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2 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

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Can LLMs Reliably Annotate Bioassay Metadata to Improve Data Readiness?
Oct 1, 2026Laura van Weesep, Riccardo Tedoldi, Jens Sjölund +6Large Language Model Annotations Molecular Property Prediction
Molecular AI, Discovery Sciences R&D, AstraZeneca · Department of Information Technology, Uppsala University · Robotics, Perception & Learning, KTH Royal Institute of Technology +3
Lessons learned from deploying imaging AI with the open PACS-AI platform
Sep 22, 2026Samuel Kadoury, Julie G. Hussin, Pascal Thériault-Lauzier +11Real-World Clinical Workflows Diagnosis
Department of Radiology, Centre Hospitalier de l'Université de Montréal (CHUM) and Centre de recherche du CHUM (CRCHUM), Montréal, QC, Canada · Department of Computer and Software Engineering, Polytechnique Montréal, Montréal, QC, Canada · Department of Medicine, Montreal Heart Institute, Université de Montréal, Montréal, QC, Canada +14
Decoupling Readiness from Release for Tail-Aware Scheduling of Agentic LLM Workflows
Sep 12, 2026Bochao Feng, Jianjiang Li, Haojie Wang +4Agentic Workflow Design Schedulers
University of Science and Technology Beijing Beijing, China · Qiyuan Laboratory Beijing, China · Tsinghua University Beijing, China
RAIL: An Automatic Classifier of the Artificial Intelligence Readiness Level
Aug 13, 2026Juan Irving Vasquez, Juan Terven, Laura-Ivoone Garay-JimenezReadiness Artificial Intelligence Systems
CIETEC-IPN Instituto Polit´ecnico Nacional M´exico City, M´exico · Instituto Polit´ecnico Nacional, UPIITA, M´exico City, M´exico
Factorized Hypothesis Search for Evidence-to-Taxonomy Retrieval
Aug 6, 2026Linhai Ma, Ethan F. Wei, Xueqing Peng +3Taxonomy Retrievers
The Fin AI, USA · Yale University, USA · Cardiff University, UK
From Chasing Ghosts to Missed Attacks: Perspectives and Perceptions of SOC Practitioners on LLM Integration, Risks, and Readiness
Aug 1, 2026Jonas Thurner, Nadine Jost, Stefan Albert Horstmann +4Cybersecurity Large Language Model Safety
FH Münster, Germany · Ruhr University Bochum, Germany · University of Cologne, Germany +2
Classical Hardware Acceleration of Quantum Autoencoders for Real-Time Anomaly Detection in Collider Experiments
Jul 22, 2026Ivan Ge, Sagar Addepalli, Abhilasha Dave +1Quantum Machine Learning Field-Programmable Gate Arrays
Department of Physics, Stanford University, 450 Jane Stanford Way, Stanford, 94305, CA, USA. · SLAC National Accelerator Laboratory, 2575 Sand Hill Road, Menlo Park, 94025, CA, USA.
Aerial Manipulation: Contact, Medium Coupling, and the Geometry of Readiness
Jul 6, 2026Antonio FranchiAerial Manipulation Robotic Systems
Robotics and Mechatronics lab, Faculty of Electrical Engineering, Mathematics & Computer Science, University of Twente, 7522 NB Enschede, The Netherlands · Department of Computer, Control and Management Engineering, Sapienza University of Rome, 00185 Rome, Italy
Automated Data Readiness for Scientific AI
Jul 2, 2026Sean R. Wilkinson, Valentine G. Anantharaj, Jong Youl Choi +8Readiness Reproducibility
Oak Ridge National Laboratory, Oak Ridge, TN, USA
IonSense-QKG: A Quantum-Readiness Metadata Framework for Lithium-Ion Battery Dataset Discovery
Jul 1, 2026Sakthi Prabhu Gunasekar, Prasanna Kumar RangarajanLithium-Ion Batteries Benchmark Datasets
Amrita School of Computing, Amrita Vishwa Vidyapeetham Coimbatore, India
Leadership as Coordination Control: Behavioral Signatures and the Recovery-Advantage Boundary in Multi-Agent LLM Teams
Jun 17, 2026Haewoon KwakMulti-Agent Large Language Model Systems Coordination
Indiana University Bloomington
Automatically Improving Simulation Physics for Articulated Objects
May 18, 2026Anh-Quan PhamPhysics Simulation Articulated Objects
University of Pennsylvania
A Readiness-Driven Runtime for Pipeline-Parallel Training under Runtime Variability
May 18, 2026Ruitao Liu, Xinyang Tian, Shuo Chen +4Readiness Runtime
Tsinghua University · Scitix AI
Predicting Plasticity in Deep Continual Learning: A Theoretical Perspective
May 9, 2026Jiuqi Wang, Jayanth Srinivasa, Claire Chen +3Plasticity Replay-Based Continual Learning
University of Virginia · Cisco Research · California Institute of Technology +1
Delayed Commitment for Representation Readiness in Stage-wise Audio-Visual Learning
May 3, 2026Xinmeng Xu, Haoran Xie, S. Joe Qin +3Audio-Visual Reasoning Audio-Visual Consistency
Division of Artificial Intelligence, Lingnan University, Tuen Mun, Hong Kong SAR · School of Computer Science and Artificial Intelligence, Wuhan University of Technology, Wuhan, China · School of Mathematics, Physics and Computing, University of Southern Queensland, Toowoomba, Australia +1
SciHorizon-DataEVA: An Agentic System for AI-Readiness Evaluation of Heterogeneous Scientific Data
Apr 29, 2026Dianyu Liu, Chuan Qin, Xi Chen +6Artificial Intelligence Evaluation Scientific Discovery
Computer Network Information Center, Chinese Academy of Sciences Beijing, China
Task-Level AI Readiness Assessment for Business Process Management:The T-IPO Model and LARA Matrix in Financial-Services IT Operations
Apr 16, 2026Mingjun Li, Xiaojun YeAgentic Workflow Design Agentic Evaluations
Tsinghua University, Beijing, China
ReaDy-Go: Real-to-Sim Dynamic 3D Gaussian Splatting Simulation for Environment-Specific Visual Navigation with Moving Obstacles
Feb 12, 2026Seungyeon Yoo, Youngseok Jang, Dabin Kim +3Sim-To-Real Reinforcement Learning Object Goal Navigation
Department of Aerospace Engineering, Seoul National University, Seoul, Republic of Korea · InnoCORE AI-Transformed Aerospace Research Center, KAIST, Daejeon, Republic of Korea

