One Model, Two Minds: Task-Conditioned Reasoning for Unified Image Quality and Aesthetic Assessment

• URL: http://arxiv.org/abs/2603.19779v1
• Date: Fri, 20 Mar 2026 09:14:33 GMT
• Title: One Model, Two Minds: Task-Conditioned Reasoning for Unified Image Quality and Aesthetic Assessment
• Abstract summary: We show that existing methods adopt a task-agnostic recipe that applies the same reasoning strategy and reward to both tasks.<n>We propose TATAR (Task-Aware Thinking with Asymmetric Rewards), a unified framework that shares the visual-language backbone while conditioning post-training on each task's nature.<n>Our results establish task-conditioned post-training as a principled paradigm for unified perceptual scoring.
• Score: 37.98880646905503
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Unifying Image Quality Assessment (IQA) and Image Aesthetic Assessment (IAA) in a single multimodal large language model is appealing, yet existing methods adopt a task-agnostic recipe that applies the same reasoning strategy and reward to both tasks. We show this is fundamentally misaligned: IQA relies on low-level, objective perceptual cues and benefits from concise distortion-focused reasoning, whereas IAA requires deliberative semantic judgment and is poorly served by point-wise score regression. We identify these as a reasoning mismatch and an optimization mismatch, and provide empirical evidence for both through controlled probes. Motivated by these findings, we propose TATAR (Task-Aware Thinking with Asymmetric Rewards), a unified framework that shares the visual-language backbone while conditioning post-training on each task's nature. TATAR combines three components: fast--slow task-specific reasoning construction that pairs IQA with concise perceptual rationales and IAA with deliberative aesthetic narratives; two-stage SFT+GRPO learning that establishes task-aware behavioral priors before reward-driven refinement; and asymmetric rewards that apply Gaussian score shaping for IQA and Thurstone-style completion ranking for IAA. Extensive experiments across eight benchmarks demonstrate that TATAR consistently outperforms prior unified baselines on both tasks under in-domain and cross-domain settings, remains competitive with task-specific specialized models, and yields more stable training dynamics for aesthetic assessment. Our results establish task-conditioned post-training as a principled paradigm for unified perceptual scoring. Our code is publicly available at https://github.com/yinwen2019/TATAR.

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