Title: Eyes Wide Open: Ego Proactive Video-LLM for Streaming Video
Venue: NeurIPS 2025
Accepted: yes
Keywords: Egocentric Video Understanding

Multimodel Large Language Model

Streaming Understanding
Abstract: Envision an AI capable of functioning in human-like settings, moving beyond mere observation to actively understand, anticipate, and proactively respond to unfolding events. Towards this vision, we focus on the innovative task where, given ego-streaming video input, an assistant proactively answers diverse, evolving questions at the opportune moment, while maintaining synchronized perception and reasoning. This task embodies three key properties: (1) Proactive Coherence, (2) Just-in-Time Responsiveness, and (3) Synchronized Efficiency.
To evaluate and address these properties, we first introduce ESTP-Bench (Ego Streaming Proactive Benchmark) alongside the ESTP-F1 metric—a novel framework designed for their rigorous assessment. Secondly, we propose a comprehensive technical pipeline to enable models to tackle this challenging task. This pipeline comprises: (1) a data engine, (2) a multi-stage training strategy, and (3) a proactive dynamic compression technique. Our proposed model effectively addresses these critical properties while achieving state-of-the-art (SOTA) performance on the standard COIN benchmark.

Eyes Wide Open: Ego Proactive Video-LLM for Streaming Video

Abstract

Envision an AI capable of functioning in human-like settings, moving beyond mere observation to actively understand, anticipate, and proactively respond to unfolding events. Towards this vision, we focus on the innovative task where, given ego-streaming video input, an assistant proactively answers diverse, evolving questions at the opportune moment, while maintaining synchronized perception and reasoning. This task embodies three key properties: (1) Proactive Coherence, (2) Just-in-Time Responsiveness, and (3) Synchronized Efficiency. To evaluate and address these properties, we first introduce ESTP-Bench (Ego Streaming Proactive Benchmark) alongside the ESTP-F1 metric-a novel framework designed for their rigorous assessment. Secondly, we propose a comprehensive technical pipeline to enable models to tackle this challenging task. This pipeline comprises: (1) a data engine, (2) a multi-stage training strategy, and (3) a proactive dynamic compression technique. Our proposed model effectively addresses these critical properties while outperforming multiple baselines across diverse online and offline benchmarks.

1 Introduction

Imagine an AI assistant that follows you through your day-assembling furniture, searching for misplaced keys [3, 15], or preparing a meal [46, 44]-not just watching, but understanding, anticipating [54], and responding proactively when needed as events unfold. To function in such human-like settings, where visual input is egocentric and continuously streaming, and user needs shift from moment to moment, the assistant must go beyond passive observation. It should be able to interpret the present, anticipate what comes next, and respond at exactly the right moment, all in real time.

As a first step toward this vision, we narrow our focus to perception and understanding in egocentric streaming video, with a particular emphasis on the following innovative task: Given ego-streaming video input, the assistant proactively answers to diverse and evolving questions at the right moment, while seeing and thinking in sync, as shown in Fig. 1. This task relies on three key properties:

Proactive Coherence: handling diverse question types, responding even when answers depend on future visual streams (proactivity), and maintaining contextual consistency across related questions. In ego-streaming scenarios, questions often go beyond the current frame, referencing future events or past observations. As shown in Fig. 1, the segment of the conversation highlighted in green is contextually dependent on the content within the segment highlighted in purple. Such queries require temporal integration of past and present information, followed by proactive answering as relevant visual evidence emerges.

Just-in-Time Responsiveness: determining when to answer based on visual readiness, neither too soon nor too late, and only when necessary. Responding before enough evidence is available can

Synchronized Efficiency:

Model Seeing and Thinking in Sync

There are some needles in

front of you that you can

use to extract fluid

The garbage is dropped to

the next to the notebook

on the counter

You can throw them from

the counter and the lab

bench into the yellow

bucket near the shelf.

[00:30

-

Silence- 01

：

26]

Can you tell me where the

garbage are when I  drop

them ?

The garbage is

dropped on

the lab bench

in front of you

[02:23]

[02:23

-

Silence- 03

：

49]

[01:26]

[03:49]

I want to clean them up

[04:00

-

Silence- 04

：

50]

[04:50]

I want to extract some liquid

from a flask. What should I do?

[00:30]

[02:00

-

[02:00]

Proactive Coherence:

Diverse, Evolving Questions and Response

···

[04:00]

Just-in-Time Responsiveness

···

Start Point

User Query

Model Action : Stay Silent

Model Action :Response and Reply

Conversation

See

Think

Real Scene

AI

X

X

一

一

lead to mistakes, while answering too late may miss the opportunity to help. Equally important is staying silent when uncertain and avoiding unnecessary repetition. As shown in the blue-highlighted segment of Fig. 1, it is necessary to remain silent until the 'face to counter'. The assistant must continuously track the evolving visual context and respond at the earliest reliable moment.

Synchronized Efficiency: ensuring that answering and visual perception proceed in sync without delay. Responses should not come at the cost of missing new visual input; perception and reasoning must remain temporally aligned. Regarding the purple segment depicted in Fig. 1, maintaining synchronization is crucial to prevent missed answers. This requires answering while continuously observing, with zero latency, while also ensuring time and memory efficiency as the number of incoming frames grows over time.

Unfortunately, existing evaluation frameworks [51, 27, 26, 61, 13] and streaming models [5, 52] fall short in supporting or measuring the unified capabilities of proactive, just-in-time, and synchronized reasoning-and often struggle even with some individual aspects. Offline video benchmarks [14, 66, 30, 50, 1] evaluate video LLMs across diverse question types and scenarios, but their offline nature limits the assessment of the three core capabilities essential for online deployment. Recent efforts toward online and streaming benchmarks address this gap by introducing proactive tasks. Nevertheless, as shown in Tab 1, they often offer limited question diversity, lack contextual continuity across queries, and-more importantly-rarely evaluate just-in-time responsiveness or synchronized efficiency. As a result, current online video LLMs remain confined to narrow tasks such as narration or simple question answering, lacking the capacity for continuous, multi-turn understanding. More critically, as illustrated in Fig. 6, these models exhibit poor just-in-time behavior-often generating under-responsive or over-extended answers. Similarly, although recent efforts [51, 33] have begun to address efficiency, they tend to focus solely on accelerating response generation-potentially at the cost of answer accuracy-while overlooking the need to balance perception and answering under synchronized constraints.

As a first step toward addressing these challenges, we introduce a new Ego STreaming Proactive (ESTP) benchmark and evaluation framework , specifically designed to capture the demands of the three key properties in streaming video. For proactive coherence , all question-answering tasks in the benchmark are proactive in nature: each question can only be answered based on future video streams

20%

40%

60%

80%

Proactive

Coherence

Just-In-Time

Efficiency

VLMs

Offline MLLMs

Online MLLMs

Ours

Table 1: Comparison of datasets based on proactive and streaming criteria. This table summarizes datasets by Question Types (OpenEnded (OE); Multiple Choice (MC); Query Matching (Q-Match & Q); and Count (C)), Proactive Types (Explicit (Exp.); Implicit (Imp.); and Contextual (Cont.)), and Just-in-Time (JIT) Responsiveness. Key JIT Responsiveness aspects include Answer Turn (Ans. Turn) (options: Single (S), Multi (M)), Precision (Is Prec.), and Timeliness. The notation '# Ques.' denotes the number of questions.

within one or more specific time intervals. To reflect different levels realistic scenarios, we group them into three types: (1) explicit, grounded in clear visual cues; (2) implicit, requiring reasoning beyond surface observations; and (3) contextual, involving temporally linked questions that demand consistent multi-turn answers. We collect 2,264 questions spanning 14 task types-such as object localization, state change understanding, and intention prediction-across over 100 types of distinct scenarios, including kitchen activities, social interactions, and daily object manipulation. For just-intime responsiveness , we emphasize the importance of response timing: each question are annotated an average of 3.96 valid answer intervals, and a prediction is considered valid only if it falls within the designated window. To assess this, we introduce ESTP-F1, a metric that integrates answer quality, response timing, and temporal precision. Additionally, 46% of questions are contextually linked, requiring coherent responses based on prior questions-highlighting the need to continuously track the evolving stream from past to future and respond at the right moment. For synchronized efficiency , we not only evaluate time and memory efficiency and answering accuracy independently, but also assess accuracy under tightly synchronized perception and response-offering a comprehensive perspective on streaming video LLM evaluation.

To address this novel task, we propose a comprehensive and novel technical pipeline-including a data engine, multi-stage training strategies, and a proactive dynamic compression technique-to enhance the streaming video LLMs. Specifically ,

The data engine automatically generates diverse, multi-turn questions and their corresponding answers to support the demands of continuous and proactive question answering. This involves a three-stage generation pipeline covering (1) one-to-one: using LVLMs to generate captions and extract initial question-answer pairs with a single temporal answer interval; (2) one-to-many: applying RAG to expand each answer into multiple valid intervals; and (3) many-to-many: composing coherent multi-turn questions from related QA pairs.

The multi-stage training strategy is employed to progressively learn: (1) passive interval responsiveness, which provides a basic ability to trigger responses by distinguishing visually similar frames with different response labels, but often results in over-responsiveness even when the correct response interval; (2) Proactive just-in-time responsiveness and accurate answering, which trains the model to actively request high-resolution frames during uncertain timestamps, allowing it to use fine-grained visual details to pinpoint both the correct response moment and the accurate answer; (3) Coherence across multi-turn QA, which enables the model to maintain consistency by reasoning over prior QA history and current context, supporting contextual consistency answering.

The proactive dynamic compression technique fully leverages the streaming nature by applying two levels of token compression based on response likelihood, including: (1) when the model anticipates a potential response, it proactively requests high-resolution inputs to improve the accuracy of perception and answering; (2) Otherwise, it applies a higher compression rate to past content to reduce token usage and improve efficiency; (3) Additionally, once a response is completed, the content preceding its timestamp is further compressed to free up resources without affecting future perception or answering.

In summary, our contributions include: the novel Ego-Streaming Proactive (ESTP) task, distinguished by its three key properties; the ESTP-Bench benchmark and the ESTP-F1 metric for robust evaluation of this task; and a comprehensive and novel technical pipeline, incorporating three key techniques, designed to address the ESTP task. Our results demonstrate that the proposed model effectively overcomes the key challenges posed by this task. Moreover, it demonstrates superior performance by substantially exceeding multiple baselines in diverse online and offline benchmarks.

2 Ego Streaming Proactivate Dataset & Benchmark

2.1 Data Source and Annotation

Data Source is validation set of Ego4D [17, 44] that includes raw annotations such as event narrations and steps for completing consistent goals. Following [28, 5], we filtered out video with missing or uncertain annotations and converted annotations into a natural language format. This process yielded 890 videos, encompassing over 100 distinct scenes and a wide array of human activities, including indoor home environments (e.g., cooking, cleaning), workspaces (e.g., working at desk, labwork, baker), and public areas (e.g., grocery shopping). Furthermore, the videos exhibit rich dynamic diversity, ranging from periods of relative stillness (e.g., observing a static scene) to highly dynamic moments involving rapid manipulation tasks or active locomotion (e.g., cooking, walking).

Annotation process follows a two-step procedure. First, initial QA pairs are automatically generated with the assistance of MLLMs [56, 49] and LLMs [10]. Second, these automatically generated questions provided inspiration for annotators, aiding them in identifying valuable instances or formulating question ideas. To ensure diversity of questions, we annotate three proactive types:

(b)

(a)

EOL

OR

AP

AR

TU

OF

OL

Explicit

IF

EOSCR

NAR

Implicit

OSCR

Context

ORC

TRC

Going to the gym - exercise machine, class, weights

istening tomusi

59

Reading books

65

Hosting a party

Labwork

Eating

the

dog

pet

Golfing

Bike

87

ld management-caring

90

Craft:

Making coffee

lioog/drawing/painting

Talking with family members

125

126

Talking to colleagues

130

Tal

Walkingon street

135

Working at desk

160

Cleaning/ laundry

175

Cooking

210

Car

commuting,"road

Indoor Navigation (walking)

trip

210

.ng

exhiiti

Frequency

100

125

150

175

200

(1). Explicit Proactive Tasks are defined as those required to identify and respond to queries by directly leveraging and interpreting visual information present in the input. This category encompasses tasks where the relevant visual cues are explicitly referenced or are central to formulating a correct response. This category is comprised of eight distinct task types: Object Recognition (OR), Attribute Perception (AP), Text-Rich Understanding (TRU), Object Localization (OL), Object State Change (OSC), Ego Object Localization (EOL), Ego Object State Change (EOSC), and Action Recognition (AR). (2). Implicit Proactive Tasks are defined as those requiring inference and deeper scene understanding that goes beyond immediate, direct observation. This category is comprised of four distinct task types: Object Function Reasoning (OFR), Information Function Reasoning (IFR), Next Action Reasoning (NAR), and Task Understanding (TU). (3). Contextual Proactive Tasks are defined as those requiring the model to maintain awareness of dialogue history and visual coherence across temporally extended interactions. This category is comprised of two distinct task types: Object Relative Context (ORC) and Task Relative Context (TRC). Fig. 3 illustrate dataset distribution.

To enable the evaluation of Just-in-Time Responsiveness and eliminate ambiguity in answer intervals, human annotators are required to mark clear time interval boundaries based on the completeness of objects within frames or the start/end of events. Simultaneously, questions with ambiguous references are filtered out (e.g., 'Remind me the location of the ceramic bowl.' where multiple ceramic bowls might be present in different locations). Each sample's question, answer, and corresponding answer interval are verified by two annotators. This rigorous verification process resulted in a dataset of 2264 verified question-answer instances. Notably, every answer in the dataset is associated with precise temporal annotations. Statistical information regarding the annotated data is presented in Fig. 3.

2.2 Evaluation Metric in ESTP

To comprehensively measure performance along three key evaluation aspects - answer quality, response timing, and temporal precision - we introduce the ESTP-F1 score. Here, we denote a ground truth item as g k with content o k , and a prediction as ˆ p l with content ˆ o l and time ˆ t l . Evaluation components are defined for matched pairs (ˆ p l , g k ) , where ˆ p l is a prediction that temporally matches g k . For answer quality, an LLM [10] is used to measure correctness, defined as a score S answer (ˆ o l , o k ) for the predicted content ˆ o l relative to the ground truth content o k . For evaluating response timing, we go beyond simply considering recall (which inherently accounts for False Negatives (FN)) and employ a score S time ( ˆ t l , g k ) to more precisely measure timeliness. Furthermore, for temporal precision, we introduce precision, utilizing False Positives (FP) as a penalty term. These components contribute to the aggregated ground truth score S ( g k ) , which replaces the traditional binary TP count. The ESTP-F1 score is computed as:

where M is number of GT. High answer quality (reflected by a high S answer score), effective response timeliness (characterized by high S time for on-time responses and a low False Negative (FN) rate), and high precision (indicated by a low False Positive (FP) rate) collectively contribute to a high ESTP-F1 score. More details are provided in the Appendix.

3 Methodology: VideoLLM-EyeWO

In this section, we introduce a technical pipeline designed for the ESTP task. For the data engine, utilizing the Ego4D [17] training set and a three-stage generation pipeline as introduced in Sec. 1, we generate 60K single-turn and 20K multi-turn questions, as shown in Fig. 4. Each generated instance includes questions, answers, and their corresponding valid answer intervals (named as ESTP-IT). See Appendix for data engine details. Subsequently, we detail the problem definition and preliminary, the multi-stage training strategies, and the proactive dynamic compression technique in respective subsections.

3.1 Problem Definition and Preliminary

Problem Definition. Given a streaming video input and a sequence of emerging queries Q = { ( q i , t q i ) } , where q i is the query content and t q i is the query timestamp. At each timestep t following a query (i.e., t > t q i ), the model must leverage its historical memory H t (including visual input history and past query-response interactions) , while concurrent observation O t , to decide whether to perform a response action and generate corresponding content. The model's decision-making process at time t can be formulated as selecting the optimal action A t from a predefined set A :

Here, θ represent model parameter, A t is the model's action at time t , and A is the set of possible actions. Notably, while previous work typically considers an action space that only includes a silence (staying silent) and a response (executing a response and generating a reply), we expands this by including the action a ask_high (requesting a high-resolution frame), as introduced in Sec. 3.2 Stage-1.

