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The Hong Kong University of Science and Technology Hong Kong SAR
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MAFIA reveals that memory-augmented LLMs can be compromised with a staggering 90.7% success rate, even under rigorous auditing conditions.
Intention-aware tool discovery can boost LLM agent performance by nearly 60% while slashing unnecessary complexity in tool management.
By aligning LLM-generated student behaviors with real-world data in hyperbolic space, L-HAKT achieves state-of-the-art knowledge tracing, capturing nuanced hierarchical relationships between concepts.
NGDB-Zoo unlocks up to 6.8x faster training for Neural Graph Databases by decoupling logical operators and integrating semantic priors from pre-trained text encoders, all while maintaining high GPU utilization.
