Back to feed
arXiv cs.CL
arXiv cs.CL
7/20/2026
EpiNarrate: Agentic Generation of Grounded Narratives from Epidemiological Scenario Projections

EpiNarrate: Agentic Generation of Grounded Narratives from Epidemiological Scenario Projections

Short summary

EpiNarrate is an agentic framework that generates grounded public health narratives from epidemiological projection data by separating structured numerical reasoning from natural-language generation. It organizes scenario axes into a partial-order schema, derives valid quantitative statements through a comparison grammar, and uses maximum-entropy-based interestingness selection to balance coverage and non-redundancy. Experiments on COVID-19 Scenario Modeling Hub data show improved factual grounding and broader coverage compared to direct LLM summarization.

  • •EpiNarrate separates numerical reasoning from NL generation to produce grounded epidemiological narratives
  • •Uses partial-order schema and comparison grammar to enforce semantic and arithmetic consistency
  • •Maximum-entropy interestingness selection balances coverage and non-redundancy; validated on COVID-19 Scenario Modeling Hub

Generated with AI, which can make mistakes.

#research-breakthrough#ai-agents
Read full article at arXiv cs.CL

Is this a good recommendation for you?

Comments

Failed to load comments. Please try again.

Explore more

The user wants me to rewrite a headline for a mobile feed based on the original title and summary.
arXiv CS.AI
Healthier LLMs: Retrieval-Augmented Generation for Public Health Question Answering
arXiv cs.CL
Discourse-Aware Policy Analysis with Argumentation: A Hybrid LLM-Symbolic Framework for Disaster Governance
arXiv cs.CL
The original title is "Prompt-to-Paper: Agentic AI System for Bioinformatics"
arXiv CS.AI