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University of North Carolina at Chapel Hill
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SeClaw reveals that existing benchmarks fall short in capturing the complexities of agent behavior, enabling a more nuanced evaluation of security risks in autonomous systems.
LLMs can now tell you how unsure they are about their long-form answers, thanks to a new interrogation-based uncertainty metric that actually works.
Solve SMoE load balancing at inference time without retraining by replicating heavily used experts and quantizing underutilized ones, achieving up to 1.4x imbalance reduction.
