mlsanityData to 7 Oct 2026

Accuracy and Cost Claims Do Not Survive Re-Execution in Agentic VideoQA

arXiv 2607.01469 ↗First posted 1 Jul 2026cs.CVcited by 0

On the map: LLM agents and tool use → Multimodal and embodied agents

Abstract (arXiv)

Agentic Video Question Answering (VideoQA) systems produce answers through adaptive reasoning and tool-use trajectories, yet standard practice evaluates each system once and estimates uncertainty only across questions. This leaves a basic question untested: would the measured method effect survive if the evaluation were run again? We show that it need not. Using Static-SAGE and Dynamic-SAGE as a controlled case study, we repeat the paired comparison twice on identical SAGE-Bench question-video pairs, holding configuration, tool library, and scoring protocol fixed. In the first execution, Dynamic-SAGE outperforms Static-SAGE by +7.33 accuracy points; in the second, the effect reverses to -4.05. Both are individually significant under paired analysis, supporting opposite conclusions. The change in the paired effect between executions is highly significant and far larger than within-execution uncertainty. The reversal is consistent across question format, modality, difficulty, and video duration, and both evaluation arms move significantly. Motivated by this failure, we introduce REPAIR (REpeated PAired Inference Reliability), a protocol that repeats the paired comparison and tests whether the method effect changes across executions, separating directional reproducibility from effect-size stability. Applied across accuracy and execution metrics, REPAIR exposes three behaviors-directional reversal, magnitude shift, and effect attenuationand shows that reductions in reasoning turns and visible tool calls do not imply reproducible reductions in primitive computation or latency. The execution-level movement is comparable to, and here larger than, median gain reported by recent agentic VideoQA systems, contextualizing its magnitude without implying those systems are unstable. Significance within a single agentic execution is insufficient evidence that a reported method effect is reproducible.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Multimodal and embodied agents, one of 16 directions in LLM agents and tool use. Builds on 16 of its 20 references that are in the map. First posted on arXiv 1 Jul 2026.

Builds on

16 of 20 references.

Closest ancestors · 6

Same direction, newest first

  1. 2026VideoSEAL: Mitigating Evidence Misalignment in Agentic Long Video Understanding by Decoupling Answer Authority12 May
  2. 2026Symphony: A Cognitively-Inspired Multi-Agent System for Long-Video Understanding18 Mar
  3. 2025LongVideoAgent: Multi-Agent Reasoning with Long Videos23 Dec
  4. 2025LVAgent: Long Video Understanding by Multi-Round Dynamical Collaboration of MLLM Agents13 Mar
  5. 2025Visual Agentic AI for Spatial Reasoning with a Dynamic API10 Feb
  6. 2024VideoAgent: Long-form Video Understanding with Large Language Model as Agent15 Mar

Other references · 10

  1. 2024GPT-4o System Cardcited by 4,467
  2. 2023ViperGPT: Visual Inference via Python Execution for Reasoningcited by 591
  3. 2022Visual Programming: Compositional visual reasoning without trainingcited by 507
  4. 2025Thinking With Videos: Multimodal Tool-Augmented Reinforcement Learning for Long Video Reasoningcited by 88
  5. 2024MoReVQA: Exploring Modular Reasoning Models for Video Question Answeringcited by 74
  6. 2024Towards Reproducible LLM Evaluation: Quantifying Uncertainty in LLM Benchmark Scorescited by 25
  7. 2026On Randomness in Agentic Evalscited by 12
  8. 2025Tool-Augmented Spatiotemporal Reasoning for Streamlining Video Question Answering Taskcited by 10
  9. 2025SAGE: Training Smart Any-Horizon Agents for Long Video Reasoning with Reinforcement Learningcited by 6
  10. 2026SVAgent: Storyline-Guided Long Video Understanding via Cross-Modal Multi-Agent Collaborationcited by 4

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Close in wording, no citation either way

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  2. 2025AVATAAR: Agentic Video Answering via Temporal Adaptive Alignment and Reasoning
  3. 2026TimeLogic Challenge @ CVPR 2026: Strong MLLMs Meet Evidence-Seeking Agents for Temporal-Logic Video Question Answering ↗
  4. 2026Watching, Reasoning, and Searching: A Video Deep Research Benchmark on Open Web for Agentic Video Reasoning
  5. 2025ViQAgent: Zero-Shot Video Question Answering via Agent with Open-Vocabulary Grounding Validation
  6. 2026VTAgent: Agentic Keyframe Anchoring for Evidence-Aware Video TextVQA
  7. 2022Measuring Compositional Consistency for Video Question Answering
  8. 2025MUPA: Towards Multi-Path Agentic Reasoning for Grounded Video Question Answering