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Ji He

Ji He contributes to research discovery and scholarly infrastructure.

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Published work

2 published item(s)

preprint2026arXiv

PASS-Enabled Covert Communications With Distributed Cooperative Wardens

This paper investigates PASS-enabled downlink covert communication in the presence of distributed surveillance, where multiple wardens perform signal detection and fuse their local binary decisions via majority-voting rule. We consider a dual-waveguide architecture that simultaneously delivers covert information and randomized jamming to hide the transmission footprint, incorporating three representative PASS power-radiation laws-general, proportional, and equal. To characterize the system-level detectability, we derive closed-form expressions for local false-alarm and miss-detection probabilities. By leveraging a probability-generating-function (PGF) and elementary-symmetric-polynomial (ESP) framework, combined with a breakpoint-based partition of the threshold domain, we obtain explicit closed-form characterizations of the system-level detection error probability (DEP) under non-i.i.d. majority-voting fusion. Building on this analytical framework, we formulate a robust optimization problem to maximize the average covert rate subject to covertness constraint. To solve the resulting nonconvex design, we develop an MM-BCD-SCA algorithm that produces tractable alternating updates for power/radiation variables and PA positions via convex surrogates and inner approximations of the DEP value function. Numerical results validate the theoretical analysis and demonstrate the impact of cooperative monitoring and PASS radiation laws on the covertness-rate tradeoff.

preprint2026arXiv

Safactory: A Scalable Agentic Infrastructure for Training Trustworthy Autonomous Intelligence

As large models evolve from conversational assistants into autonomous agents, challenges increasingly arise from long-horizon decision making, tool use, and real environment interaction. Existing agenticinfrastructure remain fragmented across evaluation, data management, and agent evolution, making it difficult to discover risks systematically and improve models in a continuous closed loop. In this report, we present \textbf{Safactory}, a scalable agent factory for trustworthy autonomous intelligence. Safactory integrates three tightly coupled platforms: a \textbf{Parallel Simulation Platform} for trajectory generation, a \textbf{Trustworthy Data Platform} for trajectory storage and experience extraction, and an \textbf{Autonomous Evolution Platform} for asynchronous reinforcement learning and on-policy distillation. As far as we know, Safactory is the first framework to propose a unified evolutionary pipeline for next-generation trustworthy autonomous intelligence.