EventFlash: Towards Efficient MLLMs for Event-Based Vision
Authors: Shaoyu Liu
Jianing Li
guanghui zhao
Yunjian Zhang
Wen Jiang
Ming Li
Xiangyang Ji
Venue: ICLR 2026 Poster (2026)
Keywords: Event-Based Vision
Event-Language Alignment
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: Event-Based Vision

Abstract: Event-based multimodal large language models (MLLMs) enable robust perception in high-speed and low-light scenarios, addressing key limitations of frame-based MLLMs. However, current event-based MLLMs often rely on dense image-like processing paradigms, overlooking the spatiotemporal sparsity of event streams and resulting in high computational cost. In this paper, we propose EventFlash, the first efficient MLLM to explore spatiotemporal token sparsification for reducing data redundancy and accelerating inference. Technically, we first build EventMind, a large-scale and scene-diverse dataset with over 500k instruction sets, providing both short and long event stream sequences to support our curriculum training strategy. Then, we present the adaptive temporal window aggregation module for efficient temporal sampling, which adaptively compresses temporal tokens while retaining key temporal cues. Finally, the sparse density-guided attention module is designed to improve spatial token efficiency by selecting informative regions and suppressing empty or sparse areas. Experimental results show that EventFlash achieves a 12.4x throughput improvement over the baseline (EventFlash-Zero) while maintaining comparable performance. It supports long-range event stream processing with up to 1,000 bins, significantly outperforming EventGPT’s 5-bin limit. We believe EventFlash serves as an efficient foundation model for event-based vision.

Published as a conference paper at ICLR 2026
EVENTFLASH: TOWARDS EFFICIENT MLLMS FOR EVENT-BASED VISION
Shaoyu Liu Jianing Li Guanghui Zhao', Yunjian Zhang', Wen Jiang?, Ming Li Xiangyang Ji
Xidian University, Tsinggua University, Beijing Institute of Technology,
Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)
1 iussyestu. xidi an edu. en, ghzhaol xidiiiidinni edu.
ABSTRACT
Event-based multimodal large language models (MLLMs) enable robust perception in high-speed and low-light scenarios, addressing key limitations of frame-based MLLMs. However, current event- based MILMS often rely on dense image-like processing paradigms, overlooking the spatiotemporal sparsity of event streams and resulting in high computational cost. this paper, we propose EventFlash, a novel, efficient MI..M to explore spatiotemporal token sparsification for reducing data redundancy and accelerating inference. Technically, we built EventMind, a large- scale and scene-diverse dataset with over 500k instruction sets, providing both short and long event stream sequences to support our curriculum training strategy. Then, we present the adaptive temporal window aggregation module for efficient temporal sampling, which adaptively compresses temporal tokens while retaining key temporal cues. Finally, the sparse density-guided attention module is designed to improve spatial token efficiency by selecting informative regions and suppressing empty or sparse areas. Experimental results show that EventFlash achieves 12.4x throughput improvement over the baseline (EventFlash-Zery while maintaining comparable performance. It supports long-range event stream processing with up to 1,000 bins, significantly outperforming EventGPT's s 5-bin limit. We believe EventFlash serves as an efficient foundation model for event-based vision. Our codes will be released in httph::///// /ttp:://ggttttttt com htt.//uu.cooooouuusse/e/eettttte lash
INTRODUCTION
Event cameras (Gallego et al.) 2020 Posch al., 2014 Li & Tian 2021). bio-inspired vision sensors, operate differently from frame- based cameras. Each pixel responds to intensity changes by generating asynchronous events (Li et al. 2022 Kudithipudi et al. 2025). Due to their high temporal resolution and high dynamic range, event cameras have been applied to various vision tasks (e.g.. scene understanding (Zhu et al. 2018 Kong et al. 2024 Yao
2024 Zhou al. 2024 Li et al. 2025a Liu et al. 2025b Li et al. 2025b Zhou & Lee 2025) in high-speed or low-light scenarios.
Recent multimodal large language models (MLLMs) (Xiang al. 2025 Li al.
2024a Tang
et al. 2025 Huang et al. 2024a Qian et al.
2024 Fang et al.) 2026a b, 2024a Xu et al. 2024
Yan al. (225b) have achieved remarkable breakthroughs processing conventional frames and
language, showing strong capabilities in scene understanding and visual question answering. However, these models are primarily designed for frame-based inputs and cannot directly handle the unique spatiotemporal properties of event streams. A straightforward approach to extending MI.LMss to event- based vision involves converting event streams into dense, image-like representations before feeding them into existing MLLMs (e.g., LLaVA (Liu al. 2023), GPT-4 (Bubeck et al. 2023), or Qwen (Bai et al. ).23)). ). However, this transformation often overlooks the inherent spatiotemporal sparsity of event data and introduces substantial redundancy (Gehrig & Scaramuzza 2024; Messikommer et al.)
2020 Wu et al.] 2024a). In other words, applying dense image-like processing paradigms to event streams not only incurs significant computational overhead but also substantially limits the effective
"Corresponding authors

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Published as a conference paper at ICLR 2026
length and efficiency of event stream understanding. Thus, developing efficient MLLMs that fully exploit the unique spatiotemporal properties of event data remains a critical and unresolved challenge.
Despite recent progress, most existing event-based MLLMs (Liu al. 2025b Li et al. 2025b
Zhou & Lee 2025 Liu et al., 2025a) still rely on dense image-like representations, which hinders computational efficiency and scalability to long event sequences. For example, EventGPT (Liu et al.
