Eagle 2.5: Boosting Long-Context Post-Training for Frontier Vision-Language Models
Authors: Guo Chen
Zhiqi Li
Shihao Wang
Jindong Jiang
Yicheng Liu
Lidong Lu
De-An Huang
Wonmin Byeon
Matthieu Le
Max Ehrlich
Tong Lu
Limin Wang
Bryan Catanzaro
Jan Kautz
Andrew Tao
Zhiding Yu
Guilin Liu
Venue: NeurIPS 2025 poster ()
Keywords: Vision-Language Model
Multimodal LLM
Multimodal Learning
Data Strategy
Primary area: applications

Abstract: We introduce Eagle2.5, a frontier vision-language model (VLM) for long-context multimodal learning. Our work addresses the challenges in long video comprehension and high-resolution image understanding, introducing a generalist framework for both tasks. The proposed training framework incorporates Automatic Degrade Sampling and Image Area Preservation, two techniques that preserve contextual integrity and visual details. The framework also includes numerous efficiency optimizations in the pipeline for long-context data training. Finally, we propose Eagle-Video-110K, a novel dataset that integrates both story-level and clip-level annotations, facilitating long-video understanding. Eagle2.5 demonstrates substantial improvements on long-context multimodal benchmarks, providing a robust solution to the limitations of existing VLMs. Notably, our best model Eagle2.5-8B achieves 72.4\% on Video-MME with 512 input frames, matching the results of top-tier commercial model such as GPT-4o and large-scale open-source models like Qwen2.5-VL-72B and InternVL2.5-78B.

Eagle 2.5: Boosting Long-Context Post-Training for Frontier Vision-Language Models
Guo Chen Zhiqi Li Shihao Wang " Jindong Jiang?, Yicheng Liu', Lidong Lu', De-An Huang", Wonmin Byeon', Matthieu Le², Max Ehrlich?, Tong Lu Limin Wang" Bryan Catanzaro', Jan Kautz², Andrew Tao Zhiding Yu Guilin Liu
Nanjing University, NVIDIA, HHong Kong Polytechnic University chenguo11176gmaal..oo
Abstract
We introduce Eagle2.5,, frontier vision-language model (VLM) for long-context multimodal learning. Our work addresses the challenges in long video comprehen- sion and high-resolution image understanding, introducing generalist framework for both tasks. The proposed training framework incorporates Automatic Degrade Sampling and Image Area Preservation, two techniques that preserve contextual integrity and visual details. The framework also includes numerous efficiency optimizations in the pipeline for long-context data training. Finally, we propose Eagle- Video- 110K, novel dataset that integrates both story-level and clip-level annotations, facilitating long-video understanding. Eagle2.5 demonstrates substan- tial improvements long-context multimodal benchmarks, providing a robust solution to the limitations of existing VLMSs. Notably, our best model Fagle2.5-88 achieves 72.4% on Video-MME with 512 input frames, matching the results of top-tier commercial model such as GPT-4o and large-scale open-source models like Qwen2.5-VL-72) and Intern VL2.5782..
1 Introduction
Despite the significant advances in multimodal learning [4, 16, 52, 53, 97], many vision- vision-language models (VLMs) remain focused on short-context tasks, with long-context understanding under- explored. This gap is particularly evident in both long video comprehension and high-resolution image/video understanding, where the processing of extended visual contexts remains an open challenge. Such extended contexts encompass multiple images, extended video sequences, high- resolution media, or combinations thereof. However, the development of long-context VLMs is still in its early stages, hindered by fundamental challenges in dataset construction, architecture design, training strategies, and computaation/mmmon bottlenecks.
To enable long context visual understanding, several approaches have been proposed to address the challenge of processing extended visual inputs by designing specialized compression or selection modules [52, 84, 57, 45, 43, 117 103, 53J. While these methods effectively circumvent the need to extend the context length of VLMs, they often introduce additional computational overhead or capacity limitations, potentially constraining model performance. A promising research direction to extend the context length of LLMs to enable native long-context understanding. While prior studies I01 126, 85] have explored this direction, challenges and key limitations still remain. First, the performance of existing methods often suboptimal, generally falling behind proprictary models. Second, these approaches struggle to achieve consistent improvements as the amount of visual input
Work done during an internship at NVIDIA.
"Co-corresponding authors.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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increases. Lastly, the optimal training strategies for state-of-the-art long-con contect VLMs remain unclear, given the complex interplay of factors such as training strategies and data recipes.
To this end, we present Fagle 2.5, a versatile multimodal model designed to efficiently pro-
Performance on Video MME (w/o subs)
GPT-40
Cemini-1-5-9y 5-Prr
cess extensive contextual information. Unlike models solely optimized for handling long mul-
Owen2 5-VL-787720
24
IntemVi-22554e
LLaV OneVision
timodal sequences without improving perfor-
72
LongViTA-148
Eagle-255-8
mance, Eagle-2.5 benefits from increased in- put length. leading to consistent performance gains besides merely accommodating longer in- puts. As shown in Fig. 1, our model achieves superior context coverage and exhibits consis- tent performance scaling with increasing frame counts. Notably, it attains competitive results compared to larger models such as GPT-4o [77] and (0wen2.5-VL-72) [5], while maintaining significantly smaller parameter footprint.
14
Model
Unkanwn
62
728
8B
Number of Frames
Figure 1: Performance comparison of Eagle2.5 with leading vision-language models on the Video-MME benchmark. Eagle2.5 demonstrates consistent improvement as the number of input frames increases.
Eagle 2.5 is driven by both the advanced train- ing strategy and data recipe. For training strat- egy, we introduce two core components for ef- fective long-context learning: information-first sampling and progressive training.
Information-Hie sampling. The information-lin first sampling strategy ensures the preservation of essential visual and semantic information through two mechanisms: (1) Image Area Preservation, which optimizes tiling to retain the majority of the original image area while maintaining aspect ratio fidelity, avoiding rigid aspect ratio constraints; and (2) Automatic Degradation Sampling (ADS), which dynamically balances visual and textual inputs by prioritizing complete text retention while adaptively optimizing visual content to maximize context length utilization and preserve multimodal information.
Progressive training. We employ progressive mixed post-training approach, wherein context length is incrementally expanded during training, enhancing the model's ability to process inputs of varying sizes. This integrated strategy significantly improves information density over static sampling methods while ensuring consistent performance across diverse input types and lengths.
For data recipe, we embrace the "diversity first, then quality" principle in curating the training data pool. Our data recipe combines open-source data (including human-annotated data as well as synthetic video data) with our self-curated Eagle- Vigle-Video-111 1 10K dataset, specifically designed to enhance long video understanding capabilities. We adopt diversity-drive collection strategy, using multiple video sources and similarity thresholding method identify novel clips that maximize content diversity. Our dataset is distinguished by dual annotation approach:
A top-down story-level method that leverages human- annotated chapters as meaningful segments instead of traditional shot-level segmentation, producing dense captions that form the basis for comprehensive long-form QA pairs capturing the entire video's 's narrative structure;
A complementary bottom-up clip-level approach that generates focused QA pairs for short clips using GPT-4o with diverse question types. To address the challenge of extending localized clip annotations full-length videos, we implement anchors that incorporates temporal references and contextual elements without revealing answers, thereby letting models understand both overarching narratives and precise spatio-tempon details within videos.
2 Related Work
Vision-language models. Advancements in large language models (LLMs) [20, 77] have sig- nificantly propelled visual understanding by integrating visual features, leading to the creation of Visual Language Models (VLMs) [52, 76, 58, 133]. Open-source VLMs [58, 60, 49, 97, 92, 18, 66, 50, 144, 67, 39] continue to achieve breakthroughs, often matching or exceeding the performance of of commercial models like GPT-4V/4o [77] and Gemini-1.5 [83]. The release of open-source VLMs [49, 95, 40, 38], complete with its training data and code base, has further

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accelerated research in this area. However, most current VLMs primarily focus on short-context understanding, handling only few images or short video clips a time. Eagle 2.5 advances this field by concentrating on long-context visual understanding through comprehensive exploration and development of training strategies and data recipes.
Long-context VLMs. Long-context VLMs were developed to address the challenges of pro- cessing large multimodal sequences. Currently, methods for long-context VLMs fall into two main categories. The first category involves specialized modules designed for context compres- sion. Question-guided compressions [84, 57, 45, 98] or selection [117, 55, 116] methods extract question-related visual cues through an additional module, while various token reduction tech- niques [43, 117, 103, 53, 57, 124, 102, 62, 62, 61] aim minimize the visual representation before LLM processing. The other category attempts to directly extend the context of LLMs. Works like VongVA [126], LongVILA [1]0], and LongViTa [85] extend the context length of L.LMs to accommodate longer multimodal sequences. While promising, these approaches often underperform proprietary models, fail to show consistent performance improvements with increasing visual input, and have underexplored constraints training strategies and data recipes. Our approach focuses on developing native long-context capabilities that enhance VLMs by exploring training data, for- mulations, and without introducing additional compression modules suffering from performance inconsistencies observed in previous expansion attempts.
Long-context multimodal data. To enhance VLMs' longecontext multimodal understanding capabil- ities, various datasets have been proposed. Some datasets focus on multimodal understanding of long documents [89, 96, 94, 80), such as slides and papers. However, they often lack temporal understand- ing. Other datasets [36, 105, 24, 82, 87, 88, 120, 121] emphasize the temporal coherence and infor- mation retrieval across long spans inherent in movies. Additionally, recent datasets [73, 13, 30, 129] covering domains further enhance VLMs' long-context multimodal understanding. Regarding the annotation methods for long-context multimodal datasets, early works [89, 96, 94, 36, 87, 73] relied on manual efforts. To reduce costs, some methods [80, 24, 82, 88, 120, 121, 30, 13, 129] use tools like GPT-4V [76] and Gemini [91] for automated or semi-automated annotation. Recent advancements in data construction emphasize hierarchical annotation strategies [30], which can preserve narrative structure in long videos. These advancements reflect trend towards creating balanced datasets that effectively assess long-context multimodal understanding while managing creation costs.
3
Method
This section introduces the model architecture, training strategies, and data recipe of Eagle2.5.
3.1
Model Architecture
We design our proposed model as a versatile multi- modal system capable of efficiently processing long- context information, rather than a specialized model solely optimized for handling extended multimodal inputs. To ensure adaptability and generalization across diverse tasks, we deliberately avoid incorpo- rating tailored compression modules that might con- strain the model's flexibility. Following the architec- ture of LLaVA [58], we employ an MLP projection layer to align vision embeddings from SigLIP [123] with the L.M representation space, as shown in
LUM
HUP connecter
AETIROAL INTELIGEED
Information First Sampling
Figure 2: Tiling-based general multimodal system.
Fig. 2. In this work, we utilize the Qwen2.5 series models [93]. To effectively handle any-resolution images, we adopt the image tiling strategy, inspired by LLaVA-1.5 [59] and Intern [16].
3.2 Training Strategy
Our approach contains two key components to achieve effective long-context training: first, an information-firs sampling strategy that establishes optimal sampling criteria; and second, a progres- sive training schedule based on this strategy, which directs the entire model training process.
3

