From Feelings to Metrics: Understanding and Formalizing How Users Vibe-Test LLMs Paper • 2604.14137 • Published Apr 16 • 10
nvidia/NVIDIA-Nemotron-Labs-3-Puzzle-75B-A9B-BF16 Text Generation • 75B • Updated 10 days ago • 3.2k • 55
nvidia/NVIDIA-Nemotron-Labs-3-Puzzle-75B-A9B-NVFP4 Text Generation • 45B • Updated 10 days ago • 50k • 120
nvidia/NVIDIA-Nemotron-Labs-3-Puzzle-75B-A9B-FP8 Text Generation • 78B • Updated 10 days ago • 23.2k • 17
Knowing When to Quit: A Principled Framework for Dynamic Abstention in LLM Reasoning Paper • 2604.18419 • Published Jun 12 • 3
Beyond IID: How General Are Tabular Foundation Models, Really? Paper • 2606.30410 • Published 19 days ago • 44
LLM Explainability with Counterfactual Chains and Causal Graphs Paper • 2606.05972 • Published Jun 4 • 18
A Matter of TASTE: Improving Coverage and Difficulty of Agent Benchmarks Paper • 2605.28556 • Published May 27 • 75
A Matter of TASTE: Improving Coverage and Difficulty of Agent Benchmarks Paper • 2605.28556 • Published May 27 • 75
Efficient Video Sampling: Pruning Temporally Redundant Tokens for Faster VLM Inference Paper • 2510.14624 • Published Oct 16, 2025 • 2
A Matter of TASTE: Improving Coverage and Difficulty of Agent Benchmarks Paper • 2605.28556 • Published May 27 • 75
Predicting Decisions of AI Agents from Limited Interaction through Text-Tabular Modeling Paper • 2605.12411 • Published May 12 • 49
MulTaBench: Benchmarking Multimodal Tabular Learning with Text and Image Paper • 2605.10616 • Published May 11 • 142
Running on CPU Upgrade Featured 3.24k The Smol Training Playbook 📚 3.24k The secrets to building world-class LLMs
TabSTAR: A Foundation Tabular Model With Semantically Target-Aware Representations Paper • 2505.18125 • Published May 23, 2025 • 113