TableDART: Dynamic Adaptive Multi-Modal Routing for Table Understanding
Authors: Xiaobo Xing
Wei Yuan
Tong Chen
Quoc Viet Hung Nguyen
Xiangliang Zhang
Hongzhi Yin
Venue: ICLR 2026 Poster (2026)
Keywords: Table Understanding
Large Language Models
Primary area: other topics in machine learning (i.e., none of the above)
TL;DR: This paper provides a training-efficient table understanding framework via dynamically modeling tabular data from multimodal perspectives by reusing existing pretrained single-modality models.

Abstract: Modeling semantic and structural information from tabular data remains a core challenge for effective table understanding. Existing Table-as-Text approaches flatten tables for large language models (LLMs), but lose crucial structural cues, while Table-as-Image methods preserve structure yet struggle with precise semantics. Recent Table-as-Multimodality strategies attempt to combine textual and visual views, but they (1) statically process both modalities for every query-table pair within large multimodal LLMs (MLLMs), inevitably introducing redundancy and even conflicts, and (2) depend on costly fine-tuning of MLLMs.  In light of this, we propose TableDART, a training-efficient framework that integrates multimodal views by reusing pretrained single-modality models. TableDART introduces a lightweight 2.59M-parameter MLP gating network that dynamically selects the optimal path (Text-only, Image-only, or Fusion) for each table–query pair, reducing redundancy and avoiding conflicts that arise when textual and visual views of the same table provide inconsistent cues. By routing to the most appropriate view, our framework improves both accuracy and efficiency. In addition, we propose a novel agent to mediate cross-modal knowledge integration by analyzing outputs from text- and image-based models, either selecting the best result or synthesizing a new answer through reasoning. This design avoids the prohibitive costs of full MLLM fine-tuning. Extensive experiments on seven benchmarks show that TableDART establishes new state-of-the-art performance among open-source models, surpassing the strongest baseline by an average of 4.02%. The code is available at: https://github.com/xiaobo-xing/TableDART.

Published as a conference paper at ICLR 2026
TABLEDART: DYNAMIC ADAPTIVE MULTI-MODAL ROUTING FOR TABLE UNDERSTANDING
Xiaobo Xing", 1 Wei Yuan',, Tong Chen', Quoe Viet Hung Nguyen', Xiangliang Zhang?
Hongzhi Yin
'he University of Queensland, Australia
Griffith University, Australia
University of Notre Dame, USA
xiaobo. xing. w. yuan,
cong.chen
h. yini Yuug edu. au
henry way..nnuuynne... @ @gr frith. edu.au,
xzhamg33end.cc
ABSTRACT
Modeling semantic and structural information from tabular data remains core challenge for effective table understanding. Existing Table-as- Text approaches flatten tables for large language models (LLMs), but lose crucial structural cues, while Table-as- Table-as-Image methods preserve structure yet struggle with precise seman- ties. Recent Mubllias-Muliimodalit strategies attempt combine textual and vi- sual views, but they (1) statically process both modalities for every query-table pair within large multimodal LLMs (MLLMs), inevitably introducing redundancy and even conflicts, and (2) depend on costly fine-tuning of MLLMs. In light of this, we propose TableDART, saaiiin--ffficinn framework that integrates mul- timodal views by reusing pretrained single- gedgalty models. TableDART intro- duces a lightweight 2.59M-parameter MI.P gating network that dynamically se- lects the optimal path (Text-only, Image-only, or Fusion) for each table Shble-qurry pair, reducing redundancy and avoiding conflicts that arise when textual and vi- sual views of the same table provide inconsistent cues. By routing to the most appropriate view, our framework improves both accuracy and efficiency. In ad- dition, propose novel agent mediate modsl knowledge integration by analyzing outputs from text- and mmage-basee models, either selecting the best result or synthesizing new answer through reasoning. This design avoids the pro- hibitive costs of full MILM fine-tuning. Extensive experiments on seven bench- marks show that TableDART establishes new state-of-the-ane performance among open-source models, surpassing the strongest baseline by an average of 4.02%. The code is available https///giiaabccm///oooooo/////a
INTRODUCTION
Tabular data is of the most ubiquitous formats in the real world, with applications spanning finance (Chen et al. 2021 Zhu et al. 2021 2024b), healthcare (Shi et al., al.] 2024 Wang et al. 2024), and many other domains that rely relational databases and spreadsheets (Fey al. 2024 Gao ctt al. 2026). Despite its prevalence, effectively modeling tabular data remains a long-standing chal- lenge, due to its unique characteristics such as heterogeneity of diverse data types across columns, permutation invariance regarding row and column order, and hierarchical structure of multi-level or
nested headers and indices (Fang et al.) (2024) Borisov et al. 2024| Chen al., 2023).
Existing approaches to tabular data understanding can be broadly categorized into two paradigms. The first is the "Table-as- Text" paradigm, where tables are linearized into sequences and processed by large language models (LLMss (Zha et al. 2023, (Su et al. (022)). While effective, this approach is sensitive serialization choices (Sui et al. 2024) and inevitably loses critical structural information (Deng et al.] 2024). The second paradigm treats TTale-as-Image
where table screenshots are processed with vision models. This approach better preserves structural information but struggles to capture precise and aligned semantic meaning (Deng al., 2024).
CCo-irst authors with equal contributions.
1 Corresponding author.

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Published as a conference paper at ICLR 2026
To combine the complementary strengths of these paradigms, recent research has moved toward "Table-as-Muuee "M-Muimmoddlli""" where tables are represented in both textual and visual forms and pro- cessed via fine-tuned multimodal LLMs (MLLMs) (Liu et al. 2025). While promising, this MLLM- based paradigm suffers from two key limitations that hinder both performance and practicality. First, regardless of the context, they mandate both modality views for every table-query pair. However, not all queries benefit from multiple views, as integrating multiple views often introduces redun- dancy and, in some cases, conflicting signals. For example, textual linearization may inadvertently impose row-order sensitivity (Fang et al. 2024). while tables in their original or visual form remain cormutationninvariann (Wu et al. 2025). Such redundancy and conflicts mislead the MLLM's table understanding, resulting in suboptimal performance. Second, existing approaches typically rely on heavy MILMM, which remain computationally prohibitive to adapt at scale even with parameter- efficient fine-tuning strategies (Hu et al. 2022), limiting their practicality under constrained training budgets and deployment pipelines (Yin et al. (025).
In this paper, propose TableDART (Dynamic Adaptive multi-modal RouTing), a novel frame- work for efficient and effective multimodal table understanding. Unlike prior work, TableDART does not naively combine all modalities' perspectives. Instead, it introduces a lightweight MLP gat- ing network that dynamically selects the optimal processing path (either Text-only, Image-only, or Fusion) for each table-query pair depending on the instance's complexity and resource efficiency. When fusion is selected, a powerful L.M agent integrates the outputs of both single-modality ex- perts, serving either an arbitrator (selecting the better output) or as a rescuer (generating an enhanced answer by reasoning over both sources). Notably, TableDART reuses existing single- modality experts (Text-only and Image-only), requiring training only for the gating network. This makes it significantly more teining-efffiientt than fine-tuning MLLMs. We validate TAR with extensive experiments on seven diverse benchmarks. Results show that TAbIeDART not only achieves state-of-ther -frte-rr---- performance among open-source models, surpassing the strongest MLLM- based baseline by an average of 4.02% accuracy, but also provides new insights into dynamic routing policies for multimodal table understanding.
In summary, our main contributions are:
(a) We propose TableDART, novel framework that adaptively selects the optimal processing path for each query to make the most of modality-specifi features for table understanding.
(b) We introduce a parameter-effictee frozen) single-modaliy experts by learning to route over experts with different output vo- cabularies, making the lightweight gating network (2.59M parameters) the only trainable part. An LLM agent is designed to further facilitate cross-modal consensus, enabling plug-and-play setup without fine-tuning any LLM or MLLM backbones.
training paradigm that fully leverages pretrained (and
(c) We conduct extensive experiments and establish new ofte--of-the-art performance on multi- ple benchmarks. addition, we provide an in-depth analysis of the learned routing policy, demonstrating its strong generalizability and effectiveness.
2 RELATED WORK
Unimodal Approaches for Table Understanding. A dominant paradigm for applying LLMs to labular data is the Table-as-Text approach, which serializes tables into linear text sequences. Early work such as TaPas (Herzig et al. (2020) demonstrated its effectiveness, inspiring specialized tabular models like TableLlama (Zhang et al. 2024) and TableGPT2 (Su et al. 2024), which achieve state- of-the-art results for table understanding. However, these models remain constrained by their input modality, as they cannot capture the rich visual and structural semantics inherent in the original table format (Deng et al. 2024). To overcome this limitation, the Table-as-Imaae paradigm has emerged, leveraging Multi-modal Large Language Models (MLLMs) to directly process table images. Pio- neering works such as Table-LLaVA (Zheng et al. 2024). TabPedia (Zhao et al. 2024), SynTab- LLaVA (Zhou et al. 2025), and generalist MLLMs like Ovis2 (Lu et al. 2024) and Qwen2.5-VL Bai et al. (025) showcase the potential of modeling visual layouts and structural cues, aligning with broader trends in vision-centric text understanding Yuaan et al. 2026 Qiao et al. 2026). Yet, this paradigm is not a universal solution, as its effectiveness diminishes for tasks where reasoning
relies heavily on text-centric pretraining knowledge (Deng et al. 2024).
