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Zhejiang University of Technology, Hong Kong University of Science and Technology
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KQFuzz uncovers bugs in quantum libraries with 18.44% better coverage than existing methods, revealing critical flaws that developers can quickly address.
OmniQEC uncovers quantum error-correcting codes that outperform established benchmarks, paving the way for more efficient fault-tolerant quantum computing.
Contrastive supervision can unlock implicit disentanglement in generative models, leading to superior content-style separation and robustness against distribution shifts.
AutoSpec achieves up to 4.8x higher F1 scores than traditional methods, transforming safety rule evolution into a precise, interpretable process for LLM agents.
LLM agents can actually get *better* at coding when you strip away the unnecessary fluff in their skills, achieving a "less-is-more" effect.
Existing defenses against indirect prompt injection in LLM agents are riddled with flaws, as demonstrated by three new adaptive attacks that easily bypass them.
