BioXArena benchmarks LLM agents on generating end-to-end ML pipelines for 76 multi-modal biomedical tasks, with MLEvolve plus Gemini-3.1-Pro scoring highest at 0.666.
Biobert: a pre-trained biomedical language representation model for biomedical text mining.Bioinformatics, 36(4):1234–1240
6 Pith papers cite this work. Polarity classification is still indexing.
6
Pith papers citing it
citation-role summary
background 4
citation-polarity summary
years
2026 6roles
background 4polarities
background 4representative citing papers
A hierarchical adversarial fine-tuning method for VLMs aligns image and text embeddings at multiple hierarchy depths with theoretical margin connections to boost robustness to leaf and superclass attacks while using multiple trees for semantic variety.
MILM fine-tunes LLMs on XML-encoded multimodal irregular time series via a two-stage process that exploits informative sampling patterns to achieve top performance on EHR classification datasets.
NanoResearch introduces a tri-level co-evolving framework of skills, memory, and policy to personalize LLM-powered research automation across projects and users.
MIMIC is a split-track encoder-decoder foundation model that unifies sequence reconstruction, prediction, and constrained design across nucleic acids, proteins, and regulatory context using partially observed multimodal inputs.
EligMeta automates trial discovery from registries and incorporates eligibility similarity into meta-analysis weighting to yield population-aligned pooled estimates, as shown by recovering all guideline trials in one case and shifting a risk ratio in another.
citing papers explorer
-
BioXArena: Benchmarking LLM Agents on Multi-Modal Biomedical Machine Learning Tasks
BioXArena benchmarks LLM agents on generating end-to-end ML pipelines for 76 multi-modal biomedical tasks, with MLEvolve plus Gemini-3.1-Pro scoring highest at 0.666.
-
Hierarchically Robust Zero-shot Vision-language Models
A hierarchical adversarial fine-tuning method for VLMs aligns image and text embeddings at multiple hierarchy depths with theoretical margin connections to boost robustness to leaf and superclass attacks while using multiple trees for semantic variety.
-
MILM: Large Language Models for Multimodal Irregular Time Series with Informative Sampling
MILM fine-tunes LLMs on XML-encoded multimodal irregular time series via a two-stage process that exploits informative sampling patterns to achieve top performance on EHR classification datasets.
-
NanoResearch: Co-Evolving Skills, Memory, and Policy for Personalized Research Automation
NanoResearch introduces a tri-level co-evolving framework of skills, memory, and policy to personalize LLM-powered research automation across projects and users.
-
MIMIC: A Generative Multimodal Foundation Model for Biomolecules
MIMIC is a split-track encoder-decoder foundation model that unifies sequence reconstruction, prediction, and constrained design across nucleic acids, proteins, and regulatory context using partially observed multimodal inputs.
-
Eligibility-Aware Evidence Synthesis: An Agentic Framework for Clinical Trial Meta-Analysis
EligMeta automates trial discovery from registries and incorporates eligibility similarity into meta-analysis weighting to yield population-aligned pooled estimates, as shown by recovering all guideline trials in one case and shifting a risk ratio in another.