Topo-R1: Detecting Topological Anomalies via Vision-Language Models

• URL: http://arxiv.org/abs/2603.13054v1
• Date: Fri, 13 Mar 2026 15:05:04 GMT
• Title: Topo-R1: Detecting Topological Anomalies via Vision-Language Models
• Abstract summary: Topological correctness is crucial for structures such as blood vessels, nerve fibers, and road networks.<n>We develop an automated data-curation pipeline that synthesizes diverse topological anomalies with verifiable annotations.<n>We then introduce Topo-R1, a framework that endows Vision-Language Models with topology-aware perception.
• Score: 42.86510112477789
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Topological correctness is crucial for tubular structures such as blood vessels, nerve fibers, and road networks. Existing topology-preserving methods rely on domain-specific ground truth, which is costly and rarely transfers across domains. When deployed to a new domain without annotations, a key question arises: how can we detect topological anomalies without ground-truth supervision? We reframe this as topological anomaly detection, a structured visual reasoning task requiring a model to locate and classify topological errors in predicted segmentation masks. Vision-Language Models (VLMs) are natural candidates; however, we find that state-of-the-art VLMs perform nearly at random, lacking the fine-grained, topology-aware perception needed to identify sparse connectivity errors in dense structures. To bridge this gap, we develop an automated data-curation pipeline that synthesizes diverse topological anomalies with verifiable annotations across progressively difficult levels, thereby constructing the first large-scale, multi-domain benchmark for this task. We then introduce Topo-R1, a framework that endows VLMs with topology-aware perception via two-stage training: supervised fine-tuning followed by reinforcement learning with Group Relative Policy Optimization (GRPO). Central to our approach is a topology-aware composite reward that integrates type-aware Hungarian matching for structured error classification, spatial localization scoring, and a centerline Dice (clDice) reward that directly penalizes connectivity disruptions, thereby jointly incentivizing semantic precision and structural fidelity. Extensive experiments demonstrate that Topo-R1 establishes a new paradigm for annotation-free topological quality assessment, consistently outperforming general-purpose VLMs and supervised baselines across all evaluation protocols.

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