Online Video Agent Harness for Long Video Understanding
Sen Yang, Boqiang Duan, Jing Yang, Weihao Bo, Jie Liu, Boyuan Tong, Ze Feng, Wenkang Zhang, et al.
cs.CV · cs.AI
Quick verdict
Claims an online 60+-tool video agent matches frontier LMMs at ~50k context; efficiency results strong, but ablation and baseline comparability need scrutiny.
Summary
The paper introduces VideoXAgent, a purely online agent harness for long video understanding that starts from the raw video file and the user query, plans and decomposes the task, invokes more than 60 expert tools on demand (spanning VLM inspection, detection, OCR, ASR, face analysis, tracking), and aggregates multimodal evidence under a hard step budget. Tool design is guided by a taxonomy of 22 atomic capabilities mined from MINERVA expert reasoning traces via LLM extraction, embedding clustering, and human refinement. Objective-evidence prompting requires VLM tools to ground answers and report uncertainty, while middleware enforces a 125-step limit with escalating warnings and forced answering. On Video-MME-Long (85.1%, ~51k context), LongVideoBench-Long (78.1%), LVBench (76.5%), and MINERVA (65.7%), the system is competitive with frontier LMMs such as Doubao-Seed-2.0-pro and Gemini-3.1-Pro at far smaller context than dense-packing baselines; notably, a text-only orchestrator (GLM-5.2) reaches 65.4% on MINERVA. Budget-control ablations on a 217-question stress set eliminate non-termination (14.7% to 0%) and raise accuracy from 35.0% to 43.8%. Limitations: ablations use a different backbone (Kimi-K2.6) than the headline configuration, on a 150-question sample without confidence intervals; MINERVA serves both as taxonomy source and benchmark; and no code release is stated in the provided text.
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Assessment at a glance
5
risk flags identified
● high 0
● medium 3
● low 2
5 strengths · 5 weaknesses · 5 author questions
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Key claims under review
- A purely online agent starting from the raw video and query can match frontier LMMs on long-video benchmarks at roughly 50k agent context per sample (85.1% Video-MME-Long, 76.5% LVBench).
- A taxonomy of 22 atomic capabilities mined from MINERVA expert reasoning traces provides a principled blueprint for a 60+ expert-tool suite.
- Objective-evidence prompting plus a 125-step budget with progressive warnings eliminates non-termination (14.7%→0% on a 217-question stress set) and curbs hallucinated evidence.
- Long-video understanding can emerge from agentic tool use alone: text-only (GLM-5.2, 65.4%) and visually weak (Claude-Opus-4.6, 65.7%) orchestrators reach frontier level on MINERVA.
Strengths
Data-driven, fully documented tool design
Appendix C specifies the whole mining pipeline — LLM extraction with mandatory quoted reasoning_evidence, merging of 458 long-tail terms at frequency ≥5, silhouette-selected K-means (86 clusters, silhouette 0.138) — consolidated into 22 capabilities mapped to 70 named tools with backends (Tables 6-9), plus the full orchestrator prompt (Appendix D).
Strong efficiency on standard benchmarks
85.1% on Video-MME-Long with 52.0 frames / ~51k tokens vs HAVEN's 82.8% at 1,645 frames; 76.5% on LVBench essentially ties the best LMM (Doubao-Seed-2.0-pro, 76.4%) while Gemini-3.1-Pro needs ~179k context for 86.2% (Table 1).
Orchestrator-agnostic performance
Exemplary budget-control analysis
Candid, self-critical reporting
Weaknesses
Ablations run on a different configuration than the headline system
No uncertainty on main tables and a small ablation sample
Heterogeneous cost accounting across baselines
MINERVA serves as both taxonomy source and evaluation benchmark
Reproducibility depends on proprietary frontier APIs
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Scope & Claims
The paper presents VideoXAgent, a purely online agent harness for long video understanding: at query time the system receives only the raw video file and the question, then plans, decomposes, invokes expert tools, and aggregates evidence (Secs. 3.1-3.3). Three claims are central: (1) online, query-conditioned tool use matches offline-index video agents (DVD, HAVEN) and frontier LMMs at far smaller context; (2) a 22-capability taxonomy mined from MINERVA expert traces gives a principled blueprint for a 60+ tool suite; (3) objective-evidence prompting plus budget middleware (L=125 node executions, warnings at 65/80/90%, forced answering) curb hallucination and non-termination. Evaluation covers full official splits of Video-MME-Long (900 Q), LongVideoBench-Long (564 Q), LVBench (1,549 Q), MINERVA's public 1,357-Q set, and a 472-question stratified subset of Video-MME-v2.
The remaining sections cover Methodology Assessment, Statistical Evidence, Results & Reproducibility, Limitations & Risks, and Verdict & Recommendations.
- All 5 strengths & 5 weaknesses, analyzed in detail
- 5 risk flags with severity, section & reviewer questions
- Methods & statistical evidence assessment
- 5 questions a peer reviewer would ask
- Full six-dimension evaluation report & recommendations
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Frequently asked questions
What is VideoXAgent?
A video-agent system from Baidu that answers questions about long videos by planning, calling more than 60 specialized tools (VLM inspection, object detection, OCR, speech recognition, face analysis), and combining the evidence online — starting from the raw video file at query time, without pre-building an index of the whole video.
How accurate is VideoXAgent on long video benchmarks?
The paper reports 85.1% on Video-MME-Long, 78.1% on LongVideoBench-Long, 76.5% on LVBench, and 65.7% on MINERVA, competitive with frontier multimodal models like Doubao-Seed-2.0-pro and Gemini-3.1-Pro while using roughly 50k tokens of agent context per video.
Can a text-only LLM drive a video agent?
Yes — a key finding of the paper is that a text-only orchestrator (GLM-5.2) reached 65.4% on MINERVA and a visually weak one (Claude-Opus-4.6) 65.7%, close to frontier multimodal models, suggesting the harness and tools rather than the orchestrator's own vision drive performance.
How does VideoXAgent avoid infinite tool-calling loops?
Each run is capped at 125 node executions (~42 tool calls); at 65/80/90% budget use the middleware injects escalating warnings, and when the budget runs out it disables tools and forces a textual answer — on a 217-question stress set this cut non-termination from 14.7% to 0%.
About this review
This is an AI-generated preliminary review by Evolve.Pub, produced from the openly available text of the paper. It is not peer review and does not represent the authors' views. The canonical record of the paper is at the publisher link above.
- The provided text is a sampled excerpt with three marked '[...sampling gap...]' spans (parts of Secs. 3-4, part of Sec. 5 Limitations, and part of Appendix C); judgments touching those specific spans rely only on the visible remainder.
- No code-release statement appears in the visible text; 'codeAvailable: false' reflects the visible text only, and such a statement could exist in an unshown section.
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