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This article critiques the over-reliance on quantifiable metrics in academia, such as citation counts and indices, and discusses the introduction of the D-index by Research.com as another problematic measure.
This paper analyzes NLP conference papers from 2020 to 2025 to examine the relationship between reported GPU resources and scholarly impact, finding that while resources are associated with higher citations, they explain little of the variance in impact.
Friend Ba Dao Liu open-sourced search result datasets from 8 domestic AI platforms, containing 620 standard questions and 210,000 citation records, along with cleaned data, a preprint paper, and an analysis report.
Introduces GRASP, a framework that combines LLM planning with graph algorithms to generate high-fidelity related work sections by modeling inter-paper relationships through a two-layer graph structure and Steiner tree pruning.
This paper proposes a Deep Research pipeline that improves literature search recall by an order of magnitude and argues that human citation lists are not reliable ground truth for evaluation.