TempFlex: Advancing MLLMs with Temporal Perception and Natively Scalable Resolution Encoding
Authors: Zhanyu Wang
Chen Tang
Haoyu He
Kuan Feng
Chao Wang
Bingni Zhang
Xiaolei XU
SHEN WANG
Luping Zhou
Venue: Accepted by TMLR ()

Abstract: Multimodal large language models (MLLMs) have made significant progress across vision-language tasks, yet many designs still suffer from two core limitations. (i) Excessive visual tokens and broken global context: Tiled Patch Encoding fragments high-resolution images, leading to token overload and disrupting global attention modeling. (ii) Lack of temporal reasoning: Most models process video as independent frames using static image encoders, failing to capture temporal dynamics. We present TempFlex-VL, a token-efficient and temporally aware MLLM that addresses both issues through lightweight architectural enhancements. First, we introduce a resolution-agnostic visual encoder that directly processes full images without tiling, preserving global context while substantially reducing visual tokens. Second, we propose Temporal Fiber Fusion (TFF), a plug-and-play module with three complementary pathways: (1) a dynamic local-convolution branch for fine-grained motion, (2) a gated memory accumulator for long-term dependencies, and (3) a periodic encoder for modeling cyclic patterns. These signals are softly fused, enabling the model to adapt to diverse temporal structures without overfitting. To support large-scale video-language pretraining, we curate TempFlex-2M, a high-quality synthetic video–text corpus generated in a single stage via GPT-4o with direct visual prompting. We instantiate TempFlex-VL using two different language backbones, Gemma3-4B and Qwen3-4B, demonstrating the generality of our design across architectures. Both variants achieve state-of-the-art or competitive results on a wide range of image and video benchmarks while markedly improving token efficiency. Code is publicly available at: https://github.com/wang-zhanyu/TempFlex.

Published in Transactions on Machine Learning Research (12/2025)
TempFlex: Advancing MLLMs with Temporal Perception and Natively Scalable Resolution Encoding
Zhanyu Wang ByteDance
shanga. haangu.wwngg@@ggedane.cccc
Chen Tang ByteDance
Sugghhen.22200gtcaamee.oom
Haoyu He
haoyw.he@@gtedanee.coo
ByteDance
Kuan Feng
ByteDance
Chao Wang
wangghoo.hhhhhhh@pyddddnc..co
ByteDance
Bingni Zhang
bingni.thang@Bbgeeddcccccmm
ByteDance
Xiaolei Xu
musicoleei@hyteddacc.com
ByteDance
Shen Wang
www.shee..summeerrbbpedddd...ee
ByteDance
Luping Zhout The University of Sydney
hupping.ahooussyyneee..ee
Reviewed on OpenReview: https: // openreview. net/ forum? id- setYdtRB3h
Abstract
Multimodal large language models (MLLMs) have made significant progress across vision- language tasks, yet many designs still suffer from two core limitations. (i) Excessive visual lokens and broken global context: Tiled Patch Encoding fragments high-resolution images, leading token overload and disrupting global attention modeling. (ii) Lack of temporal rea- soning: Most models process video independent frames using static image encoders, failing to capture temporal dynamics. We present TempFlex- VL, token-efficient and temporally aware MLLM that addresses both issnes through lightweight architectural enhancements. First, we introduce a resolution-agnostic visual encoder that directly processes full images without tiling, preserving global context while substantially reducing visual tokens. Second, we propose Temporal Fiber Fusion (TFF), a plug-and and--and--lay module with three complementary pathways: (1) a dynamic local-convolution branch for fine-grained motion, (2) gated memory accunulator for long-term dependencies, and (3) periodic encoder for modeling cyclic patterns. These signals are softly fused, enabling the model to adapt to diverse temporal structures without overfitting. To support large-scale video-language pretraining, we curate TempFlex-2M. a high-quality synthetic rideo-text corpus generated in single stage via GPT-4o with direct visual prompting. We instantiate TempFlex- VI. using two
"Project Lead.
i "Correpponding Authors.

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Published in Transactions on Machine Learning Research (12/2025)
different language backbones, Gemma3-4B and Qwen3-4B, demonstrating the generality of our design across architectures. Both variants achieve state-of-the-art or competitive results on a wide range of image and video benchmarks while markedly improving token efficiency. Code is publicly available at: https://github.com http://ggttubbcoo//waaaaaaaaaaae Baagg-hhanu/TTemppa
1
Introduction
Multimodal large
language models (MLLMss)
(Zohar el al.
2024
Li et al.
2023a
Tong el al.
2024
Lu el al.
2024a
Wu al.
2024b
Chen et al.
2025
Gu et al.) 2024
Liu et al., 2023
Zhu
et al.)
2023
Liu et al.)
2024a
Li ct al.
2024a
Ye ct al.
2024 Team
2024
Chen et al. 2024e Zhu et al.
2025
Wang et al.
2024a
Bai et al.
2025
Liu et al.
2024b
Abdin et al.
2024a b Deitke et al.
2024 Liu et al.
2024e Lu et al.
2024b
Lin et al.
2024
Cho et al.
2025
Wang et al.
2024b Yao et al.
2024 Liu et al.] 20241 Zhang et al.
2024a Chen et al.
2024d
Lin et al. 2023a) have emerged as a powerful foundation for general-purpern Al systems, enabling
unified understanding and reasoning across text, images, and videos. These models demonstrate impressive
performance in tasks such as visual question answering, image and video captioning, visual grounding, and
open-ended dialog grounded in visual context. As research shifts toward longer, higher-resolution, and lemporally complex visual inputs, improving the scalability and efficiency of MLLMs becomes increasingly
critical.
Most existing MLLMs (Wu et al.
2024b; Chen et al.
2024c Zhu et al. 2025 Liu et al. 2024a; Li ct al. 2024a
Wang et. al. 2025 Cho et al. 2025 follow a standard architecture popularized by the LLa [Luu et al. 2023 framework. In this paradigm, visual inputs are first processed by a vision encoder (e.g., CLIP (Radford et al, 2021), SigLIP (Zhai ct al.) 2023 Tschannen et al., 2025g) to extract per-frame image embeddings. These embeddings are then projected into the language model's token space via a vision-language connector module, after which the LLM performs cross-modal reasoning over the joint sequence of visual and textual tokens. Early approaches such as BLIP-2 (Li et al. 2023ab and LL (Liu et al. 2023) adopted fixed-resolution encoding strategy, where all input images are resized to pre-defined resolution (c.g., 384x prior to feature extraction. This resizing distorts the aspect ratio and potentially damages the spatial integrity of high- resolution images, leading to information loss. To alleviate this issue, recent works like LLaVA-Next (Liu et al.) 2024a) introduced a tiled patch encoding mechanism: each image is decomposed into multiple overlapping tiles (e.g., 384x384), each tile is independently encoded, and the resulting embeddings are concatenated along with the embedding of the full image. While this improves high-resolution coverage, it introduces two major limitations: 1) the lack of global spatial attention due to independent encoding of tiles, and 2) substantial visual token redundancy, which significantly increases compulational overhead. Moreover, while there have been emerging efforts to extend MLLMs to video-level tasks (Wang et al. 2025 Cho et al. 2025), most models still rely on static image encoders applied hame-by-framee, without explicit temporal modeling. This neglect of temporal dynamics such as motion patterns, causal relations, and temporal dependencies hinders their ability to reason over videos effectively. As a result, current MLLMSs struggle to jointly understand both high-resolution spatial structures and temporal semantics in visual content.
In this work, we present TempFlex- VL, token-efficient and temporally-aware MLLM designed to address both spatial and temporal limitations in existing pipelines. As shown in Figure TempFlex-VT. delivers strong results across a variety of challenging image and video understanding tasks. To improve image encoding,
we propose a naive any-resolution strategy based on SigLIP2 [Tschannen et al., 2025 J. Rather than tiling high-resolution images into fixed-size crops, we modify the encoder to directly process images of arbitrary resolution while prescrving full spatial structure. Compared to tiled-patch encoding, our method-integratod with patch merger significantly reduces the number of visual tokens (by at least. 72%) while maintaining spatial fidelity and enhancing downstream task performance, demonstrated in the results shown in Figure 3
For temporal modeling, we introduce Temporal Fiber Fusion, a lightweight, maining-friendly module that augments frame-wise image features with explicit temporal structure. TFF operates on the temporal axis of per-patch features (i.e., shape [B, F, P. DJ) and can be appended after a image encoder in a fully plug- and-play manner. It comprises three parallel temporal pathways ("fibers") that capture complementary motion patterns: (i) a Local fiber applies dynamic 1x3 convolutions using each patch's mean embedding to capture fine-grained motion over adjacent frames; (ii) Memory fiber maintains a gated accumulator whose
2

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Published in Transactions on Machine Learning Research (12/2025)
07.8
73.7
08 603
1817 54:1
81.5 92.
