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Zhengguang Gao

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#artificial intelligence Preprint Oct 2026

The Singularity Has a Distribution: Exact Timing Laws and Detection Limits for a Stochastic Intelligence Explosion

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
Preprint Sep 2026

TrajMark: Ownership Attribution and Segment-Level Tamper Localization for Coding-Agent Trajectories

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
#machine learning Preprint Sep 2026

RiPPLE: Cross-Space Performance Prediction from Early Training for Neural Architecture Search

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
#machine learning Preprint Sep 2026

DRIFT: Removing Diffusion Watermarks by Deflecting the Generative Trajectory

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
Jul 2026

TRACE: A Two-Channel Robust Attribution Watermark via Complementary Embeddings for LLM-Agent Trajectories

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.

Zhengguang Gao, Xiao-Yu Li, Xiao-Yang Feng et al. · 3 citations

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