Latent Sketch Tokens Overview

• Latent sketch tokens are vector representations that encode internal visual and reasoning states, serving as a bridge between text and visual modalities.
• They enable efficient autoregressive and attention-based computation by integrating continuous or discrete tokens directly into multimodal model contexts.
• These tokens utilize various architectures and supervision methods, such as VQ-VAE and attention-guided tokenization, to enhance interpretability, spatial reasoning, and retrieval performance.

Latent sketch tokens are a class of vector or discrete token representations designed to capture visual, structural, or reasoning information during the internal process of multimodal models—including LLMs (LLMs and MLLMs) and vision-LLMs (VLMs). Unlike pixel-level image generation or explicit sketch rendering, latent sketch tokens encode "visual thoughts" or compressed reasoning states in a form suitable for efficient autoregressive, attention-based computation. These tokens are variously continuous or discrete, integrated as first-class entities in the token stream, and typically serve as a bridge between pure text reasoning and explicit visual processing, supporting both interpretability and improved performance on tasks requiring visual imagination, spatial reasoning, or multi-stage planning (Yang et al., 20 Jun 2025, Tong et al., 18 Dec 2025, Zhang et al., 28 Oct 2025, Su et al., 5 Feb 2025, Yang et al., 3 Feb 2024).

1. Taxonomy of Latent Sketch Token Methods

Current approaches to latent sketch tokens fall into several principal categories:

• Continuous latent visual tokens: Generated directly by the model's hidden states at specific positions, integrated via self-attention for forward reasoning (e.g., Mirage (Yang et al., 20 Jun 2025), SkiLa (Tong et al., 18 Dec 2025), Latent Sketchpad (Zhang et al., 28 Oct 2025)).
• Discrete latent tokens (trace sketches): Vector-quantized or codebook-indexed, replacing subsequences of a reasoning trace to compress and abstract early reasoning steps (e.g., Token Assorted (Su et al., 5 Feb 2025)).
• Patch-level or global tokens for cross-modal similarity: Extracted from input sketches or images by attention-guided tokenization, used for retrieval or matching (e.g., MLAGT (Yang et al., 3 Feb 2024)).

Key distinctions include the discreteness (continuous vs. discrete), supervision mechanism (distillation, reconstruction, classification), and downstream purpose (reasoning, retrieval, interpretability).

2. Latent Sketch Token Generation Architectures

Latent sketch token pipelines typically intertwine two core components: a latent token generator and a mechanism for integrating these tokens into downstream autoregressive or attention-based computation.

Continuous latent token generation (e.g., Mirage, SkiLa, Latent Sketchpad):

• A special control token (e.g., ⟨vis⟩, <|sketch_start|>) switches the model from text generation to "visual mode."
• The model then outputs a vector (or sequence) in $\mathbb{R}^d$, corresponding to a latent sketch token, using the hidden state at the control point.
• For Mirage (Yang et al., 20 Jun 2025), the last-layer hidden state $h_{t_j}$ is either mapped through a small linear head or used directly as $v_j$:

$$v_j = W_v h_{t_j} + b_v, \quad W_v \in \mathbb{R}^{d \times d}$$

• In Latent Sketchpad (Zhang et al., 28 Oct 2025), "Vision Head" is a dedicated module (with attention layers) that generates visual latents in place upon a special token trigger, with both local and global context aggregation.

Discrete latent token generation (Token Assorted):

• A VQ-VAE encodes subsequences of a chain-of-thought into latent token indices $z_1, ..., z_k$ mapped from a codebook $\mathcal{E}$.

$$q(\bar{x}) = \arg\min_{e \in \mathcal{E}} \lVert \bar{x} - e \rVert_2^2$$

• During LLM training, the vocabulary is extended to include the discrete codes and special delimiters ([<boLatent>], [<eoLatent>]), and a random mixture of latent and text tokens is presented to force robustness.

Attention-guided tokenization for retrieval (MLAGT):

• Sketches and images are processed with multi-level CNNs, followed by self-attention, yielding local patch tokens and a global retrieval token RT.
• Top-K filtering and cross-attention establish correspondence prior to similarity scoring (Yang et al., 3 Feb 2024).

3. Supervision, Training Objectives, and Integration

Supervision and integration strategies vary to align latent token representations with visual, reasoning, or retrieval semantics.

Supervision mechanisms:

• Distillation from ground-truth image embeddings: Latent tokens are regressed to compressed vision encoder outputs (e.g., mean-pooled or attention-aggregated patch features) via cosine or L1 distance objectives (Yang et al., 20 Jun 2025, Zhang et al., 28 Oct 2025).
• Latent semantic reconstruction: Mean squared error loss between model-produced latents and sketch encoder outputs from ground-truth intermediate visualizations (Tong et al., 18 Dec 2025).
• VQ-VAE reconstruction: Cross-entropy for reconstructing the original token chunk, plus codebook and commitment losses for quantization (Su et al., 5 Feb 2025).
• Triplet loss for retrieval: Sketch and image tokens are optimized to minimize global RT distance for positive pairs and penalize negatives (Yang et al., 3 Feb 2024).

