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Hierarchical procedural understanding unlocks a 9.6%p performance boost in proactive AI assistance, enabling adaptive guidance that outperforms in-context learning by 57.1%p.
Idiolectal paraphrasing transforms how models learn reasoning by allowing them to express complex thoughts in their own unique language, leading to significant performance gains.
Context-Calibrated DPO can cut object hallucination in MLLMs by 36% while maintaining reasoning performance, revealing a critical gap in how existing methods leverage context.
SparSEEty reveals that even LLMs in secure environments can be vulnerable to token extraction attacks through clever exploitation of activation sparsity.
Achieve fully-supervised HOI detection performance with only image-level labels by learning to ground relationships between humans and objects.