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Published as a conference paper at ICLR 2026
Input Context
Multimodal Encoding
Gating Network
Dynamic Inference Pathways
Question:
Text-enly Path
park
manager up
Query Enceder
Table-ii- Text Decoder
number of ride tikkets
sold the post How many annusement part sell total on Thursday and Sunday?
Image-only Path
Sevialize Text Toble: Etl Nummeer
Table as image Decoder
1nnf>-//-//n ini Thursday
Enceder
2,952 Vol Fridyy| 7,807
Hlai Soturday, 6,290 Iin
Fusion Path
Sunday 7,784
Table-as-Tod
Tickets sold
LLM Ageat As Arbitator Rescuer
Day
Number tickets
Table-at-Inagge Enceder
Thuntsey
8.912
Table-as-lmag Decoder
7,807
Sunday
7,784
arg maggg
Figure 1: Architecture of TabIeDART. The framework operates in three main stages: Multimodal Encoding (Section 3.2). Gating Network (Section 3.3) and Dynamic Inference Pathways (Sec-
tion 3.4p.
Multi-modal Approaches and Dynamic Routing. Building the complementary strengths of unimodal representations, Vable-as-Multimddaliy approaches have emerged. For example, HIPPO (Liu et 2025) fine-tunes an MLLM jointly process both text and image representations of the table, achieving strong performance gains. However, these MLLM-based methods typically adopt a static, one-size-fin see-siizefit--ll fits-all strategy, applying both modalities every table-query pair. This rigid design can introduce redundancy or even conflict, particularly when single modality would suffice. To address this gap, we propose TAbIeDART, which introduces a dynamic routing mechanism that adaptively selects the optimal modality path for each instance. By learning intelligent, instance- level policy, TableDART enables more efficient and robust multimodal reasoning, moving beyond the limitations of prior paradigms.
3 METHODOLOGY
3.1 OVERVIEW
The key novelty of TableDART lies in its ability to dynamically and efficiently leverage existing single-modality models capture complementary information from tabular data for downstream tasks. To this end, TableDART integrates five components: (1) Table-as-Text model Mr, (2) Table-as-Image model M,, (3) a text embedding model to process queries, (4) a lightweight gating network, and (5) an LLM-based agent responsible for multimodal fusion. Among these compo- nents, only the gating network is trainable, ensuring that TableDART remains highly parameter- and efficient. wraniig--effcieent subsequent sections.
training-
Figure [ provides overview of TableDART's workflow, which is detailed in the
3.2 ENCODING TABLE- TABLE-AS-UULTIMONAA MULTIMODALITY
Given a raw table and query q. TableDART leverages existing models to encode them from complementary modality perspectives, thereby capturing both semantic and structural information.
This achieved through a parallel feature extraction pipeline with three concurrent streams: (1) Table-as-Text encoding: The raw table T is serialized into text and processed by the encoder & of the Table-as as- Text model My, yielding feature vector e₁- (2) Table-as-Image encoding: A screenshot of T is fed into the encoder &, of the Imaae model M.. producing e,, (3) Query encoding: The query is transformed into an embedding e, using a text embedding model Ege We apply modality-specift pooling to convert encoder outputs into fixed-dimensional vectors,
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which are concatenated to form unified multimodal representation for the gating network:
C₁ = & (((eriaaiiee
(Screenssoot())
(1)
Cy [g)
Cq. Cr, cyl
Here, x serves as the multimodal joint representation by concatenating the three embeddings, which is subsequently passed into the gating network to determine the path of dynamic inference. Note that & and & only activate small part of the corresponding expert models, constituting a minor computational overhead (i.e.. 7.15% of the Table-as-Texs model's 's and 7.63% of the Table-as-Imas model's s total parameters). Detailed analyses of the embedding construction and parameter break- down are provided in Appendix 1.3.1
3.3 GATING NETWORK AND POLICY TRAINING
As discussed in the introduction and confirmed by our experiments, not every table-query pair ben- efits from fusing both modalities, this introduce redundant address this, TaR employs lightweight gating network that dynamically selects the infer- ence path. This design both maximizes the effective use of information and reduces computational cost compared to always invoking all modality models.
conflicting signals. To
Formally, the gating network G is implemented by lightweight MI. with few trainable parameters (see Appendix forr architectural details). It takes as input the multimodal representation x from Eq. [ and outputs the raw logits z for each processing path (Text-only, Image-only, or Fusion):
z (xx)
(2)
To ensure that G selects the most appropriate strategy for each table-query pair, balancing both predictive accuracy and resource efficiency, we design the following training objective:
Lotal
Lusk + Aresouree
(3)
where À controls the strength of the resource regularization.
The task loss Lusk encourages the gating network to prioritize paths with strong empirical perfor- mance. For each training instance, pre-compute vector of binary scores (81, 83) (0.1)3 where each component 8& if the k-th inference path produces a correct answer, and otherwise. This method naturally handles instances where multiple paths are correct, each suc- cessful path receives a score of 1. To form the training signal, this score vector is converted into soft target probability distribution using temperalure--onntollled softmax. Similarly, the gating network's 's raw logits are converted into a predicted distribution. The task loss then minimizes the KL divergence between these two distributions:
Lusk- (L(softaax(s/7) softmax(z/TT))
where Tg: are temperature parameters, We use the standard forward KI. divergence, which encour- ages the gating network to assign probability to all empirically successful paths.
Beyond empirical performance on table understanding, we also account for inference overhead. To prevent over-reliance on costly strategies, we introduce a resource-aware regularizer:
Cresouree softmax(z/T,)"*.
(5)
where c an empirically measured cost vector for each inference path. By computing the expected cost of the routing policy, the resource loss Lresource penalizes high-cost inference routes, especially on simple tasks where Lusk sees minimal improvements from over- over-complex paths. As such, guides the gating policy toward more balanced choices, alleviating the information redundancy problem. The full training procedure, including the detailed protocol for measuring the cost vector c, is pro- vided in Appendix A.5

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3.4 ADAPTIVE INFERENCE
The adaptive inference process begins with deterministic routing decision guided by the trained gating network. For each query-table pair, the network produces a vector of raw logit scores, and the framework selects the single, optimal path corresponding to the highest score for execution. Once the path is selected, inference proceeds accordingly. TableDART supports three inference paths: Text-only, Image-only, and Fusion. The first directly reuse existing kinge-modality ex- pert models: the Table-as-Ted Text model M, and the table-as-Image model M.. If the gating network selects either, TableDART simply continues the inference process from the intermediate represen- tation of the chosen model. For example, when the gate selects the Text-only path, TableDART forwards M2's 's remaining layers using the intermediate encoding e₁ to produce the final output.
For the Fusion path, we design novel, training-fiee ffren LLM-based Fusion agent that synthesizes the final answer by reasoning over the outputs of the two single-modality experts, M, and Mg. Specifically, when the gating network selects the Fusion path, both M, and M, are at first executed to generate results ra and Te, along with their auxiliary outputs a₂ and ag.. These, together with the original table T, are passed to the Fusion agent. The Fusion agent operates in two possible roles depending on its reasoning process: (1) Arbitrator: M, and M, produce conflicting results, the agent resolves the disagreement, similar in spirit to aggregating consensus from multiple sources (Hung et al. (0118), by selecting the more reliable answer according to its confidence.
(2) Rescuer: If the agent believes both models provide uncertain low-confidence outputs, it synthesizes a new, more accurate answer by jointly reasoning over their partial evidence. A detailed description of the Fusion agent's implementation and prompt design is provided in Appendix A.4
4 EXPERIMENTS
4.1 EXPERIMENTAL SETUP
Evaluation Datasets and Metrics. We evaluate TableDART on seven diverse benchmarks across two tasks: Table Question Answering (TQA) and Table Fact Verification (TFV). For TQA, which requires interpreting complex queries (Ren et al. 2020 202T to perform grounded reasoning over tabular evidence. we use five benchmarks: WTQ (Pasupat & Liang 2013), TABMWP (Lu et al.) 2023). TAT-QA (Zhu et al. 2021), HiTab (Cheng et al.) 2022), and Fe TaQA (Nan et al. 2022). For TFV, we use TabFact (Chen et al. 2020) and InfoTTabs (Gupta et al. 2020). Following the established protocol (Zheng et al.) 2024 Liu al., 2025). we use Accuracy for WTQ, TABMWP, TAT-QA, HiTab, TabFact, InfoTabs, while using BLEU score for FeTaQA as it is generative free-form sponse task. Detailed statistics and further experimental setup details are provided in Appendix A.5
Baselines. We compare Tab against comprehensive set of baselines, including (1) Table- as- Text models: Llama- 2-7B (Tovronn et al. 2023). L Instruct-8B (Dubey et al. 2024),
TableLlama-78 (Zhang al. (2024). and TableGPT2-78 (Su al. (024): (2) Table-as Image mod-
els: MiniGPT-4-7H (Zhu et 2024a), mPLUG-OWI-7B (Ye et al. | mPLJG-OWI1277B (Ye
eet al. 2024
LLaVA vl.5-7B (Liu et al. 2024). Table-LLa VA-7B (Zheng et al. 2024), Qwen-VL.
7B Bai et al.
2023). InternI M-XXMMposcr22222 7B (Zhang et al. 2023). Monkey- 7B (Li al. 2024).
TabPedia-7B 7B
Zhao al. 2024). SynTab-LLaVA-1
(Zhou al. 2025). MiniCPM-
(Yao
ct al. 2024).
