From Stages to States: Unified Iterative Reasoning for MLLMs

Chuang Yu; Xuefei Ma; Yuanhao Feng; Wuxiong Zeng; Bo Wang; Minghong Cai; Dongming Wu; Fei Zhou

cs.AIOct 11, 2026

From Stages to States: Unified Iterative Reasoning for MLLMs

Authors: Chuang Yu, Xuefei Ma, Yuanhao Feng, Wuxiong Zeng, Bo Wang, Minghong Cai, Dongming Wu, Fei Zhou,

Organizations: MMLab, CUHK · PolyU · Tencent · Peking University ·

Abstract

Multimodal Chain-of-Thought (MCoT) substantially enhances the complex reasoning capabilities of Multimodal Large Language Models (MLLMs) by explicitly modeling intermediate reasoning processes. However, most learning-based MCoT methods follow a stage-centric paradigm, where different reasoning behaviors are assigned to predefined stages and optimized separately. This results in fragmented reasoning and a training-inference reasoning-state gap. To address these limitations, we propose ReState, a unified iterative reasoning framework that reformulates multimodal reasoning from a stage-driven pipeline into a state-driven dynamic process. It comprises two complementary components: Unified Reasoning State Modeling (URSM) and Closed-Loop Reasoning-State Learning (CRSL). URSM unifies reasoning construction, correct reasoning preservation, and erroneous reasoning correction as reasoning-state transitions within a shared state space, enabling these heterogeneous reasoning behaviors to be learned jointly. CRSL further incorporates self-generated historical reasoning states during training and recursively updates the current state during inference, thereby mitigating the reasoning-state gap and enabling iterative self-refinement without requiring an additional reflection module, critic, or independent correction stage. Furthermore, we construct VersaCoT-10K, a high-quality CoT dataset spanning eight visual reasoning domains, which provides diverse supervision for multi-domain reasoning and reasoning-state refinement. Extensive experiments demonstrate that ReState exhibits strong adaptability across different MLLM backbones and achieves strong performance on multiple public reasoning benchmarks, supporting the shift from stage-centric reasoning toward state-centric reasoning. Our data and code are available at https://github.com/YuChuang1205/ReState

Figures & tables

Figure 1: Comparison of reasoning paradigms and performance. The stage-centric paradigm assigns different reasoning behaviors to predefined stages, leading to fragmented modeling and a training-inference reasoning-state gap. In contrast, ReState proposes a state-centric paradigm that recursively refines explicit reasoning states with a unified state-update operator, unifying reasoning construction, preservation, and correction while leveraging model-generated historical states for closed-loop learning. Across three backbones, ReState improves performance by 0.90%-4.60% , 0.83%-2.87% , and 0.18%-13.07% on VersaCoT-10K, ScienceQA, and M 3 CoT, respectively.

Figure 2: Overview of the ReState framework. During training, URSM learns a shared state-update operator from empty, correct, and erroneous reasoning states, while CRSL feeds model-generated historical states back into training to reduce the training-inference reasoning-state mismatch. At inference, the same model recursively updates the current reasoning state and answer, enabling iterative self-refinement without additional critics, reflection modules, or separate correction stages.

Figure 3: Construction pipeline of VersaCoT-10K dataset. It consists of three stages: Data Collection and Standardization , CoT Annotation and Quality Control , and Reasoning-State Augmentation .

Model	Learning	Size	MED	DRIVE	COMMON	DIAG	SPAT	GEO	CHART	MULTI	Avg
Random	-	-	22.00	46.80	26.80	26.27	39.87	22.93	22.00	25.60	29.03
Human	-	-	66.00	89.20	90.40	88.00	92.00	94.40	96.00	85.20	87.65
Open-source Models + Zero-shot
Qwen2.5-VL-7B	Zero-shot	7B	48.00	52.00	88.80	72.40	76.80	70.40	82.40	48.80	67.45
Qwen3-VL-8B	Zero-shot	8B	51.60	57.20	88.80	79.20	89.20	68.80	83.60	54.00	71.55
Qwen3.5-9B	Zero-shot	9B	61.60	59.60	87.60	81.20	83.60	70.80	84.80	54.00	72.90

Table 1: Main results (%) on the VersaCoT-10K dataset. MED = medical, DRIVE = driving and safety, COMMON = visual commonsense, DIAG = scientific diagram, SPAT = spatial perception, GEO = geometric and mathematical, CHART = chart computation, and MULTI = multidisciplinary knowledge. Red denotes the best result, and blue denotes the second best result.

