Title: Vgent: Graph-based Retrieval-Reasoning-Augmented Generation For Long Video Understanding
Venue: NeurIPS 2025
Accepted: yes
Keywords: Long Video Understanding; Retrieval-augmented generation; GraphRAG
Abstract: Understanding and reasoning over long videos pose significant challenges for large video language models (LVLMs) due to the difficulty in processing intensive video tokens beyond context window and retaining long-term sequential information. Retrieval-Augmented Generation (RAG) has demonstrated effectiveness in processing long context for Large Language Models (LLMs); however, applying RAG to long video faces challenges such as disrupted temporal dependencies and inclusion of irrelevant information that can hinder accurate reasoning. To address these limitations, we propose Vgent, a novel \textbf{graph-based retrieval-reasoning-augmented generation framework} to enhance LVLMs for long video understanding. Our approach introduces two key innovations: (i) It represents videos by structured graphs with  semantic relationships across video clips preserved to improve retrieval effectiveness. (ii) It introduces an intermediate reasoning step to mitigate the reasoning limitation of LVLMs, which leverages structured verification to reduce retrieval noise and facilitate the explicit aggregation of relevant information across clips, resulting in more accurate and context-aware responses. We comprehensively evaluate our framework with various open-source LVLMs on three long-video understanding benchmarks. Our approach yielded an overall performance improvement of $3.0\%\sim 5.4\%$ over base models on MLVU, and outperformed state-of-the-art video RAG methods by $8.6\%$. Our code is publicly available at https://xiaoqian-shen.github.io/Vgent.

Vgent: Graph-based Retrieval-Reasoning-Augmented Generation For Long Video Understanding

Abstract

Understanding and reasoning over long videos pose significant challenges for large video language models (LVLMs) due to the difficulty in processing intensive video tokens beyond context window and retaining long-term sequential information. Retrieval-Augmented Generation (RAG) has demonstrated effectiveness in processing long context for Large Language Models (LLMs); however, applying RAG to long video faces challenges such as disrupted temporal dependencies and inclusion of irrelevant information that can hinder accurate reasoning. To address these limitations, we propose Vgent, a novel graph-based retrieval-reasoning-augmented generation framework to enhance LVLMs for long video understanding. Our approach introduces two key innovations: (i) It represents videos by structured graphs with semantic relationships across video clips preserved to improve retrieval effectiveness. (ii) It introduces an intermediate reasoning step to mitigate the reasoning limitation of LVLMs, which leverages structured verification to reduce retrieval noise and facilitate the explicit aggregation of relevant information across clips, resulting in more accurate and context-aware responses. We comprehensively evaluate our framework with various open-source LVLMs on three long-video understanding benchmarks. Our approach yielded an overall performance improvement of 3 . 0% ∼ 5 . 4% over base models on MLVU, and outperformed state-of-the-art video RAG methods by 8 . 6% . Our code is publicly available at https://xiaoqian-shen.github.io/Vgent .

1 Introduction

Multi-Modal Large Language Models (MLLMs) [6, 22, 30, 39, 40, 58] have demonstrated remarkable visual understanding and reasoning capabilities, paving the way for advancements in video tasks. Recently, numerous studies have showcased impressive progress in building Large Video Language Models (LVLMs) for video understanding [9, 7, 24, 35, 55]. Moreover, long-video understanding is particularly crucial for applications in web content, life-logging, and streaming media, where intricate narratives and evolving contexts span extended durations.

However, processing and reasoning over long-context videos remain a formidable challenge for existing LVLMs, as representing video frames requires an extensive number of tokens-for example, a 30-minute video can exceed 200K tokens [4, 22], beyond most models' context limits. To handle longer videos, existing methods resort to sparse frame sampling [55, 22] or token compression [43], but these approaches inevitably lead to visual information loss, weakening fine-grained temporal understanding and coherent reasoning.

Recent studies [1, 2, 12, 34, 52, 53] utilize Retrieval-Augmented Generation (RAG) [21] to enhance long-form video understanding by retrieving relevant information. However, they encounter certain limitations. First, some works segment lengthy videos into shorter clips, treating each as an individual document for retrieval [2], which disrupts the continuity of entities and temporal dependencies,

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Large Video Language Model

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Where was the plate before I putting a bread

slice on it?

A. cabinet

B. refrigerator C. sink D. floor

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leading to retrieval inaccuracies. Second, some methods [47, 34] rely on proprietary LLMs like GPT-4 [37] for multi-turn interactions, planning, and reasoning, making them costly and less flexible. Lastly, several approaches [47, 33] extract information from sparse key frames, neglecting temporal coherence in long videos.

Graph representation for enhanced retrieval : To address the limitation of existing RAG methods, we propose a structured graph-based representation, where video clips are modeled as nodes interconnected by recurring subjects or scenes. This graph representation not only enables effectively retrieving nodes associated with specific entities, but also facilitates capturing temporal dependencies spanning over lengthy videos. Another advantage is that the graph construction is performed offline and is query-independent. Once the graph is built, it can be reused for multiple questions on the same video, allowing retrieval to operate directly on the graph without reprocessing the video. However, feeding all retrieved clips into LLMs can cause information overload, where key details are diluted by irrelevant content [14]. In videos, the issue is further amplified, as each frame consumes hundreds of tokens, with irrelevant information overshadowing the critical content.

