ThinkOmni: Lifting Textual Reasoning to Omni-modal Scenarios via Guidance Decoding
Authors: Yiran Guan
Sifan Tu
Dingkang Liang
Linghao Zhu
Jianzhong Ju
Zhenbo Luo
Jian Luan
Yuliang Liu
Xiang Bai
Venue: ICLR 2026 Poster (2026)
Keywords: Omni-modal large language models
training-free guidance decoding
language model reasoning
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: Omni-modal reasoning is essential for intelligent systems to understand and draw inferences from diverse data sources. While existing omni-modal large language models (OLLM) excel at perceiving diverse modalities, they lack the complex reasoning abilities of recent large reasoning models (LRM). However, enhancing the reasoning ability of OLLMs through additional training presents significant challenges, including the need for high-quality data, task-specific adaptation, and substantial computational costs. To address these limitations, we propose ThinkOmni, a training-free and data-free framework that lifts textual reasoning to omni-modal scenarios. ThinkOmni introduces two key components: 1) LRM-as-a-Guide, which leverages off-the-shelf LRMs to guide the OLLM decoding process; 2) Stepwise Contrastive Scaling, which adaptively balances perception and reasoning signals without manual hyperparameter tuning. Experiments on six multi-modal reasoning benchmarks demonstrate that ThinkOmni consistently delivers performance improvements, with main results achieving 70.2 on MathVista and 75.5 on MMAU. Overall, ThinkOmni offers a flexible and generalizable solution for omni-modal reasoning and provides new insights into the generalization and application of reasoning capabilities.

Published as a conference paper at ICLR 2026
THINKOMNI: LIFTING TEXTUAL REASONING TO OMNI- MODAL SCENARIOS VIA GUIDANCE DECODING
Yiran Guan' Sifan Tu'
Dingkang Liang'
Linghao Zhu' Yuliang Liu'
Jianzhong Ju Zhenbo Luo² Jian Luan?
Xiang Bai
'Huazhong University of Science and Technology (yiranguan, dkliang, xbai) @ @hust.edu.cn
2MiLM Plus, Xiaomi Inc.
ABSTRACT
Omni-modal reasoning is essential for intelligent systems to understand and draw inferences from diverse data sources. While existing Omni-modal Large Lan- guage Models (OLLM) excel at perceiving diverse modalities, they lack the complex reasoning abilities of recent Large Reasoning Models (LRM). How- ever, enhancing the reasoning ability of OLLMs through additional training presents significant challenges, including the need for high-quality data, task- specific adaptation, and substantial computational costs. To address these lim- itations, we propose THINKOMNI, training-free framework that lifts textual reasoning to omni-modal scenarios. THINKOMnI introduces two key compo- nents: 1) LRMdds-a-Guide, which leverages off-the-shelf LRMs guide the OLLM decoding process; 2) Stepwise Contrastive Scaling, which adaptively balances perception and reasoning signals without manual hyperparameter tun- ing. Experiments six multi-modality reasoning benchmarks demonstrate that THINKOMNI consistently delivers performance improvements, with main results achieving 70.2% on Math Vista and 75.5% MMAU. Overall, THINK offers a flexible and generalizable solution for omni-modal reasoning and provides new insights into the generalization and application of reasoning capabilities. Project page: https: //1ragg// github. Lo/thinkoma
INTRODUCTION
The emergence of Large Reasoning Models (LRMs) marks paradigm shift from the traditional thinking of standard LLMs, which rely on immediate intuition, to slow thinking, which emphasizes reflective and iterative reasoning. Recent LRMs, such as DeepSeek-RI (Guo et al., 2025) and ol (OpenAI, 2025), have demonstrated exceptional performance specialized reasoning tasks like mathematical problem-solving and code generation. Nonetheless, their effectiveness remains predominantly constrained to textual inputs, thus limiting their applicability to more complex, omni- modal real-world scenarios involving text, audio, images, and videos (see Fig. ((a)..
Omni-mode reasoning is essential for synthesizing diverse data sources and enabling sophisticated inference in context-rich tasks. Strong omni-moda reasoning capabilities have profound implications for practical applications such as advanced virtual assistants (Zhang et al., 2025) and embodied robots (Gan et al., 2020). Although recent advances in Omni-modal Large Language Models (OLLM) (Xu et al., 2025; Li et al., 2025b; Liu et al., 2025c; Fu et al., 2025; Luo et al., 2025) have shown promise in comprehending various input modalities, these models typically fall short when tasked with intricate reasoning across modalities, as illustrated in Fig. 1(b). Therefore, a fundamental research challenge is how to effectively extend and elevate the reasoning capabilities of models from primarily textual inputs to truly omni-modal scenarios.
Actually, this is not trivial problem, and despite considerable efforts, existing approaches omni- modal reasoning are still limited in several critical aspects. Specifically, 1) Insufficient modality diversity. Current studies largely focus on specific modalities (e.g., image (Liu et al., 2025b;a; Lin et al., 2025), audio (Li et al., 2025a), or video (Wang et al., 2025)), rather than generalizing across
Work done Xiaomi Inc.
R Corresponding author.

---

Published as a conference paper at ICLR 2026
gradient
Aa
Reasoning LLM
Grewerr
Omni-modal
V step-bb--step reasoning
X
omni-modal Input
scarcity training
Xtsskspecltie enhancement
(8)
Ae
Omni-modal LLM
Aa
Ommi--oddal T
Reasoning
X step-by-step reasoning omni-modal input
/ training o /eneral enhancement
(6) our nethod
Off-the advanced
Omni-modal reasoning enhancement
Comparison between existing methods and THINKONNI. We integrate an OLLM with an LRM via guidance decoding, enabling advanced reasoning abilities with omni-modal input.
Figure I:
arbitrary combinations of modalities. 2) Task-specific enhancement. Enhancements proposed for existing OLLMs (Zhao et al., 2025; Zhong et al., 2025; Rouditchenko et al., 2025; Yang et al., 2025b) remain confined particular downstream tasks, lacking broader generalizability. 3) Data scarcity and high training costs. Current methods predominantly rely on extensive supervised finetuning (SFT) (Xu et al., 2024; Yang et al.. 2025c) (requiring tens of thousands of reasoning examples) or reinforcement finetuning (RFT) (Shao et al., 2024; Yu et al., 2025) approaches demanding training computational resources (e.g.. 8x40G VRAM for 7B model, 80G VRAM for 32B model). These challenges collectively motivate an important question: Is it possible to overcome the constraints of data and training conditions to bring general reasoning abilities to omni-modal content?
In this paper, we propose THINKOMNI, a novel training-free framework designed to lift textual reasoning to omni-modal scenarios (see Fig. (d)). Unlike existing approaches (see Fig. I(c) reliant on costly data annotation additional model training, THInkOMnI directly leverages off-the-shelf LRMs as decoding-time guides for OLLMs. Specifically, we first introduce the LRM-as-a- --M-idd--Guude strategy, enabling the integration of reasoning capabilities from LRMs into OLLMs. We further identify a potential issue: a fixed guidance weight is unsuitable for all the tasks, and manual, task-specific adjustment is impractical. To resolve this, we propose Stepwise Contrastive Scaling module, adaptively balancing perceptual and reasoning signals based on real-time analysis of model predictions. This module adapts various task types and facilitates coherent omni-modal reasoning.
Extensive experiments conducted on six challenging multi- modal reasoning benchmarks demonstrate the effectiveness of our method. Specifically, our method improves the state- of-the-art open souree OLLM Qwen2.5-0mni (Xu et al., 2025) by substantial margins without additional training, as shown in Fig. 2, rivaling or surpassing models that un- dergo extensive RFT. Additionally, compared to other guid- ance decoding algorithms (Li et al., 2022; Liu et al., 2024), method reduces the burden of multi-mode data input, thereby maintaining decoding efficiency.
Visusl Russoning
Qwen2.5-Omm
Omni-R1
THINKONN
50.0
51.55
31.1
71.5
15,5
modil Reosoning
Reosoning
THInKOmnI provides a flexible framework for lifting textual reasoning to a more diverse and enriched input space. By leveraging the strengths of OLLM and LRM, we explore the effective generalization of reasoning capabilities to omni-modal scenarios in a training free manner. Besides, our method is not limited to current LRMs. As new LLM technologies emerge (often developing faster than multi-modal variants), our approach can be easily adapted to improve performance across multi- modal variants and other downstream domains.
Figure Performance comparison.
2 PRELIMINARIES
2.1 NEXT TOKEN PREDICTION
Given an omni-modal input (e.g., images, audios, videos) and a sequence of text tokens Fett (21, ,., Tt-1), the OLLM M first computes the logits 2, for the next token 2t as
2

