Genesis: Visual Augmentation Primitives for Multimodal Chain-of-Thought Reasoning

Fan Yang; Lifeng Chen; Yipu Wang; Han Wang; Feilong Chen; Yidong Ma; Zhiyang Chen; Chaoyang Zhao

cs.CVOct 11, 2026

Genesis: Visual Augmentation Primitives for Multimodal Chain-of-Thought Reasoning

Authors: Fan Yang, Lifeng Chen, Yipu Wang, Han Wang, Feilong Chen, Yidong Ma, Zhiyang Chen, Chaoyang Zhao,

Organizations: Foundation Model Research Center, Institute of Automation, Chinese Academy of Sciences · University of Chinese Academy of Sciences · Beijing Jiaotong University · State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences ·

Abstract

Recent multimodal large language models achieve promising results on visual reasoning tasks, approaching human-level performance on existing STEM benchmarks. However, these capabilities degrade under complex real-world scenes. We identify that chain-of-thought reasoning remains largely confined to the linguistic space, rarely anchoring each step in visual evidence. The main cause is the absence of such data during training. In this work, we introduce a training data augmentation pipeline, Visual Augmentation Primitives (VAP), which leverages state-of-the-art vision generative models to synthesize diverse photorealistic training samples and corresponding reasoning text. VAP introduces some basic operators and applies them to a seed image and gets a variant. It also constructs coherent multimodal reasoning chains throughout the generation process. Such training data force models to perceive visual content and perform logical reasoning according to them at every step. We further introduce multimodal chain-of-thought rubric-based reinforcement learning to enhance this. It rewards not only text reasoning but also visual reasoning by scoring intermediate reasoning steps for logical validity, completeness, and consistency. Moreover, we construct the UNREAL benchmark to systematically evaluate visual reasoning on hyperrealistic scenes across controllable difficulty levels. Experiments show that a 4B model built on this framework surpasses 8B baselines, with performance improving consistently as the model scales to 32B, validating the scaling law for visual reasoning.

Figures & tables

Figure 2: Two-stage data construction pipeline. Stage 1 (Surgical Answer-Changing Editing) : the Analyst extracts the global reasoning focus and partitions visual attributes into frozen and free dimensions, the Director defines an edit primitive, the Generator executes it, and the Verifier validates the output. Stage 2 (Axis Diversification & CoT Annotation) : the Planner selects free dimensions to diversify, the Generator produces visual variants, a four-step CoT is constructed for each, and the final dataset $\mathcal{D}_{\text{syn}}$ is assembled.

Figure 3: Overview of multimodal chain-of-thought rubric-based reinforcement learning. (1) Rubric construction scores each reasoning step along three dimensions (logical validity, completeness, process consistency). (2) Reward aggregation combines format, rubric-based reasoning, and final-answer rewards, with final-answer correctness as the top priority.

Figure 4: UNREAL benchmark composition. The inner ring shows the five STEM families with their shares of the 600 samples. The outer ring shows the sixteen sub-domains with sample counts.

Model	ZeroBench	ZB-Sub	MathVista	MMMU	MMMU-Pro	EMMA	LogicVista	BabyVision
Closed-source models
GPT-5	2	-	81.3	84.2	78.4	-	71.8	-
Gemini-2.5-Pro	3	-	82.7	81.7	68.8	-	72.0	-
Claude-Opus-4.1	3	-	75.5	78.4	64.8	-	67.3	-
Open-source models
InternVL3-8B	-	-	71.6	62.7	-	-	44.1	-

Table 1: Main results on STEM visual reasoning benchmarks. Bold and underlined denote the best and second-best results among open-source models, respectively. Closed-source model results are shown for reference only and are excluded from ranking. Subscripts show the improvement over the corresponding Qwen3-VL baseline. “–” indicates a result that is still being verified.

Model	HallusionBench	MMStar	InfoVQA
Closed-source models
GPT-5	65.7	76.4	79.0
Gemini-2.5-Pro	63.7	77.5	84.2
Claude-Opus-4.1	60.4	72.1	69.4
Open-source models
InternVL3-8B	49.9	68.2	-

Table 2: Main results on VQA benchmarks. Bold and underlined denote the best and second-best results among open-source models, respectively. Closed-source model results are shown for reference only and excluded from ranking. Subscripts show the improvement over the corresponding Qwen3-VL baseline.

