VideoHarness-SRSI: Structured Recursive Self-Improvement for Video Understanding Harness

Key points

• Recursive harness evolution can automate their design, but how it can reliably discover generalizable systems from limited, noisy, and repeatedly reused feedback remains unclear.1
• We study this question in agentic video understanding, where rich multimodal evidence and coupled mechanisms make harness optimization challenging.1
• Analyzing over 4,000 evolved candidates, we find it competitive with hand-engineered systems but identify three interconnected failure modes: validation overfitting, search-budget dilution, and the early-experience trap.1
• We therefore propose VideoHarness-SRSI, an evidence-governed framework in which every search decision rests on evidence that is transferable, sufficient, and gathered before the search commits.1

Sources (1)

• [1]VideoHarness-SRSI: Structured Recursive Self-Improvement for Video Understanding Harness
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 14, 02:17 AM

  We first introduce VideoHarness-BRSI, a recursive self-improvement framework that evolves video harnesses around a frozen vision-language model.

  Recursive harness evolution can automate their design, but how it can reliably discover generalizable systems from limited, noisy, and repeatedly reused feedback remains unclear.

Extractive summary: sentences quoted from the sources.