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3.2.1 Information-Firs Sampling
In multimodal training, the sampling of visual content is essential. Multi-image documents typically comprise dozens of pages with ultra-high-resolution images, while video content can vary drastically in length from mere seconds to hours. To effectively manage this diversity, we present information- first sampling to promote information preservation from both visual and semantic dimensions.
Image area preservation (IAP). Traditional tiling methods divide an image of size W H into a rigid Tw X grid of x s tiles. While effective for han- dling high-resolution inputs, these approaches often distort the original image geometry through improper aspect ratio handling. For example, Intern VL [16] imposes strict aspect ratio constraints that force im- age downsampling, undermining the purpose of tiling. To address this, we propose an area-prioritized tiling strategy that optimizes two key objectives:
w--0on
me
Figure 3: Image area preservation. Com- pared to the tiling strategy (a) from In- tL [92], our method (b) effectively re- tains a larger portion of the original image, especially for high-resolution inputs. This ensures that more comprehensive visual in- formation is preserved, benefiting tasks that require fine-grained details.
Area Preservation: Encourage maintaining at least 60% of the original area (Aorig W H) in the tiled version (Anew rTT)ss²).
Aspect Ratio Fidelity: Align the tiling ratio = Tw/rh with the original aspect ratio Forig W/H.
For candidate tiling ratios {(Tw, Th) | Tw x rh S NJ, we select the optimal configuration by:
arg max (Fw)Th)
min
Anew Aurig
0.6
min
Vt
Torig
Torig
Area penalty
Aspect ratio aligoment
This formulation imposes penalties when Anew < 0.6.Aari but avoids over-rewarding configurations where Anew >> 0.6 Aurig- The aspect ratio term reaches maximum value when Fi = Forig: decaying symmetrically for deviations. A comparison between the strategies is shown in Fig. 3.
Automatic degradation sampling. VLMs require careful allocation of sequence length budgets between visual and textual inputs. Conventional vision-context-centt approaches sample visual content (e.g., video frames) at fixed rates with predetermined counts, risking text truncation and suboptimal token allocation. We propose Automatic Degradation Sampling (ADS), an all-context- centric strategy that dynamically optimizes this balance.
Given a training sample S {Svisual Stext) with max sequence length Lmax, where Svisual contains arbitrary combinations of images, videos, and multi-page documents: Compute fixed text token length Luex: and Derive fixed visual token budget: Lvisual = Luax Liext Thus, we keep the complete textual information by constricting the visual token budget.
For visual content optimization under Lvisua, we distinguish two types and optimize two key variables:
Optimize maximal tile count per image to maximize spatial information of M images. Temporal content (video/doc): Optimize sampling count to maximize temporal coverage.
Images:
The constrained optimization problem formulated as:
M
Σ L(I, 256m
MaX 154512, ISns Nama
(2)
M
S.L.
L(,
+ 256n S Lvis
1=1
Where optimization variables are the tile count per image (1) and temporal sampling count (n), with fixed parameters including: total image instances M (calculated from input), token function L(t, 4)

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used to calculate the tokens of i-th image I under maximal tiling number t, predefined upper bounds Tmax 12 (max tiles per image) and N Mmxx 2 duration/ pages (video/doc constraints). For temporal content, we do not use image tiling, thus the token quantity per temporal unit (frame/page) T((,,) = 256.
Given that training samples typically exhibit mutually exclusive composition (predominantly im- ages or temporal content), ADS employs a dual-phase degradation process address the above optimization problem:
Temporal degradation: Initially, we fix the max tile number t = 1 and focus on temporal sampling. We target a sampling rate of FPS for videos, and the usage of all images for multi-image documents. We also require that each visual input has at least Mmin frames; if this minimum cannot be met within the visual context budget, the sample discarded. Formally, the maximally sampled temporal units is n Eumett x56
256
Tiling degradation: After deciding the number of frames, we dynamically adjust the tiling maximize the of available context. Let T {12, 8, 6, 4, 4, represent the possible tile configurations in decreasing order. We choose the highest tile configuration t" such that: t' max ft T Σ L(t, I,) s (Lv n x 256)} This strategy preserves much visual detail as possible while ensuring the full textual input is retained, thereby optimizing the overall learning signal.
This qual-pphae approach guarantees complete text preservation while dynamically adapting visual resolution to available context budget, achieving superior information density compared to static sampling strategies.
3.2.2 Post-Training Schedule
We introduce comprehensive post- pest-training framework consisting of two complementary strategies. First, we establish a foundational mixed post-training approach, upon which we develop an enhanced progressive mixed post-training strategy to substantially improve model performance across varying context lengths.
Mixed post-training. Since the model needs to efficiently process multimodal inputs of diverse lengths, maintaining consistent performance across variable context sizes is essential. Our ADS method adaptively adjusts each training sample to the maximum sequence length C providing a Srame-agnostic training paradigm. We implement a mixed training strategy with packing [3] to optimize performance uniformly across the entire spectrum of context lengths.
Progressive mixed post-training. For scenarios with large Lmax values, balancing the distribu- tion of long and short sequences becomes computationally intensive, and achieving optimal performance through single training iteration proves challenging. To address this limitation, we propose a progressive mixed training methodology that gradually exposes the model to in- creasingly larger Lmax values, systematically enhancing its capacity to process extended contexts. Compared to conventional mixed training, our method more effectively preserves the model's s capabilities across different sequence lengths while safely generating diverse model variants at intermediate training stages. In our exeriment, we sequentially set Lmxx to 32K, 64K and 128K.
3.3
Data Recipe
Our data recipe begins with open-source data. We embrace the "diversity first, then quality" principle and gather data from various open sources. This data mainly comprises high-definition multi- image/short videos, long videos, multi multi-page documents, and extensive text data. We also find that current open-source video data often lacks sufficient length. We thus propose novel dataset, Vigll-Video-1110 to complement the length, as shown in Fig.
3.3.1 Open-Source Long-Context Data
model's 's capability intrinsically linked to the diversity of its training data. Thus, gathering the most diverse data possible represents core principle of this work, leading two main strategies:
5

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Category
Daiaset
Kineties710
Sameehing-Soneethngn [25] Activiyyeet [S], HACS Segment [100], COIN [x]].
Video Classfication
HIREST [122]. FineAction [60], (3t
Temporal Action Localization
ActivityNet [5] HACS Segment [130], FincAction [63]. Egu [26]. COIN [90]. HIREST [122].
Pereeption-Tc Test [29]
Charade-ST: [23]. OVitighlight [15], Ego [26]. Didemo [32] QueryD [.] MedVidQA [2]
Video Temporal Grounding
Youcuuk2 [ [52]] Video [..), ActivilyNet [S], HACS Segment [130]] FincAction [63], Egoi MQ [26].
COIN [90]] HIREST (122)) Perception: [29]. EggExoLearm [77]
Dense Video Captioning
ActivityNer [8], Youcook2 [[S3]. EgolxolLeam [5]]. (TTTS)) HIREST (OIN COIN po]
Temporal Segmentation
Breakfast [[i]], [iT
Temporal Ressoming
ActivityNa-RTL. [3i]
TVQA [17]. CLEVRER (115), NexOA [108], SportsQA [S1],
LLaVA- [125], Fine Video [22],
General Video QA
VidcoGiT+ [69], Oops [2]]] PestptionTTes["" EgoTaskAA CinePile [52], STAR [101]
Multi Page Document
Slid [59], DUDE [96], MP- [[]]
Video Captioning
Activity-ee [.] Youcook2 [1S]] Shat2story [50], Vrigt (112) LLaVA Video [12]]
FunQA [10%] S-MiT [74]] LLaVA-Houdd (127), Ego4D-HCap! (41 EgoEsol [37]
Momentos [%)]
Long Text
LongAlign [6], LongReward [125]
Table I: Video, multi-page document, and long text dataset used in Eagle-2.5.
Hlmann-amotatee Data: We integrate various open-souree human- annotated datasets, including established video and image-document collections such COIN [90] and SlideVQA [89], which can be directly considered as high-quality data.
Synthetic Video Data: Considering that videos naturally contain long-context information, we incorporate open-source synthetic video data, such as LLaVA VA- Video [128]. These datasets are primarily annotated automatically using state-of-the-ar models including GPT-4V/4o [76, 77], Claude-3 [2], and Gemini- Pro [83].
Combined with short-context data, all collected open-source datasets are summarized in Tab. 1, For convenience, we refer to this collective dataset as Open-Data.
3.3.2 Eagle-Video-11000
We curate Eagle-Video- 10K to enhance long video understanding capabilities. Specifically, we first collect videos using a diversity-driven strategy. We then automatically annotate these videos using both top-down and bottom-up approaches to generate comprehensive story-level and fine-grained clip-level annotations, as shown Fig.
Diversity-driven video collection. We utilize several data sources for our video collection:
Open-Data
Vidchapters [111], MiraData [44], Intern Vid-
Frequency (log scale)
10
Eagle-Video-
10M [100]. Panda-70M [14], Vript [112],
Shot2story [30], ViTT [35], and WebVid-
10"
[0) collectively referred to as A. Our ap-
10
proach prioritizes diversity, focusing on gath- cring a wide range of video content. For the
10¹
current training dataset B. we use CLIP [81]
10²
to extract temporal features at a rate of 1 frame per second. Videos from both A and
2000
4000
6000
8000
10000
12000
B are segmented into 10-second clips. We perform a pooling operation each clip's frames to derive a representative feature vec- tor. Let ( represent the clips from B, and {a, Y represent those from A. We calculate the pairwise cosine similarity between clips from B and A. For each clip aj in A, we identify its maximum similarity with any clip in B: Smax((3) = maxigigNe S(b,, o;) We then introduce similarity threshold T 0.5. Clips in A with Smax (a,) below this threshold are considered most novel relative to B: Asowl {a, E A Smax(a,) < <T}
Video Duration (seconds)
Figure 4: Comparison of video duration between open-source data and Eagle- Video- 1 10K.
The clips in Anovel and their original videos are selected to enhance the diversity of our collection.
Story-level video data. We construct seory-level annotations for long videos using a top-down approach. Unlike existing video datasets such as Shot2story [30], which employs shot detection to segment videos and construct storylines across shots, our methodology differs fundamentally. Shot- level segmentation often results in over-segmentation, producing excessively detailed annotations that are suboptimal for constructing coherent story-level text. Instead, we leverage human-annotated chapters as video segments, which provide more semantically meaningful annotations. We incorporate

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5o
16s
20s
256
30c
36s
40p
455
60
86s
60s
Top-down Approach
Skryleal
Human annolator
GPT-4o
Time anchor
Caplons
Video clp
OPT4
GPT-40 genwested Secture coritsst andhor
Bottom-up Approach
Story-level QAA pairs
Figure 5: Overview of our video annotation framework combining bottom-up clip-level and top-down story-level approaches. The diagram illustrates our dual annotation strategy. In the bottom-up approach (left), short video clips are processed by GPT-4o generate clip-level QA pairs enhanced with time anchors and textural context anchors. In the top-down approach (right), human annotators create story-level segmentations of longer videos, which are then captioned and processed by GPT-4 to generate comprehensive story story-level QA pairs. This hierarchical methodology enables both fineigriied temporal understanding and high-level semantic comprehension of video content.
content from ViTT [35] and Vidchapters [111] among the selected videos and filter out any videos with fewer than two chapters ensure they serve as effective story-level sources.
Chapter-level dense caption. For a video divided into N clips, where each clip spans from timestamp a to b, we perform visual captioning for each segment individually. For each segment, frames are sampled at a rate up to frames per second, with maximum of 50 frames. These sampled frames, together with user-provided segment titles, guide GPT-4o [77] in generating detailed visual descriptions focused on the content indicated by the titles.
Long-form QA generation. Once visual descriptions for all segments are completed, we compile the captions for the entire video along with their corresponding time intervals and chapter titles. This aggregated information is provided to GPT-4 [1], which generates diverse question-answer pairs covering multiple question types.
Clip-level video data. Story-level video data typically emphasizes high-level semantic information that unfolds over extended periods. However, for general queries, it is often necessary to focus localized spatiotemporal details. To address this need, we propose a bottom-up. computationally efficient automatic annotation method. This approach enables the generation of short clip annotations and facilitates the conversion of segment- annotations into video-level ones by incorporating temporal and contextual anchors.
Clip-level video QA generation. We generate QA pairs for each short clip in dataset A based on various question types. Specifically, we sample frames from each short clip at a rate of up to frames per second and input them into GPT-4o. From predefined question type pool, we randomly select five question types and prompt the model to generate corresponding question- answer pairs.
Clip-to-video QA conversion. Since annotations for individual clips designed for localized queries, conflicts may arise when these queries are extended to the entire video. To address this issue, we introduce two types of anchors for each clip-question pair: (1) We directly incorporate time intervals into questions to establish temporal references; (2) Using GPT--o,, we generate textual context anchors that provide additional information without revealing the answers.
4 Experiments
4.1 Comparison with State-of-the-AAt VLMs
Video benchmarks. As shown in Tab. 2, Eagle2.5-8B demonstrates strong performance across multiple video understanding benchmarks. It achieves 74.8 on MVBench [54]. 82.0 on Percep- tion test [78] and 72.2 on EgoSchema, outperforming similar-sized models like VL2.5. [92] (72.0, -) and Qwen2.5-VL. 5-LBBBBB [97] (69.6, 70.5, 65.0). Eagle2.5-80 5-BB excels in MLVU [131] (77.6) and Long Videobench [106] (66.4), surpassing even VL...5
(75.7, 63.6). For VideoMME (w/o subtitle), the performance of Fagle 2.5 (72.4) significantly surpasses models of the same size and extremely close to the 72B parameter model. On CG-Bench [11], it scores 55.8, 46.6, 45.6, 13.4 across metrics, exceeding Claude-3.5-o 3.5e-3.---Socctt [2] (56.5, 40.3, 35.6, 4.17) and Gemini-1.55 1.5-Pro [83]
7