Preliminary. LIVE [5] utilizes ground truth containing timestamps and applies cross-entropy supervision [48] to the model's action output at each timestep. Specifically, if the current time t falls within a ground truth response region (denoted as t ∈ T timestamp), the model is supervised to execute the response action ( a response) and generate a reply, incorporating a language modeling loss L LM [59, 11, 48]. Otherwise, it is supervised to remain silent ( a continue). This is formulated as:

where, ω is a balancing coefficient weighting the language modeling objective.

One-to-

many

Many-to-

many

LLM

Stage 1

Caption

Caption

One-to-one

Data Engine

Multi-Stage Training Strategy

I want to

extract

some liquid

from a

flask. What should I

do?

VideoLLM-EyeWO

Low-Resolution

Frame Tokens

Stage 2

VideoLLM-EyeWO

High-Resolution

Frame Tokens

Potential Object!

Ask for High

Resolution Tokens

I want to

extract

some liquid

from a

flask. What should I

do?

How to find it?

Stage 3

Proactive Dynamic Compression

0.5

1.0

0.5

LoRA

System Prompt

and Query

There are some needles

in front of you that you

can use to extract fluid

Compress

KV Cache for Next Turn

10 x  Compression

Low-Resolution

Frame Tokens

High-Resolution

Frame Tokens

0.5

1.0

(a)

(b)

(c)

1.0

__-to-__: Question Count \

Answer Interval Count

Stay Silent

Ask High Resolution Frame

High Resolution Frame

Response

Not in KV Cache

EG?

4D

1

↑↑↑

一

?

?

+

100000

X

+

3.2 Multi-Stage Training Strategy

Following [5], VideoLLM-EyeWO utilizes the same network architecture and is trained using LoRA [20]. However, the single-stage training and simple binary supervision strategy employed in [5] can lead to training conflict due to the high similarity of adjacent frames in streaming inputs. This conflict necessitates a difficult trade-off between over-extended and under-responsive. To address these limitations, we employ a multi-stage training strategy designed to progressively endow the model with response capabilities. The following subsections detail each stage of this training strategy.

Stage-1 : Passive Interval Responsivenes. To provide the basic ability for autonomous response triggering, we leverage the valid answer intervals within the ESTP-IT to achieve a progressive transition from silence to response. Specifically, if current time t falling within a valid answer interval (where T interval is defined as the set of all such intervals [ s i , e i ] ), we apply a weighted degree of response supervision, rather than direct binary classification, using the following loss function:

The function f is a linear decrease map used as a weighting factor applied to the response probability loss. The highlight in Equ. 4 is used to distinguish the components specific to this stage.

Stage-2 : Proactive just-in-time responsiveness and accurate answering. To use fine-grained visual details to pinpoint both the correct response moment and the accurate answer, we train the model to actively request high-resolution frames during uncertain timestamps in this stage. Specifically, we first introduce a third predefined action a ask_high. When the model executes this action at time t , it triggers the acquisition of the high-resolution frame O h t corresponding to the current observation O t using the following loss function for training: L ask_high ( t ) :

where T uncertain denotes the set of the model's uncertain (see more detail in Appendix D Stage-2 Input). We use this loss to enable the model to acquire the ability to request high-resolution frames, and then based on the more detailed information, determine whether it is the correct time to respond and provide a more accurate answer, using the following loss:

where O h t represents the high-resolution frame acquired at time t . The overall loss function at timestep t is the sum of the two components:

See appendix for detailed uncertain timestamps T uncertain identified.

Stage-3 : Coherence across multi-turn QA. Building upon the model's acquired proactive and timely response capabilities, we introduce a separate training stage. Specifically, this stage involves training solely on multi-turn question, with the aim of further improving its contextual understanding while preserving its timely responsiveness.

3.3 Proactive Dynamic Compression Mechanism

In order to ensure memory efficiency as the number of incoming frames grows over time, we propose the Proactive Dynamic Compression Mechanism, which applies two levels of token compression and employs a uniform compression method, detailed respectively in the following two subsections.

Two-Level Compression. In contrast to fixed compression rates [29, 34, 4] and steps [43, 39, 58, 63], our mechanism leverages the streaming nature to allow the model to proactively determine both when to compress and which compression level to apply. Regarding the timing of compression, after the model generates a response, the preceding visual input and the response content itself form a natural segment or processing unit. Simultaneously, lower compression rates are applied to questionrelevant content such as high-resolution frames, while higher rates are applied to other content, with these decisions proactively made by the model. Specifically, after a response, a fixed number of compression tokens (e.g., 1) are used to compress the preceding content, absorbing information from potentially many low-resolution frames or a single high-resolution frame. This approach naturally achieves a high compression rate for redundant parts of the past content, resulting in an average token usage of only about one-tenth of the original sequence.

Uniform Compression Method. For achieving two-level compression, we employ a Uniform Compression Method. Specifically, unlike methods using additional compression modules [34, 33], we insert a special compression token ( ⟨ ct ⟩ ) after segments of original input, namely after single highresolution frames, after multiple low-resolution frames, and after answer. This token is initialized using the text embedding of the '<EOS>' token. Leveraging the properties of the causal self-attention mechanism, this token prompts the LLM to compress the information from the preceding segment into a compact representation stored in the KV cache.

During training, inspired by [43], the LLM is trained to process response turns sequentially. A response turn refers to a turn of interaction, typically a comprising visual input and a model's response. Training for the Proactive Dynamic Compression Mechanism, including the integration of high-resolution frame requests, commences in Stage 2 of our multi-stage training strategy to ensure manageable training memory overhead.

4 Experiment

4.1 Baseline and Evaluation Settings

We evaluate three categories of models in this study: Offline MLLMs, VLMs, and Online MLLMs.

For Offline MLLMs we selected representative models from different open-source MLLM families, including LLaVA-OneVision [23], Qwen2-VL [49], MiniCPM-V [56], LLaVA-NeXT-Video [24], and InternVL-V2 [7]. As offline MLLMs lack inherent proactive response capability, following previous studies [27, 26, 51, 5], we employed two evaluation settings: (1) Response-in-Last : The model processes the complete video and is tasked with generating textual reply with timestamps. (2) Polling Strategy : The model is periodically queried at fixed time intervals. If the model indicates readiness, it is then prompted to generate the answer. Specific details regarding the prompts and hyperparameter used in these settings are provided in Appendix.

Regarding VLMs , following the approach of SDQES [13], we selected CLIP [36], LaViLa [64], and EgoVLP [28] for evaluation. These models were evaluated by computing the similarity between each frame and the query, using 0 . 5 as the threshold to determine responsiveness. Notably, as these models cannot generate open-ended replies, their reply score is set to 0 .

For Online MLLMs , we selected VideoLLM-Online [5] and MMDuet [51], which provide opensource weights and streaming inference code, for evaluation. For VideoLLM-Online, we experimented with different thresholds to assess its performance variations.

4.2 Benchmarking in ESTP-Bench

Comparative Analysis of Baseline Models. Tab. 2 shows the performance of different models across three proactive types and fourteen task types under various evaluation settings, the experimental results consistently demonstrate that ESTP tasks pose significant challenges for all current types of models. Analysis revealed variations across model categories, with certain models exhibiting stronger capabilities within their respective groups (e.g., MiniCPM-V [56] and QwenVL-2 [49] among offline MLLMs aligning with previous work [8], and temporal VLMs like LaViLa [64] and EgoVLP [28] outperforming spatially-focused models like CLIP [36]). Furthermore, the evaluation strategy significantly impacts performance. Specifically, offline MLLMs showed a notable disparity, performing on average better under the Polling Strategy compared to the Response-in-Last strategy, with improvements up to 5.4%. This highlights the effectiveness of ESTP-Bench in evaluating models from a timeliness perspective and underscores the limitations in temporal grounding of existing offline models.

Performance of VideoLLM-EyeWO Against Baselines. As presented in Tab. 2, our proposed model achieved significant performance improvements across all proactive tasks. Compared to the baseline videoLLM-Online [5], our model demonstrated an improvement of +19.2%. Furthermore, it outperformed the best-performing model, MiniCPM-V [56](using the Polling strategy), by +11.8%.

4.3 In-Depth Analyses in ESTP-Bench

Challenges with Coherent and Contextual Questions: Fig. 5 illustrates the average performance of different models across 14 tasks. (NAR) and (TU) exhibit significantly higher performance compared to other tasks. Upon visualizing the proportion of valid answer intervals relative to the input video duration for these two tasks, we observe that this proportion is substantially higher than for other tasks. This is attributed to these annotations originating from the raw GoalStep [44] labels, which involve segmenting continuous actions towards a consistent goal, thereby leading to a larger proportion of valid answer interval within the video and consequently, higher Recall. Conversely, for the (TRC) task, which also derives from the same original annotations and possesses a high proportion of valid answer interval, both Recall and overall performance significantly decrease. This marked performance drop underscores the significant challenge that proactive coherence and understanding contextual information pose for existing models.

Task Types

0.0

0.2

0.4

0.6

0.8

1.0

Region Value

First 10 Tasks (Avg)

Action Reasoning

Task Understanding

Object Relative Context

Task Relative Context

0.08

0.53

0.66

0.12

0.51

Performance

Recall

0

20

40

60

80

100

Performance/Recall (%)

0

20

40

60

Recall (%)

18

20

22

24

26

28

30

32

34

Precision (%)

Qwen2-VL

MiniCPM-V

LLaVA-NeXT-Video

CLIP

Lavila

LLaVA-OneVision

Qwen2-VL

MiniCPM-V

LLaVA-NeXT-Video

LIVE-Base(0.9)

MMDuet

VideoLLM-EyeWO

Model Categories

Offline MLLMs Response-in-Last

VLMs

Offline MLLMs

Online MLLMs

10

15

20

25

30

35

ESTP Performance Score

0.0

0.5

1.0

1.5

2.0

2.5

APS (Action Per Second)

Performance-Speed Tradeoff

2 FPS Threshold

LIVE(0.8)

LIVE(0.9)

MiniCPM-V

Qwen2-VL

LLaVA-NeXT-Video

EyeWO stage 2

EyeWO stage 3

Ours

Difficulty in Responding Only When Necessary: Fig. 6 presents the relationship between recall and precision for different models under various evaluation settings. We observe a prevalent negative correlation between recall and precision among most models. For instance, MMDuet achieves exceptionally high recall but at the expense of low precision. This trade-off indicates the struggle of existing models to provide proactive yet precise responses.

Synchronization Efficiency Challenges: Fig. 7 illustrates the inherent performance-speed tradeoff in ESTP tasks by plotting Action Per Second (APS) against Performance Score for various models. Existing methods often lie along a clear tradeoff curve, where higher performance is typically associated with lower APS, highlighting the difficulty in achieving both simultaneously. As seen for offline MLLMs using the Polling strategy, achieving high performance while maintaining sufficient speed for real-time synchronization remains challenging. Even approaches near the input frame rate (e.g., LIVE at ∼ 2 FPS) may demonstrate suboptimal performance. This underscores the significant challenges current models face in achieving both high task performance and effective synchronization with the dynamic video stream.

4.4 Evalutation of VideoLLM-EyeWO

Evaluating Zero-Shot Capability in Online/Offline Tasks. Table 3 presents a performance comparison of our model against the baseline on both online and offline tasks. We selected VideoLLMonline [5] as our baseline, given that it shares the same base model (LLaMA3 [16] and SigLIP [60]) and data source (Ego4D) as our own mode. For the online task, we utilize OvO-Bench [26] as a recognized benchmark. For the offline task, following [12], we evaluate our model on the multiplechoice subset of the QAEGO4D-test benchmark [3]. The 'Online' setting involves posing questions as soon as the relevant answer segment appears, whereas the 'Offline' setting involves questioning after the entire video has been presented. The experimental results demonstrate the generalization capability of our model across these distinct tasks.

Evaluating Architecture Generalizability on Offline Tasks As presented in Tab. 4, our model demonstrated comprehensive performance improvements on five tasks related to traditional temporal summarization and forecasting problems. The performance gain reached up to +2.8%, which indicates that our proposed model architecture can effectively generalize to other offline tasks.

5 Related Work

Streaming Video Understanding. Unlike traditional offline video understanding tasks [14, 66, 1, 50] which only allow for question answering after processing the entire video, Online Video Understanding tasks aim to evaluate the ability to respond to questions that arise at any time, based on past information and current observations from a sequential video stream input. Previous work [61, 26, 27] has employed various question types presented during the video stream, such as object or event perception, leveraging different spatiotemporal cues to comprehensively evaluate streaming video understanding capabilities. However, for proactive tasks where questions often require reasoning [65] beyond the current frame, existing methods often exhibit limited question diversity and lack contextual continuity across queries. Furthermore, evaluation in previous work has often overlooked efficiency and timeliness.

To specifically endow MLLMs [42] with proactive response capability, VideoLLM-Online [5] proposed the LIVE training framework, which supervises the model's output at each frame. Subsequent work has focused on improving training efficiency [52], inference speed [33], and response capability [51]. However, these methods often generate under-responsive or over-extended answers and struggle to adapt to continuous, multi-turn conversation.

Egocentric Video Understanding. Compared to traditional third-person video understanding, egocentric video data poses unique challenges, such as the scarcity of large-scale annotated datasets and the inherent narrow and often unstable viewpoint. However, recent work [17, 18, 9, 44] has contributed massive egocentric video datasets and fundamental annotations, significantly benefiting the community. Prior work has typically focused on classic egocentric video understanding tasks such as activity recognition [25, 38], temporal grounding [55, 31, 41], and hand-object detection [2, 21], which are often limited by a closed vocabulary. Other efforts have focused on offline video understanding tasks, such as QaEgo4D [3], EgoSchema [30], and MM-Ego [57]. While SDQES [13] introduced a streaming detection benchmark for egocentric video, it primarily evaluates similaritybased responsiveness and lacks the scope to assess the open-ended generative and conversational capabilities required from Online MLLMs in proactive scenarios.

6 Conclusion

We definite an novel AI assistant's task of proactive, synchronized question answering from egostreaming video, targeting the key properties of proactive coherence, just-in-time responsiveness, and synchronized efficiency. Our contributions-the ESTP-Bench with its ESTP-F1 metric for evaluation, and a novel technical pipeline incorporating a data engine, multi-stage training, and proactive dynamic compression-enable our model to effectively tackle these properties. This approach outperforms multiple baselines across diverse online and offline benchmarks.

Acknowledgement: This work is supported in part by the National Natural Science Foundation of China under Grant No.62206174 and No.62576365.

References

Kirolos Ataallah, Eslam Abdelrahman, Mahmoud Ahmed, Chenhui Gou, Khushbu Pahwa, Jian Ding, and Mohamed Elhoseiny. Infinibench: A benchmark for large multi-modal models in long-form movies and tv shows, 2025.

Siddhant Bansal, Michael Wray, and Dima Damen. Hoi-ref: Hand-object interaction referral in egocentric vision, 2024.

Leonard Bärmann and Alex Waibel. Where did i leave my keys? - episodic-memory-based question answering on egocentric videos. In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) , pages 1559-1567, 2024. ISSN: 2160-7516.

Jieneng Chen, Luoxin Ye, Ju He, Zhao-Yang Wang, Daniel Khashabi, and Alan Yuille. Efficient large multi-modal models via visual context compression, 2024.

Joya Chen, Zhaoyang Lv, Shiwei Wu, Kevin Qinghong Lin, Chenan Song, Difei Gao, Jia-Wei Liu, Ziteng Gao, Dongxing Mao, and Mike Zheng Shou. Videollm-online: Online video large language model for streaming video, 2024.

Yi Chen, Yuying Ge, Yixiao Ge, Mingyu Ding, Bohao Li, Rui Wang, Ruifeng Xu, Ying Shan, and Xihui Liu. Egoplan-bench: Benchmarking multimodal large language models for human-level planning, 2024.

Zhe Chen, Weiyun Wang, Hao Tian, Shenglong Ye, Zhangwei Gao, Erfei Cui, Wenwen Tong, Kongzhi Hu, Jiapeng Luo, Zheng Ma, Ji Ma, Jiaqi Wang, Xiaoyi Dong, Hang Yan, Hewei Guo, Conghui He, Botian Shi, Zhenjiang Jin, Chao Xu, Bin Wang, Xingjian Wei, Wei Li, Wenjian Zhang, Bo Zhang, Pinlong Cai, Licheng Wen, Xiangchao Yan, Min Dou, Lewei Lu, Xizhou Zhu, Tong Lu, Dahua Lin, Yu Qiao, Jifeng Dai, and Wenhai Wang. How far are we to gpt-4v? closing the gap to commercial multimodal models with open-source suites, 2024.

