Meta VideoHarness-SRSI：视频理解模型优化框架发布，提升6.2%准确率
递归改 harness 要先有可迁移证据：Meta VideoHarness-SRSI，held-out +6.2%、间隙 −37.9%
递归 harness 进化常在有限、噪声、重复复用的反馈上「自证」——过拟合验证、稀释搜索预算、早期经验陷阱同源。
Meta VideoHarness-SRSI (arXiv:2610.19424)
“Authors: Zihao Lin , Haoyu Dong , Haobo Xu , Chi-Chih Chang , Xuying Ning , Yinglong Xia , Shuang Yang , Zhuokai Zhao , Xinying Song , Dongf”
Authors:Zihao Lin, Haoyu Dong, Haobo Xu, Chi-Chih Chang, Xuying Ning, Yinglong Xia, Shuang Yang, Zhuokai Zhao, Xinying Song, Dongfang Liu, Lifu Huang, Yang Song, Qifan Wang
Abstract:LLM system capabilities depend not only on model parameters but also on the executable harnesses that organize information acquisition, tool use, reasoning, and memory. Recursive harness evolution can automate their design, but how it can reliably discover generalizable systems from limited, noisy, and repeatedly reused feedback remains unclear. We study this question in agentic video understanding, where rich multimodal evidence and coupled mechanisms make harness optimization challenging. We first introduce VHarness-BRSI, a recursive self-improvement framework that evolves video harnesses around a frozen vision-language model. 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. We trace all three to a common root: the same imperfect validation evidence drives both harness revisions and the proposer's search strategy. We therefore propose VHarness-SRSI, an evidence-governed framework in which every search decision rests on evidence that is transferable, sufficient, and gathered before the search commits. It restricts the proposer to content-free feedback, informs exploration with instrumented warm-up experiments, and develops mechanisms in isolated probe-race branches before factorial composition and joint refinement. Across four video understanding benchmarks, VideoHarness-SRSI improves the equally weighted mean held-out score by 6.2% relative to VideoHarness-BRSI and reduces the mean validation-test gap by 37.9%. Reliable harness evolution thus depends on how search is organized and informed, not only on how well it optimizes validation performance.
| Comments: | 50 pages, 15 figures |
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.19424 [cs.CV] |
| (or arXiv:2610.19424v2 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2610.19424 arXiv-issued DOI via DataCite |
Submission history
From: Zihao Lin [view email]
[v1]
Wed, 14 Oct 2026 02:17:07 UTC (690 KB)
[v2]
Fri, 16 Oct 2026 03:45:20 UTC (687 KB)
来源：Agent Control Flash News · arxiv.org
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