Automating AI research creates a positive feedback loop between software capability and research effort. We study a stochastic realisation of this loop in which independent, positive, multiplicative improvements arrive at a rate proportional to a power of current capability. Within this model, randomness leaves the det...
Xiao-Yu Li, Zheng-Guang Gao, Chi-Wun Yang et al.· 0 citations
Watermarking the final patch produced by a coding agent provides provenance evidence for the submitted artifact, but does not authenticate the visible process that produced it. Behavioral watermarking methods primarily provide a global detection or identifier-recovery signal, so a locally edited trajectory may retain s...
Bo-Kang Zeng, Zhengguang Gao, Xiao-Yu Li et al.· 0 citations
RiPPLE provides a whole-space ranking from a fractional anchor-training budget, with comparisons interpreted under their respective evaluation and cost protocols, and the results examine ranking quality, label efficiency, architecture selection, and the roles of readout, coverage, and propagation.
Yi-Fan Yang, Zhao-Yan Wang, Zheng-Guang Gao et al.· 0 citations
This work introduces CoRA-NAS (COarse Ranking + Anchor-residual), a two-stage framework combining a static ranking prior with low-cost learning-curve refinement that combines cross-space ranking robustness with low-cost architecture selection.
Yi-Fan Yang, Zhao-Yan Wang, Zhengguang Gao et al.· 1 citation
DRIFT, a black-box attack that combines partial forward diffusion with stochastic reverse resampling, achieves 98-100% attack success and the best image quality among the compared attacks, without secret keys, verifier internals, or per-image gradient optimization.
Rui Bao, Zheng-Guang Gao, Xiao-Yu Li et al.· 0 citations
TRACE is presented, to the authors' knowledge the first agent watermark that is distortion-free in its action choices, self-synchronizing under deletion, and unconditionally invariant under rewriting, and it is proved this behavioral watermark's signal is bought with decision entropy.