Sijie Cheng, Kechen Fang, Yangyang Yu, Sicheng Zhou, Bohao Li, Ye Tian, Tingguang Li, Lei Han, and Yang Liu. Videgothink: Assessing egocentric video understanding capabilities for embodied ai, 2024.

Dima Damen, Hazel Doughty, Giovanni Maria Farinella, Sanja Fidler, Antonino Furnari, Evangelos Kazakos, Davide Moltisanti, Jonathan Munro, Toby Perrett, Will Price, and Michael Wray. Scaling egocentric vision: The epic-kitchens dataset, 2018.

DeepSeek-AI, Aixin Liu, Bei Feng, Bing Xue, Bingxuan Wang, Bochao Wu, Chengda Lu, Chenggang Zhao, Chengqi Deng, Chenyu Zhang, Chong Ruan, Damai Dai, Daya Guo, Dejian Yang, Deli Chen, Dongjie Ji, Erhang Li, Fangyun Lin, Fucong Dai, Fuli Luo, Guangbo Hao, Guanting Chen, Guowei Li, H. Zhang, Han Bao, Hanwei Xu, Haocheng Wang, Haowei Zhang, Honghui Ding, Huajian Xin, Huazuo Gao, Hui Li, Hui Qu, J. L. Cai, Jian Liang, Jianzhong Guo, Jiaqi Ni, Jiashi Li, Jiawei Wang, Jin Chen, Jingchang Chen, Jingyang Yuan, Junjie Qiu, Junlong Li, Junxiao Song, Kai Dong, Kai Hu, Kaige Gao, Kang Guan, Kexin Huang, Kuai Yu, Lean Wang, Lecong Zhang, Lei Xu, Leyi Xia, Liang Zhao, Litong Wang, Liyue Zhang, Meng Li, Miaojun Wang, Mingchuan Zhang, Minghua Zhang, Minghui Tang, Mingming Li, Ning Tian, Panpan Huang, Peiyi Wang, Peng Zhang, Qiancheng Wang, Qihao Zhu, Qinyu Chen, Qiushi Du, R. J. Chen, R. L. Jin, Ruiqi Ge, Ruisong Zhang, Ruizhe Pan, Runji Wang, Runxin Xu, Ruoyu Zhang, Ruyi Chen, S. S. Li, Shanghao Lu, Shangyan Zhou, Shanhuang Chen, Shaoqing Wu, Shengfeng Ye, Shengfeng Ye, Shirong Ma, Shiyu Wang, Shuang Zhou, Shuiping Yu, Shunfeng Zhou, Shuting Pan, T. Wang, Tao Yun, Tian Pei, Tianyu Sun, W. L. Xiao, Wangding Zeng, Wanjia Zhao, Wei An, Wen Liu, Wenfeng Liang, Wenjun Gao, Wenqin Yu, Wentao Zhang, X. Q. Li, Xiangyue Jin, Xianzu Wang, Xiao Bi, Xiaodong Liu, Xiaohan Wang, Xiaojin Shen, Xiaokang Chen, Xiaokang Zhang, Xiaosha Chen, Xiaotao Nie, Xiaowen Sun, Xiaoxiang Wang, Xin Cheng, Xin Liu, Xin Xie, Xingchao Liu, Xingkai Yu, Xinnan Song, Xinxia Shan, Xinyi Zhou, Xinyu Yang, Xinyuan Li, Xuecheng Su, Xuheng Lin, Y. K. Li, Y. Q. Wang, Y. X. Wei, Y. X. Zhu, Yang Zhang, Yanhong Xu, Yanhong Xu, Yanping Huang, Yao Li, Yao Zhao, Yaofeng Sun, Yaohui Li, Yaohui Wang, Yi Yu, Yi Zheng, Yichao Zhang, Yifan Shi, Yiliang Xiong, Ying He, Ying Tang, Yishi Piao, Yisong Wang, Yixuan Tan, Yiyang Ma, Yiyuan Liu, Yongqiang Guo, Yu Wu, Yuan Ou, Yuchen Zhu, Yuduan Wang, Yue Gong, Yuheng Zou, Yujia He, Yukun Zha, Yunfan Xiong, Yunxian Ma, Yuting Yan, Yuxiang Luo, Yuxiang You, Yuxuan Liu, Yuyang Zhou, Z. F. Wu, Z. Z. Ren, Zehui Ren, Zhangli Sha, Zhe Fu, Zhean Xu, Zhen Huang, Zhen Zhang, Zhenda Xie, Zhengyan Zhang, Zhewen Hao, Zhibin Gou, Zhicheng Ma, Zhigang Yan, Zhihong Shao, Zhipeng Xu, Zhiyu Wu, Zhongyu Zhang, Zhuoshu Li, Zihui Gu, Zijia Zhu, Zijun Liu, Zilin Li, Ziwei Xie, Ziyang Song, Ziyi Gao, and Zizheng Pan. Deepseek-v3 technical report, 2025.

Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. Bert: Pre-training of deep bidirectional transformers for language understanding, 2019.

Shangzhe Di, Zhelun Yu, Guanghao Zhang, Haoyuan Li, Hao Cheng, Bolin Li, Wanggui He, Fangxun Shu, Hao Jiang, et al. Streaming video question-answering with in-context video kv-cache retrieval. In ICLR , 2025.

Cristobal Eyzaguirre, Eric Tang, Shyamal Buch, Adrien Gaidon, Jiajun Wu, and Juan Carlos Niebles. Streaming detection of queried event start, 2024.

Chaoyou Fu, Yuhan Dai, Yongdong Luo, Lei Li, Shuhuai Ren, Renrui Zhang, Zihan Wang, Chenyu Zhou, Yunhang Shen, Mengdan Zhang, Peixian Chen, Yanwei Li, Shaohui Lin, Sirui Zhao, Ke Li, Tong Xu, Xiawu Zheng, Enhong Chen, Caifeng Shan, Ran He, and Xing Sun. Video-mme: The first-ever comprehensive evaluation benchmark of multi-modal llms in video analysis, 2025.

Gabriele Goletto, Tushar Nagarajan, Giuseppe Averta, and Dima Damen. Amego: Active memory from long egocentric videos, 2024.

Aaron Grattafiori, Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad AlDahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Alex Vaughan, Amy Yang, Angela Fan, Anirudh Goyal, Anthony Hartshorn, Aobo Yang, Archi Mitra, Archie Sravankumar, Artem Korenev, Arthur Hinsvark, Arun Rao, Aston Zhang, Aurelien Rodriguez, Austen Gregerson, Ava Spataru, Baptiste Roziere, Bethany Biron, Binh Tang, Bobbie Chern, Charlotte Caucheteux, Chaya Nayak, Chloe Bi, Chris Marra, Chris McConnell, Christian Keller, Christophe Touret, Chunyang Wu, Corinne Wong, Cristian Canton Ferrer, Cyrus Nikolaidis, Damien Allonsius, Daniel Song, Danielle Pintz, Danny Livshits, Danny Wyatt, David Esiobu, Dhruv Choudhary, Dhruv Mahajan, Diego Garcia-Olano, Diego Perino, Dieuwke Hupkes, Egor Lakomkin, Ehab AlBadawy, Elina Lobanova, Emily Dinan, Eric Michael Smith, Filip Radenovic, Francisco Guzmán, Frank Zhang, Gabriel Synnaeve, Gabrielle Lee, Georgia Lewis Anderson, Govind Thattai, Graeme Nail, Gregoire Mialon, Guan Pang, Guillem Cucurell, Hailey Nguyen, Hannah Korevaar, Hu Xu, Hugo Touvron, Iliyan Zarov, Imanol Arrieta Ibarra, Isabel Kloumann, Ishan Misra, Ivan Evtimov, Jack Zhang, Jade Copet, Jaewon Lee, Jan Geffert, Jana Vranes, Jason Park, Jay Mahadeokar, Jeet Shah, Jelmer van der Linde, Jennifer Billock, Jenny Hong, Jenya Lee, Jeremy Fu, Jianfeng Chi, Jianyu Huang, Jiawen Liu, Jie Wang, Jiecao Yu, Joanna Bitton, Joe Spisak, Jongsoo Park, Joseph Rocca, Joshua Johnstun, Joshua Saxe, Junteng Jia, Kalyan Vasuden Alwala, Karthik Prasad, Kartikeya Upasani, Kate Plawiak, Ke Li, Kenneth Heafield, Kevin Stone, Khalid El-Arini, Krithika Iyer, Kshitiz Malik, Kuenley Chiu, Kunal Bhalla, Kushal Lakhotia, Lauren Rantala-Yeary, Laurens van der Maaten, Lawrence Chen, Liang Tan, Liz Jenkins, Louis Martin, Lovish Madaan, Lubo Malo, Lukas Blecher, Lukas Landzaat, Luke de Oliveira, Madeline Muzzi, Mahesh Pasupuleti, Mannat Singh, Manohar Paluri, Marcin Kardas, Maria Tsimpoukelli, Mathew Oldham, Mathieu Rita, Maya Pavlova, Melanie Kambadur, Mike Lewis, Min Si, Mitesh Kumar Singh, Mona Hassan, Naman Goyal, Narjes Torabi, Nikolay Bashlykov, Nikolay Bogoychev, Niladri Chatterji, Ning Zhang, Olivier Duchenne, Onur Çelebi, Patrick Alrassy, Pengchuan Zhang, Pengwei Li, Petar Vasic, Peter Weng, Prajjwal Bhargava, Pratik Dubal, Praveen Krishnan, Punit Singh Koura, Puxin Xu, Qing He, Qingxiao Dong, Ragavan Srinivasan, Raj Ganapathy, Ramon Calderer, Ricardo Silveira Cabral, Robert Stojnic, Roberta Raileanu, Rohan Maheswari, Rohit Girdhar, Rohit Patel, Romain Sauvestre, Ronnie Polidoro, Roshan Sumbaly, Ross Taylor, Ruan Silva, Rui Hou, Rui Wang, Saghar Hosseini, Sahana Chennabasappa, Sanjay Singh, Sean Bell, Seohyun Sonia Kim, Sergey Edunov, Shaoliang Nie, Sharan Narang, Sharath Raparthy, Sheng Shen, Shengye Wan, Shruti Bhosale, Shun Zhang, Simon Vandenhende, Soumya Batra, Spencer Whitman, Sten Sootla, Stephane Collot, Suchin Gururangan, Sydney Borodinsky, Tamar Herman, Tara Fowler, Tarek Sheasha, Thomas Georgiou, Thomas Scialom, Tobias Speckbacher, Todor Mihaylov, Tong Xiao, Ujjwal Karn, Vedanuj Goswami, Vibhor Gupta, Vignesh Ramanathan, Viktor Kerkez, Vincent Gonguet, Virginie Do, Vish Vogeti, Vítor Albiero, Vladan Petrovic, Weiwei Chu, Wenhan Xiong, Wenyin Fu, Whitney Meers, Xavier Martinet, Xiaodong Wang, Xiaofang Wang, Xiaoqing Ellen Tan, Xide Xia, Xinfeng Xie, Xuchao Jia, Xuewei Wang, Yaelle Goldschlag, Yashesh Gaur, Yasmine Babaei, Yi Wen, Yiwen Song, Yuchen Zhang, Yue Li, Yuning Mao, Zacharie Delpierre Coudert, Zheng Yan, Zhengxing Chen, Zoe Papakipos, Aaditya Singh, Aayushi Srivastava, Abha Jain, Adam Kelsey, Adam Shajnfeld, Adithya Gangidi, Adolfo Victoria, Ahuva Goldstand, Ajay Menon, Ajay Sharma, Alex Boesenberg, Alexei Baevski, Allie Feinstein, Amanda Kallet, Amit Sangani, Amos Teo, Anam Yunus, Andrei Lupu, Andres Alvarado, Andrew Caples, Andrew Gu, Andrew Ho, Andrew Poulton, Andrew Ryan, Ankit Ramchandani, Annie Dong, Annie Franco, Anuj Goyal, Aparajita Saraf, Arkabandhu Chowdhury, Ashley Gabriel, Ashwin Bharambe, Assaf Eisenman, Azadeh Yazdan, Beau James, Ben Maurer, Benjamin Leonhardi, Bernie Huang, Beth Loyd, Beto De Paola, Bhargavi Paranjape, Bing Liu, Bo Wu, Boyu Ni, Braden Hancock, Bram Wasti, Brandon Spence, Brani Stojkovic, Brian Gamido, Britt Montalvo, Carl Parker, Carly Burton, Catalina Mejia, Ce Liu, Changhan Wang, Changkyu Kim, Chao Zhou, Chester Hu, Ching-Hsiang Chu, Chris Cai, Chris Tindal, Christoph Feichtenhofer, Cynthia Gao, Damon Civin, Dana Beaty, Daniel Kreymer, Daniel Li, David Adkins, David Xu, Davide Testuggine, Delia David, Devi Parikh, Diana Liskovich, Didem Foss, Dingkang Wang, Duc Le, Dustin Holland, Edward Dowling, Eissa Jamil, Elaine Montgomery, Eleonora Presani, Emily Hahn, Emily Wood, Eric-Tuan Le, Erik Brinkman, Esteban Arcaute, Evan Dunbar, Evan Smothers, Fei Sun, Felix Kreuk, Feng Tian, Filippos Kokkinos, Firat Ozgenel, Francesco Caggioni, Frank Kanayet, Frank Seide, Gabriela Medina Florez, Gabriella

Schwarz, Gada Badeer, Georgia Swee, Gil Halpern, Grant Herman, Grigory Sizov, Guangyi, Zhang, Guna Lakshminarayanan, Hakan Inan, Hamid Shojanazeri, Han Zou, Hannah Wang, Hanwen Zha, Haroun Habeeb, Harrison Rudolph, Helen Suk, Henry Aspegren, Hunter Goldman, Hongyuan Zhan, Ibrahim Damlaj, Igor Molybog, Igor Tufanov, Ilias Leontiadis, Irina-Elena Veliche, Itai Gat, Jake Weissman, James Geboski, James Kohli, Janice Lam, Japhet Asher, Jean-Baptiste Gaya, Jeff Marcus, Jeff Tang, Jennifer Chan, Jenny Zhen, Jeremy Reizenstein, Jeremy Teboul, Jessica Zhong, Jian Jin, Jingyi Yang, Joe Cummings, Jon Carvill, Jon Shepard, Jonathan McPhie, Jonathan Torres, Josh Ginsburg, Junjie Wang, Kai Wu, Kam Hou U, Karan Saxena, Kartikay Khandelwal, Katayoun Zand, Kathy Matosich, Kaushik Veeraraghavan, Kelly Michelena, Keqian Li, Kiran Jagadeesh, Kun Huang, Kunal Chawla, Kyle Huang, Lailin Chen, Lakshya Garg, Lavender A, Leandro Silva, Lee Bell, Lei Zhang, Liangpeng Guo, Licheng Yu, Liron Moshkovich, Luca Wehrstedt, Madian Khabsa, Manav Avalani, Manish Bhatt, Martynas Mankus, Matan Hasson, Matthew Lennie, Matthias Reso, Maxim Groshev, Maxim Naumov, Maya Lathi, Meghan Keneally, Miao Liu, Michael L. Seltzer, Michal Valko, Michelle Restrepo, Mihir Patel, Mik Vyatskov, Mikayel Samvelyan, Mike Clark, Mike Macey, Mike Wang, Miquel Jubert Hermoso, Mo Metanat, Mohammad Rastegari, Munish Bansal, Nandhini Santhanam, Natascha Parks, Natasha White, Navyata Bawa, Nayan Singhal, Nick Egebo, Nicolas Usunier, Nikhil Mehta, Nikolay Pavlovich Laptev, Ning Dong, Norman Cheng, Oleg Chernoguz, Olivia Hart, Omkar Salpekar, Ozlem Kalinli, Parkin Kent, Parth Parekh, Paul Saab, Pavan Balaji, Pedro Rittner, Philip Bontrager, Pierre Roux, Piotr Dollar, Polina Zvyagina, Prashant Ratanchandani, Pritish Yuvraj, Qian Liang, Rachad Alao, Rachel Rodriguez, Rafi Ayub, Raghotham Murthy, Raghu Nayani, Rahul Mitra, Rangaprabhu Parthasarathy, Raymond Li, Rebekkah Hogan, Robin Battey, Rocky Wang, Russ Howes, Ruty Rinott, Sachin Mehta, Sachin Siby, Sai Jayesh Bondu, Samyak Datta, Sara Chugh, Sara Hunt, Sargun Dhillon, Sasha Sidorov, Satadru Pan, Saurabh Mahajan, Saurabh Verma, Seiji Yamamoto, Sharadh Ramaswamy, Shaun Lindsay, Shaun Lindsay, Sheng Feng, Shenghao Lin, Shengxin Cindy Zha, Shishir Patil, Shiva Shankar, Shuqiang Zhang, Shuqiang Zhang, Sinong Wang, Sneha Agarwal, Soji Sajuyigbe, Soumith Chintala, Stephanie Max, Stephen Chen, Steve Kehoe, Steve Satterfield, Sudarshan Govindaprasad, Sumit Gupta, Summer Deng, Sungmin Cho, Sunny Virk, Suraj Subramanian, Sy Choudhury, Sydney Goldman, Tal Remez, Tamar Glaser, Tamara Best, Thilo Koehler, Thomas Robinson, Tianhe Li, Tianjun Zhang, Tim Matthews, Timothy Chou, Tzook Shaked, Varun Vontimitta, Victoria Ajayi, Victoria Montanez, Vijai Mohan, Vinay Satish Kumar, Vishal Mangla, Vlad Ionescu, Vlad Poenaru, Vlad Tiberiu Mihailescu, Vladimir Ivanov, Wei Li, Wenchen Wang, Wenwen Jiang, Wes Bouaziz, Will Constable, Xiaocheng Tang, Xiaojian Wu, Xiaolan Wang, Xilun Wu, Xinbo Gao, Yaniv Kleinman, Yanjun Chen, Ye Hu, Ye Jia, Ye Qi, Yenda Li, Yilin Zhang, Ying Zhang, Yossi Adi, Youngjin Nam, Yu, Wang, Yu Zhao, Yuchen Hao, Yundi Qian, Yunlu Li, Yuzi He, Zach Rait, Zachary DeVito, Zef Rosnbrick, Zhaoduo Wen, Zhenyu Yang, Zhiwei Zhao, and Zhiyu Ma. The llama 3 herd of models, 2024.