---

Published as a conference paper at ICLR 2026
2, (x< O), where E RV and V is the vocabulary size. The probability distribution P for Iy is then given by
P(z, Eecr, 0) Softmax(z)). (2).
(1)
Then Be(+1 = (x1, ..,..., zt). The model computes distribution and decodes token at each step, resulting an auto-regressive generation process.
2.2 INFERENCE-TIME GUIDANCE DECODING
Finetuning large language models is time-consuming and costly, highlighting the need for methods to modify or control models' behaviors without additional training. In this subsection, we introduce the following works to understand our method better: Contrastive Decoding (Li et al., 2022), Visual Contrastive Decoding (Leng et al., 2024), Proxy Tuning (Liu et al., 2024), and Proxy ProxyThinker (Xiao al.. 2026). For models within the same family (i.e., sharing the same token vocabulary), these methods guide base model decoding by introducing contrastive pair at the logits level:
base +
contrastive pair
where controls the influence of the guidance signal. Here, z+ and = represent the logits from the positive and negative references, respectively. These encourage or discourage certain behaviors in the model's output. This mechanism is analogous differential amplifier circuit, which amplifies the desired signals while suppressing noise. Consequently, the model can reduce hallucinations or achieve preference alignment during inference without additional training.
In Contrastive Decoding (Fig. 3(a)), the contrastive pair is formed by com- paring the responses to the same prompt from the original guiding model and an additional amateur model, with = set base, In Visual Contrastive Decoding (Fig. 3(b)), the contrastive pair is created by applying different input conditions to the same model. Specifically, = is obtained by adding Gaussian noise to the input image and then performing inference. In contrast to these approaches, Prox- y yuming and Proxy Thinker (Fig. 3())) construct contrastive pairs across different models within the same family, aiming transfer behav- iors from more minor, guiding models to larger, amateur models.
Ourpst
Output
Xa
x1
kft.+to
Gurd. LLM
Amat LLM
Multi-modal LLM
Large Amat. LLM
Guid, LLM
Arat
(al Contrastive Decoding
[b) Visual Contrastive Decoding
(c) Tunningnnng, Prozy ProxyThinkee Thinker
Figure 3: Guidance decoding methods. "Guid." " denotes the guiding model, and "Amat." denotes the amateur model.
Existing guidance decoding methods limited to scenarios with consistent input modalities and available expert models. There is often no suitable expert model in omni-modal settings or other downstream tasks, making technically challenging to construct effective guidance signals. More- over, the heterogeneity of modalities complicates the alignment and integration of guidance during inference. Our work addresses these challenges by designing a framework for cross-mag cross-modes -odal guidance decoding, enabling preference alignment without requiring modality-speciff expert models.
3 METHOD
This section outlines the implementation roadmap of THINKOMNI, starting with a straightforward guidance decoding approach. We first introduce IRM--ss-- -aiide, which separates the input modal- ities of the Omni-modal Large Language Model (OLLM) and incorporates an off-the-shelf Large Reasoning Model (LRM) as a guiding component. While this approach is practical, coordinating fixed guidance decoding hyperparameters remains challenging due to the varying demands for rea- soning signals across different tasks and scenarios. To address this shortcoming, we propose Stepwise Contrastive Scaling, module that dynamically adjusts parameters based on real-time analysis of model predictions, thereby adapting automatically to each decoding scenario. An overview of our framework is provided in Fig. 4.
3

---

Published as a conference paper at ICLR 2026
LRM--as-a-Guide
Step-wise Contrastive Scaling
LRMias--eGGuide
Step-wise Contrastive Scaling
Omni-modal
Ommit-meddll
content
OLLM
OLLM
OLLM
94s3
Sampling
Sumplin &Cincat
Rict
LRM
&Canno
LRM
Textual Input
Adaptive logits mixture
Textual Input
Adaptive logits mixture
OLLM
29
P(R: x..,, Softmax (2:)
Omni-modal LLM
Reasoning LLM
Text token
Token sequence
Next token prediction process
Figure 4: Overview of THINKOMNL. The framework begins by separating input modalities of the OLLM and introducing the LRM as a guiding model. Stepwise Contrastive Scaling dynamically adjusts guidance parameters based real-time prediction analysis, enabling adaptive and effective decoding across diverse tasks.
3.1 LRM-AS-A-GUIDE
As discussed in Sec. 2.2, to address the gap where current guidance decoding approaches are limited models with matched input modalities, we introduce the I.RM-as-a-Guide, which lifts advanced textual reasoning into the omni-modal content through collaborative decoding with OLLM.
Let Mo denote the OLLM and Ma denote the LRM. As shown in Fig. 6(a), we compute the base logits with full omni-modal input, 2 base Mo(x O). Then we discard the omni-modal content and feed Mo only the textual prefix der-- The results are treated as the negative logits =- (o( The positive logits are produced by the LRM on the same prefix 2 = Mn(x<)) As formulated in Eq. (2), the token probability distribution will then serve as
p p= Softmax Mo(Fct, 0) (Ma(set)- Mo(
where the scalar determines the extent to which the LRM influences the OLLM. After obtaining the mixed logits, normalize them probabilities and then sample the token usual.
Although the LRM cannot access omni-modal information, we mitigate this disadvantage and amplify the reasoning preference through the logits contrastive. During the generation process, the OLLM and LRM collaborate in a complementary manner. The OLLM, serving as the primary agent, extracts and integrates omni-modal clues, while the LRM provides deeper reasoning over the textual trace. As decoding progresses, the LRM can compensate for the lack of omni-modal information by leveraging the already decoded tokens, and the OLLM achieves logical reasoning through the reasoning preferences supplied by the LRM. Their strengths are seamlessly fused through logit mixing, resulting in unified decoding framework that effectivelyy integrates perception and reasoning.
3.2 STEPWISE CONTRASTIVE SCALING
While LRM-as-a- a-Guide effectively enables collaboration between the LRM and OLLM, there mains room for improvement regarding the choice of the fixed guidance weight a. fixed a may not consistently achieve the optimal balance between perception and reasoning across different tasks.
User: Hosed on the given andin, identify which sound connol be keard.
USerr: Use sector paper ahort with contnal angle of 128.0 and eadius 6.8 to soll conical paper the botton perimeter paper
Audio Content.
"Male Speech and Child
Speech with music"
Los LXM rxidance
gusdincs Lde,
Low grldance
High LKM graline
Ansuer: The audio contains
Ausmer: Since I can't hour the information given...
very confored the constractioe of paper cup because can't cae' ger..
Ansver Step I: Univesttnd the Gromenry Langth..
background muri speech..
andio,
Stop 2. Calcutate
The audio clearly inchudes vostt....
Stop 3: Reinte the Ave Lenghh to
(a) Study
(b) Case Stydy from MathVVista
Figure 5: Case studies from (a) MMAU and (b) Math (Sakshi al., 2025; Lu et al., 2024) Tasks require different levels of LRM involvement. Using a fixed limits the ability of the model to optimally adapt task-specific needs, highlighting the need for more flexible approach.
4

