A multi-agent framework uses natural language to generate and execute Python code for dynamic bibliometric analysis including networks, clustering, and automated reports.
LLMs for science: Usage for code generation and data analysis
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Empirical evaluation of quantization effects on eight LLMs across bit widths, showing performance generally declines at lower precision but with model-size-dependent resilience and acceptable accuracy at 2 bits for many cases.
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AI-Augmented Bibliometric Framework: A Paradigm Shift with Agentic AI for Dynamic, Snippet-Based Research Analysis
A multi-agent framework uses natural language to generate and execute Python code for dynamic bibliometric analysis including networks, clustering, and automated reports.
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K-Quantization and its Impact on Output Performance
Empirical evaluation of quantization effects on eight LLMs across bit widths, showing performance generally declines at lower precision but with model-size-dependent resilience and acceptable accuracy at 2 bits for many cases.