Input-Blind Controls Produce Substantial Oracle Headroom for Layer Programs in Multiple-Choice Evaluation

7 Oct 2026Reported by arXiv21

Input-Blind Controls Produce Substantial Oracle Headroom for Layer Programs in Multiple-Choice Evaluation. It centres on Benchmarks, and also names Inference and Llama. Reported by arXiv. Bharat Hunt files it under AI Research and AI Models — the section covering papers, benchmarks, evaluations, interpretability and safety results.

Written by Bharat Hunt from the headline and the coverage below. The original reporting is the source of truth.

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Reported by arXiv

First seen 7 oct 2026

arXivInput-Blind Controls Produce Substantial Oracle Headroom for Layer Programs in Multiple-Choice Evaluation7 Oct 2026Yibei Guo

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Measuring the Serving Stack Instead of the Model: Hidden Confounds in Local Tool-Use Evaluation. It centres on Benchmarks, and also names Ollama and Phi. Reported by arXiv. Bharat Hunt files it under AI Tools and AI Models — the section covering applications people use — assistants, editors, search, productivity.

arXiv22 Sept 2026

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Principled Under Pressure: Post-Training Decides Whether LLMs Act on Their Own Moral Judgment

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Input-Blind Controls Produce Substantial Oracle Headroom for Layer Programs in Multiple-Choice Evaluation

Companies, models and people in this story

Reported by arXiv

Measuring the Serving Stack Instead of the Model: Hidden Confounds in Local Tool-Use Evaluation

Principled Under Pressure: Post-Training Decides Whether LLMs Act on Their Own Moral Judgment

Fine-Tuning LLMs for Translation: General Forgetting Mitigation Does Not Preserve MT-Specific Instruction Following

Stale-Document Poisoning: When Outdated Retrieval Overrides Correct Model Answers

Companies, models and people in this story

Reported by arXiv

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Measuring the Serving Stack Instead of the Model: Hidden Confounds in Local Tool-Use Evaluation

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