---

Published as a conference paper at ICLR 2026
are three people it should be celfist. Wail, there two playing cello. Hmmn.. I think the one sittngg the center is more likely.
video, ...
User: Who leuding the ensemble now?
Text
Encoder
Encoder
Tokonizor
Sofmax
Impoct Omni- modaliit Condition decoding step.
Large Language Model
MolRas
DISCARD
System Token
Sotmax
Large Language Model
Vaual Token
Mo(ka)
Same Condition
Offfrent Medel
Audio Token
Softmxx
Large Reasoning Model
Impoct Reasoning Ability this decoding
Docoding Token
Ma(Fet)
(a) LRMias-a-Guide
Step-wise Contrastive Scaling
Figure 6: Detailed process of THINKOMNI. (a) The OLLM handles multi-modal inputs, while the LRM focuses textual reasoning. By mixing their logits, the system effectively integrates perception and reasoning during token generation. (b) Each decoding step balances perception and reasoning dynamically by comparing logit distributions under different conditions and models.
The OLLM prefers smaller to emphasize omni-modal cues, while the LRM benefits from larger a to strengthen its guidance. Additionally, since = and = do not possess comprehensive lead to recognition bias
omni-modal content, excessive reliance them (adopting large such as hallucination (Fig. 5(a)). Conversely, setting a too low may diminish the effectiveness of guidance, thereby constraining the logical reasoning capabilities (see Fig. 5(b)). Motivated by this, we propose Stepwise Contrastive Scaling, which dynamically apportions a token's prediction budget between perception and reasoning through online analysis of logits.
We introduce a stepwise influence metric to determine whether each decoding step is dominated by perception reasoning. Specifically, all the generated logits are first transformed into probability distributions with softmax function, let Po. PR. P denote the corresponding distributions for Mo ( O). (x(xet), and (x respectively. The pairwise distances between these distri- divergence, which is employed in DoLa (Chuang al., 2024) to measure the disagreement between two logits. This metric is symmetrie, bounded, and numerically stable, making it well-suited for our purposes:
butions are then quantified by the
DR = J JS(P P),
Dp Dp=JS(Pa P).
(4)
Intuitively, DR reflects the unique influence of reasoning preference, whereas Dr captures the contri- bution from perceptual mmni-modalitics A larger pairwise distance signifies that the corresponding factor (perception or reasoning) impacts the current decoding step more. Building on this metric, we proceed to reformulate Eq. (3) and introduce an additional contrastive logits term:
p Softmax Mo(x< +)+af (M (M(((=))))(((()))))++)) (oM 0) 0)-Mo(zz))) (5)
where of acts as the original guidance weight, capturing enhanced reasoning capability, whereas the difference contributed by of serves as an aggressive visual contrastive term (Leng al., 2024) (i.e., by directly removing non-textual inputs rather than adding noise), reflecting augmented perceptual capability. To improve decoding stability, we employ a normalization strategy to ensure of + 1, with the coefficients determined by the relative magnitudes of DR and Dp. During the initial decoding steps, we constrain the magnitude of a, to implement a warmup for the reasoning task. More implementation details provided in Appendix B.
As shown in Fig. 4, the entire THINKOMNI procedure is training free, requiring no additional finetun- ing corpus statistics. Leveraging stepwise contrastive scaling, LRM-as-a a-Guide autonomously evaluate the relative contributions of perceptual and reasoning signals at each generation step, seam- lessly balancing these complementary abilities without manual hyperparameter tuning.
5

---

Published as a conference paper at ICLR 2026
Table 1: Model performance on several omni-modal reasoning benchmarks. Here, DeepSeck refers to DeepSeek-R1 1-DistiDll Distill--wen-7H (Guo et al., 2025), and Qwen3 denotes Qwen3-8B (Yang al., 2025a). The numbers parentheses indicate the performance changes compared the base OLLMs Qwen2.5-Omnt
7B (Xu et al., 2025) and Omni-RI (Zhong et al., 2025). Models marked with *** are evaluated using our own evaluation scripts.
MathVista test-mini
MathVision test
MathVerse test-mini
MMA 15.25) test-mini
DailyOmni test
OmniBench
Model
Close-Sourse Models
GPT-4o
63.8
30.4
50.8
62.5
56.5
Gemini-2.0-Flaas
73.1
41.3
59.3
70.5
67.8
Open-Sourse Omni Models
Miichuan-Omni-Ie
63.6 68.4
66.2
50.0
42.9
Ola
70.3
50.71
Open-Sourse RFT Models
Omni-R1"
64.7
25.4
39.8
70.5
59.6
43.0
HumanOmniV22
68.8
25.4
37.3
75.3
58.5
41.9
0mmiin33
Qwen2.5-
56.0
18.2
32.0
69.4
56.6
37.5
+ DeepSeek
56.1(+0.1)
20.2(+2.0)
33.5(+1.5)
70. (++007)77)
57.1(+0.5)
39.9(42.4)
Qwen3
58.1(+2.1)
25.3(+7.1)
38.8(+6.8)
70.6(+1.2)
57.3(+0.7)
39.5(+2.0)
BuNNOOaat-Qwen2.5-Omm
66.8
25.0
40.2
71.5
57.9
42.1
+ DeepSeck
68 (8.8()2.0)
28 28-2()))2)
4..0(+1.8)
73.8(+2.3)
59.8(+1.9)
43.2(+1.1)
+ Qwen3
70.2(+3.4)
32.9(+7.9)
45.1(+4.9)
75.5(+4.0)
59.5(+1.6)
43.6(+1.5)
7
Omni-RI
64.7
25.4
39.8
70.5
$9.6
43.0
DeepSeck
66.1(+1.4)
27.0(+1.6)
43.1(+3.3)
73.1(+2.6)
60.3(+0.7)
43.5(+0.5)
+ Qwen3
71.3(+6.6)
31.5(+6.1)
45.2(+5.4)
75.4(+4.9)
59.8(+0.2)
43.4(+0.4)
4 EXPERIMENT
4.1 EXPERIMENT SETUP
Models To validate the effectiveness of THINKOMNI, we conduct experiments on three OLLMs: Qwen2.5-Ommi-33 7B (Xu al., 2025) and Omni-RI (Zhong et al., 2025). We utilize the DeepSeek-RI-Distil series (Guo et al., 2025) and the Qwen3 series (Yang et al., 2025a), both in thinking mode, as our LRMs guide decoding.
Benchmarks To demonstrate the generalizability of THINKOMNI, we evaluate on omni-modal scenarios using six benchmarks, comprising over 10, 000 test samples in total: Vista (test- mini) (Lu et al., 2024), Math Visinnsion (Wang et al., 2024), Math Verse (test-mini) (Zhang et al., 2024), MMAU-v05.1 15.25 (test-mini) (Sakshi et al., 2025), Daily-Omnl (Zhou al., 2025), OmniBench (Li al., 2024). More details are provided in Appendix B.
Evaluation We first use template matching for multiple-chon questions to extract the option from the model's output. If the answer cannot be extracted directly, and then compare the extracted answer to the gold answer. For free-form questions, we first use GPT-4o extract the answer from the model's output, then compare the extracted answer to the gold answer to determine if their meanings are consistent. This process is designed to account for various expressions in the answers.
GPT-4o extract it
4.2
MAIN RESULT
To evaluate the generality and scalability of THINKOMII, we benchmark the improvements of different LRM guides on several OLLMS with varying capability levels. Our main results are presented in Tab. 1. The experiment result shows that THINKOMNI brings extensive improvements across all OLLMs, LRMs, and benchmarks. For example, with the Qwen3 guide, THINKOMNI brings remarkable improvement Qwen2.5-0mni-77 on MathVisias by 7.9%, achieving the final score of 32.9%. Since LRMs do not have access to omni modal data contents, our results demonstrate that ThinKO indeed lifts the complex reasoning of LRMss the omni-modal scenario.

---

Published as a conference paper at ICLR 2026
Table 2: Comparison with several training-free methods. All are built upon Qwen2.5-0mni-77
Math Vista lest-mini
MMAU
OmniBench
Method
test-mini
Base Model
66.8
71.5
42.1
Average Logits Fusion
55.0(-11.8)
55.7(-15.8)
36.1(-6.0)
Caption-then-Answerr
61.0(-5.8)
59.7(-11.8)
32.3(-9.8)
VCD
66.5(-0.3)
72.2(+0.7)
43.1(+1.0)
THINKOMNI (Ours)
68.8(+2.0)
73.8(+2.3)
43.2(+1.1)
We compare our approach with methods trained using reinforcement learning finetuning (RFT) (i.e., Omni-RI (Zhong et al., 2025) and HumanOmniV2 (Yang et al., 2025b)). Based on the same foun- dation model, Aween..--Omii-7B. our DeepSeek-guided model achieves comparable performance, while our Qwen3-guided model consistently outperforms all the RFT-based methods. Moreover, our approach can be applied to models already undergoing RFT, further demonstrating broad perfor- mance improvements.
In addition, we observe differences in performance gains, which the following factors can explain: 1) the capabilities of LRM models, newer models like Qwen3, with stronger logical understanding and reasoning abilities, achieve greater improvements compared to DeepSeek under identical settings; 2) the training data of LRMs is biased towards scientific and mathematical content, leading to more pronounced gains these tasks; 3) the tested tasks themselves differ in their demands for reasoning ability, with scientific and mathematical tasks typically requiring more reasoning than audio or general omni- modil tasks.
4.3 COMPARE WITH TRAINING- METHODS
We use the original evaluation results of the OLLMs baseline. In addition, compare method with several other training- free methods: 1) Average Logits Fusion, which directly averages the output logits of the OLLM and LRM during inference. 2) Caption-then-Ame OLLM generates detailed caption for the omni-modal input, and the LRR answers the question based this caption. 3) Visual Contrastive Decoding (VCD) (Leng al., 2024), which mitigates hallucinations by contrasting predictions from original inputs with those from distorted ones. In this ablation, we implement the negative baseline by directly removing the multi-modal inputs. As shown in Tab. 2, THiNKOMII significantly outperforms the base OLLM. For Average Logits Fusion, although simple mixing of logits allows the model to generate outputs, it negatively impacts answer accuracy due to improper integration. The Ciption-then-Answee experiment demonstrates that when LRM alone responsible for answering, even with multi-modal information provided by the OLLM, performance drops significantly because information transmission is one-way. The OLIM cannot respond to the LRM's specific needs. VCD is designed to enhance attention to multi-modal information rather than reasoning ability, its performance declines on Math Vista, which requires stronger reasoning skills.
where the
4.4 ABLATION STUDY
Ablation study on fixed a and adaptive a OLLM has limited capability in complex reasoning, while LRM cannot access multi-modal content. Over-reliance on either component leads sub- optimal performance. As shown in Fig. 7(a), adjusting the fixed guiding weight a markedly impacts results: when 0, performance matches the original OLLM, and extreme values reduce scores on both benchmarks. In contrast, our Stepwise Contrastive Scaling (Full THINKOMNi) consistently achieves superior results across both benchmarks. Furthermore, Fig. 7(b) visualizes the distribution of the dynamic a", revealing distinct shifts across different tasks and underscoring the adaptive nature of our method autonomously tuning parameters to meet specific task requirements.
Ablation study different LRMs Fig. 8 demonstrates the performance of Qwenn.55Ommi-7 under the guidance of LRM across different model sizes and series. When the LRM size is too small, performance Math Vista and OmniBench both degrades, the language capabilities of