2025b) converts event streams into dense token sequences for language modeling. EventVL. LL et al.
2025b integrates RGB frames with event data to enhance multimodal reasoning. LLaFEA (Zoou &
Lee (025) employs frame-event fusion for region-level spatiotemporal grounding. Although these models perform well in challenging scenarios such as high-speed motion and low-light conditions, their dense processing of sparse event data leads to significant overhead and limits real-time or long- range understanding. Meanwhile, the scene diversity of their datasets is relatively limited, and the event streams are short, making it difficult to support general-pune -arrpol- models for long event-stream understanding.
In this paper, we propose EventFlash, a novel efficient MLLM that leverages spatiotemporal token sparsification to reduce data redundancy and accelerate inference. Unlike prior works that focus on maximizing reasoning accuracy, our goal is to address three key challenges in efficient MLLMs: (i) Temporal inefficiency: The microsecond resolution of event streams results in prohibitively large token volumes when processed over long temporal durations; (ii) Spatial inefficiency: The inherent sparsity of event data leads to numerous empty or low-information tokens that incur computational overhead due to uniform attention allocation; (iii) Dataset limitations: Existing instruction-augmentea datasets are not publicly available and often lack diversity, contain low-quality annotations, and cover short temporal sequences, making them inadequate for training generalizable models.
To address these challenges, our EventFlash presents a density-aware spatiotemporal token sparsi- fication strategy that exploits the inherent sparsity and high temporal resolution of event streams, Specifically, we propose an adaptive temporal window aggregation module for efficient temporal sampling. which dynamically compresses temporal tokens while preserving essential temporal cues. Then, sparse density- -uided attention module is presented to enhance spatial token efficiency by selecting informative regions and suppressing empty or low-density areas. Moreover, we design a progressive curriculum learning strategy following a short-to-long paradigm to improve EventFlash 's generalization and generative capabilities. To support this, we built a large-scale scene-diverse dataset over 500k instruction sets, including both short and long event stream sequences. Experimental results show that EventFlash achieves a 12.4x improvement in throughput over our baseline (EventFlash- Zero) while maintaining comparable performance. Notably, EventFlash enables long-range event stream processing of up to 1,000 bins compared to only 5 bins in the competing EventGPT.
In summary, the main contributions of this work are:
We propose EventFlash, an efficient event-based vision MLLM, which explores a spatiotemporal token sparsification strategy for raw event streams reduce redundancy, accelerate inference (12.4x throughput), and enable long-range event stream understanding (up to 1,000 bins).
We present density-aware spatiotemporal token sparsification strategy for event-based MLLMs, which effectively reduces redundancy while maintaining comparable reasoning accuracy by leveraging the fine-grained temporal resolution and inherent sparsity of raw event streams. We build large-scale scene-diverse dataset for long-rang event stream understanding. We believe this standardized dataset will accelerate future research in event-based MLLMs.
2 RELATED WORK
Event-based Vision with MLLMs. Early works (Wu et al. 2023 Zhou et al. (0223) ) have explored
the alignment between event data and textual information. Event- CL.IP (Wu al.. 2023) builds on pre-trained Aasion-language models (Radford et al. 2021: Yang et al.) 2023 Klenk et al., 2024 Huang et al.) 2024b; Tong et al. 2025) for event-based recognition, and EventBind (Zhou et al. 2023) incorporates an event encoder to unify images, events, and texts. Yet both overlook the world knowledge embedded in LLMs, constraining nuanced scene understanding. More recently, emerging event-based MLLMs (Liu et al. [022bb Li al. 2025b Zhou & Lee| (2025) have demonstrated strong reasoning capabilities in challenging conditions. For example, EventGPT (Liu et al. (025b) is the
first to design event-based ML.LM for accurate generation. EventVL. (Li et al. 2025b) enhances
2

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MCQA
FIGGA OR
de Eant
Scune Caption
00:90
08:01
M6:02
09:83
04:04
Stmple
Task 1:Simple Caption
Task 4: MCQA
Task Event Query: What is the baby divinz
Captisn
baly is playiigg the rifa
(A) Sleeping peacefully on the sofa
ergagral
Query: activity is baby
wodss Options:
bubly playinz vifi..
(b) Data distribution for nuuttiple tasks
Modion Captiins
Task 2:
Human Action QA
(B) Flay esergetioally on the sola
depicied evest secams?
Query: humox action
(D) Fating smacks on the sufa
(C) Watching TV while ciring will
Task T: Motion Caption
Nomber Samples
200k
68L
Humai Action QA
kimple Coppian
Asswer: is junging safa. The
Anower: (R) Playing eurgentiosly...
Query: How does Be ball''s sonvmmont coelve frim insina contact with the sofa
baby apeneaches the sods and lesps onto is landing with thair body....
EeerrO4
Pogz
FGQA
98L
Task 3e Semee Caption
crasiling
Query: What slocs the baby do ofter
suatained jumping action?
the The baby approaohes grigg trasstonss into
cusiioned sufa, beby uctvvly
Answer:
The baby mands up while
copaaiig.....
holding anto the lackknd of the sola.
cravfing position...
Stage
Stage
Stage
(a) Multiple task Instructions
(c) Data distribution across training stages
Figure 1: Instructions and data statistics of our EventMind. (a) Seven tasks instructions for event stream understanding. (b) Data distributions of each task. (c) Data distributions of the three stages.
robustness by fusing complementary modalities from event streams and RGB frames. LLaFEA (Zhou
& Leee 2025) achieves region-level spatiotemporal grounding through the complementary fusion of
frame and event modalities. However, these event-based L.MSs rely dense image-like processing
of inherently sparse events (Peng et al. 2024 Perot et al. [2200] Vemprala et al., 2021 Zhu et al.