Qwen2.5- LLBBB (Bai et al., 2025). and Ovis2-8B (L et al. 2024); (3) Table-as-
Multimodality models: HIPPO-8B (Liu al. 2025) and Google Gemini 2.0 Flash (Comanici al.
2025).
We employ TableGPT2-7B and Ovis2-8B as TableDART's primary single-modality models. To demonstrate generalization, we additionally evaluate a variant by replacing the Table-as-Image model Ovis2-8B with Qwen2.5-VI-7B while retaining TableGPT2-77B, We use Google Gemini 2.0 Flash to implement the Fusion agent. Comparing TabieDART against these base models directly demonstrates the effectiveness of our framework. Appendix [A.5.2 provides a detailed rationale for model selection and clarifies the sourcing of baseline results adopted from previous studies. Notably, TableDART is a general framework that can be seamlessly integrated with a wide range of existing
LLMs, VLMs and MILMs.
Implementation Details. Our training set is 10,000-sample mixture constructed by sampling from five diverse table understanding benchmarks, following the protocol of prior work (Liu al. 2025)
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Table Main performance comparison on seven benchmarks for Table Question Answering (TQA) and Table Fact Verification (TFV). The Average column shows the mean accuracy across all bench- marks that use Accuracy as the evaluation metric. The best and second-best results are marked.
IQA
TFV
Summary
Method
WTQ
TABMWP
TAT-QA (Acc.)
HiTab (Acc.)
FeTaQA (BLEU)
TabFact (Acc.)
InfoTabs (Acc.)
Average (Acc.)
(Acc.)
(Acc.)
Constiment Models of TobleDART LableGiP1-77B (Test-only Path) Ovis2 8B (Image only Path)
58.76
87.00
47.67
68.59
34.70
80.80
74.11
69.49
Toble-as-Tett Bavelines
Llama 2:7B
16.39
22.82
13.73
10.72
10.93
9.20
38.92
18.63
Llama3-Instruct-BB
TableLLamu 7B
24.97
10.10
19.04
46.57
38.38
79.37
46.57
37.77
Table-ss-Imuge
MiniGPT-4-7B
0.90
0.22
0.13
0.20
0.39
0.00
0.10
0.26
mPLUGG-OW-77
1.76
0.13
0.25
7.42
7.46
5.53
mnPLUG-Ow12-7B
0.67
6.83
0.39
0.13
11.91
8.21
26.19
7.07
L.LVV v1.5
1.24
6.05
2.97
2.03
8.24
18.90
28.31
9.92
Table-LLaVA-7B
Qwen-VLL.7B VI.
0.09
3.30
0.13
0.06
0.45
1.12
0.65
0.89
Intern TDeemmMMXCoompse222211
0.26
Monkey-7B
19.07
13.26
12.31
6.41
3,41
22.56
22.11
15.95
IabPedia-7B
23.53
10.66
13.08
6.54
14.31
35.49
2.43
15.29
SynTab-LLaWA-78
39.59
88,30
51.94
35.66
35.45
70.78
69.42
$9.28
MiniCPM-V-2.6-8R
47.97
83.68
51.55
56.53
32.68
78.48
73.03
65.21
Qwee2.5-VL-7B
75.81
Table-as Maliimodiii (MLLM based) Baselines
HIPPO-8B
55.77
60.75
63.00
33.18
82.27
75.74
Gemini 2.0 Flash
63,56
46.29
35.62
60.41
10:57
81.33
$4.31
56.92
Table as-
Miole--s-Mutimadaatl Multimodality (Dynamic Adaptive Roating!
TableDART (TableGPT2 7B Qwen2.5 VL_77) VL-7B)
69.29
72.61
59.07
71.13
29.87
77.94
71.46
70.25
TableDART (TableGPT2-7B Ovic2-8B)
70.58
84.54
62.05
74.37
36.11
81.37
76.22
74.86
We train only the lightweight MLP gating network while keeping all large I.IM models frozen. The complete details regarding our data construction, hyperparameter settings, and computational environment are provided in Appendix A.5
4.2 EFFECTIVENESS ANALYSIS
State-of-the-Art Performance. Table ! shows the comparison results of TableDART with base- lines. From the results, we can observe that: (1) Compared to single-modality based methods, TableDART consistently achieves superior or highly competitive results, including surpassing its constituent models like TableGPT2-7B and Ovis2-8B, demonstrating that the framework success- fully combines their strengths to achieve result neither could reach alone. (2) Compared to multi- modality based methods, TableDART's dynamic routing mechanism proves more effective than MLLM-based HIPPO, outperforming it five of the seven benchmarks.
(3) TableDART outper- forms the powerful Fusion agent model (Google Gemini 2.0 Flash), confirming that the performance gains driven by the intelligent routing mechanism, not just the capacity of the Fusion agent's backbone. (4) TableDART demonstrates strong generalization across backbone models. When in- stantiated by replacing the Table-as-Image model with Sowen2.5VVL-7. while retaining TableGPT2- 7B, the framework achieves a substantial +7. 14.14% average accuracy gain over the single-modality Qwen2.5-VL baseline, validating its model-agnostic effectivvneess. Furthermore, the 'Average' col- umn summarizes the average performance, showing that TableDART achieves the strongest results among all baselines, surpassing the best multi-modality tabular model, HIPPO-SB, by a decisive +4.02% in average accuracy and +2.93 points in BLEU score.
Zero-Shot Generalization Performance. To assess TableDART's generalization capability, we compare it with the strongest MLLM-based baseline, HIPPO-8B, in a zero-shot setting on unseen datasets excluded from training. As shown Table TableDART demonstrates superior generaliza- tion. It achieves + +18.05 % accuracy improvement and +2.93 BLEU score gain over HIPPO-8B Notably, Tab maintains nearly identical performance on seen (74.95%) and unseen datasets (74.37%), whereas HIPPO-88 8B suffers sharp drop from 72.41% to 63.00%. This substantial advan- tage highlights that TableDART learns a genuinely generalizable routing policy, capable of adapting tables rather than overfitting to the training distribution.

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Table 2: Generalization performance on seen versus unseen datasets. Seen datasets were included in the training mixture, while unseen datasets were excluded from training.
Seen Datasets (Avg. Acc.)
Unseen Datasets (Avg. Acc. / /BEUU)
Method
HIPPO-8B (Best MLLM-buxed Baseline)
72.41
63.00 / 33.18
TableDART
74,95
74.37 / 36.11
Improvement
+3.51 %
+18.05% / 42.93 pts
4.3 EFFICIENCY ANALYSIS
Training Efficiency. TRT demonstrates exceptional training efficiency rooted its archi- tectural design. Our framework freezes all large constituent models, requiring only the training of lightweight MLP gating network with just 2.59M parameters. This contrasts sharply with MLLM- based approaches like HIPPO, which fine-tune their 8B backbone model using LoRA (Hu et al.) 2022). For their model architecture and LoRA configuration. the total number of trainable parame- ters reaches 25.87M, meaning that HIРРО trains nearly 10 times more parameters than our entire framework. This vast difference highlights the profound training efficiency and the plug-and-play scalability of our modular design.
Inference Efficiency. To quantify the practical benefits of dynamic adaptive routing, benchmark TableDART's optimal configuration (A = 0. 15) against a Non-Adaptive Fusion baseline that pro- cesses every instance via the full multimodal pipeline. Both settings use identical backbone models and Fusion agents, ensuring a fair comparison to isolate the efficiency gains of our adaptive policy. The detailed evaluation protocol is in Appendix [A.6] The results, presented in Table demonstrate substantial efficiency gains. For example, TableDART achieves an average reduction of 24.5% in latency compared to the Non-Adaptive Fusion baseline, decreasing the mean inference time from 2.92s to 2.20s per sample. These improvements are direct consequence of TableDART's learned routing policy, which reserves the computationally expensive Fusion path for instances that truly re- quire multimodal reasoning, while assigning simpler cases to the more efficient unimodal paths. As shown in Appendix[A.7] and Appendix B.3] the model routes multimodally complex instances the Fusion path while assigming simpler ones to the appropriate unimodal path. In particular, Figure 6 shows that 34.8% of TAT-QA instances cannot be solved by either single-modality model alone, and Figure [0] confirms that the learned policy routes 88.7% of these instances to the powerful Fusion path. In contrast, for simpler datasets such as TABMWP, where single-modality models are highly effective, the routing policy assigns 97.2% of the instances to the more efficient Image-only path. Overall, TableDART's inference efficiency derives from its intelligent dynamic routing mechanism, which preserves effectiveness while substantially reducing unnecessary computational cost.
Table 3: Inference efficiency for TableDART with its Dynamic Adaptive Routing policy versus the Non- Adn-Aidatiiee Fusion baseline. TPS is short for Tokens per Second. "I" indicates lower is better, while "t" indicates higher is better.