---

Published as a conference paper at ICLR 2026
Hean
75
Accuracy
orea bauk
Figure Ablation on guidance weight. Left: Performance varies with the constant guidance weight a. Each task's optimal range differs, with = 0 as the OLLM baseline. Right: THINKOMNI uses adaptive dynamic weights, and the dynamic a" shows a similar distribution shift, indicating that stepwise contrastive scaling can flexibly adapt to different task requirements.
OmniBench
Axcuracy (h4)
-
Diepiies
Dispseet Gaseire
Denzown
Mode: Size (8)
Model Stee
Medal Sice (B)
Figure 8: LRM-as-a-Guide performance scaling, We replace the Guide LRM on several bench- marks study the impact different LRM sizes and different LRM series on the performance of our method. The baseline refers to the performance of the Qwen2.5-Ommi--
LRM and the base OLLM are insufficiently matched. A sufficiently strong LRM positively impacts all benchmarks, with larger LRMs generally yielding better results than smaller ones. However, increasing the size to Qwen3- 14B does not lead to further improvements on MMAU and OmniBench, suggesting that enhanced reasoning ability has a limited effect on these tasks, although the results still surpass the baseline.
4.5 ANALYSIS
Qualitative analysis Fig. 9 illustrates the generation process of THINKOMNI, where darker colors indicate greater LRM contributions to each token. These tokens, often logical connectives (c.g., "but", "Therefore") and key terms (e.g., "traditional", "common"), are evenly distributed, showing that LRM consistently guides reasoning rather than merely supplementing OLLM. This highlights LRM's role in analyzing multi-modal clues and driving logical inference. In contrast, lighter tokens are mainly function words and specific terms reflecting multi-modal content, suggesting that OLLM focuses on retrieving information and constructing fluent responses under LRM's guidance. For more cases, see Appendix D.
Failure case analysis We identified several representative failure cases from the response. 1) ThinkOmni demonstrates correct multimodal perception, but conflicting information within the input leads to erroneous reasoning. As shown in Fig. 10(a), the model correctly recognizes that the highest visible marking on the beaker 400ml. However, due to the beaker being labeled as 600ml, the model incorrectly infers that only portion of the beaker is visible in the image, resulting in the wrong final answer. 2) Insufficient perceptual ability and limited sensitivity to subtle differences in the input lead to incorrect answers. As illustrated in Fig. 10(b), the model fails accurately detect the actual onset of the drum kit in the audio and mistakenly identifies the beginning of the audio as the start of the drum kit's performance, ultimately producing the wrong answer.
8

---

Published as a conference paper at ICLR 2026
Why would the audience find this prrformance novel?
INC peTtonAiCE
Becaow
i cuspicely
diffionn insiuuuns, mesiing únion
onginal sungantion
performucce
weee Wid Inctrunninns ERSGEAY IN FHE DE TURE JANC MA1E
Shonnl chassiol
DERETTI 10 (NotAar
Becaue performance included vocal parts presont the wrighna cospuutioo.
[scmnson idtencco limanna HOSOMONN 189 CAMMSTY (NSIRA WINT
makeo: the performance mecommn innosel and INE Ieyy BORK Mui I (SURS))
ia [eoneretei entioree Sell ieprumcets (NtEIT)) INCKAM (RSSUR) (1
Ueique tin (Theretoon)) the (narnict annwer
unconvestioaal volu..
score
Figure 9: An OmniBench case study. This case study visualizes the reasoning process of HHNNKOMNN-QQeen2555mmii--- highlighting the stepwise contrastive scaling coefficient a".
Ouestion: Whar is the highest amownn this class messurss?
Onestion: At whot point does the drom kit begin to play in the undin?
G7: 400
(A) After the intrudtction
(3) di the very degliniing
(C) Durtng the choras
When the bass starts
GTA A
To diserntiee the lights amsncr riv beoker out mecooue, la's anabaz the earkings on the beaker:
400
J. The beaker in faleled is "BOROSILICATE ASS 60tma.
warkings Booker show
autiiiiio
Drum begin
Charus
600mm
J. sighest vtrible aurking the
Guae the feaker spucyfically ishuted or sontol but the wartings only go up to distel, yt appears that the image only shows portion of the lockeer. Therefooe, the fioal atswer is 600.
The drass Atr tattores its prrformence at the stort of the sudio, vhish is warked by she presence flager Thangfore, the
Cornect perception but Wrong recacring
percepticn loads Wirnng recconing
is
(a) Failure case in MathVista
(b) Failure case in MMAU
Figure 10: Failure case study. (a) THINK makes a reasoning error due to conflicting information between the visible beaker markings (400ml) and the label (600ml). (b) THINKOMNI fails because it cannot accurately detect the drum kit's true start time in the audio.'
Efficiency analysis Although our method introduces some additional computation, it remains efficient. We measured generation latency on an H800-80G GPU with KV cache in the generate stage, benchmarking VCD (Leng et al., 2024), ProxyTuning (Liu et al., 2024), and THInkOMnI using 100 random OmniBench (Li et al., 2024) samples. VCD performs two forward passes per step, while ProxyTuning and THINKOMNI require three. As shown Tab. 3, our method (7B+7B setting) incurs 1.38x x in the prefill stage (first token generation) and 2.88x in the generate stage (response generation utilizing KV cache). Importantly, during decoding, the guiding model in THINKOMNI processes only text, which helps to reduce latency.
Table 3:
Generation latency comparison. Prefill Time: first token generation latency; Generate Time: response generation with KV cache. Results are averaged over 100 samples from OmniBench.
Guidence Decoding Method
Model Size
Prefill Time |
Generate Time +
None (Baseline)
7B
0.138s
0.025s
VCD (Leng et al., 2024)
7B
0.262s (1.89x)
0,050s (2.00x)
Proxy Tuning (Liu et al., 2024)
7B + 3B 3B
0.406s (2.91x)
0.086s (3.1xx)
THINKONni (Ours)
7B +7B
0.191s (1.38x)
0.072s (2.88x) x)
THINkOMNI (Ours)
7B +1.5B
0.191s (1.38x)
0.069s (2. 76x)
5 RELATED WORK
Omni-modal Large Language Models With the rapid advancement of large language models, expanding their capabilities to omni-modal domains has become a key focus. Omni-modal Large Language Models (OLLM) align and process information from multiple modalities, capturing richer semantics and context than single-modal systems. Proprietary models (Hurst et al., 2024; Deepmind, 2025) demonstrate impressive real-time multi-modas interaction, while open-source efforts such as
For the reader's understanding, the colored markings in Fig. 10 are visual aids added afterward and were provided to the model.

