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Uni-CoT: Towards Unified Chain-of-Thought Reasoning Across Text and Vision

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 5.6Download Report PDF
Unified Model;Multi-Modal;Chain-of-Thought; Image Generation; Image Editing;
Abstract:

Chain-of-Thought (CoT) reasoning has proven effective in enhancing Large Language Models (LLMs) on complex tasks by decomposing problems into step-wise solutions. However, extending CoT to multi-modal settings remains challenging, as it requires modeling transitions of visual states alongside textual reasoning. Existing approaches often underperform due to limited capacity to model visual transitions or fragmented architectures. To overcome this limitation, we introduce Uni-CoT, a Unified Chain-of-Thought framework that captures structured visual transitions and seamlessly aligns them with textual logic, enabling coherent multimodal reasoning. To mitigate the computational and training challenges inherent to multi-modal reasoning, Uni-CoT introduces a two-level reasoning paradigm: a macro-level CoT for high-level planning and a micro-level CoT for localized subtask execution. This hierarchical design reduces computational overhead while maintaining coherence. Additionally, Uni-CoT incorporates a structured training paradigm with auxiliary tasks to stabilize optimization and improve generalization. Experiments on reasoning-driven image generation and understanding benchmarks demonstrate that Uni-CoT achieves state-of-the-art performance and remarkable generalization, underscoring its potential for complex multi-modal reasoning.

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This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
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Overview

Overall Novelty Assessment

The paper proposes Uni-CoT, a unified multi-modal Chain-of-Thought framework with two-level hierarchical reasoning (macro-level planning and micro-level execution). It resides in the 'Two-Stage and Hierarchical CoT Frameworks' leaf under 'Foundational Multi-modal CoT Architectures and Training Methods'. This leaf contains only two papers total, including the original work, indicating a relatively sparse research direction within the broader taxonomy of fifty papers across thirty-six topics. The framework aims to model visual state transitions alongside textual logic, addressing challenges in extending CoT reasoning to multi-modal settings.

The taxonomy tree reveals that neighboring leaves include 'Interleaved and Continuous Latent-Space Reasoning' (three papers on paired visual-textual rationales) and 'Retrieval-Augmented and Knowledge-Enhanced CoT' (two papers on demonstration selection). The 'Visual Grounding and Spatial Reasoning in CoT' branch (three leaves, six papers) focuses on localization and region-based reasoning, while 'Domain-Specific Multi-modal CoT Applications' (six leaves, seventeen papers) represents the most populated branch. Uni-CoT's position in foundational architectures suggests it targets general-purpose reasoning infrastructure rather than domain-specific adaptation, distinguishing it from the heavily populated application-oriented work.

Among thirty candidates examined via semantic search and citation expansion, the analysis identified limited prior work overlap. The unified framework contribution examined ten candidates with one potentially refutable match. The two-level hierarchical paradigm also examined ten candidates with one refutable match, suggesting some architectural precedent exists within the limited search scope. The structured training paradigm with auxiliary tasks examined ten candidates with zero refutable matches, indicating this aspect may be more novel among the papers reviewed. These statistics reflect a constrained literature search rather than exhaustive coverage of the field.

Based on the top-thirty semantic matches examined, the work appears to occupy a moderately explored niche within foundational multi-modal CoT architectures. The hierarchical reasoning paradigm shows some overlap with prior approaches, while the training methodology may offer more distinctive contributions. The sparse population of the specific taxonomy leaf (two papers) contrasts with the broader field's fifty papers, suggesting the particular combination of unified framework design and two-level decomposition represents a less crowded research direction, though definitive novelty assessment would require broader literature coverage.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
30
Contribution Candidate Papers Compared
2
Refutable Paper