Kristen Grauman, Andrew Westbury, Eugene Byrne, Zachary Chavis, Antonino Furnari, Rohit Girdhar, Jackson Hamburger, Hao Jiang, Miao Liu, Xingyu Liu, Miguel Martin, Tushar Nagarajan, Ilija Radosavovic, Santhosh Kumar Ramakrishnan, Fiona Ryan, Jayant Sharma, Michael Wray, Mengmeng Xu, Eric Zhongcong Xu, Chen Zhao, Siddhant Bansal, Dhruv Batra, Vincent Cartillier, Sean Crane, Tien Do, Morrie Doulaty, Akshay Erapalli, Christoph Feichtenhofer, Adriano Fragomeni, Qichen Fu, Abrham Gebreselasie, Cristina Gonzalez, James Hillis, Xuhua Huang, Yifei Huang, Wenqi Jia, Weslie Khoo, Jachym Kolar, Satwik Kottur, Anurag Kumar, Federico Landini, Chao Li, Yanghao Li, Zhenqiang Li, Karttikeya Mangalam, Raghava Modhugu, Jonathan Munro, Tullie Murrell, Takumi Nishiyasu, Will Price, Paola Ruiz Puentes, Merey Ramazanova, Leda Sari, Kiran Somasundaram, Audrey Southerland, Yusuke Sugano, Ruijie Tao, Minh Vo, Yuchen Wang, Xindi Wu, Takuma Yagi, Ziwei Zhao, Yunyi Zhu, Pablo Arbelaez, David Crandall, Dima Damen, Giovanni Maria Farinella, Christian Fuegen, Bernard Ghanem, Vamsi Krishna Ithapu, C. V. Jawahar, Hanbyul Joo, Kris Kitani, Haizhou Li, Richard Newcombe, Aude Oliva, Hyun Soo Park, James M. Rehg, Yoichi Sato, Jianbo Shi, Mike Zheng Shou, Antonio Torralba, Lorenzo Torresani, Mingfei Yan, and Jitendra Malik. Ego4d: Around the world in 3,000 hours of egocentric video, 2022.

Kristen Grauman, Andrew Westbury, Lorenzo Torresani, Kris Kitani, Jitendra Malik, Triantafyllos Afouras, Kumar Ashutosh, Vijay Baiyya, Siddhant Bansal, Bikram Boote, Eugene Byrne, Zach Chavis, Joya Chen, Feng Cheng, Fu-Jen Chu, Sean Crane, Jing Dong, Maria Escobar, Cristhian Forigua, Abrham Gebreselasie, Jing Huang, Mohaiminul Islam, Suyog Jain, Rawal Khirodkar, Kevin J Liang, Jia-Wei Liu, Sagnik Majumder, Yongsen Mao, Miguel Martin, Effrosyni Mavroudi, Tushar Nagarajan, Francesco Ragusa, Santhosh Ramakrishnan, Arjun Somayazulu, Yale Song, Shan Su, Zihui Xue, Edward Zhang, Angela Castillo, Changan Chen, Xinzhu Fu, Ryosuke Furuta, Cristina Gonzalez, Prince Gupta, Jiabo Hu, Yifei Huang, Yiming Huang, Weslie Khoo, Anush Kumar, Sach Lakhavani, Miao Liu, Mi Luo, Zhengyi Luo, Brighid Meredith, Austin Miller, Oluwatumininu Oguntola, Xiaqing Pan, Penny Peng, Shraman Pramanick, Merey Ramazanova, Fiona Ryan, Wei Shan, Kiran Somasundaram, Chenan Song, Audrey Southerland, Huiyu Wang, Yuchen Wang, Takuma Yagi, Mingfei Yan, Xitong Yang, Zecheng Yu, Shengxin Cindy Zha, Chen Zhao, Ziwei Zhao, Zhifan Zhu, Jeff Zhuo, Pablo Arbelaez, Gedas Bertasius, David Crandall, Dima Damen, Jakob Engel, Giovanni Maria Farinella, Antonino Furnari, Bernard Ghanem,

Judy Hoffman, C V Jawahar, Richard Newcombe, Hyun Soo Park, James M Rehg, Yoichi Sato, Manolis Savva, Jianbo Shi, Mike Zheng, and Michael Wray. Ego-exo4d: Understanding skilled human activity from first- and third-person perspectives.

Matthew Honnibal, Ines Montani, Sofie Van Landeghem, and Adriane Boyd. spaCy: Industrial-strength Natural Language Processing in Python. 2020.

Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. Lora: Low-rank adaptation of large language models, 2021.

Shuhei Kurita, Naoki Katsura, and Eri Onami. Refego: Referring expression comprehension dataset from first-person perception of ego4d, 2023.

Jie Lei, Linjie Li, Luowei Zhou, Zhe Gan, Tamara L. Berg, Mohit Bansal, and Jingjing Liu. Less is more: Clipbert for video-and-language learning via sparse sampling, 2021.

Bo Li, Yuanhan Zhang, Dong Guo, Renrui Zhang, Feng Li, Hao Zhang, Kaichen Zhang, Peiyuan Zhang, Yanwei Li, Ziwei Liu, and Chunyuan Li. Llava-onevision: Easy visual task transfer, 2024.

Feng Li, Renrui Zhang, Hao Zhang, Yuanhan Zhang, Bo Li, Wei Li, Zejun Ma, and Chunyuan Li. Llava-next-interleave: Tackling multi-image, video, and 3d in large multimodal models, 2024.

Yanghao Li, Tushar Nagarajan, Bo Xiong, and Kristen Grauman. Ego-exo: Transferring visual representations from third-person to first-person videos, 2021.

Yifei Li, Junbo Niu, Ziyang Miao, Chunjiang Ge, Yuanhang Zhou, Qihao He, Xiaoyi Dong, Haodong Duan, Shuangrui Ding, Rui Qian, Pan Zhang, Yuhang Zang, Yuhang Cao, Conghui He, and Jiaqi Wang. Ovo-bench: How far is your video-llms from real-world online video understanding?, 2025.

Junming Lin, Zheng Fang, Chi Chen, Zihao Wan, Fuwen Luo, Peng Li, Yang Liu, and Maosong Sun. StreamingBench: Assessing the gap for MLLMs to achieve streaming video understanding. In None , 2024.

Kevin Qinghong Lin, Alex Jinpeng Wang, Mattia Soldan, Michael Wray, Rui Yan, Eric Zhongcong Xu, Difei Gao, Rongcheng Tu, Wenzhe Zhao, Weijie Kong, Chengfei Cai, Hongfa Wang, Dima Damen, Bernard Ghanem, Wei Liu, and Mike Zheng Shou. Egocentric video-language pretraining, 2022.

Xiangrui Liu, Yan Shu, Zheng Liu, Ao Li, Yang Tian, and Bo Zhao. Video-xl-pro: Reconstructive token compression for extremely long video understanding, 2025.

Karttikeya Mangalam, Raiymbek Akshulakov, and Jitendra Malik. Egoschema: A diagnostic benchmark for very long-form video language understanding, 2023.

Zaira Manigrasso, Matteo Dunnhofer, Antonino Furnari, Moritz Nottebaum, Antonio Finocchiaro, Davide Marana, G. Farinella, and C. Micheloni. Online episodic memory visual query localization with egocentric streaming object memory.

Xavi Puig, Eric Undersander, Andrew Szot, Mikael Dallaire Cote, Ruslan Partsey, Jimmy Yang, Ruta Desai, Alexander William Clegg, Michal Hlavac, Tiffany Min, Theo Gervet, Vladimir Vondrus, VincentPierre Berges, John Turner, Oleksandr Maksymets, Zsolt Kira, Mrinal Kalakrishnan, Jitendra Malik, Devendra Singh Chaplot, Unnat Jain, Dhruv Batra, Akshara Rai, and Roozbeh Mottaghi. Habitat 3.0: A co-habitat for humans, avatars and robots, 2023.

Rui Qian, Shuangrui Ding, Xiaoyi Dong, Pan Zhang, Yuhang Zang, Yuhang Cao, Dahua Lin, and Jiaqi Wang. Dispider: Enabling video llms with active real-time interaction via disentangled perception, decision, and reaction, 2025.

Rui Qian, Xiaoyi Dong, Pan Zhang, Yuhang Zang, Shuangrui Ding, Dahua Lin, and Jiaqi Wang. Streaming long video understanding with large language models, 2024.

Lu Qiu, Yi Chen, Yuying Ge, Yixiao Ge, Ying Shan, and Xihui Liu. Egoplan-bench2: A benchmark for multimodal large language model planning in real-world scenarios, 2025.

Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever. Learning transferable visual models from natural language supervision, 2021.

Shuhuai Ren, Linli Yao, Shicheng Li, Xu Sun, and Lu Hou. Timechat: A time-sensitive multimodal large language model for long video understanding, 2024.

Ivan Rodin, Antonino Furnari, Kyle Min, Subarna Tripathi, and Giovanni Maria Farinella. Action scene graphs for long-form understanding of egocentric videos, 2023.

Michael S. Ryoo, Honglu Zhou, Shrikant Kendre, Can Qin, Le Xue, Manli Shu, Jongwoo Park, Kanchana Ranasinghe, Silvio Savarese, Ran Xu, Caiming Xiong, and Juan Carlos Niebles. xgen-mm-vid (blip-3video): You only need 32 tokens to represent a video even in vlms, 2025.

Manolis Savva, Abhishek Kadian, Oleksandr Maksymets, Yili Zhao, Erik Wijmans, Bhavana Jain, Julian Straub, Jia Liu, Vladlen Koltun, Jitendra Malik, Devi Parikh, and Dhruv Batra. Habitat: A Platform for Embodied AI Research. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) , 2019.

Yuhan Shen, Huiyu Wang, Xitong Yang, Matt Feiszli, Ehsan Elhamifar, Lorenzo Torresani, and Effrosyni Mavroudi. Learning to segment referred objects from narrated egocentric videos. In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , pages 14510-14520. IEEE.

Cheng Shi, Yizhou Yu, and Sibei Yang. Vision function layer in multimodal llms, 2025.

Yan Shu, Zheng Liu, Peitian Zhang, Minghao Qin, Junjie Zhou, Zhengyang Liang, Tiejun Huang, and Bo Zhao. Video-xl: Extra-long vision language model for hour-scale video understanding, 2024.

Yale Song, Gene Byrne, Tushar Nagarajan, Huiyu Wang, Miguel Martin, and Lorenzo Torresani. Ego4d goal-step: Toward hierarchical understanding of procedural activities. In Thirty-seventh Conference on Neural Information Processing Systems Datasets and Benchmarks Track , 2023.

Andrew Szot, Alex Clegg, Eric Undersander, Erik Wijmans, Yili Zhao, John Turner, Noah Maestre, Mustafa Mukadam, Devendra Chaplot, Oleksandr Maksymets, Aaron Gokaslan, Vladimir Vondrus, Sameer Dharur, Franziska Meier, Wojciech Galuba, Angel Chang, Zsolt Kira, Vladlen Koltun, Jitendra Malik, Manolis Savva, and Dhruv Batra. Habitat 2.0: Training home assistants to rearrange their habitat. In Advances in Neural Information Processing Systems (NeurIPS) , 2021.

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, 2019.

Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurelien Rodriguez, Armand Joulin, Edouard Grave, and Guillaume Lample. Llama: Open and efficient foundation language models, 2023.

Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need, 2023.

Peng Wang, Shuai Bai, Sinan Tan, Shijie Wang, Zhihao Fan, Jinze Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, Yang Fan, Kai Dang, Mengfei Du, Xuancheng Ren, Rui Men, Dayiheng Liu, Chang Zhou, Jingren Zhou, and Junyang Lin. Qwen2-vl: Enhancing vision-language model's perception of the world at any resolution, 2024.

Weihan Wang, Zehai He, Wenyi Hong, Yean Cheng, Xiaohan Zhang, Ji Qi, Xiaotao Gu, Shiyu Huang, Bin Xu, Yuxiao Dong, Ming Ding, and Jie Tang. Lvbench: An extreme long video understanding benchmark, 2025.

Yueqian Wang, Xiaojun Meng, Yuxuan Wang, Jianxin Liang, Jiansheng Wei, Huishuai Zhang, and Dongyan Zhao. Videollm knows when to speak: Enhancing time-sensitive video comprehension with video-text duet interaction format, 2024.

Shiwei Wu, Joya Chen, Kevin Qinghong Lin, Qimeng Wang, Yan Gao, Qianli Xu, Tong Xu, Yao Hu, Enhong Chen, and Mike Zheng Shou. Videollm-mod: Efficient video-language streaming with mixture-ofdepths vision computation, 2024.

Guangxuan Xiao, Yuandong Tian, Beidi Chen, Song Han, and Mike Lewis. Efficient streaming language models with attention sinks, 2024.

Jingkang Yang, Shuai Liu, Hongming Guo, Yuhao Dong, Xiamengwei Zhang, Sicheng Zhang, Pengyun Wang, Zitang Zhou, Binzhu Xie, Ziyue Wang, Bei Ouyang, Zhengyu Lin, Marco Cominelli, Zhongang Cai, Yuanhan Zhang, Peiyuan Zhang, Fangzhou Hong, Joerg Widmer, Francesco Gringoli, Lei Yang, Bo Li, and Ziwei Liu. Egolife: Towards egocentric life assistant, 2025.

Xitong Yang, Fu-Jen Chu, Matt Feiszli, Raghav Goyal, Lorenzo Torresani, and Du Tran. Relational space-time query in long-form videos. In 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , pages 6398-6408. IEEE.

Yuan Yao, Tianyu Yu, Ao Zhang, Chongyi Wang, Junbo Cui, Hongji Zhu, Tianchi Cai, Haoyu Li, Weilin Zhao, Zhihui He, Qianyu Chen, Huarong Zhou, Zhensheng Zou, Haoye Zhang, Shengding Hu, Zhi Zheng, Jie Zhou, Jie Cai, Xu Han, Guoyang Zeng, Dahai Li, Zhiyuan Liu, and Maosong Sun. Minicpm-v: A gpt-4v level mllm on your phone, 2024.

Hanrong Ye, Haotian Zhang, Erik Daxberger, Lin Chen, Zongyu Lin, Yanghao Li, Bowen Zhang, Haoxuan You, Dan Xu, Zhe Gan, Jiasen Lu, and Yinfei Yang. Mm-ego: Towards building egocentric multimodal llms for video qa, 2025.

Xubing Ye, Yukang Gan, Xiaoke Huang, Yixiao Ge, and Yansong Tang. Voco-llama: Towards vision compression with large language models, 2025.

