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Towards Multi-Label Graph Foundation Models: from Single-Vector Representation Learning to Multi-Semantic Basis Learning

arXiv:2608.06394v1 Announce Type: new Abstract: Multi-label node classification is an important yet challenging task in graph learning, where nodes exhibit multiple semantics simultaneously. Existing methods for multi-label node classification can effectively model multiple labels, while only considering in-domain scenarios where the model needs to be trained and tested within the same graph domain, resulting in…

These startups are chasing the next big thing in LLMs

MIT Technology Review’s What’s Next series looks across industries, trends, and technologies to give you a first look at the future. You can read the rest of them here. Way back in the summer of 2017, AI researchers at Google put out a paper called “Attention Is All You Need,” in which they described a new…

AI for science needs reasoning, not just data

Every few decades, someone announces that science has reached its end. In 1903, the revered physicist Albert Michelson wrote that the “facts of physical science have all been discovered.” In the 1980s, Stephen Hawking predicted that theoretical physics might be finished by the end of the century. With the explosive arrival of artificial intelligence, the…

The Download: AI agents for science, and the “censorship-industrial complex”

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. AI for science needs reasoning, not just data —Eric Schmidt, the former CEO of Google and the cofounder of Schmidt Sciences, and Suhas Mahesh, who leads the AI for science work…

How to Implement Structured Output with Local LLMs

Why use it? How to implement it? What can we do when it fails? The post How to Implement Structured Output with Local LLMs appeared first on Towards Data Science.

I Thought Loading Data Was the Finish Line. It Was the Starting Point.

Building my first dbt models and learning what “analysis-ready” data actually means The post I Thought Loading Data Was the Finish Line. It Was the Starting Point. appeared first on Towards Data Science.

Before Q, K, and V: Reconstructing the Transformer

Many Transformer explainers start with the finished architecture. We ask why it looks the way it does. The post Before Q, K, and V: Reconstructing the Transformer appeared first on Towards Data Science.

Building a Streamlit UI for My LangGraph AI Agent

Building a production-ready web interface for a stateful LangGraph agent The post Building a Streamlit UI for My LangGraph AI Agent appeared first on Towards Data Science.

Loop Engineering for Listing Questions: When the Answer Is Every Passage, Not the Top One

Enterprise Document Intelligence [Vol.1 #12] - The category of question most RAG pipelines silently fail on, and the pipeline shape that handles them The post Loop Engineering for Listing Questions: When the Answer Is Every Passage, Not the Top One appeared first on Towards Data Science.

Matplotlib vs Plotly: Which Python Chart Tool Should You Choose?

From Static Plots to Interactive Data Exploration The post Matplotlib vs Plotly: Which Python Chart Tool Should You Choose? appeared first on Towards Data Science.