Model	Overall	Math.	Nat. Sci.	Percep.	Phys. & Eng.	Reas. & Log.
Closed-source models
Claude-Opus-4.7	32.9	28.6	46.8	28.3	32.4	41.2
Claude-Sonnet-4.6	30.7	23.4	45.2	32.6	35.3	36.1
Doubao-Seed-1.8	35.9	27.5	45.2	39.1	44.1	42.3
Gemini-3.1-Pro	37.1	23.2	51.6	43.5	53.9	44.3
GPT-5.5	59.6	62.9	51.6	54.3	56.9	62.9

Table 3: Results on the UNREAL benchmark, stratified by domain. Bold denotes the best result per column. Scores are accuracy (%).

Reward (32B)	ZeroBench	ZB-Sub	MathVista	MMMU	MMMU-Pro	EMMA	LogicVista
RLVR	3	28.44	82.20	75.22	66.42	45.23	66.22
Rubric	3	29.94	82.40	75.44	66.47	46.48	68.46

Table 4: Rubric-based RL vs. RLVR at 32B, holding data and optimizer constant.

RL Algorithm (4B)	ZeroBench	ZB-Sub	MathVista	MMMU	MMMU-Pro	EMMA	LogicVista
SAPO	0	20.96	77.2	63.39	52.20	34.61	59.06
GRPO	0	17.07	77.6	63.44	53.87	32.78	59.96

Table 5: Comparison of policy-optimization algorithms under rubric-based reward (4B).

Appendix figures & tables11 assets

Supplementary material from the paper’s appendix.

Appendix

Family	Sub-domains
Physics (15)	Mechanics, Thermodynamics, Optics, Electromagnetism, Wave Motion, Fluid Dynamics, Kinematics, Gravitation, Nuclear Physics, Quantum Mechanics, Acoustics, Solid-State Physics, Astrophysics, Relativity, Circuit Analysis
Biology (8)	Cell Biology, Genetics, Ecology, Anatomy, Microbiology, Botany, Zoology, Evolutionary Biology
Chemistry (13)	Organic Chemistry, Inorganic Chemistry, Physical Chemistry, Analytical Chemistry, Biochemistry, Electrochemistry, Thermochemistry, Polymer Chemistry, Nuclear Chemistry, Environmental Chemistry, Crystallography, Spectroscopy, Chemical Kinetics
Mathematics (14)	Algebra, Geometry, Calculus, Statistics, Probability, Number Theory, Trigonometry, Linear Algebra, Combinatorics, Graph Theory, Topology, Differential Equations, Set Theory, Logic

Appendix

Table 6: STEM families and their sub-domains covered in our dataset.

Figure 5: Word cloud of question keywords in the training corpus. Word size is proportional to frequency. The distribution highlights the emphasis on quantitative reasoning (“number”, “value”, “total”, “calculate”), spatial understanding (“left”, “right”, “top”, “bottom”), and visual attributes (“color”, “shape”, “pattern”).

Category	Dimensions
Illumination	Lighting direction, intensity, color temperature
Viewpoint	Camera angle, distance, orientation
Object	Color, texture, shape, size, material
Background	Scene type, clutter level, color scheme
Layout	Object position, spatial arrangement, scale
Style	Rendering style, contrast, saturation

Appendix

Table 7: Visual taxonomy used for attribute decomposition. For each image, axes are partitioned into a frozen subset

\mathcal{F}

(must be preserved pixel-wise to keep the test point intact) and a free subset

\mathcal{V}=\mathcal{A}\setminus\mathcal{F}

that Stage 2 aggressively varies.

Figure 6: An example of a compositional visual reasoning dataset sample, where answering the question requires reasoning over object attributes such as color, shape, size, and material.

Figure 7: An example of a spatial visual reasoning dataset sample, where answering the question requires reasoning over relative object positions and counting objects satisfying spatial constraints.

Figure 8: An example of a visual counting dataset sample, where answering the question requires identifying and counting target objects in the scene.

Hyperparameter	4B	8B	32B
Learning rate	$1{\times}10^{-6}$	$1{\times}10^{-6}$	$1{\times}10^{-6}$
Train batch size	32	32	96
PPO mini-batch size	16	16	48
PPO micro-batch / GPU	4	4	1
Rollout group size $n$	8	8	8
Max prompt length	4096	8192	4096

Appendix

Table 8: Training hyperparameters at three model scales. The 4B and 8B variants share the same base configuration, while the 32B variant uses larger batch sizes, higher per-GPU token budgets, and multi-node training with rollout tensor parallelism.

