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papersTODAY 04:00 UTC

Study Separates Inference Topology From Diversity in Multi-Agent LLM Emotion Detection

A new arXiv paper examines multi-agent LLM pipelines by treating two design choices as independent variables: how agent calls are wired together and where the differences between agents come from. The authors evaluate this on multilingual, low-resource emotion detection, where labeled data is scarce. The goal is to clarify which gains come from the structure of the agent network versus from the diversity introduced between agents.

papersSEP 10 04:00 UTC

Study proposes evaluation of diachronic semantic change in Sinhala

A new arXiv paper examines how word meanings in Sinhala have shifted over long historical periods, focusing on a language with limited textual resources. The authors tackle obstacles such as sparse historical corpora and the drawbacks of static embedding alignment techniques when measuring semantic drift. The work contributes an evaluation approach for diachronic semantic change in low-resource NLP settings.