Dynamic Adaptive Routing
Non-Adaptive Fusion
Dataset
Latency (s) +
TPS t
Latency (s)
TPS 1
WTQ
2.39
8.90
2.84
1.33
TABMWP
1.81
26.09
2.53
1.04
TAT-QA
2.35
22.36
3.17
2.22
HiTab
2.56
13.97
3.19
1.70
FeTaQA
1.93
18.98
2,99
11.11
TabFact
2,21
16.48
2,91
4.43
InfoTabs
2.17
17.60
2.78
5.09
Average
2.20
17.77
2.92
3.85
4.4 ANALYSIS OF EACH INFERENCE PATH
To better understand the contribution of each inference path in TableDART, we categorize instances based whether the single-modality Fusion paths could produce correct solution. The statis-

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(a) Overall Model Analysis Across Datasets
(b) Dataset-specific Model Analysis
Only Text Correct
(Onl Fusion Correct)
Synergistic Rescue
28.1%
Complemet farily iele (S)
20
2.4%
Unsolved (All Paths Fall)
25-24.15
Synwey Succwe Raie (N)
Only Image Correct
6.8%
14.9%
17.25
20
Mate
15
$0.15
5.1%
58.7%
1.7
Both Mingll-Modality Correct
wra
TABMWWP
TATCH
PeTadd
Inlo Toba
Datavt
Figure 2: Performance amalysis of inference paths. (a) The chart counts instances based on which path(s), if any. produced a correct answer, across all datasets. (b) A per-dataset analysis of two key metries: the Complementarity Rate, which the percentage of instances where correctness is achieved by only one of the two single-modality models, and the Synergy Success Rate, which measures the fraction of hard cases (instances where both single-modality models fail) that are suc- cessfully resolved by the Fusion path.
tics in Figure provide three key insights. (1) Unimodal paths have complementary performance, as 24.0% of test instances fall into a complementarity zone where only single-modality paths gen- erate correct answers (17.2% for the image-only path vs. 6.8% for the text-only path) shown in Figure aa). This underscores the importance of maintaining distinct single-modality inference paths. Moreover, the complementarity rate varies across data sets according Figure (b)). high- lighting the need for instance-specific dynamic routing. (2) Hard cases, where both single-modality inference paths fail, account for 17.3% of the test set. While 14.9% of these remain unsolved, the Fusion path successfully rescues an additional 2.4% (Figure 2p). This capability is further reflected in the Synergy Success Rate (i.e., the proportion of hard cases resolved by Fusion), which averages 14.0% across benchmarks (Figure ( These results confirm that our Fusion agent is not only an Arbitrator between modalities but also a Rescuer, capable of synergistic reasoning beyond the limits of individual models. (3) Easy cases where both unimodal inference paths succeed make up the majority of the data, accounting for 58.7% the test set (Figure 24). For this substantial portion, multimodal fusion is unnecessary,
either single-modaliyy path suffices to produce the correct an- swer. This result reinforces our core motivation for dynamic adaptive routing, rather than defaulting to multimodal fusion in all cases.
4.5 ABLATION STUDY ON KEY COMPONENTS
Impact of Dynamic Gating, We ablate the routing strategy to validate our dynamic approach by comparing it with two non-adaptive baselines in Table 4 The results first confirm the effective- ness of our dynamic strategy, as TableDART significantly outperforms the Random Routing base- line. Compared with the Non-Adaptive Fusion baseline, which uses the same backbone models as TableDART but applies brute-force strategy where every instance is routed through our LLM Agent-based Fusion path, our method not only achieves comparable results on most benchmarks but also delivers clear gains on datasets such as TABMWP and HiTab. This indicates that TableDARTss effectiveness is not solely attributable to the strong Fusion agent, but arises from the dynamic routing mechanism itself. As shown in Figure T0 the learned policy routes most instances to the appropri- ate unimodal paths and uses Fusion only when needed. In many cases, a single modality already provides sufficient information, and forcing unnecessary multimodal processing introduces extra cost and potential noise, which is why TableDART surpasses the Non-Adaptive Fusion baseline on several datasets. Overall, these observations demonstrate the balance of resource-efficiency and performance- -efrrancceeeeeeeieeeessssee of our dynamic routing design.
Impact of Resource-Aware Objective (A). We analyze the effect of the resource loss weight X, highlighting its two key roles in shaping the final routing policy. (1) Effective Routing Policy Con- trol. As shown in Figure X directly regulates the framework's behavior. For instance, reducing its
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The best and Published as a conference paper at ICLR 2026
TQA
TFV
Method
WTQ
TABMWP
TAT-QA
HiTab
FeTaQA
TabFact
InfoTabs
TableDART w/ Random Routing
65.40
75.50
58.94
70.49
30.87
79.50
69.57
TableDART w/ Non- -on-Aiaptive Fusion
70.97
81.47
63.34
73.35
34.82
81.56
76.83
TableDART (Dynamic Routing)
70.58
84.54
62.05
74.37
36.11
81.37
76.22
Table 4: Ablation on routing strategies.
Benchmark
À 1.0
À = 0.15
A= 0.1
0.05
À 0.00
Table Question Answering (TQA)
WTQ
64.94
70.58
67.96
71.80
71.96
TABMWP
83.86
84.54
84.79
85.08
85.47
TAT-QA 61.79
62.05
63.21
64.12
64.38
HiTab 73.79
74.37
72.14
74.30
73.22
FeTaQA 35.28
36.11
35.37
34.34
35.87
Table Facr Verification (TFV)
TabFact 77.30
81.37
79.55
81.39
79.81
InfoTabs 70.13
76.22
70.44
73.59
74.19
Avg. Acc. 71.97
74.86
73.02
75.05
74.84
Table 5: Impact of the resource loss weight (A) on TableDART's performance. second-bes results are highlighted.
value increases reliance the computationally expensive Fusion path, causing queries to be routed to the Fusion inference path.
(2) Regularization for Improved Generalization. Thble that proposed resource-award objective also serves as an effective regularizer. The highest performance, both in terms of average results and across four of the seven benchmarks, is attained with a purely performance-driven policy (A = 0.00), but rather at a non-zero X. By penalizing computational cost, the objective dis- courages the gate from over-relying on the Fusion path, a tendency that risks overfitting training artifacts. Instead, it promotes more balanced routing policy that generalizes more effectively. In addition, varying À has direct impact on inference efficiency. As illustrated in Appendix B.4 (Fig- ure IT and Table IT. efficiency generally improves as À increases, since larger penalty discourages unnecessary use of the Fusion path. However, the trend not strictly monotonic, because each induces a distinct routing policy that balances accuracy and computational cost differently. Based on this observation. we select À = 0.15 for the final model, which achieves the second-best aver- age accuracy (7486%, within 0.19 points of the best) while requiring 8.4% less inference latency, representing a well-regularized operating point on the performance-effiiene figuration enables truly adaptive policy, which tailors routing to the task type, directing 97.2% of queries to the Image-only path for the structure-heavy TABMWP, while shifting to 67.5% Text-only for the semantics-focusel InfoTabs (see Appendix B.3 and Fig. 10 for full breakdown).
frontier. This con-
4.6
CASE STUDY
To qualitatively illustrate the sophisticated reasoning of our Fusion path, Figure 4 presents two representative cases that highlight the LLM agent's primary roles. (1) The first case demonstrates the agent as an Arbitrator. Tasked with a probability calculation, the Table-as-Ten Text model fails by incorrectly summing subset of the data for its denominator, while the Table-as-Iman reasons correctly. The Fusion agent resolves this conflict by validating both reasoning paths against the source table and selecting the correct output. (2) The second case showcases the Fusion agent as a Rescuer. Here, Fusion agent reasons that both single-modality models fail to identify the two required occupations, with each providing only exhibits true synergy by synthesizing a new, fully correct answer, combining the valid fragments from both failed outputs while discarding the errors. This synthesis demonstrates the Fusion agent's ability to generate novel, correct answers from incomplete and confiicing information from two hingle-oddali models.
model
correct and one hallucinated answer. The agent

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Published as a conference paper at ICLR 2026
HiTab
TAT-QA
08-
Rouring Distribttion
Processing Path
Tex--ony
04-
Imsegeeely
Fusion
62
8.5
X(Reson oe Loss Weight)
(Resource Loss Weight)
Figure 3: Inference path selection distribution vs. the resource loss weight (A) on two representative benchmarks. Each bar shows the percentage of instances routed to the Text-only (blue). Image- only (green), and Fusion (orange) paths. A red star (*) marks the configuration with the highest performance for each dataset (see Table 5p. This selection highlights TableDART's adaptability to diverse challenges. Full results on all seven datasets are provided in Appendix B.1
(a) Conflict Resolution: Fusion as an Arbitrator
Iaput Context
Outputs
Tabl-as- Tixt Midal
Qurstia:: prltaiiity randenly selected
X
dost was marked states was otserved aherrsoe?
Explanation: The protabiliy cakorieed by dividing number ennre clasts aborved theiefomoon (1)by toe mumberot
Sertallaed Table dhr Table-o--eed Text
Tahle Image (he Table-s-image
olocrations 17+2
Medel::
Modell:
TaMe Image Madel Ourpeti
3/10
Explanation Snippct: "...
tible clords atsevel alemoos
divide fhis
obsered, which 19."
Fiskas Medel Fizsi Amserr: 3/19 of
(b) Symergy: Fusion Rescuer
Iaput
Model Outputs
Dutast:: Hilab
Outpat
Quastier: Which two sccopotions firmal worken lune the hight pareaagee
Answer "S accepttiics ant culture memorioo spon",
servicn pecepatione
Seefalleed Table (Sur Table-as-Tost Text
Table Table-as-nngg Modeli:
Explanation Seippet: didetiiioss peooragee thi dio dhe an...] and 22 der vaice.
Made
pemomaage workerd [m
Tabeoas Madel
odacaton (e63
Answer: accopeticns odacation, social coumunsitt pover stinst
sccspations 1a6-
Sccccppations calture recreation and yoor
alocand
Explanation Salppet: Men top percerages rekection, 7 and the (hr
(-P... Itn..
enitss) ..a
Fisios Medel Finsl Amswrr: "4, occpotios education, law and social commenrntyy and governnenn servies, 6 sites service occepetions "gy
mantteetttig...