Structured post-retrieval reasoning : To address the aforementioned issue and fully harness the benefits from our GraphRAG, we introduce the structured reasoning step in the post-retrieval stage. Instead of generating answers directly from the retrieved clips, it decomposes the question and systematically verifies the relevance of each clip. As shown in Figure 1, this process refines the retrieved set by identifying clips that mention critical elements-such as the plate, sink, cabinet, and bread-and then aggregates information across them for temporal reasoning (e.g., the plate moving from the sink to the cabinet). This process mitigates noise, facilitates information aggregation across refined clips, and thus creates a more reliable pathway for producing accurate responses.

Weevaluate our framework upon seven different LVLMs with sizes ranging from 2B to 7B across three long-video benchmarks: MLVU [57], VideoMME [13] and LongVideoBench [50]. Experimental results demonstrate that our framework consistently improves the performance of existing LVLMs by 3.0%-5.4%. We further show that our framework surpasses existing RAG-based video understanding works by 8.6%.

Contribution. Our contribution is summarized as follows:

We developed a novel graph-based RAG framework for long-video understanding, where video clips are represented as nodes within a graph, interconnected through shared entities, thereby preserving semantic relationships and temporal dependencies across clips, facilitating more effective retrieval.

Wepropose structured reasoning to tackle the limited reasoning ability of LVLMs, which can be distracted by hard negative retrieved samples. Our approach introduces an intermediate reasoning step for retrieval verification and aggregates information across verified clips to enhance generation accuracy.

Our graph-based retrieval-reasoning-augmented framework demonstrates 3.0%-5.4% improvements over various LVLMs ranging from 2B to 7B and surpasses existing RAG-based video understanding works by 8.6% in long-video understanding tasks.

2 Related Work

2.1 Large Video Language Models

Multimodal large language models (MLLMs) [6, 58, 31, 30, 45] have demonstrated remarkable progress in vision-language tasks. Recent advancements have further extended their capabilities to video understanding tasks [2, 9, 24, 25, 28, 32]. Large Video Language Models (LVLMs) process videos by extracting and encoding frames, and then rearranging them into final video representations. Some approaches [24, 25, 9] leverage the Q-Former module from BLIP-2 [23] to integrate visual and textual features, while others [2, 28, 32] directly concatenate frame features. However, these models struggle with processing hour-long videos in a single pass as the number of video tokens exceeds their training context size. To address these limitations, most existing works train on sparsely sampled frames no matter how long the video is [24, 2, 9, 55, 22], while others try to handle long videos by token pooling [35, 27, 44], token compression [43], or memory aggregation [19]. However, they struggle to effectively capture and reason about temporal dependencies spanning hour-long videos.

2.2 Agent-based Video Understanding

A dominant trend in long-context video question-answering involves equipping Large Language Models (LLMs) with tools that heavily rely on proprietary models to process queries and handle video clips. MM-VID [29] uses a video-to-script generation with GPT-4V [37] to transcribe multimodal elements into a long textual script. VideoAgent [47] integrates diverse foundation models through a unified memory architecture. DrVideo [34], VideoTree [49], VideoAgent [12] and OmAgent [53] dynamically invoke tools to enhance query processing and accuracy. Such methods consequently suffer from high operational costs and a critical reliance on external, closed-source systems, limiting their adaptability. In contrast, our work targets the development of a self-contained pipeline designed for flexible deployment with open-source LVLMs.

2.3 Video Retrieval-Augmented Generation

Retrieval-Augmented Generation (RAG) enhances large language models (LLMs) by retrieving relevant information to improve long context memory, factual accuracy, and reduce hallucinations. The process involves three stages: (i) indexing, which organizes raw data into a knowledge base; (ii) retrieval, which searches for relevant information based on user queries; and (iii) generation, where the model takes the retrieved context to generate the final response. Recent advancements of RAG in LLM mainly follow two directions, i.e. chunk-based methods [15, 5] and graph based methods [11, 17, 26, 18], both of which have been applied in video understanding tasks. Goldfish [3] chunks long videos into shorter clips, processes each clip independently, and retrieves the most relevant clip in response to user queries. Wang et al. [48] applied graph structures for action recognition in short clips and Hussein et al. [20], Luo et al. [33] employ scene graphs for video understanding. However, constructing graphs for long videos and effectively retrieving information from the noisy and complex graph remains a challenge. Only recently, a concurrent work [42] constructs graphs for long-context video understanding, but they heavily relies on external proprietary LLM for graph construction, while graph-based video RAG with open-sourced LVLM itself remains unexplored, which our work aims to address.

3 Method

We introduce a novel, training-free framework, Vgent, for long-context video understanding. Unlike conventional Retrieval-Augmented Generation (RAG), our pipeline proposes a graph-based retrieval-reasoning-augmented generation paradigm, specifically designed to address complex video scenarios with improved contextual comprehension and structured reasoning. As illustrated in Figure 2, our proposed pipeline contains four stages: (1) Offline video graph construction (Section 3.1): Builds a video graph offline by extracting knowledge from long videos. (2) Graph-based

Offline Graph Construction

Video Clip

Entities

(                                 )

Question:

Please identify the option that corresponds to the order of events as they

occur in the video. [Zumba, clean and jerk, milking cow, playing trombone]

Query Keywords Extraction

Strutured Query Refinement

Answer: milking cow --> zumba --> clean and jerk --> playing trombone

['zumba', 'clean', 'milking

cow', 'playing trombone']

Retrieval-Reasoning Augmented Generation

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Q1: Is 'zumba' shown in the video?

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Q4: Is 'trombone' shown in the video?