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Moldi
MVBeach Peveeption
EgoSchema MMB Vileo
MLVU LVBench Val
Video-MME solo suleilie
CG-Bendh
Hou VouoVaoo
Chanade-S7 STA mfali
Vil
fulbet
Via
sabiikke
Cluc
Long
Open mlol)
Dox
Text
ClosedSourcee Models
4o [P]]
71.9
17.2
44.9
35.7
Clande 35 Sornet [C]
60.0
62.9
56.5
40.3
35.6
4.17
Genini-1.-PPo [S3]
72.2
1.30
64.0
75.0
$1.3
50.9
37.5
26.7
385
37.2
37.4
Ommini-2.5-Poo
87.0
Sccal.5-VLL [15]
74.3
77.6
Publiely Asailable Models
V26-88 (
60.9
63.7
41.4
19.9
26.3
2.27
Los-VILA-BB [15]
67.1
1.. [17]
72.0
50.0
64.3
66.9
LLava Video 3B [129]
58.6
67.9
57.3
70.8
58.2
63.3
05.7
S-1 [S]
69.6
70.5
65.0
1.79
56.0
66.1
71A
44.5
15.5
2.48
436
VidooChut
[1]]
74.0
76.2
43.1
ImcmWL225-758 [17]
76.4
1.97
75.7
63.5
72.1
14.0
59.5
44.2
34.2
3.90
5wen2.5-VL-722 [1]
70.4
73.2
75.2
2.02
74.6
60.7
73.3
79.1
50.9
LaVA-Vias-778
64.1
88ple25-881
74.8
81.0
72.2
1.94
66.4
72.4
75.5
55.8
46.6
46.5
41.8
Table 2: Comparison with SoTA models on Various Video Benchmarks. We sample each video FPS by default and disable tiling, and limit the minimum sampling frame number to 8 frames. Among them, the maximum frame number of Video-MME is 512, and the others are 256. Perception- Test turns on tiling enable high-resolution testing.
Model
DecYQA CharQA
IntoVQA TeuvQA OLRBench MMMstur RWQA AEDD MMMU MMB,
MMNet Halli Vasti
Arg
Test
Tist
Text
Text
Test
Test
Test
Test-Mini
Score
Closed-Source Models
GPT-1u-0806 [77]
79.2
75.4
55.0
Tiaale-3.--Semnc [?]
05.2
90.8
74.3
74.1
788
65.1
60.1
81.2
68.3
80.9
70.1
55.5
67.7
74.0
Gemini-1.5-P [83]
Publicly Available Models
MiniCPM-V2.66688 [1:3]
N2.4
S0.1
57.5
.9..8
60.01
48.1
LLaVA One Vision 8B [19]
$7.5
80.0
68.8
622
61.7
66.3
$1.4
48.8
30.9
57.5
31.6
63.2
LitemVL2.--88 [17]
84.5
77.6
79.1
62.8
56.0
62.8
50.1
64.4
Qwen2.5 VL-BBB [5]
95.7
87.3
82.6
84.9
864
63.9
68.5
83.9
58.6
82.6
67.1
52.9
68.2
25.6
LLaVA MWV One---si-nn Vision [19g
91.7
R3.7
74.9
741
66.1
71.9
85.6
56.6
84.5
60.6
47.5
68.4
11aMa-3 2-008-Vision [10]
85.5
783
60.3
77.3
44.1
57.3
Exple2.5:3B
94.1
87.5
50.4
83.7
869
66.2
76.7
84.5
55.8
81.7
62.9
54.7
67.8
75.6
Table 3: Comparison with SoTA models Various Image Benchmarks. The avg score is computed as the average of all benchmark scores, with OCRBench score divided by 10.
(50.9, 37.8, 28.7, 3.85). With 44.5 on Hour [10] dev set and 41.8 on test set, all surpassing Gemini-1.5-PPy [83]. Finally, on Charade-STr, [23]. Eagle 2.5 outperforms other models significantly, demonstrating strong temporal perception capabilities. Eagle2.5-88 shows effective long-form video understanding, highlighting its robust visual reasoning using less parameters.
Image benchmarks. As shown in Tab. 3, Eagle2.5-8B demonstrates competitive performance across diverse image understanding benchmarks. It achieves strong results on document understanding (94.1 on VQA [71D, chart interpretation (87.5 on ChartQA [70]), and general information extraction (80.4 on InfoVQA [72]. 83.7 on TextVQA [86]). The model also performs well in optical character recognition with 869 on OCRBench [65], comparable to other models its category. Eagle2.5-8B
shows balanced capabilities across multimodal general perception and reasoning tasks, scoring 66.2
on MMstar [12], 76.7 on RWQA [107], and 81.7 on MMB [64], and 62.9 on MM [118]. Its performance extends to knowledge domain (55.8 MMMU [119], 84.5 on A12D [33]), visual hallucination benchmark (54.7 on HallB [27]), and mathematical reasoning (67.8 on Vista [68]).
Overall, Eagle2.5-81 5-B achieves competitive 75.6 average score, demonstrating its effectiveness as versatile languag model that balances performance across various visual understanding tasks.
4.2 Ablation Studies
In this section, we conduct experiments on various benchmarks to evaluate our method. We mainly design experiments to study the following questions.
Q1: How do video and image data influence each other's benchmarks? Tab. 4 studies the impact of long context data on the image benchmark performance. We compare the image benchmark performance without training with long-context data and with training long-context data under different Lmxx- The results show that increasing the long-context data, under our training recipe, does not harm the short-context images and even slightly benefits it. To assess the impact of image data and pre- pre-training video benchmarks, conduct comparison using the Lmax = 32K model.
8

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The VQA ChiriQA InfoV/Q TeiVVQ OCRfiech MMane RWIQA
ARD
MMMIT MMB,
MMNN Halli MathVisti Aug
Training & Disa rocipe
Tist
Val
EN-Mi
Test
Test-Miii
Seore
E.ple2.5-92
92.5
58.3
78.8
34,5
868
66.5
74.4
83.5
54.0
85.5
57.3
53.4
65.1
74.8
Fag Zus 32K
92.3
86.6
77.6
82.8
861
66.7
75.9
83.7
55.5
84.8
63.6
55.4
68.3
75.3
fuss 64X
92.5
57.0
784
83.9
865
66.8
76.8
R3.9
55.7
85.2
63.3
5522
67.3
75.6
Eagl225-22 525 S2, Luss 1288
93.2
87.5
78.5
83.7
809
66.2
76.7
84.5
55.8
85.5
62.9
54.7
67.8
75.7
Table 4: Impact of long- data on performance of image benchmarks.
IntoVQA DoeVQA TeavQQ
PT MLVU Video-MME
Training Data rocipe
Recipe
Val
w/o subiiile
Val
Val
Val
Val
Val
wlo subtille
SI-+52
70.4
67.4
64.9
buseline
77.6
92.3
828
76.3
71.5
65.4
S2 (OD+EV-110K) S1-S115-82 (Image0D++EV11100]
72.9
70.9
65.2
w/o LAP
76.2
91.9
82.4
73.3
71.2
64.9
73.1
71.5
65.4
w/o ADS
77.0
92.1
82.8
75.5
70.1
65.0
Table 5: The impact of image data and pretraining on the performance of video benchmarks. S1/S1.5 denotes the stage-1 and stage- 1.5 similar to Ea- gle2 [3]. "OD" is short for Open-Data.
Table 6: The impact of information firstt sam- pling on performance of image and video bench- marks. The baseline equipped with IAP and ADS strategy. "PT" is short for Pereption Test.
For each benchmark, sampled at 2FPS, ensuring maximum of 32 frames. As shown in Tab. 5, extensive image pre-training significantly enhances performance on short video benchmarks like MVBench, well on the relatively simple long video benchmark, MLVU. However, for the more challenging and held-out long video benchmark, Video-MME, the improvements are less pronounced.
Q2: The effect of information-firs sampling on performance? Tab. 6 illustrates the impact of the information- first sampling strategy on image and video tasks. Without the Image Area Preservation strategy, high-resolution image benchmarks like Info and fine- ane-grained video benchmarks such as Perception-tes. suffer significant performance degradation. The effect on other benchmarks is less pronounced. While the Automatic Degradation Sampling strategy offers convenience for processing various visual inputs, experiments indicate that omitting it poses a risk. The vision-context-cemtii strategy may truncate supervision signals, leading performance loss.
Q3: The impact of different post-training schedules? Tab. 7 illustrates the performance im- pact of progressive mixed training from 32K to 64K compared to direct 64K mixed training the video benchmarks. The results demonstrate that progressive training outperforms direct 64K mixed training. possibly due to two reasons: 1) Direct 64K hybrid training disperses samples across the 64K space, diluting the focus on shorter contexts. 2) Some longer samples challenging learn without gradual learning process that transitions from easy to difficult. Fig. 6 shows the effect of progressive mixed training on the Video-MME benchmark. It reveals that as progressive training advances, the model's capacity to process more frames is gradually enhanced.
MVBench MLVU Video-MME
Training & Data recipe
Val
w/o sbutitle
32K 32K- Open-Data
73.0
74.5
68.1
64K, Open Data
71.3
74.0
67.9
32K -64K, Open- Data 10K
73.9
75.1
68.8
The impact of Eagle-Video- Video- 10K dataset and different post-training schedules on the performance of video benchmarks.
Table 7:
Performance on Video-MME (wlo subs)
Q4: The impact of Eagle- Video- 110K data on performance? We assess the impact of Eagle- Video- 110K model performance. Table 7 shows that it improves several long and short video benchmarks. Figure 6 demonstrates that training
22
88
with Eagle- Video- 10K enhances the model's 's abil- ity process many frames (2 128 frames) by incorporating long videos absent from the open- source training dataset.
Eagle2 5-88-16K
Eagle2 5-58-32K
Eagle2 5-88-6KK
Espli2 5-58-12500
Eagle2 5- 9-80-44K w/o Eagle-Viceo-110
5 Conclusion
128
286
512
Number of Frames
Figure 6: The impact of Eagle- Video- 1 10K dataset and different post-training schedules on the performance of Video-MME.
In this work, we present Eagle 2.5, an advanced vision-language model family designed for long- context multimodal understanding. Through inno- vative training approaches- including information-fiee sampling and progressive mixed post-training combined with our dual-annotated Eagle- Video- 1 11K dataset, significantly enhance long-context

