biomedical

This paper proposes a context-augmented training framework for multi-hop question-answering, showing that combining context graphs with knowledge graphs and using reinforcement learning improves performance in biomedical domains.

This paper introduces BioCheck Agent, an LLM-based agent that generates structured biomedical fact-checking reports using RL-enhanced agentic search, showing improved accuracy and reduced hallucinations compared to base models.

MIT engineers have developed a tiny ingestible capsule that can continuously measure core body temperature with high accuracy, enabling improved health monitoring in various applications such as infection detection and fever tracking.

AstraZeneca describes Research Assistant, an internal LLM-based multi-agent system that lets scientists explore biomedical data via chat, with evidence grounding and citation links, deployed to 15,000 internal users.

This article covers how horseshoe crab blood has been crucial for testing medicine safety, the environmental impact of harvesting the crabs, and Eli Lilly's transition to a lab-made alternative that eliminates the need for crab blood.

This paper introduces THBKG, a temporal heterogeneous biomedical knowledge graph covering 110k entities and 11.1M edges with yearly evidence timestamps, designed to predict whether target-disease pairs entering Phase II trials advance to Phase III using only evidence available at the decision time. The graph-based approach outperforms direct-evidence baselines, especially for pairs lacking direct evidence, and the authors release it as a continually updated resource.

The article discusses a potential strategy to prevent protein misfolding by inhibiting the folding process itself, which could have implications for diseases related to misfolded proteins.

AIPOCH has released a curated library of over 550 medical research agent skills supporting evidence insights, protocol design, data analysis, and academic writing, compatible with multiple AI agent platforms like Claude Code and Codex.

This paper presents a systematic study of cross-lingual transfer for biomedical machine translation into low-resource Arabic-script languages, using Arabic and Persian as pivots. The authors evaluate LoRA adapter merging as a zero-data transfer strategy, showing it works surprisingly well for closely related languages like Dari.

A novel framework called FAIR GraphRAG integrates FAIR Digital Objects with graph-based retrieval to enhance retrieval-augmented generation for semantic data analysis, improving question answering accuracy and adherence to FAIR principles, demonstrated on a biomedical dataset.

This paper evaluates Claude Fable 5 on eight biomedical benchmarks, finding that despite high refusal rates (8-99.4%), the model achieves superior accuracy when it does answer, highlighting willingness to engage as the primary constraint.

Introduces Drift-Aware Temporal Graph Rewiring (DATGR) to dynamically update co-occurrence edges in biomedical text graphs, capturing semantic drift without full retraining. Evaluated on BIOMRC, it achieves a mean AUROC improvement of 0.066 over static baselines while maintaining precision.

This paper investigates the effects of domain-specific expert pruning on both utility and factual reliability of Mixture-of-Experts (MoE) models in the biomedical domain. It finds that moderate pruning preserves in-domain utility without immediate reliability loss, but extreme pruning increases hallucination risks, and generalization degrades rapidly in cross-domain settings.

This paper introduces MedKGTab, a knowledge-injected framework that uses biomedical knowledge graphs to expand cross-domain features in tabular medical data, addressing data scarcity by generating high-fidelity biomedical profiles.

Proposes DDIAgents, a mechanism-conditioned multi-agent framework for drug-drug interaction prediction that dynamically routes relevant biomedical knowledge to specialized expert agents and aggregates their analyses, outperforming existing feature-based, graph-based, and LLM-based methods.

A new benchmark paper, OpenBioRQ, reveals that AI agents rarely fabricate citations but often cite papers that do not support the claim, with 15.9% of citations being mismatched in biomedical contexts.

Hybrid-IR introduces a dual-path retrieval framework combining graph-based and dense retrieval with iterative reasoning to improve complex medical QA, addressing limitations in existing RAG methods. Experiments on three benchmarks show effectiveness.

OpenBioRQ is a new benchmark of 12,553 unsolved biomedical research questions that tests agentic models' ability to verify sources and avoid false citations. It reveals that current models often link to wrong papers and suffer from agentic collapse on hard questions.

BioInsight is a multi-agent system that transforms static biomedical reports into interactive, evidence-centered interfaces by organizing disease-specific evidence through structured artifacts and deterministic citation normalization.

TxBench-PP is a benchmark for evaluating AI agents on small-molecule preclinical pharmacology tasks. Across 16 model-harness configurations, the best system achieved only 59.3% accuracy, indicating significant room for improvement.