12.1
452
MMStar
Hallusionbench
MathWssaa
MMMU(vall
A120
VideoMME sub)
MIVU(val)
Gamma3-48
Qw0-2
IntemMVL2.544B
CempFlox-Gemmaa..o
Cempplax-Qwen3..o
Figure 1: Performance Comparison of TempFlex- VL with the previous advanced image/ /video MLLMs on various representative benchmarks.
update rate controlled by learned gates a = (W2), enabling integration of long-term dependencies and static context; and (iii) Periodic fiber encodes frame positions using complex exponentials across multiple frequencies to detect recurring patterns such flashes oscillations. A learned fusion layer dynamically video sequence, allowing the model to adapt to diverse motion characteristios
weights these pathways while reducing the risk of overfitting.
We push the limit of large-scale video-language pretraining by introducing TempFlex-2M, 2M, a synthetic multimodal video dataset containing million samples. Previous pipelines for creating synthotic data for video instruction tuming generally follow multi-stage approach (Zhang al. 2024b c): they first generate frame-level image captions, and then prompt large language models to produce question-answer pairs based these captions. However, this process inevitably introduces an information bottleneck during caption generation. In contrast, our TempFlex-2MM 2M is constructed in single stage by prompting GPT-4o with visual input directly, producing temporally aligned and semantically rich narrations.
Our main contributions are summarized as follows:
(1) We propose an any-resolution visual encoding strategy that processes high-resolution images directly without tiling, preserving global context and reducing visual tokens by at least. 72% compared to tiled patch encoding, while achieving favorable accuracy-efficinnc trade-off.
(2) We design a plug-and Temporal Fiber Fusion module to enhance temporal modeling across video frames, which quickly adapts to pre-trained visual encoders.
(3) We construct a large-scale and high-quality synthetic video dataset TempFlex-2M tailored for multimodal video-language pretraining, which brings consistent improvements across multiple video benchmarks.
(4) We demonstrate the generality and efficiency of TempFlex-Vi VL by instantiating it with Gemma3-4B and Qwen3-4B backbones, achieving competitive state-of-the-arl performance across diverse image and video understanding benchmarks (Figure 1 with all code, models, and data to be released publicly.
2
Related Works
Vision-language models. Leveraging the capabilities of large language models (LLMs), a growing body
of Multimodal large language models (MLLMs) has been introduced for image understanding (Li al. Gu et al.
2023a
2024 Liu et al.
2023
2024a
Lin al. 2024 Gao et al. [024b] Deitke et al. 2024 Abdin
et al.
2024a:
Jeddi et al. 2025
video understanding (Zohar et al.) 2024 Lin et al.) 2023b Shu et al.
2024
Li ct al.
2023b Wang ot al.
2025
Chen et al.]
2024d Zhhng et al. 2024a
Wang et al.| 2024b Jiang Chen el al. 2024e Zhu el al. 2025
et al.
2025).
and unified image-vides comprehension [Wu el al. 2024b
Wang et al. 2024a Bai et al. 2025 Chen et al. 2025 Li et al. 2024a Cho et al. 2025 Gao et al. 2024a).
These models incorporate advances such as dynamic high-rosolution inputs (Li al. 2024a), ) adaptive token
compression (Jiang et al. 2025 Jeddi et al. (202)). and multimodal positional embeddings (Wang al.
2024a:
Bai et al. 2025 enabling more fine-grained image and video understanding. In the same spirit, we
3

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Published in Transactions on Machine Learning Research (12/2025)
introduce TempFlex- VL, unified vision-language model designed for both image and video understanding, featuring token-efficient any-resolution encoding strategy and a plug-and-play temporal fusion module to achieve state-of-the-art results across a broad range of MLLMs tasks.
Multimodal synthetic dataset. Training data is critical for MLLM development, with synthetic data emerging as a key ingredient for instruction tuning (Abdin et al. 2024b). While many methods exist for image- based instruction datasets (Zhao al.) 2024 Yang al. 2025b Liu al. 2023; Deitke et al. 2024 Chen al.] 2024a Su et al., 2023 synthetic video-based data remains scarce and less diverse (Zhang et al,
2024c b). Recent approaches like LLa Video-1788 (Zhang et al. 2024c) follow two-stage pipeline: first generating detailed captions using GPT-4o (Hurst et al. 2024), followed by constructing VQA pairs based solely on the generated captions. However, this may overlook fine-grained visual or temporal details, leading to semantically biased supervision. To overcome this, we introduce CempFlex-2M, a large-scale synthetic dataset where GPT-4o (Hurst et al., (024)) directly processes raw visual content and auxiliary text (e.g., OCR, ASR, titles) generate VQA data in single stago, enhancing visual grounding and temporal richness.
3 Method
3.1
Network Architecture
As illustrated in Figure 2 our architecture consists of four main components: visual encoder, temporal Fiber Fusion module for temporal modeling, a Patch Merger module for visual token compression, and a large language model.
Visual Encoder. We adopt the pretrained Sig..P22-patch16-nafes as our visual encoder. Unlike patch-basod approaches that require fixed input sizes tiling (e.g., 384x388 crops), SigLIP2-naflex natively supports arbitrary imput resolutions by design. This is enabled by its flexible positional encoding mechanism and the ViT-style patch embedding. To better accommodate high-resolution images, we extend the original positional embedding length from 256 to 2048 using bilincar interpolation:
p Interp(p.j), j 1, 2048,
where p is the pretrained positional embedding. This extension enables efficient encoding of large images while preserving spatial context. Details of the visual encoding process are provided in Appendix A.1
Temporal Fiber Fusion Module. As SigLIP2 is trained for still image understanding, it lacks temporal modeling capabilities necessary for video tasks. To address this, we introduce a lightweight Temporal Fiber Fusion module to enhance temporal reasoning across frames. TFF captures short-term motion, long- range dependencies, and periodic patterns through specialized temporal pathways. Details are provided in Section 3.2
PetchMerger. To reduce the number of visual tokens and improve token efficiency, we apply a pixel unshuffle operation on the image feature maps. This reduces spatial dimensions while preserving information in the channel dimension. The resulting compact tokens then projected via two-layer MLP to match the input space of the language model:
MLP((nnhuffle(2))
where: 2 are the patch-level features from SigLIP2.
Large Language Model. We the pretrained Gemma3 Team et al. 2025 and Qwen3 Yang et al. 2025al model as our language backbone. Text is tokenized and embedded, then concalenated with the processed visual tokens before being passed to the LLM. The model is trained using a standard cross-entropy loss for next-token prediction.
3.2 Temporal Fiber Fusion
We propose Temporal Fiber Fusion, lightweight yet expressive module that injects temporal reasoning into the frame-patch representations produced by the vision encoder. Let xe BxFxxxX denote the batch
4

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Published in Transactions on Machine Learning Research (12/2025)
Large Language Model
Patch Merger
Text
Tokenizer
Temporal Fiber Layer
x4
Temporal Fiber Fusion
Residual refinement
Visual Encoder
Adaptive fusion
Native any resolution inputs
This is TempFlex-VL
depli-oise conv
Dynamic 1"3
Gated Camuuative Sum
Sinuacidal Moduration
Wate
Local Path
Memory Path
Periodic Path
120 Toee
WAk E2
Imagge
Widdi 690
Video
frame Voual tokens
Figure 2: Overview of TempFlex-Vi VL framework. TempFlex natively accommodates image and vidco inputs of arbitrary resolutions. Its Temporal Fiber Fusion module reinforces temporal modeling, while the Patch Merger effectively reduces the dimensionality of visual tokens.
of visual tokens, where B is the batch size, F the number of frames, P the number of spatial patches per frame, and D the feature dimension.
E FxD a temporal fiber describing how that spatial location evolves over time. TFF treats each fiber as a onc-dimensional signal and derives three orthogonal temporal viewpoints: local, memory, and periodic. These viewpoints fused with data-dependent weights and finally broadcast across patches to share frame-level context.
Intuition. For a fixed patch index p, the slice xp - (X XF.))
3.2.1 Module Formulation
For every fiber p we derive three complementary temporal views and fuse them adaptively.
Local Path.
two-layer MLP dioc: : TR. R3D maps the fibre mean x, E, Xy. to a depth-wise kernel kp kpp 1. kp.1, 133x D, The local output is the circular convolution
Lfp Σ kp C (x(+)) mod F.p
(1)
where O denotes element-wise, channel-wise multiplication.
Memory Path. A gated accumulator captures slow trends. With trainable matrix W. W.ERDXD we set Of (W.. e (0, 1) where a(-) is the element-wise logistic sigmoid and update
M Ofg O my-1pp (1 -.f.)) 2 xf
M fi
m mfp:
(2)
initialised with m-1.p 1 0.
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Published in Transactions on Machine Learning Research (12/2025)
Periodic Path. A small dictionary of integer frequencies ! [ui...;wkk ( (K=5 in practice) detects
rhythmic motion:
P pSP
Σ cod22wffFF) W..ps
(3)
where W. ERDx are learned projections.
Adaptive fusion and contextual broadcast. the three paths:
Fibre-wise weights 3, softmax W Wxxp)
R combine
VS. - L B + -/000 P PSP
(4)
Restoring the original layout yields Y RBxPxPXD For each frame we compute the global context. o +E, fs and broadcast it with learnable scale yERD Y Ys
70c
Residual refinement. A gated food-forward network FFN(z) = GLU(W, GELU(W1z)) further mixes channels. The final putput of one TFF layer is
F = Xs, + LayerNorm
Y FFN(F (.))
