Paper List
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Formation of Artificial Neural Assemblies by Biologically Plausible Inhibition Mechanisms
This work addresses the core limitation of the Assembly Calculus model—its fixed-size, biologically implausible k-WTA selection process—by introducing...
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How to make the most of your masked language model for protein engineering
This paper addresses the critical bottleneck of efficiently sampling high-quality, diverse protein sequences from Masked Language Models (MLMs) for pr...
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Module control in youth symptom networks across COVID-19
This paper addresses the core challenge of distinguishing whether a prolonged societal stressor (COVID-19) fundamentally reorganizes the architecture ...
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JEDI: Jointly Embedded Inference of Neural Dynamics
This paper addresses the core challenge of inferring context-dependent neural dynamics from noisy, high-dimensional recordings using a single unified ...
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ATP Level and Phosphorylation Free Energy Regulate Trigger-Wave Speed and Critical Nucleus Size in Cellular Biochemical Systems
This work addresses the core challenge of quantitatively predicting how the cellular energy state (ATP level and phosphorylation free energy) governs ...
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Packaging Jupyter notebooks as installable desktop apps using LabConstrictor
This paper addresses the core pain point of ensuring Jupyter notebook reproducibility and accessibility across different computing environments, parti...
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SNPgen: Phenotype-Supervised Genotype Representation and Synthetic Data Generation via Latent Diffusion
This paper addresses the core challenge of generating privacy-preserving synthetic genotype data that maintains both statistical fidelity and downstre...
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Continuous Diffusion Transformers for Designing Synthetic Regulatory Elements
This paper addresses the challenge of efficiently generating novel, cell-type-specific regulatory DNA sequences with high predicted activity while min...
Developing the PsyCogMetrics™ AI Lab to Evaluate Large Language Models and Advance Cognitive Science
Marywood University | The University of Scranton | University of North Carolina Wilmington | California State University Dominguez Hills
30秒速读
IN SHORT: This paper addresses the critical gap between sophisticated LLM evaluation needs and the lack of accessible, scientifically rigorous platforms that integrate psychometric and cognitive science methodologies for non-technical stakeholders.
核心创新
- Methodology Introduces the first cloud-based platform applying Classical Test Theory (CTT) and psychometric validity principles (Cronbach's α > .70, AVE > .50) to systematically evaluate LLMs as cognitive entities rather than mere tools.
- Methodology Implements a three-cycle Action Design Science framework (Relevance-Rigor-Design) with nested Build–Intervene–Evaluate loops, bridging Popperian falsifiability, Cognitive Load Theory, and stakeholder requirements into a unified evaluation system.
- Biology Validates that modern LLMs (GPT-4, LLaMA-3) satisfy core psychometric validity criteria—including convergent, discriminant, predictive, and external validity—and outperform earlier models (GPT-3.5, LLaMA-2) across these dimensions.
主要结论
- The PsyCogMetrics™ AI Lab successfully operationalizes psychometric principles with demonstrated reliability metrics (Cronbach's α > .70) and validity frameworks (convergent/discriminant validity) for LLM evaluation.
- The platform addresses three critical pain points: mitigates benchmark saturation through dynamic evaluation, reduces data contamination via reproducible workflows, and expands coverage through cognitive science methodologies.
- Design validation shows GPT-4 and LLaMA-3 satisfy psychometric validity criteria and outperform earlier models, with GPT-4 reaching six-year-old human parity on Theory of Mind vignettes (Strachan et al., 2024).
摘要: This study presents the development of the PsyCogMetrics™ AI Lab (https://psycogmetrics.ai), an integrated, cloud-based platform that operationalizes psychometric and cognitive-science methodologies for Large Language Model (LLM) evaluation. Framed as a three-cycle Action Design Science study, the Relevance Cycle identifies key limitations in current evaluation methods and unfulfilled stakeholder needs. The Rigor Cycle draws on kernel theories such as Popperian falsifiability, Classical Test Theory, and Cognitive Load Theory to derive deductive design objectives. The Design Cycle operationalizes these objectives through nested Build–Intervene–Evaluate loops. The study contributes a novel IT artifact, a validated design for LLM evaluation, benefiting research at the intersection of AI, psychology, cognitive science, and the social and behavioral sciences.