# A Guideline-Augmented Multi-Agent Framework for Schema-as-Code Biomedical Named Entity Recognition

arXiv: https://arxiv.org/abs/2610.02970 · HTML: https://mlsanity.com/paper/2610.02970/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Medical and scientific domain agents, one of 16 directions in LLM agents and tool use.
- Builds on 17 of its 31 references that are in the map.
- First posted on arXiv 2 Oct 2026.

## Placed in

- LLM agents and tool use / [Medical and scientific domain agents](https://mlsanity.com/agents/medical-and-scientific-domain-agents/)

## Builds on (most cited first, 17 of 17)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Qwen2.5 Technical Report](https://mlsanity.com/paper/2412.15115/) (2024)
- [GPT-4o System Card](https://mlsanity.com/paper/2410.21276/) (2024)
- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing](https://mlsanity.com/paper/2007.15779/) (2020)
- [GPT-NER: Named Entity Recognition via Large Language Models](https://mlsanity.com/paper/2304.10428/) (2023)
- [CodeIE: Large Code Generation Models are Better Few-Shot Information Extractors](https://mlsanity.com/paper/2305.05711/) (2023)
- [Inspire the Large Language Model by External Knowledge on BioMedical Named Entity Recognition](https://mlsanity.com/paper/2309.12278/) (2023)
- [Dual-Pathway Circuits of Object Hallucination in Vision-Language Models](https://mlsanity.com/paper/2605.13156/) (2026)
- [A Cooperative Multi-Agent Framework for Zero-Shot Named Entity Recognition](https://mlsanity.com/paper/2502.18702/) (2025)
- [ConMem: Contribution-Aware Memory for Long-Horizon Manufacturing Inspection Logs](https://mlsanity.com/paper/2607.28126/) (2026)
- [MemGuard: Persisting Verifier Signals for LLM-Agent Memory Governance](https://mlsanity.com/paper/2608.21867/) (2026)
- [Biomedical Nested NER with Large Language Model and UMLS Heuristics](https://mlsanity.com/paper/2407.05480/) (2024)
- [Retrieval augmented generation based dynamic prompting for few-shot biomedical named entity recognition using large language models](https://mlsanity.com/paper/2508.06504/) (2025)
- [Learning to Generate and Extract: A Multi-Agent Collaboration Framework For Zero-shot Document-level Event Arguments Extraction](https://mlsanity.com/paper/2603.02909/) (2026)
- [UltraG-Bench: A Multi-task Benchmark for assessing Large Vision-Language Models on Pixel-level Evidence Grounding in Ultrasound](https://mlsanity.com/paper/2609.30928/) (2026)

## Built on by (most cited first, 1 of 1)

- [Conditional Rank Allocation for Taxonomy-Aware Medical Language Model Adaptation](https://mlsanity.com/paper/2610.06765/) (2026)

## Abstract (arXiv)

Large language models (LLMs) have shown promising potential for biomedical named entity recognition (BioNER) through instruction following and in-context learning. However, existing LLM-based BioNER methods still face two key limitations. First, retrieved demonstrations and external biomedical knowledge provide limited support for dataset-specific annotation semantics, leaving entity boundaries, type scopes, and annotation conventions ambiguous. Second, free-form generation lacks sufficient structural control, often leading to invalid formats, hallucinated mentions, duplicated entities, and boundary errors. To address these limitations, we propose GAMA, a guideline-augmented multi-agent framework for schema-as-code BioNER. GAMA first induces candidate annotation rules from labeled training instances and verifies them against annotated data to construct reliable dataset-specific guideline memory. Guided by these verified rules, a planning component generates ranked span-type hypotheses with rationales, and a coding component converts them into schema-constrained entity objects. A verification module then checks span grounding, type validity, and structural compliance, and performs dual-loop refinement to correct invalid or low-confidence predictions. Experiments on five widely used BioNER datasets with multiple LLM backbones show that GAMA consistently outperforms strong LLM-based baselines. Ablation and parameter analyses further verify the effectiveness of the proposed components.