(5)
3.3 Vision- Proomped Dataset Construction
To facilitate the training of TempFlex-Vi VL, construct high-quality video language instruction dataset tailored for diverse temporal and visual rcasoning tasks. Existing datasets such as ALaVA-Hound (Zhang
(et al. 2024b), ShareGPT4Vides (Chen et al., 2024c), and LLaVA-Video-178), (Zhang et al. 2024c lypically follow a two two-stage generation pipeline: an LLM first generates a dense caption from video content, and subsequently produces VQA pairs conditioned solely on that caption. This Coption - VQA strategy suffers from information bottlenecks, as unmentioned visual cues such as fine-grained motions, or non-salient interactions irrevocably discarded, leading to generic or context-insensitive supervision.
In contrast, propose single stage Video Text Caption+ VQA pipeline. Given raw video frames along with auxiliary metadata (e.g., titles, tags, and ASR transcripts where available), we prompt GPT-40 to jointly produce (i) a concise clip description and (ii) a diverse set of question-anie -onswer answer pairs. These QA pairs are designed to span twelve complementary reasoning skills: Object Description, Cansal Reasoning, Temporal Reasoning, Scene Description, Human Activity, Appearance Description, Spatial Awareness, Sequence Ordering, Attribute Evolution, Count and Quantity, Narrative Understanding, Fine-grain Action Recognition, and Motion Analysis. This direct promppng strategy preserves fine-grained information and yields high-utility, multimodal instruction data.
Our resulting dataset, TempFlex-28 2M, comprises 210K curated and de-duplicated videos sourced from FineV- idco FFaré et al.] 2024), OpenVid-1M (Nan et al. 2024), Vatex (Wang et al. 2019). and Virpt (Yang et al.) 2024). The dataset contains 1.77 million vision-grounde
instruction pairs, covering approximately 1,500 hours of footage across domains such as everyday life, sports, how-to videos, documentaries, and short. films. A comparative summary is presented in Table [ and detailed category-wiss distributions and the prompts used for data generation are provided in the Appendix A.2
Table Comparison of TempFlex 2M and other video-language datasets.
Datasets
Pipeline
Video
Total Video Length
Average
Caption
VQA
V...V-Houund Zhang
2024h
Video Caption VQA
900K
3K hr
0.008
900K
906K
ShareGPTVidee Chen et al
20
Video - Caption VQA
10K
0.2K lr
0.15
40K
Videen-n-175 Zhang et
Video Caption VQA
TempFlex-2M
Video + Text Caption VQA
210K
1.5K hr
1
210K
1.77M

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Published in Transactions on Machine Learning Research (12/2025)
Table Comparison with state-of-the-art methods on image benchmarks. The best results are highlighted in bold, and the second-best results are underlined. Most of the comparison results in the table are sourced from well as all our TempFlex
OpenCompass (Contributors )023); for entries not covered by OpenCompass, rosults, we performed consistent evaluation using VLMEvalKit (Duan et al. 2024
OCR-related Understanding
College-level
Math
Models
A12D
DocVQAval
Text
MMMU val
MathVistamii
Paligemma-30 3B Beeer et al.
2024
68.3
73.9
70.1
34.9
28.5
Gemma3-4B Team al.
2025
70.7
62.3
60.3
47.3
46.3
Phi3.5-vision-4B [bbdin ot al.
2024a
77.8
70.3
65.2
44.6
43.3
Phi4-viston-5.6B (Abdin et al.
2024b
83.0
92.6
76.9
56.0
65.8
Qwen2-VI.s VL-2B Wang et al.
2024a
74.7
89.2
79.9
42.2
48.0
Qwen2.5-VI--B [ai et al.
2025
81.4
93.0
79.3
51.2
61.2
Mini-InneenVL-hhattaee
Gao
al.
2024b
77.0
86.7
73.0
45.1
55.0
InteraVL2-1B Teeam 2024
79.1
88.1
74.7
48.3
58.5
InteruVL.2.5-1B (Chen et 2024e
81.4
91.1
78.8
51.8
60.8
Vemppleex-emma333 (Ours)
82.0
90.1
77.8
44.0
63.6
TempFlex- Qwerxx4-een3BBBB (Ours)
80.9
91.0
79.1
52.1
67.8
Ceneral Visual Question Answering
Models
Overall
MMStar
MMBench-EN EN
Realworldqa
HallBench
Paligemma-33 Beyer et
2024
48.3
65.6
55.2
32.2
53.0
Gemma3-4B Team et al.
2025
47.9
66.4
55.6
40.8
55.3
Phi3.5-vision-IB AB Abdin al.
20248
47.5
67.4
53.6
40.5
56.7
Phi4-vision-5.6B
Abdin et al.
2024b
58.9
77.2
64.1
40.5
68.3
Qwen2-V1-22 Wang al.
2024a
47.5
72.2
60.7
42.4
61.9
Qwen2.5- VL-33B [Bi et
2025
56.3
46.6
67.9
Mini-IntenVVL-Chatt-B
Gao et al.
2024b
53.1
69.7
60.8
43.0
62.6
IntemVLL2-4B Team 2024
53.9
73.6
60.5
42.4
64.3
IntemVL12.--B (Chen et al. 2024e
58.7
78.2
64.6
46.6
68.0
Gemmaale- (Ours)
58.7
79.2
64.6
43.9
67.1
SeuppleexQween3-BB (Ours)
60.4
78.5
66.1
47.6
69.3
4 Experiments
4.1 Implementation Details
Architecture. We adopt the 1asip22-so000m-pachhheee encoder, initialized with public pretrained weights. To handle longer visual sequences, we expand its original positional embodding from 256 2048 tokens. For the language backbone, we use the docoder-only LLM component of Gemma3-4B (Team et al. 2025) and Qwen3-4B (Yang al., 2025a). The TFF module consists of four stacked layers with hidden size 1152. Each layer generates a 3 1152 dynamic depthwise kernel via a two-layer MLP with hidden size of 1152, encodes periodicity using five fixed frequencies {1, 2, 4, 8, 16), and applies a linear fusion gate to predict three blending weights per patch fiber. A gated feedforward network with hidden size 2304 is appended to each layer. For patch merger, we apply a stride-2 pixel unshuffle operation to the patch embeddings, resulting a 4x reduction sequence length.
(Tschannen et al. 2025 model as our visual
Training Recipe. All models are trained on 128 NVIDIA H100 GPUs with 80GB VRAM in four distinct
stages:

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Published in Transactions on Machine Learning Research (12/2025)
Table 3: Comparison with state-of-the-art methods on video benchmarks. The best results are highlighted in bold, and the second-best results are underlined. Most of the comparison results in the table are sourced from OpenCompass (Contributors )023); for entries not covered by OpenCompass, as well as all our TempFlex results, we performed a consistent evaluation using VLMEvalKit (Duan et al. 2024
Models
Frames
MMBBench- Videeo
MLVU
TempCompass
Loug VodgeBBeeeenc
VideoMME (wa/ww sub)
Kangaroo-SB (Lä el al.
2021b
64
1.4
61.1
62.5
51.8
56.0
57.6
TimeMarler-Sn SD Chen ot al.
2024d
128
1.53
63.9
60.4
56.3
57.3
62.8
IntecuVL2-BB Team
2024
1.28
56.5
65.1
56.9
MidCPMM--22.--BB Yau el ml
2024
64
1,76
52.5
57.1
60.9
63.6
LongVA-7B Zhong
2024n
128
56.3
57.0
47.8
52.6
54.3
Video-XI-7B (Sbu xk 2024
128
619
50.7
55.5
61.0
LLavn-OneVision 7B [T ct al.
2024m
32
64.7
56.3
58.2
Qveu2-VI--B Wang
20/244m
1.44
54.8
59.1
Plid4-visioo-5.6B Abdia el al.
b0244
64
0.87
45.6
50.4
42.4
Phi3.5-visson-4B
Abdia ct
2024m
16
1.20
52.5
59.7
51.1
InteenVT.2-4B Tewm [0144]
64
1.45
50.9
53.0
53.9
37.0
1- C Chen ot al.
A024e
64
1.73
68.3
35.2
62.3
63.6
Qwen2.5 VL--BB Bai
2125
768
1.63
64.4
07.6
TempFlex Gemmad--mmllll 1B
128
1.58
69.0
63.8
57.2
63.3
61.3
HmmpFlex-Qwen--41
1.86
70.9
68.9
65.9
Stage 1: Visual-Language Alignment and Encoder Adaptation. We jointly fine-tune the visual en- coder and the MLP projector, setting the learning rates to 5c-5 and 1c-3, respectively. This stage uses 13.67 million high-quality imago-caption pairs with diverse resolutions to align the visual and language modalities and adapt the pretrained visual backbone.
Stage 2: Image Instruction Fine-Tuning. In this stage, train the visual encoder, MLP projector, and LLM simultancously. The visual encoder is trained with learning rate of 2e-6, while the remaining components use 1e-5. This stage utilizes 27.94 million multi-task instruction samples spanning general question answering, OCR, document/ /cchar/scrreen understanding, mathematical reasoning, and pure language tasks.
