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Ming Jin

3 papers indexed

arxivcs.LG2026-07-01

LeNEPA: No-Augmentation Next-Latent Prediction for Time-Series Representation Learning

Alexander Chemeris, Ming Jin, Randall Balestriero

Time series are central to modern data mining applications, from industrial telemetry and server metrics to finance and physiology, yet time-series self-supervised learning often depends on view and augmentation choices that encode domain-specific invariances. We study how an SSL…

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arxivcs.LGcs.AI2026-07-01

Aionoscope: Debugging Latent-State Accessibility in Time-Series Representations

Alexander Chemeris, Ming Jin, Randall Balestriero

Time-series models are often evaluated by what they can forecast or classify, but those scores do not show whether their representations preserve the process state a user may want to inspect: event timing, phase, amplitude, frequency, or regime variables. We introduce Aionoscope,…

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arxivcs.CRcs.AI2026-06-29

Understanding and Evaluating Claw-like Agent Security Through a Computer-Systems Lens

Peizhi Niu, Wenjie Qu, Shangding Gu, Tianneng Shi, Yuankai Li, Ahmad Tawaha, et al.

Claw-like AI agents (e.g., OpenClaw) are always-on processes with persistent access to credentials, files, tools, and external services. They take on system-level responsibilities -- installing packages, maintaining state, scheduling subtasks, and mediating I/O -- making security…

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