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SCOPE framework improves LLM reasoning for clinical trial data analysis

By PulseAugur Editorial · [3 sources] ·

Researchers have developed SCOPE, a novel multi-LLM framework designed to improve reasoning over clinical trial data. This approach addresses the issue of "bad reasoning" in current LLMs by explicitly structuring the planning process before answer generation. SCOPE decomposes the task into row selection, structured planning, and execution, making source fields, rules, and constraints clear. Evaluations show SCOPE enhances accuracy for complex reasoning questions and offers a better accuracy-efficiency trade-off compared to other methods. AI

IMPACT Introduces a structured planning framework to improve LLM accuracy on complex clinical trial data reasoning tasks.

RANK_REASON This is a research paper detailing a new framework for LLM-based reasoning over clinical trial data.

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 3 sources. How we write summaries →

SCOPE framework improves LLM reasoning for clinical trial data analysis

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This is a research paper detailing a new framework for LLM-based reasoning over clinical trial data.
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COVERAGE [3]

  1. arXiv cs.CL TIER_1 English(EN) · Suparno Roy Chowdhury, Manan Roy Choudhury, Tejas Anvekar, Muhammad Ali Khan, Kaneez Zahra Rubab Khakwani, Mohamad Bassam Sonbol, Irbaz Bin Riaz, Vivek Gupta ·

    Diagnosis, Bad Planning & Reasoning. Treatment, SCOPE -- Planning for Hybrid Querying over Clinical Trial Data

    arXiv:2604.25120v1 Announce Type: new Abstract: We study clinical trial table reasoning, where answers are not directly stored in visible cells but must be reasoned from semantic understanding through normalization, classification, extraction, or lightweight domain reasoning. Mot…

  2. arXiv cs.CL TIER_1 English(EN) · Vivek Gupta ·

    Diagnosis, Bad Planning & Reasoning. Treatment, SCOPE -- Planning for Hybrid Querying over Clinical Trial Data

    We study clinical trial table reasoning, where answers are not directly stored in visible cells but must be reasoned from semantic understanding through normalization, classification, extraction, or lightweight domain reasoning. Motivated by the observation that current LLM appro…

  3. Hugging Face Daily Papers TIER_1 English(EN) ·

    Diagnosis, Bad Planning & Reasoning. Treatment, SCOPE -- Planning for Hybrid Querying over Clinical Trial Data

    We study clinical trial table reasoning, where answers are not directly stored in visible cells but must be reasoned from semantic understanding through normalization, classification, extraction, or lightweight domain reasoning. Motivated by the observation that current LLM appro…

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