Stage 3: Temporal Fusion Alignment. We activate the TFF module and the MLP projector, using learning rates of 50-4 and 1c-5, respectively. This stage is trained on 900K vidco-captioning pairs to guide temporal reasoning and dynamic representation learning.
Stage 4: Video Instruction Fine-Tuning. All model parameters are updated in this stage. The visual encoder is trained with a learning rate of 20-6, and the rest of the model with 1c-5. The total training data includes 6.68 million samples: million high-quality video instruction samples synthesized in-house, 1.4 million public video instruction-tuning samples, and 3.28 million image- based instruction samples to retain the model's image understanding ability. During training, maximum of 64 frames is sampled per video, while 128 frames are used at test time.
For public data sourcing, we follow VLaVA-OneVision Li et al. 2024a and Infinity-MM (Gu et al. 2024 in constructing the instruction tuning corpus. All datasets undergo low-quality filtering, and we provide detailed dataset composition for each stage in Appendix A.3
In terms of training settings, Stage is trained for epochs with batch size of 256 while all other stages use batch size of 512 and are trained for epoch. We employ a cosine learning rale scheduler with a warmup ratio of 0.03. To enhance training efficiency and scalability, we apply DeepSpeed ZeRO-2 optimization, gradient checkpointing, and Flash Attention throughout the training process.
Evaluation. We conduct comprehensive evaluations on a diverse set. of image and video benchmarks. For image- based tasks, we report results on MMStar (Chen et al., 2024b), MMBench [Liu et al. 2024c). RealWorldQA, Hallusionbench (Guan et al.] 2024). MathVista (Lu et al. 2023), , MMMU (Yue et al. 2024), AI2D (Kembhavi et al., 2016),, Doe (Mathew et al. 2021), and TextVQA (Singh et al. For video-level evaluation, we include VideoMME (Fu et al.] 2024), Long VichoBench (Wu et al. 2024a
2019
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TempCompass (Liu et al. 2024d), HLMBench-Videe (Fang et al, 2024]. and MLVU Li et al 2024b). We
compute accuracy using the standardized VL.MvvlKit.. (Duan et al. 2024 open-source evaluation framework
to ensure consistency and reproducibility across different tasks.
4.2 Main Results
Comparison with SoTAs in Image Understanding, Table 2 shows that our TempFlex family sets
new performance bar for 4B-scale rision-languan models (Beyer et al.) 2024; Team et al. 2025; Abdin
et al. 2024a b Bai et al. 2025 Wang et al.] 2024a Gao et al.) 2024b Chen et al. 2024e; Team 2024]
across nine image understanding benchmarks. of 69.3%, surpassing the previous best 4B model, InternVL2.5-4B (Chen al. 2024c). by 1.3 percentage
FeepFlex-eeee33 attains the highest overall average
points (pp) and even edging out the larger 5.6 B Phi4-vision (Abdin et al. 2024b) by 1.0 pp. This lead is driven by consistent gains on both general vistal-guestoon-nnsvvrinn tasks and specialist benchmarks: VempFlex-Qwen3- establishes new state-of-the-art results on MMStar (60.4%), RealWorkdQA (66.1%) HallBench (47.6%), and MathVistamini (67.8%), while matching or exceeding prior bests on the OCR-centric Vex (79.1%) and Doc (91.0%). Temp likewise performs strongly, posting the top result on the comprchensive reasoning benchmark MMBench-ENaas at 79.2% and ranking a close sccond to Phi4-vision (Abdin al.) 2024b) on AI2D (82.0% vs. 83.0%). is worth emphasizing that, compared to Fee--mmma3 elevates the overall average from 55.3% 67.1%, marking a notable improvement of 11.8 percentage points. This gain, achieved by simply adopting different visual-encoding strategy, underscores the strength of TempFlex's natively any-resolution visual representation.
the original Gemma3 (Team et al. 2025),
Comparison with SoTAs in Video Understanding. Table 3 benchmarks our two 4B-paramed
variants Temp-lee--Gemaa--- Gemma3-4B and TempFlex Qwen3- 4-B against the strongest published video-capable
MLLLSs [Liu al.] 2024b Chen et al. 2024d Team 2024 Yao et al.,
2024 Zhang et al. 2024a Shu et al.,
2024 Li et al.] 2024a] Wang al.) [2024a] Bai et al.) 2025 Abdin al. 2024b a Chen et al. 2024ep spanning
3B to 8B parameters. Despite their compact size, both TempFlex models either match surpass every larger competitor on all five representative benchmarks.
attains the best overall performance across all benchmarks. It achieves 1.86 on
MMBench- Video (Fang et al. 70.9 on MLVUval (Zhou et al. 2024], surpassing InternVL2.5-4B (68.3) and Qwen2.5-VLie VL-3B (68.2); 60.4
2024), outperforming InternVT.2.5-4B (1.73) and TimeMarker-SB (1.53);
on Long VidcoBench.al (Wu et al. and 65.9 on VideoMME (Fu et al. TempCompass (Liu et al. 2024d], it reaches 68.9, the highest score to date, indicating improved temporal
2024a), ahcad of TimeMarker-8B and LLava-OneVise (both 56.3); 2024h, exceeding InternVL...544B (62.3) and Kangaroo-SB (56.0). On
alignment and multimodal reasoning.
TempFlex -eem--a33 also shows consistently strong performance: 1.58 on MMBench- Video, 69.0 on MLVU 57.2 on LongVideoBenchy.. 63.3 on VideoMME,, and 63.8 on TempCompass. These results outperform most 4B and even some 7B 8B baselines, such as InternVL2-41 Phi3.5-Vision-4B, and LAngVA- 7B.
It is worth noting that the strong performance of CempFlex-Qwenn-eee TempCompass further confirms the effectiveness of the proposed Temporal Fiber Fusion module in enhancing temporal understanding across diverse video reasoning tasks.
Qualitative Results. In Figure 4 we illustrate our model's exceptional visual understanding and text generation capabilities across various scenarios, encompassing both image and video. More qualitative results are provided in the Appendix A.4
4.3 Ablation Study
Effect of TempFlex encoding. To rigorously assess the impact of the visual-encoding module, we
compare TempFlex's native any-resolution strategy with two prevailing alternatives: (1) Fix-resolution
encoding (Liu et al. 2023 Zhu al. 2023 Li al.) 2023a), which resizes every image a fixed spatial
size before feature extraction; and (2) Tiled Tiled-patch encoding (Liu et al. 2024al Li et al. 2024a Wu et. al.
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10
80
792
# Visual Tokens
10a
Accuracy
(log
102
60
10a
40
A12D
MMBanch
MMStar
DocVOA
RealWoridOA
TbrVOO
52x32
384x384
768x768
1152x1152
1448x1448
Ri--rs encoding
Tleed-pacch encading
TempFlex encading
Figure 3: Comparison of performance (left) and efficiency (right) across different visual encoding strategies. Efficiency is measured by the number of visual tokens generated under varying imput resolutions for each encoding method.
Table 4: Performance evaluation of Temporal Fiber Fusion module on vidco benchmarks. TG denotes TempFlex-Gemnn while TQ refers to TempFllex-Qen-3-- model.
Models
VideoMME (w/o subs]
#
TempCompass
Local fiber
Memory fiber
Periodic fiber
Overall
Short video
Medium video
Long video
57.1
70.9
54.1
46.2
58.2
58.4
72.2
55.3
47.7
59.4
TG
V
57.9
71.9
55.0
46.8
58.8
/
V
58.2
72.0
55.1
47.5
59.1
6
/
58.8
72.7
55.7
47.9
59.7
58.4
72.5
54.6
48.1
61.7
59.2
73.2
55.8
48.6
63.0
TQ
V
58.7
72.9
55.0
48.2
62.6
48.5
62.9
/
59.6
48.7
2024b Team 2024
Chen et al. 2024e),
which tessellates the inpul into 384 384 crops whose features
are concatenated into a long visual token sequence. All experiments are conducted under identical training scttings with Gemma3 (Team et al. 2025) as the LLM backbone and the only variation the visual encoding strategy. This ensures a lair and coutrolled comparison.
As shown in Figure (left), TempFlex consistently outperforms both baselines across six image benchmarks. It yields +3.55 percentage points (pp) improvement on AI2D (Kembhavi et al.) 2016) and +4.2 pp MMBench (Liu et al. 2024ch, while achieving comparable performance MMStar CChen et al. advantage becomes more prominent in high-resolution, detail-centric benchmarks. On DocVQA (aathew et al. 2021), TempFllx achieves 90.1% accuracy, surpassing fix-resolution (47.6%) and tiled-patch (85.4%) by a large margin. Similarly, on TextVQA (Singh et al. 2019), TempFlex reaches 77.8%, outperforming fix-resolutinn (42.2%) and tiled-patch (73.5%).
2024b). The
Figure (right) compares token efficiency across resolutions. Fix-resolution encoding, which compresses all inputs to 384x384 pixels, yields a constant number of visual tokens of 729 regardless of input size. While this approach is computationally efficient, it leads to significant performance drop high-resolution benchmarks such VQA. Tiled-patch encoding preserves more detail but significantly increases visual token count, reaching 7,290 tokens at 1152x 1 152 resolution -verooo--vvee 5.6x higher than TempFlex at the same resolution. contrast, our encoding strategy is significantly more efficient, aided by patch merging: producing only tokens 32x32, 576 768x768, and 2,048 at 4448x1448. These results highlight TempFlex's favorable accuracy-efficinns, trade-off: it enables global attention over high-resolution inputs while reducing token overhead by at least. 72% compared to patch-wise encoding.
