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Graduate School of Artificial Intelligence, POSTECH
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Harness-aware post-training can drastically improve LLM agent performance, especially when facing shifting tool environments, revealing a key design dimension often overlooked in AI systems.
Machine learning resolves 20,000 ambiguous X-ray source matches, revealing the limitations of traditional spatial cross-matching methods.
Ortho-ReID achieves a remarkable boost in clothes-changing person re-identification accuracy by leveraging instance-adaptive low-rank subspaces, outperforming existing methods significantly.
Current concept unlearning methods for diffusion models are easily bypassed by paraphrasing prompts, but PURE leverages cross-attention activations to achieve significantly better erasure.
Optimal transport provides a surprisingly tight and efficiently computable bound on transductive generalization in graph node classification, revealing how GNN depth impacts representation geometry.
Skip the retraining: compose pre-trained GFlowNets at inference time to tackle multi-objective generation, even with complex non-linear reward functions.