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Milking cow is shown first,

followed by zumba, then

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Information Aggregation

retrieval (Section 3.2): Retrieves relevant video clips from graph based on the user query. (3) Structured Reasoning (Section 3.3): Refines the retrieved clips using structured queries and aggregates information across the filtered clips. (4) Multimodal Augmented Generation (Section 3.4): Combines refined clips and intermediate reasoning results to generate the final response.

3.1 Video Graph Construction

To better capture the complex relationships and dependencies in long-context videos, we propose a graph-based representation to store video content and enhance semantic connections. Specifically, given a video V with F frames, we first partition it into a sequence of short video clips { V 1 , V 2 , . . . , V ⌈ F K ⌉ } , where each video clip V i consists of K frames. We then dynamically construct the graph by a series of structured steps, as detailed below.

Visual Entity Extraction. For each video clip, we leverage the LVLM to extract the key semantic entities (i.e., the primary subjects, actions, or scenes) from both the spoken content (subtitles) C i and video clip V i .

where the set of entity is denoted as E i = { e 1 i , e 2 i , . . . } and its corresponding description set is denoted as T i = { t 1 i , t 2 i , . . . } . In this step, the LVLM captures subjects, actions, and scene dynamics, seamlessly linking visual entities with spoken content to extract meaningful knowledge. Please refer to Appendix B.1 for illustrative examples.

Graph Construction. Based on extracted information, we construct a video knowledge graph G = ( V , E ) , where V denotes the nodes set representing video clips, and edges in E represents the connectivity between nodes. Additionally, we define a global set of unique prototype entities U = { u ∈ E i , i = 1 , . . . , ⌈ F K ⌉} that spans across all nodes. As more video clips are processed, we will dynamically add newly extracted unique entity u to the set or link it to an existing entity. We define t u as the description of each entity u ∈ U .

Entity Merging and Node Connection. Since LVLMs process video clips independently, it is essential to identify and unify semantically equivalent entities across clips. Given a newly extracted entity-description pair ( e j i , t j i ) from video clip V i , we determine whether it belongs to an existing entity in the global entity set U . Specifically, we compute the similarity score between the textual descriptions t i j and descriptions of entities in U based on their respective text embeddings. If the similarity score > τ , the entity e i j is considered semantically equivalent to an existing entity and these two are merged into a single entity representation. Otherwise, e i j is treated as a distinct entity and added to U . This process is formulated as follows:

Once entity is merged, we then build edges from the node v i associated with the video clip V i to all the nodes that have the same entity u ∗ , denoted as V ( u ∗ ) .

As new video clips are processed, the graph is dynamically updated such that nodes containing the same entity are connected, which preserves semantic relationships and contextual dependencies. This forms a structured representation that facilitates effective video retrieval in subsequent processing stages.

3.2 Graph-based Retrieval

Keywords Extraction. Direct retrieval based on the original query may not provide sufficient context, especially when reasoning across multiple temporal clips is required. To address this, we extract keywords from the query for effective retrieval. Specifically, we prompt the LVLM to identify key semantic elements, denoted as K , from the query Q . The detailed prompt is provided in Appendix B.2.

Graph-based Clip Retrieval. Next, we leverage these extracted keywords for graph-based retrieval. Specifically, for each keyword k ∈ K and each entity u ∈ U , we compute a similarity score sim ( k, t u ) to determine whether the entity matches the keyword. If sim ( k, t u ) > θ , we include all nodes associated with entity u as the target retrieval node set R :

After obtaining the retrieval node set R , we refine the results by re-ranking the nodes based on the similarity between the query's keywords and the extracted information of each node, including entities, corresponding textual descriptions, and subtitles if available. Finally, we select the TopN nodes with the highest average similarity scores across all associated information of each video clip.

3.3 Structured Reasoning

Feeding all relevant clips directly into LLMs can lead to information overload, diluting the focus on key details with irrelevant content [14]. Our empirical analysis also reveals that in roughly 40% of failure cases, the correct clip is successfully retrieved, yet the model still generates incorrect responses-even though it can answer correctly when provided with that clip alone. We then introduce structured reasoning in the post-retrieval stage that refines the retrieved clips and aggregates useful information towards final generation.

Structured Query Refinement. We introduce the divide-and-conquer strategy to refine the retrieval through structured query verification. Specifically, we prompt the LVLM to generate structured subqueries, denoted as Q , based on the original query Q and extracted keywords K . These subqueries focus on verifying the presence of relevant entities or quantifying their occurrences, whose answers are expected to be binary (yes/no) or numerical value. Please refer to Appendix B.3 for the detailed prompt and Figure 3 for an example of generated subqueries.

After generating the subqueries, we process the TopN retrieved video clips using the LVLM, producing either binary (yes/no) or numerical responses for each subquery. As shown in Figure 2, this structured verification systematically assesses the relevance of each clip to the original query, filtering out irrelevant clips that were wrongly retrieved based on semantic embedding similarity. Denoting 1 to yes and 0 to no in binary questions, this refined clip set R ′ can be formulated as:

where f ( v i , q j ) denotes the response of retrieved clip v i to subquery q j . We keep at most r clips after refinement. This refinement step ensures that only video clips satisfying the structured queries are retained, effectively eliminating hard negatives from the initial retrieval.

Information Aggregation. As shown in Figure 2, we then let LVLM aggregate and summarize all useful information from structured queries and their corresponding results for each video clip, providing an enriched auxiliary context that enhances the final inference.

3.4 Multimodal Augmented Generation.

We incorporate both the intermediate reasoning results and the filtered video clips as multimodal context inputs to the LVLM for the final response. This enriched input allows the model to leverage both structured reasoning and relevant visual information, enabling it to generate a more accurate and contextually grounded final response to the original question.

