Multimodal agents often struggle with long-form video inputs due to unclear scene transitions, mixed audio-visual signals, or dense motion patterns. The client required a dataset that could:

• Evaluate scene segmentation, audio captioning, and transcript extraction with high temporal resolution
• Capture foreground motion, camera movement, visual text, and causal relationships
• Handle STEM domains requiring instructional focus such as math lectures, science experiments, and engineering tutorials
• Provide a clear reference for agentic evaluation, especially in videos with overlapping sounds, slides, or background distractions

Dataset design

Each task consisted of:

• Global overview for visual and audio context, capturing setting, tone, and speaker structure
• Context-level segments annotated for overall scene structure and flow
• Fine-grained segments capturing semantically rich content and moment-specific interactions

Each video was annotated using context-level segmentation for scene structure and fine-grained segmentation for semantically rich moments.

Segment-level annotations:

• visual_caption: detailed description using the watch → observe → note → draft process
• visual_text: on-screen text reproduction with exact spelling and timestamps
• audio_caption: description of ambient audio, speaker tone, or key non-speech sound
• transcription: verbatim, timestamped speech with speaker IDs and character descriptions

All STEM-related videos were annotated to prioritize foreground instructional content and minimize focus on trivial background details.

Annotation and QA flow

• Annotators used Turing’s labeling tool integrated with LLM-based preannotation for visual captioning and transcription support
• 75+ annotators were trained through a structured bootcamp covering segmentation, narrative clarity, and STEM-specific guidelines
• Implemented dual-layer manual QA to review every sample:
  - Layer 1: scene accuracy, audio-visual alignment, object detail
  - Layer 2: grammar, label correctness, and metadata compliance
• Programmatic checks were incorporated for segmentation consistency & hygiene, visual text extraction accuracy, and tag correctness. 

The annotation and review guidelines enforced consistent tone, LaTeX formatting for math, timestamp fidelity, and a high level of accuracy across all tasks.

• Delivered more than 1,500 long-video tasks, each with segment-aligned captions, tags, and QA metadata
• More than 500 videos contained audio, with structured transcripts and speaker labeling
• More than 250 STEM videos captured causal reasoning, step-by-step instruction, and multimodal synthesis
• Achieved 100% human-audited segmentation, with dual-pass QA for scene boundaries, caption accuracy, and visual tag coverage
• Achieved consistent tagging coverage across motion, scene, visual text, audio, and camera movement

The resulting dataset gave researchers a fine-grained tool to benchmark video models on:

• Long-form video coherence
• Instructional fidelity in STEM settings
• Audio-visual reasoning and transcript precision
• Agentic evaluation with multi-turn, multimodal supervision

The client appreciated the annotation quality, LLM-assisted tooling, and guideline compliance.