CACM improves language-based drug discovery agents by 36.4% via protocol auditing, a grounded diagnostician, and compressed static/dynamic/corrective memory channels that localize failures and bias corrections.
Wang, Iskandar Sitdikov, Ciro Salcedo, Alireza Seif, and Zlatko K
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pArticleMap combines article embeddings, graph-based frontier extraction, and agentic LLMs to map nanomedicine literature and generate hypotheses, achieving 10.8% gold recovery and 61% future-neighborhood rate in retrospective benchmarks.
Empirical study of real NISQ order-finding data identifies dominant verified mass fraction as the strongest predictor of whether standard post-processing recovers the true order.
Zero-shot LLM agents with human personas predict individual social media reactions better than chance (MCC 0.29) but worse than conventional text classifiers (MCC 0.36).
AI will evolve from a research tool into a collaborator, fundamentally reshaping scientific collaboration, discovery, publishing, and evaluation while requiring continuous learning and idea diversity for original contributions.
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Constraint-Aware Corrective Memory for Language-Based Drug Discovery Agents
CACM improves language-based drug discovery agents by 36.4% via protocol auditing, a grounded diagnostician, and compressed static/dynamic/corrective memory channels that localize failures and bias corrections.
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Evidence-Grounded Frontier Mapping and Agentic Hypothesis Generation in Nanomedicine
pArticleMap combines article embeddings, graph-based frontier extraction, and agentic LLMs to map nanomedicine literature and generate hypotheses, achieving 10.8% gold recovery and 61% future-neighborhood rate in retrospective benchmarks.
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When Noisy Quantum Order Finding Remains Recoverable for Shor's Algorithm
Empirical study of real NISQ order-finding data identifies dominant verified mass fraction as the strongest predictor of whether standard post-processing recovers the true order.
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LLM Agents Predict Social Media Reactions but Do Not Outperform Text Classifiers: Benchmarking Simulation Accuracy Using 120K+ Personas of 1511 Humans
Zero-shot LLM agents with human personas predict individual social media reactions better than chance (MCC 0.29) but worse than conventional text classifiers (MCC 0.36).
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The Agentification of Scientific Research: A Physicist's Perspective
AI will evolve from a research tool into a collaborator, fundamentally reshaping scientific collaboration, discovery, publishing, and evaluation while requiring continuous learning and idea diversity for original contributions.