Chain-of-Visual-Thought (CoVT) Overview

Updated 28 November 2025

Chain-of-Visual-Thought (CoVT) is a framework that systematically integrates visual and linguistic reasoning by interleaving explicit visual thought representations with textual analysis.
Core methodologies leverage various visual formats—such as natural language descriptions, structured annotations, and edited images—coupled with transformer-based models for multi-step reasoning.
Empirical studies show that CoVT enhances accuracy and efficiency in tasks like visual question answering, video analysis, and robotics, achieving performance gains of up to 16%.

Chain-of-Visual-Thought (CoVT) refers to a broad family of frameworks and methodologies that systematically interleave visual and linguistic intermediate states in multi-step reasoning. These frameworks are designed to address the limitations of text-only chain-of-thought (CoT) approaches in vision-language and vision-language-action problems, enhancing interpretability, accuracy, and compositional generalization across domains ranging from visual question answering to code analysis, video generation, chart summarization, robotics, and interactive human-in-the-loop reasoning.

1. Formal Definition and Taxonomy

CoVT generalizes standard (textual) chain-of-thought methods by introducing intermediate "visual thoughts," which are explicit representations of visual reasoning steps. Visual thoughts may take multiple forms, including:

Natural Language (N-LANG): Textual descriptions of visual content.
Structured Language (S-LANG): Formal scene graphs, JSON, or other structured annotations.
Edited Images (E-IMG): Images modified (e.g., segmented, masked, cropped) to emphasize aspects of the reasoning process.
Generative Images (G-IMG): Images synthesized by generative models conditioned on the current reasoning context.
Continuous Latents: Compact vectors encoding visual content distilled from expert networks (e.g., segmentation, depth, edge, patch features) (Qin et al., 24 Nov 2025).
Diagrammatic/SVG States: Symbolic vector graphics rendered as intermediate sketches (Meng et al., 2023).

Let $V_I$ be the input image, $Q_T$ the question, $I_{E_{VT}}$ the instruction specifying visual thought type, and $R_{<i}$ the set of prior reasoning steps. The generation of the $i$ -th visual thought $R_i$ is generally formulated as:

$R_i = \arg\max_{R^{E_{VT}}} \, \pi( R_{<i}, V_I \to R^{E_{VT}} \mid V_I, Q_T, I_{E_{VT}}, R_{<i} )$

where $\pi$ is a model distribution over possible visual thought expressions (Cheng et al., 21 May 2025). These visual thoughts act as caches, relaying distilled visual information into subsequent reasoning stages and mediating deep cross-modal interactions within transformer architectures.

Empirically, four principal forms have been shown to provide strong, task-dependent gains: N-LANG (free-form text), S-LANG (structured text), E-IMG (edited images), and G-IMG (generative images) (Cheng et al., 21 May 2025). Explicit CoVT traces can also occur implicitly in hidden states (e.g., latent dynamics in video models (Zhong et al., 25 Aug 2025, Ma et al., 25 Nov 2025), or VLM dense continuous tokens (Qin et al., 24 Nov 2025)).

2. Core Methodologies and Representative Frameworks

A variety of architectures instantiate CoVT across domains:

2.1 Interleaved and Multimodal CoVT

VisualCoder: Each reasoning step links a code line $C_i$ to a corresponding node $N_{M(i)}$ in a Control Flow Graph (CFG) image. Stepwise rationales are constructed by prompting for explicit reference to CFG visual nodes, focusing attention and avoiding irrelevant branches (Le et al., 30 Oct 2024).
VICoT-Agent: Combines a stack-based "Think Module" (LLM-driven) and a "Vision Module" (vision tool invoker) in an agent with an explicit reasoning stack $S_t = (φ_t, m_t, e_t)$ . Tool calls (detector, cropper, super-resolver, denoiser, etc.) are interleaved into the chain-of-thought via the Model Context Protocol, yielding transparent, modular visual reasoning (Wang et al., 25 Nov 2025).

2.2 Continuous and Latent-Space CoVT

CoCoVa: Deploys an iterative Latent Q-Former (LQ-Former) to produce a chain of continuous latent thoughts, dynamically selecting salient visual regions at each reasoning step. Contrasts, diffusion, and autoregressive objectives align this latent chain with both vision and language, providing interpretable trajectories in latent space (Ma et al., 4 Nov 2025).
Dense Visual Token CoVT: Embeds discrete language interleaved with 20–32 continuous visual tokens distilled from segmentation, depth, edge, and patch experts; these tokens are supervised to reconstruct expert outputs and jointly optimized with text generation (Qin et al., 24 Nov 2025).