Model	Learning	Size	NAT	SOC	LAN	TXT	IMG	NO	G1-6	G7-12	Avg
Random	-	-	40.28	46.13	29.25	47.45	40.08	33.66	39.35	40.67	39.83
Human	-	-	90.23	84.97	87.48	89.60	87.50	88.10	91.59	82.42	88.40
ViLT	Fine-tune	113M	60.48	63.89	60.27	63.20	61.38	57.00	60.72	61.90	61.14
Patch-TRM	Fine-tune	90M	65.19	46.79	65.55	66.96	55.28	64.95	58.04	67.50	61.42
VisualBERT	Fine-tune	111M	59.33	69.18	61.18	62.71	62.17	58.54	62.96	59.92	61.87
LLaMA-Adapter	Fine-tune	6B	84.37	88.30	84.36	83.72	80.32	86.90	85.83	84.05	85.19

Table 2: Main results (%) on the ScienceQA dataset. NAT = natural science, SOC= social science, LAN = language science, TXT = text context, IMG = image context, NO = no context, G1-6 = grades 1-6, G7-12 = grades 7-12. Red denotes the best result, and blue denotes the second best result.

Model	Size	Science			Commonsense			Mathematics
		Lang	Natural	Social	Physical	Social	Temporal	Algebra	Geometry	Theory	Total
Random	-	32.70	30.62	26.71	32.97	22.22	20.33	35.71	27.50	23.81	28.56
Human	-	97.83	92.62	94.31	96.28	92.41	88.71	87.23	88.75	85.71	91.61
Tool-Usage Methods
HuggingGPT	175B	17.57	20.93	10.33	8.70	14.75	9.76	11.35	22.50	9.52	14.60
VisualChatGPT	>175B	30.09	36.28	7.78	43.48	29.92	33.33	21.99	21.25	28.57	25.92

Table 3: Main results (%) on the M 3 CoT dataset. The “Random” and “Human” results are averaged over three trials. Red denotes the best result, and blue denotes the second best result.

Scheme	URSM	CRSL		Qwen2.5-VL-7B	Qwen3-VL-8B	Qwen3.5-9B
Scheme	URSM	SHS	ISR	Qwen2.5-VL-7B	Qwen3-VL-8B	Qwen3.5-9B
Baseline	✗	✗	✗	75.15 ( +1.90 )	75.65 ( +3.60 )	77.80 ( +4.10 )
Baseline w/ URSM	✓	✗	✗	75.65 ( +1.40 )	78.20 ( +1.05 )	78.95 ( +2.95 )
Baseline w/ URSM, CRSL(SHS)	✓	✓	✗	76.20 ( +0.85 )	78.50 ( +0.75 )	81.10 ( +0.80 )
Baseline w/ URSM, CRSL(ISR)	✓	✗	✓	76.20 ( +0.85 )	78.65 ( +0.60 )	79.45 ( +2.45 )
ReState (Final)	✓	✓	✓	77.05	79.25	81.90

Table 4: The Break-Down ablation of ReState on the VersaCoT-10K dataset. SHS and ISR denote the training-time self-history state mechanism and inference-time iterative self-refinement in CRSL.

Figure 4: Performance evolution over iterative self-refinement rounds across different MLLMs and datasets. Round 0 denotes the initial reasoning, with most gains achieved in the first few rounds.

Appendix figures & tables22 assets

Supplementary material from the paper’s appendix.

Appendix

Figure 5: Prompt Design for CoT Annotation during the construction of the VersaCoT-10K dataset. Italicized text represents assignment variables.

Figure 6: Prompt Design for CoT filtering during the construction of the VersaCoT-10K dataset. Italicized text represents assignment variables. A quality score of 8 or higher is deemed acceptable.

Figure 7: Statistics of VersaCoT-10K dataset. Left: data sources and splits across eight reasoning categories. Middle: CoT length distribution. Right: distribution of candidate-option counts.