---

comprehension capabilities. Eagle 2.5 achieves leading performance on video and high-resolution image benchmarks, matching larger models like GPT-4V and Gemini 1.5 Pro despite its smaller size. With advanced training strategies and diverse data, Eagle 2.5 sets strong foundation for future research, paving the way for efficient and versatile VI.Ms complex real-world scenarios.
Limitations. The training of Eagle2.5 required substantial computational resources, specifically cluster of 128 H100 GPUs. This high resource demand may limit the reproducibility and accessibility of our approach for researchers or practitioners without access to large-scale GPU infrastructure. Future work could explore more sesource-efficien training strategies or model architectures to reduce computational requirements.
6
Acknowledgement
This work is supported by the National Key R&D Program of China (No. 20222D0160900,, the Basic Research Program of Jiangsu (No. BK20250009), the National Natural Science Foundation of China (Grant 23.62232223 and U24A20330), and in part by Nanjing University-Ching Mobile Communications Group Co., Ltd. Joint Institute under Grant NJ20250037.
References
[1] Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, lige Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, al. GPT-4 technical report. arXiv:2303.0877 2023.
[2] Anthropic. The claude 3 model family: Opus, sonnet, haiku. https:
/ //www, anthropic com, 2024.
URL https://wwt cdn. anthropic com/ Careel__arrd___aaaeaeaeeee pdf.
[3] Anonymous Authors. Eagle 2: Building post-training data strategies from scratch for frontier vision-language models. In Submission, 2025.
[4] Jinze Bai, Shuai Bai, Shusheng Yang, Shijie Wang, Sinan Tan, Peng Wang, Junyang Lin, Chang Zhou, and Jingren Zhou. Qwen- VL: frontier large vision-language model with versatile abilities. arXiv:2308. 120:6666,. 2023.
[5] Shuai Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, Sibo Song, Kai Dang, Peng Wang. Shijie Wang, Jun Tang, et al. Qwen2.5-VLL technical report. M.XXi:25022.3923 2025,
[6] Yushi Bai, Xin Lv, Jiajie Zhang, Yuze He, Ji Qi, Lei Hou, Jie Tang, Yuxiao Dong, and Juanzi Li. Longalign: A recipe for long context alignment of large language models. arXiv:240I. 18058, 2024.
[7] Max Bain, Arsha Nagrani, Güil Varol, and Andrew Zisserman. Frozen in time: A joint video and image encoder for end-to-end retrieval. In ICCV, 2021.
[8] Fabian Caba Heilbron, Victor Escorcia, Bernard Ghanem, and Juan Carlos Niebles. Activitynet:
A large-scale video benchmark for human activity understanding. In CVPR, 2015.
[9] Joao Carreira and Andrew Zisserman. Quo vadis, action recognition? a new model and the kinetics dataset. In CVPR, 2017.
[10] Keshigeyan Chandrasegaran, Agrim Gupta, Lea M Hadzic, Taran Kota, Jimming He, Cristóbal Eyzaguirre, Zane Durante, Manling Li, Jiajun Wu, and Fei-Fei Li. Hourvideo: 1-hour video- language understanding. In NeurlPS, 2025.
[11] Guo Chen, Yicheng Liu, Yifei Huang, Yuping He, Baoqi Pei, Jilan Xu, Yali Wang, Tong Lu, and Limin Wang. Cg-bench: Clue- Clue-grounded question answering benchmark for long video understanding. arXiv:2412. 2024.
[12] Lin Chen, Jinsong Li, Xiaoyi Dong, Pan Zhang, Yuhang Zang, Zehui Chen, Haodong Duan, Jiaqi Wang, Yu Qiao, Dahua Lin, al. Are we on the right way for evaluating large vision- language models? arXiv:2403.20330, 2024.
10

---

[13] Lin Chen, Xilin Wei, Jinsong Li. Xiaoyi Dong, Pan Zhang, Yuhang Zang, Zehui Chen, Haodong Duan, Bin Lin, Zhenyu Tang, al. Sharegpt4video: Improving video understanding and generation with better captions. arXiv:2406.04325 2024.
[14] Tsai-Shien Chen, Aliaksandr Siarohin, Willi Menapace, Ekaterina Deyneka, Hsiang-wei Chao, Byung Eun Jeon, Yuwei Fang, Hsin-Ying Ying Lee, Jian Ren, Ming-Hsuan Yang. et al. Panda-70m: Captioning 70m videos with multiple cross-modality teachers. In CVPR, 2024.
[15] Yukang Chen, Fuzhao Xue, Dacheng Li, Qinghao Hu, Ligeng Zhu, Xiuyu Li, Yunhao Fang, Haotian Tang, Shang Yang, Zhijian Liu, et al. Longvila: Scaling long-context visual language models for long videos. arXiv:2408. 10188, 2024.
[16] Zhe Chen, Jiannan Wu, Wenhai Wang, Weijie Su, Guo Chen, Sen Xing, Zhong Muyan, Qinglong Zhang, Xizhou Zhu, Lewei Lu, et al. Intern VL: Scaling up vision foundation models and aligning for generic visual-linguistic tasks, arXiv:2312. 142388 2023.
[17] Zhe Chen, Weiyun Wang, Yue Cao, Yangzhou Liu, Zhangwei Gao, Erfei Cui, Jinguo Zhu, Shenglong Ye, Hao Tian, Zhaoyang Liu, et al. Expanding performance boundaries of open- source multimodal models with model, data, and test-time scaling, arXivv2412.05271,, 2024.
[18] Wenliang Dai. Nayeon Lee, Boxin Wang. Zhuoling Yang, Zihan Liu, Jon Barker, Tuomas Rintamaki, Mohammad Shoeybi, Bryan Catanzaro, and Wei Ping. NVLM: Open frontier-class multimodal llms. arXiv:2409. 402 2024.
[19] Google DeepMind. Build rich, interactive web apps with an updated gemini 2.5 pro. https: // http//meminnnnnnnien /ttp:///g//oooccctte///////////a
1ariii2-5-- 2-----------aeeees-------------- 2025. Accessed: 2025.
[20] Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Amy Yang, Angela Fan, et al. The llama 3 herd of models. arXiv:240..21783, 2024.
[21] Dave Epstein, Boyuan Chen, and Carl Vondrick. Oops! predicting unintentional action in video. In CVPR, 2020.
[22] Miquel Farré, Andi Marafioti, Lewis Tunstall, Leandro Von Werra, and Thomas Wolf. Finev- 2024.
ideo.
co/ dasrarets//tugggggFece//////ee
[23] Jiyang Gao, Chen Sun, Zhenheng Yang, and Ram Nevatia. Tall: Temporal activity localization via language query. In ICCV, 2017.
[24] Ridouane Ghermi, Xi Wang, Vicky Kalogeiton, and Ivan Laptev. Long story short: Story-level video understanding from 20k short films, 2025. URL https:// http:///aaaxxv..gaa/aa////////// org//bs/2405. 10221.
[25] Raghav Goyal, Samira Ebrahimi Kahou, Vincent Michalski, Joanna Materzynska, Susanne Westphal, Heuna Kim, Valentin Haenel, Ingo Fruend, Peter Yianilos, Moritz Mueller-Freitag, et al. The" something something" video database for learning and evaluating visual common sense. In ICCV, 2017.
[26] Kristen Grauman, Andrew Westbury, Eugene Byrne, Zachary Chavis, Antonino Furnari, Rohit Girdhar, Jackson Hamburger, Hao Jiang, Miao Liu, Xingyu Liu, et al. Ego4d: Around the world in 3,000 hours of egocentric video. In CVPR, 2022.
[27] Tianrui Guan, Fuxiao Liu, Xiyang Wu, Ruiqi Xian, Zongxia Li, Xiaoyu Liu, Xijun Wang, Lichang Chen, Furong Huang, Yaser Yacoob, et al. HallusionBench: An advanced diagnostic suite for entangled language hallucination & visual illusion in large vision-language models. ftti::2310.15560 2023.
[28] Dong Guo, Faming Wu, Feida Zhu, Fuxing Leng, Guang Shi, Haobin Chen, Haoqi Fan, Jian Wang, Jianyu Jiang, Jiawei Wang, et al. Seed1. 5-vl technical report. arXiv preprint vrXiv:2555.0766) 2025.
[29] Deepak Gupta, Kush Attal, and Dina Demner-Fushhan. dataset for medical instructional video classification and question answering. Scientific Data, 10(1):158, 2023.
11

---

[30] Mingfei Han, Linjie Yang, Xiaojun Chang, and Heng Wang. Shot2story20k: A new benchmark for comprehensive understanding of multi-shot videos. arXiv:2312.10300, 2023.
[31] Mingfei Han, Linjie Yang, Xiaojie Jin, Jiashi Feng, Xiaojun Chang, and Heng Wang. Video recognition in portrait mode. In CVPR, 2024.
[32] Xuchai He, Yichen Zhang, Luntian Mou, Eric Xing, and Pengtao Xie. PatVVQA 30000+ questions for medical visual question answering. arXiv:2003. 10286, 2020.
[33] Tuomo Hiippala, Malihe Alikhani, Jonas Haverinen, Timo Kalliokoski, Evanfiya Logacheva, Serafina Orekhova, Aino Tuomainen, Matthew Stone, and John A Bateman. Ai2d-rst: multimodal corpus of 1000 primary school science diagrams. Language Resources and Evaluation, 55:661-688, 2021.
[34] De-An Huang. Shijia Liao, Subhashree Radhakrishnan, Hongxu Yin, Pavlo Molchanov, Zhiding Yu, and Jan Kautz. LITA: Language instructed cemporal-localization assistant. In ECCV, 2024.
[35] Gabriel Huang, Bo Pang, Zhenhai Zhu, Clara Rivera, and Radu Soricut. Multimodal pretraining for dense video captioning. CoRR, abs/2011.11178 2020.
[36] Qingqiu Huang, Yu Xiong, Anyi Rao, Jiaze Wang, and Dahua Lin. Movienet: holistic dataset for movie understanding. In 2020.
[37] Yifei Huang, Guo Chen, Jilan Xu, Mingfang Zhang. Lijin Yang, Baoqi Pei, Hongjie Zhang, Lu Dong, Yali Wang, Limin Wang, et al. Egoexolearn: dataset for bridging asynchronous ego-and exo-centric view of procedural activities in real world. In CVPR, 2024.
[38] Yifei Huang, Jilan Xu, Baoqi Pei, Yuping He, Guo Chen, Lijin Yang, Xinyuan Chen, Yaohui Wang, Zheng Nie, Jinyao Liu, et al. Vinci: A real-time embodied smart assistant based on egocentric vision-language model. arXiv preprint arXiv:2412.2 1080, 2024.
[39] Yifei Huang, Jilan Xu, Baoqi Pei, Lijin Yang, Mingfang Zhang. Yuping He, Guo Chen, Xinyuan Chen, Yaohui Wang, Zheng Nie, et al. Vinci: A real-time smart assistant based on egocentric vision-language model for portable devices. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 9(3):1-33, 2025.
[40] IDEFICS, Introducing IDEFICS: An open reproduction of state-of-the-art visual language model. https::/huuggingface cttbloog/ceee/////o/ooec http:///aaggaaaaacac.oooo/////ea
2023.
[41] Md Mohaiminul Islam, Ngan Ho, Xitong Yang, Tushar Nagarajan, Lorenzo Torresani, and
Gedas Bertasius. Vidco recap: Recursive captioning of hour-long videos. In CVPR, 2024.
[42] Baoxiong Jia, Ting Lei, Song-Chun Zhu, and Siyuan Huang. Egotaskqa: Understanding human tasks in egocentric videos. In NeurlPS, 2022.
[43] Peng Jin, Ryuichi Takanobu, Wancai Zhang, Xiaochun Cao, and Li Yuan. Chat-univi: Unified visual representation empowers large language models with image and video understanding. In CVPR, 2024.
[44] Xuan Ju, Yiming Gao, Zhaoyang Zhang, Ziyang Yuan, Xintao Wang, Ailing Zeng, Yu Xiong, Qiang Xu, and Ying Shan. Miradata: A large-scale video dataset with long durations and structured captions. In NewrIPS, 2025.
[45] Bruno Korbar, Yongqin Xian, Alessio Tonioni, Andrew Zisserman, and Federico Tombari. Text-conditioned resampler for long form video understanding. In ECCV, 2024.
[46] Hilde Kuehne, Ali Arslan, and Thomas Serre. The language of actions: Recovering the syntax and semantics of goal-directed human activities. 2014.
[47] Jie Lei, Licheng Yu, Mohit Bansal, and Tamara L Berg. Tvqa: Localized, compositional video question answering. arXiv: 01666 2018
[48] Jie Lei, Tamara I.. Berg, and Mohit Bansal. Detecting moments and highlights in videos via natural language queries. 2021.
12