2.3 Visual State Transition and Goal-Conditioned CoVT

VisualCoT in Video/Action: Video and VLA models (e.g., FlowVLA, CoT-VLA, VITA) structure reasoning as an interleaved sequence of visual states, motion plans, and subgoal predictions:
- FlowVLA: $v_t \to f_t \to v_{t+1}$ , with explicit optical flow $f_t$ as a motion plan prior to appearance synthesis, decoupling dynamics from pixel prediction (Zhong et al., 25 Aug 2025).
- CoT-VLA: $s_t \to s_{t+n} \to \{a_t, ..., a_{t+m}\}$ , with subgoal image $s_{t+n}$ predicted before planning an action sequence (Zhao et al., 27 Mar 2025).
- VITA: Implicitly disperses visual and action information in a shared discrete latent space; tokens are mapped to both action trajectories and future image predictions, enabling bidirectional alignment and more robust motor plans (Ma et al., 25 Nov 2025).

2.4 Unified and Hierarchical Reasoning

Uni-CoT: Employs a single transformer supporting both vision-understanding and image-generation, structured into Macro-level (high-level CoT planning) and Micro-level (MDP-formulated visual chain-of-state/action execution) reasoning (Qin et al., 7 Aug 2025). This hierarchical approach supports both image generation and editing tasks with explicit planning/execution abstraction.

2.5 Interactive, Human-in-the-Loop CoVT

Vis-CoT: Lineraizes LLM CoT traces as an interactive visual reasoning graph $G=(V,E)$ . Nodes $V$ encapsulate reasoning steps (text, type, confidence, state); edges capture dependencies. Users can prune or graft nodes, with graph-based interventions directly shaping the final answer, thus enhancing interpretability, correctness, and user trust (Pather et al., 1 Sep 2025).

2.6 Minimal-Region and Bounding-Box CoVT

Visual CoT Benchmark/VisCoT: CoVT is realized by a two-turn process: (i) localize the minimal region (bounding box) needed to answer a visual question, (ii) generate an answer based on both global and cropped visual context (Shao et al., 25 Mar 2024). Datasets consist of 373 K annotated triples (image, question, minimal-region box), supporting multi-step visual reasoning evaluation.

3. Training Objectives, Supervision, and Optimization

Training regimes for CoVT systems are adapted to the underlying architecture and reasoning style. Representative objectives include:

Autoregressive Mixed-Modal Modeling: Joint likelihood over interleaved visual and text tokens (Qin et al., 24 Nov 2025, Ma et al., 4 Nov 2025, Ma et al., 25 Nov 2025, Zhao et al., 27 Mar 2025).
Knowledge Distillation: Supervision of continuous visual tokens via L1, Dice, cross-entropy, or MSE alignment with vision experts’ output for dense perception tasks (Qin et al., 24 Nov 2025).
Contrastive and Reconstruction Objectives: InfoNCE losses (aligning latent thoughts with vision/language pools), diffusion-based reconstruction (inverting latent to image), and cross-modal retrieval (Ma et al., 4 Nov 2025).
Two-level Supervision: Macro-level CoT aligns planning traces (text/image), micro-level CoT optimizes action, state, reward, and auxiliary prediction losses—curriculum learning, multi-task, and RL (DPO) can be employed (Qin et al., 7 Aug 2025).
Region Supervision: For region localization, explicit bounding-box regression and accuracy@IoU metrics augment standard answer loss (Shao et al., 25 Mar 2024).

Ablation studies consistently show that chain-based frameworks—particularly those balancing clarity and conciseness in visual thoughts—yield up to 16% relative gains across perception, localization, depth, and counting (Qin et al., 24 Nov 2025, Cheng et al., 21 May 2025).

4. Quantitative Performance and Empirical Impact

CoVT variants achieve consistent improvements over text-only or unimodal CoT baselines across tasks:

Visual QA/Benchmarking: VisCoT-7B increases average ChatGPT answer scores by 23% over LLaVA-7B on composed visual reasoning; DOCVQA scores more than double (24.4→49.3) (Shao et al., 25 Mar 2024).
Chart Summarization: V-CoT outperforms traditional summarizers in BLEU, CIDEr, and human reasoning correctness scores, especially as chart complexity increases (Choi et al., 24 Feb 2025).
Action/Perception: CoT-VLA outperforms prior VLA models by 17% (real world) and 6% (simulation) on robot benchmarks, through explicit future visual goal inference (Zhao et al., 27 Mar 2025). VITA exhibits 14.5%–12.1% gains over strong action-only baselines (Ma et al., 25 Nov 2025).
Image–Text Reasoning: All forms of VT (N-LANG, S-LANG, etc.) surpass “no-VT” baselines, with image-based VTs excelling on fine-grained or multi-step benchmarks (Cheng et al., 21 May 2025).
Interactive CoVT: Human-in-the-loop interventions via Vis-CoT yield dramatic answer accuracy boosts (GSM8K, 74.8%→91.7%), 30% time reduction, and significant increases in perceived trust and understanding (Pather et al., 1 Sep 2025).
Latent CoVT: Compact (<32) continuous visual tokens improve Qwen2.5-VL by 3%–16%, beating even GPT-4o on vision-centric benchmarks (Qin et al., 24 Nov 2025).