Figure 4: Case studies illustrate the key synthesis roles the Fusion Model, which implemented by an LLM agent. (a) As Arbitrator (example from the TABMWP dataset), it resolves conflict between a correct and an incorrect numerical reasoning path. (b) As a Rescuer (example from the HiTab dataset), it demonstrates synergy by synthesizing correct answer from two distinct, incorrect outputs, showcasing its ability to combine partially correct reasoning fragments.
CONCLUSION
In this work, we introduced TableDART, a dynamic adaptive multi-modal routing framework for table understanding. We replace the conventional MLLM-bassed approach with lightweight MIP- based gating network that learns to route each input to an optimal processing path: Text-only, Image-only, or Fusion. This instance-level routing is learned efficiently by training only the 2.59M- parameter gate while keeping the large base models entirely frozen, preserving their pre-trained ca- pabilities and ensuring alug-and-play flexibility. Our experiments across seven diverse benchmarks demonstrate that TableDART consistently achieves strong performance, validating that its dynamic, resource-aware routing is more effective and efficient paradigm for complex table reasoning tasks. Please refer Appendix A. for detailed reproducibility statement.
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ACKNOWLEDGMENTS
The Australian Research Council partially supports this work under the streams of Future Fellowship (Grant No. F1210100624), the Discovery Project (Grant No. DP240101 108 and DP260100326), and the Linkage Project (Grant No. LP230200892 and LP240200546).
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A APPENDIX
A.1 REPRODUCIBILITY STATEMENT
We are committed to ensuring the reproducibility of our work. All source code, trained gating net- work checkpoints, and experiment configurations provided at the following GitHub repository: duction.
Below a summary of the key components for repro-
The overall architecture TableDART is detailed in Section 3 Spe- cific details of the embedding extraction pipeline and the MLP gating network are provided in Appendix A.3 The primary constituent models are TableGPT2-77
Model Architecture:
7B (Su et al. 2024
and Ovis2-8B (Lu et al. 2024h. with Qwen2.5-VL-7B (Bai al. 2025 additionally em- ployed to validate generalization. Google's 's Gemini 2.0 Flash model (Comanici et al. 2025 implements the Fusion agent.
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Training Procedure: Our parameter-efficien training methodology for the gating network, including the resource-awand objective function, is described in Section 3.3 Full imple- mentation details are provided in Appendix A.5
Datasets and Preprocessing: The datasets used for training and evaluation are described in Section 4 Our training mixture was constructed from five datasets: WTQ (Pasupat & Liang 2015), TABMWP (Lu et al. 2023). TAT-QA (Zhu al. 2021), TabFact (Chen al.)
2020 and Iabo (Gupta et al. 2020). We evaluate on these five datasets plus two zero-
shot datasets: HiTab (Cheng
(022) and FeTaQA (Nan et al. 2022). Our specific data
construction protocol is detailed in Section 4.1 and Appendix A.5
Hyperparameters and Computational Infrastructure: A comprehensive list of all hy- perparameters is provided Table [O All experiments were conducted on single NVIDIA H100 80GB GPU. These computational resources
provided by the Bunya percomputer (The University of Queensland Research Computing Centre (024). operated by The University of Queensland Research Computing Centre (RCC). Full details the training pipeline, hyperparameters, and computational setup are located in Appendix(A.3
A.2 LARGE LANGUAGE MODELS USAGE
Large Language Models were employed fix the grammar issues in this paper. The authors remain fully responsible for all content.
A.3
MODEL ARCHITECTURE DETAILS
A.3.1
EMBEDDING ARCHITECTURE AND COMPUTATIONAL ANALYSIS
The gating network requires fied-dimensiionl feature representations enable consistent routing decisions across variable-length inputs. We employ different pooling strategies optimized for each modality's 's characteristics:
Table-as-Ter. Model Features: 3,584 dimensions obtained through attention-masked
mean pooling of TableGPT2-78 input embeddings. This approach weights each token by its attention importance, preventing padding tokens from diluting the semantic representa- tion while preserving global table structure information essential for routing decisions.
Table-as-Image Model Features: 6,144 dimensions derived from spatial-temporal pool- ing of Ovis2-8B visual tokenizer outputs. Mean pooling across spatial dimensions cap- tures holistic visual layout characteristics rather than fine-gu aie-ggained spatial details, providing sufficient information for modality appropriateness assessment without overwhelming the routing mechanism with local visual features.
Question Embedding: 384 dimensions from all-MiniLM--66-2 RRimerrs Gurevych 2019) sentence transformer. This pre-trained model provides question-type agnostic se- mantic representations that complement the features from the aagle-modalii, models, en- abling the gating network to learn routing patterns based on question semantics rather than model-specific encodings.
Total Concatenated Dimension: 3,584 + 6, + 384 10,112 dimensions. This unified multi- modal representation serves the input to the MLP gating network, which learns to map these heterogeneous features to processing path selection probabilities.
Parameter Analysis: The embedding extraction phase utilizes only small fraction of each model's total parameters: (1) The Table-as- Text model requires 545M parameters from the input embedding layer (152K vocab x 3,584 dimensions), representing 7.15% of TableGPT2-7B's 7.62B parameters; (2) The Table-as- Image model requires 682M parameters from the Vision Transformer component aimv2- www.-butthhhe...oooo 14-448 for visual feature extraction, representing 7.63% of Ovis2-8B's 8.94B pa- rameters. This computational efficiency enables lightweight routing decisions while preserving rich semantic representations from the pre-trained models.
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A.3.2 GATING NETWORK ARCHITECTURE SPECIFICATION
Following standard practice in dynamic routing and mixtureeof-exxpee systems in the prior works
(Lepikhin et al. 2021; Zhu et al. 2022 Xue & Marculescu 2023), the MLP gating network im-
plements a standard two-layer architecture designed for efficient and effective routing. The detailed
forward pass is defined as:
h ReLU(W x bi)
(6)
h' Dropout (h,p 0.1)
(7)
2 W2h' + b2
(8)
where x E R10112 is the concatenated multi-modal input features, and z E Rd represents the final expert selection logits. During inference, these logits are converted to probabilities via a Softmax function to select the optimal expert.
Implementation Details. The hidden dimension is set to 256, choice empirically found to bal- ance representational capacity for learning complex routing patterns with computational efficiency. Dropout with probability of 0.1 is applied after the hidden layer to prevent overfitting. We use the ReLU activation function for its computational efficiency and stable gradient properties. All weight matrices are initialized using Xavier uniform initialization (Glorot & Bengio, 2010), and bias vectors are initialized to zero to ensure stable training dynamics.
Parameter Count. The lightweight nature of our gating network is evident in its parameter count, detailed in Table 6 The total of 2.59M trainable parameters is negligible compared to the billions of parameters in the frozen LLM/MLLM models, underscoring the high parameter efficiency of our TableDART framework.
Component
Computation
Parameters
First Layer
Weight matrix W1
10,112 x 256 256
2,588,672
Bias vector b
256
First layer subtotal
2,588,928
Second Layer
Weight matrix W2
256 x 3 3
768
Bias vector by
3 771
Second layer subtotal
Total Network Parameters
2,589,699 As 2.59M
Table 6: Detailed parameter breakdown of the MLP gating network.
A.4 FUSION PATH DETAILED DESIGN
This section provides the detailed design of the Fusion path, which serves as the sophisticated syn- thesis mechanism within the TableDART framework. As described in the main text, this component is implemented as an LLM agent that interfaces with Google's 's Gemini 2.0 Flash model via REST API calls. For each instance, the LLM agent receives four key inputs: (1) the original question, (2) the complete table data in a structured markdown format, (3) the output from the Table-as-Teex model containing its answer and explanation, and (4) the output from the Table-as- Image model con- taining its answer and explanation. These inputs are formatted into prompt whose logical structure illustrated in Figure 5 To ensure output format compatibility across benchmarks, the prompt is dynamically adapted based the target dataset. As detailed in Table 7 specific instructions appended to the prompt to meet the unique formatting requirements of each benchmark, such as requiring JSON outputs for TabFact full-sentence responses for FeTaQA.
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Question
Prompt Logle
Role: You synhesizer.
Inputs: Question, Full table data (markiown format),
Table-as- Text model oulput (enwwer explonation)
Full lable Data (Mulkdoww)
Table-as-image model ourput (anewer explatation).
Core Iastructions:
Amayye the table compure the two models'
anowers and explination.
Fision Model
Constraiat: Synhesize unly from models"
Table-as- Text Model
outpus. Use table atracture understanding only
Handle Scenarios:
complementary: Merge key information.
conflicting: Reason produce most plussible answer.
Toble-as-Imnge Model Answer
Dutaset- Specitic Adaptation (Format adapted based. on target benehmark)
Output Format: extremely
Figure 5: The data flow and logical structure of the prompt for the Fusion path's L.M agent. This structure guides the synthesis final answer from the outputs of the single-modality models.
Dataset(s)
Additional Instruction
TabFact
Generate JSON response with "answer" field containing ["True"l or
["False")
InfoTabs
Generate JSON response with "answer" field: ["Entail"), ["Contra- dict"], ["Neutral"]
TabMWP
Output numeric answers without units when applicable Provide complete sentence responses, not keywords or phrases Standard JSON format with concise, direct answers
FeTaQA
WTQ. HiTab, TAT-QA
Table 7: sptaset- prompt adaptations for the Fusion path's 's LLM agent.
