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New research explores temporal retrieval for evolving scientific document collections

By PulseAugur Editorial · [2 sources] ·

A new paper analyzes temporal retrieval for scientific document collections that grow over time, using the LongEval-Sci benchmark. The research found that temporal full-text retrieval methods, particularly those incorporating citation features, achieved the best official results. Internal diagnostics revealed that while full-text retrieval is a strong foundation, temporal integration can enhance longitudinal effectiveness, though citation evidence requires further refinement. AI

IMPACT Provides insights into improving information retrieval systems for dynamic scientific literature.

RANK_REASON Academic paper detailing a new evaluation and diagnostic analysis of retrieval methods.

Read on arXiv cs.IR (Information Retrieval) →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New research explores temporal retrieval for evolving scientific document collections

COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Yingdong Yang, Haijian Wu ·

    Submitted and Diagnostic Analysis of Full-Text Temporal Retrieval for LongEval-Sci

    arXiv:2607.04088v1 Announce Type: cross Abstract: LongEval-Sci evaluates scientific retrieval under collection change, where a system should be effective on the current corpus and remain usable as documents accumulate over time. This paper reports both official Task 1 results and…

  2. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Haijian Wu ·

    Submitted and Diagnostic Analysis of Full-Text Temporal Retrieval for LongEval-Sci

    LongEval-Sci evaluates scientific retrieval under collection change, where a system should be effective on the current corpus and remain usable as documents accumulate over time. This paper reports both official Task 1 results and development diagnostics for LongEval-Sci 2026. We…

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