Scheme	URSM	CRSL		Qwen2.5-VL-7B	Qwen3-VL-8B	Qwen3.5-9B
Scheme	URSM	SHS	ISR	Qwen2.5-VL-7B	Qwen3-VL-8B	Qwen3.5-9B
Baseline	✗	✗	✗	79.42 ( +5.57 )	81.62 ( +6.34 )	84.73 ( +4.27 )
Baseline w/ URSM	✓	✗	✗	84.12 ( +0.87 )	86.58 ( +1.38 )	88.65 ( +0.35 )
Baseline w/ URSM, CRSL(SHS)	✓	✓	✗	84.17 ( +0.82 )	86.67 ( +1.29 )	88.83 ( +0.17 )
Baseline w/ URSM, CRSL(ISR)	✓	✗	✓	84.43 ( +0.56 )	87.14 ( +0.82 )	88.65 ( +0.35 )
ReState (Final)	✓	✓	✓	84.99	87.96	89.00

Appendix

Table 5: The Break-Down ablation of ReState on the M 3 CoT dataset. SHS and ISR denote the training-time self-history state mechanism and inference-time iterative self-refinement in CRSL.

Scheme	URSM	CRSL		Qwen2.5-VL-7B	Qwen3-VL-8B	Qwen3.5-9B
Scheme	URSM	SHS	ISR	Qwen2.5-VL-7B	Qwen3-VL-8B	Qwen3.5-9B
Baseline	✗	✗	✗	96.11 ( +1.32 )	96.89 ( +1.27 )	95.59 ( +2.78 )
Baseline w/ URSM	✓	✗	✗	97.31 ( +0.12 )	97.95 ( +0.21 )	98.04 ( +0.33 )
Baseline w/ URSM, CRSL(SHS)	✓	✓	✗	97.38 ( +0.05 )	98.07 ( +0.09 )	98.37 ( +0.00 )
Baseline w/ URSM, CRSL(ISR)	✓	✗	✓	97.31 ( +0.12 )	97.95 ( +0.21 )	98.07 ( +0.30 )
ReState (Final)	✓	✓	✓	97.43	98.16	98.37

Appendix

Table 6: The Break-Down ablation of ReState on the ScienceQA dataset. SHS and ISR denote the training-time self-history state mechanism and inference-time iterative self-refinement in CRSL.

Scheme	$\phi$	${{\cal R}^{+}}$	${{\cal R}^{-}}$	${{\cal R}^{h}}$	Qwen2.5-VL-7B	Qwen3-VL-8B	Qwen3.5-9B
ReState (Final) w/o $\phi$	✗	✓	✓	✓	75.70 ( +1.35 )	77.95 ( +1.30 )	80.15 ( +1.75 )
ReState (Final) w/o ${{\cal R}^{+}}$	✓	✗	✓	✓	75.65 ( +1.40 )	78.50 ( +0.75 )	79.50 ( +2.40 )
ReState (Final) w/o ${{\cal R}^{-}}$	✓	✓	✗	✓	76.45 ( +0.60 )	78.90 ( +0.35 )	81.75 ( +0.15 )
ReState (Final) w/o ${{\cal R}^{h}}$	✓	✓	✓	✗	76.20 ( +0.85 )	78.65 ( +0.60 )	79.45 ( +2.45 )
ReState (Final)	✓	✓	✓	✓	77.05	79.25	81.90

Appendix

Table 7: Ablation of different reasoning states in ReState on VersaCoT-10K.

\phi\;

{{\cal R}^{+}}

{{\cal R}^{-}}

, and

{{\cal R}^{h}}

denote empty, positive, negative, and self-generated historical reasoning states, respectively.

Model	Learning	Size	MED	DRIVE	COMMON	DIAG	SPAT	GEO	CHART	MULTI	Avg
Random	-	-	22.00	46.80	26.80	26.27	39.87	22.93	22.00	25.60	29.03
Human	-	-	66.00	89.20	90.40	88.00	92.00	94.40	96.00	85.20	87.65
Open-source Models + Zero-shot
Qwen2.5-VL-7B	Zero-shot	7B	48.00	52.00	88.80	72.40	76.80	70.40	82.40	48.80	67.45
Qwen3-VL-8B	Zero-shot	8B	51.60	57.20	88.80	79.20	89.20	68.80	83.60	54.00	71.55
Qwen3-VL-32B	Zero-shot	32B	60.00	51.20	87.20	81.20	77.60	80.40	92.80	53.60	73.00

Appendix

Table 8: Main results (%) on the VersaCoT-10K dataset. Learning = Learning and training methods. Size = backbone model size. MED = medical, DRIVE = driving and safety, COMMON = visual commonsense, DIAG = scientific diagram, SPAT = spatial and perceptual, GEO = geometric and mathematical, CHART = chart and quantitative, and MULTI = multidisciplinary. Red denotes the best result, and blue denotes the second best result.

