-ended web environments. The code and datasets will be released in https://github.com/pc-inno/ICA_MM_deepsearch.git.

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