---

[49] Bo Li, Yuanhan Zhang, Dong Guo, Renrui Zhang, Feng Li, Hao Zhang, Kaichen Zhang, Yanwei Li, Ziwei Liu, and Chunyuan Li. LLaVA-One Vision: Easy visual task transfer. arXiv:2408. 003.:2200..03226,, 2024.
[50] Dongxu Li, Yudong Liu, Haoning Wu, Yue Wang, Zhiqi Shen, Bowen Qu, Xinyao Niu, Guoyin Wang, Bei Chen, and Junnan Li. Aria: An open multimodal native mixture-of-experts model. arXiv preprint
241000993, 2024.
[51] Haopeng Li, Andong Deng, Qiuhong Ke, Jun Liu, Hossein Rahmani, Yulan Guo, Bernt Schiele, and Chen Chen. Sports-qa: A large-scale video question answering benchmark for complex and professional sports. arXiv:2401.01555 2024.
[52] Junnan Li, Dongxu Li, Silvio Savarese, and Steven Hoi. BLIP-2: Bootstrapping language- image pre-training with frozen image encoders and large language models. In ICML, 2023.
[53] KunChang Li, Yinan He, Yi Wang, Yizhuo Li, Wenhai Wang, Ping Luo, Yali Wang. Limin Wang, and Yu Qiao. VideoChat: Chat-centric video understanding. arXiv:2305.06335 2023.
[54] Kunchang Li, Yali Wang, Yinan He, Yizhuo Li, Yi Wang, Yi Liu, Zun Wang. Jilan Xu, Guo Chen, Ping Luo, et al. Mvbench: A comprehensive multi-modal video understanding benchmark. CVPR, 2024.
[55] Pengyi Li, Irina Abdullaeva, Alexander Gambashidze, Andrey Kuznetsov, and Ivan Oseledets. Maxinfo: A training-free key-frame selection method using maximum volume for enhanced video understanding. arXiv:2502.03/1 2025.
[56] Xinhao Li, Yi Wang. Jiashuo Yu, Xiangyu Zeng, Yuhan Zhu, Haian Huang, Jianfei Gao, Kunchang Li, Yinan He, Chenting Wang. et al. Videochat-flaste Hierarchical compression for long-context video modeling. arXiv preprint arXiv:2501.00574 2024.
[57] Yanwei Li, Chengyao Wang, and Jiaya Jia. Llama-vid: An image worth tokens in large language models. In ECCV. Springer, 2024.
[58] Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee. Visual instruction tuning. In NeurIPS, 2023.
[59] Haotian Liu, Chunyuan Li. Yuheng Li, and Yong Jac Lee. Improved baselines with visual instruction tuning. In CVPR, 2024.
[60] Haotian Liu, Chunyuan Li. Yuheng Li, Bo Li. Yuanhan Zhang, Sheng Shen, and Yong Jae Lee. LLaVA-NeXT: Improved reasoning, ocr, and world knowledge. January 2024. URL https://llavee vl. github. io/blog////// 2024202220000 01-0000-3000011000 le-lllaaaneeee nextt.
[61] Xuyang Liu, Yiyu Wang, Junpeng Ma, and Linfeng Zhang. Video compression comman- der: Plug-and-play inference acceleration for video large language models. arXiv preprint arXiv:2505. 14454, 2025.
[62] Xuyang Liu, Ziming Wang, Yuhang Han, Yingyao Wang, Jiale Yuan, Jun Song, Bo Zheng, Linfeng Zhang, Siteng Huang, and Honggang Chen. Global compression commander: Plug- and-play inference accelcration for high-resolution large languala models. arXiv preprint (xXiv:2501.05111 2025.
[63] Yi Liu, Limin Wang, Yali Wang, Xiao Ma, and Yu Qiao. Fineaction: A fine- fine-grained video dataset for temporal action localization. IEEE transactions on image processing, 31:6937- 6950, 2022.
[64] Yuan Liu, Haodong Duan, Yuanhan Zhang, Bo Li, Songyang Zhang, Wangbo Zhao, Yike Yuan, Jiaqi Wang, Conghui He, Ziwei Liu, et al. Mmbench: is your multi-modal model an all-around player? In ECCV, 2024.
[65] Yuliang Liu, Zhang Li, Mingxin Huang, Biao Yang, Wenwen Yu, Chunyuan Li, Xu-Cheng Yin, Cheng-Lin Liu, Lianwen Jin, and Xiang Bai. Ocrbench: on the hidden mystery of ocr in large multimodal models. Science China Information Sciences, 62(1))220010, 2024.
13

---

[66] Zhijian Liu, Ligeng Zhu, Baifeng Shi, Zhuoyang Zhang, Yuming Lou, Shang Yang. Haocheng Xi. Shiyi Cao, Yuxian Gu, Dacheng Li. Xiuyu Li, Yunhao Fang, Yukang Chen, Cheng- Yu Hsieh, De-An Huang, An-Chieh Cheng, Vishwesh Nath, Jinyi Hu, Sifei Liu, Ranjay Krishna, Daguang Xu, Xiaolong Wang. Pavlo Molchanov, Jan Kautz, Hongxu Yin, Song Han, and Yao Lu. NVILA: Efficient frontier visual language models. In CVPR, 2025.
[67] Lidong Lu, Guo Chen, Zhiqi Li, Yicheng Liu, and Tong Lu.. Av-reasoner: Improving and benchmarking clue-grounded audio-visual counting for milms, 2025. URL https://arxit: org/ abaaaba//0066 05328.
[68] Pan Lu, Hritik Bansal, Tony Xia, Jiacheng Liu, Chunyuan Li, Hannanch Hajishirzi, Hao Cheng, Kai-Wei Chang, Michel Galley, and Jianfeng Gao. Math Vista: Evaluating mathematical reasoning of foundation models in visual contexts. arXiv:2310.02255, 2023.
[69] Muhammad Maaz, Hanoona Rasheed, Salman Khan, and Fahad Khan. Videogpt+: Integrating image and video encoders for enhanced video understanding. arXiv:2406.09416 2024.
[70] Ahmed Masry, Xuan Long Do. Jia Qing Tan, Shafiq Joty, and Enamul Hoque. ChartQA: A benchmark for question answering about charts with visual and logical reasoning. In ACL, 2022.
[71] Minesh Mathew, Dimosthenis Karatzas, and CV Jawahar. DocVQA: dataset for vqa document images. In WACV, 2021.
[72] Minesh Mathew, Viraj Bagal, Rubèn Tito, Dimosthenis Karatzas, Ernest Valveny, and CV Jawa- har. Infographic In WACV, 2022.
[73] Antoine Miech, Dimitri Zhukov, lean-Baptisse Alayrac, Makarand Tapaswi, Ivan Laptev, and Josef Sivic. Howto100m: Learning a text-video embedding by watching hundred million narrated video clips. In ICCV, 2019.
[74] Mathew Monfort, YouYoung Jin, Alexander Liu, David Harwath, Rogerio Feris, James Glass, and Aude Oliva. Spoken moments: Learning joint audio-visual representations from video descriptions. In CVPR, 2021.
[75] Andreea-Maria Onceseu, Joao Henriques, Yang Liu, Andrew Zisserman, and Samuel Albanie. Queryd: A video dataset with high-quality text and audio narrations. In ICASSP, 2021.
[76] OpenAl.
Gpt-4v(ision) system card, 2023.
URL https://openai.com wwtpinai...omiieeeeee
Ept-4v-system-carct
[77] OpenAl. Hello gpt-4o. https://openai: htt.:/ppaa...ammm/e//eeeelll---a 2023. Accessed: 2024-11-12.
[78] Viorica Patraucean, Lucas Smaira, Ankush Gupta, Adria Recasens, Larisa Markeeva, Dylan Banarse, Skanda Koppula. Mateusz Malinowski, Yi Yang, Carl Doersch, et al. Perception test: A diagnostic benchmark for multimodal video models. In NeurlPS, 2023.
[79] Viorica Patraucean, Lucas Smaira, Ankush Gupta, Adria Recasens. Larisa Markeeva, Dylan Banarse, Skanda Koppula, Mateusz Malinowski, Yi Yang, Carl Doersch, et al. Perception test: A diagnostic benchmark for multimodal video models. In NewrIPS, 2023.
[80] Shraman Pramanick, Rama Chellappa, and Subhashini Venugopalan. Spiqa: dataset for multimodal question answering on scientific papers. In NeurIPS, 2025.
[81] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. ICMI.. PmL.R, 2021.
[82] Ruchit Rawal, Khalid Saifullah, Miquel Farré, Ronen Basri, David Jacobs, Gowthami Somepalli, and Tom Goldstein. Cinepile: long video question answering dataset and benchmark. arXiv:2405. 2888::2405.08813, 2024.
14

---

[83] Machel Reid, Nikolay Savinov, Denis Teplyashin, Dmitry Lepikhin, Timothy Lillicrap. Jean- baptiste Alayrac. Radu Soricut, Angeliki Lazaridou, Orhan Firat, Julian Schritwieser. et al. Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context. arXiv:2403.05530, 2024.
[84] Xiaoqian Shen, Yunyang Xiong, Changsheng Zhao, Lemeng Wu, Jun Chen, Chenchen Zhu, Zechun Liu, Fanyi Xiao, Balakrishnan Varadarajan, Florian Bordes, al. Longvu: Spa- tiotemporal adaptive compression for long video-language understanding. arXiv:2410. 17434, 2024.
[85] Yunhang Shen, Chaoyou Fu, Shaoqi Dong, Xiong Wang, Peixian Chen, Mengdan Zhang, Haoyu Cao, Ke Li, Xiawu Zheng, Yan Zhang, et al. Long-vita: Scaling large multi-modal models to million tokens with leading short-context accuray. arXiv:2502.05177 2025.
[86] Amanpreet Singh, Vivek Natarajan, Meet Shah, Yu Jiang, Xinlei Chen, Dhruv Batra, Devi Parikh, and Marcus Rohrbach. Towards vqa models that can read. In CVPR, 2019.
[87] Enxin Song, Wenhao Chai, Guanhong Wang, Yucheng Zhang, Haoyang Zhou, Feiyang Wu, Haozhe Chi, Xun Guo, Tian Ye, Yanting Zhang, et al. Moviechat: From dense token to sparse memory for long video understanding. In CVPR, 2024.
[88] Zhende Song, Chenchen Wang, Jiamu Sheng. Chi Zhang, Gang Yu, Jiayuan Fan, and Tao Chen. Moviellm: Enhancing long video understanding with ai-generated movies. arXiv:2403.01422, 2024.
[89] Ryota Tanaka, Kyosuke Nishida, Kosuke Nishida, Taku Hasegawa, Itsumi Saito, and Kuniko Saito. Slidevqa: A dataset for document visual question answering on multiple images. In AAAI, 2023.
[90] Yansong Tang, Dajun Ding, Yongming Rao, Yu Zheng, Danyang Zhang, Lili Zhao, Jiwen Lu, and Jie Zhou, Coin: A large-scale dataset for comprehensive instructional video analysis. In CVPR, 2019.
[91] Gemini Team, Rohan Anil, Sebastian Borgeaud, Jean-Baptiste Alayrac, Jiahui Yu, Radu Soricut, Johan Schalkwyk, Andrew M Dai, Anja Hauth, Katie Millican, et al. Gemini: a family highly capable multimodal models. 1rXiv:2312.11800 2023.
[92] OpenGVLab Team. Internv12: Better than the best expanding performance boundaries of open-source multimodal models with the progressive scaling strategy. pttps://intervl.. github. http://ooooo//24-00000
07-0222022--------2-2222220020-0 InternVL-2 2. 2024.
[93] Qwen Team. Qwen2.5: A party of foundation models, September 2024. URL https: //quenlm github. http.o/gggeee2...////// qw.n2.//nn.....mm
[94] Rubèn Tito, Dimosthenis Karatzas, and Ernest Valveny. Hierarchical multimodal transformers for multipage docvqa. Pattern Recognition, 144:109834, 2023.
[95] Shengbang Tong, Ellis Brown, Penghao Wu, Sanghyun Woo, Manoj Middepogu, Sai Charitha Akula, Jihan Yang. Shusheng Yang, Adithya Iyer, Xichen Pan, et al. Cambrian-1: 1: A fully open, vision-centric exploration of multimodal 1ms. arXiv:2406. 16860, 2024.
[96] Jordy Van Landeghem, Rubèn Tito, Lukasz Borchmann, Michal Pietruszka, Pawel Joziak, Rafal Powalski, Dawid Jurkiewicz, Mickaël Coustaty, Bertrand Anckaert, Ernest Valveny, et al. Document understanding dataset and evaluation (dude). In ICCV, 2023.
[97] Peng Wang, Shuai Bai, Sinan Tan, Shijie Wang, Zhihao Fan, Jinze Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, et al. Qwen2-vl: Enhancing vision-language model's perception of the world at any resolution. arXiv:2409. 12191, 2024.
[98] Shihao Wang, Guo Chen, De-An Huang, Zhiqi Li, Minghan Li, Guilin Liu, Jose M. Alvarez, Lei Zhang, and Zhiding Yu. Videoitg: Multimodal video understanding with instructed temporal grounding. arXiv preprint arXiv:2507.13333. 2025.
15