A.5
EXPERIMENTAL SETUP AND IMPLEMENTATION DETAILS
A.5.1 DATASETS AND METRICS
Our experimental evaluation is conducted on seven diverse benchmarks across two primary tasks: Table Question Answering (TQA) and Table Fact Verification (TFV). We follow the established pro- tocol of previous works (Zheng et al. 2024 [iu et al. (025) for evaluation metrics, using Accuracy for most tasks and BLEU score for the generative FeTaQA benchmark. Table g provides detailed breakdown of these datasets, including the number of instances used for training mixture and the full size of the official test sets.
Task Type
Dataset
Train
Validation
Test
TABMWP
2,000
300
7,686
WTQ
2,000
300
4,344
Table Question Answering (TQA)
HiTab
1,586
TAT-QA
2,000
300
FeTaQA
2,003
Table Fact Verification (TFV)
TabFact
2,000
300
6,845
InfoTabs
2,000
300
5,400
Total
10,000
1,500
28,636
Table 8: Statistics of the datasets used for experiments. The "Test" column reflects the official benchmark size, while the 'Train' and 'Validation' columns indicate the number of instances we sampled from the official training data to construct training mixture, detailed in Section 4.1
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A.5.2 COMPARATIVE MODELS AND RATIONALE
We compare TableDART against comprehensive set of baselines, strategically selected to validate our core contributions across several axes:
Constituent Models: To demonstrate that TableDART's performance stems from its dy- namic framework, evaluate its core constituent models standalone baselines. These include: (1) the Table-as-Tew model, TableGPT2-7B (Su et al., 2024); (2) the primary Table-as-Image model, Ovis2-8B (Lu al., 2024 (3) the additional visual expert used for generalization, Qwen2.5-VEL 5-LL-BB (Bai et al. 2025); and (4) the backbone of our Fusion agent, Google Gemini 2.0 Flash (Comanici et (025). We selected these backbone mod- els because they are recent and strong representatives of their respective paradigms. Eval- uating the single-modality experts in isolation establishes a performance baseline. Specif- ically, including Qwen2.5-Vi -B enables us to test the framework's 's generalization capa- bilities across different visual backbones, while comparing against Gemini as a standalone MLLM verifies that performance lift arises from the routing mechanism rather than only the capacity of the Fusion agent alone.
MLLM-based Baselines: To highlight the superiority of our dynamic adaptive routing paradigm over static, one-size-fits-ll approaches, we compare against two key models. First, HIPPO (Liu et al.] 2025). a recent model representing the MLLM paradigm that jointly processes both text and image representations for all inputs. Second, Google Gemini 2.0 Flash (Comanici et al. 2025). Since Gemini serves as the backbone for the LLM agent in our Fusion path, including it as standalone MLLM baseline allows us demonstrate that the observed improvements stem from our dynamic routing framework itself, rather than solely from the capacity of the Fusion agent's 's underlying model.
To position Tab within the broader field, we benchmark it against an extensive suite of single-modality baselines in the 7-8B param- eter range. Our selection of Table-as- Text models includes generalist LLMS like Llama- 2-7B (Touvron et al.) 2023) and Allama3-Instruct-8 (Dubey et al. 2024), as well as the specialized TableLlama-1 (Zhang et al. 2024). The set of Table-as- lable-as-Image MLLMs is
Broader Competitive Landscape:
equally comprehensive, featuring MiniGPT-4-7B (Zhu et al. 2024a), mPLUG-OW1-77 (Yc
et al. 2023), mmLmUG-WW22-2 (Ye al. 2024). LLaVA v 1.57B (Liu et al. 2024), Table-
LLaVA-7B (Zheng al. 2024), Qwen- VL-7B (Bai et al. 2023), MiterLL--XCooppeee
7B (Zhang et al. 2023), Monkey-7B (Li et al. 2024). TabPedia-7B (Zhao et al.) 2024),
SynTab-LLLaVA-11 7B (Zhou et al. 2025) Qwen2.5-VL
(Bai et al. 2025) and MiniCPM-
V-2.6-8B (ao et al. 2024).
For a comprehensive and fair comparison, we report several baseline results directly from their orig- inal publications. Specifically, the results for the following models are adopted from the HIPPO pa- per (Liu et al. 2025): Llama-2-778
TableLlama-75 7B, MiniGPT-4-7B, mPLUG- MiniCPM-V-2.6688
Owl-7B, mPL.UG-OW2277 LLaVA v1.5, Rable-LLaVA--- Monkey-7B, and the HIPPO model itself. Furthermore, the results for TableGPT2-7B are sourced from its proposal paper (Su et al. 2024), and the results for SynTab- LLaVA are sourced from Zhou et al. (2025). We also adopt the results for TabPedia and Ovis2-8B (except TABMWP dataset) from Jiang et (2025). All other model results not mentioned above were generated by our own experimental runs.
Owen-VLL-70
MeemLLMMXCCoppser22777
A.5.3 TRAINING AND IMPLEMENTATION DETAILS
Training Data Construction. Our training set is a 10,000-sample mixture, constructed by ran- domly sampling 2,000 instances from five datasets: TABMWP, WTQ, TAT-QA, TabFact, and In-
foTabs (Lu et al., 2023: Pasupat & Liang 2015 Zhu et al. 2021 Chen et al. 2020 Gupta et al.) 2020). Following the protocol of HIPPO (Liu et al. 2025). TaQaQA and HiTab are excluded from training due to their non-accuracy-bart based evaluation metric (BLEU) and challenges in serializing
complex hierarchical structures, respectively (Nan et al., 2022 Cheng et al., 2022). An additional 15% validation split (1,500 samples) is generated using the same random sampling procedure with fixed seed to ensure reproducibility.
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Processing Path Cost Measurement Protocol. The resource cost vector used in resource- aware objective is derived from empirical measurements to provide a realistic estimate of each path 's computational expense. We measured these costs representative testbed of 70 samples (10 from each benchmark), averaging over 10 timed runs following 5 warm-up iterations. Our final cost metric offers holistic balance of latency (seconds per instance) and throughput (tokens per second), defined as: Cost = 0.5 x (Avg Latency) + 0.5 x (1.0/ Avg TPS). For the Fusion path, its latency assumes parallel execution of its base models plus a 0.3s API average overhead, reflecting a realistic deployment scenario. The resulting empirically derived cost values are summarized in Table
Table 9: Breakdown of the processing path cost calculation. The Fusion path's latency assumes parallel execution the two single-modalle models (taking the max latency) plus measured API overhead. Its TPS is consequently bottlenecked by the slower Table-as Table-as-Image model.
Processing Path
Avg. Latency (s)
Avg. TPS
Final Cost (c)
Text-only
1.445
44.19
0.73
Image-only
1.559
18.78
0.81
Fusion
1,859
18.78
0.96
Hyperparameter Configuration. Table T0 summarizes the complete hyperparameter settings used for training the gating network. The resource loss weight = 0.15 is selected based on exten- sive ablation studies (Section 4.5). which demonstrate optimal cerformance-effccineey trade-offss at this value. These hyperparameters are selected through preliminary experiments to balance training stability and convergence speed. The target temperature 0.3 creates sufficiently sharp target distributions while preventing degenerate solutions, while the gate temperature Tg = 1.0 maintains standard softmax behavior during training.
Parameter
Value
Learning Rate
lc-4
Batch Size
Gradient Accumulation Steps
Effective Batch Size
32
Weight Decay
0.01
Gradient Clipping (Max Norm)
1.0
Hidden Dimension
Dropout Rate
0.1
Target Temperature (7)
0.3
Gate Temperature (r₂)
1.0
Resource Loss Weight (X)
0.15
LR Warmup Ratio
0.05
Training Epochs
Table 10: Complete hyperparameter configuration for TableDART training.
Training Pipeline. Expert model parameters remain frozen throughout training, with only the lightweight 2-layer MLP gating network being optimized. This parameter--effiiient approach dra- matically reduces computational requirements compared to joint fine-tuning alternatives. The learn- ing rate follows cosine annealing schedule with 5% linear warmup, starting from zero and reaching the maximum learning rate of le-4 after the warmup period. We employ the Adam W optimizer with weight decay 0.01 to prevent overfitting of the gating network. Gradient accumulation over 4 steps achieves an effective batch size of 32 while maintaining memory efficiency on single-GPU training. Gradient clipping with maximum norm 1.0 ensures training stability, particularly important given the dynamic target generation process.
Computational Configuration and Convergence. Training is conducted on a single NVIDIA H100 80GB GPU with mixed precision optimization. Expert models utilize bfloat16 precision to reduce memory consumption, while the gating network maintains float32 precision for numerical stability during gradient computation. The complete training process requires approximately 13.5
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hours to complete epoch. Training converges rapidly within this single epoch, which pirically determined to be optimal to prevent overfitting. The model is evaluated on the held-out validation set, and the checkpoint with the highest validation accuracy is saved as the best model for inference.
A.6 EFFICIENCY BENCHMARK PROTOCOL
This section provides a detailed account of the protocol used for the efficiency analysis presented in Section We outline the methodology for data sampling, performance measurement under parallel assumption. and the precise definitions for all reported metries ensure full reproducibility.
A.6.1 BENCIMARK SETUP AND DATA SAMPLING
To create representative and manageable testbed, constructed evaluation set via stratified random sampling from the seven benchmark test sets. We randomly sampled a balanced set of 50 instances from each dataset, resulting in comprehensive benchmark suite of 350 unique samples. To ensure statistical stability, the entire measurement process was repeated three times with dif- ferent random seeds, and all reported metrics the average across these independent runs. All benchmarks were executed on a single NVIDIA H100 80GB GPU.
