Agentic and Non-Agentic Multi-Hop Systems for Medical Question Answering
Bibliographic record
Abstract
Abstract This paper presents two systems developed for the MedHopQA Shared Task on multi-hop biomedical question answering. Our first system, Agentic-Qwen-Wikipedia, uses a lightweight agentic framework using SmolAgents to iteratively retrieve and reason over Wikipedia content via sub-query decomposition. Our second system, LLM-Qwen-Wikipedia-PubMed, offers a non-agentic, explicitly controlled pipeline that decomposes questions, retrieves evidence from both Wikipedia and PubMed, and synthesizes answers through a structured multi-hop process. The systems employ Qwen2.5-Coder-32B-Instruct-GPTQ-Int4 and Qwen-3-8B-AWQ, respectively. They are deployed efficiently using vLLM. The non-agentic system achieves higher performance on the shared task. This article is part of the Proceedings of the BioCreative IX Challenge and Workshop (BC9): Large Language Models for Clinical and Biomedical NLP at the International Joint Conference on Artificial Intelligence (IJCAI).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".