rishabh ranjan

Parul Pandey Verified account @pandeyparul · 3d

Relational databases are a lingua franca for business data as most enterprise data is stored across multiple tables. However, many models for structured data expect one flat table.

Yes, we can join tables and create summaries, but that results in the loss of some fine-grained information.

This is where relational foundation models come in. They work directly with related tables and preserve their structure. Because they are already pre-trained, we can use them without training a new model on our own data. @NVIDIA's Kumo Relational and Prior Labs’ TabPFN-Rel are some examples of such models.

To see how this works, let’s look at Kumo Relational. Like other tabular foundation models, it uses in-context learning i.e it makes predictions from examples we show it at prediction time, without being trained on our data.

It mainly works in three stages:
• Intra-table row embeddings wherein each table is converted into embeddings, one for every row, and the known outcomes of the context rows are added here.
• Inter-table message passing where rows linked across tables exchange embeddings over several rounds, so each row learns from the rows connected to it.
• finally, In-context learning

I’ve put together a small Kaggle notebook that runs Kumo Relational with NVIDIA’s Structured Data Models library — kaggle.com/code/parulpand…

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Vignesh Kothapalli Verified account @kvignesh1420 · 2d

🎉 Super excited to introduce Memorilla 🦍

A memory system that turns long textual inputs and agent trajectories into latent inputs for a frozen ❄️ LLM.

⚡ Memorilla can use up to 4000x fewer input tokens and be 27x faster than full context inference without training the LLM!

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Liana Verified account @lianapatel_ · Oct 21

Introducing ✨Token-Gated Language Models✨ for up to 11x faster prefill by learning to skim context, conditionally forwarding only tokens that matter.

Akin to the shift from dense models to MoEs, the next frontier of efficient LLMs is conditional computation over a new scaling axis: context tokens🧵👇

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Jure Leskovec Verified account @jure · Oct 13

🚀 I'm excited to announce the Stanford Data-Centric AI Workshop 2026 (aka the Stanford Graph Learning Workshop), happening Tuesday, November 3, 2026 at @Stanford University (with online livestream). Free registration! Submit a poster. 📍

This year's workshop will spotlight three fast-moving frontiers in data-centric AI:
-- AI Foundations — Making frontier models fast and scalable
-- AI for Science and Biology — Agents and foundation models across the scales of biology
-- Relational and Tabular Foundation Models — Learning directly on the structured data that runs the world

We're bringing together Stanford researchers and industry partners for a full day of research talks, case studies, and collaborative discussion. Whether you're working in industry, academia, or startup land, there will be something to spark your curiosity and drive your work forward.

🔍 Want to share your work? Our Call for Posters and Demos is also open now.

✅ Register now (free): snap.stanford.edu/graphlearning-…

📅 Save the date: Nov 3, 2026

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