A Cost-Aware, Paired Protocol for Auditing Dynamic Tool Synthesis in Agentic Video Question Answering

2026-07-01 · Source: Computer Vision and Pattern Recognition · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, quick

Summary

A new cost-aware, paired protocol has been developed to audit dynamic tool synthesis in agentic Video Question Answering (VideoQA) systems. This protocol addresses the limitation of standard accuracy metrics by jointly reporting net differences in accuracy and cost between two paired systems on the same input. It categorizes outcomes into six groups based on correctness and changes in visible tool calls, using McNemar's test and paired bootstrap confidence intervals for significance. The protocol was instantiated on Dynamic-SAGE, an agentic VideoQA framework that synthesizes, validates, and persistently registers composite tools for reuse. An audit against the SAGE baseline on SAGE-Bench revealed Dynamic-SAGE improved accuracy by 7.5 points (p < 0.001) and reduced reasoning turns and visible tool calls by approximately 28%. However, it shifted inference cost, increasing token usage by 34% and overall cost by 26%. Gains were most significant for visual and open-ended questions.

Key takeaway

For Machine Learning Engineers evaluating agentic VideoQA systems, you should adopt a cost-aware, paired auditing protocol beyond simple accuracy metrics. While Dynamic-SAGE improves accuracy by 7.5 points and reduces reasoning turns, be aware that it increases token usage by 34% and overall cost by 26%. Your evaluation must jointly consider both accuracy and inference cost to understand the true impact of dynamic tool synthesis, especially for visual and open-ended questions.

Key insights

A paired, cost-aware protocol reveals dynamic tool synthesis improves VideoQA accuracy while shifting, not reducing, inference costs.

Principles

• Agentic VideoQA systems can synthesize composite tools for reuse.
• Scalar accuracy metrics are insufficient for evaluating agentic systems.
• Jointly measuring accuracy and cost reveals a multi-axis performance profile.

Method

The protocol pairs two complete agentic systems on identical inputs, sorting outcomes into six groups based on joint correctness and changes in visible tool calls, reporting significance via McNemar's test and paired bootstrap confidence intervals.

In practice

• Implement Dynamic-SAGE for improved VideoQA accuracy.
• Audit agentic systems using a paired, cost-aware protocol.
• Focus on visual and open-ended questions for Dynamic-SAGE gains.

Topics

• Agentic AI
• Video Question Answering
• Tool Synthesis
• Inference Cost Auditing
• Dynamic-SAGE
• Performance Evaluation

Code references

• KurbanIntelligenceLab/Dynamic-SAGE

Best for: Research Scientist, Computer Vision Engineer, AI Scientist, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computer Vision and Pattern Recognition.