Adaptive Video Understanding Agent: Enhancing efficiency with dynamic frame sampling and feedback-driven reasoning

1 Introduction

• Existing uniform sampling and selective retrieval can reduce frame counts but often lack adaptability, risking redundant processing amid long-video computational costs.Long-context methods may also require specialized architectures and training paradigms.Source: §1 · p. 1
• The LLM-based agent dynamically chooses frames from the video context and query, sampling at inference rather than processing the full frame set.This is intended to make selection both relevant and efficient compared with static sampling.Source: §1 · pp. 1–2
• The approach draws on the idea that people allocate attention and filter details according to the task at hand.The passage contrasts reviewing a whole video for a goal with focusing on its initial segment to identify a bird's color.Source: §1 · p. 2
• Reflective statements provide verbal reinforcement that updates the agent's policy, while long-term memory stores and retrieves relevant prior reasoning trajectories.The paper motivates retrieval of semantically similar past experiences as a way to enhance reasoning behavior.Source: §1 · p. 2
• Across benchmarks, the framework reports higher accuracy while maintaining the lowest number of frames accessed.The introduction presents this as evidence of effectiveness and adaptability across video-understanding tasks.Source: §1 · p. 2

2.1 Long Context Multimodal Agents

• Other multimodal agents combine reasoning with tools, including Monte Carlo Tree Search or ReAct for video input processing.The cited methods are [39] and [4], respectively.Source: §2.1 · p. 2
• Method [31] reduces frame access with predefined frame counts and dynamic sampling, but targets short-form videos and straightforward question answering.This scope differs from long-context video understanding.Source: §2.1 · pp. 2–3
• Long-context agent methods can use runtime memory retrieval, but preprocessing may be resource-intensive and static with respect to the question.Memory retrieval can localize detailed information yet may be redundant depending on query type; predefined sampling for captioning can require extensive frame access.Source: §2.1 · pp. 2–3

2.2 Frame Sampling Methods

• Question- or text-guided samplers include CLIP retrieval and learned significance selection, but may require fixed frame counts or access to nearly all frames.The passage notes that exhaustive access can become inefficient as video length grows.Source: §2.2 · p. 3
• The proposed approach selects frames from the question or prior extracted information and avoids preprocessing such as KNN clustering.Table 1 characterizes the approach as addressing long-context videos with query-adaptive sampling and long-term memory.Source: §2.2 · p. 3, Table 1 · p. 3

3 Adaptive Video Understanding Agent

• AVUA starts with video metadata and a question, then generates a policy analyzing question type, sampling strategy, and key elements for reasoning.The policy guides planning and may be abstracted for long-term memory.Source: §3 · p. 3, Figure 2 · p. 4, Figure 3 · p. 5
• The agent chooses tool actions and frame inputs from its policy and prior action-observation trajectory, while a sampler suggests frames from that trajectory.Each tool returns observations extracted from selected frames; the agent aims to reduce frame access while maintaining performance.Source: §3 · pp. 3–4
• An evaluator judges the final answer as true or false with a confidence score, and a refiner diagnoses the trajectory and proposes a refined plan.Refinement occurs regardless of the evaluation; it can guide the next trial or be stored with a successful trajectory.Source: §3 · p. 4
• Long-term memory stores experiences and short-term memory stores accessed-frame information; long-term experiences are retrieved by semantic similarity of question type.The framework stores the final result in long-term memory.Source: §3 · p. 4, Figure 2 · p. 4, Figure 3 · p. 5

4.1 Tools

• The framework invokes multimodal tools for video, image, and audio, including LaViLa for frame captions, Video-LLaVa for frame analysis, and RT-DETR for object tracking.LaViLa and Video-LLaVa each use four frames for information extraction; RT-DETR identifies objects above 0.6 confidence, while the text-caption tool outputs text only when present in the frame.Source: Table 2 · p. 5, §4.1 · pp. 5–6

4.2 Evaluation Datasets

• The evaluation spans EgoSchema, Ego4D NLQ, MovieChat, and NextQA, covering long-form video question answering as well as shorter clips.EgoSchema uses 500 question-answer pairs; Ego4D NLQ uses its validation set, with videos averaging 8.7 minutes and prediction windows averaging 9.3 seconds. MovieChat uses the Global-mode test set and counts answers correct only when evaluator confidence exceeds 80; its videos average 9.4 minutes. NextQA averages 44 seconds and includes questions with and without textual cues.Source: §4.2 · p. 6, Table 3 · p. 6

4.3 Baselines

• Baselines include multimodal LLMs, masked vision transformers, and agent-based systems that process video with external tools.ShortViViT and LongViViT use fixed inputs of 32 and 256 frames, respectively; agent-based baselines typically sample at a predefined frame rate such as 1 fps.Source: §4.3 · pp. 6–7
• Figure 4 compares frames-accessed ratio with accuracy and describes AVUA as reducing frame access while maintaining high accuracy.The supplied caption does not report numerical values for that comparison.Source: Figure 4 · p. 7

5.1 Performance Analysis

• 66.98% accuracy on Egoschema exceeded LifelongMemory (62.4%) by more than 4%, while accessing approximately 93% fewer frames.The reported frame-access reduction accompanies the accuracy improvement.Source: §5.1 · p. 7
• At IoU=0.3, Ego4D NLQ surpassed baselines by 2%; at IoU=0.5, it was around 10% above the agent approach and 11% above the supervised approach.Frames accessed were 80% fewer on average than for the agent method.Source: §5.1 · p. 7, Table 5 · p. 8
• MovieChat accuracy increased by more than 22% over baseline models while the method accessed only 0.1% of frames.The result is reported in Table 6.Source: §5.1 · p. 7, Table 6 · p. 8
• NextQA overall average accuracy improved by 1.4%, with around 6.9% absolute improvement over the next-best method on temporal reasoning.Table 7 reports 2.4% fewer frames accessed alongside the overall accuracy gain.Source: §5.1 · p. 8, Table 7 · p. 9