---

Published as a conference paper at ICLR 2026
Qwen2.5-Omnt (Xu et al., 2025; Li al., 2025b; Liu al., 2025c; Yang al., 2025b; Fu et al., 2025; Xie & Wu, 2024; Chen et al., 2025) are quickly closing the gap in modality alignment and deployment.
Large Reasoning Model Recent advances in large-scale reasoning models, such as ol (OpenAI, 2025) and DeepSeek-RI (Guo et al., 2025). highlight the challenge of robust general reasoning. Early methods used supervised fine-tuning with thain-of-thoughe data (Xu et al., 2024; Linger et al., 2025), while recent work leverages reinforcement learning (Shao et al., 2024: Xiaomi et al., 2025; Team et al., 2025; Yu et al., 2025; Tan et al., 2025; Zhu et al., 2026) for autonomous reasoning. As OLLMs tackle complex cross-modal reasoning, bridging perception and reasoning remains a core challenge.
Decoding-time Algorithm Decoding-time algorithms refine language model outputs at inference in training-fret free manner. Contrastive Decoding (O'Brien & Lewis, 2023) improves long-form gen- eration by avoiding degenerate outputs, and Visual Contrastive Decoding (I (Leng et al., 2024) reduces hallucination via visual input perturbation. ProxyTuning (Liu al., 2024) combines expert outputs and injects knowledge from finetuned models. ProxyThinkes (Xiao et al., 2026) extends Proxy Tun- ing to multi-modal reasoning tasks. Beyond visual modalities, ThiNKOmI adaptively integrates reasoning with omni-modal perception, achieving a flexible fusion of fast and slow thinking.
6 CONCLUSTON
We present THINKOMNI, training-free inference-ting framework that achieves robust and general- izable reasoning enhancement by introducing an off-the-shelf LRM guide decoding with a stepwise adaptive scaling mechanism. Across six challenging multi-modal benchmarks, it delivers consis- tent gains, often matching or surpassing reinforcement weteemment-iie-tueet fine-tuned models, suggesting a general and extensible paradigm for omni-modal reasoning.
Limitation THINKOMni requires shared vocabularies between the OLLM and LRM for logit fusion and introduces extra inference overhead due additional forward passes. Nevertheless, believe our approach offers valuable insights for bridging the gap between multi-modal and textual LLMs and provides sustainable direction for future LLM improvements.
10

---

Published as a conference paper at ICLR 2026
AKNOWLEDEGMENT
This work was done during the research internship of Yiran Guan, Sifan Tu, Linghao Zhu, and Dingkang Liang Xiaomi Inc. MiLM Plus team.
This work was supported by the National Natural Science Foundation of China (NO. 62441615, 62225603, 62576147).
ETHICS STATEMENT
This work introduces training-free framework using only publicly available models (Qwen2.5- Omni eX
et al., 2025); Omni-R1 (Zhong et al., 2025), DeepSeek-R1 (Guo et al,, Qwen3 (isemnaaann (Yang
al., 2025a)) and benchmarks (see Appendix B), without collecting new human. biometric, or sensitive data. Risks stem from inherited biases, possible hallucinated cross-modal attributions, and over-trust in generated reasoning chains; the method does not ensure factuality or safety in high-stakes domains. Before deployment, we advise bias auditing, human oversight, and external safety / factuality filters. Environmental impact is reduced relative finetuning because no additional training is performed.
REPRODUCIBILITY STATEMENT
Reproducibility is supported by: 1) the explicit inference formulation (Eq. 3 and Eq. 5) 2) unified decoding hyperparameters (see Appendix B); 3) public benchmark splits and the two-stage answer extraction pipeline follow Xiao et al. (2026); 4) hardware specification (80G VRAM GPU, KV cache enabled). The code will be coming up soon upon acceptance.
LLM USAGE
LLMs were used only for 1) minor code scaffolding / refactoring (boilerplate, log parsing), 2) linguistic polishing after technical content finalized, and 3) answer string normalization in the evaluation pipeline (format extraction, not scoring). They were not used for ideas, model method design, experiments, analyses, claims, interpretations. The researchers authored, validated, and cross-checked all algorithms, equations, results, and conclusions. Every LLM-assisted output was manually reviewed to prevent hallucination. Thus, L.M involvement provides creative contribution and does not affect the authenticity or reliability of the paper.
REFERENCES
Lichang Chen, Hexiang Hu, Mingda Zhang, Yiwen Chen, Zifeng Wang, Yandong Li, Pranav Shyam, Tianyi Zhou, Heng Huang, Ming- Hsuan Yang, et al. Omnixr: Evaluating omni-modality language models on reasoning across modalities. Proc. of Intl. Conf. on Learning Representations, 2025.
Ming-Hsuan
Yung-Sung Chuang, Yujia Xie, Hongyin Luo, Yoon Kim, James Glass, and Pengcheng He. Dola: Decoding by contrasting layers improves factuality in large language models. Proc. of Intl. Conf. Learning Representations, 2024.
Google Deepmind. https: //tp:://bloo googl blog.google.
Gemini 2.5:
Our most intelligent AI model www.go//ocoooooooooooooooooooooe e/technol poyy/googi e- deepmind/ maarch-20255 #egeeniiminnnnn 2-w-aii-kii-ttttnnn------- thinkking
gemini www-iii-moel-tte wwwoee-tthininnnee sooodel--chinkkng-uudteeee
2025.
Chaoyou Fu, Haojia Lin, Xiong Wang, Yi-Fan Zhang, Yunhang Shen, Xiaoyu Liu, Haoyu Cao, Zuwei Long, Heting Gao, Ke Li, al. Vita-1.5: Towards gpt-4o level real-time vision and speech interaction. arXiv preprint 2r00i:2501.011
2025.
Chuang Gan, Yiwei Zhang. Jiajun Wu, Boqing Gong, and Joshua B Tenenbaum. Look, listen, and act: Towards audio-visual embodied navigation. In 2020 IEEE International Conference on Robotics and Automation, pp. 9701-9707. IEÉE, 2020.

---

Published as a conference paper at ICLR 2026
Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Peiyi Wang, Qihao Zhu, Runxin Xu, Ruoyu Zhang, Shirong Ma, Xiao Bi, et al. Deepseek-rl incentivizes reasoning in llms through reinforce- ment learning. Nature, (4:38 638. 2025.
Aaron Hurst, Adam Lerer, Adam Goucher, Adam Perelman, Aditya Ramesh, Aidan Clark, AJ Os- trow, Akila Welihinda, Alan Hayes, Alec Radford, et al. Gpt-4o system card. arXiv preprint arXiv:2410.212777 2024.
Sicong Leng, Hang Zhang, Guanzheng Chen, Xin Li, Shijian Lu, Chunyan Miao, and Lidong Bing. Mitigating object hallucinations in large rision-language models through visual contrastive decoding. In Proc. of IEEE Intl. Conf. on Computer Vision and Pattenn Recognition, pp. 13872- 13882, 2024.
Gang Li, Jizhong Liu, Heinrich Dinkel, Yadong Niu, Junbo Zhang, and Jian Luan. Reinforcement learning outperforms supervised fine-tuning: A case study on audio question answering. arXiv preprint arXiv:2503. 1199, 2025a.
Xiang Lisa Li, Ari Holtzman, Daniel Fried, Percy Liang, Jason Eisner, Tatsunori Hashimoto, Luke Zettlemoer, and Mike Lewis. Contrastive decoding: Open-ended text generation optimization. arXiv preprint arXiv:2210. 15097, 2022.
Yadong Li, Jun Liu, Tao Zhang, Song Chen, Tianpeng Li, Zehuan Li, Lijun Liu, Lingfeng Ming, Gu- osheng Dong, Da Pan, al. Baichuan-omni-lk. 1.5 technical report. arXiv preprint arXiv:2501. 15368, 2025b.
Yizhi Li, Ge Zhang. Yinghao Ma, Ruibin Yuan, Kang Zhu, Hangyu Guo, Yiming Liang, Jiaheng Liu, Zekun Wang, Jian Yang, et al. Omnibench: Towards the future of universal omni-language models. arXiv preprint 240::2229..22.. 2024.
Zhiyu Lin, Yifei Gao, Xian Zhao, Yunfan Yang, and Jitao Sang. Mind with eyes: from language reasoning to multimodal reasoning. arXiv preprint arXiv:2503.18071, 2025.
Deng Linger, Linghao Zhu, Yuliang Liu, Yu Wang, Qunyi Xie, Jingjing Wu, Gang Zhang, Yingying Zhu, and Xiang Bai. Theorem-validatee reverse thaiin-ff-thoglle problem generation for geometric reasoning. Proc. of Conf. on Empirical Methods in Natural Language Processing, pp. 718-735, 2025.
Alisa Liu, Xiaochuang Han, Yizhong Wang, Yulia Tsvetkov, Yejin Choi, and Noah A Smith. Tuning language models by proxy. First Conference on Language Modeling, 2024.
Yuqi Liu, Bohao Peng, Zhisheng Zhong, Zihao Yue, Fanbin Lu, Bei Yu, and Jiaya Jia. Seg- zero: Reasoning-
guided segmentation via cognitive reinforcement. arXiv preprint arXiv:2503.06520, 2025a.
Ziyu Liu, Zeyi Sun, Yuhang Zang, Xiaoyi Dong, Yuhang Cao. Haodong Duan, Dahua Lin, and Jiaqi Wang. Visual-rft: Visual reinforcement fine-tuning. Porc. of IEEE Intl. Conf. on Computer Vision, 2025b.
Zuyan Liu, Yuhao Dong, Jiahui Wang, Ziwei Liu, Winston Hu, Jiwen Lu, and Yongming Rao. Ola: Pushing the frontiers of omni-modal language model with progressive modality alignment. arXiv e-prints, pp. ar02 2025c.
Pan Lu, Hritik Bansal, Tony Xia, Jiacheng Liu, Chunyuan Li, Hannanch Hajishirzi, Hao Cheng, Kai-Wei Chang. Michel Galley, and Jianfeng Gao. Mathvista: Evaluating mathematical reasoning of foundation models in visual contexts. Proc. of Inil. Conf. on Learning Representations, 2024.
Run Luo, Ting-En Lin, Haonan Zhang, Yuchuan Wu, Xiong Liu, Min Yang, Yongbin Li, Longze Chen, Jiaming Li, Lei Zhang, et al. Openomni: Advancing open-source omnimodal large lan- guage models with progressive multimodal alignment and real-time self-aware emotional speech synthesis. Proc. of Advances in Neural Information Processing Systems, 2025.
Sean O' Brien and Mike Lewis. Contrastive decoding improves reasoning in large language models.
arXiv preprint arXiv:2309.091 17, 2023.
12