Oct 1, 2026, cs.CLJ/K move · Enter open · S save

Can LLMs Reliably Annotate Bioassay Metadata to Improve Data Readiness?

Laura van Weesep, Riccardo Tedoldi, Jens Sjölund, Hossein Azizpour+5

Molecular AI, Discovery Sciences R&D, AstraZeneca · Department of Information Technology, Uppsala University · Robotics, Perception & Learning, KTH Royal Institute of Technology+3

Large Language Model Annotations Molecular Property Prediction Readiness

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The emergence of foundation models for molecular property prediction requires a high degree of AI data readiness, including reliable metadata annotation. However, both public repositories and industrial screening databases suffer from missing, inconsistent, or conflated assay annotations. In this work, we quantify the extent of missing annotations in PubChem for the BioAssay Ontology (BAO) assay format and physical detection method fields and investigate whether open-source and proprietary large language models (LLMs) can reliably predict and audit metadata annotations directly from the assay text. In our assessment, we found that the annotation coverage across PubChem's

\sim

2 million bioassays is critically sparse, 36% lacking an assay format, 89% a BioAssay type, and >99.9% any BAO-mapped assay format or detection technology term. This motivates the need for automated test-metadata curation. Using evaluation sets derived from PubChem and ChEMBL, we assess the agreement of seven open-source and proprietary LLMs with existing silver labels. Recall is at least 0.96 for biochemical and cell-based assay formats, with a similar pattern for detection technology, although disagreements increase on under-represented classes. Manual inspection shows that many of these disagreements trace back to inconsistencies between silver sources rather than to LLM error. Moreover, in a qualitative study with a senior industrial curator, LLM-generated evidence prompted the expert to revise some of their own labels, showing LLMs can flag potentially mislabeled assays. Across the study, performance differences between proprietary and open-source models were small. Together, these results suggest LLMs can support the large-scale annotation and auditing of assay metadata, though per-class reliability estimates and targeted human review remain necessary before such labels enter downstream ML pipelines.

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