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Great Bay University, Tsinghua University, Guangdong Provincial Key Laboratory of Mathematical and Neural Dynamical Systems
Tsinghua AI6
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Time-varying transition dynamics in Poisson-Gamma models lead to a marked improvement in predictive accuracy for count time series.
Identity drift in generative agents reveals that anti-self-deception is a dominant modification behavior, challenging assumptions about agent fidelity under pressure.
Simulating 8.3 billion diverse personas reveals nuanced user interactions that traditional evaluations miss, transforming how we assess AI systems.
Trustworthy medical agents could evolve autonomously through interactive learning, rather than relying solely on parameter scaling.
AgentX can autonomously iterate on recommendation algorithms, outpacing human-driven processes and fundamentally changing how we approach system development.
A simple change in the source distribution can boost generative robot policy success rates by over 25%.