---

Published as a conference paper at ICLR 2026
OpenAI. OpenAl ol System Card o1 sl--syteem-card. pdf, 2025.
openai.com.
https://cch..openal com/
Andrew Rouditchenko, Saurabhchand Bhati, Edson Araujo, Samuel Thomas, Hilde Kuehne, Rogerio Feris, and James Glass. Omni-rl: Do you really need audio to fine-tune your audio llm? arXiv preprint arXiv:2505. 09439, 2025.
S Sakshi, Utkarsh Tyagi, Sonal Kumar, Ashish Seth, Ramaneswaran Selvakumar, Oriol Nieto, Ramani Duraiswami, Sreyan Ghosh, and Dinesh Manocha. Mmau: A massive multi-task audio understanding and reasoning benchmark. Proc. of Inil. Conf. on Learning Representations, 2025.
Zhihong Shao, Peiyi Wang. Qihao Zhu, Runxin Xu, Junxiao Song, Xiao Bi, Haowei Zhang, Mingchuan Zhang, YK Li, Y Wu, et al. Deepseekmath: Pushing the limits of mathematical reasoning in open language models. arXiv preprint arXiv:2402.033000 2024.
Wenhui Tan, Jiaze Li, Jianzhong Ju, Zhenbo Luo, Jian Luan, and Ruihua Song. Think silently, think fast: Dynamic latent compression of IIm reasoning chains. Proc. of Advances in Neural Information Processing Systems, 2025.
Kimi Team, Angang Du, Bofei Gao, Bowei Xing, Changjiu Jiang, Cheng Chen, Cheng Li, Chenjun Xiao, Chenzhuang Du, Chonghua Liao, et al. Kimi k1. 5: Scaling reinforcement learning with Ilms. arXiv preprint arXiv:2501. 12599, 2025.
Ke Wang, Junting Pan, Weikang Shi, Zimu Lu, Houxing Ren, Aojun Zhou, Mingjie Zhan, and Hongsheng Li. Measuring multimodal mathematical reasoning with math-vision dataset. Proc. of Advances in Neural Information Processing Systems, 37:95095-95169 2024.
Ye Wang, Ziheng Wang, Boshen Xu, Yang Du, Kejun Lin, Zihan Xiao, Zihao Yue, Jianzhong Ju, Liang Zhang, Dingyi Yang, al. Time-rl: Post- tratiiga large vision language model for temporal video grounding. Proc. of Advances in Neural Information Processing Systems, 2025.
Zilin Xiao, Jaywon Koo, Siru Ouyang, Jefferson Hernandez, Yu Meng, and Vicente Ordonez. Proxythinker: Test-time guidance through small visual reasoners. Proc. of Intl. Conf. on Learning Representations, 2026.
LLM Xiaomi, Bingquan Xia, Bowen Shen, Dawei Zhu, Di Zhang, Gang Wang, Hailin Zhang, Huaqiu Liu, Jiebao Xiao, Jinhao Dong, et al. Mimo: Unlocking the reasoning potential of language model-from pretraining to posttraining. arXiv preprint arXiv:2505.07660 2025.
Zhifei Xie and Changqiao Wu. Mini-omni2: Towards open-source gpt-4o with vision, speech and duplex capabilities. arXiv preprint arXiv:2410.11190 2024.
Guowei Xu, Peng Jin, Li Hao, Yibing Song. Lichao Sun, and Li Yuan. Llava-ol: Let vision language models reason step-by-step. arXiv preprint 2411::2411.100 10440, 2024.
Jin Xu, Zhifang Guo, Jinzheng He, Hangrui Hu, Ting He, Shuai Bai, Keqin Chen, Jialin Wang. Yang Fan, Kai Dang, et al. Qwen2. 5-omni technical report. arXiv preprint 2rXiv:2503.202/5. 2025.
An Yang, Anfeng Li, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chang Gao, Chengen Huang, Chenxu L.v, al. Qwen3 technical report. arXiv preprint arXiv:2505.092) 09388, 2025a.
Qize Yang, Shimin Yao, Weixuan Chen, Shenghao Fu, Detao Bai, Jiaxing Zhao, Boyuan Sun, Bowen Yin, Xihan Wei, and Jingren Zhou. Humanomniv2: From understanding to omni-modal reasoning with context. arXiv preprint arXi:2250..1277, 2025b.
Yi Yang, Xiaoxuan He, Hongkun Pan, Xiyan Jiang, Yan Deng, Xingtao Yang, Haoyu Lu, Dacheng Yin, Fengyun Rao, Minfeng Zhu, et al. R1-onevision: Advancing generalized multimodal reason- ing through cross- modal formalization. arXiv preprint arXiv:2503, 10615, 2025c.
Qiying Yu, Zheng Zhang, Ruofei Zhu, Yufeng Yuan, Xiaochen Zuo, Yu Yue. Tiantian Fan, Gaohong Liu, Lingjun Liu, Xin Liu, et al. Dapo: An open-source Ilm reinforcement learning system at scale. Proc. of Advances in Neural Information Processing Systems, 2025.
13

---

Published as a conference paper at ICLR 2026
Renrui Zhang. Dongzhi Jiang, Yichi Zhang, Haokun Lin, Ziyu Guo, Pengshuo Qiu, Aojun Zhou, Pan Lu, Kai-Wei Chang, Yu Qiao, et al. Mathverse: Does your multi-modal Ilm truly see the diagrams in visual math problems? In Proc. of European Conference Computer Vision, pp- 169-186. Springer, 2024.
Shaolei Zhang, Shoutao Guo, Qingkai Fang, Yan Zhou, and Yang Feng. Stream-omni: Si- multaneous multimodal interactions with large seeguage-viiio-speeee model. arXiv preprint arXiv:2506.13642, 2025.
Jiaxing Zhao, Xihan Wei, and Liefeng Bo. Rl-omni: Explainable omni-multimodal emotion recog- nition with reinforcement learning. arXiv preprint urXiv:2503.05379 2025.
Hao Zhong, Muzhi Zhu, Zongze Du, Zheng Huang, Canyu Zhao, Mingyu Liu, Wen Wang. Hao Chen, and Chunhua Shen. Omni-r1: Reinforcement learning for omnimodal reasoning via two-system collaboration. Proc. of Advances in Neural Information Processing Systems, 2025.
Ziwei Zhou, Rui Wang, and Zuxuan Wu. Daily-omni: Towards audio-visual reasoning with temporal alignment across modalities. arXiv preprint arXiv:2505. /7862, 2025.
Linghao Zhu, Yiran Guan, Dingkang Liang, Jianzhong Ju, Zhenbo Luo, Bin Qin, Jian Luan, Yuliang Liu, and Xiang Bai. Shuffle-rl: Efficient rl framework for multimodal large language models via data-centric dynamic shuffle. Proc. of Intl. Conf. on Learning Representations, 2026.
14