Gokul Yenduri, Ramalingam M, Chemmalar Selvi G, Supriya Y, Gautam Srivastava, Praveen Kumar Reddy Maddikunta, Deepti Raj G, Rutvij H Jhaveri, Prabadevi B, Weizheng Wang, Athanasios V. Vasilakos, and Thippa Reddy Gadekallu. Generative pre-trained transformer: A comprehensive review on enabling technologies, potential applications, emerging challenges, and future directions, 2023.

Xiaohua Zhai, Basil Mustafa, Alexander Kolesnikov, and Lucas Beyer. Sigmoid loss for language image pre-training, 2023.

Haoji Zhang, Yiqin Wang, Yansong Tang, Yong Liu, Jiashi Feng, Jifeng Dai, and Xiaojie Jin. Flash-vstream: Memory-based real-time understanding for long video streams, 2024.

Jiahao Zhang, Stephen Gould, and Itzik Ben-Shabat. Vidat-ANU CVML video annotation tool. https: //github.com/anucvml/vidat , 2020.

Peitian Zhang, Zheng Liu, Shitao Xiao, Ninglu Shao, Qiwei Ye, and Zhicheng Dou. Long context compression with activation beacon, 2024.

Yue Zhao, Ishan Misra, Philipp Krähenbühl, and Rohit Girdhar. Learning video representations from large language models, 2022.

Ge Zheng, Bin Yang, Jiajin Tang, Hong-Yu Zhou, and Sibei Yang. Ddcot: Duty-distinct chain-of-thought prompting for multimodal reasoning in language models, 2023.

Junjie Zhou, Yan Shu, Bo Zhao, Boya Wu, Zhengyang Liang, Shitao Xiao, Minghao Qin, Xi Yang, Yongping Xiong, Bo Zhang, Tiejun Huang, and Zheng Liu. Mlvu: Benchmarking multi-task long video understanding, 2025.

NeurIPS Paper Checklist

1. Claims

Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?

Answer: [Yes]

Justification: The abstract and introduction provide an accurate and faithful summary of the paper's contributions, with no overstatement or omission of key elements.

Guidelines:

The answer NA means that the abstract and introduction do not include the claims made in the paper.

The abstract and/or introduction should clearly state the claims made, including the contributions made in the paper and important assumptions and limitations. A No or NA answer to this question will not be perceived well by the reviewers.

The claims made should match theoretical and experimental results, and reflect how much the results can be expected to generalize to other settings.

It is fine to include aspirational goals as motivation as long as it is clear that these goals are not attained by the paper.

2. Limitations

Question: Does the paper discuss the limitations of the work performed by the authors?

Answer: [Yes]

Justification: A detailed discussion of the limitations will be included in the supplementary material.

Guidelines:

The answer NA means that the paper has no limitation while the answer No means that the paper has limitations, but those are not discussed in the paper.

The authors are encouraged to create a separate "Limitations" section in their paper.

The paper should point out any strong assumptions and how robust the results are to violations of these assumptions (e.g., independence assumptions, noiseless settings, model well-specification, asymptotic approximations only holding locally). The authors should reflect on how these assumptions might be violated in practice and what the implications would be.

The authors should reflect on the scope of the claims made, e.g., if the approach was only tested on a few datasets or with a few runs. In general, empirical results often depend on implicit assumptions, which should be articulated.

The authors should reflect on the factors that influence the performance of the approach. For example, a facial recognition algorithm may perform poorly when image resolution is low or images are taken in low lighting. Or a speech-to-text system might not be used reliably to provide closed captions for online lectures because it fails to handle technical jargon.

The authors should discuss the computational efficiency of the proposed algorithms and how they scale with dataset size.

If applicable, the authors should discuss possible limitations of their approach to address problems of privacy and fairness.

While the authors might fear that complete honesty about limitations might be used by reviewers as grounds for rejection, a worse outcome might be that reviewers discover limitations that aren't acknowledged in the paper. The authors should use their best judgment and recognize that individual actions in favor of transparency play an important role in developing norms that preserve the integrity of the community. Reviewers will be specifically instructed to not penalize honesty concerning limitations.

3. Theory assumptions and proofs

Question: For each theoretical result, does the paper provide the full set of assumptions and a complete (and correct) proof?

Answer: [NA]

Justification: The paper does not include theoretical results.

Guidelines:

The answer NA means that the paper does not include theoretical results.

All the theorems, formulas, and proofs in the paper should be numbered and crossreferenced.

All assumptions should be clearly stated or referenced in the statement of any theorems.

The proofs can either appear in the main paper or the supplemental material, but if they appear in the supplemental material, the authors are encouraged to provide a short proof sketch to provide intuition.

Inversely, any informal proof provided in the core of the paper should be complemented by formal proofs provided in appendix or supplemental material.

Theorems and Lemmas that the proof relies upon should be properly referenced.

4. Experimental result reproducibility

Question: Does the paper fully disclose all the information needed to reproduce the main experimental results of the paper to the extent that it affects the main claims and/or conclusions of the paper (regardless of whether the code and data are provided or not)?

Answer: [Yes]

Justification: We include thorough descriptions of our experimental setup, implementation, and evaluation protocols.

Guidelines:

The answer NA means that the paper does not include experiments.

If the paper includes experiments, a No answer to this question will not be perceived well by the reviewers: Making the paper reproducible is important, regardless of whether the code and data are provided or not.

If the contribution is a dataset and/or model, the authors should describe the steps taken to make their results reproducible or verifiable.

Depending on the contribution, reproducibility can be accomplished in various ways. For example, if the contribution is a novel architecture, describing the architecture fully might suffice, or if the contribution is a specific model and empirical evaluation, it may be necessary to either make it possible for others to replicate the model with the same dataset, or provide access to the model. In general. releasing code and data is often one good way to accomplish this, but reproducibility can also be provided via detailed instructions for how to replicate the results, access to a hosted model (e.g., in the case of a large language model), releasing of a model checkpoint, or other means that are appropriate to the research performed.

While NeurIPS does not require releasing code, the conference does require all submissions to provide some reasonable avenue for reproducibility, which may depend on the nature of the contribution. For example

(a) If the contribution is primarily a new algorithm, the paper should make it clear how to reproduce that algorithm.

(b) If the contribution is primarily a new model architecture, the paper should describe the architecture clearly and fully.

(c) If the contribution is a new model (e.g., a large language model), then there should either be a way to access this model for reproducing the results or a way to reproduce the model (e.g., with an open-source dataset or instructions for how to construct the dataset).

(d) We recognize that reproducibility may be tricky in some cases, in which case authors are welcome to describe the particular way they provide for reproducibility. In the case of closed-source models, it may be that access to the model is limited in some way (e.g., to registered users), but it should be possible for other researchers to have some path to reproducing or verifying the results.

5. Open access to data and code

Question: Does the paper provide open access to the data and code, with sufficient instructions to faithfully reproduce the main experimental results, as described in supplemental material?

Answer: [No]

Justification: Code will be released after acceptance.

Guidelines:

The answer NA means that paper does not include experiments requiring code.

Please see the NeurIPS code and data submission guidelines ( https://nips.cc/ public/guides/CodeSubmissionPolicy ) for more details.

While we encourage the release of code and data, we understand that this might not be possible, so 'No' is an acceptable answer. Papers cannot be rejected simply for not including code, unless this is central to the contribution (e.g., for a new open-source benchmark).

The instructions should contain the exact command and environment needed to run to reproduce the results. See the NeurIPS code and data submission guidelines ( https: //nips.cc/public/guides/CodeSubmissionPolicy ) for more details.

The authors should provide instructions on data access and preparation, including how to access the raw data, preprocessed data, intermediate data, and generated data, etc.

The authors should provide scripts to reproduce all experimental results for the new proposed method and baselines. If only a subset of experiments are reproducible, they should state which ones are omitted from the script and why.

At submission time, to preserve anonymity, the authors should release anonymized versions (if applicable).

Providing as much information as possible in supplemental material (appended to the paper) is recommended, but including URLs to data and code is permitted.

6. Experimental setting/details

Question: Does the paper specify all the training and test details (e.g., data splits, hyperparameters, how they were chosen, type of optimizer, etc.) necessary to understand the results?

Answer: [Yes]

Justification: We provide detailed experimental settings.

Guidelines:

The answer NA means that the paper does not include experiments.

The experimental setting should be presented in the core of the paper to a level of detail that is necessary to appreciate the results and make sense of them.

The full details can be provided either with the code, in appendix, or as supplemental material.

7. Experiment statistical significance

Question: Does the paper report error bars suitably and correctly defined or other appropriate information about the statistical significance of the experiments?

Answer: [No]

Justification: We observe substantial performance gains, supported by comprehensive analyses that validate our conclusions.

Guidelines:

The answer NA means that the paper does not include experiments.

The authors should answer "Yes" if the results are accompanied by error bars, confidence intervals, or statistical significance tests, at least for the experiments that support the main claims of the paper.

The factors of variability that the error bars are capturing should be clearly stated (for example, train/test split, initialization, random drawing of some parameter, or overall run with given experimental conditions).

The method for calculating the error bars should be explained (closed form formula, call to a library function, bootstrap, etc.)

The assumptions made should be given (e.g., Normally distributed errors).

It should be clear whether the error bar is the standard deviation or the standard error of the mean.

It is OK to report 1-sigma error bars, but one should state it. The authors should preferably report a 2-sigma error bar than state that they have a 96% CI, if the hypothesis of Normality of errors is not verified.

For asymmetric distributions, the authors should be careful not to show in tables or figures symmetric error bars that would yield results that are out of range (e.g. negative error rates).

If error bars are reported in tables or plots, The authors should explain in the text how they were calculated and reference the corresponding figures or tables in the text.

8. Experiments compute resources

Question: For each experiment, does the paper provide sufficient information on the computer resources (type of compute workers, memory, time of execution) needed to reproduce the experiments?

Answer: [Yes]

Justification: We provide resources details in appendix.

Guidelines:

The answer NA means that the paper does not include experiments.

The paper should indicate the type of compute workers CPU or GPU, internal cluster, or cloud provider, including relevant memory and storage.

The paper should provide the amount of compute required for each of the individual experimental runs as well as estimate the total compute.

The paper should disclose whether the full research project required more compute than the experiments reported in the paper (e.g., preliminary or failed experiments that didn't make it into the paper).

9. Code of ethics

Question: Does the research conducted in the paper conform, in every respect, with the NeurIPS Code of Ethics https://neurips.cc/public/EthicsGuidelines ?

Answer: [Yes]

Justification: The research conducted in the paper conform, in every respect, with the NeurIPS Code of Ethics.

Guidelines:

The answer NA means that the authors have not reviewed the NeurIPS Code of Ethics.

If the authors answer No, they should explain the special circumstances that require a deviation from the Code of Ethics.

The authors should make sure to preserve anonymity (e.g., if there is a special consideration due to laws or regulations in their jurisdiction).

10. Broader impacts

Question: Does the paper discuss both potential positive societal impacts and negative societal impacts of the work performed?

Answer: [NA]

Justification: There is no societal impact of the work performed.

Guidelines:

The answer NA means that there is no societal impact of the work performed.

If the authors answer NA or No, they should explain why their work has no societal impact or why the paper does not address societal impact.

Examples of negative societal impacts include potential malicious or unintended uses (e.g., disinformation, generating fake profiles, surveillance), fairness considerations (e.g., deployment of technologies that could make decisions that unfairly impact specific groups), privacy considerations, and security considerations.

The conference expects that many papers will be foundational research and not tied to particular applications, let alone deployments. However, if there is a direct path to any negative applications, the authors should point it out. For example, it is legitimate to point out that an improvement in the quality of generative models could be used to generate deepfakes for disinformation. On the other hand, it is not needed to point out that a generic algorithm for optimizing neural networks could enable people to train models that generate Deepfakes faster.

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.

If there are negative societal impacts, the authors could also discuss possible mitigation strategies (e.g., gated release of models, providing defenses in addition to attacks, mechanisms for monitoring misuse, mechanisms to monitor how a system learns from feedback over time, improving the efficiency and accessibility of ML).

11. Safeguards

Question: 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 (e.g., pretrained language models, image generators, or scraped datasets)?

Answer: [NA]

Justification: The paper poses no such risks.

Guidelines:

The answer NA means that the paper poses no such risks.

Released models that have a high risk for misuse or dual-use should be released with necessary safeguards to allow for controlled use of the model, for example by requiring that users adhere to usage guidelines or restrictions to access the model or implementing safety filters.

Datasets that have been scraped from the Internet could pose safety risks. The authors should describe how they avoided releasing unsafe images.

We recognize that providing effective safeguards is challenging, and many papers do not require this, but we encourage authors to take this into account and make a best faith effort.

12. Licenses for existing assets

Question: Are the creators or original owners of assets (e.g., code, data, models), used in the paper, properly credited and are the license and terms of use explicitly mentioned and properly respected?

Answer: [Yes]

Justification: We cite all the original paper that produced the code package or dataset.

Guidelines:

The answer NA means that the paper does not use existing assets.

The authors should cite the original paper that produced the code package or dataset.

The authors should state which version of the asset is used and, if possible, include a URL.

The name of the license (e.g., CC-BY 4.0) should be included for each asset.

For scraped data from a particular source (e.g., website), the copyright and terms of service of that source should be provided.

If assets are released, the license, copyright information, and terms of use in the package should be provided. For popular datasets, paperswithcode.com/datasets has curated licenses for some datasets. Their licensing guide can help determine the license of a dataset.

For existing datasets that are re-packaged, both the original license and the license of the derived asset (if it has changed) should be provided.

If this information is not available online, the authors are encouraged to reach out to the asset's creators.

13. New assets

Question: Are new assets introduced in the paper well documented and is the documentation provided alongside the assets?

Answer: [Yes]

Justification: We will release the dataset and benchmark we collected.

Guidelines:

The answer NA means that the paper does not release new assets.

Researchers should communicate the details of the dataset/code/model as part of their submissions via structured templates. This includes details about training, license, limitations, etc.

The paper should discuss whether and how consent was obtained from people whose asset is used.

At submission time, remember to anonymize your assets (if applicable). You can either create an anonymized URL or include an anonymized zip file.

14. Crowdsourcing and research with human subjects

Question: For crowdsourcing experiments and research with human subjects, does the paper include the full text of instructions given to participants and screenshots, if applicable, as well as details about compensation (if any)?

Answer: [NA]

Justification: The paper does not involve crowdsourcing nor research with human subjects.

Guidelines:

The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.

Including this information in the supplemental material is fine, but if the main contribution of the paper involves human subjects, then as much detail as possible should be included in the main paper.

According to the NeurIPS Code of Ethics, workers involved in data collection, curation, or other labor should be paid at least the minimum wage in the country of the data collector.

15. Institutional review board (IRB) approvals or equivalent for research with human subjects

Question: Does the paper describe potential risks incurred by study participants, whether such risks were disclosed to the subjects, and whether Institutional Review Board (IRB) approvals (or an equivalent approval/review based on the requirements of your country or institution) were obtained?

Answer: [NA]

Justification: The paper does not involve crowdsourcing nor research with human subjects.

Guidelines:

The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.

Depending on the country in which research is conducted, IRB approval (or equivalent) may be required for any human subjects research. If you obtained IRB approval, you should clearly state this in the paper.

We recognize that the procedures for this may vary significantly between institutions and locations, and we expect authors to adhere to the NeurIPS Code of Ethics and the guidelines for their institution.

For initial submissions, do not include any information that would break anonymity (if applicable), such as the institution conducting the review.

16. Declaration of LLM usage

Question: Does the paper describe the usage of LLMs if it is an important, original, or non-standard component of the core methods in this research? Note that if the LLM is used only for writing, editing, or formatting purposes and does not impact the core methodology, scientific rigorousness, or originality of the research, declaration is not required.

Answer: [NA]

Justification: The core method development in this research does not involve LLMs as any important, original, or non-standard components.

Guidelines:

The answer NA means that the core method development in this research does not involve LLMs as any important, original, or non-standard components.

Please refer to our LLM policy ( https://neurips.cc/Conferences/2025/LLM ) for what should or should not be described.

In the Appendix, we provide additional information regarding,

Ablation Study of VideoLLM-EyeWO in Sec. A

ESTP-Bench: Dataset and Evaluation Details in Sec. B

Model Input Example in Sec. C

Training Implementation Detail in Sec. D

ESTP-Bench Evaluation Details in Sec. E

Limitation and Future Work in Sec. F

Data Examples in Sec. G

Qualitative Results in Sec. H

A Ablation Study of VideoLLM-EyeWO

Tab. 5 details the results of our ablation study on the ESTP benchmark:

( +ESTP-IT ) enhanced the LIVE baseline's performance on both Single and Contextual Question tasks, increasing it by +7.1 and +6.8 respectively, thereby demonstrating the effectiveness of ESTP-IT .