---

[99] Xiyao Wang, Yuhang Zhou, Xiaoyu Liu, Hongjin Lu, Yuancheng Xu, Feihong He, Jaehong Yoon. Taixi Lu, Gedas Bertasius, Mohit Bansal, al. Mementos: A comprehensive benchmark for multimodal large language model reasoning over image sequences. 2.Vi::2401.15222 2024.
[100] Yi Wang, Yinan He, Yizhuo Li, Kunchang Li, Jiashuo Yu, Xin Ma, Xinyuan Chen, Yaohui Wang. Ping Luo, Ziwei Liu, et al. InternVid: A large-scale video-text dataset for multimodal understanding and generation. ICLR, 2024.
[101] Yi Wang, Kunchang Li, Xinhao Li, Jiashuo Yu, Yinan He, Guo Chen, Baoqi Pei, Rongkun Zheng, Jilan Xu, Zun Wang, et al. Intern Video2: Scaling video foundation models for multimodal video understanding. urXiv:2403. /5377, 2024.
[102] Yuxuan Wang. Cihang Xie, Yang Liu, and Zilong Zheng. Videollamb: Long-context video understanding with recurrent memory bridges. arXiv:2409.01071, 2024.
[103] Yuetian Weng, Mingfei Han, Haoyu He, Xiaojun Chang, and Bohan Zhuang. Longvlm: Efficient long video understanding via large language models. In ECCV. Springer, 2024.
[104] Bo Wu, Shoubin Yu, Zhenfang Chen, Joshua B. Tenenbaum, and Chuang Gan. STAR: A benchmark for situated reasoning in real-world videos. CoRR, abs/2445.099711, 2024.
[105] Chao- Yuan Wu and Philipp Krahenbuhl. Towards long-form video understanding. In CVPR, 2021.
[106] Haoning Wu, Dongxu Li, Bei Chen, and Junnan Li. Longvideohench: benchmark for long-context interleaved video-language understanding. In NeurlPS, 2025.
[107] X.ai. Grok-1.5 vision preview. https: awtaaaa/bblggaeee groggg/ggooo11 1.kk-1..1 5v, 2024.
[108] Junbin Xiao, Xindi Shang, Angela Yao, and Tat-Seng Chua. Next-qa: Next phase of question- answering to explaining temporal actions. 2021.
[109] Binzhu Xie, Sicheng Zhang, Zitang Zhou, Bo Li, Yuanhan Zhang, Jack Hessel, Jingkang Yang. and Ziwei Liu. Funqa: Towards surprising video comprehension. In ECCV, 2024.
[110] Fuzhao Xue, Yukang Chen, Dacheng Li, Qinghao Hu, Ligeng Zhu, Xiuyu Li, Yunhao Fang, Haotian Tang, Shang Yang. Zhijian Liu, et al. Longvila: Scaling long- context visual language models for long videos. arXiv:2408. 10188, 2024.
[111] Antoine Yang, Arsha Nagrani, Ivan Laptev, Josef Sivic, and Cordelia Schmid. Vidchapters 7mh Video chapters at scale. In NewrlPS, 2023.
[112] Dongjie Yang, Suyuan Huang, Chengqiang Lu, Xiaodong Han, Haoxin Zhang, Yan Gao, Yao Hu, and Hai Zhao. Vript: A video is worth thousands of words. In NeurlPS, 2025.
[113] Yuan Yao, Tianyu Yu, Ao Zhang, Chongyi Wang, Junbo Cui, Hongji Zhu, Tianchi Cai, Haoyu Li, Weilin Zhao, Zhihui He, et al. Minicpm-v: gpt-4v level mllm on your phone. arXiv:2408.0m 01800, 2024.
[114] Jinhui Ye, Zihan Wang, Haosen Sun, Keshigeyan Chandrasegaran, Zane Durante, Cristobal Eyzaguirre, Yonatan Bisk, Juan Carlos Niebles, Ehsan Adeli, Fei-Fei, et al. Re-thinking temporal search for long-form video understanding. In Proceedings of the Computer Vision and Pattern Recognition Conference, pages 8579-8591, 2025.
[115] Kexin Yi, Chuang Gan, Yunzhu Li, Pushmeet Kohli, Jiajun Wu, Antonio Torralba, and Joshua B. Tenenbaum. CLEVRER: Collision events for video representation and reasoning. In ICLR, 2020.
[116] Shoubin Yu, Jaemin Cho, Prateek Yadav, and Mohit Bansal. Self-chained image-language model for video localization and question answering. In NeurIPS, 2023.
[117] Sicheng Yu, Chengkai Jin, Huanyu Wang, Zhenghao Chen, Sheng Jin, Zhongrong Zuo, Xiaolei Xu, Zhenbang Sun, Bingni Zhang, Jiawei Wu, et Frame-voyager: Learning to query frames for video large language models. 240.v22410 2024.
16

---

[118] Weihao Yu, Zhengyuan Yang, Linjie Li, Jianfeng Wang, Kevin Lin, Zicheng Liu, Xinchao Wang, and Lijuan Wang. Me-Vet: Evaluating large multimodal models for integrated capabilitiis. N.Nv:2308.004999 2023.
[119] Xiang Yue, Yuansheng Ni, Kai Zhang, Tianyu Zheng, Ruoqi Liu, Ge Zhang, Samuel Stevens, Dongfu Jiang, Weiming Ren, Yuxuan Sun, et al. Mmmu: A massive multi-discipline multi- modal understanding and reasoning benchmark for expert agi. In CVPR, 2024.
[120] Zihao Yue, Qi Zhang, Anwen Hu, Liang Zhang, Ziheng Wang, and Qin Jin. Movie A new movie understanding benchmark. 1rXiv:2305.121-44 2023.
[121] Zihao Yue, Yepeng Zhang, Ziheng Wang, and Qin Jin. Movie10lv2: Improved movie narration benchmark. 1xXi::2404.13370 2024.
[122] Abhay Zala, Jaemin Cho, Satwik Kottur, Xilun Chen, Barlas Oguz, Yashar Mehdad, and Mohit Bansal. Hierarchical video-moment retrieval and step-captioning. In CVPR, 2023.
[123] Xiaohua Zhai, Basil Mustafa, Alexander Kolesnikov, and Lucas Beyer. Sigmoid loss for language image pre-training. In ICCV, 2023.
[124] Haoji Zhang, Yiqin Wang, Yansong Tang, Yong Liu, Jiashi Feng, Jifeng Dai, and Xiao- jie Jin. Flash-vstream: Memory-based real-time understanding for long video streams. arXiv:2406. 08085, 2024.
[125] Jiajie Zhang. Zhongni Hou, Xin Lv, Shulin Cao, Zhenyu Hou, Yilin Niu, Lei Hou, Yuxiao Dong. Ling Feng, and Juanzi Li. Longreward: Improving long-context large language models with ai feedback. arXiv:2410.21252 2024.
[126] Peiyuan Zhang, Kaichen Zhang, Bo Li, Guangtao Zeng, Jingkang Yang, Yuanhan Zhang, Ziyue Wang. Haoran Tan, Chunyuan Li, and Ziwei Liu. Long context transfer from language to vision. arXiv:2406. 1685,2 2024.
[127] Ruohong Zhang, Liangke Gui, Zhiqing Sun, Yihao Feng, Keyang Xu, Yuanhan Zhang, Di Fu, Chunyuan Li, Alexander Hauptmann, Yonatan Bisk, et al. Direct preference optimization of video large multimodal models from language model reward. arXiv:2404.01258. 2024.
[128) Yuanhan Zhang, Jinming Wu, Wei Li, Bo Li, Zejun Ma, Ziwei Liu, and Chunyuan Li. Video instruction tuning with synthetic data. arXiv:2410.02713, 2024.
[129] Yuanhan Zhang, Jinming Wu, Wei Li, Bo Li, Zejun Ma, Ziwei Liu, and Chunyuan Li. Video instruction tuning with synthetic data. arXiv:2410.02713, 2024.
[130] Hang Zhao, Antonio Torralba, Lorenzo Torresani, and Zhicheng Yan. Haes: Human action clips and segments dataset for recognition and temporal localization. In ICCV, 2019.
[131] Junjie Zhou, Yan Shu, Bo Zhao, Boya Wu, Shitao Xiao, Xi Yang, Yongping Xiong, Bo Zhang, Tiejun Huang, and Zheng Liu. Mlvu: A comprehensive benchmark for multi-task long video understanding. 24t::2406.04264, 2024.
[132] Luowei Zhou, Chenliang Xu, and Jason Corso. Towards automatic learning of procedures from web instructional videos. In AAAI, 2018.
[133] Deyao Zhu, Jun Chen, Xiaoqian Shen. Xiang Li, and Mohamed Elhoseiny. MiniGPT-4: Enhancing vasion-language understanding with advanced large language models. ICLR, 2024.
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1. Claims
Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?
Answer: [Yes]
The abstract and introduction clearly state the main claims: Eagle2.5 is frontier vision-language model (VLM) for long-context multimodal learning, introducing a generalist framework for both long video comprehension and high-resolution image understanding. The paper claims the introduction of two new techniques (Automatic Degrade Sampling and Image Area Preservation), a new dataset (Eagle- Video- 10K), and substantial improvements on long-context benchmarks, with performance matching or surpassing top-tier commercial and open-source models. These claims supported by the theoretical and experimental sections, and the scope well-aligned with the results presented in the paper.
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The authors should provide scripts to reproduce all experimental results for the new reproducible, they (b) If the contribution is primarily new model architecture, the paper should describe the architecture clearly and fully.
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Answer: [Yes]
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For each experiment, does the paper provide sufficient information on the com- puter resources (type of compute workers, memory, time of execution) needed reproduce the experiments?
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Question: Does the paper discuss both potential positive societal impacts and negative societal impacts of the work performed?
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The paper does not include a discussion of broader socictal impacts, either positive or negative. There is no section paragraph addressing potential misuse, fairesss privacy, or security implications releasing a powerful long-context VLM or dataset.
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The answer NA means that there no societal impact of the work performed.
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The authors should consider possible harms that could arise when the technology is being used as intended and functioning correctly, harms that could arise when the technology is being used as intended but gives incorrect results, and harms following from (intentional or unintentional) misuse of the technology.
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Does the paper describe safeguards that have been put in place for responsible release of data or models that have a high risk for misuse (c.g., pretrained language models, image generators, or scraped datasets)?
Question:
Answer: [Yes]
The paper does not describe any safeguards for responsible release of the model or dataset, nor does it discuss filtering for unsafe content or mechanisms to prevent misuse of the released assets.
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The answer NA means that the paper poses no such risks.
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Datasets that have been scraped from the Internet could pose safety risks. The authors should describe how they avoided releasing unsafe images.
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Are the creators or original owners of assets (e.g., code, data, models), used in the paper, properly credited and the license and terms of use explicitly mentioned and properly respected?
Question:
Answer: [Yes]
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The answer NA means that the paper does not use existing assets.
The authors should cite the original paper that produced the code package dataset.
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If this information is not available online, the authors are encouraged to reach out to the asset's s creators.
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Question: Are new assets introduced in the paper well documented and is the documentation provided alongside the assets?
Answer: [Yes]
The paper introduces Eagle- Video- 110K as new dataset but does not provide documentation, access instructions, or details about licensing, consent, or terms use for the new asset.
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Answer: [NA]
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The answer NA means that the paper does not involve crowdsoureing nor research with human subjects.
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24


