SLDBench v2 is introduced with two extensions, and active scaling law discovery is introduced: an agent chooses which experiments to run, pays each one's recorded cost, and revises its law as outcomes arrive, and is scored on the same hidden extrapolation targets as in the passive setting.
Ling-Bei Meng, Rui Huang, Zhi-Rong Shen et al.· 0 citations
We study environment scaling as a way to increase agent-benchmark difficulty while holding core task objectives fixed. Our framework, Recursive Environment Scaling (RES), transforms existing benchmark environments while preserving their objectives and completion requirements. A proposer changes information access, reso...
Lin Jing, Zheng-Bo Jiao, Yi-Chen Xiang et al.· 0 citations
A statistical observation that the residuals between the features of full computation steps using caching methods and reference full-compute trajectory locally exhibit a zero-mean Gaussian distribution is presented, and a plug-and-play GP-Refiner correction framework is proposed.
Zhi-Rong Shen, Rui-Xin Huang, Chang Zou et al.· 0 citations
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