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Published in Transactions on Machine Learning Research (12/2025)
Effect of Temporal Fiber Fusion. To validate the effectiveness of the proposed TFF module, conducted detailed ablation study to evaluate the impact of each of the three branches in the TFF on two TempFlex- VI. vairants. All experiments were conducted on the VideoMME (Fu et al., 2024) and TempCompass (Liu ict al. 2024d benchmark. For training efficiency, we sampled 30% of the training data while keeping other experimental seitings consistent to ensure fair comparability of the results. As shown in the Table 1 the checkmarks (V) indicate the presence of each fiber branch. From the experimental results, is evident that all three branches local, memory, and periodic contribute to significant performance improvements.
Table 5: Comparison of the model performance with and without synthetic data on VideoMME.
Overall
Short Video
Medium Video
Long Video
Models
w/o subs
subs
w/o subs
w subs
w/o subs
w subs
w/o subs
subs
TempFlex-VT.ee VT.-BB
63.3
64.3
76.3
78.2
63.3
65.1
50.3
49.6
w/o TempFlex-24 2M
62.5
63.5
75.3
77.0
60.7
63.8
51.7
49.8
Effects of Synthetic Data. To assess the contribution of synthetic video-text pairs, we conduct an ablation study on the Video-MME (Fu et al. 2024) benchmark by removing the TempFlex- 2M synthetic subset from the training corpus. Table 5 shows performance with and without synthetic data across different video lengths and subtitle availability.
Removing synthetic data leads to consistent drops in performance, both in the presence and absence of subtitles. The overall accuracy declines by 0.74 0.78% on average, and more notably on mediun-length videos, suggesting that synthetic supervision enhances temporal reasoning.
Big Tasty
ITC Limited PoPomT ACCOUNT 2013
Saver Menu
User: How many posters are deppayed on the left side of entrance doors! visible tee left side athentie diffinent mons liems aeailabic MeDoould's, The fins for 'lig Iaay" and scrondl promotaa the "Sncer Mena."
User: What tee rame of company?
TemppFee::
doors. These
Tempplex: ITU Limned,
This conchsioo como dicalls from der bealer of the dacament which cleady statcs 'ITC LIMITED REPORT AND ACCOUNTS 2013".
User: what phrase prinned the Good paster inmodiately the left the entanne doces?
brndd TempFlis: Auslivvadd Anw Muligmire.
User What's
fasurcal in adverisemmn??
TempFlee:: The plause "Big Tuay" is piited on the food poster immodiatly to the left of the entrane dooes can stemihod by looking at the posters diellayo in from the Mel honslfh, store
This conchacion was meched by icomiiiing the spezific product mentional the which stiee indicntos the the heand being adventised Andhinoed Milligrrde per the infocmationn provided given image.
erernce where they advertise ther mens hemss tod "Big Tioe" stadd out preminenaly agiie the bockgrouudd
"AASHIRAA ATTA MUTTIGRANN
poster.
(a) Fine-grained Visual Perception
(b) Document Understanding
FREE BLITES ANTTESTMEEE TPS:
PRENREX PREAPPLE
Please desorie this detuils
Fle::The válze begins pensan kithee setting, weaingg stirt, sunding af counter various and sign that "FOOD BUSKER. "FOOD"
"BUSKER" appear sareen. soese tussitions case-up of pinzapple oz woodes suftee, 'Food Busker's Minute
Prepure Piscapole" diplayed person
to prguer a pincopple aseing romsingg top the tpp penson
quie-arsse pincoplecs usal fini's being The pinseple, coasing ring pincapplc
posso's volco plassi
eyes The
fni.,
lokcy hand
shills parson huiding ring nig
pineapple
(c) Video Understanding: Action Recognition and Temporal Perception
Figure& 4: Quantitative Results for Image and Video dialogues.
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5 Conclusion and Limitation
This work introduces TempFlex-VL, a fully reproducible multimodal language model that overcomes two persistent bottlenecks in vision language research: resolution rigidity and the lack of explicit temporal reasoning. By pairing a native amy-resolttion adaptation of SigLIP2 with a lightweight Temporal Fiber Fusion module, MampFlex-VT-44F delivers statc-of-the-art performance across a broad suite of image and video benchmarks while using far fewer visual tokens than prior systems. We further release the accompanying TempFlex-22 2M synthetic video corpus, model weights, and training code to facilitate transparent, community- driven progress.
Limitation While TempFlex-V VLL benefits from strong supervised pre-training, it has not yet been post-trained with reinforcementlearningg techniques that could better align its outputs with downstream user preferences. We leave the integration reward modelling and preference optimisation as promising future work to push the model's capabilitics beyond the current supervised ceiling.
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A
Appendix
Table 6: Comparison of visual encoding strategies 1280 x 720 input image.
Method
Token Count
Distortion
Memory Cost
Feature Quality
Remarks
Fix-Resolution
576
Low
Moderate
Simple but lossy due to resizing
Tiled Patch
4032
x
High
Medium
Fragmented tokens, weak cross-tile links
TempPlex (Ouss)
900
x
Moderate
High
No resising, full-resolttion attention
A.1 Details of visual encoding process
In this section, we provide a detailed explanation of the three visual encoding strategies considered in our
Encoding, Tiled Patch Encoding, and our proposed TempFler Encoding. We also compare these strategics quantitativoly in terms of visual token efficiency and computational overhead.
Fix- Rix-Resolution Encoding This approach resizes every input image to fixed spatial resolution (H, W) (384, 384):
I' Resize 38, 384),
where I 1o is the original image. The resized image is then fed into a frozen SigLIP2 encoder:
Vix Lvis Lvis(T') R2766
where each visual token corresponds to 16 x 16 patch and D is the embedding dimension. This method is simple but introduces spatial distortion when processing images with diverse aspect ratios.
Tiled Patch Encoding To retain high-resolution features, the image is divided into non-overlapping tiles of size 384 384, each processed independently by the encoder:
(+))_ fvis (I(K) E 5766 k= K,
where K denotes the number of tiles. Additionally, resized global view Iglobal Resize I, 384, 384) is encoded:
Svis (ighoog)) 576
global
The final representation the concatenation:
V eiled Concat (1))
(K) V Vllbbal) (576K
While this strategy preserves detail and introduces global context, it significantly increases token length and lacks inter-tile continuity.
TempFlex Encoding (Ours) its native resolution. Given I R Hox Wox3 we first obtain patch-level embeddings using a fixed patch size of 16:
Our proposed method avoids resizing and tiling by processing the input at
We T6
Vo fpatch (I) IR
We then apply a pixel unshuffle operator with stride = 2, effectively reducing the token count:
V tempflex = Unshufflez V IR Mo Wg
) x(D))
This operation maintains spatial integrity, allows native resolution input, and significantly reduces the number of visual tokens.
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Token Efficiency and Computational Comparison We benchmark the encoding strategies using representative resolution of 1280 x 720 and summarize their properties in Table G
The token count for TempFlex is approximately an order of magnitude lower than the tiled patch approach while maintaining the native spatial structure of the input. This makes it significantly more efficient and flexible for both short and long video sequences.
A.2 Details regarding the construction of the TempFlex-2M
In this section, we provide detailed information on the construction of the TempFlex-2M dataset, supplementing the brief introduction in the main paper. As described in the main text, TempFlex-2M is a large-scale synthetic video-language instruction dataset curated to facilitate instruction tuning for multimodal large language models (MLLMs). The datasct is synthesized using GPT-4o based on four primary data sources: Virpt Yang al., 2024h, VATEX (Wang al., 2019), OpenVid (Nan et al. 2024). and FineVideo (Farré et al. 2024
To ensure diversity and comprehensive vidco understanding, we define twelve instruction dimensions that reflect distinct reasoning or perceptual challenges, including but not limited to temporal reasoning, motion tracking, event prediction, object interaction, and spatial relationships. GPT-4o is guided to generate both caption-style and open-ended VQA-style annotations for each vidco segment, tailored each of these dimensions.
Figure Faa illustrates the distribution of generated samples across the twelve instruction dimensions, showing balanced and diverse composition. Figure (b) reports the sample statistics from each of the four data sources, separately for caption and open-ended VQA types, highlighting the multi-format coverage contributed by each source.
We further detail our prompt engineering strategies in Figures G and prompts used to guide GPT-4o in generating instruction-followe data. Finally, Figure g presents a complete example from the TempFlex-2M dataset.
where we present representative
Distribution Video Understanding Dimensiors
Sample Statistics per Dataset
Caption
Oprr
Object Docrption
Caupk Bewczinn
830000
mpprr
Descritioo
630000
Sequerce Ordaring
ditibuute Exslution
Doun and Ouantivy
250000
Fime grann Action Tasogeition
Nation Arahys
Crumt
Vigelang
Openttd
Freilces
(a) Distribution of video understanding dimensions.
(b) Sample statistics per dataset.
