AI RESEARCH
Attention Flows: Tracing LLM Conceptual Engagement via Story Summaries
arXiv CS.LG
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ArXi:2604.06416v1 Announce Type: cross Although LLM context lengths have grown, there is evidence that their ability to integrate information across long-form texts has not kept pace. We evaluate one such understanding task: generating summaries of novels. When human authors of summaries compress a story, they reveal what they consider narratively important. Therefore, by comparing human and LLM-authored summaries, we can assess whether models mirror human patterns of conceptual engagement with texts.