4 Experiments

4.1 Experimental Setups

Baselines. We apply our framework Vgent on open-sourced LVLMs including InternVL2.5 [8], Qwen2 [46], Qwen2.5-VL [4], LongVU [43] and LLaVA-Video [56] as base video understanding model. We further compare Vgent against state-of-the-art RAG baselines as follows: NaïveRAG [3], Video-RAG [33], and proprietary LLM-based methods including VideoAgent [47], LLoVi [52], DrVideo [34] and VideoTree [49]. More details can be found in Appendix A.3.

Benchmarks. We evaluate the performances of each model across three long-video benchmarks. Video-MME [13] is a widely used benchmark designed to evaluate LVLMs' capability to process detailed, real-world videos. It comprises three subsets categorized by video length, ranging from 11 seconds to 1 hour. MLVU [57] is a long-video understanding benchmark with videos ranging from 3 minutes to 2 hours, with an average length of about 12 minutes. LongVideoBench (LVB) [50] focuses on referred reasoning tasks that require models to analyze long frame sequences. These questions depend on extensive temporal context and cannot be effectively addressed using a single frame or a small set of sparsely sampled frames.

Implementation Details. During the offline video graph construction, we sample videos at 1.0 FPS, segmenting the long video into clips, each containing K = 64 frames. We use the BAAI/bge-large-en-v1.5 [51] embedding for similarity calculation. The entity merging threshold is set to τ = 0 . 7 . In the online retrieval stage, we use BAAI/bge-large-en-v1.5 to retrieve the top N = 20 clips based on extracted keywords (maximum to 20 to discard low-relevance, with a similarity threshold θ = 0 . 5 ). After structured query refinement, we retain a maximum of r = 5 clips. Thresholds are set as the same for all three benchmarks, with hyper-parameter selection details provided in the supplementary. For MLVU [57], we extract spoken content using openai/whisper-large , while for VideoMME [13] and LongVideoBench [50], we use the provided subtitles from benchmark. All experiments are conducted on A100 80G GPUs.

4.2 Main Results

Comparison with LVLMs. In Table 1 and 6, we present the performance of our proposed Vgent framework on the MLVU [57] benchmark, where we consistently observe substantial improvements across all models. Specifically, our framework enhances LongVU [43] by 5.4% and boosts Qwen2.5VL [4] (7B) by 3.3%. Notably, when applied to Qwen2.5VL (3B), Vgent achieves

an accuracy of 70.4%, surpassing its larger 7B counterpart and improving the base model by 4.2%. This result underscores the effectiveness of our approach in bridging the performance gap between small models and their larger counterparts. At the category level (Table 6), our framework notably improves Count and Order tasks, which demand event-level understanding and multi-clips reasoning.

In Table 1, we showcase the results of Vgent on the VideoMME [13] benchmark, where it consistently outperforms base models across all video lengths, achieving an average performance gain of 4.2%. Notably, our framework excels in long-video scenarios, surpassing the best baseline by 5.4%. These findings highlight the strength of our structured graph-based retrieval and reasoning approach, demonstrating its ability to enhance long-video comprehension by effectively capturing cross-segment dependencies and refining information retrieval for improved reasoning and final response generation.

Comparison with SoTA RAG Methods. In Table 2, we provide a comprehensive comparison of Vgent against state-of-the-art RAG methods on MLVU [57] and VideoMME [13] benchmarks.

(1) Our framework consistently outperforms the RAG baseline, Video-RAG [33], across three different LVLM base models. Unlike Video-RAG [33], which relies on CLIP [41]-based keyframe selection and external tools such as object detection and OCR for frame-level information extraction, Vgent eliminates these dependencies by leveraging LVLMs themselves for graph construction, verification, and intermediate reasoning. This structured approach significantly enhances retrieval precision and reasoning accuracy, leading to more reliable final responses.

(2) Our framework also surpasses proprietary RAG-based methods for long-video understanding. Compared to closed-source API-dependent methods which heavily rely on closed-source APIs, our framework is more flexible and effective solution for long-video understanding.

4.3 Ablation Studies

NaïveRAG vs GraphRAG. As shown in Table 3, integrating GraphRAG yields an average improvement of 2.9% over NaïveRAG, with a particularly notable 4.1% gain on MLVU [57]. This is because NaïveRAG's difficulty in handling complex queries that requires temporal reasoning across multiple clips, as it treats each video clip as an independent document. In contrast, our GraphRAG effectively preserves semantic relationships between clips, enabling more accurate retrieval and reasoning. By structuring video content into a graph representation, our approach addresses retrieval inconsistencies inherent in traditional RAG methods.

However, the improvement remains marginal compared to the base models. Upon checking failure cases in MLVU, we observe that in 44% of the failures, the correct clip is actually present within the model's retrieved set, which indicates that while the retrieval was successful, irrelevant retrievals still distract the model, hindering accurate responses. Consequently, a post-retrieval stage is necessary to amplify the potential of our GraphRAG by refining the retrieved nodes and improving reasoning towards more precise answers.

Structured Reasoning (SR). By refining retrieved nodes through intermediate reasoning with structured queries, we achieve an additional 2.6% improvement on MLVU [57] and 1.6% on VideoMME [13], resulting in an overall 3.4% average gain over the base model. This interme-

diate reasoning step decomposes complex queries into targeted sub-questions and generates binary or numerical answers. These structured response are then used to systematically filter out irrelevant clips and aggregate relevant information across clips, guiding the model toward the correct final answer. Our findings also indicate that the final improvement is contingent upon Graph-based RAG. Specifically, if SR is applied to NaïveRAG, the inherent inaccuracy of NaïveRAG's retrievals restricts the potential for significant improvement through refinement alone.

