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Real-world documents distribute evidence across text, tables, figures, and captions within complex page layouts. Answering complex questions over such documents therefore requires more than retrieving relevant passages: systems must recover the evidence topology that connects heterogeneous evidence units. Existing Grap...
Attention Diversion explains why inference-time strategies, including chain-of-thought prompting, spell-checking, self-repair, and stronger repair models, fail to consistently recover performance: each addresses one channel at a time.
This study finds that existing MCN benchmarks present data quality issues and underexplored data fusion potential, and data quality enhancement and LLM-based controlled-variety data augmentation help alleviate overlapping phrases and long-tail issues.
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