Research Landscape Overview

Core task: Multi-modal chain-of-thought reasoning across text and vision. The field has evolved around several complementary branches that address different facets of integrating visual and textual information for step-by-step reasoning. Foundational Multi-modal CoT Architectures and Training Methods explore how to build and train models that generate intermediate reasoning steps, often employing two-stage or hierarchical frameworks that separate rationale generation from answer prediction, as seen in Multimodal Chain-of-Thought[1] and Uni-CoT[0]. Visual Grounding and Spatial Reasoning in CoT focuses on anchoring reasoning to specific image regions or 3D contexts, exemplified by works like Visual CoT[4] and Situated Reasoning 3D[11]. Domain-Specific Multi-modal CoT Applications tailor these techniques to specialized tasks such as embodied AI (EmbodiedGPT[42], Embodied Chain-of-Thought[6]) or scientific question answering (T-sciq[17]), while Benchmarking and Evaluation of Multi-modal CoT provides datasets and metrics to assess reasoning quality (Mathverse[12], MME-CoT[16]). Advanced Reasoning Paradigms and Extensions push beyond standard CoT by incorporating retrieval mechanisms (Retrieval Augmented Multimodal[3]), interleaved modalities (Interleaved-modal CoT[13]), or continuous latent reasoning (Continuous Thought[14]). Within the foundational architectures, a particularly active line of work centers on two-stage and hierarchical frameworks that decompose reasoning into distinct phases, aiming to reduce modality interference and improve interpretability. Uni-CoT[0] sits squarely in this cluster, emphasizing a unified approach to hierarchical reasoning that balances rationale generation with answer synthesis. This contrasts with Multimodal Chain-of-Thought[1], which pioneered the two-stage paradigm but may handle modality fusion differently, and with retrieval-augmented methods like Retrieval Augmented Multimodal[3] that inject external knowledge rather than relying solely on internal hierarchical decomposition. Meanwhile, works such as CoT-VLA[2] and Mint[5] explore how to integrate CoT reasoning into vision-language-action loops or multi-task settings, highlighting trade-offs between architectural simplicity and task-specific customization. Open questions remain around optimal stage granularity, the role of explicit versus implicit rationale supervision, and how to scale these frameworks to more complex visual scenes without sacrificing reasoning transparency.

Claimed Contributions

Uni-CoT unified multi-modal Chain-of-Thought framework

The authors propose Uni-CoT, a framework that extends Chain-of-Thought reasoning to multi-modal settings by integrating structured visual state transitions with textual reasoning within a unified model architecture, enabling coherent reasoning across both vision and language modalities.

10 retrieved papers
Can Refute
Two-level hierarchical reasoning paradigm with macro and micro CoT

The authors introduce a hierarchical reasoning structure where macro-level CoT handles task decomposition and synthesis while micro-level CoT executes individual subtasks. This design reduces computational complexity from quadratic to near-linear while maintaining reasoning coherence.

10 retrieved papers
Can Refute
Structured training paradigm with auxiliary tasks

The authors develop a training approach that decomposes multi-modal CoT learning into macro-level and micro-level components, augmented with four auxiliary tasks for MDP-based self-reflection, enabling stable and efficient training for complex multi-modal reasoning.

10 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Uni-CoT unified multi-modal Chain-of-Thought framework

The authors propose Uni-CoT, a framework that extends Chain-of-Thought reasoning to multi-modal settings by integrating structured visual state transitions with textual reasoning within a unified model architecture, enabling coherent reasoning across both vision and language modalities.

Contribution

Two-level hierarchical reasoning paradigm with macro and micro CoT

The authors introduce a hierarchical reasoning structure where macro-level CoT handles task decomposition and synthesis while micro-level CoT executes individual subtasks. This design reduces computational complexity from quadratic to near-linear while maintaining reasoning coherence.

Contribution

Structured training paradigm with auxiliary tasks

The authors develop a training approach that decomposes multi-modal CoT learning into macro-level and micro-level components, augmented with four auxiliary tasks for MDP-based self-reflection, enabling stable and efficient training for complex multi-modal reasoning.

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