Number of retrieval r We conduct an ablation study to examine the impact of the number of retrieved clips after structured query refinement. Table 4 presents both the overall performance and results across several MLVU [57] subcategories. Among these, Count and Order are two tasks that heavily require reasoning across multiple video clips. Count involves identifying the number of events or actions throughout an entire video, while Order requires the model to arrange multiple events in chronological sequence. r represents the maximum number of video clips retained after refinement. Our findings indicate that increasing the number of retrieved clips consistently improves performance, particularly for tasks demanding multi-clip reasoning, with the highest performance observed at r = 5 .

Further details, including category-level performance on MLVU (A.1), limitations (D), ablation studies on the number of retrievals N (A.5), confidence-based refinement (A.2), retrieval threshold τ (A.6) are provided in the Appendix.

4.4 Inference Time Analysis

We analyze the computational trade-offs and report the processing times in Table 5 for the APIbased method VideoAgent [47], Video-RAG [33] as well as our framework built on Qwen2.5VL [4]. VideoAgent [47] leverages a proprietary LLM (GPT-4 [36]) to iteratively perform self-reflection for frame selection and aggregating key information from the video. Video-RAG [33] relies on querydependent key frame selection and per-frame object detection, introducing online computational overhead. In contrast, our framework can offline constructs a query-independent graph from the video, which takes 20.13 seconds. Once the graph is built, the online retrieval, reasoning and generation process requires only 3.93 seconds per minute-video.

Our offline graph construction further improves efficiency in multi-question scenarios (e.g., three questions per video in VideoMME [13]). Unlike query-dependent methods that reprocess the entire

Question: Did I open the laptop?

A. Maybe   B. No   C. I don't know   D. Yes

Q1: Is there a laptop shown in the video?

Q2: Is someone visible interacting with

the laptop in the video?

Q3: Does the video show the laptop

opened?

Reasoning

A laptop is shown in the video as not

opened, and someone is interacting with

it. Then, the laptop is shown opened.

Graph-based Clip Retrieval

[5,6,7,9,11]

Answer: B. No

Answer: D. Yes

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video for each question, our approach constructs the graph once, allowing the model to retrieve relevant clips based on entity descriptions-without the need to rewatch the entire video. As a result, our method achieves a 1.73× speedup over Video-RAG [33] when performing inference on VideoMME [13].

4.5 Qualitative Examples

We show a qualitative example in Figure 3, 5 and 6. Our graph construction effectively connects relevant video clips through shared entities. In Figure 3 the graph-based retrieval system can identify relevant nodes that contains a laptop, with Clip 6 providing crucial evidence to answer the query. However, the model incorrectly responded 'No" to the question 'Did I open the laptop?", presumably due to hard negatives from multiple clips featuring a opened laptop, hallucinating the model to overlook the closed laptop and the action of opening it.

In contrast, with an intermediate reasoning step, we validate each retrieved node with structured subqueries (e.g., 'Is there a laptop open?" 'Is someone interacting with the laptop?"). This verified information is aggregated to form an enhanced reasoning chain, allowing the model to correctly infer that the laptop was opened, overcoming the distraction from hard negatives.

5 Conclusion

In this work, we introduced a novel graph-based Retrieval-Augmented Generation (RAG) framework designed for long-video understanding. Our approach represents video clips as nodes in a graph and leverages entities to maintain semantic relationships, thereby enhancing retrieval effectiveness. To address retrieval noise, we proposed a structured query refinement strategy that systematically filters out irrelevant clips, ensuring a more precise selection of relevant video content. Additionally, we introduced an intermediate reasoning step that summarizes the response to the structured query, using the filtered retrieved clips as multimodal context to significantly improve the accuracy of the final answer generation. Our framework outperforms state-of-the-art video RAG methods by 8.6%, demonstrating its effectiveness in enhancing long-video understanding tasks. This work paves the way for more accurate and context-aware long-form video retrieval and reasoning systems.

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A Experiments

A.1 Category-level performance on MLVU

Table 6 shows performance on the multiple-choice task of MLVU [57]. Our framework consistently improves all models, enhancing LongVU by 5.4% and Qwen2.5VL (7B) by 3.3%. Notably, Vgent achieves 70.4% accuracy on Qwen2.5VL (3B), surpassing its 7B counterpart and improving the base model by 4.2%. Significant gains are observed in Count and Order tasks, highlighting the effectiveness of our approach in cross-segment reasoning and long-video understanding.

A.2 Confidence-based Refinement

Astraightforward solution is to filter out the hard negative retrievals by their relevance scores. Initially, we experimented with confidence-based refinement, as used in VideoAgent [47], where the model self-reflect the relevance of retrieved nodes. However, this approach proved ineffective in our case, as the confidence score failed to reliably reflect video clip relevance, leading to an average improvement of only 0.2%, as shown in Table 7.

A.3 Baseline Details

NaïveRAG: Following GoldFish [3], we construct a NaïveRAG baseline for video understanding by representing each video clip as plain text and retrieving relevant clips based on similarity to the query.

Video-RAG: [33]: This method selects keyframes by evaluating CLIP similarity between each frame's features and the text embeddings of keywords extracted from the question. Additionally, an object detection model and an Optical Character Recognition (OCR) model are applied to each keyframe to extract detailed information.

Proprietary LLM-based: VideoAgent [47], LLoVi [52], DrVideo [34] and VideoTree [49] utilizes interactive reasoning and planning of proprietary LLM APIs to enhance long-video understanding.

