Welcome to Inference Providers on the Hub 🔥

The New and Fresh analytics in Inference Endpoints

Categories of Inference-Time Scaling for Improved LLM Reasoning

And an Overview of Recent Inference-Scaling Papers

Introducing Three New Serverless Inference Providers: Hyperbolic, Nebius AI Studio, and Novita 🔥

Kimina-Prover: Applying Test-time RL Search on Large Formal Reasoning Models

Apriel-H1: The Surprising Key to Distilling Efficient Reasoning Models

Inference for PROs

Lifted Causal Inference

arXiv:2606. 28024v1 Announce Type: new Abstract: Lifted inference exploits indistinguishabilities in probabilistic graphical models by using a representative for indistinguishable objects, thereby speeding up query answering while maintaining exact answers.

Approximating Probabilistic Inference in Statistical EL with Knowledge Graph Embeddings

arXiv:2407. 11821v2 Announce Type: replace Abstract: Statistical information is ubiquitous but drawing valid conclusions from it is prohibitively hard.

Renormalising Generative Models for Active Inference: Foundations, Derivations, and Verification

arXiv:2608. 09512v1 Announce Type: new Abstract: Active inference offers a unified framework for perception, learning, and action, but scaling discrete active-inference models to rich spatial and temporal domains remains difficult.

Renormalising Generative Models for Active Inference: Foundations, Derivations, and Verification

Active inference offers a unified framework for perception, learning, and action, but scaling discrete active-inference models to rich spatial and temporal domains remains difficult. Renormalising generative models (RGMs) address this challenge by composing discrete generative models across spatial and temporal scales, coarse-graining lower-level states and paths into higher-level causes for objects, events, and action.

SciR: A Controllable Benchmark for Scientific Reasoning in LLMs

arXiv:2606. 13020v1 Announce Type: new Abstract: Three paradigmatic forms of inference recur across scientific reasoning: deduction, induction, and causal abduction.

Generative language modeling for automated theorem proving