CoT Traces for VLMs

• CoT traces for VLMs are explicit intermediate reasoning steps encoded as text, images, or code that enable detailed model introspection.
• They represent structured sequences of decision-making, integrating visual, spatio-temporal, and code-driven formats to improve interpretability.
• These traces facilitate advancements in autonomous driving, robotics, and scientific problem solving by clarifying complex, multimodal reasoning paths.

Chain-of-Thought (CoT) traces for Vision-LLMs (VLMs) denote explicit intermediate reasoning steps—represented as text, images, graphs, or code—surfaced during the model’s prediction process rather than compressed into a single output. Unlike purely textual CoT, visual or multimodal CoT traces encode world states and sub-decisions with rich spatial, temporal, or structural information essential for tasks requiring high-fidelity reasoning, such as planning, complex scene interpretation, object detection, robotics, and scientific problem solving. Recent research has extended CoT methodology to pipelines that dynamically produce images, bounding boxes, graph traversals, or code-executed renderings as “thought steps”, yielding significant gains in interpretability, control, and robustness, but also introducing new fragility, computational complexity, and architectural requirements.

1. Taxonomy and Representation of CoT Traces in VLMs

CoT traces in VLMs manifest in diverse formats, tailored to multimodal reasoning requirements:

• Visual Chain-of-Thought (Visual CoT): Sequences of intermediate image edits—such as annotated crops, bounding boxes, overlays, or generated canvases—mirroring stepwise scrutiny of salient regions (Xu et al., 28 Sep 2025, Zeng et al., 23 May 2025, Li et al., 22 Jul 2025).
• Spatio-Temporal CoT: Stacks of annotated images showing predicted future states, embedding both spatial and temporal relationships (e.g., lanes, obstacles, scene motion), as in autonomous driving (Zeng et al., 23 May 2025, Li et al., 23 Jun 2025).
• Textual CoT/Chain-of-Reasoning (CoR): Multi-step natural language rationales linked to spatial decisions, used for affordance extraction, robot control, and simple visual tasks (Park et al., 3 Nov 2025, Xu et al., 15 Nov 2024).
• Code-driven CoT: Alternating text/code-image steps, where the model reasons by emitting plotting code, executing it, and conditioning further steps on the rendered figure (Duan et al., 13 Oct 2025).
• Graph Traversal CoT: Ordered prediction of graph elements (e.g., atoms/bonds in molecular graphs), logging each discovery as an explicit step (Wang et al., 9 Jun 2025).
• Stage-tagged Multilingual CoT: Structured reasoning pipelines with tagged evidence extraction, language identification, object captioning, and logical reasoning (Huang et al., 12 Sep 2025).
• Pseudo-labeling CoT: Discrete perception–recognition–grounding steps used to generate robust pseudo-labels and background negatives for open-vocabulary detection (Choi et al., 16 Oct 2025).

A canonical visual CoT trace can be denoted as:

$$(i, B^{(1)}, p^{(1)}) \to (i, B^{(2)}, p^{(2)}) \to \dots \to a$$

where $i$ is the input image, $B^{(t)}$ denotes the $t$th annotation (e.g., box), $p^{(t)}$ the corresponding crop, and $a$ the final answer.

2. Methodologies for Constructing and Generating CoT Traces

VLMs employ varied methodologies to construct, represent, and leverage CoT traces:

• Autoregressive Decoding: Unified image–token generation followed by planning, as in FutureSightDrive, where the model predicts Q_CoT (visual tokens for frames/overlays), then waypoints (Zeng et al., 23 May 2025).
• Progressive Generation: Multi-stage decoder progressing through coarse (lane), mid-level (detection), and fine-grained (frame) token generation; each step conditions on previously emitted tokens (Zeng et al., 23 May 2025).
• Prompt Engineering: Staged prompting templates (e.g., [SCENE]→[ANALYSIS]→[SOLUTION]→[FORMAT]) steer the decoder to produce discrete CoT stages (Ren et al., 3 Mar 2025, Xu et al., 15 Nov 2024).
• Fine-tuning on CoT-annotated Data: Supervised loss over structured traces, with explicit stage tags such as <SUMMARY>, <CAPTION>, <REASONING>, <CONCLUSION> (Xu et al., 15 Nov 2024, Wang et al., 22 Jun 2025).
• Multimodal Interleaving: Training VLMs to output interleaved sequences of text and image tokens, facilitating stepwise sketching, planning, or graph traversal (Li et al., 22 Jul 2025, Duan et al., 13 Oct 2025, Wang et al., 9 Jun 2025).
• RL-based Trace Alignment: Use of Group Relative Policy Optimization (GRPO) with multi-aspect rewards (accuracy, format, semantic alignment, language tags) to align generated CoT traces with ground-truth reasoning and task outcomes (Wang et al., 22 Jun 2025, Li et al., 23 Jun 2025, Huang et al., 12 Sep 2025).
• Automated CoT Data Curation: LLM-driven pipelines generate, verify, and refine CoT traces, enforcing logical coherence and factual correctness per step (Park et al., 3 Nov 2025, Huang et al., 12 Sep 2025).

3. Applications and Impact Across Domains

CoT trace methodologies furnish critical capabilities in several domains:

• Autonomous Driving (AD): Spatio-temporal CoT traces enable visual prediction of future states, explicit spatial overlays for lane/obstacle detection, and image-conditioned motion planning (inverse dynamics modeling) (Zeng et al., 23 May 2025, Li et al., 23 Jun 2025).
• Robotic Affordance Extraction: Textual CoR traces externalize spatial reasoning for coordinate prediction, dramatically improving placement accuracy and interpretability (Park et al., 3 Nov 2025).
• Traffic Management: Multistage CoT-prompted reasoning orchestrates scene analysis, anomaly diagnosis, solution synthesis, and translation to simulator control commands (Ren et al., 3 Mar 2025).
• Object Detection (Open Vocabulary): Visual CoT adds perceptual and background separation steps to pseudo-labeling, bolstering robustness in crowded or occluded scenes (Choi et al., 16 Oct 2025).
• Video Understanding: Chain-of-Thought traces over video clips support temporally grounded object tracking, event localization, and relational inference (Zhang et al., 10 Jun 2025).
• Mathematical and Scientific Reasoning: Code-driven CoT enables VLMs to “think with images” by executing plotting code and iteratively refining logical/graphical subgoals (Duan et al., 13 Oct 2025, Li et al., 22 Jul 2025).
• Multilingual VQA: Language-aware CoT composes evidence extraction, language identification, object captioning, and stepwise logical inference, enforced via reward optimization (Huang et al., 12 Sep 2025).
• Molecular Recognition (OCSR): Graph traversal CoT decomposes molecule parsing into sequential atom-bond steps, handling nonstandard abbreviations and yielding traceable outputs (Wang et al., 9 Jun 2025).

Empirical improvements resulting from CoT traces are significant: +7.7 AP50 on novel-class COCO detection (Choi et al., 16 Oct 2025), +4–17% accuracy on robotic affordance (Park et al., 3 Nov 2025), +12.7pp test accuracy in geometric reasoning (Li et al., 22 Jul 2025), and >70% collision reduction in AD planning (Li et al., 23 Jun 2025).

4. Architectural and Training Paradigms

Implementing CoT traces in VLMs requires careful architectural and training design:

• Tokenization Schemes: Visual/language tokens bracketed with explicit tags > , <trajectory>, <summary>, <image_start>, enhancing supervision and decoupling subtask losses (Xu et al., 15 Nov 2024, Zeng et al., 23 May 2025). > > - Vision Encoders: Frozen ViTs or CLIP-style backbones supply continuous attention maps for cross-stage conditioning (Zeng et al., 23 May 2025, Li et al., 22 Jul 2025). > > - Decoders: Unified autoregressive decoders with staged update logic (e.g., concatenated key/value conditioning) propagate downstream dependencies between reasoning steps (Zeng et al., 23 May 2025). > > - Reward Functions: Multi-aspect reward design covering accuracy, structure, format, semantic match, language tags, or trajectory metrics, balancing granular trace fidelity and final outcome alignment (Li et al., 23 Jun 2025, Huang et al., 12 Sep 2025). > > - Loss Terms: Stage-specific cross-entropy losses, format and accuracy rewards for RL stages, and contrastive background alignment for robust detection (Zeng et al., 23 May 2025, Choi et al., 16 Oct 2025). > > - RL Paradigm: GRPO (Group Relative Policy Optimization) supports groupwise advantage normalization, enabling policy updates that preserve format correctness, reward alignment, and mitigates over-exploration in low-capacity models (Wang et al., 22 Jun 2025, Li et al., 23 Jun 2025). > > Fine-tuning and reward strategies must address trade-offs in verbosity, generalization, and reasoning style. Hybrid SFT–RL approaches commonly induce trade-offs rather than synergy, requiring adaptive frameworks for optimal trace depth per instance (Chen et al., 10 Jul 2025). > > ## 5. Robustness, Limitations, and Failure Modes > > While CoT traces enhance interpretability and in-distribution performance, they also introduce specific vulnerabilities: > > - Fragility to Visual Corruption: Intermediate crops, overlays, or generated canvases amplify the effects of input perturbations (noise, blur, pixelation, adversarial attack), leading to sharper accuracy drops than standard VLMs (Xu et al., 28 Sep 2025). Empirical analysis attributes degradation primarily to decreased localization quality (IoU contraction). > > - Redundancy and Self-Monitoring: Stabilization requires multi-proposal cropping (Grounding DINO plug-and-play), confidence-weighted aggregation, adversarial training for bounding-box prediction, and fallback logic when localization is unreliable (Xu et al., 28 Sep 2025). > > - Computational Overhead: Generation of visual, code, or graph traces incurs measurable inference and runtime costs, especially when integrating external execution or multi-stage attention (Duan et al., 13 Oct 2025). > > - Trace Quality Assurance: Automated and manual filtering, per-step verification, and curriculum strategies are vital to maintain logical consistency, step completeness, and factual grounding in annotated datasets (Zhang et al., 10 Jun 2025, Park et al., 3 Nov 2025). > > - Difficulty Matching Reasoning Depth: SFT improves only the hardest questions (introducing verbosity elsewhere), RL supports brevity but may lose needed depth; naive combinations result in accuracy-style-length dilemmas (Chen et al., 10 Jul 2025). > > A plausible implication is that future CoT trace design should build in redundancy, per-step diagnostic logging, adaptive reasoning depth, and modular fallback mechanisms for corrupted or ambiguous input. > > ## 6. Guidelines for Future CoT Trace Design and Applications > > Best practices and recommendations converge around several axes: > > - Structured Annotation: Systematic templates, explicit delimiters, domain-specific tags, and multi-stage encoding robustly organize trace information and facilitate downstream supervision and evaluation (Xu et al., 15 Nov 2024, Zhang et al., 10 Jun 2025, Ren et al., 3 Mar 2025). > > - Curriculum Learning: Dynamically scale reasoning trace complexity according to problem difficulty, balancing performance on both simple and complex inputs (Zhang et al., 10 Jun 2025, Li et al., 23 Jun 2025). > > - Automated Verification: Integrate LLM-driven stepwise evaluators and correction loops; score traces on fidelity, consistency, and coverage before export (Park et al., 3 Nov 2025, Huang et al., 12 Sep 2025). > > - Reward-Balanced Training: Blend structural, semantic, and format-aligned rewards using RL or mixed supervised-objective frameworks, tuning reward ratios for task-specific gains (Li et al., 23 Jun 2025, Huang et al., 12 Sep 2025). > > - Redundancy and Robustness: Employ multi-crop pipelines, self-diagnosing attention, confidence-weighted composition, and adversarial pretraining of region selectors for corrupted input resilience (Xu et al., 28 Sep 2025). > > - Interpretable Logging: Store and visualize trace evolution—graph traversal steps, code–image pairs, bounding-box overlays, or planning waypoints—enabling transparent model debugging and scientific inspection (Wang et al., 9 Jun 2025, Zeng et al., 23 May 2025, Duan et al., 13 Oct 2025). > > - Adaptive Inference: Pursue frameworks that predict task difficulty and select SFT-style or RL-style decoding at runtime, with mechanisms for reasoning depth and trace length adjustment (Chen et al., 10 Jul 2025). > > This suggests that the field is moving toward highly structured, adaptive, and redundancy-rich CoT trace systems, supporting robust, multimodal reasoning across both simple and complex tasks in VLMs.