A.4 Retrieval Embedding

We explore different types of retrieval embeddings, i.e., CLIP [41], BERT [10] and BGE [51] on VideoMME [13] benchmark with Qwen2.5-VL [4] backbone, as shown in Figure 4 (left).

A.5 Number of Retrieval N

We conduct ablation on the number of retrieval N before structured reasoning (SR) on VideoMME [13] benchmark with Qwen2.5-VL [4] backbone, as shown in Figure 4 (middle). We set N = 20 by default.

A.6 Retrieval Threshold τ

We investigate retrieval threshold τ on VideoMME [13] benchmark with Qwen2.5-VL [4] backbone, as shown in Figure 4 (right). As the value of τ increases, less video clips are retrieved based on similarity scores, potentially leading to the loss of relevant information. We set τ = 0 . 5 by default.

A.7 Qualitative Results

We show a qualitative example in Figure 3, 5 and 6. Our graph construction effectively connects relevant video clips through shared entities. In Figure 3 the graph-based retrieval system can identify relevant nodes that contains a laptop, with Clip 6 providing crucial evidence to answer the query. However, the model incorrectly responded 'No" to the question 'Did I open the laptop?", presumably due to hard negatives from multiple clips featuring a opened laptop, hallucinating the model to overlook the closed laptop and the action of opening it.

In contrast, with an intermediate reasoning step, we validate each retrieved node with structured subqueries (e.g., 'Is there a laptop open?" 'Is someone interacting with the laptop?"). This verified information is aggregated to form an enhanced reasoning chain, allowing the model to correctly infer that the laptop was opened, overcoming the distraction from hard negatives.

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0.6

0.7

Question: Arrange the following events from the video in the

correct chronological order: (1) The man attempts to walk

across the rope but falls and holds onto the rope; (2) A seal

sits on a rock near an ocean; (3) The man films from a beach

cliff next to a tent; (4) The man walks across the rope all the

way to the attached rock.

Q1: Does the man attempts to walk

across the rope but falls and holds

onto the rope?

Q2: Is there a seal sitting on a rock

near an ocean' shown in video?

Q3: Does the man film from a beach

cliff next to a tent?

Q4: Does the man walk across the rope

all the way to the attached rock?

Reasoning

A seal sitting by the ocean is an

initial scene. There is a man attempting

to walk across the rope, who then falls,

holds onto it and arrives the rock. Then

the man filming from a beach cliff.

Graph-based Clip Retrieval

[0,2,3,4,6]

Answer: 2->1->4->3

0

1

2

3

4

5

6

7

8

9

10

11

rock

seal

...

...

rope

man

Clip 0

Clip 1

Clip 2

Clip 3

Clip 4

Clip 5

Clip 6

Clip 7

...

...

...

Video Graph

Construction

Strutured Queries

GraphRAG w/o reasoning

GraphRAG with reasoning

Answer: 2->4->1->3

Clip

Q1

Q2

Q3

Q4

0

2

3

4

6

tent

x

@

...

Question: In this video, how many instances are

there of the 'cooking sausages' action scene in total?

Q1: Is there cooking sausages in the video?

Reasoning

The video shows instances of cooking

sausages in two segments.

Graph-based Clip Retrieval

[2,4,9,10]

Answer: 2

1

2

3

4

5

6

7

8

9

10

11

man

deer

...

pan

tree

Clip 1

Clip 2

Clip 3

Clip 4

Clip 5

Clip 9

Clip 10

Clip 11

...

...

...

Video Graph

Construction

Strutured Queries

GraphRAG w/o reasoning

GraphRAG with reasoning

Answer: 3

Clip

Q1

2

4

11

12

sausage

x

B Prompts

B.1 Visual Entity Extraction

Figure 7 illustrates the prompts used to describe entities, actions, and scenes given a video segment for the LVLM.

B.2 Keyword Extraction

Figure 8 presents the prompt designed for the LVLM to perform task identification and extract keywords from the original question to facilitate retrieval.

B.3 Subqueries Generation

Figure 9 presents the prompt designed for the LVLM to generate structured subqueries for retrieved nodes refinement.

Prompt: Describe entities, actions, scenes.

Please analyze the given video and extract key information in a structured JSON format in English. Identify and describe:

Entities: List all distinct objects, people, animals, or other significant elements present in the video.

Actions: If the entities are interacting, describe their actions and relationships in a structured manner.

Scenes: Identify and describe the locations, environments, or contexts where the events occur. If the video is filmed from a first-person point of view, please also describe "subject" as "me" and actions or interactions from this person.

Ensure the output strictly follows the JSON format below:

{ 'entities': ['entity name': '', 'description': ''], 'actions': ['entity name': 'action description'], 'scenes': ['location': ''] }

The 'entity name' in actions should belong to 'entity name' in entities.

Each section should be detailed but concise, capturing all relevant interactions and contextual elements from the video. Avoid unnecessary text outside the JSON output.

Prompt: Task Identification and Keyword Extraction.

Given a question of a long video and potential candidates:

Question:

{query}

Candidates:

{candidates}

You need to retrieve the relevant video segments to answer the question. Note that you do not need to see the video. But based on the question please think step by step what are the important things for retrieval.

[keywords] Please identify the information, like entities, scene, action from the question that is important to retrieve the segments for further answer the question. Do not include the candidates in the keywords.

[candidates_necessary] Do you think the information in the candidates is necessary for retrieval? Answer yes or no.

[multiple] Do you think it needs to aggregate the information from multiple segments to answer the question? ONLY answer yes or no.

