VideoToolBench: Benchmarking Video Tools

• VideoToolBench is a unified benchmark framework that standardizes the evaluation of video tool use through structured instructions and tool orchestration.
• It integrates diverse methodologies such as continual training, ReAct-based scheduling, and modular pipelines to enhance tool selection accuracy and retention.
• The approach supports multiple use cases, including video-centric datasets, VIoT applications, and multimodal analysis through standardized JSON interfaces.

VideoToolBench denotes a benchmark-oriented research direction for tool use in video analysis, but the supplied literature does not use the name for a single universally fixed artifact. In one usage, it is a video-centric tool-use instruction tuning dataset collected for continual tool learning in video LLMs, introduced alongside COLT (Liu et al., 23 Sep 2025). In another, the supplied details for VIoTGPT label the VIoT-Tool benchmark as “VideoToolBench,” emphasizing ReAct-based scheduling of multiple vision models in Video Internet of Things settings (Zhong et al., 2023). A third usage applies the label to a modular audio/video analysis toolchain organized around Dockerized specialist services and JSON interfaces (Pham et al., 2024). Taken together, these usages place VideoToolBench at the intersection of benchmark design, tool orchestration, and multimodal systems engineering.

1. Terminological scope and referents

Across the supplied sources, “VideoToolBench” functions as an umbrella label for benchmarked tool use in video systems rather than as a single canonical dataset. The usages differ in task definition, supervision format, and system boundary.

Usage in supplied sources	Core artifact	Source
VideoToolBench	Video-centric tool-use instruction tuning dataset for COLT	(Liu et al., 23 Sep 2025)
“VideoToolBench” in VIoTGPT details	VIoT-Tool benchmark for scheduling 11 vision tools	(Zhong et al., 2023)
VideoToolBench toolchain	Modular audio/video analysis pipeline with six specialist modules	(Pham et al., 2024)

This naming variability suggests that the term is best understood functionally: it refers to structured environments in which a LLM or orchestration layer must select, invoke, and integrate specialist audio-visual tools. A plausible implication is that the common research object is not any one data release, but the evaluation of tool-use competence under video-specific constraints such as temporal grounding, multimodal evidence fusion, and multi-step reasoning.

2. VideoToolBench in COLT: dataset construction and schema

In COLT, VideoToolBench is a video-centric tool-use instruction tuning dataset designed to support continual acquisition of tool-use ability in a successive tool stream without catastrophic forgetting (Liu et al., 23 Sep 2025). The dataset contains 10 distinct “tools”, comprising 8 single-tool skills + 2 compositional tools, with ≈ 5 000 instruction-following examples generated per tool, for a total of roughly 50 000 raw samples. After format-checks and manual semantic filtering, the retained samples are split 9∶1 into train vs. test, yielding ≈ 45 000 train and ≈ 5 000 test.

The tool inventory spans both video understanding and video generation. The supplied details enumerate Action Recognition (AR) using VideoMAE, Dense Video Captioning (DVC) using PDVC, Temporal Action Localization (TAL) using InternVideo, Video Object Segmentation (VOS) using VisTR, Optical Character Recognition (OCR) using EasyOCR, Automatic Speech Recognition (ASR) using Whisper, Video Relation Detection (VRD) using VidVRD, and Text-to-Video Generation (T2V) using Text2Video-Zero. The two compositional tools are AR + ASR and AR + VOS. The associated videos and distributions are inherited from the specialist model corpora, including Kinetics, ActivityNet Captions, ActivityNet, YouTube-VIS, HierText, OpenAI’s Whisper, ImageNet-VidVRD, and InternVid.

The annotation protocol is explicitly staged. First, sample videos are fed to each specialist model to produce “ground-truth” tool outputs such as action labels, bounding-box masks, or transcripts. Second, GPT-3.5-turbo is prompted with in-context examples to generate multi-turn “instruction→tool call” dialogues in a structured format containing thought, action, and value fields, where action is a JSON array of {API_name, API_params} calls. Third, the tool outputs are injected into the second round of the dialogue through a human turn stating “tool output: …”, after which GPT writes the final value. Fourth, automatic format-checks enforce that every sample has exactly two rounds and that each GPT response contains the three fields; manual spot checks verify semantic correctness.

