12,445 papers · continuously updated · last export: 10 Aug 2026livingmeta.ai
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Research theme

Evaluation & Benchmarks

The Evaluation & Benchmarks theme comprises 1,104 papers in this corpus published between 2002 and 2026. Work here is dominated by Benchmarking, Empirical Study, Experimental. 5 open research gaps have been surfaced in this area.

Methodology profile

  • Benchmarking533 (48%)
  • Empirical Study211 (19%)
  • Experimental137 (12%)
  • Design Science84 (8%)
  • Literature Review47 (4%)
  • Conceptual15 (1%)

Research domains

  • Data Analysis461 (42%)
  • Research Productivity150 (14%)
  • Research Integrity103 (9%)
  • Scholarly Infrastructure86 (8%)
  • Knowledge Synthesis71 (6%)
  • AI Governance67 (6%)

Frequent sub-topics

LLM performance in clinical decision support · 2machine learning for mass cytometry data analysis in chronic lymphocytic leukaemia · 1information extraction from epidemiological data using generative AI · 1Explainable machine learning for predictive modeling · 1domain-specific LLM evaluation for supply chain management · 1inter-model agreement in LLM-based cultural annotation of consumer reviews · 1adversarial robustness of multimodal medical RAG systems · 1Topic modeling for health discourse analysis using SC-BERTopic · 1

Open research gaps

  • The authors identify that existing benchmarks are confined to single domains and languages, and fail to evaluate generalization capabilities for real-world industrial applications or reflect coding pr
  • The abstract does not explicitly identify future research directions or gaps, though it implies questions about why LLMs fail to match theoretical predictions and whether different architectural or tr
  • Not explicitly stated in the provided abstract
  • The paper does not explicitly identify future research directions in the provided abstract. However, it implicitly suggests the need for further investigation into how safety-tuning mechanisms affect
  • The authors identify that "existing evaluation frameworks do not adequately address multi-agent systems that combine simulation, retrieval, and manufacturing preparation," indicating this gap motivate

Representative papers

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