Figure 5: Overview of vidco undorstanding tasks and datasct compositions.
A.3 Details of the training data composition
This scction supplements the training data composition details. For imago-text data, we follow the data setup from InfinityMMM (Gu et al., (024) and LLa LLaVA-OneVision (Li et al. 2024a), while applying additional filtering to remove low-quality samples, such as those generated by GPT-4V.
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For video-text data, primarily the -LaVA-Videe--1e (Zhang et al. 2024c dataset and our synthetic dataset, TempFlex-2M, which provides high-quality, instruction-oriented annotations.
Table 7 summarizes the amount and composition of training data used in each stage, showing the progression from image-based to video-based multimodal supervision.
Table 7: Overview of training data composition across all stages. We categorize data into image caption, video caption, and comprchensive instruction data. Each row details the data type, total volume, and fine-grained composition per stage.
Training Stage
Data Category
Size
Data Composition
Alignment and Adaptation
Image Caption Data
13.67M
General Instruction Data 13.67M
General Instruction Data 9.24M
OCR Data 2.6M
#2 Image Instruction Tuning
Comprehensive Data
27.94M
Doc/Charde Poc//hart//creen Data 5.8M
MathhRResoning Data 1.3M
Text Instruction Data 9M
#3 Temporal Fusion Alignment
Video Caption Data
900K
General Instruction Data 900K
General Instruction Data 1.3M
OCR Data 0.34M
Doc/Chart/Scrent Data 0.1M
#4 Video Instruction Tuning
Comprehensive Data
6.68M
Math Bathh/Reaonnng Data 0.14M
Synthetic data 1.4M
Public Ilava-video- 178k 1.4M
Our TempFlex Video Data 2M
A.4 More Qualitative Results
In this section, we provide additional qualitative case studies further demonstrate the capabilities of our models. Specifically, we present results from both TempFlex- Semppllll--emma--1111 and TempFlexx-Qen-1IB across a diverse set of multimodal tasks.
The examples cover a wide range of scenarios, including image understanding 10 optical character recognition
(OCR) 11 document and chart comprehension I2 and video-based instruction following 13 14 These cases illustrate the generalization ability and robustness our models across both static and temporal visual inputs.
Through these qualitative results, we aim to provide more comprehensive view of the strengths of the TempFlex framework in handling complex and diverse multimodal tasks.
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GPT40's Prompt for Constructing Video Instruction Tuning dataset
You are an Al assistant tasked with generating questions und answers about video content to create video instruction tuning dataset.
##RTASK:
1. Users will provide multiple stitched images, each containing 9 frames. These frames are extracted from the original video at a frame rate of I fps and are arranged from left to right and top to bottom in tic-tac-tee stylo(3x3).. The order the squares is as follows: the first row from left to right represents 1, 2, second row from left right represents 4, 6; and the third row from left right represents 8, Each row images sequentially coherent in terms of time from left right. Multiple stitched images are also coherent over time. For example, the 9
frames in the first stitched image represent frumes from to seconds, while the frames the second stitched image represent frumes from 9 to 17 seconds, Please note that any white images within the stitched images are meuningless and can be igmored.
2. Users will also provide 1. ASR text correspending the video; 2. Activities happening in the video; Objects/ Props that appeur in the vidco; 4. Video brief summary:
3. Generate detailed and accurate description of a video based on multiple stitched images and clip captions provided. Instructions for writing the detailed description: Focus on describing key visual details such as appearance, motion, sequence of actions, objects involved, and interactions between elements
video. Leave out any descriptions about the atmosphere, mood, style, aesthetics, proficiency, emotional tone of the video. Make the description is more than 20 sentences. Combine and organize information from all frames into clear and detuiled description, removing any repeated or conflicting details. Emphasize importunt points like the order of events, appearunce and actions of people or objects. and any significant changes or movements.
4. Generate two different types of VQA: vga-typel: VQA based solely on video content: vga-type2: VQA combining video content and video ASR text information. All castion-answer pairs should be faithful to the content of the video description and developed from different dimensions to promote comprehensive understanding of the video.
Here are some question dimensions and their explanations and exampled question-answer pairs for reference:
(task definitions)
#owe Guidelines For Question-Answer Pairs Generation:
Read the stitched frumes provided carefully, paying attention to the content, such the scene where the video takes place, the main characters and their behaviors, und the development of the events.
Generate vqa-typel question- answer pains: VQA based solely on (1 Sttttchee images and (2)Props that appear in the video, neither the Answer
nor the Question should contain any inferences that require ASR text or Activities.
Generate vqa-type2 question-ante answer pairs: VQA combining (I) Stitched images; (2)ASR text corresponding to the video; (3)Activities happening in the video; (4)Props that appear in the video; VSddo brief summary. This means you can use all the information provided by the user to generate VQA.
All guestion-answer pairs should be faithful comprehensive understanding of the video. The question-answer pairs should cover as many question dimensions and not deviste from the content of the video.
content of the video description and developed from different dimensions promote
Each VQA type generate 5 question-answer pairs, total 10 question-answer pairs. You need to select the most suitable 5 dimensions out of 15 question dimensions for this vidco. and generate questions based on these dimensions.
How to choose question dimensions: Evaluate which dimensions are suitable for generating questions based on the visual content of the video.
Ensure the accuracy of the generated Questions and Answers as much as possible. Prioritize selecting dimensions where both Questions and video. Avoid choosing dimensions that cannot directly displayed by video visuals require
Answers are clearly demonstrated further inference.
The question should be objective and avoid using subjective words like infer, suggest, likely, indicate, convey, and The questions must be complex and require at least 2 steps of thinking to reach the correct answer. complex question does not mean
question asking combination of elements.
Questions from different dimensions should be as distinct as possible, meaning both the questions and answers need to be diverse, rather than repeatedly asking about a specific action from different dimensions.
##i Output Format:
1. Your output should be formed in a JSON file.
2. Only provide the Python dictionary string.
Figure 6: GPT4o's Prompt for Constructing TempFlex-2M 2M Video Instruction Tuning datasct
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Task Definition Generating Video Instruction Tuning Data Using GPT-4o
TASKS
reasoning about temporal relationships between actions/events, Questions involve previous, present, actions. ## Question-1: What activity did the child perform just before deciding to sit on the baby chair? Ht Answer-l: I: The child was playing with toy.
Temporal Reasoning:
Designed
Spatial Awareness: Tests the ability to perceive spatial relationships between observed instanees in video seene.
84 Question-I: I: Where were the objects located in relation the main character before they moved them!?
Answer-1: I: The objects were initially placed on the side
next window.
suddenly? # Causal Reasening: Focuses on explaining actioms/events, deternining intentions of actions or causes for subvequent events.
Question-1: What prompted person towards #it Answer-1: I: The person heard loud noise coming from outside the door.
Analysis: Involves discerning variations speed, including absolute and relative speeds.
## Question-1: How did the speed of the car change from the beginning to the end the chase scene?
## Answar-1: The car accelerated rapidly at first but slowed down as it approached the crowded intersection.
Binary Questions: Involves yes questions related the video content.
H# Question-1: Did the child look at the window before moving to the chair? (frem a scene where this detail might be subtic)
84 Answer-1: Yes.
Ht Question-2: 2: Was there naticeable change in lighting before the action started??
## Answer-2: No.
Count and Quantity: Tests the ability count instances of objects, people, actions, and to distinguish between old and new elements #* Question-I: How many books did the child pick up from the floor throughour the video?
## Answer-:: The child picked up three books from the floor.
I Narrative Understanding: Challenges the ability to interpret the plat in the video.
Question-1:: I: What underlying renson for character's behavior throughout video?
84 Answer-1: 1: The character trying find hidden object that crucial to the story.
Object Deseription: Assesses the ability to describe attributes objects, like their appeamnce and function.
#* Question-1: Deseribe appearance and possible function the object placed
the lamp.
H# Answer-:: The object is a spherical device with a digital screen, likely used as an alarm clock.
Soquence Ordering: Challenges recognition of temporal sequence activitics vidcos.
HH Question-I: Arrange the following events in the order they occurre: the child playing with the toy, the child sitting on the chair, the child picking up the book. Answer-1. I: The child was playing
toy, then picked up book, and finally sat on the chair.
8 Fine grain Action Recognition: Creates questions challenging comgreehension of subtle actions.
HM Question-1: I: What specific gestures did the character use to communicate with the other person?
## Answer-l: The character used hand signals and nodding communicate without speaking.
Human Activity and Appearance Description: Involves describing actions or attributes of people, such as their activities and appearances.
## Question-1: Deseribe attire and actions of the main choracter during the garden
## Answer-1: The main character wearing blue shirt and jeans, and they are watcring the plants.
Attribute Evolution: Centers how attributes of objects the entire video change time, like size, shape, color, and Htt Question- I; How did the color of the sky change from the beginning to the end of the scene?
H4 Answer-1: I: The sky changed from a bright blue to deep orange as the sun set.
# Camera Movement Direction: Focuses the direction of camera movement.
## Question-1: 1: which direction did the camera pan during the opening sccnc?
Answer-1: The camera panned
right during opening scene.
Object Movement Direction: Emphasizes perception of object movement direction.