( Stage-0 ) addressed the training conflicts stemming from simple binary supervision, enabling performance improvements without requiring any manual threshold tuning, which demonstrates the model's acquisition of a basic ability to trigger responses .

With the increased proactive dynamic compression mechanism, the model's KV cache consumption was significantly reduced, requiring on average only about 0.11% of the baseline .

( +Stage-1 ) significantly boosted Single Question performance to 34.0 and Contextual Question performance jumped to 38.7 by incorporating the mechanism for actively requesting highresolution frames for scrutiny alongside initial compression .

( +Stage-2 ) further improved contextual coherence and refined compression , enabling the model to achieve a gain of +4.9 on Contextual tasks, reaching 43.6. Simultaneously, the more accurate and efficient responses further reduced memory consumption to minimal levels.

B ESTP-Bench: Dataset and Evaluation

In this section, we first introduce the detailed description for each task within ESTP-Bench. Subsequently, we present the annotation tool and pipeline employed for these tasks. Finally, we provide a detailed description of the ESTP-F1 metric.

B.1 Task Explanation

Explicit Proactive Tasks require detecting and responding to queries that directly reference visual cues. This category includes eight distinct task types:

[OR] Object Recognition : Detect and identify specific objects.

SH

RT-0

2001.3

[AP] Attribute Perception : Recognize object attributes.

[TRU] Text-Rich Understanding : Interpret and explain text content.

[OL] Object Localization : Identify spatial relations between objects.

[OSC] Object State Change : Detect object state transitions.

[EOL] Ego Object Localization : Localize objects relative to the ego.

[EOSC] Ego Object State Change : Track object state changes from the ego's view.

[AR] Action Recognition : Detect and classify specific actions.

Implicit Proactive Tasks require performing inference and scene understanding beyond direct observation. Task types include:

[OFR] Object Function Reasoning : Identify functional objects and guide their use.

[IFR] Information Function Reasoning : Extract and provide information from source objects.

[NAR] Next Action Reasoning : Predict and recommend next user actions.

[TU] Task Understanding : Comprehend user goals and offer task guidance.

Contextual Proactive Tasks require maintaining dialogue history and visual coherence over time. Task types include:

[ORC] Object Relative Context : Track an object's attributes, position changes, and function across question sequences.

[TRC] Task Relative Context : Monitor user task progress, action correctness, and provide sequential guidance.

See Sec. G for data examples.

B.2 Annotation Tool and Pipeline

Annotation Tool. As shown in Fig. 8, our annotation tool [62] facilitates the labeling process: annotators can adjust the start and end timestamps of valid answer intervals in the upper region, while authoring questions in the bottom-left area and completing answers in the corresponding region on the right. Notably, a single question can be associated with multiple answers and their corresponding intervals, which are visually linked through a shared color for clarity.

Annotation Construction Pipeline. As a first step, we leverage LLMs [10] to convert raw annotations into initial question-answer pairs. For Action Recognition (AR) tasks, we utilize Ego4D [17]

basic Narration raw annotations, which comprise descriptions of action-related events and their corresponding timestamps. For Next Action Reasoning (NAR), Task Understanding (TU), and Task Relative Context (TRC) questions, we leverage Ego4D GoalStep [44] annotations, which provide a task goal, multiple task steps, and their corresponding temporal intervals. For these aforementioned tasks, we directly prompt LLMs for question generation. For all other task types, question generation is performed through the data engine.

Subsequently, the data undergoes a human annotation phase. For Action Recognition (AR), Next Action Reasoning (NAR), Task Understanding (TU), and Task Relative Context (TRC) tasks, given their origin from existing dataset [44], annotators primarily focus on three aspects: (1) detecting question ambiguity; (2) assessing the question-answer pair's matching quality and correcting erroneous wording; and (3) calibrating the answer interval timestamps. For all other task types, annotators are additionally required to generate questions and complete answers, inspired by the initial QA pairs, before executing the aforementioned refinement steps. Simultaneously, a double-check mechanism is implemented, where a second annotator independently re-executes the aforementioned three steps for verification.

B.3 Evaluation Metric (ESTP-F1)

Let G = { g k = ( o k , [ t start k , t end k ] , t opt k ) } M k =1 denote a set of M ground truth items, where o k is the content, [ t start k , t end k ] is the temporal interval, and t opt k ∈ [ t start k , t end k ] is the optimal response timestamp. Let P = { ˆ p l = (ˆ o l , ˆ t l ) } N l =1 represent the set of N predictions, where each prediction ˆ p l consists of the predicted content ˆ o l and a prediction timestamp ˆ t l . Following [13], we also introduce temporal tolerances: τ ant (set to 1 second) for allowed anticipation before t start k , and τ lat (set to 2 seconds) for allowed latency after t end k .

A prediction ˆ p l is considered a valid match for a ground truth g k if its timestamp ˆ t l falls within the interval [ t start k -τ ant , t end k + τ lat ] . We define P k = { ˆ p l ∈ P | ˆ p l is a valid match for g k } as the set of all such valid matching predictions for g k .

For each prediction ˆ p l that is a valid match for g k , we compute a match quality score S (ˆ p l , g k ) ∈ [0 , 1] to comprehensively evaluate its quality. This score is the average of a answer accuracy score and a timeliness score:

The Scoreanswer (ˆ o l , o k ) ∈ [1 , 5] evaluates the semantic correctness of the predicted content ˆ o l against the ground truth content o k , typically assessed by a advanced Large Language Model [10].

The Scoretime ( ˆ t l , g k ) ∈ [0 , 5] assesses the temporal accuracy of the prediction timestamp ˆ t l relative to the optimal response timestamp t opt k . The definition of t opt k varies by task type:

For tasks emphasizing immediate perception upon appearance (e.g., Object Recognition (OR), Attribute Perception (AP), Text-Rich Understanding (TRU), Object Localization (OL), Object Function Reasoning (OFR), Information Function Reasoning (IFR), and Object Relative Context (ORC)), t opt k is set to t start k .

For all other task types that may require more observation of event progression, t opt k is the midpoint of the interval [ t start k , t end k ] , allowing sufficient time for information processing before a response.

The timeliness score is then calculated as:

where scale k = max(1 , ( t end k -t start k ) + τ ant + τ lat ) is a normalization factor representing the total effective time window. This factor is used to prevent division by zero for point intervals (where t start k = t end k ).

For each ground truth item g k , an aggregated score S ( g k ) ∈ [0 , 1] is determined by averaging the quality scores of its matching predictions:

̸

This S ( g k ) provides a nuanced measure of performance for each ground truth item.

Finally, the ESTP-F1 score is calculated as:

̸

where I ( · ) is the indicator function. This metric offers a comprehensive evaluation of a model's justin-time proactive capability by jointly considering content accuracy and temporal alignment through the S ( g k ) scores. In contrast to classic detection tasks, which may generate redundant proposals for the same region, streaming proactive tasks necessitate a single, well-considered judgment at each timestamp. Therefore, rather than selecting the best among multiple redundant proposals as a True Positive (TP), our evaluation metric calculates the average of all valid matches. This approach comprehensively assesses each of the model's actions, reflecting the unique temporal nature of streaming environments.

B.4 Data Cleaning and Filtering

To ensure the high quality and suitability of the egocentric video data for the ESTP tasks, a comprehensive data cleaning and filtering pipeline is applied, involving the following key steps:

Initial quality control was performed in accordance with [28] to identify and remove corrupted, incomplete, or visually ambiguous segments, ensuring the foundational integrity of the data.

Segments with a duration shorter than 250 seconds were filtered out to ensure sufficient temporal context for learning proactive behaviors.

Segments where the narration annotation coverage fell below a threshold of 0.8 were discarded to ensure robust linguistic supervision and data richness.

This multi-stage filtering process results in a refined dataset optimized for the challenges of streaming proactive perception, ensuring both visual and linguistic fidelity.

B.5 Data Engine: ESTP-Gen

As shown in Fig. 9, for automatically generates diverse, multi-turn data, we propose ESTP-Gen , a data engine that leverages VLMs [56, 49] and LLMs [10] to generate diverse proactive QA data from large-scale ego video datasets like Ego4D [17]. We apply a multi-stage pipeline including Caption Generation , Key Segment Extraction , and Multi-Level QA Generation . For simplification, in the main paper, we consolidate the Caption Generation and Key Segment Extraction steps into the one-to-one stage. For videos spanning several minutes, directly generating QA for all segments proves challenging for entities that are persistently visible and static, as defining clear response boundaries for such entities is often ambiguous. Instead, we solely generate relevant QA from extracted key segments and complete answers by leveraging all available captions. This approach simultaneously ensures clear response boundaries and answer completeness. The specific details of each stage below:

Multi-Perspective Caption Generation:

Narration: Origin narration [44, 17] provides basic descriptions of human actions and their corresponding timestamps, serving as a foundational reference for our caption generation process.

Event Caption: The original narration is typically structured as a simple subject-verb-object phrase, merely including basic category information for the actor, action, and manipulated object. It often lacks detailed information regarding object attributes, spatial-temporal location, state, or actions beyond the basic level. To generate more detailed captions specifically for events, we first follow the video clipping approach of EgoVLP [28] to obtain event-relevant video clips based

MLLM

One-to-

One

One-to-

Many

Many-to-

Many

VLM sim

VLM

sim

Key

Segment

Human

Narration

Prompt focus

Scene | Event

Specific

Event

Caption

Caption

Sce.

Caption

Caption

Caption

Eve.

Caption

Caption

Caption

Caption

Caption

Event Clip

You spit.

You look around.

You put the package down.

SCENE 1

SCENE 2

SCENE 3

SCENE 4

Low vision similarity , Clip !

You hold a pipette.

Temporal Window

Key segment 1:

[Event]: As you put down the package, it

rests next to the notebook.

……

[Scene]:

……

A notebook is placed on the left

side of the counter.

……

Key segment 1:

[Event]:

……

A bright yellow bucket sits on the

floor near the shelf, adding a pop of color to

the otherwise muted tones of the room.

……

Specific Event

Similarity too Low

Relative Caption

Caption

Scene 3 Caption:

[Scene]:

……

Various small

containers, tubes, packages

and other lab items are

scattered around, indicating

active use. .

……

Explicit:  Spatial  Understanding

Question  : Can you tell me where the garbage are when I  drop them ?

Answer  :

[02:23] The

garbage

is dropped to the

next to the notebook on the

counter.

[03:49] The

garbage

is dropped

on the lab bench in front of you.

Implicit:  Object Function Reasoning

Question  : I want to clean up some things. Is

there anything that can help me ?

Answer :

[04:50] You can clean up some things into the

yellow bucket

near the shelf.

Contextual QA : Object Relative

Question 1 : …..

[02:23] : ….

[03:49] : ….

Question 2 :  I want to clean them up

[04:50 ]:  You can throw them from the counter and the

lab bench into the yellow bucket near the shelf.

Prompt

LLM

LLM RAG

LLM

on the origin narration timestamps. Then, using lexical analysis [19], we extract the actions and interacted objects. Subsequently, we use an MLLM [56] to provide detailed descriptions of the action and the interacted object.

Scene Caption: The Event Captions primarily focus on foreground directly involved in human interaction, potentially overlooking the broader background content within ego videos. Therefore, a detailed description of the video's background information is necessary to complement the Event Captions. First, we perform video scene segmentation. Then, for each video segment, we prompt an MLLM [56] to generate detailed descriptions of the background context. Specifically, we utilize SigLIP [60] to extract features from each video frame and compute the change from the previous frame using cosine similarity. Potential video scene boundaries are identified where the similarity is significantly below the video's average similarity. This segmentation method, based on visual content changes, preserves the comprehensive scene content within ego videos, facilitating effective description by the MLLM.

Key Segment Extraction: Avoiding questions about persistently visible visual and static content is a key challenge for data generation of proactive task. We address this by employing two criteria for key segment extraction aimed at identifying moments suitable for generating proactive QA: specific egocentric actions and visual similarity change. The key insight is that moments in ego videos characterized by significant visual changes and scene transitions are necessarily accompanied by specific egocentric events of the actor (e.g., 'turn around,' 'walk,' 'step into'). In conjunction with visual similarity analysis, we extract visual segments exhibiting viewpoint changes to serve as the basis for QA generation. As illustrated in the portion of Fig. 9, we extract key segments for QA generation. These segments are identified by significant visual changes (e.g., 'scene 1') and specific event (e.g., 'You look around')

One-to-One QA: Using diverse prompting strategies, we guide LLMs to generate questions, visual cues, and answers from captions. As illustrated in the portion of Fig. 9, the caption of discarding

a package prompted the LLM [10] to generate a question concerning the 'location of dropped garbage', while the yellow bucket prompted the LLM [10] to generate a question regarding the 'function of the item for helping clean'.

One-to-Many Expansion: We refine timestamps by aligning QA pairs with captions that fully contain the visual cues needed to answer, improving temporal coverage and QA consistency. As shown in the portion of Fig. 9, utilizing the LLM's Retrieval-Augmented Generation (RAG) capabilities, segments with similar visual cues are identified from all remaining captions to complete all related answers.

Many-to-Many Contextual QA: Logically grouped questions from the same video are combined via LLMs to form coherent multi-turn contextual QA sets. As illustrated in the portion of the Fig. 9, the questions 'location of dropped garbage' and 'function of the item for helping clean' exhibit logical relevance, and are therefore merged into a coherent multi-turn contextual QA set.

C Model Input Example

In this section, we utilize input examples to more clearly illustrate the concepts presented in Sec. 3 of the main paper. We first present input examples for LIVE [5] (corresponding to Sec. 3.1 of the main paper), followed by input examples for our multi-stage training strategy (corresponding to Sec. 3.2 of the main paper). Finally, we will demonstrate Compression Token Insertion and related training details, further elucidating key aspects introduced in Sec. 3.3 of the main paper.

LIVE Input. An input example for the LIVE [5] model is provided below. Visual tokens [F] denote the tokens per frame. The number of visual tokens per frame, | [F] | , is set to 10 , comprising 1 CLS token and 3 × 3 average-pooled spatial tokens for enhanced visual understanding. Tokens marked in purple indicate an output of a response, while green tokens correspond to an output of a continue. Text highlighted in red is subject to language model loss [59, 11, 48], whereas other textual elements receive no supervision. Some chat template strings have been omitted for better visualization.

Stage-1 Input. An input example for the Stage-1 training is provided below. The core distinction from the LIVE [5] example is the transformation of the abrupt supervision signal into a progressive one. Visually, as the purple color deepens, the weight of the supervision signal increases, indicating that the supervision signal becomes stronger as it approaches the optimal response timestamp.

Stage-2 Input. First, the set of uncertain timestamps T uncertain (shown in Equ. 5) is automatically identified by perceiving timesteps where the model is likely to be confused or uncertain. Specifically, an initial training phase is conducted for one epoch using the L ask_high ( t ) loss, with T uncertain initially set equal to T interval. An illustrative example is provided below. Compared to Stage 1, the a response is now replaced by a ask_high, with the blue tokens indicating receiving this new supervision.

Subsequently, by comparing the model's predicted sequence against the ground truth sequence, we identify timestamps of erroneous a ask_high predictions ( t error). For each such error timestamp t error, we locate the nearest preceding timestamp t visual_change_prev characterized by a significant visual feature change, measured using visual [60] similarity. The set T error is then defined as the union of intervals [ t visual_change_prev , t error ] , where for each response turn, t error corresponds to the erroneous prediction with the highest logit if multiple errors are present within that turn. An example is provided below, where red tokens indicate timesteps where the ground truth was a continue but the model erroneously predicted a ask_high .

T uncertain is then constructed by merging T error with the Ground Truth relevant regions T interval. This allows the model to learn from mistakes and focus training on challenging moments, encouraging it to utilize scrutiny before making correct decisions in these uncertain scenarios. An illustrative example is provided below.

Proactive Dynamic Compression Mechanism Example. An example illustrating the insertion of compression tokens is provided below. Compared to the Stage-2 example, ⟨ ct ⟩ tokens are inserted after segments of input, specifically following multiple low-resolution frames, single high-resolution frames, and text replies.