[time] Please identify if it can tell the question is asking which part of the video. Answer begin, end or none.

[tool] Do you think it needs additional step for answering the question, please select from [object counting, action counting, order, none].

[global] Can this question be answered based on the overall understanding of the whole video? (e.g., 'What is the main topic of the video?' or 'What is the main content of the video?')

Please output the final answer in json format, for example:

{'multiple": 'no", 'keywords": ['man in black"], 'time": "begin", 'tool": none, 'candi-dates_necessary": 'yes", 'global": 'yes"}

Prompt: Subqueries Generation. Given a question of a long video and potential candidates: Question: {query} Candidates: {candidates} Given a multiple-choice question about a video, break it down into several sub-questions that analyze the key elements required to answer it step by step. First, identify the key subject or event in the question (e.g., an object, an animal, an action, or a location). Form yes/no or counting questions to verify the presence of the subject or event in the video (e.g., "Does the video show [subject/event]?"). Ensure the sub-questions cover all necessary aspects to reach the correct answer. ==important== Please give me the answer in JSON format. Do not include references to specific time positions in the video when generating questions (e.g., "at the beginning," "in the middle," or "at the end") Do not go through all the numbers in the candidates for counting quesitons.

C Model Output Examples

C.1 Visual Entity Extraction

D Limitation

In this work, we represent video content using textual descriptions-such as entities and their associated details-as a lightweight and efficient alternative to raw visual features. However, we do not incorporate visual embeddings or frame-level features into our graph. While computing similarity across frames can be computationally intensive, it remains a promising direction for future improvement.

Additionally, our framework is model-agnostic and compatible with any LVLM, meaning its performance is inherently bounded by the capabilities of the base LVLM. As more powerful LVLMs emerge, our pipeline can be readily adapted to take advantage of their enhanced video understanding and reasoning abilities.

Peer reviews:
comments: SUMMARY:
This paper presents Vgent, a comprehensive RAG-based framework for long-form video understanding. The key idea is to build the GraphRAG that models video clips as graph nodes, followed by a structured reasoning module, where subqueries are automatically generated to verify and filter noisy content. Extensive experiments on benchmarks like MLVU, LongVideoBench, and VideoMME show that Vgent consistently improves the performance of open-source LVLMs and outperforms prior RAG baselines.

STRENGTHS AND WEAKNESSES:
Strengths:

The graph-based retrieval module is query-independent and constructed offline, making it efficient and scalable across multi-turn or multi-query QA tasks. Although this feature isn’t particularly novel, it remains essential.

The proposed approach achieves consistent performance improvements over baselines. Ablation studies have also been well done.

The paper is also well structured and clearly presented.

Weaknesses:

The proposed framework improves existing training-free, RAG-based long-form video understanding methods. Although the proposed modules are solid and effective, the overall framework may not have sufficient novelty to be considered a top paper. 

The potential and future work of the proposed method are unclear. I may raise my rating if the authors can well address this part.

QUESTIONS:
Some additional questions:

How does Vgent perform without subtitle input? 

Is there any special reason not including other datasets such as LVBench and MVBench in the testing?

LIMITATIONS:
Yes
TITLE: Official Review
CLARITY: 3
IMPACT: 2
ORIGINALITY: 2
DATE: 2025-06-30