Integration into autoregressive or attention flow:

• Continuous tokens are incorporated into the self-attention cache as part of model context, indistinguishable at the architectural level from text tokens, enabling their use throughout downstream reasoning (Yang et al., 20 Jun 2025, Tong et al., 18 Dec 2025, Zhang et al., 28 Oct 2025).
• Discrete tokens are treated as vocabulary elements, requiring LLMs to learn new embeddings and next-token models conditioned on both types (Su et al., 5 Feb 2025).

Reinforcement learning (optional):

• In Mirage, reinforcement learning with rewards for final answer accuracy and format regularizes the full multimodal trace (Yang et al., 20 Jun 2025).

4. Applications and Empirical Performance

Latent sketch tokens have been systematically evaluated on spatial reasoning, mathematical problem solving, maze planning, general multimodal benchmarks, and cross-modal retrieval.

Spatial and vision-centric reasoning:

• Mirage's latent tokens (vs. text-only or explicit image generation) yield consistent gains, e.g., VSP Spatial Reasoning: $0.89$ vs. $0.85$, COMT Geometry: $0.77$ vs. $0.75$, Blink-Jigsaw: $0.88$ vs. $0.83$ (Yang et al., 20 Jun 2025).
• SkiLa demonstrates $+9.3$ absolute point improvement over Qwen2.5-VL baseline on MMVP, with broad gains across MMBench, RWQA, BLINK, and MME-Reason (Tong et al., 18 Dec 2025).
• Latent Sketchpad improves success rates and visualization consistency in MazePlanning tasks (e.g., Gemma3+Latent Sketchpad SR $72.2\%$ vs. $70.0\%$) and increases layout consistency rate to $\approx 99\%$ (Zhang et al., 28 Oct 2025).

Reasoning trace compression and efficiency:

• Token Assorted reduces token counts in mathematical and logical tasks by $17\%$–$92\%$ while increasing or matching accuracy ($+13.3$ points on Fresh-Gaokao-Math) (Su et al., 5 Feb 2025).
• Attention analysis indicates a model trained on latent tokens focuses more on semantically critical elements.

Cross-modal matching and retrieval:

• MLAGT achieves improved remote-sensing image retrieval by tokenizing sketch and image modalities into multi-level patches, followed by cross-modal attention and similarity scoring on global RT tokens (Yang et al., 3 Feb 2024).

5. Interpretability, Limitations, and Extensions

Interpretability:

• In continuous latent systems, the internal visual reasoning is not generally human-interpretable; external decoders (sketch decoders, VQ-VAE decoders) can reconstruct approximate sketches from latents for inspection, as in Latent Sketchpad and Token Assorted (Zhang et al., 28 Oct 2025, Su et al., 5 Feb 2025).
• The latent token mechanism acts as an internal sketchpad, providing structure for subsequent steps rather than explicit visualizations.

Limitations:

• Small codebooks may limit discrete sketch representation, while large codebooks increase computational cost (Su et al., 5 Feb 2025).
• Fixed compression rates may not align with the granularity needed for all problem instances.
• Continuous latents, while efficient, are opaque and may hinder direct debugging or interpretability.
• Some methods require pretraining or alignment with frozen vision encoders; transferability to novel modalities is underexplored.

Potential extensions:

• Adaptive compression, hierarchical latent structures, integration with external retrieval modules, or jointly end-to-end fine-tuned sketch token and model architectures have been proposed (Su et al., 5 Feb 2025).

6. Comparative Summary of Key Methods

The following table summarizes methodological distinctions across representative latent sketch token frameworks:

Method	Token Type	Integration	Decoder/Interpretability	Notable Results
Mirage (Yang et al., 20 Jun 2025)	Continuous latent vectors	Insert into autoregressive context	No pixel/image output; reasoning only	+4–9 pts on spatial reasoning
Token Assorted (Su et al., 5 Feb 2025)	Discrete codebook tokens	Extend LLM vocabulary, hybrid traces	VQ-VAE reconstructs text/approximate sketch	17–92% token reduction, +3–13 pts accuracy
SkiLa (Tong et al., 18 Dec 2025)	Continuous sketch vectors	Interleaved text and latent via special tokens	Visual semantics via MSE to sketch encoder	+9.3 pts MMVP, +12.5 pts MME-Reason
Latent Sketchpad (Zhang et al., 28 Oct 2025)	Continuous latent vectors	Autoregressively generated by Vision Head	Sketch decoder reconstructs images	+2.2 pts SR, +0.5 pts PR, >99% LCR
MLAGT (Yang et al., 3 Feb 2024)	Patch/global tokens (image/sketch)	Feature extraction, cross-attention, Euclidean similarity	No generative decoder; tokens for retrieval	Outperforms sketch-based retrieval baselines

Empirical evidence across these lines of research demonstrates that latent sketch token mechanisms enable efficient, multimodal, and more accurate internal reasoning, particularly for tasks involving spatial relationships, visual planning, and abstract chain-of-thought compression without explicit image generation.