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.
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...
The paper introduces a unified pre‑training framework for medical representations that incorporates hierarchical sub‑token aggregation, partial masking, and cross‑reference mechanisms to better capture the structure of medical codes. The resulting model outperforms existing BERT‑based approaches on pre‑training tasks and downstream clinical predictions, such as dementia onset and hospitalization. An in‑silico drug repositioning study for Alzheimer’s disease demonstrates the framework’s ability to rediscover known drugs and prioritize new hypotheses without external literature, establishing a workflow for hypothesis generation and prioritization based on observational data.
Hakken is a domain‑agnostic system that predicts and explains future scientific discoveries by combining transformer‑based models trained on temporal knowledge graphs with large language model semantic knowledge. It identifies novel relationships between scientific concepts that extend beyond the deductive hull of existing knowledge and provides explanations to help scientists assess these predictions. In the biomedical domain, Hakken set a new benchmark for time‑aware multi‑label relation prediction, generated 1.5 million high‑confidence hypotheses about aging, and experimentally confirmed two predictions that revealed previously undocumented interactions relevant to drug discovery.
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.
The paper "Medical Causal Hypothesis Verification with Large Language Models" reports a small-scale study evaluating eight LLMs on 17 medical causal hypotheses. The authors introduce an evaluation framework and annotate 1,067 evidence points across six criteria, using nine metrics to assess performance. Results show that while LLMs have strong recall, they frequently fail to provide valid scientific articles, evidence, or reject unsupported hypotheses, revealing a critical limitation for their use in healthcare.
arXiv:2609.22174v1 Announce Type: cross Abstract: Anticipating emerging research directions is a critical goal of AI-assisted science. Existing methods mainly predict which concepts will co-occur in...
arXiv:2603. 03322v2 Announce Type: replace-cross Abstract: Recent advancements in Large Language Model (LLM) agents have demonstrated remarkable potential in automatic knowledge discovery.
EvidenceNet is a disease‑specific dataset that transforms full‑text biomedical literature into structured evidence records and graph representations, preserving study design, provenance, and quantitative support. Using an LLM‑assisted pipeline, it extracts experimentally grounded findings, normalizes entities, scores evidence quality, and links related records via typed semantic relations. The released subsets—EvidenceNet‑HCC and EvidenceNet‑CRC—contain thousands of evidence records and richly connected graphs, with high extraction and relation‑type accuracy, enabling retrieval‑augmented question answering and graph‑based tasks such as link prediction and target prioritization.
arXiv:2607. 01131v1 Announce Type: cross Abstract: Autonomous scientific discovery systems offer the potential to accelerate research by automating the process of hypothesis generation and validation.
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:2511.21075v4 Announce Type: replace-cross Abstract: Engineering LLMs to accelerate life sciences research requires a robust alignment with biomedical knowledge. We observe that biomedical text...
arXiv:2602. 20459v2 Announce Type: replace Abstract: Can AI systems trained on the existing scientific record forecast the advances that will follow?