DynLP is a parallel dynamic batch update algorithm for label propagation that achieves significant speedups by updating only relevant parts of the graph on GPUs.
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iCoRe improves bug reproduction test generation by combining differentiated code/test retrieval, function-call-structure similarity, and iterative generation-to-retrieval feedback, achieving state-of-the-art results on SWT-bench Lite and TDD-bench Verified.
AGREE boosts visual document retrieval by adding local relevance signals from MLLM attention maps to global document labels during retriever training.
citing papers explorer
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DynLP: Parallel Dynamic Batch Update for Label Propagation in Semi-Supervised Learning
DynLP is a parallel dynamic batch update algorithm for label propagation that achieves significant speedups by updating only relevant parts of the graph on GPUs.
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iCoRe: An Iterative Correlation-Aware Retriever for Bug Reproduction Test Generation
iCoRe improves bug reproduction test generation by combining differentiated code/test retrieval, function-call-structure similarity, and iterative generation-to-retrieval feedback, achieving state-of-the-art results on SWT-bench Lite and TDD-bench Verified.
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Attention Grounded Enhancement for Visual Document Retrieval
AGREE boosts visual document retrieval by adding local relevance signals from MLLM attention maps to global document labels during retriever training.