Question-1: Which direction did the move when entering frame? Ht Answer-1; 1: The car moved from the right to the left when entering the frume.
Scene Description: Assesses ubility describe major scene videa, like where takes place and enviromment.
Hit Question- I: Describe the setting and atmosphere of the final hattle scene.
## Answer-l: The final battle took place in a dimly lit warchouse filled with crates, creating tense and gritty atmosphere. new
Figure Task Definition for Generating TempFlex-29 Vidco Instruction Tuning Data Using GPT-4o
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(GPT4o's Prompt for Constructing Video Instruction Tuning dataset)
The video takes place a well li room with contemporary secup. feasuring vibrat bocklighting that adds depth color to background The speaker, dressed casually, discusses features not inchuded in specific gaming dedk, Invially, the speaker seated and gesturing towards empty space to his siće, implying the missing foutures. The video then shows close images of those testures ftem other products sull-surface mouse pad being unrolied onto another gaming des,, illastrting suee and it. Following dtis, an online shopping pige display a diffrret gaming desk with buill-in
Caption:
lights, highlightig RGB lighting fisture empitanizee
like langas
discussdl preduct. The
spesker
standing, making swiping gestree
lights.
VQA
"dimersion": "Temporal Reasoning",
"questio": "What the speaker after gesturing sowards the empry space to his side". "answer": "The video then shows close- up images of features from other products, such full-surfsce mouse padi being unrelled onto snother gaming desk.
"dinession" "Spatial Awarensss",
"queetion" "Whrre the spesker positionnd rolution to the gaming clnsc cherin the discrusion?",
"answer" "The speaker scated next to the gaming desk, gestring sowards an empty space his side."
'dimersionn "C "Cneal Ressoning".
"quceiio""
desk?", "unseer" "The speaker swiping gesture over desk emplascae of lights."
speaker swiping
"iinersion" "Ojjast Descripiin",
"questoo": "Deseriee the footures of the giming desk shown the online shopping page."
"answer": "The gaming
shopping page
lights accessories headphone hangens"
"dimension"" "Sqperce Ondering',
"quctioo"" : "Araagg the following events the under they occaredd: showing the online emppy space, the close-up the and then showing the shopping paga."
the spenker yosurrng uownnd the and clesoup the pad!"
"mnswes"
'dimersion'' "Human Activity and Agpearance Description':
"question "Wha is the speaker wearing and what actions does he perforn during the vides?" "unsur"" :: "The spenker dresal casslly and per forms actions such gestring towartk emppty aprcc, showing closeop Seaturos, and makk ing swiging gesture unlii deak."
"dimersion" "Scane Description,
"quseion" "Deseribe the welling the vidca."
"answer": "he video takes place in well li room with comemgoorry setup, featuring vibeant backlighting that adds depth and color the background'
Figure 8: An example from the TempFlex- 2M dataset. The top part of the image shows the input to GPT-4o, including 3x3 grid of 9 concatenated frames along with the prompt illustrated in Figure The bottom part of the image presents the output, consisting of a caption and several open-ended VQA pairs.
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TempFlex-Gemma's
Please describe this image in details.
The image depicts tranquil and serene scene of wooden pier extending into calm body of water, which appears to be lake wide river. The pier is constructed straight line, with wood planks arranged in parallel, and ends with two small floating platforms. The are two distinct mountain ranges visible, implying that the pier is located mountains. The mountains are covered with mix coniferous and deciduous trees. The mountains are partially obscured by clouds or mist, giving them soft, hazy outline. The sky above is overcast, with clouds casting a diftused light over the entire scene, enhancing the peaceful and serene atmosphere.
is remarkably still, reflecting the surrounding environment with crystal clarity.
the background,
valley surrounded by
The foreground is dominated by the pier, which leads the viewer's eye towards the center of the image, emphasizing the calm and stillness of the water. There quality the landscape.
signs of wildlife human activity, accentuating the natural and untouched
Given the absence any human presence and the pristine condition the pier, it can be inferred that this location might used recreational purposes such as fishing, boating, or simply enjoying quiet moment nature. The location's natural beauty and tranquility make it an ideal setting relaxation In summary, the image showcases long wooden pier leading into a calm lake or river, surrounded by distant mountains and cloudy sky. The reflection of the landscape the water and the absence of human activity emphasize the location's serene and natural appeal, making perfect spot for relaxation and enjoying the outdoors.
reflection.
Figure 9: Qualitative result of CempPlex-Gemma333
image understanding.
Cempplex-Qwenn-4p
Please describe this image in details.
image features picturesque urban scene observed from a high vantage point. Dominating upper part image prominent double rainbow. The rainbow curves gracefully from the left to the right of the frame, with the outer are displaying vibrant array of colors that transition from red to violet. The inner are is less distinct and more subdued in color.
Below the rainbow, the cityscape unfolds with multitude of buildings. The foreground occupied tall, multi-story residential building with numerous balconies, of which visible due the perspective. The building's facade is mix light and dark shades, creating textured appearance.
the midground, various other buildings can be seen. These buildings vary height and design, with some appearing to be commercial or office spaces due to their flat rooftops and the presence of signage. The signs include texts such as "Westfield," indicating the presence of a commercial district or shopping area.
The background extends further into the horizon, where more urban structures are faintly visible, blending into the sky. The sky itself gradient of colors, suggesting either sunrise or sunset, which complements the vibrant hues of the rainbow. A crane is visible the left side of the image, indicating ongoing construction development within the city. The presence the crane adds dynamic element to the otherwise static urban landscape.
overall composition of the image captures the juxtaposition of natural beauty (the rainbow) and urban development, creating a visually striking scene that highlights coexistence nature man-mo man-made structures modern city.
Figure 10: Qualitative result of TempPlex-Qwen3-1 on image understanding.
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Dream World
TempFlex-Owen3-44
he attor
And
Nos uneve
evrp far, And wal cines seeid
Pheer Zonpula
Dream A World
I dream a world where man No other man will scom, Where love will bless the earth And peace its paths adorn I ddeam world where all
Please output all the text in this image.
Will know sweet freedom's way, Where greed no longer saps the soul day.
Nor avariee brights
world dream where black or white, Whatever race you be,
Will share the bounties of the earth And every man is free,
Where wretchedness will hang its head And joy, like pearl.
Attends needs of all mankind- Of such dream, my world!
Poem by Langston Hughes
Figure 11: Qualitative result of Semppllex-Qwene-ee on OCR.
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TempFlex-Qwent
ENTVERSIVVPPA POPSSTT DEEGO
1130 /22
2.0401
Dute
WHILE YOU WERE OUT
W (SS-
Seoo Clinca
Peferphonel L Welshrrn REEE
Cl place
MARS
Br progsiy de thom It will fectclley See 1e weel mrrzeh (1983) tothis then latter letterled lesty PPect Micned to ee fice.))
MESSAGE
Sehie and aaaasf tnites vislt wolld diatedit2222
What name of university?
University of California
The header the document clearly states the name of the institution as "University Of California, San Diego". This information provides us with the identity of the university mentioned in the document.
Where is the university located ?
San Diego.
The header of the document clearly states that the University of California, San Diego. This information indicates that the institution is situated in San Diego
To whom is the document sent?
Paul.
The name 'Paul' appears at the top of the document under the heading "To", indicating that this person the intended recipicnt of the document.
Figure 12: Qualitative result of TempFllex-Qwen3-44 on Document understanding
25

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Published in Transactions on Machine Learning Research (12/2025)
TempFlex-Gemma3- GBmmma3-BBB
CRISIS
QUANIITATIV EASING
Please describe this video in details.
The video opens with the Telegraph logo and the words Teelegraph.co.uu//ee The words "BEGINNING OF THE FINANCIAL CRISIS appear. Then pinball machine graphic appears with globe top and the numbers 2007 and 2008. 2009 appears the globe falls into the pinball machine. Then the words "QUANTITATIVE EASING" appear.
The next graphic of bank building on background. The building morphs include stacks of money.
The money is then printed by a printer in a light blue background.
The next graphic is of computer screen showing bank interface with balance of zero pounds. The balance is then increased to 1,000,000,000 pounds. A bond certificate graphic appears with the words "BONDE CERTIFICATE" and the word "SINCE 2009" appears below. The certificate changes British flag. The graphic shows person and then graph. The graph shows the "yield and rises. person is shown again which starts high and then declines. The person shown again with the graph.
safe assets," which starts low the graph. The graph changes "yield curve on riskier assets,"
The next graphic is of a share graph. The word "SHARES" appears. Then two people are shown, and one shakes the other's 's hand, with pile of money on the ground between them. The video concludes with the word "ASSETS" and graphic of bank building. The money is shown series of bubbles.
Figure 13: Qualitative result of TemppFeex on Video understanding
HempFlex-Qwen3-4B
Please describe this video in details.
The video is a commercial for Viator, travel company that helps users book tours, activities, and experiences in New York City. The video opens with view of the Statue of Liberty, the Brooklyn Bridge, and fountain in Central Park. The narrator discusses how the Statue of Liberty is a symbol of freedom and opportunity. The video then shows different images of the statue, including a small coin with the statue on it cover the publication Weekly, edited by George Harvey.
