arXiv Machine Learning

Automatic Construction of Strong Simple Baselines

The paper proposes using large language models to automatically build simple baselines for deep learning studies in the physical and life sciences. By assembling 36 supervised classification and regression tasks from high‑impact journals, the authors show that the generated baselines recover a median of 95.5% of the performance gap between a trivial predictor and the deep learning model, and in 12 studies even match or surpass the model’s performance. The work also explores how baseline quality varies across domains, venues, and the influence of test‑time compute and contextual information.

arXiv AI

6d ago

Learning Scientific Trajectories to Predict Future Biomedical Discoveries

ForeSCITE is a framework that uses large language models to analyze temporal patterns in biomedical conclusions. It clusters published conclusions, estimates a direction that separates earlier from more recent findings within each cluster, and projects conclusion embeddings along these directions. The projected coordinates are decoded into natural‑language hypotheses, and retrospective evaluations show that ForeSCITE expands semantic coverage, introduces novel biomedical entity pairs, and identifies plausible CAR‑T hypotheses that experts deem biologically relevant.

By Chia-Hsuan Chang, Brian Ondov, Jiang Lv, Lan Jiang, Yunmai Wang, Haerin Song, Xueqing Peng, Qiaozhu Mei, Xiaolei Su, Hua Xu

llms rag

arXiv Machine Learning

Aug 18

Deep Learning for BioImaging: What Are We Really Learning?

arXiv:2603. 13377v2 Announce Type: replace-cross Abstract: Representation learning has driven major advances in natural image analysis by enabling models to acquire high-level semantic features.

By Ivan Svatko, Maxime Sanchez, Ihab Bendidi, Gilles Cottrell, Auguste Genovesio

benchmarks

Hugging Face Trending Papers

Oct 28

Learning Scientific Trajectories to Predict Future Biomedical Discoveries

AI co-scientists have expanded the use of large language models for generating scientific hypotheses and guiding experimental investigation. Here we investigate whether temporal patterns in the semant...

llms rag

arXiv AI

Aug 10

Fisher-R1: Training LLM Agents for Reliable Hypothesis Testing

arXiv:2608. 07437v1 Announce Type: new Abstract: Reliable hypothesis testing is the foundation of many empirical scientific claims.

By Jiacheng Miao, Jin Mu, Guanhua Chen, James Zou

llms agents reinforcement-learning benchmarks

arXiv Computation and Language

Sep 15

A Hybrid Hierarchical 1D-CNN-BiLSTM Framework for Extractive Summarization of Biomedical and Clinical Text

arXiv:2609.13481v1 Announce Type: new Abstract: Large language models have made abstractive summarization remarkably fluent, but generated summaries can hallucinate facts, posing serious risks in bio...

By Saad Bin Ather, Muhammad Saif, Ali Hassan Khan, Manzer Abbas, Hajra Waheed

llms rag nlp

arXiv Machine Learning

4d ago

Do Language Models Know What Compelling Scientific Evidence Looks Like?

The paper introduces EDB-Bio, a benchmark of 96,000 episodes drawn from real biological studies—drug‑perturbation transcriptomics, CRISPR screens, and GWAS—to test whether language models can weigh empirical evidence and predict hypothesis truth. Initial results show that both open‑weight and frontier models are systematically underconfident, matching a trivial base‑rate forecaster in Brier score, but post‑training with proper scoring rules improves calibration and discrimination. In a downstream experiment‑selection task, a calibrated model (Ibis0) outperforms the best frontier model by 2.5×, and Bayes‑factor analysis reveals how models update beliefs, align with expert relevance judgments, and expose biases.

By Conor Igoe, Gabriel Grand, Zane Koch, Albert Bou, Eli Moss, Michaela M. Hinks, Samuel G. Rodriques, Andrew White, Siddharth Narayanan

llms agents benchmarks

arXiv AI

Jun 30

Beyond IID: How General Are Tabular Foundation Models, Really?

arXiv:2606. 30410v1 Announce Type: cross Abstract: Foundation models for predictive machine learning on tabular data have recently gained significant traction in academia and industry.

By Lennart Purucker, Andrej Tschalzev, Nick Erickson, Gioia Blayer, David Holzm\"uller, Alan Arazi, Alexander Pfefferle, Mustafa Tajjar, Ga\"el Varoquaux, Frank Hutter

benchmarks

arXiv AI

Aug 3

Can Large Language Models Derive New Knowledge? A Dynamic Benchmark for Biological Knowledge Discovery

arXiv:2603. 03322v2 Announce Type: replace-cross Abstract: Recent advancements in Large Language Model (LLM) agents have demonstrated remarkable potential in automatic knowledge discovery.

By Chaoqun Yang, Xinyu Lin, Shulin Li, Wenjie Wang, Ruihan Guo, Fuli Feng, Tat-Seng Chua

llms agents benchmarks

arXiv AI

Jul 13

TheBioCollection: Unified Pre-Training Scale LLM Corpus for Biology

arXiv:2607. 08803v1 Announce Type: cross Abstract: The push toward large language models for biology (BioLM) has created a need for training corpora that can endow models with a genuine understanding of biology.

By Hyunjin Seo, Hyeon Hwang, Gyubok Lee, Jay Shin, Jimin Park, Taesoo Kim, Sanghoon Lee, Hongjoon Ahn, Sungjun Han, Sangwon Jung

llms

arXiv AI

Jul 3

PreScience: A Dataset and Benchmark for Scientific Forecasting

arXiv:2602. 20459v2 Announce Type: replace Abstract: Can AI systems trained on the existing scientific record forecast the advances that will follow?

By Anirudh Ajith, Amanpreet Singh, Jay DeYoung, Nadav Kunievsky, Austin C. Kozlowski, Oyvind Tafjord, James Evans, Daniel S. Weld, Tom Hope, Doug Downey

llms rag agents benchmarks

arXiv Machine Learning

Jul 10

A Tool Bottleneck Framework for Clinically-Informed and Interpretable Medical Image Understanding

arXiv:2512. 21414v2 Announce Type: replace-cross Abstract: Recent tool-use frameworks powered by vision-language models (VLMs) improve image understanding by grounding model predictions with specialized tools.

By Christina Liu, Alan Q. Wang, Joy Hsu, Jiajun Wu, Ehsan Adeli

llms multimodal benchmarks

arXiv AI

Sep 17

REPAIR: Resolving Long-Tail Confusion in Scientific Retrievers via Fact-Verified Iterative Refinement

REPAIR is a self‑evolving data augmentation framework designed to improve scientific dense retrievers by addressing long‑tail concept gaps and fact sensitivity. It iteratively generates training data through diagnosis of long‑tail concepts, API‑guided evidence expansion, and hard negative mining, thereby grounding retrieval in factual reality. Experiments show that REPAIR outperforms 19 strong baselines across nine materials science and biomedical benchmarks.

By Yerim Oh, Gunhee Kim

llms benchmarks safety

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