A.6.2 MEASUREMENT PROTOCOL AND PARALLEL ASSUMPTION
Our measurement protocol designed simulate realistic nario. For each sample, the total inference latency is calculated by summing the durations of three sequential phases, with parallelism applied within phases where appropriate.
deployment sce-
Phase Parallel Feature Extraction. The framework concurrently extracts embeddings from the input question, the serialized text table, and the table image. The duration of this phase is determined by the maximum latency among these three parallel operations: Thhsse1 maxxTiext.mmbed Tvision.embed; T quuestion.embeuu.
Phase 2: Gating and Routing. The concatenated embeddings are fed into the lightweight gating network to yield routing decision. This phase only applicable to TableDART; for the Non-Adaptive Fusion baseline, its duration is zero.
Phase 3: Generation and Fusion. The execution path depends on the routing decision:
For TableDART (Unimodal Path): If the Text-only Image-only path is selected, the duration is simply the generation time of the chosen model (T (Tuext.ge T ven. For TableDART (Fusion Path) and the Non-Adaptive F Fusion baseline: Both the Table-as- and Table-as-Image models perform generation in parallel, followed by a call to the Fusion API. The duration is the maximum of the two generation times plus the API call latency: Thhase3 max (TaxxTrext.gmm Timagg..cm (aannggggn) Tusion.api.
The total latency for each sample is the sum these three phases: Lparaltel Tphase! + Tphase2 + phases3
A.6.3 METRIC DEFINITIONS
We the following two primary metrics report efficiency:
Latency (s): The total time in seconds to process single sample, calculated as Lurraliel under the parallel assumption described above. This is our primary metric for comparing the end-to-end speed of different frameworks. Lower values are better.
Tokens per Second (TPS): A measure of throughput, calculated by dividing the number generated output tokens by the total parallel latency. The token count is estimated using the Text Expert's 's tokenizer. Higher values are better.
A.7 DETAILED ARCHITECTURAL ANALYSIS
This section provides detailed visualizations to supplement the summary analysis Section Figure full, per-dataset breakdown of the performance overlap between the single-modality
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100
13.4%
20.9%
19.8%
12.7%
17.4%
Only Image Correct
Only Text Correct
34.8%
Both Correct
Both Wrong
Percentage (%)
51.3%
67.6%
50. 1.%
60.0%
40
47.0%
14.0%
26.4%
10.0%
24.5%
12.3%
14.8%
1.2%
8.3%
4.4%
7.4%
11.5%
WTO
TABNWP
TATQA
HiTab
FeTaQA
TabFact
InfoTabe
Dataset
Figure 6: Per-dataset breakdown of performance overlap between the single- modallty models. The stacked bars show the percentage of instances solved by only the Table-as-Text Text model, only the Table-as-Imadt model, both, or neither. The distribution varies significantly across benchmarks, highlighting the need for adaptive routing.
Hard Cases Fusion Rescue
(b) Synergy Success Rate
Buth
20.0
1000
17.5
19.4%
15.0
13.8%
14.1%
Nambbe of Cansee
800
Synergy Rate Pl)
32.5
9.8%
600
7.5
4.7%
2.5
WT0
TUBAW
TA
Himb
Taiifett
WTQ
TRTSH
intaa
nwsreer
Dataser
Dataset
Figure 7: Detailed analysis of Fusion Path Synergy. (a) The absolute number of hard cases (where both base models failed) and the number of those cases successfully rescued by the Fusion path (synergy). (b) The Synergy Success Rate (Fusion Path Correct Both Base Models Wrong) for each dataset demonstrates the consistent effectiveness of the Fusion path.
models. Figure provides a more detailed view of the synergy analysis, showing both the absolute number of hard cases and the corresponding synergy success rates for each benchmark. These detailed charts serve as the underlying evidence for the aggregate statistics and trends discussed in the main paper.
B FURTHER IN-DEPTH ANALYSIS
B. FULL GATING DECISION ANALYSIS ON ALL DATASETS
To supplement the analysis in the main paper. this section provides the complete visualizations of the processing path selection distribution for all seven benchmarks. Figure 3 in the main text highlights two representative datasets, while Figure below shows the results on the remaining five.
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Tah Pact
TARMWP
WTQ
Routing Dienibution
0.
013
61
8
InfoTabs
PeTQQA
Routing Pharabbttio
Processing Path
Tos-only
Imegevonly
02
A.Rcnsoncc Laos Waigh)
X(Rovum Loss Weight
Figure 8: Processing path selection distribution vs. the resource loss weight (A) on the five additional benchmarks. A red star (*) marks the best- cest--effrrmig configuration for each dataset. The data demonstrates consistent adaptive behavior across these diverse tasks.
The trends observed here are consistent with our main findings. For instance, on the math-heavy TABMWP dataset, the policy heavily favors the Image-only path. On the visually complex HiTab benchmark, the routing policy maintains a significant reliance on both the Image-only and Fusion paths across all À values. Finally, the generative FeTaQA task shows strong preference for the Fu- sion path when performance is prioritized. These varied, dataset-specific behaviors further validate that TableDART successfully learns adapt its strategy to the underlying characteristies of the data.
B.2 ANALYSIS OF THE LEARNED ROUTING POLICY
Our final in-depth analysis dissects the sophisticated nature of the learned routing policy, revealing a crucial trade-off between aligning with a simple heuristic and achieving globally-optimal task performance. To investigate this, adopt behavioral analysis approach, common technique in policy analysis and explainable AI (Guidotti et al. 2019). by defining Heuristic Alignment Score. This metric measures the percentage of times TableDART's routing policy matches simple, greedy heuristic: always choose the most cost-effective processing path (first Text-only, then Image-only, then Fusion) that is known to solve given problem correctly. The procedure for calculating this metric is detailed in Algorithm [ We then plot this alignment score against the final average task performance for each of our configurations.
The result, shown in Figure illustrates a clear non-linear relationship, resembling a Pareto frontier. We observe that policy laser-focused on maximizing heuristic alignment (e.g.. X 1.0. the rightmost point) adheres most closely to the simple greedy rule but yields suboptimal overall task performance. This demonstrates that a greedy, locally-focused strategy can detrimental to the global objective.
Crucially, our chosen configuration, = 0.15, resides at the apex of this performance curve. Its modest Heuristic Alignment Score indicates that it has learned more sophisticated, non-greedy policy. It quantitatively proves that the globally optimal strategy, learned via end-to-end training, involves strategically investing in cheaper option is technically viable. This finding validates that TableDART learns a truly effective, globally-optimized routing policy that transcends simple heuristics.
computationally expensive processing paths, even when
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Algorithm Heuristic Alignment Score Calculation
1: Input: Set of test samples S, Table-as- Textt model results Rrest, Table-as lablee model results Rimages TableDART's routing decisions DTaMeDART:
2: Output: Heuristic Alignment Score Salign.
3:
4: rotiened.routes f-
5: Ntotal +- |S|
6:
7: for each sample 8; E E do
t RTeat (8:) ) correct 9: is.image_corm correct &- Rimage (s:) is correct T.RT
8:
+- D DbaleeDAR
11:
D Define the simple, greedy heuristic action
13:
if is wettt_ttorrreee then
14:
heuristic seuristic_choice &- "Text-only"
15:
else if is wimaaamccooreeee then heuristic.choice + "Image-only"
16:
17: else
18:
heuristicn sehristtccchoiceeeeooo + "Fusion"
end if
20:
if DRR -choice heuristic_choice then
22:
N rolsteed.routes &- Naligned +
end if
24:
end for
26:
Satign (Natigned voutes/ Neotat) X 100
27:
return Salign
B.3 ANALYSIS OF PER-DATASED ROUTING POLICIES
To provide quantitative evidence of TableDART's adaptive capabilities, we analyze the routing de- cisions of our final model (trained with X = 0.15) each of the seven test benchmarks. Figure TO visualizes the learned policy, revealing how the framework dynamically allocates resources based on the specific demands of each dataset.
The analysis highlights several key adaptive behaviors:
The model demonstrates a profound understanding of modality-task alignment. For TABMWP, a benchmark requiring structural and mathemat- ical reasoning. the policy routes an overwhelming 97.2% of instances the Image-only path. Conversely, for InfoTabs, a fact-verification task dependent on fine-grad tine-grained seman- tics, the strategy shifts dramatically favor the Text-only path (67.5%).
Adaptation to Modality Strengths:
Adaptation to Task Difficulty: On challenging benchmarks where single-mon modality mod- els are likely to fail, such as TAT-QA, the policy learns to prioritize correctness by invoking the powerful but costly Fusion path for 88.7% of cases. This demonstrates an ability to gauge task difficulty and escalate to more robust strategy when necessary.
Balanced, Nuanced Strategies: For benchmarks with a mix of challenges like WTQ and FeTaQA, the model learns more balanced policy, distributing queries across all three paths. This indicates that the routing not coarse grained but makes fine-tuned, instance- level decisions.
In summary, this analysis provides compelling evidence that TableDART operates as truly adaptive framework, which is the key to its state-of-the-art performance and efficiency.
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Average laakk Parfor coffrrrmnnc e%a
67
64
56
Alignment Score
Figure 9: The trade-off between aligning with simple greedy heuristic (x-axis) and achieving globally-optimal task performance (y-axis). The curve shows that strictly following the greedy heuristic hurt final performance, and TableDART's best configuration (A = 0.15) learns supe- rior, non-greedy policy.
