Beyond Accuracy: Benchmarking Cross-Task Consistency in Unified Multimodal Models

• URL: http://arxiv.org/abs/2604.25072v1
• Date: Mon, 27 Apr 2026 23:57:29 GMT
• Title: Beyond Accuracy: Benchmarking Cross-Task Consistency in Unified Multimodal Models
• Abstract summary: Unified Multimodal Models (uMMs) aim to support both visual understanding and visual generation within a shared representation.<n>Existing evaluation protocols assess these two capabilities independently and do not examine whether they are semantically aligned.<n>We introduce XTC-Bench, a scene-graph-grounded evaluation framework that measures cross-task visual semantic consistency.
• Score: 12.660879089125684
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Unified Multimodal Models (uMMs) aim to support both visual understanding and visual generation within a shared representation. However, existing evaluation protocols assess these two capabilities independently and do not examine whether they are semantically aligned. As a result, it remains unclear whether current uMMs learn coherent unified representations that remain consistent across tasks given a visual concept. We introduce XTC-Bench, a scene-graph-grounded evaluation framework that measures cross-task visual semantic consistency. By deriving both generation prompts and understanding queries from a structured scene graph, our framework enables fact-level alignment analysis across objects, attributes, and relations. We propose Continuous Cross-Task Agreement (CCTA), a fine-grained metric that quantifies semantic agreement between generation and understanding over matched atomic facts, isolating internal consistency from standalone task accuracy. Extensive experiments on eight open-source and one commercial unified models reveal that high generation or understanding performance does not imply strong cross-task alignment, and architectural analysis shows consistency is governed by how tightly learning objectives are coupled across modalities, not by architectural unification alone. XTC-Bench provides a reproducible and model-agnostic framework for diagnosing representation-level misalignment, offering a concrete direction for advancing unified multimodal modeling beyond isolated task performance.

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