2022 Tulyakov et al. 2019 Qu et al. [224]]24]] Lin al. (Shrestha & Orchard [018 Wu et al.
2024b; Engelken [023] Cho et al. [2024] Wan et al. 2022 Mei et al. 2023 Li et al. 2025c 2023
Chen et al. 2024). ), leading to excessive computation and hindering sng-seeuence inference.
Efficient Token Sparsification in MLLMs. Recent MLLMs (Weng et al., (0244) Jiang et al. 2025
Qian et al. 2024, Yan et al.) 2025a) have revealed that visual tokens extracted from foundation models like CLIP contain substantial redundancy, leading to significant computational overhead. Consequently, several token sparsification strategies (Ychezkel et al., 2024| He et al. 2024 Zhang ct al. 2024) have been attempted to reduce token counts while preserving essential semantics in video tasks. However, asynchronous events differ fundamentally from structured frames: while video redundancy mainly stems from spatial repetition within a regular patch grid, event streams consist of sparse spatiotenporal points with redundancy arising from uneven temporal sampling. Their tokens are distribuled irregularly and vary in density, making frame-based sparsification not only computationally costly but also ineffective for long event stream understanding. Thus, this work presents a novel spatiotemporal token sparsification strategy specifically tailored for event streams.
EVENTMIND DATASET
Data Collection. To support the curriculum learning strategy in our EventFlash, construct large- scale multimodal dataset named EventMind for event stream understanding. EventMind provides long temporal sequences, diverse scenes, multiple tasks, and high-quality instructions. The raw event data is sourced from both real-world and synthetic domains. Real-world data includes short-duration event sequences from DSEC (Gehrig al. (02T)) and N-ImageNet (Kim et al., 202T), as well as longer-duration streams from HARDVS (Wang et al.) 2024b) and E2VID (Rebecq et al.. 2019). Synthetic data are generated by converting large-scale video datasets (i.e.. Kinetics- 700 (Carreira et al. 2019), UCF-101 (Soomro et al. 2012), W0 M (Bain et al. (02T). PL.M-Dat (Cho et al. 2025), and MotionBench (Hong et al. 2025)) into event streams using the V2E simulator (Hu et al. 2021. To ensure high-quality simulated events, we use GPT-4o to automatically filter videos using their captions before simulation. To align with our curriculum stages, we categorize them into three groups: short (0-50 ms), medium 55000,000 ms), and long (5,000-2) 2,,00200000 ms).
Instruction Generation. To evaluate the modeling capacity and generalization ability of our EventFlash, we define seven distinct task types for event stream understanding. As shown in Fig. (()), these tasks include motion captioning, event question answering (Event QA), human action QA, multiple-choice QA (MCQA), simple captioning, fine-grained QA (FGQA), and scene captioning.
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EventFlash: The main motion is sndden and forceful impact that the doll viotently shatler, Frugments scutler outward in multiple dircetions before gradually setling down.
Large Language Model Decoder (Qwen2.5)
Prompt: the main motion deserined scene?
Evant-Language Projector
Adaptive Temporal Window Aggregation
Event Encoder
Sparse Density-guided Attention
Tleer
Caoage yhe dlare Vegalla
Lavedradity
Enpoy
Delete
Simibbity Score
Score
Event Stream
Deasity Encoding
Dassiy pradicat
The pipeline of efficient MLLMs (EventFlash). The adaptive temporal window aggregation module is presented for efficient temporal sampling, which adaptively compresses temporal tokens while retaining key temporal cues. Besides, the sparse density- siiidy--guddddddd attention module is designed to improve spatial token by selecting informative regions and suppressing empty or sparse areas.
Figure 2:
Text instructions are constructed via two pathways: (i) For samples with existing textual annotations, we use GPT-4o to refine the descriptions by removing static attributes and irrelevant visual details (e.g., texture, color), ensuring better alignment with event streams. (ii) For samples lacking ground- truth text, we leverage Owen-VL-Mau video inputs, enabling a scalable and consistent data synthesis pipeline. In addition, we organize a multi-person team to manually inspect and filter the generated instruction sets for quality assurance.
automatically generate annotations from corresponding
Dataset Statistics. We analyze the composition of the EventMind dataset from a curriculum learning perspective (see Fig. 1(b)). It is structured into three stages based on event sequence length and task complexity. In Stage 1, short sequences are used for the simple captioning task, contributing 200k instruction samples. Stage utilizes medium-length sequences for scene captioning and human action understanding, with a total of 1 10k instructions. Stage 3 focuses on long sequences for more complex tasks such as motion captioning. EventQA, FGQA, and MCQA, comprising 190k instructions. Overall, our EventMind comprises 500k instruction samples spanning seven task types (see Fig. I())): 200k for simple captioning, 90k for scene captioning, 30k for motion captioning, 90k for EventQA, 60k for FGQA, 10k for MCQA, and 20k for human action QA.
design make EventMind a highly compet- itive dataset with several key strengths: (i) High temporal sampling resolution at the microsecond level from event streams; (ii) Coverage of temporal sequences of various lengths; (iii) Diverse scene types supporting distinct tasks; (iv) A large-scale high-quality instruction set with 500k samples.