Model	Learning	Size	NAT	SOC	LAN	TXT	IMG	NO	G1-6	G7-12	Avg
Random	-	-	40.28	46.13	29.25	47.45	40.08	33.66	39.35	40.67	39.83
Human	-	-	90.23	84.97	87.48	89.60	87.50	88.10	91.59	82.42	88.40
MCAN	Fine-tune	95M	56.08	46.23	58.09	59.43	51.17	55.40	51.65	59.72	54.54
Top-Down	Fine-tune	70M	59.50	54.33	61.82	62.90	54.88	59.79	57.27	62.16	59.02
BAN	Fine-tune	112M	60.88	46.57	66.64	62.61	52.60	65.51	56.83	63.94	59.37
DFAF	Fine-tune	74M	64.03	48.82	63.55	65.88	54.49	64.11	57.12	67.17	60.72

Appendix

Table 9: Main results (%) on the ScienceQA dataset. Learning = Learning and training methods. Size=backbone model size. NAT = natural science, SOC= social science, LAN = language science, TXT = text context, IMG = image context, NO = no context, G1-6 = grades 1-6, G7-12 = grades 7-12. Red denotes the best result, and blue denotes the second best result.

		Science			Commonsense			Mathematics
Model	Size	Lang	Natural	Social	Physical	Social	Temporal	Algebra	Geometry	Theory	Total
Random	-	32.70	30.62	26.71	32.97	22.22	20.33	35.71	27.50	23.81	28.56
Human	-	97.83	92.62	94.31	96.28	92.41	88.71	87.23	88.75	85.71	91.61
Tool-Usage Methods
HuggingGPT	175B	17.57	20.93	10.33	8.70	14.75	9.76	11.35	22.50	9.52	14.60
VisualChatGPT	>175B	30.09	36.28	7.78	43.48	29.92	33.33	21.99	21.25	28.57	25.92

Appendix

Table 10: Main results (%) on the M 3 CoT dataset. The “Random” and “Human” results are averaged over three trials. Red denotes the best result, and blue denotes the second best result.

Figure 8: Qualitative comparison on an example of small loan dilemma reasoning in VersaCoT-10K.

Figure 9: Qualitative comparison on an example of rock cycle reasoning in VersaCoT-10K.

Figure 10: Qualitative comparison on an example of fossil age reasoning in ScienceQA.

Figure 11: Qualitative comparison on an example of solution concentration reasoning in ScienceQA.

Figure 12: Qualitative comparison on an example of species recognition in M 3 CoT.

Figure 13: Qualitative comparison on an example of tangram recognition in M 3 CoT.

Figure 14: Iterative reasoning-state refinement of ReState on VersaCoT-10K.

Figure 15: Iterative reasoning-state refinement of ReState on ScienceQA.

Figure 16: Iterative reasoning-state refinement of ReState on M 3 CoT.

Setting	VersaCoT-10K $\rightarrow$ ScienceQA			VersaCoT-10K $\rightarrow$ M 3 CoT
Setting	Qwen2.5-VL-7B	Qwen3-VL-8B	Qwen3.5-9B	Qwen2.5-VL-7B	Qwen3-VL-8B	Qwen3.5-9B
Origin (No SFT)	84.39 ( +0.66 )	90.62 ( -0.52 )	91.25 ( +0.69 )	59.79 ( +1.38 )	61.43 ( +0.69 )	62.81 ( +2.29 )
Origin (Direct SFT)	84.70 ( +0.35 )	89.55 ( +0.55 )	91.54 ( +0.40 )	58.93 ( +2.24 )	57.98 ( +4.14 )	63.11 ( +1.99 )
ReState (Ours)	85.05	90.10	91.94	61.17	62.12	65.10

Appendix

Table 11: Cross-dataset generalization of ReState from VersaCoT-10K to ScienceQA and M 3 CoT.