The resulting schema is a two-round dialogue with an average length of 4 turns (human→GPT→human→GPT). The average instruction length is ~15–25 words, and the average number of tools invoked per sample is 1 for single-tool instances and 2 for compositional ones. No explicit difficulty levels are provided; the focus is on breadth of tool coverage rather than calibrated task hardness.

3. Learning objectives, metrics, and benchmark results in COLT

VideoToolBench in COLT is evaluated at both the level of immediate tool invocation and the level of continual retention (Liu et al., 23 Sep 2025). The primary task metric is tool-call accuracy, defined as the percentage of samples for which the model invokes exactly the correct tool(s). A second metric is a GPT-evaluated score, a 0–5 “quality” rating assigned by GPT-3.5 to the final natural-language answer. For streaming-tool experiments, the paper uses Average Accuracy at step $k$,

$\mathrm{AA}_k=\frac1k\sum_{j=1}^k a_{k,j},$

and Average Forgetting at step $k$,

$$f_j^k=\max_{\ell<k}a_{\ell,j}-a_{k,j},\quad \mathrm{AF}_k=\frac1{k-1}\sum_{j=1}^{k-1}f_j^k,$$

where lower $\mathrm{AF}_k$ indicates better retention of past tool skills.

The COLT training procedure is staged. Stage 1 uses standard autoregressive LM cross-entropy over GPT tokens. Stages 2–3 add codebook quantization & commitment losses tied to the selected tool prompt $P_{n^*}$ and the stop-gradient operator $\mathrm{sg}[\cdot]$. Architecturally, COLT incorporates a learnable tool codebook as a tool-specific memory system, and relevant tools are dynamically selected based on the similarity between user instruction and tool features within the codebook. The supplied details characterize this automatic tool selection as robust and efficient.

On the VideoToolBench test split, the reported baselines are VideoLLaVA, VideoChat2, and VideoChat, while the main proposed variants are COLT$_{\text{joint}}$, COLT$_{5\times2}$, and COLT$_{10\times1}$. For AR, the baseline accuracies/scores are 47.7 / 2.89 for VideoLLaVA, 51.4 / 3.13 for VideoChat2, and 29.0 / 2.31 for VideoChat; the COLT variants report 77.9 / 3.84, 74.8 / 3.60, and 73.5 / 3.48, respectively. For VRD, the baseline values are 6.0 / 0.82, 14.0 / 1.36, and 4.5 / 0.90, compared with 24.0 / 1.75, 21.3 / 1.48, and 19.6 / 1.27 for the three COLT variants. For ASR, the corresponding baseline values are 1.8 / 0.77, 3.6 / 0.87, and 2.8 / 1.28, while COLT reports 24.2 / 1.95, 22.6 / 1.50, and 21.9 / 1.48. The key reported outcome is that COLT achieves a 20–25 point absolute gain in tool-invocation accuracy over prior video-LLMs.

The dataset and benchmark also foreground specific research challenges. The supplied details identify the difficulty of maintaining a uniform two-round “thought–action–value” dialogue format across tools, verifying semantic correctness of GPT-generated instructions and responses, and ensuring that the dataset can scale “continuously” to new tools without regenerating old data. On the evaluation side, the central challenge is to measure both tool selection and open-ended generation quality, while also capturing long-term retention.

4. VIoTGPT and the VIoT-Tool benchmark labeled as VideoToolBench

The VIoTGPT paper describes a framework for scheduling domain-specific perceiving models over video data in Video Internet of Things (VIoT), and the supplied details explicitly present its benchmark as “VideoToolBench: A Unified Benchmark for Scheduling Vision Tools in VIoT” (Zhong et al., 2023). In this formulation, the underlying artifact is the VIoT-Tool dataset, consisting of a training set of 200 000 ReAct instruction–response pairs involving 11 vision tools, a testing set of 1 841 held-out samples, and a knowledge base of 125 city-level video “locations,” each containing 10–20 clips drawn from public, web-collected, or self-made surveillance sources.

