{"total":21,"rows":[{"id":"2606.12736","title":"Benchmarking AI Agents for Addressing Scientific Challenges Across Scales","created":"2026-06-10","cited_by_count":12,"area":null},{"id":"2601.20996","title":"MADE: Benchmark Environments for Closed-Loop Materials Discovery","created":"2026-01-28","cited_by_count":7,"area":null},{"id":"2602.06448","title":"Principle-Evolvable Scientific Discovery via Uncertainty Minimization","created":"2026-02-06","cited_by_count":7,"area":null},{"id":"2606.10587","title":"Towards Diverse Scientific Hypothesis Search with Large Language Models","created":"2026-06-09","cited_by_count":3,"area":null},{"id":"2609.15973","title":"Discovery Foundation Models: Toward Open-Ended Discovery Intelligence","created":"2026-09-14","cited_by_count":3,"area":null},{"id":"2603.00883","title":"Knowledge without Wisdom: Measuring Misalignment between LLMs and Intended Impact","created":"2026-03-01","cited_by_count":2,"area":null},{"id":"2603.12249","title":"SciMDR: Advancing Scientific Multimodal Document Reasoning","created":"2026-03-12","cited_by_count":2,"area":null},{"id":"2603.24877","title":"More Than \"Means to an End\": Supporting Reasoning with Transparently Designed AI Data Science Processes","created":"2026-03-25","cited_by_count":2,"area":null},{"id":"2605.28778","title":"Can LLMs Use Linguistic Uncertainty Markers to Reliably Reflect Intrinsic Confidence?","created":"2026-05-27","cited_by_count":2,"area":null},{"id":"2607.11881","title":"Metacognition in LLMs: Foundations, Progress, and Opportunities","created":"2026-07-13","cited_by_count":1,"area":null},{"id":"2601.15130","title":"The Plausibility Trap: Using Probabilistic Engines for Deterministic Tasks","created":"2026-01-21","cited_by_count":0,"area":null},{"id":"2602.17027","title":"Transforming Behavioral Neuroscience Discovery with In-Context Learning and AI-Enhanced Tensor Methods","created":"2026-02-19","cited_by_count":0,"area":null},{"id":"2603.23514","title":"DepthCharge: A Domain-Agnostic Framework for Measuring Depth-Dependent Knowledge in Large Language Models","created":"2026-03-05","cited_by_count":0,"area":null},{"id":"2604.04791","title":"How Far Are We? Systematic Evaluation of LLMs vs. Human Experts in Mathematical Contest in Modeling","created":"2026-04-06","cited_by_count":0,"area":null},{"id":"2606.05206","title":"Ontology-constrained multi-LLM scoring of hypothesis support in the predictive processing literature","created":"2026-05-23","cited_by_count":0,"area":null},{"id":"2606.29630","title":"SFBench: The SciFy Scientific Feasibility Benchmark","created":"2026-06-28","cited_by_count":0,"area":null},{"id":"2609.30489","title":"BioEVAL: A global, multi-institutional benchmark of large language and multimodal models for bioengineering","created":"2026-09-24","cited_by_count":0,"area":null},{"id":"2609.35315","title":"Collaborative Principle Evolution via Evidence Transfer for Scientific Discovery","created":"2026-09-28","cited_by_count":0,"area":null},{"id":"2609.36187","title":"EvoMO-SR: Multiobjective LLM-based Evolution of Symbolic Expressions with substructure guidance","created":"2026-09-28","cited_by_count":0,"area":null},{"id":"2609.36726","title":"Can AI Scientists Change Their Minds? Prior-Evidence Conflict in Synthetic Universes","created":"2026-09-29","cited_by_count":0,"area":null},{"id":"2610.05197","title":"From Scientific Observations to Mechanisms: Benchmarking Hypothesis Generation by AI Scientists","created":"2026-10-04","cited_by_count":0,"area":null}],"next_cursor":null}