Failure is Feedback: History-Aware Backtracking for Agentic Traversal in Multimodal Graphs

• URL: http://arxiv.org/abs/2602.03432v1
• Date: Tue, 03 Feb 2026 11:54:38 GMT
• Title: Failure is Feedback: History-Aware Backtracking for Agentic Traversal in Multimodal Graphs
• Authors: Joohyung Yun, Doyup Lee, Wook-Shin Han,
• Abstract summary: Open-domain multimodal document retrieval aims to retrieve specific components from large and interconnected document corpora.<n>Existing graph-based retrieval approaches rely on a uniform similarity metric that overlooks hop-specific semantics.<n>We propose Failure is Feedback (FiF), which casts subgraph retrieval as a sequential decision process.<n>FiF achieves state-of-the-art retrieval on the benchmarks of MultimodalQA, MMCoQA and WebQA.
• Score: 13.855117422052315
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Open-domain multimodal document retrieval aims to retrieve specific components (paragraphs, tables, or images) from large and interconnected document corpora. Existing graph-based retrieval approaches typically rely on a uniform similarity metric that overlooks hop-specific semantics, and their rigid pre-defined plans hinder dynamic error correction. These limitations suggest that a retriever should adapt its reasoning to the evolving context and recover intelligently from dead ends. To address these needs, we propose Failure is Feedback (FiF), which casts subgraph retrieval as a sequential decision process and introduces two key innovations. (i) We introduce a history-aware backtracking mechanism; unlike standard backtracking that simply reverts the state, our approach piggybacks on the context of failed traversals, leveraging insights from previous failures. (ii) We implement an economically-rational agentic workflow. Unlike conventional agents with static strategies, our orchestrator employs a cost-aware traversal method to dynamically manage the trade-off between retrieval accuracy and inference costs, escalating to intensive LLM-based reasoning only when the prior failure justifies the additional computational investment. Extensive experiments show that FiF achieves state-of-the-art retrieval on the benchmarks of MultimodalQA, MMCoQA and WebQA.

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