Figure 9: Overview of the UNREAL benchmark construction pipeline. Top: the four sequential stages, namely (1) data collection and organisation across five STEM families and sixteen sub-domains, (2) LLM-driven question generation with MCQ $\rightarrow$ OE conversion, (3) structure-preserving image-to-image augmentation using the Axis Bank, and (4) human and automated quality control. Middle: the benchmark composition over the five STEM families together with format and scale statistics. Bottom: the key characteristics of the benchmark, including rule competition, structure preservation, high difficulty, mixed answer formats, and broad STEM coverage.

Figure 10: UNREAL benchmark illustration.

Family	Samples	Share
Mathematics	259	43.2%
Physics & Engineering	96	16.0%
Reasoning & Logic	92	15.3%
Perception	91	15.2%
Natural Science	62	10.3%
Total	600	100%

Appendix

Table 9: UNREAL benchmark composition by STEM family.

Family	Sub-domain	Samples
Mathematics	Combinatorial Geometry	63
	Metric Geometry	40
	Combinatorics & Counting	38
	Descriptive Geometry	32
	Solid Geometry	25
	Transformation Geometry	23

Appendix

Table 10: UNREAL sub-domain distribution across the five STEM families.

VLM Reasoning

Vision-language model reasoning studies how multimodal systems use visual evidence and language to answer questions, plan, and explain.

See all 846 papers

Distilling Visual Reasoning into Text Space
Sep 28, 2026Wenhan Yang, Nilay Naharas, Ali Payani +1CoT Distillation VLM Distillation
University of California, Los Angeles · Cisco Research
VisReason: A Large-Scale Dataset for Visual Chain-of-Thought Reasoning
Nov 21, 2025Lingxiao Li, Yifan Wang, Xinyan Gao +3Visual Spatial Reasoning Visual Reasoning
Boston University, Boston, MA, USA · Stony Brook University, Stony Brook, NY, USA · MMLab, The Chinese University of Hong Kong, Hong Kong SAR, China
Gen-VCoT: Generative Visual Chain-of-Thought Reasoning via Diffusion-Based RGB Intermediate Representations
Jun 15, 2026Zhiqiang Zhou, Junliang Dai, Xu lingVLM Interpretability Multimodal CoT Reasoning
Hunan Chemical Industry Vocational and Technical College, Zhuzhou, Hunan 412000, China

Sep 28, 2026J/K move · Enter open · S save

Distilling Visual Reasoning into Text Space

Wenhan Yang, Nilay Naharas, Ali Payani, Baharan Mirzasoleiman

University of California, Los Angeles · Cisco Research

CoT Distillation VLM Distillation Large Vision-Language Models VLM Reasoning

Open paper AskPDF arXiv

Large Vision-Language Models (LVLMs) have shown strong promise for multimodal reasoning, yet often struggle with tasks requiring concepts beyond what is directly observable in the input image. Existing methods generate intermediate images or latent visual tokens to guide reasoning, but these representations can introduce errors and increasingly interfere with textual reasoning as reasoning progresses. We propose Visual-to-Text Chain-of-Thought Distillation (V2T), a framework that enables LVLMs to internalize visual reasoning without generating intermediate visual representations at inference time. V2T first trains a teacher LVLM using interleaved visual and textual chains of thought, and then uses knowledge distillation to train a student LVLM using the teacher's logits and cross-entropy supervision from ground-truth textual reasoning. When reasoning images can be mapped to the original image, V2T can additionally distill the teacher's attention to corresponding regions, while ground-truth bounding boxes can further guide a subsequent reinforcement learning stage. Experiments across multiple multimodal reasoning benchmarks show that V2T consistently outperforms the teacher and existing baselines, improving average accuracy by 14.3% on a held-out set and 2.7% on the broader visual evaluation suite. Moreover, lightweight SFT and substantially reduced RL make V2T up to 42x faster to train than state-of-the-art baselines.

Abstract

VLM Reasoning

Explore similar work

Distilling Visual Reasoning into Text Space

VisReason: A Large-Scale Dataset for Visual Chain-of-Thought Reasoning

Gen-VCoT: Generative Visual Chain-of-Thought Reasoning via Diffusion-Based RGB Intermediate Representations