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LLM-enhanced GNNs may boost performance but also expose sensitive information, revealing a stark vulnerability to privacy attacks that traditional models avoid.
Benign experiences in self-evolving LLMs can be weaponized, revealing a hidden attack surface that undermines safety guarantees.
AREX achieves superior performance in deep research tasks by recursively refining answers through a novel self-improvement mechanism that outpaces traditional search methods.
Audio-Zero reveals that fine-grained auditory reasoning can be achieved without any external labels, transforming how we approach audio model training.
Autonomous agents' broad operational privileges are a ticking time bomb, but ClawKeeper offers a comprehensive, multi-layered framework to defuse them before model errors turn into real-world disasters.
