StepBack: Step-Level Error-Localized Resampling for Efficient Test-Time Reasoning

YesterdayReported by arXiv39

StepBack: Step-Level Error-Localized Resampling for Efficient Test-Time Reasoning. It centres on Reasoning Models, and also names Benchmarks and Inference. Reported by arXiv. Bharat Hunt files it under AI Research — the section covering papers, benchmarks, evaluations, interpretability and safety results.

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Reported by arXiv

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arXivStepBack: Step-Level Error-Localized Resampling for Efficient Test-Time ReasoningYesterdayYang Ouyang

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GeoReader: Learning Latent Geometry for Spatial Reasoning in Vision-Language Models

GeoReader: Learning Latent Geometry for Spatial Reasoning in Vision-Language Models. It centres on AI Safety, and also names Fine-tuning and Inference. Reported by arXiv. Bharat Hunt files it under AI Models and AI Research — the section covering a new or updated model, its capabilities, benchmarks or availability.

arXiv13 Oct 2026

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HARISSA: Inference-Time Self-Checks for Efficient and Safe Local Language Model Deployment

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StepBack: Step-Level Error-Localized Resampling for Efficient Test-Time Reasoning

Companies, models and people in this story

Reported by arXiv

GeoReader: Learning Latent Geometry for Spatial Reasoning in Vision-Language Models

HARISSA: Inference-Time Self-Checks for Efficient and Safe Local Language Model Deployment

SEABench: Benchmarking Endogenous Misalignment In Self-Evolving Agents

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Companies, models and people in this story

Reported by arXiv

Related AI coverage

GeoReader: Learning Latent Geometry for Spatial Reasoning in Vision-Language Models

HARISSA: Inference-Time Self-Checks for Efficient and Safe Local Language Model Deployment

SEABench: Benchmarking Endogenous Misalignment In Self-Evolving Agents

Nested Shared-Operator Recursion on ARC-AGI-1: An Empirical Study of HRM, TRM and URM