During training, inspired by [43], the LLM is trained to process response turns sequentially.The initial sequence provided to the LLM is as follows:

Past visual tokens are compressed into a ⟨ ct ⟩ . The System Prompt, containing essential system-level instructions [53], remains uncompressed. The subsequent sequence processed by the LLM is as follows, where gray portions indicate tokens stored in the KV Cache:

Following this, solely the user's query content and the ⟨ ct ⟩ are maintained in the KV cache.

D Training Implementation Detail

In this section, we provide detailed implementation configurations of our training methodology.

ESTP-Bench. Our training methodology employs a three-stage strategy to progressively endow the VideoLLM-EyeWO model with advanced proactive capabilities. Tab. 6 summarizes key details of each stage's specific configuration and learning objectives, while Tab. 5 presents the corresponding ablation results.

COIN-Benchmark. Tab. 7 presents the training plan on the COIN [46] dataset. For a fair comparison with VideoLLM-Online [5], we utilized the same base model (LlaMa3 [16] and SigLIP [60]) and adopted identical training hyperparameters.

E ESTP-Bench Evaluation Details

In this section, we first present the parameter scales and hyperparameters for various models. Subsequently, we illustrate the corresponding prompts used in the evaluation.

E.1 Various MLLMs Hyperparameter

For Offline MLLMs , considering Synchronized Efficiency, we employed models around the 7B/8B scale. For the Response-in-Last strategy, a single query was issued after processing the complete video segment. Regarding the Polling strategy, the querying frequency was set to 0 . 175 queries per second. This frequency was chosen to match the model's response efficiency (Action Per Second), as illustrated in Fig. 7. Given that our videos are often significantly longer than the typical input frame capacity of these MLLMs, we followed the open-source code from [27], utilizing the corresponding sampling frequencies or input frame counts for different models.

For VLMs , we adopted model parameters and input sampling frequencies consistent with [13]. Given their inherent lack of text generation capabilities and consequently, their relatively low computational cost, the query frequency was set to match the input sampling frequency.

For Online MLLMs , we utilized model parameters, input frequencies, and query frequencies as reported in [5, 51].

E.2 Evalutation Prompt

Below, we present the prompt used for the Offline MLLMs Response-in-Last strategy. Inspired by [37], we guided the model within the system prompt to output all answers corresponding to a given question, along with their respective frame indices. These frame indices are then converted into corresponding timestamps via a defined sampling relationship. Contextual information, encompassing both timestamps and content, is incorporated into the prompt following the approach of [27].

Below, we present the prompt used for the Offline MLLMs Polling Strategy, following the approach of [27]. The sole distinction lies in that proactive tasks in [27] solely require the model to judge if it can answer a question at the current moment, outputting only 'yes' or 'no'. In contrast, our approach additionally requires the model to generate a textual reply. Therefore, at timesteps where the model indicates readiness (by outputting 'yes'), we execute an additional query, prompting the model to generate the answer.

Prompt Used for Response-in-Last You are an advanced video AI assistant. Given a video and a question, carefully analyze each frame of the video, identify all relevant moments that help answer the question, and provide the corresponding frame numbers along with the answer. The format should be: '[frame idx] answer'. For example, [6] The object is a cup. [60] The object is a cup. [100] The object is a yellow cup. Here are the contextual information related to the video. Please answer the questions based on the contextual information: At timestamp {}, the following question occurred: {} At timestamp {}, the following answer occurred: {} Here is the question. Answer it and don't confuse it with the previous conversation Question: {} The answer is:

Prompt Used for Polling Strategy

You are an advanced video question-answering AI assistant. You have been provided with video and a question related to the video. Your task is to carefully analyze the video and provide the answer to the question. You need to carefully confirm whether the video content meet the conditions of the question, and then output the correct content.

Here are the contextual information related to the video. Please answer the questions based on the contextual information:

At timestamp {}, the following question occurred: {}

Here is the question. Answer it and don't confuse it with the previous conversation Question: Is it the right time to answer the question '{}'? Y ou need to answer yes or no. /

Please answer the question: '{}'

The answer is:

F Limitation and Future Work

To build a truly intelligent agent akin to Jarvis, it is essential to move beyond passive perception and incorporate active interaction with the environment. In this work, we focus on a constrained setting where the agent operates solely on pre-recorded videos. The objective is to train a model that can perceive the visual world and respond promptly within a fixed observation window. While this setting enables us to study perceptual grounding and response capabilities in a controlled manner, it inevitably limits the agent's ability to engage in real-time decision making, action planning, or closed-loop interaction.

This limitation highlights an important future direction: extending perceptual agents into embodied settings where they can interact with the environment through physical actions. For instance, integrating vision-language models into real-world robots-such as home assistants, warehouse manipulators, or autonomous drones-would require the ability to reason about action consequences, update beliefs based on new observations, and adapt behavior on the fly. Existing platforms like Habitat [32, 40, 45] for embodied AI or Meta's HomeAssist offer promising testbeds for such developments.

Therefore, future work should explore how to bridge the gap between static visual perception and dynamic agent-environment interaction. This includes developing methods for real-time perceptionaction loops, task-aware decision making, and cross-modal alignment under continual feedback. Strengthening interaction with the environment is a crucial step toward building general-purpose, autonomous agents that go beyond observation to perform useful actions in the real world.

G Data Examples

Task Type: Object Recognition

Question:

What is the object on the desk near the tablet?

The object on the desk near the tablet is a white cup.

A white cup.

Task Type: Attribution Perception

Question: What color is the frisbee held by the person in the frame?

blue

blue

Silence

Silence

Silence

Silence

Silence

Silence

Silence

Silence

1350.50s

1358.50s

1371.50s

1390.00s

1397.00s

1409.50s

1422.50s

Silence

Silence

Silence

Silence

Silence

Silence

1429.50s

1430.00s

1430.50s

1439.00s

1447.50s

1460.50s

1470.25s

1473.00s

Silence

Silence

Silence

Silence

Silence

Silence

Silence

1486.00s

1498.50s

1510.50s

1515.00s

1519.50s

1524.00s

1536.50s

1545.00s

Silence

Silence

Silence

Silence

Silence

Silence

Silence

0.50s

8.50s

11.00s

16.50s

20.00s

23.50s

37.50s

53.50s

Silence

Silence

Silence

Silence

Silence

Silence

Silence

Silence

70.00s

80.50s

89.50s

96.50s

112.50s

128.50s

144.50s

155.50s

Silence

Silence

Silence

Silence

Silence

Silence

156.00s

159.00s

162.00s

176.50s

214.00s

225.00s

Task Type: Text-Rich Understanding

Question: Can you remind me of the specific brand name written on the signboard above the

store entrance?

Task Type: Object Location

Question:

Where's the blue bike's position in the room?

'tamimi markets express'

'tamimi markets express'

'Welcome to Tamimi Express '

The blue bike is positioned elevated on a

repair stand in the room.

The blue bike is positioned elevated on a

repair stand in the room.

Silence

Silence

Silence

Silence

Silence

Silence

S.AHW

HIHII用

0.00s

5.00s

12.50s

20.50s

28.00s

35.50s

43.50s

51.00s

Silence

Silence

Silence

Silence

Silence

Silence

Silence

Silence

59.00s

65.75s

66.50s

74.00s

82.00s

89.50s

97.50s

105.00s

Silence

Silence

Silence

Silence

Silence

Silence

Silence

112.50s

128.00s

131.50s

133.50s

85.50s

137.50s

138.50s

140.00s

141.50S

143.00s

146.50s

148.50s

150.50s

151.00s

152.00s

Silence

Silence

Silence

Silence

Silence

540.00s

562.50s

563.75s

585.50s

587.50s

89.50s

608.00s

Silence

Silence

Silence

Silence

631.00s

653.50s

663.75s

676.50s

699.50s

729.50s

736.00s

737.00s

Task Type: Object State Change

Question: Can you remind me when the state of the fridge changes?

The fridge has been opened.

The fridge has been closed.

The fridge has been opened.

The fridge has been closed.

Silence

Silence

Silence

Silence

Silence

Silence

Silence

79.00s

90.00s

101.50s

112.50s

124.00s

135.00s

135.50s

136.50s

Silence

Silence

Silence

Silence

Silence

137.50s

145.50s

146.50s

154.00s

155.00s

156.00s

161.50s

164.50s

Silence

Silence

Silence

Silence

172.50s

173.50s

175.00s

175.00s

175.50s

177.00s

Question: How far the copper-colored pot is from me?

The copper-colored pot is on the

sink's edge, within arm's reach.

The copper-colored pot is located to your

right on the countertop, within arm's reach

Silence

Silence

Silence

Silence

Silence

0.50s

10.50s

11.75s

20.50s

23.00s

31.00s

39.50s

50.50s

Silence

Silence

Silence

Silence

Silence

Silence

Silence

60.50s

70.50s

83.50s

85.50s

95.50s

105.50s

120.50s

128.00s

Silence

Silence

Silence

Silence

148.00s

168.50s

170.50s

185.50s

195.50s

205.50s

220.50s

225.00s

Task Type: Ego-Object State Change

Question: When does the tree change its position relative to me?

It moves closer to you.

It moves closer to you and finally

disappear on your left side.

Silence

Silence

Silence

Silence

Silence

Silence

Silence

Silence

540.50s

548.00s

556.00s

568.00s

569.75s

576.00s

588.00s

596.00s

Silence

Silence

Silence

Silence

Silence

601.00s

603.50s

609.00s

612.00s

615.00s

623.50s

632.50s

Task Type: Action Recognition

Question: I want to know when I pick up items from the shelf in the market.

Could you let me know?

Silence

Silence

Silence

Silence

Silence

Silence

Silence

Silence

0.00s

20.50s

41.00s

61.50s

81.25s

82.50s

103.00s

123.50s

Silence

Silence

Silence

Silence

151.00s

162.50s

164.00s

168.75s

171.50s

174.50s

Silence

Silence

Silence

Silence

Silence

175.50s

181.25s

187.50s

188.00s

189.00s

192.50s

213.00s

218.25s

Silence

Silence

Silence

Silence

Silence

Silence

Silence

Silence

233.50s

248.00s

248.50s

249.50s

275.00s

275.75s

302.50s

Task Type: Object Function

Question: What could help if I want to load a lot of cargo to a far position?

You could use the tractor to help you

with the task.

You could use the tractor

to help you with the task.

Silence

Silence

Silence

Silence

Silence

Silence

Silence

Silence

540.50s

541.50s

543.50s

545.00s

547.00s

548.00s

549.75s

551.50s

Silence

Silence

Silence

Silence

Silence

Silence

Silence

552.50s

554.50s

556.00s

559.00s

561.00s

563.50s

564.00s

Silence

Silence

Silence

Silence

Silence

Silence

Silence

Silence

566.00s

567.00s

569.00s

570.50s

572.50s

573.50s

574.25s

576.50s

Silence

Silence

Silence

Silence

Silence

577.50s

579.50s

581.00s

583.00s

585.00s

585.50s

586.50s

587.50

Question: What time is it now?

According to the time displayed on

the phone, it's 16:56.

Silence

Silence

Silence

Silence

Silence

Silence

Silence

270.00s

277.50s

288.50s

300.00s

301.75s

311.50s

322.50s

333.50s

Silence

Silence

Silence

Silence

Silence

Silence

335.50s

338.00s

341.50s

353.00s

364.00s

375.50s

387.00s

398.00s

Task Type: Task Understanding

Task Type:  Next Action Reasoning

Question: What should I do next to continue dicing ingredients after completing the earlier tasks: washing utensils and slicing meat? Please provide clear instructions

First, wash the cucumber thoroughly under running

water to ensure it clean and ready for preparation.

Next, slice the cucumber into even pieces. This will

make it easier to dice them into smaller, uniform cubes.

Question: Please explain how to achieve my goal of seasoning vegetables, starting from the very beginning and detailing each step.

To begin, pour lime juice over the vegetables. This will

add a tangy flavor and help the seasoning adhere better.

Next, add your chosen seasoning to the vegetables. This could

include salt, pepper, herbs, or any other spices you prefer.

Finally, mix the seasoning thoroughly into the vegetables to

ensure an even distribution of flavors.

Silence

Silence

Silence

Silence

915.50s

915.50s

916.00s

922.50s

928.50s

29.00s

953.50s

978.00s

Silence

Silence

Silence

Silence

Silence

Silence

978.45s

978.50s

79.00s

999.00s

999.50s

1000.50s

Silence

Silence

Silence

1001.00s

1001.50s

1002.00s

1003.50s

1004.00s

1004.39s

1005.50s

Silence

Silence

Silence

Silence

Silence

Silence

Silence

Silence

1005.50s

1006.00s

1007.00s

1007.50s

6

1008.00s

1009.00s

1009.50s

1010.50s

Silence

Silence

Silence

Silence

Silence

Silence

528.50s

532.50s

533.00s

533.50s

534.00s

534.50s

538.50s

542.50s

543.00s

543.50s

544.00s

548.50s

549.00s

554.50s

560.50s

561.00s

Task Type: Object Relative Context

Question:  [270s] Could you remind me when the dog is pooing?

The dog is pooing.

Question:  [332s] I want to clean it up. Give me some advice.

You can use the green plastic bag in

your right hand to collect them.

You can drop it into the dark bin

on the left of you..

You can drop it into the dark

bin on the left of you.

Silence

Silence

Silence

Silence

Silence

Silence

Silence

Silence

270.00s

274.00s

278.50s

283.00s

290.00s

294.50s

299.00S

305.50s

Silence

Silence

Silence

310.00s

312.50S

317.00s

322.00s

327.00s

332.00s

332.50s

334.50s

Silence

Silence

Silence

Silence

Silence

337.50s

340.00S

342.50s

345.00s

348.00s

350.50s

357.00s

361.50s

Silence

Silence

Silence

Silence

Silence

Silence

365.00s

366.00s

372.50s

377.00s

381.50s

382.50s

383.00s

884.00s

Silence

Silence

Silence

Silence

Silence

Silence

Silence

Silence

385.00s

386.00s

390.50s

397.00s

401.50s

406.00s

408.50s

415.00s

Silence

Silence

Silence

Silence

Silence

419.50s

424.00s

431.00s

431.50s

432.50s

433.50s

434.50s

436.00s

Task Type: Object Relative Context

Question:  [98.1s] Can you analyze my current approach?

Currently, you're moving the

mushrooms around in the pan, which

is great for ensuring even cooking.

I noticed you're adjusting the heat this is a good

practice to maintain control over the cooking process.

Question:  [150s] What are the resources I'll need for the next task?

You've just added paprika spice,

which will enhance the flavor profile.

Now that you've added chili flakes,

the dish is gaining a nice kick of heat

For the next task, you'll need a spatula or spoon to continue moving the mushrooms, a heat source to maintain the cooking temperature, and possibly a timer to keep track of the cooking duration.

Question:  [162.4s] What are the resources I'll need for the next task?

Your current goal is to cook the mushrooms thoroughly while ensuring the spices (oregano, paprika, and chili flakes) are evenly distributed.

You're now checking the recipe from the pack this is a

good step to ensure you're on track.

Silence

Silence

Silence

Silence

Silence

81.00s

88.00s

95.00s

102.005

107.00s

107.00s

107.50s

108.00s

Silence

Silence

Silence

Silence

108.00s

108.50s

111.50s

115.00s

118.50s

122.00s

123.00s

123.50s

Silence

Silence

125.50s

130.00s

135.00s

139.50s

144.00s

144.50s

146.00s

148.00s

Silence

Silence

150.00s

152.50s

152.75s

153.50s

155.50s

157.50s

159.50s

161.50s

Silence

Silence

Silence

Silence

Silence

162.00s

162.50s

164.00s

165.00s

166.50s

167.50s

169.00s

170.00s

Silence

Silence

Silence

Silence

171.50s

174.50s

175.50s

177.00s

178.50s

180.00s

H Qualitative Results

Question:  Where is the fire extinguisher located relative to the microwave?

silence

[1187.5s]The fire extinguisher is

mounted on the wall to the left

of the microwave.

VideoLLM-Online /

MiniCPM-V

Ours

Silence

Silence

Silence

Silence

Silence

Silence

Silence

Silence

1182.00s

1182.00s

1182.50s

1183.00s

1183.50s

1184.00s

1184.50s

1185.00s

Silence

Silence

Silence

Silence

Silence

Silence

Silence

1185.50s

1186.00s

1186.50s

1187.00s

1187.50s

1188.50s

1189.50s

1190.50s

Question:  Can you outline the subsequent steps for making a rice sushi roll after completing the previous steps: setting a nori sheet on the rolling mat, spreading rice on the nori sheet, and transferring rice from a rice cooker into the bowl with a spoon? Provide detailed guidance.