The 11 representative vision models are grouped into three categories. The human-centric group includes Face Recognition (YuNet + SFace), Person Re-identification (FastReID), Gait Recognition (OpenGait), and Crowd Counting (P2PNet). The vehicle-centric group includes Vehicle Re-identification (FastReID variant) and License Plate Recognition (YoloV4-based detector). The event-related group includes Fire & Smoke Detection (YoloV4), Human Pose Estimation (MMPose), Action Recognition (MMAction2 I3D), Scene Recognition (Places365), and Violence Detection (I3D + context aggregation). The three task categories are correspondingly Human-centric (4 tasks), Vehicle-centric (2 tasks), and Event-related (5 tasks).

The annotation pipeline is semi-automatic and proceeds in three stages. First, domain experts define a minimal set of ReAct instruction templates specifying tool name and input format. Second, ChatGPT-based augmentation paraphrases each template into multiple natural-language instructions. Third, experts review and correct all generated instructions to ensure correct tool invocation, valid knowledge-video references, and consistency of the step-by-step “Thought/Action/Action Input” format. The supplied details state that this process ensures both diversity and correctness.

Evaluation in this benchmark targets the quality of ReAct scheduling itself rather than only the final answer. The scheduling action at step $\mathrm{AA}_k=\frac1k\sum_{j=1}^k a_{k,j},$0 is represented as

$\mathrm{AA}_k=\frac1k\sum_{j=1}^k a_{k,j},$1

where $\mathrm{AA}_k=\frac1k\sum_{j=1}^k a_{k,j},$2, $\mathrm{AA}_k=\frac1k\sum_{j=1}^k a_{k,j},$3 is the selected tool, and $\mathrm{AA}_k=\frac1k\sum_{j=1}^k a_{k,j},$4 is its input. The supervised objective is

$\mathrm{AA}_k=\frac1k\sum_{j=1}^k a_{k,j},$5

The benchmark reports Decision accuracy ($\mathrm{AA}_k=\frac1k\sum_{j=1}^k a_{k,j},$6), Tool-name accuracy ($\mathrm{AA}_k=\frac1k\sum_{j=1}^k a_{k,j},$7), Tool-input accuracy ($\mathrm{AA}_k=\frac1k\sum_{j=1}^k a_{k,j},$8), and Whole-response accuracy ($\mathrm{AA}_k=\frac1k\sum_{j=1}^k a_{k,j},$9). In the reported experiments, Llama 7B and Vicuna 7B with only in-context ReAct prompts yield 0 % whole-response accuracy. With instruction tuning, Llama + IT achieves 87.4 % / 52.5 % / 73.1 % / 47.2 % for Decision/Tool/Input/Whole, while Vicuna + IT reports 93.5 % / 67.8 % / 73.7 % / 39.6 %. The supplied details summarize this as an absolute gain of ~40–50 pp in whole-response accuracy over baselines.

The benchmark is framed as the first unified evaluation protocol for LLM-based scheduling of multiple, domain-specific vision algorithms in a VIoT setting. Its limitations are also explicit: a fixed set of 11 tools, a static video base (125 cities), and the absence of tests for continuous streaming or spatio-temporal indexing. Future extensions listed in the supplied details include adding object detection, segmentation, multi-object tracking, long-term event reasoning, real-time video streams, and dynamic tool invocation budgets.

5. Toolchain-centered interpretation: multimodal audio/video analysis

A third usage of VideoToolBench in the supplied material refers to a modular multimodal toolchain for comprehensive audio/video analysis (Pham et al., 2024). The pipeline begins with an Input Video (e.g. MP4 container) and splits it into an audio track and a video track. The audio side includes Speech-to-Text (S2T) via Whisper, Acoustic Event Detection (AED) via PANN, and Acoustic Scene Classification (ASC) via AIT-ASC; the vision side includes Visual Object Detection (VOD) via DETR, Image Captioning (IC) via VEDA, and Video Captioning (VC) via SWINBERT. Each module outputs a JSON file, and downstream applications include Audio/Video Clustering, Comprehensive Summary, and Riot/Violent Context Detection.