All in all, the novel teent-text modality and
METHOD
4.1
EVENTFLASHI OVERVIEW
This work aims designing an efficient MLLM for event stream understanding, termed EventFlash, which presents spatiotemporal token sparsification strategy to reduce redundancy and accelerate inference. As illustrated in Fig. 2 our framework consists of five modules: adaptive temporal window aggregation module. sparse density-guided attention module, event encoder, event-language projector, and large language model (LL.M) decoder. More precisely, the adaptive temporal window aggregation module first segments the continuous event stream into uniform short bins and adaptively merges adjacent bins based on token similarity or event density. These processed bins are then passed by an event encoder (e.g., CLIP) to extract semantic embeddings. In parallel, the sparse density- guided attention module improves spatial token efficiency by emphasizing informative regions and suppressing empty or low-density areas. The event-languee projector aligns the event tokens with text tokens to enable coherent multimodal fusion. Finally, the compact event tokens are fused with text tokens and processed by LLM decoder (e.g., Qwen-2.5) for multimodal generation tasks.
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4.2
TEMPORAL SPARSE
The microsecond-level resolution of raw event streams generates an excessive number of temporal tokens, resulting in high computational overhead. To address this, introduce two-stage density- guided adaptive temporal window aggregation (ATWA) module that compresses event streams while preserving key motion dynamics. The event stream first divided into fine-grained bins, which are iteratively merged based on an asynchronous spatiotemporal spike metric (Li et al. (022)) Each bin treated as a polarity -wwre spatiotemporal point process with an intensity function AB:
(x (I-II)) xn)²
(y Mn)
(t - tz)
\n(2,,,4,p)
Σ S(n) exp E.EB
(I)
20²
20
207
where fP,) encodes the polarity for an event (Inz Vns tn Pa) Cg og. and 02 are the parameters of the Gaussian kernel. The similarity distance between two bins B; and Bu+1 can be computed as:
D(B, B.+1) = || AB
(2)
where lower D indicates higher temporal correlation between two bins. We iteratively merge adja- cent bins when the distance is below threshold forming meta event windows (M1, M2,..., ..... Mx }.
In the second stage, we perform semantic-aware aggregation of meta bins. Each window M, is passed through event encoder (e.g.. VIT (Arnab et al. (02T)) to obtain CLS token representation 2,, and similarity S; between adjacent windows defined as cosine similarity as follows:
2 11+1
S;
| 2++|
To incorporate event sparsity, we define a normalized event density factor ry = [M, Ee,, Ien. and compute density-awand weight. The final adaptive merging score can be formulated by:
A S; exp(-a--r - rz),
where a controls the decay sensitivity. which jointly considers semantic similarity and event sparsity. We iteratively merge windows with high A, to obtain a compressed yet semantically meaningful temporal sequence that preserves key temporal cues with reduced computational cost.
4.3 SPATIAL SPARSE
While temporal aggregation re- duces sequence length, spatial redundancy still persists due to the inherent sparsity and uneven event distribution across the sensor plane. To tackle this, propose the sparse density-guided atten- tion (SDGA) module (see Fig. 3). which adaptively prunes uninfor- mative tokens based on both vi- sual semantics and event density. For each aggregated event bin, we use encoder (i.e., ViT) to ex- tract patch-level features {I, )=
Query
Kry
QR? V4,
MIHA
Token Selector
(())
Daneky Eaceding Usit
[(D)
Dessity Compute
Sparse Density-guided Amention Module (SIGA)
Figure 3: The architecture of the sparse censity-guides attention module. It enhances spatial token efficiency by selecting infor- mative regions and suppressing empty or low-density areas.
which are fed into multi-head self-attention mechanism
QKT
Attention @ K, V) = softmax
V.
(5)
vdl
where Q, K, and V are the projected queries, keys, and values from {zy and d, is the key dimension.
In parallel, we compute the event density D, of each token region based on the number of events falling within its receptive field. This scalar value is then passed through density encoding unit consisting of linear transformation followed by GELU activation:
S(D;) GELnar) (D;)),
(6)

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Published as a conference paper at ICLR 2026
where f(D,) is soft modulation signal that reflects the importance of each spatial token. The encoded density is added to the attention scores to focus on denser and more important areas as:
Q.Kj vdk.
A.
+ f(D)).
Finally, we apply a Token Selector operation that ranks the aggregated attention responses and discards low-importance tokens, which can be formulated as follows:
: TokenSelector
Σ softmax(A.g) V
In summary, this density-guided token pruning strategy enables EventFlash to keep important spatial details while greatly cutting down redundant computations. By combining semantic relevance with event density, SDGA produces more compact tokens for the efficient MLLM.
4.4 SHORT-TO-LONG CURRICULUM LEARNING
To support scalable training across different event durations and enhance generalization, we propose a progressive short-to-long curticulum learning strategy, Unlike prior event-based MLLMs such as EventVL (Li et al. 2025b) and EventGPT (Liu et al., 2025b), which train different modules in separate stages, our curriculum emphasizes a gradual progression from short to long event streams. This design facilitates smoother training dynamics, enabling EventFlash to evolve from mastering simple alignments to bandling complex reasoning and long-range event understanding.
To be specific, Stage focuses on event-language alignment by training on 200k short sequences (0-50 ms) paired with simple scene descriptions establish basic cross-modal understanding. Stage 2 expands to 110k medium sequences (50-5,000 ms) featuring complex motions like human actions, enhancing the model's 's reasoning and ability to handle instruction- nosllwitionfoollowingg and event-based QA over longer inputs. Stage 3 fine-tunes the model on 190k long sequences (5,000-20,008 ms) with rich scene descriptions, enabling holistic scene understanding and open-ended language generation.
