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KDPH's innovative use of Kent distributions allows it to absorb gradient conflicts, leading to superior performance in cross-modal hashing tasks.
Hallucinations in LVLMs can be cut by over 43% without sacrificing grounded object coverage, thanks to a novel verifier-guided approach.