The system architecture is explicitly microservice-based. The video is demultiplexed into raw .wav (16 kHz mono) and image frames in JPEG format. The audio pre-processor resamples and normalizes; Whisper and PANN/ASC operate on log-mel spectrograms (128 bands, 25 ms window, 10 ms hop). Frames are center-cropped/resized to 800×800 for DETR and 224×224 for VEDA/SWINBERT. Each model is packaged as a standalone Docker micro-service exposing the same FastAPI endpoint, “/analyze”, and returning a JSON array of predictions. An orchestration layer built with FastAPI + Streamlit calls the containers in parallel, collects their JSONs, stores them, and triggers clustering, summarization, or riot-detection modules.

Fusion is performed at the decision level, not at the joint feature level. The supplied details state explicitly that there is no joint feature-level fusion in the core toolchain; downstream tasks simply read and merge the JSON outputs. For clustering, the toolchain extracts an audio embedding $k$0 from the penultimate layer of AIT-ASC or PANN, and a visual embedding $k$1 by averaging DETR’s 100 object-feature vectors, concatenates them into $k$2, and applies t-SNE for 2-D projection. Summary generation is template-based and simply formats the six JSONs into human-readable text.

The riot/violent context detection use case is built on keyword matching over the generated summary. The supplied details define three alert levels: Blue (normal), Yellow (medium), and Red (high). In the simulation, the thresholds are $k$3 per 10 s segment and $k$4 per 10 s segment; if ALERT remains “RED” for >5 s, the system raises a real-time notification. In the paper’s demo video, the toolchain correctly flagged the riot segment (60–100 s) with precision=1.0, recall=1.0, F1=1.0 (synthetic ground truth). The supplied details add that, for large-scale deployment, one would compute true/false positives over a labeled test set of riot vs non-riot clips.

This toolchain usage of the term emphasizes an engineering interpretation of VideoToolBench: a standardized, JSON-centric interface for integrating heterogeneous specialist models. Its extensibility procedure is concrete: package a new model as a Docker image with an /analyze endpoint, declare it in the orchestrator’s YAML, and update the Streamlit frontend to display the new JSON or derived charts.

6. Relation to adjacent evaluation toolkits and open technical issues

The broader research setting contains adjacent efforts aimed at standardizing evaluation and integration for AI-generated or tool-mediated video systems. The abstract of “AIGVE-Tool: AI-Generated Video Evaluation Toolkit with Multifaceted Benchmark” reports that existing metrics lack a unified framework for systematically categorizing methodologies, and that fragmented implementations and the absence of standardized interfaces lead to redundant processing overhead (Xiang et al., 18 Mar 2025). It introduces AIGVE-Tool as a unified framework with a structured and extensible evaluation pipeline, organized within a novel five-category taxonomy, and paired with AIGVE-Bench, a large-scale benchmark dataset created with five SOTA video generation models and evaluated across nine critical quality dimensions. Although this is not presented as VideoToolBench, it occupies the same design space of benchmark/toolkit unification.

Across the supplied VideoToolBench-related sources, several technical issues recur. One is interface standardization: COLT formalizes tool use through thought–action–value dialogues, VIoTGPT through ReAct traces, and the multimodal toolchain through a common /analyze JSON interface (Liu et al., 23 Sep 2025, Zhong et al., 2023, Pham et al., 2024). Another is evaluation granularity: COLT distinguishes tool-call accuracy, GPT-evaluated answer quality, and continual-learning retention, whereas VIoTGPT decomposes scheduling into decision, tool-name, tool-input, and whole-response accuracies. A third is scalability to evolving tool sets. COLT explicitly targets environments where tool data is perpetually evolving and streaming in, while VIoTGPT identifies the limitation of a fixed set of 11 tools and a static video base.

A plausible implication is that VideoToolBench, as used in this literature, marks a transition from static video understanding benchmarks toward tool-governed evaluation environments in which correctness depends on selecting the right expert subsystem, formatting the right call, and integrating returned evidence into a final answer. In that sense, the benchmark object is inseparable from the protocol for orchestration. The central open problems, as directly reflected in the supplied sources, are reliable tool differentiation, robust multi-step scheduling, standardized yet extensible interfaces, and evaluation regimes that jointly capture short-horizon task success and long-horizon retention under continual expansion of the tool inventory.