5
EXPERIMENTS
5.1 EXPERIMENTAL SETUP
Implements Details. We initialize the event encoder with CLIP- CLIP-WiT-Large-Patchh (Radford et al.. 2021) and use Qwen2.5 (Bai et al. (023) the LLM backbone. A two-layer MLP serves as the Event-Language Projector to align the event and semantic spaces. EventFlash is implemented in both 3B and 7B variants and trained on 8 A100 GPUs. For throughput evaluation, the inference is conducted on an A A100 GPU using Hugging Face deployment. Our three-stage curriculum learning strategy proceeds follows: only the Event-Language alignment module is trained in Stage 1, using a learning rate of x 10 and a batch size of 64. For Stage 2 and Stage 3, all model parameters are unfrozen and trained with learning rate of 2 x 10-5, batch size of and gradient accumulation step of 4. cosine learning rate decay schedule is applied throughout training. We set the temporal aggregation interval to 10 ms and use a density attemuation factor a of 0. for spatial sparsification.
Evaluation Metrics. To thoroughly evaluate the generalization and reasoning capabilities of our EventFlash, we adopt four metrics aligned with protocols established LLaVA (Liu et al., 2023) and other widely used benchmarks (Fang et al. 2024b). More precisely, we use the following evaluation metries: (i) Global detailed captioning (GDC) to assess scene-level summarization, (ii) Fine-grained question answering (FGQA) to evaluate the model's understanding of localized event details, (iii) Human action question answering (HAQA) to measure temporal reasoning at the action level, and (iv) Multiple choice question answering (MCQA) to assess caaruction-followiii and discriminative reasoning. For open-ended tasks (GDC and FGQA), we employ LLM-based evaluation using GPT-4o (i.e., LLM-Judge) consistent with prior benchmarks. For HAQA and MCQA, report the accuracy based on exact matches with ground-truth answers. In addition, throughput and maximum event bin capacity used to evaluate the efficiency of all MLLMs. Throughput is typically defined as the number of tokens generated per second during inference, while maximum event bin capacity refers to the largest number of event bins the model can process in single input.

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Published as a conference paper at ICLR 2026
Question 1:
What is happening in this high-speed motion some? Please give the simple description.
Human Caption:
A doll stunds on platform when a sudden bullet is fired, plercing through it In. the next moment, the doll shatters violently, with fragments scattering through the air, vividly capturing the dromatic instant destroction.
(Ground Truth)
EventF lash:
A standing object: is struck by a speed projectile, lt, resulting in violent explosion. The object shatters into mulliple fragments that disperse repicly into the air, creating dense burst event activily.
Question 2:
heppens the objeet the high speed impert? Options:
(A) remains intact but is pushed beokwards. (8) It breaks into pieces and the fragments scaller.
without damage.
catches
EventFlash:
breaks into pieces and the fragments scaller.
Representative visualization tests motion captioning and multiple-choice question answering (MCQA) are conducted in high-speed scenarios. Our EventFlash demonstrates superior accuracy in recognizing fast-moving objects, such as sudden bullet being fired at doll.
Figure 4:
5.2 QUALITATIVE RESULTS
Table 1: Comparison of video-basee MLLMs and event-based MLLMs on our EventMind dataset and EventChat-Sun dataset (Liu et al.) (2025)). Notably, it can process significantly longer event bins
than the event- based competitor EventGPT.
Models
Purams
LLM Backbone
Max Bins
Throughput (Tokem's)
EventChat-Sub
GDC
FGQA
HAQA
MCQA
GDC
PGQ4
Videu-Base ...B Scale MILMSs
Qwen2.5 VL Bai 2023
Qwen2.5
768
20.6
41.7
23.8
34.6
34.5
51.2
VideoChat2- Flahh Ti Frela
20244b
26
Qwen2.5
1,000
31.6
38.9
16.2
43.6
36.9
43.8
Qwen2.5
Video-Base -7B Scale MILMS
VidrooChat--Paah
al
Qwen2.5
1,000
18.9
48.2
53.1
LLaVA-Next Video Liu
2003
7B
Qwen2.5
56
31.2
44.6
22.8
42.7
16.3
54.8
Qwenn25 VI. Rai 05
78
Qwen2.5
768
22.1
43.9
28.6
41.8
41.6
53.2
M225
Event- Base MLLMs
Vicuns-v1.5
EventFlash-Zee Zero
3B
Qwen-2.5
1,000
2.3
15.3
60.4
85.0
58.2
70.1
77.1
RventFlash-388 (Oues*)
SB
Qwen-2.5
1,000
28.5
46.8
61.1
84.9
60.0
71.5
78.6
EventFlash-7B (Ours*)
7B
Qwen-2.5
1,000
24.0
52.3
64.2
87.6
63.1
74.1
79.5
To evaluate the effectiveness and efficiency of EventFlash, we compare it against four state-of-the-an video-based MLLMs and the only open- sourced event-based MLLM (i.e., EventGPT (Liu et al. 2025b)). We select strong video-based models both the 3B and 7B scales, including Qwen2.5-VL (Bai al. 2023), VideoChat2-Flash (Li et al. 2024b), LLaVA- Video (Liu et al. 2023). and Intern InternV2 2.5 (L et al. (2025). EventGPT uses fixed bin encoding for event stream understanding. We also construct a baseline, EventFlash- Zero, by removing spatiotemporal sparsification from EventFlash.
