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BioASQ 2026 system uses GPT-5.5 and novel retrieval for medical QA

By PulseAugur Editorial · [1 sources] ·

A research paper details a system designed for the BioASQ Task 14B 2026, focusing on retrieval and multi-model combination strategies. The system employs a hybrid retrieval pipeline incorporating dense models, BM25, and reciprocal rank fusion, alongside an agent-driven approach that decomposes questions for targeted retrieval. This method optimizes re-retrieval costs while improving performance on specific metrics. For answer generation, the system explores selection and fusion techniques for combining multiple language models, with GPT-5.5 showing strong performance on certain metrics. AI

IMPACT This research demonstrates advanced retrieval and multi-model combination techniques that could influence future medical question-answering systems.

RANK_REASON Academic paper detailing a system for a specific task (BioASQ). [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

BioASQ 2026 system uses GPT-5.5 and novel retrieval for medical QA

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Academic paper detailing a system for a specific task (BioASQ). [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Marian-Andrei Rizoiu ·

    Cost-Pragmatic Quality Gating and Selection-Fusion Multi-Model Combiners for BioASQ Phases A+ and B

    We describe our BioASQ Task 14B 2026 system. The work centers on two design decisions: how aggressively to re-retrieve when first-stage retrieval is weak, and how to combine multiple language-model answers. Retrieval unions two parallel pipelines - a hybrid first stage (dense BGE…

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