Model	Overall	Correctness	Relevance	Coherence	Solving Process
Origin (No SFT)	7.30 ( +0.82 )	5.85 ( +0.97 )	8.44 ( +0.61 )	8.62 ( +0.34 )	6.29 ( +1.36 )
Origin (Direct SFT)	7.82 ( +0.30 )	6.44 ( +0.38 )	8.84 ( +0.21 )	8.61 ( +0.35 )	7.38 ( +0.27 )
Multimodal-CoT	7.56 ( +0.56 )	6.29 ( +0.53 )	8.37 ( +0.68 )	8.40 ( +0.56 )	7.20 ( +0.45 )
MIND	8.00 ( +0.12 )	6.63 ( +0.19 )	9.01 ( +0.04 )	8.88 ( +0.08 )	7.49 ( +0.16 )
ReState (Ours)	8.12	6.82	9.05	8.96	7.65

Appendix

Table 12: CoT quality evaluation on VersaCoT-10K with Qwen2.5-VL-7B.

Model	Overall	Correctness	Relevance	Coherence	Solving Process
Origin (No SFT)	8.21 ( +0.64 )	6.93 ( +0.80 )	9.19 ( +0.28 )	9.15 ( +0.28 )	7.55 ( +1.23 )
Origin (Direct SFT)	8.49 ( +0.36 )	7.25 ( +0.48 )	9.28 ( +0.19 )	9.13 ( +0.30 )	8.29 ( +0.49 )
Multimodal-CoT	8.27 ( +0.58 )	7.28 ( +0.45 )	8.87 ( +0.60 )	8.85 ( +0.58 )	8.06 ( +0.72 )
MIND	8.35 ( +0.50 )	7.57 ( +0.16 )	9.13 ( +0.34 )	8.65 ( +0.78 )	8.04 ( +0.74 )
ReState (Ours)	8.85	7.73	9.47	9.43	8.78

Appendix

Table 13: CoT quality evaluation on VersaCoT-10K with Qwen3-VL-8B.

Model	Overall	Correctness	Relevance	Coherence	Solving Process
Origin (No SFT)	7.97 ( +0.54 )	6.68 ( +0.64 )	9.01 ( +0.31 )	8.78 ( +0.51 )	7.42 ( +0.71 )
Origin (Direct SFT)	8.25 ( +0.26 )	6.81 ( +0.51 )	9.12 ( +0.20 )	9.06 ( +0.23 )	8.00 ( +0.13 )
Multimodal-CoT	8.17 ( +0.34 )	7.04 ( +0.28 )	8.81 ( +0.51 )	8.91 ( +0.38 )	7.93 ( +0.20 )
MIND	8.42 ( +0.09 )	7.22 ( +0.10 )	9.25 ( +0.07 )	9.25 ( +0.04 )	7.98 ( +0.15 )
ReState (Ours)	8.51	7.32	9.32	9.29	8.13

Appendix

Table 14: CoT quality evaluation on VersaCoT-10K with Qwen3.5-9B.

Multimodal Reasoning

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CoLT: Teaching Multi-Modal Models to Think with Chain of Latent Thoughts

Lianyu Hu, Shengqian Qin, Zeqin Liao, Qing Guo+3

Nanyang Technological University, Singapore · Shanghai Jiao Tong University, China · NKIARI, Shenzhen Futian, China+2

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Chain-of-thought (CoT) reasoning has enabled multi-modal large language models (MLLMs) to tackle complex visual reasoning tasks by generating explicit intermediate reasoning steps in natural language. However, this text-based reasoning paradigm is inherently slow at inference time with even thousands of tokens and fundamentally constrained by the expressiveness of natural language. In this paper, we propose CoLT, (Chain of Latent Thoughts), a novel framework that teaches multi-modal models to reason through a chain of latent thought representations instead of verbose text tokens, which can perform thinking with as few as 3 steps. Naively forcing the model to think with latent states easily produces meaningless semantics and makes training unstable. To effectively regulate the latent reasoning process, we introduce a lightweight external decoder that provides step-level supervision for each latent reasoning step in two complementary directions: a forward mode that decodes latent thoughts into the textual reasoning of the next step, and a backward mode that aligns decoder hidden states with the model's latent thoughts given preceding textual context. We further incorporate internal supervision that encourages coherent step-by-step latent transitions. The decoder and internal supervision are removed during inference to maintain high efficiency of latent reasoning. Extensive experiments on eight benchmarks demonstrate that CoLT not only outperforms existing latent reasoning methods such as CODI and SIM-CoT, but also surpasses latent visual reasoning approaches that rely on auxiliary images with costly annotation requirements. Compared to text CoT methods, CoLT can notably reduce the inference time by 10.1

\times

and text decoding time by 22.6

\times

. Code is released at https://github.com/hulianyuyy/CoLT.

Abstract

Multimodal Reasoning

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