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Learning from real-world environments follows a precise log-sigmoid scaling law, with agent performance and learning speed improving dramatically over time.
By surgically removing "hallucination patterns" from a model's hidden state, HulluEdit offers a reference-free, single-pass method to dramatically reduce object hallucinations in LVLMs without sacrificing visual grounding.