Comparison with State-of-the-Art MILMs.
Qualitative Evaluation. As illustrated in Table T EventFlash outperforms four video-based
MLLMs and the event-based EventGPT on all four tasks (i.e., GDC, FGQA, HAQA, and MCQA). This demonstrates that EventFlash excels at understanding and describing dynamic event scenes. While EventGPT implements a fixed configuration of 5 event bins, EventFlash can process up to 1,000 event bins, achieving a 200 increase in processing capacity. In other words, our EventFlash is enabled by our efficient sparsification strategy for longer-term understanding. In addition. EventFlash reaches a speed of 28.5 tokens per second during inference. This is 12.4x faster than our baseline FventFlash-Zero (2.3 tokens per second), and still maintains comparable performance all tasks.

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Published as a conference paper at ICLR 2026
Question 1:
is main contant presented sccmss Pleuse briefly desoribe
Human Caption:
scene cuptures a quiet urban street streedlights illuminate road, reveoling a pedestrion crosswalk ahsad. Several cars parked along the right side of the street, and residential buildings line both sides, partially obscured by shadows and trees.
round Truth)
The scene deplcts urban street with multiple cars parked along both sides of the road. Tall trees and residential buildings line each side the street.
EventFlash:
Q Question 2:
W What types of objects are present along both sides of the street? Please give the simple description.
EventF
There are trees, residential buildings, and car parked on the side of the street
Figure 5: Representative visualization tests questioning answering (QA) and scene caption are conducted low-light scenarios. EventFlash showcases strong scene description and reasoning capabilities, such identifying car in nighttime scene where barely visible on RGB images.
Visualization Evaluation. We further evaluate EventFlash under challenging scenarios, such as high- speed motion and low illumination. As shown in Fig. Ag Fig. our model demonstrates strong descriptive and reasoning capabilities both cases. In high-speed case: The scene depicts goblin being struck by high-velocity projectile, resulting in mid-air explosion with scattered fragments. EventFlash generates an accurate and fine-grained description of this dynamic event and correctly answers multiple-choice question. In low-light case: The scenario involves vehicle driving through darkness. Despite the absence of frame-based visual cues, EventFlash generates a coherent and precise description, along with accurate response to the corresponding QA prompt. These results validate EventFlashh's ability to understand complex dynamics in edge-case environments where traditional frame-based models often fail.
To further demonstrate the ad- vantages of EventFlash on long- duration event streams, we com- pare it with EvenGGPT on 10,000 ms sequence. As shown in Fig. [ EventGPT operates on fixed number of bins (e.g.. 0-50 ms), limiting under- standing to moment-level seg- ments. contrast, EventFlash leverages its high maximum event bin capacity to process ex- tended sequences, enabling co- herent reasoning across the full temporal window and capturing sequence-level motion dynamics. As result, EventFlash generates more contextually accurate de- scriptions, highlighting its potential for real-world applications that require long-range understanding. such surveillance analysis and autonomous driving.
10ms
10,000ms EventF lash
EventG 0-50 :2s
EventG
0-50nss
person upward toss, appearing to manipulate ball performance.
perform
0-10,000 Ias
0-30,000ms
(Moment)
EventFlash
The subject performs a fluid contact juggling routine with two spheres, smoothly rolling them between hands using precise fingur. Their body remains still, emphasizing controlled, conti mouss motion of the objects in seamless circalar patterns.
(Sequence)
Figure 6: Comparison of EventFlash and EventGPT on long- duration event streams from our EventMind dataset.
This gap stems from their different temporal modeling strategies. EventGPT relies on short, fixed- duration bins, which fragment long-term motion and hinder the capture of cross-bin dependencies. In contrast, EventFlash maintains a unified representation over extended event streams, preserving temporal continuity and enabling consistent reasoning across long time horizons. As result, EventFlash produces descriptions that better reflect the overall motion evolution the scene, rather than isolated moment-level level observations.
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Published as a conference paper at ICLR 2026
5.3 ABLATION STUDY
Contribution of Each Component. To explore the impact of each component overall performance, we conduct an ab- full model against three variants: a baseline without any sparsification (EveenFFash--Zero), model with only temporal sparsification, and model with only spatial sparsifica- tion. As shown in Table 2
Table 2:
The contribution of each component to our EventMind dataset. The baseline uses our EventFlash without the spatiotemporal token sparsification strategy.
lation study by comparing
EventMind
Model T
Token/s
GDC
FGQA
HAQA
MCQA
Baseline
x
x
2.3
45.3
60.4
85.0
58.2
A
x
5.3.33x
46.3
61.2
85.1
59.6
B Ours
x
/
14.0,61x
47.1
60.6
83,8
60.3
our full model
28.5m124x
46.8
61.1
84.9
60.0
achieves a 12.4x increase throughput (28.5 5 tokens/s vs. 2.3 tokens/s) while maintaining compa-
rable performance across four evaluation metrics (i.e., GDC, FGQA, HAQA, and MCQA). With temporal sparsification alone, the model achieves 14.0 tokens/s, representing 6.1x speedup over the baseline. In contrast, spatial sparsification alone yields 2.3x improvement, reaching 5.3 tokens/s. The results show that both temporal and spatial sparsification contribute to efficiency gains.
Influence of the Aggregation Inter- val Length. To explore how the ini- tial temporal bin duration affects perfor- mance and efficiency, we evaluate model throughput and accuracy across different initial event bin durations. As shown in Table we compare four settings with bin lengths of ms, 10 ms, 20 ms, and 30 ms. We observe that shorter bin dura-
Table The influence of aggregation interval length on our EventMind dataset.
