ThinkOmni: Lifting Textual Reasoning to Omni-modal Scenarios via Guidance Decoding
Overview
Overall Novelty Assessment
ThinkOmni proposes a training-free framework that enhances omni-modal reasoning by leveraging large reasoning models to guide decoding in omni-modal large language models. The paper resides in the 'Reward-Guided and Process-Supervised Decoding' leaf, which contains only three papers total, including ThinkOmni itself. This represents a relatively sparse research direction within the broader taxonomy of fifty papers across thirty-six topics, suggesting the specific combination of process supervision and omni-modal reasoning remains an emerging area rather than a saturated one.
The taxonomy tree reveals that ThinkOmni's leaf sits within 'Decoding-Time Guidance Mechanisms,' which encompasses four sibling categories: reward-guided methods, attention-guided approaches, cross-modal contrastive decoding, and uncertainty-guided techniques. Neighboring branches include 'Reasoning Process Enhancement' (focusing on chain-of-thought and stepwise reasoning) and 'Cross-Modal Alignment and Fusion' (addressing architectural integration of modalities). ThinkOmni bridges these directions by applying process-level guidance specifically to omni-modal scenarios, diverging from purely attention-based or contrastive methods that do not incorporate explicit reasoning model supervision.
Among twenty-five candidates examined across three contributions, the framework-level contribution shows two refutable candidates out of ten examined, while the LRM-as-a-Guide strategy found zero refutations among five candidates. The Stepwise Contrastive Scaling module identified two refutable candidates from ten examined. These statistics indicate that within the limited search scope, the LRM-as-a-Guide component appears more distinctive, whereas the overall framework and scaling mechanism encounter some overlapping prior work. The modest candidate pool suggests the analysis captures top semantic matches rather than exhaustive field coverage.
Based on the limited literature search of twenty-five candidates, ThinkOmni demonstrates partial novelty, particularly in its guidance strategy, while operating in a sparsely populated taxonomy leaf. The analysis does not cover the full breadth of multi-modal reasoning literature, focusing instead on top semantic matches and citation-expanded candidates. The framework's positioning at the intersection of process supervision and omni-modal reasoning appears less explored than adjacent attention-based or contrastive decoding methods.
Taxonomy
Research Landscape Overview
Claimed Contributions
The authors introduce ThinkOmni, a framework that enables omni-modal reasoning without additional training by leveraging off-the-shelf Large Reasoning Models to guide Omni-modal Large Language Models during decoding, addressing limitations of data scarcity and high computational costs in existing approaches.
The authors propose a strategy that uses Large Reasoning Models as guiding components during decoding to inject advanced reasoning capabilities into Omni-modal Large Language Models through logit-level contrastive mixing, enabling collaboration between perception and reasoning.
The authors develop a module that dynamically adjusts guidance parameters at each decoding step by analyzing real-time model predictions using Jensen-Shannon divergence, automatically balancing perceptual and reasoning signals across different tasks without requiring manual tuning.
Core Task Comparisons
Comparisons with papers in the same taxonomy category
[1] PRM-BAS: Enhancing Multimodal Reasoning through PRM-guided Beam Annealing Search PDF
[4] Controlling Multimodal LLMs via Reward-guided Decoding PDF
Contribution Analysis
Detailed comparisons for each claimed contribution
ThinkOmni training-free framework for omni-modal reasoning
The authors introduce ThinkOmni, a framework that enables omni-modal reasoning without additional training by leveraging off-the-shelf Large Reasoning Models to guide Omni-modal Large Language Models during decoding, addressing limitations of data scarcity and high computational costs in existing approaches.
[51] Socratic models: Composing zero-shot multimodal reasoning with language PDF
[54] Training-Free Reasoning and Reflection in MLLMs PDF
[52] See&Trek: Training-Free Spatial Prompting for Multimodal Large Language Model PDF
[53] MedAgents: Large Language Models as Collaborators for Zero-shot Medical Reasoning PDF
[55] Freeret: Mllms as training-free retrievers PDF
[56] Vision-by-language for training-free compositional image retrieval PDF
[57] E-FreeM2: Efficient Training-Free Multi-Scale and Cross-Modal News Verification via MLLMs PDF
[58] Training-Free Multimodal Large Language Model Orchestration PDF
[59] Multimodal PEAR chain-of-thought reasoning for multimodal sentiment analysis PDF
[60] SelfCheck: Using LLMs to Zero-Shot Check Their Own Step-by-Step Reasoning PDF
LRM-as-a-Guide strategy
The authors propose a strategy that uses Large Reasoning Models as guiding components during decoding to inject advanced reasoning capabilities into Omni-modal Large Language Models through logit-level contrastive mixing, enabling collaboration between perception and reasoning.
[71] Enhancing Multimodal Large Language Models: From Multimodal Alignment, Fine-Grained Perception to Robust Reasoning PDF
[72] Moment and Highlight Detection via MLLM Frame Segmentation PDF
[73] Dark Side of Modalities: Reinforced Multimodal Distillation for Multimodal Knowledge Graph Reasoning PDF
[74] Multimodal Reasoning with Fine-grained Knowledge Representation PDF
[75] Object-Guided Visual Tokens: Eliciting Compositional Reasoning in Multimodal Language Models PDF
Stepwise Contrastive Scaling module
The authors develop a module that dynamically adjusts guidance parameters at each decoding step by analyzing real-time model predictions using Jensen-Shannon divergence, automatically balancing perceptual and reasoning signals across different tasks without requiring manual tuning.