CauSim turns scarce causal reasoning labels into scalable supervised data by having LLMs incrementally construct complex executable structural causal models.
Large language models surpass human experts in predicting neuroscience results
7 Pith papers cite this work, alongside 111 external citations. Polarity classification is still indexing.
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NeuroAgent uses a hierarchical LLM agent framework with Generate-Execute-Validate loops to automate neuroimaging preprocessing, reaching 84.8% end-to-end correctness and 0.9518 AUC for Alzheimer's classification on 1470 ADNI subjects using four modalities.
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).
ED3R is an energy-aware distributed framework for hierarchical robotic wildfire detection that reports up to 97% success rate, 36% lower energy use, and 41% faster detection than baselines in simulations.
A survey of reasoning language model adoption across 28 ERC scientific disciplines finds large maturity gaps, especially when only public resources are counted.
The paper proposes a four-role framework for LLMs in scientific innovation and reviews methods, benchmarks, and limitations across Assistant, Collaborator, Scientist, and Evaluator roles.
A cross-disciplinary review of 151 studies concludes LLMs accelerate research workflows while introducing recurring technical and ethical risks, including ten it flags as underexplored.
citing papers explorer
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CauSim: Scaling Causal Reasoning with Increasingly Complex Causal Simulators
CauSim turns scarce causal reasoning labels into scalable supervised data by having LLMs incrementally construct complex executable structural causal models.
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NeuroAgent: LLM Agents for Multimodal Neuroimaging Analysis and Research
NeuroAgent uses a hierarchical LLM agent framework with Generate-Execute-Validate loops to automate neuroimaging preprocessing, reaching 84.8% end-to-end correctness and 0.9518 AUC for Alzheimer's classification on 1470 ADNI subjects using four modalities.
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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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ED3R: Energy-Aware Distributed Disaster Detection Enabled by Cooperative Robotic Agents
ED3R is an energy-aware distributed framework for hierarchical robotic wildfire detection that reports up to 97% success rate, 36% lower energy use, and 41% faster detection than baselines in simulations.
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Reasoning4Sciences: Bridging Reasoning Language Models to All Scientific Branches
A survey of reasoning language model adoption across 28 ERC scientific disciplines finds large maturity gaps, especially when only public resources are counted.
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Evolving Roles of LLMs in Scientific Innovation: Assistant, Collaborator, Scientist, and Evaluator
The paper proposes a four-role framework for LLMs in scientific innovation and reviews methods, benchmarks, and limitations across Assistant, Collaborator, Scientist, and Evaluator roles.
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From Text to Discovery: How Large Language Models Are Reshaping Research Across Scientific and Humanistic Disciplines
A cross-disciplinary review of 151 studies concludes LLMs accelerate research workflows while introducing recurring technical and ethical risks, including ten it flags as underexplored.