---

Published as a conference paper at ICLR 2026
A A DETAILED EXPLANATION FOR THINKOMNI
At each decoding step t, the Omni-modal Large Language Model (OLLM) generates two token probability distributions: conditioned on the full omni-modal input and the textual only input. In input.
contrast, the Large Reasoning Model (LRM) outputs a single distribution based solely on the textual
We would like to decide how much of the next-token decision should be driven by perception (P(") and how much by abstract reasoning (P((). To gauge the disagreement between these signals, we compute two Censen-Shannon divergences
D JS(PR) || P( DØ JS(P || p(t)).
D is large when the reasoner (LRM) wants a different token than the perceiver (OLLM text-only); R
DS is large when masking the multi-modal content changes the OLLM's belief crucial, i.e. when perception.
1. If the current step mainly requires perception (c.g. reading label in the image detecting a sound), masking the modalities hurts the OLLM, so D large while D stays small. Consequently aj 0 and the model trusts the perception logits.
2. the step calls for reasoning (e.g. algebraic manipulation after the visual information is extracted), the text-only LRM disagrees with p(t), so D' dominates and a s 1. The generation is therefore guided by the stronger logical signal from the LRM.
3. For mixed cases, ag smoothly interpolates between the two extremes, allowing the decoder to blend perception and reasoning in real time.
B EXPERIMENT DETAILS
Hyperparameter Settings During our experiments, we used the following inference parame- ters to ensure standard model outputs and to prevent performance degradation and endless rep- ctitions, following (Yang al., 2025a): temperat of 0.6, top-p value of 0.95, repetition. ieeeeiitin..peatt of 1.03, and a max max.new.tokens of 4096. In addition, all LRMs append <think> tag to the end of the prompt during inference ensure the reasoning state is activated (Guo et al., 2025).
Dynamic Weight and Mixing. Given the calculated divergences DR and Dp, we determine the reasoning weight of by measuring the reasoning surplus, clamped to [0, 1]:
a clip (DR Dp, 1.0).
(6)
To ensure stability during the initial phase, apply linear warmup for the first T Twam 5 steps:
of < min (af 0.1
Finally, the mixed logits computed efficiently via the following implementation, which integrates the base, reasoning, and negative distributions:
(2 a.) Mo 0) + of MR(( (T(=2))-M((Re)))
(8)
The datasets used in our experiments include MathVist (Lu et al., 2024), Visin (Wang et al., 2024), MathVerse (Zhang et al., 2024), MMAU (Sakshi al., 2025), Daily-Omni (Zhou et al., 2025) and OmniBench (Li et al., 2024).
Benchmarks Details
Math Vista (Lu et al., 2024): We evaluate on the test-mini split Math (1,000 samples), unified benchmark for mathematical reasoning in visual contexts, which includes three newly introduced datasets (IQTest, FunctionQA, and PaperQA), as well as MathQA and 19 VQA datasets from previous work.
15

---

Published as a conference paper at ICLR 2026
MathVision Vision (Wang al., 2024): We evaluate on the Math Vision dataset (3,040 samples), a curated collection of high-quality mathematical problems with visual contexts. Covering 16 mathematical disciplines and five difficulty levels, MATHVision offers comprehensive and diverse benchmark for assessing the mathematical reasoning abilities of LMMs.
Math Verse (Zhang al., 2024): We evaluate on the test-mini split of Math Verse (3,940 samples), visual math benchmark with 2.612 multi-subbect problems and diagrams. Each issue is annotated into six multi-modal versions, totaling 15K samples, to assess MLLMs' understanding of visual information in math reasoning.
MMAU (Sakshi et al., 2025): We evaluate on the test-mini split of MMAU (1,000 samples), which consists of 10K audio clips with natural language questions and answers across speech, sounds, and music. Covering 12 retrieval and 15 reasoning types, MMAU challenges models with expert-level, domain-specific audio understanding and reasoning.
Daily-Omni (Zhou et al., 2025): We evaluate on Daily-Omni (1,197 samples), which contains 684 videos from 11 YouTube categories and questions requiring integration of audio, visual, and textual information. The benchmark covers 30-second and 60-second videos assess multi-modal reasoning abilities.
OmniBench (Li et al., 2024): We evaluate on OmniBench (1,142 samples), which covers wide range of reasoning and cognitive skills, from perception complex reasoning. Tasks include object recognition, temporal and spatial reasoning, symbolic and quantitative processing, and various audio types, including speech, sound events, and music.
C
FUTURE WORK
THINKOMNI represents an attempt to introduce omni-modal capabilities based on textual reasoning abilities. We plan explore additional modalities, such as 3D point clouds, protein structures, and reasoning applications in image/video generation scenarios. Moreover, we are also curious about "what truly works during the reasoning process", which is of great significance for understanding why reasoning abilities in the textual domain can generalize to a wider range of modalities.
16

---

Published as a conference paper at ICLR 2026
D
MORE CASES
Please answer the following question based on the given image and audio:
where this person in white close might be.
Please choose only one answer from the following options:
A. woman in jail
voman shelter
man court
man police station
Audio content:
(Key to open the door conversation close the door): (1I come back when the paperwork complete.
Answer:
The dim lighting, bare walls, and the presence of a simple bed suggest a confined space typical jail cell. sound effects metal clanging and door slomming reinforce the idea of prison environment. The person's attire is modest, which could indicate prisoner.
While chelters can have similar settings, the specific sound effects and the presence of hed rather than a cot sleeping bag make it less likely, Shelters typically have more varied farwishings and may have the level of confinement.
The setting does not resemble courtnom, which usually has a bench, desks, and more formal attire. The sounds heard do not match those of court proceeding.
A police station night
similar elements, the presence sound effects and the specifie description of the room cell point towards a jail rather than police station. Police stations often have more varied spaces and different types of furniture.
The answer is:
Figure 1I: Omni-modal Reasoning.
Question: What the most likely environment time of day for these sounds?
Choices:
(A) Urban street night
(B) Quiet home interior in the morning
(C) Busy office during lunch hour
(D) Forest trail dusk
ajulp
Audio content:
The audio indudes the sounds of cats and binds, and the ambient sound is relatively open.
Answer:
The eat's meowing, combined with chirping, suggests a qulet setting where both onimals active, which
natural environment like forest trail
during duslt. The presence of biral chirping also indicates daylight, eliminating the possibilit of it being at night or during busy office hour. The quietness of the environment points towards making the forest at dusk environment quiet home interior in the morning.
interion, but the presence of birds suggests an outdoor setting, likely choice. Therefore, the probable
The answer B.
Figure 12: Audio Reasoning.
17

---

Published as a conference paper at ICLR 2026
Question: derivative of fix) xtxx
x
Choices:
(A) larger than
(B) equal to
(C) smaller than
Answer: determine the relationship between derivative of at x-2 and x-5, we need analyze the graph function f(x) 12x 31 1.
Identify the critical point: The function f(s) 12x 31
V-shape, which
it has where expression inside the absolute value changes sign. This occars 2x O, which gives 3/2.
2. Analyze the slope on either side of the vertex:
For <. 3/2, the expression inside the absolute value is negative, so f(x) = -f2x-
+ 1 -2x + 4 -2x 4. The slope here is -2-
For > 3/2, the expression inside the absolute value is positive, so f(x)) - (2x 3)
11
The slope here 2.
Evaluatee the slopes x x=5:
-Atx=2, which greater than 3/2, slope
-A which also greater than 3/2, the slope still
Since the slope (derivative) the same for both x-2 and x-5, the derivative of f(x x-2 is equal to that at x-5.
Therefore, the correct answer is B.
Figure 13: Visual Reasoning.
18