Reviews:
[{"review_id": "TdLizgANZM", "reviewer": "Reviewer_36zH", "summary": "This paper proposes Eagle 2.5, a vision-language model (VLM) designed for long-context multimodal understanding. It systematically studies training strategies and data recipes for this setting, introducing methods such as information-first sampling with image area preservation and automatic degradation sampling, as well as progressive mixed post-training to enhance long-context capabilities. Additionally, the authors curate Eagle-Video-110K, a novel long-video dataset with combined story-level and clip-level annotations. Experiments show that Eagle 2.5 achieves competitive performance across a range of image and video understanding benchmarks.", "questions": "Please check the questions in the weaknesses above.", "limitations": "Yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strengths:\n\n1. The paper provides a detailed description of the training strategies and data recipe for building an effective framework for long-context VLMs, which will benefit future community work in this area.\n\n2. The proposed Eagle 2.5 framework achieves strong results, demonstrating competitive performance across a variety of image and video understanding benchmarks.\n\n3. The paper includes detailed ablation studies that validate the effectiveness of the proposed components, studying the impact of elements such as information-first sampling, post-training schedules, and the Eagle-Video-110K dataset.\n\nWeaknesses:\n\n1. While the data and training strategies are well-executed, the core model architecture follows existing VLM designs. The novelty of the work is relatively limited. \n\n2. Continuing from the previous point, the paper does not appear to mention what base model Eagle2.5-8B is built upon, and there is also no baseline performance reported for this base model in the experiments. This makes it difficult to assess what really contributes to the superior performance of Eagle, especially since the paper discusses many engineering efforts, which feels a bit messy without a clear baseline for reference.\n\n3. Based on Table 7, it appears that training with the Eagle-Video-110K dataset brings only marginal improvements for long-video benchmarks (comparing the first and third rows). In Figure 6, the improvements from Eagle-Video-110K under the 64K context length setting also appear to be quite limited. It would be helpful if the authors could help understand this observation and clarify under what conditions Eagle-Video-110K contributes more.", "was_revised": true, "final_justification": "The authors’ rebuttal has addressed my concerns, and I will maintain my positive rating for the paper.", "rebuttal": "## About the novelty of this work\n\nThe success of large-scale multimodal models stems from a combination of factors, including—but not limited to—model design, large-scale multimodal data, and effective training strategies. In this work, we focus on these holistic innovations and improvements. Specifically, we treat **data sampling and tiling methods** as part of model preprocessing, as they are distinct from the raw data itself or the training method. As noted by both Reviewer 4qXs and Reviewer YT9W, our contributions also include **a novel training strategy and a valuable video dataset** enriched with dual annotations, which together enhance the capabilities and robustness of models tackling the challenging task of long-form video understanding.\n\n## About the base model\n\nEagle2.5-8B is **built on SigLIP2-400M and Qwen2.5-7B, without relying on any existing VLM base model.** Compared with Eagle2, which has three training stages (Stage 1, Stage 1.5, Stage 2), Eagle2.5 adopts five stages (Stage 1, Stage 1.5, Stage 2, Stage 3, Stage 4). The first two stages are consistent between the two models, focusing on pretraining with large-scale image–language data.\n\nIn Eagle2, Stage 2 trains the model on high-quality image-language data. In contrast, Stages 2–4 of Eagle2.5 progressively train the model on long-context data. A slight difference exists in Stage 1.5, resulting in Eagle2.5-Stage 1.5 achieving better performance than Eagle2-Stage 1.5 and Eagle2-stage2.\n\nTo isolate the impact of our improvements, we present the following key benchmark comparisons.\n\n| Benchmark | Eagle 2 (Stage2) | Eagle 2.5-Stage1.5 | Eagle 2.5 Final |\n| --- | --- | --- | --- |\n| DocVQA | 92.6 | 92.6 | **94.1** |\n| ChartQA | 86.4 | **88.3** | 87.5 |\n| InfoQA | 77.2 | 78.8 | **80.4** |\n| TextVQA | 83.0 | **84.6** | 83.7 |\n| OCRBench | 868 | 868 | **869** |\n| MMstar | 62.6 | **66.5** | 66.2 |\n| RWQA | 69.3 | 74.4 | **76.7** |\n| AI2D | 83.9 | **85.5** | 84.5 |\n| MMMU | **56.1** | 54.0 | 55.8 |\n| HallB | 49.3 | 53.4 | **54.7** |\n| MathVista | 63.8 | 65.1 | **67.8** |\n| Video-MME (16 frames) | 56.6 | 58.9 | **65.0** |\n| Video-MME (128 frames) | 58.7 | 61.1 | **70.8** |\n\nThese results show that Eagle2.5-8B substantially outperforms Eagle 2 and Eagle2.5-stage1.5 model on video understanding tasks, while maintaining competitive performance on image-language tasks. This confirms that the introduced extensions are effective and generalizable.\n\n## Performance Improvement of Eagle-Video-110K\n\nOverall, incorporating Eagle-Video-110K yields only a modest overall improvement because gains on long-form videos are partially counterbalanced by small drops on short clips, in some video benchmarks, especially Video-MME.\n\nVideo-MME includes three subsets: long, medium, and short. Therefore, when adding long video data to the original data, the evaluation results on different subsets will be offset. This will lead to limited improvement in overall performance. For example, on the setting in Table 7 (testing up to 64 frames), the performance of different subsets with or without eagle-video-100k is as follows:\n\n| Model | Long | Medium | Short | Total |\n| --- | --- | --- | --- | --- |\n| 32K→64K, Open-Data | 59.5 | 63.4 | 81.3 | 68.1 |\n| 32K→64K, Open-Data + Eagle-Video-110K | 61.8 | 64.6 | 80.0 | 68.8 |\n| gain | **+2.3** | **+1.2** | **-1.3** | **+0.7** |\n\nAdding Eagle-Video-110K yields a sizeable +2.3-point gain on the *long* subset (and +1.2 on *medium*), but a –1.3-point drop on *short*, translating to a modest +0.7 overall. These findings suggest that jointly tuning for both long- and short-context videos remains an open, worthwhile direction for future work."}, {"review_id": "xncXy9FVsm", "reviewer": "Reviewer_4qXs", "summary": "The paper introduces Eagle 2.5, an 8 B-parameter vision-language model (VLM) aimed at native long-context reasoning across both video and high-resolution image domains. Key ingredients are:\n\n1) **Information-First Sampling (IFS)** – consisting of Image-Area Preservation (IAP) to keep ≥ 60 % of the original pixels during tiling, and Automatic Degradation Sampling (ADS) that dynamically trades visual tokens for text tokens so the text prompt is never truncated.\n\n2) **Progressive training** – the context window of the model is gradually expanded, which the authors show yields better length-robustness than a single long-window run.\n\n3) **Eagle-Video-110 K**, a 110 k-clip dataset with dual story-level and clip-level annotations to enhance long-form video understanding capabilities.", "questions": "Please see Strengths and Weaknesses Section for a list of questions.", "limitations": "Yes.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths:\n1) **Timely focus on long-form video understanding.** Scaling vision-language models to hour-long content is a pressing challenge; meaningful progress here benefits both research and downstream applications.\n\n2) **Valuable resource.** The Eagle-Video-110 K dataset (≈110 k clips with story- and clip-level annotations) should become a useful test-bed for future work on extended-duration reasoning and retrieval.\n\n3) **Impressive efficiency/accuracy frontier (Table 2).** The proposed 8B parameter model matches or beats far larger models on Video-MME, LVBench, and, notably, outperforms Gemini 1.5 on the challenging HourVideo benchmark.\n\n\nWeaknesses:\n1) **HourVideo breakdown:** Could you provide per-category results for HourVideo (analogous to Table 2 in the HourVideo [A] paper)? This would clarify whether gains come primarily from visual reasoning, or other tasks such as summarization or perception.\n\n2) **Reproducibility & release plan:** Will you release all training code, model weights, and the full Eagle-Video-110 K dataset to the community? \n\n3) **Terminology clarity:** “Information-First Sampling” is not a standard terminology. A brief justification would improve clarity.\n\n4) **Standard deviations missing:** No variance metrics are given. Please include standard deviations for a representative subset of experiments.\n\n5) Missing related work on long-form video understanding: [B]\n\nThought experiment: Do you think long-form video understanding is fundamentally a long-context modelling problem?\n\n==\n\n[A] Chandrasegaran, Keshigeyan, et al. \"Hourvideo: 1-hour video-language understanding.\" Advances in Neural Information Processing Systems 37 (2024): 53168-53197.\n\n[B] Ye, Jinhui, et al. \"Re-thinking temporal search for long-form video understanding.\" Proceedings of the Computer Vision and Pattern Recognition Conference. 2025.", "was_revised": true, "final_justification": "Thank you authors for the rebuttal. Authors have addressed my concerns.\n\nI will maintain my positive rating.", "rebuttal": "## About reproducibility & release plan\n\nThe model weight and inference code are open to the public now. The training code and dataset are under legal review. They will be released when the legal review is done.\n\n## About the clarity of information-first sampling\n\nIn brief, **Information-First Sampling is a dynamic token-allocation strategy that, under a fixed context budget, selects the most informative text spans and image regions so that critical content remains intact.** It has two complementary components: \n\nImage Area Preservation (IAP) – fully preserves spatial image information.\n\nAutomatic Degradation Sampling (ADS) – ensures temporal inputs and their supervision signals are retained in full.\n\nTogether, ADS and IAP let the model devote its limited tokens to the meaningful patches. \n\n## About standard deviations missing\n\nExisting closed-source model APIs enable sampling during inference, which introduces uncertainty and can lead to bias in multiple tests. **However, for open-source VLMs, the common practice is to directly use greedy decoding.** Our experiments are run with decoding temperature τ = 0 that is common practice, thus we employ greedy decoding for every evaluation. Because this setup is fully deterministic, repeated evaluations yield identical predictions and standard deviations are not applicable. \n\nIn addition, we fix **the same random seed throughout the entire training pipeline**—covering Python’s `random`, NumPy, PyTorch, and data-loader workers—so that initialization, data shuffling, and gradient updates are fully reproducible.\n\nWe will make these settings explicit in the revised manuscript to enhance transparency and avoid any potential confusion.\n\n## About the missing related work\n\nWe acknowledge that the current draft requires more comprehensive coverage of prior work in this domain and will address this in the final manuscript.\n\n## Do you think long-form video understanding is fundamentally a long-context modelling problem?\n\nNot entirely. For images, visual understanding can largely be regarded as a short-context spatial modeling task (e.g., modeling objects, scenes, and spatial relationships). Videos, however, introduce the temporal dimension. As a result, video understanding requires both spatial modeling of individual frames and temporal modeling across frames (e.g., modeling events, motion, and changes over time). Because the temporal span can grow without bound, understanding long-form videos within **general** multimodal large language models ultimately manifests as a long-context modeling problem. Moreover, the information density of video is typically much lower than that of text, leading to substantial variability in sparsity when processed by general-purpose models. Various sparsity-oriented methods—some designed specifically for video and others applicable to long-context modeling more broadly—can be leveraged to address this issue. Exploring these directions is an important avenue for future work, though it lies beyond the scope of this paper."}, {"review_id": "goXxR15PI3", "reviewer": "Reviewer_YT9W", "summary": "Eagle2.5, a Vision-Language Model (VLM) aimed at advancing long-context multimodal understanding. The authors tackle the challenges of processing long videos and high-resolution images by introducing a novel training framework and dataset. The core contributions are threefold: An \"Information-First Sampling\" strategy to dynamically balance and preserve visual and textual data, a \"Progressive Mixed Post-Training\" schedule that incrementally increases the model's context length , the Eagle-Video-110K dataset, which provides much-needed long-form video content with a dual-annotation approach for both narrative and detail. Through extensive experiments, the 8B-parameter Eagle2.5 is shown to achieve state-of-the-art performance, often matching or outperforming significantly larger models.", "questions": "1. Regarding the Nature of IAP/ADS:The IAP and ADS methods are query-agnostic, pre-processing heuristics, applied before the model sees the user's actual question. Given that the optimal strategy for information retention is highly context-dependent , how does this approach mitigate the risk of discarding crucial visual details before their relevance can be assessed in light of the specific question being asked? How to prove this heuristic actually preserve more information than direct sample and tiling method? Is it necessary to utilize this sampling strategy during training for it to be effective at inference, or could it be applied post-hoc to other pre-trained VLMs?\n2. Regarding Analysis of IAP/ADS: To better understand the behavior of the sampling strategies, would it be possible to provide a specific case study? For instance: A visualization of a high-resolution image tiled using IAP versus a baseline method. A quantitative example of the trade-off made by ADS for a specific long-video sample that contains lengthy text, showing how many frames are preserved versus discarded. \n3. Is there a clear plan to open-source the methods and dataset? I believe this could greatly benefit the field.", "limitations": "yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "**Strengths:**\n1. Significance: The paper tackles a highly relevant and challenging frontier in multimodal AI: long-context visual understanding.