13%
Inference Path
14.8%
14.5%
Text-only
Image-only
35.2%
Fusion
Routing Policy Distribation (%)
60
88.7%
79.6%
40
55.2%
57.7%
67.5%
20
17.6%
11.3%
11.2%
7.1%
WTQ
TABMWP
TAT-QA
HiTab
FeTaGA
TabFact
InfoTabs
Dataset
Figure 10: The learned routing policy distribution for each benchmark using the final TableDART model (A = 0.15). The distinct strategies across datasets provide strong evidence of the framework's adaptive nature.
B.4 EFFECT OF ON INFERENCE EFFICIENCY
To more clearly characterize how the resource-aware objective influences efficiency, we report em- pirical latency and throughput measurements across different values of À in Table 11 As shown,
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has direct impact inference cost. While larger À generally encourages efficient routing by discouraging unnecessary use of the Fusion path, the trend is not strictly monotonic. This is because each À induces distinct routing policy that balances accuracy and computational cost differently.
Figure IT visualizes this relationship by plotting average latency against average accuracy. The resulting curve illustrates natural eerformance-efffcien 0.05) tend to overuse the Fusion path, while very large values (e.g.. 1.0) favor efficiency at the cost of reduced accuracy. The configuration À 0.15 provides the best overall balance, achieving the second-highest average accuracy (within 0.19 points of the best) while maintaining strong efficiency (8.4% less inference latency).
frontier: smaller values (e.g.. 0.0 and
73.5
2-0.05
0=1.5
A-0.0
74.5
Average Accuracy (%)
73.5
A=0.1
73.0
2=1.0
72.0
71.5
Average Latency (8) 1
Figure 11: Performance-efficinne trade-off across different values the resource loss weight (A). The red star marks the selected configuration.
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Table 11: Inference efficiency for different values of X. Higher values encourage more efficient routing, though the effect is not strictly monotonic due to the distinct routing policies induced by each setting.
Latency (s)
Throughput (TPS)
0.00
2.3675
15.14
0.05
2.4021
13.72
0.10
2.0832
23.85
0.15
2.2008
17.77
1.00
1.8490
29.56
26


Reviews:
[{"review_id": "f9m1IDrWww", "reviewer": "Reviewer_YExZ", "summary": "The paper proposes TableDART, a dynamic adaptive multi-modal routing framework for table understanding tasks. The key innovation lies in replacing traditional monolithic multimodal LLM fine-tuning with a lightweight 2.59M-parameter gating network that dynamically selects among three inference paths, Text-only, Image-only, or Fusion, depending on each table–query pair’s characteristics. The fusion stage employs an LLM agent that arbitrates between unimodal outputs or synthesizes a new answer when both fail. TableDART reuses frozen pretrained unimodal models, drastically reducing training cost. Experiments on seven benchmarks show TableDART surpasses strong baselines.", "questions": "- How sensitive is the routing policy to the composition of the training mixture? Would domain-specific fine-tuning alter routing distributions?\n\n- Does the gating network rely primarily on query semantics or table structure cues when choosing between text and image paths?", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The dynamic adaptive routing approach is impressive. It represents a conceptual shift from static multimodal fusion to instance-specific modality selection.\n\n- The paper demonstrates strong empirical rigor.\n\n- TableDART addresses a key bottleneck in multimodal table understanding, redundant modality processing and high fine-tuning cost", "weaknesses": "- While the paper highlights the LLM-based Fusion agent's \"Arbitrator\" and \"Rescuer\" roles, its decision logic is black-box. There is no systematic evaluation of when or why it succeeds beyond anecdotal cases.\n\n- The use of Gemini 2.0 Flash for fusion raises reproducibility and accessibility concerns. A variant using a fully open LLM would improve transparency.\n\n- The gating network's supervision relies on pre-computed correctness vectors, but the procedure for labeled paths is not critically examined.\n\n- It would be instructive to show a variant where fusion uses a simple weighted ensemble rather than an LLM agent, to disentangle the benefits of adaptive routing at the fusion stage.", "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": "dgDp6rX6l6", "reviewer": "Reviewer_nQLA", "summary": "This paper proposes TableDART—a training-efficient framework that reuses pretrained single-modality models via a lightweight 2.59M-parameter MLP gating network, dynamically selecting the optimal path (Text-only, Image-only, or Fusion) for each table–query pair to mitigate redundancy and conflicts.", "questions": "see the weakness", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "TableDART addresses limitations of existing table understanding methods by introducing a training-efficient framework with a lightweight MLP gating network, dynamically selecting optimal (text, image, or fusion) paths per table-query pair to reduce redundancy/conflicts. It further incorporates a cross-modal agent for knowledge integration without costly MLLM fine-tuning, achieving SOTA on seven benchmarks.", "weaknesses": "1. The paper is difficult to comprehend, as many concepts are employed directly without any corresponding explanation of their meanings—\n   - For instance, what does the term \"fine-grained\" on line 46 specifically refer to?\n   - As noted in the abstract: \"statically process both modalities for every query-table pair within large multimodal LLMs (MLLMs), inevitably introducing redundancy and even conflicts,\" what exactly does the term \"conflicts\" refer to herein?\n   - In the Introduction, it is mentioned that table data exhibits the property of heterogeneity—could the authors provide an explanation for this?\n   - What modality exactly is Table T on line 145—textual or visual?\n2. The literature review is inadequate, and the compared methods lack several representative approaches, such as the domain-specific VLM TabPedia、SynTab-LLaVA, and the general VLM Qwen2.5VL.\n3. How are the three token sequences (e_q, e_t, e_v), which vary in length and differ in token dimension, concatenated?\n4. As the authors state, \"We employ TableGPT2-7B and Ovis2-8B as TableDART’s single-modality experts,\" it is queried whether ablation experiments have been conducted using other alternative single-modality experts to verify the robustness and generalizability of the proposed framework.", "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": "isyySRjfCE", "reviewer": "Reviewer_Z7NP", "summary": "This paper proposes TableDART, a framework that adaptively selects the optimal processing path for each query to make the most of modality-specific features for table understanding.", "questions": "See weakness.", "limitations": "", "rating": 2, "confidence": 5, "soundness": 2, "presentation": 2, "contribution": 1, "strengths": "Proposes a novel dynamic routing framework that adaptively selects the optimal modality (text, image, or fusion) for each table–query pair.\n\nAchieves good performance gains across multiple benchmarks with minimal additional parameters. The parameter-efficient training makes it practical and resource-friendly.", "weaknesses": "1. Your results seem insufficient to demonstrate that you achieve Inference Efficiency (the main text only contains a one-sentence description of the results). From the appendix, it can be seen that most tasks on the datasets can be completed with a single modality; however, a fusion strategy is still predominantly used.\n\n2. The two single-modality based methods you used already perform far beyond most models, making many methods seem completely meaningless for comparison. Aren't there any other relatively stronger baselines in this field?\n\n3. Three paths are a bit too few for routing. More models should be adopted to provide more options.\n\n4. How does computational efficiency change as $lambda$ varies? This should be presented clearly. Furthermore, I believe it would be normal for the performance to be optimal when $ \\lambda$ is 0, but this does not seem to be the case.\n\n5. From the results, Fusion appears to be the primary source of your performance improvement, and this deserves a detailed explanation.\n\n6. Why not use Qwen2.5-VL or even Qwen3-VL as your baseline? \n\n7. The experimental results in Table 1 on Ovis2 seem partially the same as \"Multimodal Tabular Reasoning with Privileged Structured Information\" (https://arxiv.org/pdf/2506.04088), except for the result on the TABMWP dataset. However, no reference or explanation can be found in the paper. The authors also did not compare with this paper. Could the authors explain the reason?", "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": "x5pi8XEEoP", "reviewer": "Reviewer_P1qR", "summary": "This paper proposed TableDart, a method designed for understanding semantic and structual information in table data. It adopted an MLP gating network that dynamically selected the optimal path, avoiding prohibitive costs of full MLLM fine-tuning. It also designed an LLM-based Agent to deal with outputs of other two single-modality experts.", "questions": "Please refer to section Weaknesses.", "limitations": "", "rating": 2, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "- This method only have few parameters to train, avoiding cost of fully finetuning\n- The designed dusion agent consider different conditions of other two outputs of single-modality experts, including similar and conflict answers\n- This paper do many experiments to validate its performance and efficiency", "weaknesses": "There are some core issues that have significant influence on my rating:\n- How do the MLP gating network choose the path after giving the logits? Choose the path with the highest score or paths whose scores exceed certain thresholds. If it is the later condition, what if the gating network choose more than one path?\n- In **Inference efficiency of section 4.3**, what is the model used in Non-Adaptive baseline? Why the mean inference time can be reduced? From my perspectives, if single-modality expert is chosen, the inference time remain the same. And if the fusion path is chosen, the other two path will be executed, where the inference time will double.\n- What's the standard when you choose the single-modality MLLM and the fusion agent as these three models have a significant influence on the performance of your methods.\n- Part results in Table 1 are the same with paper \"Multimodal Tabular Reasoning with Privileged Structured Information\" but no reference is mentioned.\n\nOther discussions:\n- Why do you choose three paths, including text-only, image-only and fusion. Why not add another path with MLLM modality? I take the thought that this path will provide more insight of the information than the single-modality experts do.\n- I wonder the number of times each path is chosen as this maybe can help understand the inference efficiency.", "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)