Reviews:
[{"review_id": "9lNXqlB19S", "reviewer": "Reviewer_Xh2x", "summary": "The paper introduces THINKOMNI, a training-free framework designed to enhance the complex reasoning capabilities of Omni-modal Large Language Models (OLLMs). The authors identify that while OLLMs are good at perceiving diverse modalities (audio, video, image), they often lack the deep reasoning abilities found in recent text-only Large Reasoning Models (LRMs). To bridge this gap without expensive training or data annotation, the authors propose a decoding-time strategy with two main components:\n- LRM-as-a-Guide: Leverages off-the-shelf text-only LRMs to guide the OLLM's token generation process via logit fusion.\n- Stepwise Contrastive Scaling: A dynamic mechanism that adjusts the guidance weights at each decoding step based on the Jensen-Shannon divergence between different model distributions. This adaptively balances signals from perception (Omni-modal inputs) and reasoning (textual inputs).\n\nExperiments across six multi-modal reasoning benchmarks (including Math Vista, MMAU, and OmniBench) demonstrate that THINKOMNI consistently improves the performance of base OLLMs (like Qwen2.5-Omni), sometimes matching or exceeding models trained with Reinforcement Fine-Tuning (RFT)", "questions": "See weaknesses.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "- The idea of using a text-only reasoning model to guide an omni-modal model during inference is a clever approach to addressing the scarcity of high-quality omni-modal reasoning data. While guidance decoding exists, applying it specifically to lift reasoning capabilities from one modality to many is a novel application.\n\n- The proposed Stepwise Contrastive Scaling is a technically sound contribution. By using Jensen-Shannon divergence to measure the \"disagreement\" or unique contribution of reasoning ($D_R$) versus perception ($D_P$) at each step, the model avoids manual hyperparameter tuning and adapts to different task types (e.g., audio perception vs. math reasoning) on the fly.\n\n- The framework is entirely training-free, avoiding the high computational costs and data requirements of Supervised Fine-Tuning (SFT) or RFT. This makes it practically valuable for rapidly deploying improved reasoning in existing omni-modal systems.", "weaknesses": "- (minor, ack'ed by the authors) The framework relies on logit fusion, which strictly requires the OLLM and the LRM to share the exact same tokenizer vocabulary. This is a major limitation to the claim of using \"off-the-shelf\" LRMs, as it restricts acceptable pairings to models within the same family (e.g., Qwen-based OLLMs with Qwen-based LRMs).\n\n- Since the LRM never sees the omni-modal input and only sees the text trace, there is a theoretical risk that in highly visual/auditory tasks where the text prefix is ambiguous, the LRM might confidently \"reason\" in a wrong direction based only on text priors. It would be great to study such tricky cases / failure modes.\n\n- While the method beats standard baselines, it would be useful to know if the ~3x compute cost at inference time would be better spent just using a much larger standard OLLM if one is available, rather than guiding a smaller one.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "hESzjGBCDj", "reviewer": "Reviewer_myT4", "summary": "The paper proposes ThinkOmni, a training-free framework that enhances omni-modal reasoning by leveraging off-the-shelf Large Reasoning Models (LRMs) to guide the decoding process of Omni-modal Large Language Models (OLLMs). The method consists of two main components: LRM-as-a-Guide, which uses textual reasoning models to influence OLLM outputs via logit mixing, and Stepwise Contrastive Scaling, which dynamically balances perception and reasoning signals during inference. The approach is evaluated on six omni-modal benchmarks, showing consistent improvements over baseline OLLMs and competitive performance against reinforcement fine-tuned models.", "questions": "- What is the theoretical justification for using Jensen-Shannon divergence to measure disagreement between distributions?  Were other metrics explored?\n- How does the method perform when the OLLM and LRM are from different model families or have significant architectural differences?\n- How does ThinkOmni handle cases where the LRM provides incorrect reasoning?  Is there a risk of propagating textual reasoning errors into omni-modal outputs?", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The proposed Stepwise Contrastive Scaling mechanism is a meaningful contribution, enabling adaptive tuning of guidance weights without manual hyperparameter search.\n- Extensive experiments across six benchmarks demonstrate the generality and scalability of the method.\n- The framework is model-agnostic and can be applied to various OLLM and LRM combinations, enhancing its potential impact.\n- The idea of leveraging pre-trained LRMs to enhance OLLM reasoning without additional training is novel and practical, especially given the high cost of omni-modal fine-tuning.", "weaknesses": "- The approach requires external LLMs to guide the response. This led to doubts about where the performance gain comes from. Does LLM introduce extra information for solving the question? \n- Although training-free, the approach incurs non-trivial inference overhead due to multiple forward passes per decoding step, which may hinder real-time deployment.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "EP3kkciRXZ", "reviewer": "Reviewer_kNcB", "summary": "This paper proposes the ThinkOmni framework, which aims to enhance the reasoning capabilities of multimodal large language models (OLLMs) by utilizing off-the-shelf large reasoning models (LRMs). By introducing two innovations—LRM-as-a-Guide and Stepwise Contrastive Scaling—ThinkOmni can flexibly balance perceptual and inference signals without additional training, thereby improving multimodal reasoning performance. Experimental results show that ThinkOmni achieves significant performance improvements on multiple benchmark tasks, outperforming existing models that rely on reinforcement fine-tuning.", "questions": "Please see weakness.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 4, "presentation": 4, "contribution": 4, "strengths": "1. Innovative Framework: The paper proposes ThinkOmni, a training-free, decoding-time method that cleverly integrates a Large Reasoning Model (LRM) into an omni-modal LLM (OLLM) pipeline. The LRM-as-a-Guideand Stepwise Contrastive Scaling mechanisms are novel and well-motivated.\n2. Strong Empirical Results: The method shows consistent and significant improvements across six diverse omni-modal reasoning benchmarks. The gains are particularly notable given that no additional training is required.", "weaknesses": "1. Limited Focus on Math Domain:  The benchmark is currently restricted to the math domain at the image-level. It would be beneficial to expand the experiments to include perception-level tasks, such as MME, as well as reasoning benchmarks like MMLU, to provide a more comprehensive evaluation.\n\n2. Confusion Regarding Figure 4: There is some confusion regarding the notation in Figure 4, specifically the terms \\( x_{<t} \\) and \\( x_{<t+1} \\). These appear to suggest a multi-turn conversational setup, which warrants further clarification.\n\n3. Minor Performance Gain Relative to ProxyTuning:  The observed performance gain is relatively minor when compared to ProxyTuning, which calls for a deeper analysis of the effectiveness of the proposed approach in this context.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "qUxJqtJ5QT", "reviewer": "Reviewer_2VXa", "summary": "The paper introduces ThinkOmni, a training-free framework designed to enhance the reasoning capabilities of Omni-modal Large Language Models by leveraging the strengths of text-based Large Reasoning Models (LRMs). The core problem is that while OLLMs excel at perceiving diverse inputs , they lack the advanced reasoning skills of specialized LRMs. Bridging this gap usually requires costly fine-tuning or reinforcement learning, demanding large datasets and significant computation. It doesn't need training by using an LRM to guide the OLLM's decoding process at inference time. Experiments show ThinkOmni significantly boosts the performance of OLLMs on challenging multimodal reasoning benchmarks.", "questions": "1. It's unclear how the framework robustly handles situations where the OLLM's direct perception strongly contradicts the LRM's reasoning based on the potentially lossy text representation.\n\n2. How significant is the increase in inference time and computational cost when using ThinkOmni compared to running the base OLLM alone?\n\n3. How does the framework handle tasks where the crucial reasoning step relies heavily on non-translatable visual, auditory, or temporal patterns (e.g., recognizing subtle visual defects, interpreting musical harmony, understanding complex physical interactions in a video)? Can specific examples of failure cases due to information loss in text conversion be provided?\n\n4. What happens when the guiding LRM makes a fundamental reasoning error? Does the Stepwise Contrastive Scaling mechanism effectively detect and mitigate the impact of incorrect guidance, or can it lead the OLLM further astray?\n\n5. How sensitive is the performance to the quality of the text summarization/description of the omni-modal input fed to the LRM? Who generates this text, and how is its fidelity ensured?\n\n6. It mentions \"maintaining decoding efficiency\" (line 104). How is this efficiency achieved when two large models are involved in the decoding loop?\n\n7. How does ThinkOmni perform on tasks requiring common-sense reasoning grounded in multimodal contexts, rather than specialized mathematical or logical reasoning?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. It avoids the need for expensive data collection, annotation, and computationally intensive fine-tuning or RL, because of its training-free nature.\n\n2. it effectively combines the perceptual strengths of OLLMs with the reasoning strengths of state-of-the-art LRMs in a modular way.\n\n3. it demonstrates substantial performance gains on difficult benchmarks, showing the practical effectiveness of the approach.", "weaknesses": "1. running both an OLLM and an LRM during inference will inevitably increase computational cost and latency compared to running a single model, even if optimized. \n\n2. the framework's performance ceiling is inherently tied to the capabilities of the chosen LRM. Errors or limitations in the LRM's reasoning will directly impact the guided OLLM.\n\n3. the LRM guide operates on a textual representation of the omni-modal input. Complex non-textual information (e.g., intricate spatial relationships in an image, temporal dynamics in a video, nuances in audio) might be lost or distorted in this conversion, limiting the LRM's ability to provide accurate guidance.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)