Aggregation interval
Throughput (Token/s)
EventMind
GDC
FGQA
MCQA
HAQA
5ms
15.8
47.1
61.8
84.6
58.2
10ms
28.5
46.8
61.1
84.9
60.0
20ms
52.6
43.2
56.3
72.6
48.4
30ms
63.3
36.8
48.2
61.8
46.2
tions ( 5 ms) provide finer temporal resolution but significantly increase the number of windows, resulting in lower throughput (15.8 tokens/s) compared to our default setting of 28.5 tokens/s at 10 ms. Despite the increased computational load, the model maintains strong performance across all tasks. Conversely, increasing the bin size to 20 ms and 30 ms improves throughput to 52.6 and 63.3 tokens/s, respectively, indicating greater efficiency. However, this comes at the cost of performance degradation on GDC, FGQA, MCQA, and HAQA. In this work, a bin duration of 10 ms offers a trade-off between accuracy and efficiency, and is therefore adopted as our default setting.
Impact of Density Attenuation Factor a. We investigate how the density atten- uation factor a affects model throughput and task performance (see Table 4). To ex- plore the trade-off between density-guided and siillarity-guidee token merging, we evaluate four values of a to identify the optimal balance between accuracy and ef- ficiency. The results show that increasing leads to higher throughput, indicating that stronger density suppression accelerates the token aggregation process. For example, FGQA and MCQA stay mostly stable when is between 0.2 and 0.4. However, GDC and HAQA rely more detailed timing information. Because of this, their performance drops when a gets higher. The results confirm the effectiveness of our density-awand weighting mechanism. Notably, a = 0.1 and 0.4 achieve a favorable trade-off, providing substantial speed gains while preserving strong task performance.
Table 4: The influence of density factor on throughput and performance on our EventMind dataset.
Throughput (Token/s)
Density Factor a
GDC
FGQA
MCQA
HAQA
0.1
28.5
46.8
61.1
84.9
60.0
0.2
27.6
45.6
61.4
85.2
58.4
0.4
28.8
45.3
61.6
85.2
58.4
0.6
26.8
47.2
60.8
83.2
60.1
5.4 EXTENSIVE APPLICATION
We further investigate additional down- stream applications enabled by our EventFlash. For instance, EventFlash can be readily fine-tuned to support action recognition tasks. As shown in [] we evaluate its performance the DailyDVs-200 (Wang et al.) (2024a) dataset, where EventFlaahh predicts
Table 5: Action recognition results on processed
DailyDVS-200 (Wang et al. 2024a) dataset.
Methods
Venue
Impot Type
Backbone
top-1
Swin-T T Liu
2022
CVPR'22
Frame
Transformer
48.06
GET Pong et all
1023
ICCV'23
Event
Transfommr
37.28
SDT
Van
NeurlPS'24
Event
Transfomeer
35.43
ESTF
Wing ct
2024b
AAAF24
Event
ResNec50
24.68
EventFlash
Oun>
Event
Qwen2.5
48.36

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Published as a conference paper at ICLR 2026
tion categories in open-ended QA setting. Our EventFlash achieves outstanding performance and strong generalization capability.
CONCLUSION
This paper presents EventFlash, a novel efficient MLLM that leverages spatiotemporal token sparsi- fication to reduce data redundancy and accelerate inference. We also built large-scale dataset for event stream understanding. The results show that EventFlash achieves 12.4x improvement in throughput over baseline (EventFlash-Zero) while maintaining comparable performance. Notably, EventFlash enables long-range event stream processing of up to 1,000 bins compared to only bins in the EventGPT. Our EventFlash serves an efficient foundational model for event-based vision.
7 ACKNOWLEDGMENTS
This work is supported by the National Natural Science Foundation of China (Grant No. 62502317).
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15


Reviews:
[{"review_id": "jgmYj3fdwJ", "reviewer": "Reviewer_UMdA", "summary": "This paper addresses the inefficiency of applying dense, frame-like processing to sparse event-based MLLMs. It makes two main contributions: (1) EventMind, a new 500k-sample instruction dataset for event vision, enabling a short-to-long curriculum learning strategy; and (2) EventFlash, an efficient MLLM using adaptive temporal (ATWA) and sparse spatial (SDGA) token sparsification(2). Experiments show EventFlash achieves a 12.4x throughput gain over its non-sparse baseline and can process much longer event sequences (1,000 bins).", "questions": "See weaknesses above.", "limitations": "", "rating": 6, "confidence": 2, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The creation of the 500k-sample EventMind dataset is a major contribution that addresses a critical resource gap for training and benchmarking event-based MLLMs.\n2. The paper targets the correct bottleneck: the inefficiency of applying dense methods to sparse data. The proposed spatiotemporal sparsification modules (ATWA and SDGA) are an intuitive and direct solution to this problem.", "weaknesses": "1. Insufficient comparison to SOTA event-based models: The paper fails to benchmark EventFlash against its direct competitors. On its new EventMind dataset, it only compares against frame-based models (Table 1). On the existing EventChat-Sub dataset, it only compares against EventGPT, omitting other SOTA event models like EventVL mentioned in the related work. This makes the SOTA performance claims unsubstantiated.\n2. Missing Key Methodological Ablations: The core Adaptive Temporal Window Aggregation (ATWA) module is a complex two-stage process (a spike-based merge followed by a semantic-based merge. However, the ablation study (Table 2) only validates the entire \"+T\" (Temporal) block at once. It never justifies the necessity of this complex two-stage design over a simpler, single-stage alternative.