Is it agentic enough? Benchmarking open models on your own tooling

A comparative review of modern large language model paradigms: GPT-4, BERT, Gemini, and DeepSeek

Comparison of GPT-4, BERT (bidirectional encoder representations from transformers), Gemini, and DeepSeek large language models (LLM), focusing on architectures, training methodologies, and real-world applications reveals GPT-4 excels in natural language generation and complex reasoning, supporting up to 128K tokens with moderate latency and higher costs making it effective for conversational artificial intelligence (AI).

LLMs Leak Training Data Beyond Verbatim Memorization: Extraction via Membership Decoding

The Membership Decoding method is a plug-and-play replacement for standard decoding that requires only black-box token probabilities, and a new token-level membership inference method is proposed by leveraging likelihood from reference models, shifting the generation from the original token distribution to the member token distribution.

A Multiagent Large Language Model–Based System for Early-Stage Building Layout Planning

A multiagent large language model (LLM)–based system for early-stage building layout planning, which enables flexible design requirement inputs and robust spatial reasoning and demonstrated significant improvements in both geometric quality and semantic alignment over a baseline LLM-only system.

A semi-supervised multi-stage pipeline for low-resource industrial condition monitoring by injecting domain knowledge via cross-modal multimodal large language model fine-tuning