[783.0s] First, season the rice in the bowl

with a mixture of rice vinegar, sugar, and

salt to enhance its flavor.

Ours

[815.0s] The next step is to spread the rice

evenly on the nori sheet. This ensures a

consistent layer for rolling.

Ours

[787.0s-814s] Put / Add the

rice on the nori sheet.

MMDuet

[823.0s-830s] Place rice on the

nori sheet.

MMDuet

[839.0s]

After setting a nori sheet on the rolling  mat, spreading rice on the nori sheet, and transferring rice from a rice cooker

into the bowl with a spoon, the subsequent steps for making a rice sushi roll are as follows:\n\n1. **Prepare Ingredients**:

Gather all necessary ingredients such as fish (e.g., salmon), vegetables  (e.g., cucumber, avocado), and any desired fillings  like

mayonnaise or wasabi.\n\n2. **Trim Nori Sheet**: Lightly moisten the edge of the nori sheet to prevent it from sticking

together during assembly. Use a wet brush or your fingers to dampen the edges.\n\n3. **Lay Out Ingredients**: Place the

prepared ingredients on the center of the rice-covered  nori sheet, leaving  about an inch of empty space at one end. The

arrangement should be neat and even.\n\n4. **Roll the Sushi**: Starting from the end closest to you, use the mat to gently

press down on the ingredients while simultaneously rolling  them up towards yourself. Apply firm but gentle pressure to ensure

the ingredients stay in place without crushing them.\n\n5. **Shape the Roll**: Once the sushi is rolled, unroll the mat and

hold the sushi firmly with both hands. Gently press the ends of the roll inward to create a cylindrical  shape.\n\n6. **Slice the

Sushi**: Using a sharp knife, carefully  slice the sushi roll into individual  pieces. For perfect slices, dip the tip of the knife in

water between each cut.\n\n7. **Serve**: Arrange the sliced sushi rolls on a plate or platter,  garnish if desired, and serve

immediately.\n\nBy following  these detailed steps, you can successfully make a delicious rice sushi roll after completing the

previous steps mentioned.

[840.0s]Once the rice is spread, you can

begin shaping the nori sheet into a roll.

Ours

MiniCPM-V

Silence

Silence

Silence

Silence

Silence

Silence

Silence

783.00s

783.00s

790.50s

798.50s

806.00s

813.725

814.50s

Silence

Silence

Silence

Silence

Silence

Silence

Silence

Silence

815.50s

817.00s

818.50s

820.00s

820.27s

821.50s

830.50s

Silence

Silence

Silence

Silence

Silence

835.00s

839.50s

839.86s

840.00s

840.50s

843.50s

847.00s

850.00s

Question:  What can help in case of fire?

[2050.5s] A fire extinguisher is

mounted on the wall near the

kitchen entrance

[2045s] The object is a fire

extinguisher.

[2063.5s] A fire extinguisher is

mounted on the wall near the

kitchen entrance

[1922.5s] cut the fire hose.

Ours

MiniCPM-V

[1903.5s] A fire extinguisher is

mounted on the wall near the

kitchen entrance, slightly to the

right of the central workspace.

Ours

Ours

[1903.5s] The person puts the pot

on the stove and turns on the

stove.

MMDuet

MMDuet

[1975.5s] put the fire

extinguisher in the kitchen.

MMDuet

[2054.5s,2055.0s,2056.0s] put

the fire extinguisher in the

kitchen.

MMDuet

[2057.0s,2057.5s,2058.0s,2059

.0s] put the bag into the

refrigerator.

MMDuet

Silence

Silence

Silence

Silence

Silence

Silence

Silence

1890.00s

1896.75s

1901.00s

1903.50s

1904.00s

1905.00s

1915.50s

1927.005

Silence

Silence

Silence

Silence

Silence

Silence

Silence

1934.50s

1945.50s

1956.50s

1967.50s

1975.00s

1977.50s

1986.50s

1997.50s

Silence

Silence

Silence

Silence

Silence

Silence

Silence

2008.50s

2016.00s

2027.00s

2038.50s

2049.50s

2050.50s

2052.00s

2053.50s

Silence

Silence

Silence

Silence

Silence

2055.00s

2056.50s

2056.75s

2057.50s

2059.00s

2061.00s

2065.00s

Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper proposes a new task where, given ego-streaming video input, an assistant proactively answers questions while maintaining synchronized perception and reasoning. To enable this capability, the paper proposes an MLLM with calibrated benchmarks (ESTP-Bench) and metric (ESTP-F1). The model is trained in three stages and includes a dynamic compression technique to enable multi-turn QA while being efficient. The final model outperforms prior Online MLLMs as well as modified Offline MLLMs.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n\n1. Originality in terms of (1) task, (2) benchmark, and (3) loss design.\n\n(1) The paper proposes a new task for MLLMs to proactively answer diverse given egocentric streaming videos.\n\n(2) A new benchmark (ESTP-Bench) with a new metric that emphasizes (a) Proactive Coherence, (b) Just-in-Time Responsiveness, and (c) Synchronized Efficiency.\n\n(3) A modified loss that addresses the training conflict due to the high similarity of adjacent frames in streaming inputs when using vanilla binary cross-entropy loss. \n\n2. Significance of results.\n\n(1) Clear improvement over prior Online MLLMs as well as modified Offline MLLMs.\n\n(2) Generalizable on Offline Tasks (i.e., COIN benchmark). \n\nWeaknesses\n\n1. Notations are not always clear; sometimes inconsistent.\n\n(1) L168, $\\mathcal{T}\\_\\text{timestamp} $ = $ \\mathcal{T}\\_\\text{GT} $? $ \\mathcal{T}\\_\\text{GT} $ has never been used ever since.\n\n(2)  Given the description, Eq (4) is simply a modulated version of Eq (3) with weighted supervision. $ \\mathcal{T}\\_\\text{interval} $ should be the same as $\\mathcal{T}\\_\\text{timestamp} $?\n\n(3) Eq (5): $\\mathcal{T}\\_\\text{uncertain} $ needs to be defined in the main text for completeness. \n\n(4) Eq (6): It is $ O_t^h$  rather than $H_t$ that has to be highlighted.\n\n(5) Eq (7): $\\mathcal{L}\\_\\text{scrutinize}$ is never defined.\n\n2. The two-level Compression makes sense. However, the implementation is not clear (L219 - L230). According to the paper, you are using a learnable special token \\<ct\\> to compress the input low-res multi-frame (or high-res single-frame). My question is, how do you supervised train this? How does the training enforce the compression? Some technical details and intuitions need to be added here.\n\n3. \"Triangle of Impossibility\" in Figure 2 caption. This term refers to the trilemma where you cannot simultaneously achieve three goals. This contradicts the claim of the paper that the proposed method does well on all axes. I also believe that they are not essentially exclusive, and it's just a matter of dataset and model, which the paper's contribution lies in. \n\n4. To get the number on COIN, what training data did you use? According to L149, the training dataset is Ego4D. If so, it is somewhat surprising to see that the model trained on egocentric videos does well on exocentric videos too.\n\n5. Supplementary L130, since $\\tau\\_\\text{ant} = 1$, $\\tau\\_\\text{lat}=2$, $\\text{scale}_k$ should always be greater than 3 even without $\\max(1, \\cdot)$?\n\nQUESTIONS:\nMy biggest concerns lie in the clarity. Please refer to the weakness section for more details.\n\nOne more question: any open-source plan for code and dataset?\n\nLIMITATIONS:\nno. \n\nI would suggest the authors think of this carefully and add it in the revision. Possible limitations would include: how fast does the model run? Is it ready for real-time purpose? If so, on what kind of hardware device?", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-22"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper defines the task of proactive question answering for AI assistants, using ego-streaming video as the input. The key properties emphasized are proactive coherence, just-in-time responsiveness, and synchronized efficiency. The authors construct a benchmark for this task, complete with an evaluation metric. They also propose a technical pipeline that addresses the challenge and demonstrates state-of-the-art (SOTA) results on both the proposed benchmark and a traditional video QA benchmark.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n1. The paper introduces a novel proactive egocentric streaming video QA task aimed at helping developing AI assistants. The task design addresses key real-world application characteristics such as Proactive Coherence, Just-in-Time Responsiveness, and Synchronized Efficiency, making it highly valuable for research.\n2. The authors propose a comprehensive benchmark and evaluation metric for the task, alongside a tailored solution to address the specific challenges of proactive video question answering. The results achieved are promising, and the work is thorough.\n\nWeaknesses\n1. The description of the proposed three-stage generation pipeline is insufficiently detailed. For instance, the use of RAG techniques for the one-to-many expansion is not clearly explained. Additionally, the integration of multi-turn QA and how to define the response time is unclear. Any manual annotations are needed? If needed, how to combine the automatically generated data and annotations is also not fully clarified. This lack of detail diminishes the perceived innovation in the dataset construction process and its potential for scalability.\n2. The ESTP-F1 metric and the VideoLLM-EyeWO solution presented in the paper appear to have limited novelty. VideoLLM-EyeWO follows the framework of VideoLLM-online, with additional loss functions introduced at different stages to impose constraints on the model. However, these constraints are not exhaustive, which raises concerns about the scalability and generalizability of the approach.\n\nQUESTIONS:\n1. The process for generating QA pairs and the corresponding response time annotations is not clearly explained in the main paper. Could you provide more details on how these annotations are generated?\n2. In the ESTP-F1 metric, how are the components $S_{time}$ and $S_{answer}$ aggregated to obtain $S(g_k)$? A clearer explanation of this fusion process would be helpful.\n3. Is there a more generalizable model solution that could address the various challenges mentioned in the task, beyond the current specific approach?\n\nLIMITATIONS:\nNot discussed in the main paper. Maybe limitations about the scalability and efficiency of training on more large-scale data can be discussed. Additionally, the scalability of the data generation process itself could be a concern.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper presents a compelling advancement in proactive video-language models, with a well-designed benchmark and innovative techniques. However, broader applicability, reproducibility, and statistical rigor could be strengthened. The work sets a strong foundation for future research in real-time, interactive AI systems.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. The paper introduces a well-defined, challenging task (ESTP) that bridges the gap between passive observation and proactive AI assistance in streaming video scenarios.\n2. The proposed benchmark rigorously evaluates the three key properties (proactivity, timeliness, synchronization) with diverse question types and contextual dependencies.\n\n\nWeaknesses:\n1. The \"scrutinize loss\" in Eq. (7) is not clearly referenced.\n\n2. The authors should also report the performance of their proposed model on other online/streaming benchmarks, such as StreamingBench [1] and OVO-Bench [2], to prove its generalization ability.\n\n3. In L79-L81, the authors claimed data engine as a key component in solving the ESTP task, however, I could not find more details about how this engine works except from L82-87.\n\n4. The formatting of the references is completely incorrect; the authors should carefully review them before submission.\n\n[1] Lin et al. StreamingBench: Assessing the gap for MLLMs to achieve streaming video understanding.\n\n[2] Li et al. OVO-bench: How far is your video-LLMs from real-world online video understanding?\n\nQUESTIONS:\n1. In Figure 7, why the model trained only on multi-turn QA (Stage 3) achieves higher APS compared to Stage 2?\n\n2. In L212-218, the description of the compression is confusing. I could not understand (1) what are the two levels; (2) how many tokens are compressed; (3) what low/high compression rates did you choose.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-01"}

comments: SUMMARY:
The paper introduces ESTP-Bench, a benchmark for ego-centric streaming QA that emphasises (i) proactive coherence, (ii) just-in-time responsiveness and (iii) synchronised efficiency. It further proposes VideoLLM-EyeWO, a three-stage LoRA-based training pipeline with a data-generation engine, an action space that can request high-resolution frames, and a proactive compression token. On ESTP-Bench and offline tasks (COIN), EyeWO reportedly outperforms prior VLMs and VideoLLM variants.

STRENGTHS AND WEAKNESSES:
Strengths: (i) first introduce ESTP-Bench (Ego Streaming Proactive Benchmark) alongside the ESTP-F1 metric—a novel framework designed for their rigorous assessment. (ii) propose a comprehensive technical pipeline to enable models to tackle this challenging task.

Weaknesses:
i. Many components appear incremental: (a) the streaming ability is directly from LIVE.  (b) uniform compression resembles earlier token-compression work (Video-XL, VoCo-LLaMA). The data engine largely follows existing LVLM-based QA generation pipelines. Overall methodological novelty is limited.
ii. EyeWO is trained on ESTP-IT derived from Ego4D, whereas key baselines (VideoLLM-online, MMDuet) are evaluated zero-shot using released weights, as the authors explicitly state.  A fair comparison would (a) fine-tune those baselines on ESTP-IT or (b) report EyeWO in zero-shot.
iii. Experiments are only conducted on Ego4D-derived ESTP-Bench and COIN; no results are reported on other relevant ego-centric, streaming benchmarks (e.g., LIVE) or other offline datasets, making generalisation claims unsubstantiated.

QUESTIONS:
Please see weakness

LIMITATIONS:
Please see weakness
TITLE: Official Review
CLARITY: 3
IMPACT: 2
ORIGINALITY: 2
DATE: 2025-07-01

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis work introduces a novel task for an AI assistant that can proactively answer a wide range of evolving questions from an egocentric, streaming video input. The authors identify three key properties for this task: Proactive Coherence, Just-in-Time Responsiveness, and Synchronized Efficiency. To address these properties, the paper makes several contributions. First, it introduces the Ego Streaming Proactive Benchmark (ESTP-Bench) and a new evaluation metric, ESTP-F1, designed to assess these capabilities. Second, it proposes a comprehensive technical pipeline to enable models to perform this task. This pipeline includes a data engine for generating diverse, multi-turn questions and answers, a multi-stage training strategy to progressively build the model's capabilities, and a proactive dynamic compression technique to manage the growing volume of video data efficiently. The proposed model, VideoLLM-EyeWO, is shown to effectively address the challenges of the task and achieves SOTA performance on the COIN benchmark.\n\nSTRENGTHS AND WEAKNESSES:\nStrength:\n1. Novel and Important Problem Formulation: The paper tackles a timely and significant challenge in AI: moving from passive observation to proactive assistance in real-world, dynamic environments. The formulation of the Ego-Streaming Proactive (ESTP) task, with its emphasis on proactive, just-in-time, and synchronized responses, is a valuable contribution to the field.\n2. Comprehensive Benchmark and Evaluation: The development of ESTP-Bench and the ESTP-F1 metric provides a much-needed framework for evaluating models on this complex task. The benchmark's inclusion of diverse question types (explicit, implicit, and contextual) and its focus on response timing and precision are particularly noteworthy.\n3. Strong Empirical Results: The paper presents compelling experimental results, demonstrating the effectiveness of the proposed model, VideoLLM-EyeWO. The model's significant performance improvements over existing baselines on the ESTP-Bench and its state-of-the-art results on the COIN benchmark validate the proposed approach.\n\nWeakness:\n\n1. Limited Discussion of Generalization: While the paper demonstrates the model's performance on the Ego4D dataset, it would be beneficial to see a more in-depth discussion of how the proposed approach might generalize to other datasets and real-world scenarios. More evaluation results on online and offline video understanding and reasoning benchmarks are expected.\n2. The main novelty of the framework seems to add an additional special token requesting a high-resolution frame. The ablation studies lack an analysis of this aspect.\n3. Ablation Studies: While the paper presents a comprehensive evaluation, more detailed ablation studies could provide further insights into the specific contributions of each component of the proposed pipeline. For example, it would be interesting to see the impact of each stage of the training strategy or the effect of different compression levels.\n4. Complexity of the Pipeline: The proposed technical pipeline is quite complex, with multiple components (data engine, multi-stage training, dynamic compression). This complexity could make it challenging for other researchers to reproduce and build upon the work.\n5. The basic setting of the model VideoLLM-EyeWO is unclear. What is the input format for the video frames? Are they processed frame-by-frame or several frames together? How is the information from the historical trajectory stored and managed within the KV cache? What are the specific resolutions for the high and low-resolution frames?\n\nQUESTIONS:\n1. The proactive dynamic compression mechanism is a key component of your proposed pipeline. Could you provide more details on how the model learns to determine when to compress and which compression level to apply? Is this a learned policy, and if so, how is it trained?\n2. The paper mentions that the model can request high-resolution frames when it is uncertain. Could you elaborate on how the model's uncertainty is quantified and used to trigger this request?\n\nLIMITATIONS:\nPlease see weakness.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-02"}