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper proposes Vgent, a novel graph-based RAG framework designed to improve long video understanding in Large Video Language Models. By representing videos as structured graphs and incorporating an intermediate reasoning step with information aggregation, Vgent addresses key challenges in long-context retrieval and reasoning. Experimental results on three main long-video benchmarks demonstrate consistent performance gains over base models and improvement over prior RAG-based methods.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\n⦁\tThe proposed method of representing video segments as graph nodes is inspiring, enabling the construction of structured connection between video clips.\n\n⦁\tThe divide-and-conquer strategy employed in the Retrieval-Reasoning Augmented Generation effectively refines the retrieved video clips, which alleviates the limitations of LVLMs in graph construction and reasoning. This approach is intuitive and makes sense.\n\n⦁\tThe paper conducts extensive experiments on three widely used long-video benchmarks, with very detailed discussion on the results. The proposed method consistently improves performance across major LVLMs and outperforms existing approaches, demonstrating both effectiveness and generalizability.\n\nWeaknesses:\n\n⦁\tThe paper lacks a more in-depth analysis of the Offline Graph, including its construction efficiency, accuracy, and both qualitative and quantitative evaluations.\n\nQUESTIONS:\n1.\tIn both the entity merging and clip retrieval stages, the similarity is computed based solely on the text embeddings of entity names. How does the method address issues of coreference and ambiguity (e.g., when two different people are both referred to as “a man”)? A more detailed discussion on this would be helpful.\n\n2.\tBuilding an accurate graph from video content is inherently challenging. Has there been any deeper analysis of the quality of the constructed graphs, including ablation studies on parameters like τ?\n\n3.\tSince graph construction is likely to be computationally intensive, a time analysis of this component would be valuable for understanding the overall efficiency.\n\n4.\tSome implementation details could be clarified. In Table 1, for the different LVLM+Vgent settings, was the graph constructed using the corresponding LVLM in each case? Also, what were the inference-time model settings used in these experiments? It would be good to report them.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 5, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 4, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes Vgent, a novel graph-based retrieval-reasoning-augmented generation framework that enhances open-source Long Video Language Models (LVLMs) in long video understanding tasks. Vgent constructs a Video Knowledge Graph with semantic relationships across video clips preserved to improve retrieval effectiveness and introduces Structured Query Refinement (SQR) to perform intermediate reasoning and information aggregation. Evaluations on three popular benchmarks (MLVU, VideoMME, LongVideoBench) according to Table 1 demonstrate consistent performance improvements across various LVLM backbones.\n\nSTRENGTHS AND WEAKNESSES:\n### **Strengths:**\n\n1. The proposed training-free graph-based retrieval and reasoning framework eliminates dependency on proprietary models or large external backbones, providing a flexible and self-contained solution.\n2. Vgent achieves consistent improvements on three widely-used long video understanding benchmarks (MLVU, VideoMME, LongVideoBench), and an experimental comparison is made with recent work on similar graph structure methods such as Video-RAG.\n\n### **Weaknesses:**\n\n- **Concerns about fair comparison.** As stated in lines 236–238, the authors used `openai/whisper-large` for spoken content extraction in MLVU and used benchmark-provided subtitles for VideoMME and LongVideoBench. It is unclear whether the same preprocessing pipeline was applied in comparison baselines  in Table 1 and 2(especially Table 2, e.g., “Qwen2.5-VL + Video-RAG” and “LLaVA-Video + Video-RAG”). Moreover, Table 1 reports results for LongVU on MLVU that align with the original paper, while the LLaVA-Video results differ. Many existing long video models (e.g., LongVA, LongVILA, VideoChat-flash, Apollo) do not include audio-derived transcripts during evaluation, as such textual tokens may heavily influence model performance. A comparison under *pure visual settings* with SOTA methods would better validate the visual reasoning capability of Vgent.\n- The proposed **Structured Query Refinement (SQR)** involves decomposing a query into multiple sub-questions and validating against multiple retrieved clips with VLM. While this increases accuracy, it may introduce significant **inference latency**, especially when scaling to longer videos or larger candidate sets.\n- The paper lacks ablation studies on critical hyperparameters such as **Top-N retrieval size**. The choice of N directly impacts whether the reasoning module can access necessary clips for answering decomposed binary questions. More discussions are needed on how N influences model performance and efficiency trade-offs.\n\nQUESTIONS:\n- Were the same subtitle/transcript settings applied to all baseline models in Table 1 and 2? How would Vgent perform under *pure visual settings* (e.g., VideoMME-w/o subs or visual-only MLVU)?\n- Could the proposed cross-clip graph reasoning approach be further validated on benchmarks focusing specifically on **cross-segment temporal reasoning**?\n- What is the upper limit of video frame length that Vgent can handle with its graph-based retrieval structure? Can it scale to hour-long videos? What are the average processing times on VideoMME-long and LVBench?\n- Appendix Figure 5 suggests that Top-N selection has limited impact on VideoMME performance—does the same trend hold on other datasets like MLVU and LongVideoBench?\n\nLIMITATIONS:\nThe authors discuss limitations in detail in Appendix D.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nIt introduces the Vgent, a graph-based Retrieval-Augmented Generation (RAG) framework tailored for long video understanding. The method addresses the inherent challenges of applying RAG in the video domain, such as disrupted temporal dependencies and noise from irrelevant content. This paper proposes a novel pipeline that includes (i) offline video graph construction to preserve semantic and temporal relationships across clips, and (ii) a structured post-retrieval reasoning step that filters retrieved clips and aggregates relevant information for more accurate responses. The Vagent framework demonstrates consistent improvements (3.0%–5.4%) over base open-source LVLMs and outperforms state-of-the-art RAG-based methods by 8.6% across three long-video benchmarks.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\n1. This paper provides a quite solid and interesting method. It combines graph-based representation and middle-stage reasoning into one pipeline, which doesn’t need extra training. The idea is fresh and practical.\n\n2. The method is introduced clearly, and the diagrams are easy to understand. The whole paper has good structure and is not difficult to read.\n\n3. The problem of long video understanding is very meaningful. Especially now, large vision-language models have some trouble when facing long input. The results in experiments show that Vgent improves both retrieval accuracy and reasoning ability a lot.\n\n4. Although this work is based on some previous ideas like RAG and scene graph, the usage of offline entity video graph and structured sub-query checking is new and not so simple. It shows the authors did deep thinking.\n\nWeaknesses:\n\n1. It uses multiple calls for LVM tools, and there is accumulated errors in the graph construction and filtering process.\n\n2. The novelty is not limited compared to the existing scene-graph based VLLM works. the innovation lies it introduces a problem decomposition and verification step it is good but not so novel.\n\n3. Limited ablation on graph construction strategy: The authors assume the effectiveness of entity-level similarity and merging, but do not explore alternative graph construction techniques, like, motion cues, which is very important in some queries where dynamic information in videos are needed.\n\n4. The method relies heavily on accuracy improvements, but lacks deeper analysis on error types, qualitative improvements, or user studies for interpretability gains.\n\nQUESTIONS:\n1. This work selects the clips according to the embedding similarity, which might have problems of false-positive and true-negative. The method introduces structured query refinement to try to filter the false-positive question. So how the framework resists the true-negative problem if some key information is lost in the early stage of the pipeline?\n\n2. Have you considered non-entity-based or multi-modal graph construction (e.g., action sequences, visual similarity)? Would incorporating motion features (e.g., optical flow) improve temporal coherence?\n\n3. In Section 4.3, the Ablation Studies section, you show that adding Structured Reasoning (SR) shows more improvement than the GraphRAG module, showing the importance of the SR approach, but the source of this improvement remains somewhat abstract. Have you conducted a finer-grained analysis on where SR contributes the most? This is what i am very curious.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 4, "DATE": "2025-07-03"}