5. Practical Implementations and Usage Patterns

Several CoVT paradigms have crystallized into practical workflows:

Two-turn Inference: First, localize (e.g., via bounding box), then answer with focused context, as in Visual CoT and region-centric benchmarks (Shao et al., 25 Mar 2024).
Interleaved Reasoning: Alternating thinking and acting steps (LLM reasoning, tool call, VLM interpretation) using stacks, memory, or explicit CoVT traces (VICoT-Agent) (Wang et al., 25 Nov 2025).
Latent-State Transitions: Autoregressively generating latent tokens predictive of both future perception and action, as in hybrid-motor pipelines (VITA) (Ma et al., 25 Nov 2025) or world-models with explicit motion planning (FlowVLA) (Zhong et al., 25 Aug 2025).
Continuous Latent Unrolling: Dynamically selecting visual regions and iteratively fusing them in LQ-Former pipelines, optimizing for both efficiency and accuracy (CoCoVa) (Ma et al., 4 Nov 2025).

Performance and efficiency are enhanced by tuning the number and form of visual thoughts—e.g., 8 segmentation tokens giving optimal CV-bench tradeoff (Qin et al., 24 Nov 2025). Unified models like Uni-CoT achieve SOTA on cross-modal image generation/editing using hybrid macro–micro reasoning and efficient pretraining (Qin et al., 7 Aug 2025).

6. Theoretical and Empirical Analysis of Visual Thought Utility

Technical analyses demonstrate that:

Attention Redistribution: Transformer attention shifts away from raw image tokens to visual thought (VT) tokens as models reason through the chain, with deep-layer attention and gradient saliency decisive for prediction accuracy (Cheng et al., 21 May 2025).
Clarity/Conciseness Correlation: Human scores for VT clarity ( $\rho,r>0.8$ with accuracy) and conciseness are tightly linked to downstream performance (Cheng et al., 21 May 2025).
Visual Thought Mediation: Information is routed through VT tokens in a two-stage pipeline, facilitating efficient and accurate cross-modal transfer (Cheng et al., 21 May 2025).
Implicit vs. Explicit CoVT: Models leveraging latent graphical (CFG nodes (Le et al., 30 Oct 2024)), region (bounding box (Shao et al., 25 Mar 2024)), or continuous expert-aligned latents (Qin et al., 24 Nov 2025) can outperform more verbose or less-structured approaches.
Scalability: CoVT improves with model and backbone scale (e.g., 7B–13B LLMs). Token efficiency is generally improved (–15% to –30% output length), supporting scalable deployment (Ma et al., 4 Nov 2025).

7. Limitations, Open Research Challenges, and Future Directions

While CoVT frameworks consistently improve reasoning and interpretability, open problems include:

Ambiguity in Region/State Selection: When tasks demand multiple, disjoint or abstract regions, single-step visual CoT may fail. Multi-hop region refinement and co-annotation with textual CoT are active research directions (Shao et al., 25 Mar 2024).
Implicit Chain Interpretability: Latent-based or implicit VTs (continuous tokens or shared discrete states) may offer less direct interpretability than image/text-based steps.
Context Fragmentation: Adaptive cropping and iterative region focus (e.g., CoFFT) risk context loss for global tasks; tuning hyperparameters remains non-trivial (Zhang et al., 26 Sep 2025).
Pipeline Complexity: Architectures requiring joint optimization of vision, language, actions, and expert-aligned decoders (e.g., VITA, Uni-CoT) introduce training and inference complexity.
Integration of External Knowledge and Multi-hop Reasoning: Extending CoVT to multi-step or tree-of-thought structures, or integrating symbolic knowledge, is an open challenge (Wu et al., 2023).
Efficient Inference and Scalability: Autoregressive multi-modal modeling incurs inference penalties relative to direct action or end-to-end baselines; compression/distillation (VICoT-HRSC, LoRA) can help (Wang et al., 25 Nov 2025).
Human-interactive CoVT: Balancing transparency, user agency, and model autonomy remains an open field in interactive settings (Pather et al., 1 Sep 2025).

CoVT constitutes a fundamental architectural prior for next-generation VLMs, VLA agents, and multi-step reasoning systems, bridging the gap between symbolic linguistic reasoning and dense perceptual understanding through explicit, interpretable, and oftentimes compact interleaving of visual and linguistic thought. Further research is needed to optimize hybrid, hierarchical, and adaptive CoVT pipelines for real-time, generalist, and interactive applications across domains.