The narrator explains that the Statue of Liberty was gift from France to the United States in 1886 to celebrate the centennial of American independence. It was gift of friendship, and the statue symbolizes the American ideals of liberty and democracy. The statue designed by French sculptor Frédéric Auguste Bartholdi and built by Gustave Eiffel, the engineer who designed the Eiffel Tower.
The video shows several people taking photos and enjoying their time Liberty Island, the site where the statue is located. The narrator mentions that Liberty Island is free to visit, but the Statue of Liberty Museum is not. The museum costs $16, but there is a special deal where can reserve visit and pay later. The video then shows various exhibits at the Statue of Liberty Museum, including the original torch that was given the statue, banners that say "Dissent," and large bronze mask. The narrator mentions that the museum great place to learn about history of the statue and immigrants who came to America. The video also shows images of Ellis Island, the immigration station where people came to the United States, and ferry that takes visitors to Liberty Island.
The video concludes with the narrator encouraging viewers to book visit to the Statue of Liberty Museum through Viator, and visit other parts of New York City. The video ends with Viator logo and links subscribe, watch more videos, and book today.
Figure 14: Qualitative result of TempFlex-Qwen3--1F on Video understanding
26


Reviews:
[{"review_id": "9aaC1zRkAw", "reviewer": "Reviewer_nkzC", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "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": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "This paper addresses two core limitations of existing MLLMs: excessive visual tokens with broken global context in image processing, and lack of temporal reasoning in video understanding. To this end, this paper presents TempFlex-VL, a token-efficient and temporally aware multimodal large language model (MLLM). TempFlex-VL has two key innovations: (1) a resolution-agnostic visual encoder (based on SigLIP2) that processes full high-resolution images directly without tiling, preserving global context while reducing visual tokens by at least 72% compared to tiled patch encoding; (2) a plug-and-play Temporal Fiber Fusion (TFF) module with three complementary pathways (local convolution for fine-grained motion, gated memory for long-term dependencies, periodic encoder for cyclic patterns) to capture temporal dynamics in videos. Additionally, the authors curate TempFlex-2M, a high-quality synthetic video-text corpus generated via single-stage GPT-4o visual prompting, to support large-scale video-language pretraining. Instantiated with Gemma3-4B and Qwen3-4B backbones, TempFlex-VL achieves state-of-the-art or competitive performance on diverse image (e.g., MMStar, DocVQA) and video (e.g., VideoMME, TempCompass) benchmarks, with plans to release code, models, and data publicly.\n\nStrengths:\n\n1. The problem to address in this work is critical. This work helps eliminate tiling for high-resolution images as well as filling the gap of explicit temporal modeling with a lightweight, modular design, avoiding the computational overhead of heavy video-specific architectures.\n\n2. Along with the proposed approach, this paper also introduces a synthetic dataset to addresses the scarcity of diverse video-language data by adopting a single-stage \"Video+Text → Caption+VQA\" pipeline. The dataset has a broad coverage of 12 reasoning skills (e.g., causal reasoning, motion analysis) for comprehensive video-language pre-training.\n\n3. The authors conduct rigorous experiments, including head-to-head comparisons with SOTA models (e.g., InternVL2.5-4B, Qwen2.5-VL-3B), ablation studies for the TFF module (validating each pathway’s contribution) and encoding strategy (token efficiency vs. accuracy), and qualitative results (image description, OCR, video understanding). This multi-faceted evaluation strongly supports the claims of the proposed innovations.\n\nWeaknesses:\n\n1. The design choice of the propose framework lacks justification. For example, the paper highlights that TempFlex-2M uses a single-stage pipeline to avoid information loss from multi-stage captioning, but it does not explicitly compare TempFlex-2M with a \"multi-stage variant\" (e.g., generating captions first, then VQA) using the same video sources. Moreover, the paper mentions using up to 128 frames at test time, but it does not discuss how TempFlex-VL performs with extremely long videos (e.g., hour-scale content, as targeted by Video-XL). \n\n2. The comparison of inference latency is missing. The analysis only considers token efficiency. However, it is not clear the inference latency (e.g., per-image/video processing time on a standard GPU) compared to baselines like InternVL2.5-4B or Qwen2.5-VL-3B. \n\nMinor: The presentation can be improved. \n\na. There are too many references in the first sentence of the introduction, which spans 6 lines.\n\nb. The terms used are inconsistent: this paper sometimes employs MLLM and other times uses VLMs.\n\nc. Abbreviations are defined in multiple times, e.g., Temporal Fiber Fusion (TFF).\n\nd. Figure 4 can be moved before the conclusion section.", "claims_and_evidence_yn": "Yes", "claims_explanation": "The claims made in the submission are supported by comprehensive experiments (head-to-head comparisons with SOTA models on diverse image/video benchmarks), detailed ablation studies (validating the TFF module and resolution-agnostic encoder), a high-quality synthetic dataset (TempFlex-2M) with transparent construction, and standardized evaluation via VLMEvalKit, along with qualitative results demonstrating the model’s capabilities in image description, OCR, and video understanding .", "audience_yn": "Yes", "audience_explanation": "The topic studied in this paper, i.e., the scalability and efficiency of MLLMs, would be of interest to a large group of audiences.", "requested_changes": "1. Clarify the design choice of the proposed framework.\n2. Elaborate the comparison of the latency.\n3. Improve the presentation quality.", "broader_impact_concerns": "NA", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "ZZJIwnBSp8", "reviewer": "Reviewer_vnUU", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "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": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "This paper introduces a lightweight architecture to tackle two core limitations of current MLLMs. For image encoding, it replaces tiled patching with a natively scalable, any-resolution encoder that preserves global structure. For video encoding, it proposes the Temporal Fiber Fusion (TFF) module, which jointly models short-term motion, long-term memory, and periodic patterns through adaptive fusion.  Extensive evaluations show that the proposed approach achieves state-of-the-art or competitive performance on both image and video benchmarks, with significant gains in token efficiency.", "claims_and_evidence_yn": "Yes", "claims_explanation": "The paper presents a clear problem statement, identifying the shortcomings of existing image and video encoding approaches in MLLMs. It then provides a well-elaborated description of the proposed model structure, explaining how the any-resolution encoder and the Temporal Fiber Fusion module directly target these issues. The claims are further supported by extensive experiments that are carefully designed to highlight the specific advantages of the proposed architecture. The results consistently demonstrate solid improvements over strong baselines across multiple benchmarks, which makes the evidence both convincing and credible.", "audience_yn": "Yes", "audience_explanation": "he paper addresses a standing challenge in multimodal LLMs—efficient image encoding and explicit temporal modeling for video—by introducing a relatively simple yet effective architectural addition. This makes the work both accessible and impactful. Many readers would be interested in these findings, as they demonstrate how lightweight modifications can lead to meaningful gains in performance and efficiency on image and video tasks, which remain central problems in the field.", "requested_changes": "1.  for evaluation part, I recommend that the authors add a few sentences on the evaluation metrics used and a bit more context in the main text for readers without prior knowledge to be more easily understand the numbers in tables.\n\n2.The paper emphasizes token efficiency, but more explicit analysis of how accuracy scales with different token budgets or input resolutions would make the contribution clearer for practitioners.\n\n3.While TFF is lightweight and effective, it would be useful to discuss or empirically compare against attention-based temporal modules. Such structures also retain memory and can capture dependencies with relatively low computation, offering an informative baseline.", "broader_impact_concerns": "NA", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "AT6oRug8A1", "reviewer": "Reviewer_Y7r4", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "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": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "This paper proposes a MLLM called TempFlex for improving both efficiency and temporal reasoning in vision-language training tasks. The model supports any-resolution visual encoding with a temporal fiber fusion module. The paper also constructs a TempFlex-2M dataset that provides richer temporally aligned supervision. The performance of the model is demonstrated via comprehensive empirical studies. \n\nSome potential weaknesses:\n1. While the dataset boosts performance, it is synthetic and may not be generalizable to real-world scenarios. \n2. Training can be more costly when compared to existing models.\n3. The TTF module introduce significant model complexity, which may complicate users integrating it into other pipeline. Though the paper is well-written, but it can be challenging for readers not familiar with the field as the description of the pipeline is brief.", "claims_and_evidence_yn": "Yes", "claims_explanation": "1. The evidence of \"any-resolution visual encoding reduces tokens while preserving performance\" is clear and convincing, with support from solid empirical evidence.\n2. The improvement in temporal reasoning via TFF is also strongly supported and well-structured. \n3. The generated dataset is demonstrated to enhance video-language pretraining. \n4. TempFlex is slightly outperforming the state-of-the-art approaches.", "audience_yn": "Yes", "audience_explanation": "The paper’s findings would interest TMLR’s audience, particularly those working on multimodal large language models, video understanding, and efficiency in model design. The proposed any-resolution encoding, lightweight temporal fusion module, and large-scale synthetic dataset address well-known bottlenecks and demonstrate strong empirical gains, making the work relevant to both researchers and practitioners.", "requested_changes": "1. Describe the pipeline in more details, with notation and terminologies clearly explained.\n2. Provide more insight on how the generated dataset contributes to real-world tasks.", "broader_impact_concerns": "NA", "additional_comments": "NA", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Decision: Accept (poster)