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

Wen Yin; Cencen Liu; Dingrui Liu; Bing Su; Yuan-Fang Li; Tao He

cs.CVMar 20, 2026

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

Authors: Wen Yin, Cencen Liu, Dingrui Liu, Bing Su, Yuan-Fang Li, Tao He

Organizations: University of Electronic Science and Technology of China, Chengdu, Sichuan, China · Jiigan Technology, Chengdu, Sichuan, China · Faculty of Information Technology, Monash University, Melbourne, Australia · The Laboratory of Intelligent Collaborative Computing of UESTC, Chengdu, Sichuan, China

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.

Figures & tables

Figure 1. From shared to task-conditioned post-training. Prior unified methods apply the same response behavior and reward to both IQA and IAA, conflating two tasks with fundamentally different decision regimes. TATAR decouples what is shared (the VL backbone) from what is adapted (post-training): Stage 1 instills task-specific reasoning modes via fast–slow SFT, and Stage 2 refines scoring with asymmetric rewards matched to each task’s supervision geometry.

Panel of Figure 2 — Figure 2. Motivation for task-conditioned post-training. (a) Adding a CoT instruction yields asymmetric effects: it consistently helps IAA but hurts IQA, demonstrating that a uniform reasoning strategy is suboptimal. (b) Under shared-reward RFT, IQA converges to short, stable completions while IAA produces long, variable ones, revealing that the two tasks inhabit different response and optimization regimes.

Figure 3. Task-conditioned reasoning construction. IQA rationales are synthesized by score-conditioned reverse inference from image–score pairs, while IAA rationales are obtained by summarizing structured aesthetic annotations. A unified judge then filters low-quality generations, producing the final fast–slow reasoning corpus used in Stage 1 SFT.

Figure 4. Overview of TATAR. TATAR is a unified framework for IQA and IAA with task-conditioned reasoning and reward design. In Stage 1, format-oriented SFT aligns the model to a shared <think>/<answer> output schema while teaching distinct response modes, with concise reasoning for IQA and more deliberative reasoning for IAA. In Stage 2, task-conditioned GRPO further refines the model with a shared optimization procedure but asymmetric rewards, where IQA is optimized with a Gaussian score reward $\mathcal{R}_{\mathrm{fmt}}+\mathcal{R}_{\mathrm{score}}$ and IAA is optimized with a ranking reward $\mathcal{R}_{\mathrm{fmt}}+\mathcal{R}_{\mathrm{rank}}$ .

Figure 5. Two examples from the constructed reasoning corpus ( QACoT-score dataset). IQA (top) samples use concise, distortion-focused rationales, while IAA (bottom) samples use longer and more integrative aesthetic rationales.

Models	IAA task					IQA task
Models	ArtiMuse-10K	AVA	TAD66K	FLICKR-AES	Avg.	KonIQ-10K	SPAQ	KADID	PIPAL	Avg.
Proprietary Models	Large-scale pretraining, cross-domain testing
GPT-4o ( Achiam et al., 2023 )	33.3/27.6	50.9/48.5	27.8/28.2	60.5/59.7	43.1/41.0	69.5/74.4	87.4/88.1	67.7/64.6	32.5/34.9	64.3/65.5
Llama-4 ( AI, 2025 )	20.4/14.7	34.5/32.9	23.6/21.0	54.8/50.6	33.3/29.8	50.3/65.3	-4.1/0.7	-9.9/-0.4	-0.7/2.3	8.9/17.0
Gemini-2.5-Pro ( Team et al., 2023 )	18.7/3.5	24.8/10.0	14.3/3.7	35.7/20.6	23.4/9.5	58.2/31.6	8.7/21.2	43.6/27.4	22.5/-1.9	33.3/19.6
Claude-4.5 ( Lindsey et al., 2025 )	4.1/2.7	0.3/1.3	4.0/4.7	3.7/4.9	3.0/3.4	-3.7/-4.3	3.6/8.5	22.3/27.3	-13.1/-8.8	2.3/5.7

Table 1. Performance comparison on unified IAA and IQA scoring. Results are reported as SRCC/PLCC (%). For specialized models, red columns denote in-domain and green columns denote out-of-domain evaluation.

\clubsuit

marks results cited from UniPercept ( Cao et al., 2025a ) ,

\spadesuit

denotes single-task evaluation, and * denotes models retrained under our protocol.

Settings			IAA task					IQA task
SFT	RL	Reward	ArtiMuse	AVA	TAD66K	FLICKR	Avg.	KonIQ	SPAQ	KADID	PIPAL	Avg.
✓			45.9/45.1	30.3/34.2	18.9/19.7	36.3/37.8	32.9/34.2	89.2/91.8	81.5/82.3	70.9/69.0	40.2/41.2	70.5/71.1
	✓	Hard-only	40.0/43.1	56.1/55.1	30.5/31.3	53.4/53.4	45.0/45.7	90.1/92.2	82.1/81.7	70.3/68.5	41.3/41.5	70.9/71.0
	✓	Gauss.-only	40.8/43.7	52.1/52.3	31.2/31.9	54.0/54.1	44.5/45.5	90.8/92.6	82.6/82.1	69.9/68.2	42.0/42.1	71.3/71.2
	✓	Rank-only	41.3/44.1	55.8/51.4	30.9/31.6	54.6/54.8	45.6/45.5	91.2/92.9	82.3/81.9	70.8/69.1	42.6/42.8	71.7/71.7
	✓	Task-cond.	41.7/44.6	55.6/51.3	31.3/32.0	55.0/55.2	46.0/45.9	91.7/93.2	82.8/82.3	71.3/69.6	43.0/43.2	72.2/72.1

Table 2. Ablation study on training paradigm and reward design. Results are reported as SRCC/PLCC (%). Bold and underlined values indicate first and second place. Task-cond. means Gaussian for IQA and Rank for IAA.

Panel of Figure 6 — Figure 6. Training dynamics analysis covering completion behavior, reward trends, and optimization stability.

Figure 7. Case study comparing TATAR and UniPercept on representative IQA (top) and IAA (bottom) examples. Ground-truth scores and prediction errors are shown for each model.

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Xiangyong Chen, Xiaochuan Lin, Haoran Liu, Xuan Li+2

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Image Quality Assessment VLM Reasoning RL for VLMs

Open paper AskPDF arXiv

Recent advances in reasoning-induced image quality assessment (IQA) have demonstrated the power of reinforcement learning to rank (RL2R) for training vision-language models (VLMs) to assess perceptual quality. However, existing approaches operate at a single granularity, predicting only an overall quality score, while overlooking the multi-dimensional nature of human quality perception, which encompasses attributes such as sharpness, color fidelity, noise level, and compositional aesthetics. In this paper, we propose MG-IQA (Multi-Granularity IQA), a multi-granularity reasoning framework that extends RL2R to jointly assess overall image quality and fine-grained quality attributes within a single inference pass. Our approach introduces three key innovations: (1) an attribute-aware prompting strategy that elicits structured multi-attribute reasoning from VLMs; (2) a multi-dimensional Thurstone reward model that computes attribute-specific fidelity rewards for group relative policy optimization; and (3) a cross-domain alignment mechanism that enables stable joint training across synthetic distortion, authentic distortion, and AI-generated image datasets without perceptual scale re-alignment. Extensive experiments on eight IQA benchmarks demonstrate that MG-IQA consistently outperforms state-of-the-art methods in both overall quality prediction (average SRCC improvement of 2.1%) and attribute-level assessment, while generating interpretable, human-aligned quality descriptions.

Abstract

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