5.2 Ablation Analysis

• Default ReAct reasoning without intervention showed low accuracy and reduced frame access; the authors report that internal interventions improve accuracy, even when more frames are accessed.Table 8 describes the default ReAct model as having the lowest accuracy and frame-access ratio.Source: §5.2 · p. 8, Table 8 · p. 9
• Questions with textual cues used an average of 10.56 frames (.008%), versus 12.26 frames (.01%) for questions without cues.The analysis reports that access ratios correlate with textual cues in the query, such as “Start” cues and frame access near the beginning.Source: §5.2 · p. 8
• Ablating any component consistently reduced accuracy, but its effect on frame access varied by benchmark.Removing the evaluator caused the largest accuracy drop on Egoschema, while removing the sampler had the greatest impact on Ego4D; sampler or refiner ablations generally increased access, but decreased it on Ego4D.Source: §5.2 · p. 8

6 Conclusion

• Static or uniform frame sampling can be inefficient and redundant, motivating query-adaptive selection of relevant frames.The agent selects frames in response to specific queries.Source: §6 · p. 9
• The framework uses an LLM-based agent to adaptively select relevant frames for specific queries.Source: §6 · p. 9
• Benchmark results are presented as validating the framework’s effectiveness and adaptability across diverse video-understanding tasks, with greater efficiency than traditional methods.Source: §6 · p. 9

7 Limitations

• Variations in tool performance can directly affect the framework’s overall effectiveness.Source: §7 · p. 9
• Reliance on API calls introduces potential latency issues.Source: §7 · p. 9
• Dependence on external APIs may affect processing consistency and speed.Source: §7 · p. 9

8 Broader impact

• Dynamic LLM-based agents for adaptive frame sampling are proposed as a more efficient and effective approach to long-form video comprehension.Source: §8 · p. 9
• Potential application areas include automated video content analysis and surveillance.Source: §8 · p. 9
• The authors also identify multimedia indexing, where large volumes of video data must be processed, as a potential application.Source: §8 · p. 9

A.Case Study

• The refined second trial produces the correct evaluation after refinement based on the first trial.The example is an EgoSchema refinement case.Source: Figure 6 · p. 13
• The revised policy adds updated question analysis and more granular guidance on when to switch between dense and sparse sampling.Compared with the original, the refined policy is more detailed; Figure 7 highlights added instructions.Source: A.Case Study · p. 13, Figure 7 · p. 14
• Runtime-only video processing reduces latency compared with LifelongMemory [33] and Videoagent [3], which require preprocessing.Figure 8 compares latency across the agent approaches.Source: Figure 8 · p. 14

C. Prompts Configuration

• The planning prompt classifies question types and chooses a sampling strategy, such as starting sparsely and sampling more densely when a relevant frame appears.The prompt gives examples of question types and sampling intervals.Source: C. Prompts Configuration · p. 14
• The agent prompt supplies video metadata and uses repeated tool interactions, while separate prompts evaluate answers and refine reasoning trajectories.The tool cycle uses thought, action, input, and observation; refinement diagnoses possible failures and proposes a revised path.Source: C. Prompts Configuration · p. 15
• Ablating any component lowers accuracy, while the trends in frames accessed vary across benchmarks.The table caption reports these patterns for MovieChat and NextQA.Source: Table 9 · p. 16
• The sampler example illustrates sparse frame calculation, dynamic changes in sampling rate, and dense sampling of relevant frames.Source: Figure 9 · p. 16

NeurIPS Paper Checklist

• The checklist asks that stated claims match theoretical and experimental results and reflect how far results may generalize.It also calls for clearly stating contributions, assumptions, and limitations.Source: NeurIPS Paper Checklist · p. 17

• The authors state that they discuss the work’s limitations in Section 7.Source: §2 · p. 17
• For reproducibility, the authors say Sections 3 and 4 detail methods and settings, the data are public, and they plan to release necessary code with instructions.Source: §2 · pp. 18–19
• The authors say experiments are deterministic (e.g., temperature 0), making statistical significance inapplicable; the paper focuses mainly on empirical experiments.Source: §2 · pp. 18, 20
• The authors say they discuss broader impact in Section 8 and report that the work involves little risk.Source: §2 · p. 21

Safeguards

• The guidance encourages safeguards for models with misuse or dual-use risks and a best-faith effort to address them, including describing how unsafe scraped images were avoided.Suggested safeguards include usage restrictions or safety filters.Source: §2 · p. 22

12. Licenses for existing assets

• The paper says existing assets were credited by paper citation or URL, while the checklist guidance calls for explicit license, copyright, and terms-of-use details.For repackaged datasets, both the original and any changed derived-asset license should be provided.Source: §2 · p. 22

New assets

• The authors state that no new asset was released; the checklist guidance recommends documenting new assets alongside their release.It also recommends structured submission templates covering details such as training, license, and limitations.Source: §2 · p. 22

14. Crowdsourcing and Research with Human Subjects

• The authors report no crowdsourcing; the checklist guidance calls for participant instructions and compensation details when crowdsourcing or human-subjects research is conducted.When human subjects are a main contribution, as much detail as possible should appear in the main paper.Source: §2 · p. 23

15. Institutional Review Board (IRB) Approvals or Equivalent for Research with Human Subjects

• The authors state that IRB approval was not required; the guidance says to describe participant risks and disclose any approval obtained when human-subjects research is involved.Procedures vary by institution and location, and authors are expected to follow applicable institutional guidelines and the NeurIPS Code of Ethics.Source: §2 · p. 23