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "OOkW2NjStg", "reviewer": "Reviewer_aCxZ", "summary": "This paper presents an efficient MLLM for event-based vision tasks. The authors claim that this is the first efficient MLLM to explore spatio-temporal token sparsification, aiming to reduce redundancy, accelerate inference, and enable long-range event stream understanding. To address temporal inefficiency, the authors propose a two-stage density-guided Adaptive Temporal Window Aggregation (ATWA) module; to address spatial redundancy, they introduce a Sparse Density-Guided Attention (SDGA) module. To demonstrate performance on long-range event tasks, the authors also generate a large dataset containing 500k events. Experiments are conducted to evaluate the proposed framework on the self-generated dataset across seven tasks: motion captioning, event question answering (Event QA), human action QA, multiple-choice QA (MCQA), simple captioning, fine-grained QA (FGQA), and scene captioning. The authors report throughput performance compared to state-of-the-art (SOTA) methods using an A100 GPU. Some qualitative results are also provided.", "questions": "See weakness. I am frustrated that the author did not make corresponding revisions to the submitted paper based on the NeurIPS review comments. This work has so many limitation, although its packaging is good. I think the event community need the solid and usefull MLLM-based works, not limited in the Line MLLM+some existing techinuqes+datasets''.", "limitations": "", "rating": 4, "confidence": 5, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "1. A novel self-generated dataset that could be useful for the event-based MLLM research community.\n2. Two interesting modules appear to help improve throughput while achieving comparable accuracy.\n3. Some qualitative analyses are provided.", "weaknesses": "1. The experimental comparison to actual SOTA MLLMs (e.g., Qwen2.5-VL, InternVL, LLaVA-v1.6) is critically undermined by the paper's failure to state whether these baselines were finetuned on event data. This strongly suggests an unfair comparison against zero-shot models, rendering the performance results unreliable.\n2. The reliance on \"RGB-style\" (image-like) representations is a significant limitation. The method's effectiveness is never validated against true native event representations (e.g., point clouds, graphs), which would be necessary to prove its value for event-based vision.\n3. The framework appears to be an incremental application of common MLLM techniques (e.g., standard token sparsification, three-stage training) to a pre-processed, image-like data format, rather than a fundamental innovation for \"raw event streams.\"\n4. The paper's second major contribution, the EventMind dataset, is undermined by critical concerns. Significant doubts exist regarding the quality of annotations generated by GPT-4o, especially given its visual token limits and the challenge of processing long event sequences. The \"human checking\" and validation process lacks sufficient detail to be verifiable.\n5. The paper's headline claim of a \"12.4× throughput improvement\" is highly misleading. This gain is measured against \"EventFlash-Zero,\" an internal baseline, not against established SOTA models.\n6. Benchmarking is incomplete, omitting comparisons against key models like EventGPT-13B and failing to report results on established metrics from prior work (e.g., EventGPT's VQA setting).\n7. A fundamental contradiction exists between the paper's premise and its implementation. The paper claims to explore a \"sparsification strategy for raw event streams,\" but the methodology reveals it operates on \"image-based tokens\" from a standard ViT encoder.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "cjUihGtiOX", "reviewer": "Reviewer_U2bJ", "summary": "This paper proposes an event-based multimodal large model named EventFlash. The method explores reducing data redundancy and accelerating inference for event cameras through spatiotemporal token sparsification. It introduces an adaptive temporal window aggregation module and a sparse density-guided attention module to perform efficient sampling and selection of spatiotemporal information. In addition, the authors construct a large-scale, scene-diverse dataset called EventMind. Experimental results demonstrate that the proposed approach significantly improves throughput while maintaining competitive performance.", "questions": "1. When processing the event data, do the authors first stack events into event counts at fixed time intervals, and then aggregate them into W₁, W₂, …, Wₙ using the Adaptive Temporal Window Aggregation module?\n\n2. In Table 1, are the video-based methods also using event data as input? Have these video-based methods been fine-tuned on event data before comparison?\n\n3. Can the proposed Adaptive Temporal Window Aggregation module and Sparse Density-Guided Attention module be applied to other event-based tasks, such as semantic segmentation or optical flow estimation?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "1. The paper presents a clear motivation. It effectively addresses the high temporal resolution and sparsity characteristics of event camera data, significantly reducing data redundancy and improving the efficiency of large language model (LLM) inference.\n\n2. It provides a scene-driven large-scale event dataset, EventMind, which makes a valuable contribution to the advancement of this field.\n\n3. The experimental results are comprehensive, including images, tables, videos, and code. The writing is fluent, logically structured, and easy to follow.", "weaknesses": "1. The authors emphasize the advantages of their method in terms of efficiency and throughput. However, in Table 1, EventFlash-7B does not show a throughput advantage compared with EventGPT-7B. The authors should provide a discussion to clarify this discrepancy.\n\n2. EventGPT-7B was not evaluated on the EventMind dataset. According to Figure 6, EventGPT-7B appears to work on the EventMind dataset, so why are there no quantitative comparison results presented in Table 1? The authors should explain this omission.\n\n3. The application scope seems limited. When constructing the EventMind dataset, the authors used GPT-4o and Qwen-VL-Max to automatically generate annotations. This suggests that similar tasks could potentially be handled using standard RGB cameras combined with LLMs. Therefore, the authors should clarify why event cameras and EventFlash are necessary in this context, and how the advantages of event cameras are effectively leveraged.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)