New agentic memory framework uses 118K tokens per query. LangMem burns through 3.26M.

NUS researchers' MRAgent framework reduces LLM agent memory retrieval to 118K tokens per query — vs. 3.26M for LangMem — using step-by-step reasoning.
The Tech Edvocate

How to connect to MySQL remotely

Spread the love“`html 1. Understanding MySQL and Its Importance MySQL is one of the most popular relational database management systems (RDBMS) in the world, powering countless applications ranging ...

How to use Google query expansion to improve content visibility

Your content may already be surfacing for searches you never planned for. Here's how to identify those opportunities and act ...
Nature

A comparative study of large language models (ChatGPT/DeepSeek) in generating structured thyroid ultrasound reports

We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors ...
IEEE

Example Generalizing Network Configuration Synthesizer via Graph-Informed Large Language Models

Abstract: Network configuration synthesis promises to increase the efficiency of network management by reducing human involvement. However, despite significant advances in this field, existing ...
IEEE

Characterizing Secretion System Effector Proteins With Structure-Aware Graph Neural Networks and Pre-Trained Language Models

Abstract: The Type III Secretion Systems (T3SSs) play a pivotal role in host-pathogen interactions by mediating the secretion of type III secretion system effectors (T3SEs) into host cells. These ...