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

Welcome to Inference Providers on the Hub 🔥

Accelerated Inference with Optimum and Transformers Pipelines

DODR: Deterministic Operator-Driven Reasoning in Latent Space

The paper introduces DODR, a deterministic operator‑driven reasoning architecture that models reasoning as graph computations in a high‑dimensional linear‑algebraic space, replacing token‑level sampling with matrix operations. Reasoning states are snapshot vectors of semantic units, and three trainable matrix operators—deduction, induction, and abduction—implement Peirce’s inference types. Experiments on 503 records demonstrate near‑perfect deduction, high generalization for induction, and significant gains for abduction, while the design guarantees zero hallucination and supports continual learning.

OpenAI o1-mini

Advancing cost-efficient reasoning

Jalapeño’s first results show industry-leading speed and efficiency in AI inference

OpenAI’s new custom inference chip, Jalapeño, achieves industry-leading speed and efficiency in AI inference. It delivers faster, more power‑efficient performance with higher throughput and lower latency for modern models. The chip represents a significant hardware advancement for deploying AI workloads.

Enhancing Small Language Models Reasoning through Knowledge Graph Grounding

arXiv:2607. 14149v1 Announce Type: new Abstract: Although large language models (LLMs) have set benchmarks for zero-shot reasoning, their deployment remains cost-prohibitive and environmentally taxing.

BAM! Bayesian Anything Model: a foundation model for generative computational imaging

arXiv:2609.39660v1 Announce Type: new Abstract: Generative models are transforming Bayesian computational imaging, yet the field still lacks physics-aware foundation models. Current practice falls in...

Pre-Compiled Pipeline Shards for Distributed LLM Inference on Intel AI PC Fleets

The paper presents a method for distributing large language model inference across multiple Intel AI PCs by splitting the model into pipeline shards, each pre‑compiled into an OpenVINO graph. Three key techniques—beam_idx Gather to enable GPU optimizations, speculative decoding on stateful models, and interleaved micro‑batching—allow a two‑node Llama 3.1 8B INT4 pipeline to serve two users at 1.79× the throughput of a single‑node model, while a four‑node deployment can run a 70B model that no single PC can hold. The authors provide code, benchmark logs, and reproduction scripts on GitHub.

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.

Symbolic Separation: Grounding Deep Agents in Knowledge Graphs for Trustworthy Operational Data Analytics

The paper introduces Symbolic Separation, a method that grounds deep learning agents in knowledge graphs to improve reliability in operational data analytics. By restricting agent actions to an ontology-constrained Virtual Knowledge Graph with deterministic pre-execution validation, the approach transforms complex queries into validated graph traversals rather than relying on LLM-inferred joins. In experiments on 49.9 TB of supercomputer telemetry, the Neurosymbolic Deep Analyst achieved an 86% task‑success rate, eliminated silent data‑integrity errors, and reduced token costs by 2.4× compared to a non‑symbolic baseline.

Neuro-symbolic learning over OWL 2 DL via consequence-based compilation to differentiable circuits

Baobab compiles an OWL 2 DL (ΣROIQ) ontology with a finite ABox into a Sentential Decision Diagram (SDD), saturating a propositional core and instantiating remaining DL features over the active domain. The resulting evidence‑conditioned weighted model count trains a perception network to recognize real images under partial ABox supervision, enabling a CNN to recover latent ontology concepts that an independent perception would miss. When supervision allows multiple ontology‑consistent completions, Baobab’s mixture indexed by query justifications represents the calibrated posterior, achieving Bayes‑optimal performance on a real‑image MNIST task where single‑WMC and learned mixtures fail, thereby characterizing and mitigating reasoning shortcuts in a non‑Horn description logic.

DeepSWIP: Quotient-WMC Counterfactuals for Neural Probabilistic Logic Programs

arXiv:2606. 20526v1 Announce Type: new Abstract: Neurosymbolic systems such as DeepProbLog combine neural perception with probabilistic logic, but standard inference is associational.