The New and Fresh analytics in Inference Endpoints

Welcome to Inference Providers on the Hub 🔥

Categories of Inference-Time Scaling for Improved LLM Reasoning

And an Overview of Recent Inference-Scaling Papers

Industrializing Prediction-Powered Inference: The GLIDE Library for Reliable GenAI and Agentic Systems Evaluation

arXiv:2605. 31278v2 Announce Type: replace-cross Abstract: Reliable evaluation of agentic systems requires unbiased estimates with valid uncertainty, but standard practice navigates between costly human annotation and biased LLM-as-judge proxies.

More Correct Mass, Worse Answers: Why Power Sampling Can Fail and How to Fix It

arXiv:2608. 14420v1 Announce Type: new Abstract: Power Sampling sharpens a language model's distribution over complete generation trajectories, offering a verifier-free way to improve reasoning at inference time.

Statistical Early Stopping for Reasoning Models

arXiv:2602. 13935v2 Announce Type: replace Abstract: While LLMs have seen substantial improvement in reasoning capabilities, they also sometimes overthink, generating unnecessary reasoning steps, particularly under uncertainty, given ill-posed or ambiguous queries.

Debiased Inference for AI-Generated Data without Gold-Standard Labels: Identification via Multiple Imperfect Measurements

The paper introduces Debiased Inference with Multiple Imperfect Measurements (DMM), a framework that uses several error‑prone AI measurements to perform valid downstream statistical inference without requiring costly gold‑standard labels. By assuming conditional independence of the measurements given the true label and unit‑level features, DMM leverages CP decomposition and semiparametric theory to prove consistency and asymptotic normality of its estimator. Simulations demonstrate that DMM yields valid inference and can improve efficiency when additional imperfect measurements are available, and the authors provide diagnostics for the key independence assumption.

Agentic-SQL Revisited: Autonomy-Based Taxonomy and Empirical Benchmark Analysis for LLM Text-to-SQL

arXiv:2608. 15389v1 Announce Type: new Abstract: LLM-based Text-to-SQL progress is reported across heterogeneous benchmarks, backbones, and inference protocols, making cross-system comparison fragile.

Test-Time Scaling in Reasoning LLMs: Inference Regimes, Evaluation, and Reproducibility

arXiv:2608. 04001v1 Announce Type: cross Abstract: Large language models can solve substantially harder reasoning problems with more inference-time compute.

Valid Inference with Synthetic Data via Task Exchangeability

arXiv:2606. 13629v1 Announce Type: cross Abstract: There is a proliferation of work arguing for the use of synthetic data in scientific research.

Optimal Self-Consistency for Efficient Reasoning with Large Language Models

arXiv:2511. 12309v2 Announce Type: replace-cross Abstract: Self-consistency (SC) is a widely used test-time inference technique for improving performance in chain-of-thought reasoning.

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.

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.