\n2. Noval Training Strategy and Data Curation: The paper introduces a well-motivated progressive training schedule to effectively scale context length. Furthermore, the development of the Eagle-Video-110K dataset addresses a recognized scarcity of long-form video data for training, and its dual annotation strategy is a thoughtful approach to capturing both semantic and temporal details.\n3. Rigorous Evaluation: The claims are substantiated by extensive experiments across a wide array of benchmarks and detailed ablation studies that validate the effectiveness of each component of the proposed framework.\n4. Competitive Performance: The 8B model demonstrates strong empirical results, achieving performance on par with or exceeding much larger open-source and proprietary models.\n5. Clarity and Organization: The paper is well-written, logically structured, and easy to follow. The authors clearly articulate the problems with existing long-context VLMs and present their solutions in a coherent manner.\n\n**Weaknesses:**\n1. Lack of Qualitative Analysis: The paper would be strengthened by a qualitative analysis of its core sampling strategies. It currently lacks case studies or visualizations to provide intuition for how IAP and ADS make decisions. For example, showing how an image is tiled by IAP or which frames are dropped by ADS in a given scenario would offer valuable insight into their practical behavior.\n2. The dataset proposed in the article holds significant importance, as it appears to contribute most noticeably to the improvement in results. However, the manuscript does not seem to provide a clear plan for the dataset's release, which could impact the reproducibility and overall significance of the work.", "was_revised": true, "final_justification": "After carefully reading the authors' response, the other reviews, and the ensuing discussion, my concerns have been addressed, I believe the work is solid and meets the bar for acceptance. Therefore, I have raised my score to 5 (Weak Accept).", "rebuttal": "## Risk of Dropping Task-Critical Visual Cues\n\nBefore being fed into the LLM, all visual operations—such as sampling, slicing, visual encoding, visual token compression, and the visual-to-LLM projection—are query-agnostic. Consequently, two key factors determine the effectiveness of visual cue extraction: (1) preserving the integrity of the original input source, which directly sets the upper bound of visual feature information available to the LLM; and (2) providing complete textual semantic supervision, ensuring the visual encoder can extract universal visual features adaptable to any query. Our IAP ensures the upper limit of visual information, while ADS complements it by guaranteeing textual semantic supervision. Through end-to-end training, the visual encoder becomes task-agnostic and capable of extracting general visual features for query-aware decoding in the LLM.\n\n## How to prove heuristic method actually preserve more information.\n\nWe show this through practical experimental results. For IAP, we scan all images in the training set, compare the results of IAP and traditional tiling methods from InternVL and LLaVA, and remove results that did not match the tiling. We calculate the average coverage of the original image after tiling, as shown in the table below.\n\n|  | IAP | vanilla tiling |\n| --- | --- | --- |\n| Average Pixel Coverage | 73.82% | 44.19% |\n\nThe results show that IAP can retain more pixels input to the model.\n\nFor ADS strategy, we scan all video data in the training data, compare the results of ADS and non-ADS sampling strategies. non-ADS strategy samples frames at fixed FPS and is limited by the maximum number of frames. We calculate the label truncation rate and sample discard rate, as shown in the table below. We set `max_len = 16k`.  ADS minimizes both the label truncation rate and the sample dropout rate by adaptively calculating the number of sampling frames. \n\n|  | Average label truncation rate | Average sample discard rate |\n| --- | --- | --- |\n| ADS | 0.2% | 0.4% |\n| non-ADS (fps2, max 8 frames) | 0.5% | 0.1% |\n| non-ADS (fps2, max 16 frames) | 2.1% | 0.3% |\n| non-ADS (fps2, max 32 frames) | 11.3% | 4.8% |\n| non-ADS (fps2, max 48 frames) | 22.3% | 10.4% |\n\nIn this table, the label truncation rate counts samples with partially truncated labels, and the sample discard rate counts samples with completely truncated labels.\n\n## Need for sampling strategy at training and testing time\n\nIAP/ADS can be applied post-hoc to any pre-trained VLM because it is a pure input-layer transformation; no model parameters are modified. Nevertheless, matching the sampling distribution during training yields the best performance. We therefore recommend \"train-time + test-time\" for new models. ADS ensures that, given any input of multiple rounds of text-and-image dialogues, the processor automatically performs heuristic sampling of visual tokens according to the set `max_len`, thereby ensuring that labels are not truncated and reducing the cost of hyper-parameter tuning.\n\n## Visualization of IAP\n\nAs is shown in the Figure 3 of the paper, when the input image size is 1300×2000 and the maximum number of tiles 12, the target aspect ratios include:[(1, 1), (1, 2), (2, 1), (3, 1), (1, 3), (2, 2), (4, 1), (1, 4), (5, 1), (1, 5), (1, 6), (6, 1), (3, 2), (2, 3), (7, 1), (1, 7), (4, 2), (2, 4), (1, 8), (8, 1), (1, 9), (3, 3), (9, 1), (2, 5), (5, 2), (10, 1), (1, 10), (11, 1), (1, 11), (12, 1), (3, 4), (4, 3), (1, 12), (6, 2), (2, 6)].\n\nIn InternVL's tiling method, the closest ratio to the image aspect ratio of 13:20 is 2:3, and thus the final tiles cover an area of 896×1344 pixels.\n\nIn contrast, IAP (our method) considers not only aspect ratio proximity but also the matching degree of tile area to the original image, which helps avoid generating excessive redundant tiles or overly small tiles that lead to information loss. According to Eq. (1), compared to a score of 0.4516 for the 2:3 configuration, the 3:4 configuration achieves a higher score of 0.5200. As a result, our method selects the 3:4 layout and tiles an area of 1344 × 1792, which better preserves visual information and reduces unnecessary padding or information loss.\n\n## **About release plan**\n\nThe model weight and inference code are open to the public now. The training code and dataset are under legal review. They will be released when the legal review is done."}, {"review_id": "EGm0Af1ron", "reviewer": "Reviewer_bMM9", "summary": "This paper proposes a frontier multi-modal large language model named Eagle2.5, which focuses on long-context multi-modal learning. Eagle2.5 is a generalist framework to addresses the challenges in long video comprehension and high-resolution image understanding. It proposes Automatic Degrade Sampling and Image Area Preservation to preserve contextual integrity and visual details. Also, the work also proposes a diverse and high-quality dataset named Eagle-Video-110K to facilitating long-context training. Extensive experiments demonstrate the effectiveness of Eagle2.5.", "questions": "In addition to the questions in Weakness part, I have another important question. \n\nIn Table 3, Eagle2.5 does not perform better than Qwen2.5-VL-8B in terms of the overall average metric. For example, Eagle2.5 is inferior to Qwen2.5-VL-8B on DocVQA. Could you please show me some insights or conjectures about the reasons of these phenomena? I think some explanation or analysis will surely improve the quality of the paper.", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "The paper proposes a new frontier multi-modal large language model to address long-context modeling, and their experiments have demonstrated the effectiveness of the proposed model. Thus, I think this paper does make some contributions. \n\nHowever, I have some concerns about the techniques and experiments, which are listed as follows:\n- In my understanding, the proposed Image Area Preservation involves more patches/tokens from the the original videos/images, such that it would cover more complete content in videos/images. However, it will surely involve more computation cost. Have you analyze the budget? \n- Eagle-Video-110K involves synthetic video data, whose captions are automatically generated by advanced MLLMs like GPT-4o and Gemini-1.5-Pro. Have you make some efforts to ensure the quality of generated captions as these MLLMs have hallucinations. \n- In Line 245, why you use CLIP? Could you please give me some reasons? Have you try any other encoders?\n- I think it will be better that you can highlight the best/second-best results in experiment tables, e.g., Table 2 and 3.", "was_revised": true, "final_justification": "I initially gave a positive rating. After rebuttal, my concerns have been addressed. Thus I recommend accepting this paper.", "rebuttal": "## About the computation cost of IAP sampling\n\n**Computation cost does not necessarily increase**. Our IAP, which maintains the same performance as traditional tiling for regular images, does not increase the actual number of visual tokens. IAP primarily targets long-context sampling of images, particularly for images with long-tail aspect ratios similar to those found in InfoVQA. For example, for an image with a height and width of (1366, 6350) in InfoVQA, the number of visual tokens generated by Eagle2.5, InternVL3, and Qwen2.5VL (these models use 28x28 compression ratio for each image) is as follows.\n\n|  | vision token (1366, 6350) | inference speed (1366, 6350) | InfoVQA val set |\n| --- | --- | --- | --- |\n| InternVL-3 8B | 4864 | 0.45s | 76.8 |\n| Qwen2.5VL 8B | 10848 | 0.78s | 82.6 |\n| Eagle-2.5 8B | 5376 | 0.47s | 80.4 |\n\nCompared to traditional tiling methods like InternVL3 and LLaVA, IAP preserves more contextual information for large images, resulting in better results (as shown in Table 6 in the main paper), while only slightly increasing computational overhead (0.45s → 0.47s). Compared to Qwen2.5VL's anyres sampling strategy, Eagle2.5-8B saves about 50% vision tokens, yet its performance is only slightly lower (qwen2.5vl: 82.6).\n\n## About the data quality\n\nMultiple works[1][2] have proved that integrating or collaborating multiple VLM models can reduce the response illusion and accuracy of queries. Hence, to curb hallucination in automatically generated captions and QA pairs, we performed a mutual review cycle in which GPT-4o and Gemini-1.5-Pro iteratively scored and revised each other’s annotations until consensus was reached. This procedure markedly improved caption fidelity, but it also introduced substantial **time and money costs**. In pilot studies with a 7B-parameter LLM, the marginal gain in downstream performance did not justify the extra cost. Consequently, we prioritized incorporating a broader range of newly rollout of GPT4o/Gemini 1.5 Pro—after basic sanity checks—into the full training set, deferring more intensive refinement to future work. \n\n## Why use CLIP for video clip retrieval?\n\nWe initially considered several models as potential options, including CLIP, SSCD, InternVideo, and SigLIP. However, after careful evaluation, we ultimately selected CLIP for our deployment. Our decision was based on several factors. First, although SSCD is effective for capturing detailed visual textures, we observed that the queries in existing video benchmarks are more semantic in nature, rather than focusing on fine-grained visual details. InternVideo and SigLIP are also strong candidates, but they come with higher computational overhead, which requires further consideration in our deployment context. In contrast, CLIP offers a good balance between semantic understanding and computational efficiency, and its ONNX format allows for fast and easy inference.\nIn summary, while we considered multiple alternatives, we chose CLIP because it best meets the semantic requirements of our tasks while ensuring efficient inference.\n\n## About result highlighting in table 2 and 3\n\nWe appreciate the reviewer’s suggestion regarding the presentation of experimental results. We will revise the tables in the final version to highlight the best and second-best performance values for better clarity.\n\n## About the performance comparison with Qwen 2.5 VL\n\nQwen2.5 VL differs from Eagle 2.5 in many aspects. It uses a larger vision encoder than Eagle 2.5 (Qwen vision encoder 600m vs. siglip-400m used in Eagle2.5) for visual encoding. In addition, and most importantly, it samples much more pre-training data than Eagle 2.5 and does not publish the training data list. Qwen2.5VL trains on 18T vision-language tokens. Eagle 2.5 trains on a total of 0.67T vision-language tokens across its four stages. Consequently, the gap we observe on DocVQA and the overall average metric is likely driven less by architectural shortcomings in Eagle 2.5 but more by this stark disparity in encoder capacity and pre-training scale; we expect that scaling Eagle 2.5 to comparable data volumes and a higher-capacity vision backbone would substantially narrow the performance difference.\n\n[1] Enhancing LLMs with an Ensemble of Critics for Mitigating Hallucination\n\n[2] LVAgent: Long Video Understanding by Multi‑Round Dynamical Collaboration of MLLM Agents"}]

Author rebuttal:

Meta-review: The paper introduces Eagle 2.5, an 8 B-parameter vision-language model (VLM) aimed at native long-context reasoning across both video and high-resolution image domains. Key contributions recognized by reviewers are: Information-First Sampling (IFS) strategy, progressive training recipe, and a 110K clip dataset named Eagle-Video-110K. 

All reviewers agree that this work tackles a highly relevant and challenging frontier in multimodal AI: long-context visual understanding. The proposed data curation and training strategies are well-motivated and shown to be effective. The proposed 8B model achieves strong empirical results, and notably outperforms Gemini 1.5 on the challenging HourVideo benchmark. 

Two reviewers raised concerns about reproducibility and release plan of the work, and asked for some detailed quantitative & qualitative results. The authors addressed these concerns in the rebuttal. As a result, all four reviewers reached a consensus to accept/borderline accept the paper.
Decision: Accept (poster)