arXiv Machine Learning June 24, 2026 By Wietse Stienstra

One Ruler: A Same-Hands Re-Evaluation of Bivariate Causal Direction on Tuebingen, with a Parameter-Free Compression Baseline

Read the original on arXiv Machine Learning →

arXiv:2606. 23767v1 Announce Type: new Abstract: Headline accuracies on the Tuebingen cause-effect pairs are routinely compared across papers even though each is measured under its authors' own protocol -- different pair subsets, weightings, model-selection, and decision rates.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

efficiency

arXiv Machine Learning

1d ago

Certifying Compressed Language Models: An Audit and a Statistical Toolkit

arXiv:2608. 15046v1 Announce Type: new Abstract: A fraction of a point of benchmark accuracy is the usual evidence that a compressed model is equivalent to its original.

By Amogh Singh

llms benchmarks

arXiv Machine Learning

Jul 8

Heckman-Corrected Epistemic Uncertainty: Selection on Unobservables Defeats Importance Weighting

arXiv:2607. 05806v1 Announce Type: new Abstract: Training data for machine learning is routinely collected by a selection process the model never sees: loans are observed only when granted, outcomes only when a test was ordered.

By Gunner Levi Howe

safety

arXiv Machine Learning

Jul 1

Calibration, Not Compilation: Detecting and Repairing Misspecified Probabilistic Programs Written by Language Models

arXiv:2606. 31630v1 Announce Type: new Abstract: Language models increasingly write probabilistic programs (in NumPyro, Stan, or Pyro), but a program that compiles, runs, and passes every unit test can still be \emph{statistically} wrong -- a Gaussian likelihood for heavy-tailed data, a Poisson for over-dispersed counts, an invalid prior support, or a pathological parameterization.

By Jian Xu, Delu Zeng, John Paisley, Qibin Zhao

llms benchmarks

arXiv Machine Learning

Aug 4

UpliftBench: Revealing Outcome-Regime and Objective Mismatch in Uplift Evaluation

arXiv:2608. 00915v1 Announce Type: new Abstract: Uplift modeling (conditional-average-treatment-effect estimation) drives personalized targeting, yet published uplift benchmarks frequently disagree on which estimator performs best; we show the disagreement is substantially about metrics, not models.

By Binshuang Li

benchmarks safety

arXiv Machine Learning

Aug 11

Which Decisions Low-Bit Quantization Breaks, and How to Predict Them

arXiv:2608. 06564v2 Announce Type: replace Abstract: Quantization is how large language models are actually deployed, and below four bits it hurts.

By Zekun Wu, Swati Dhiman, Adriano Koshiyama

llms agents efficiency benchmarks safety

arXiv Machine Learning

Jul 31

Sample More, Reflect Less: Self-Refine and Reflexion Lose to Repeated Sampling at Equal Token Cost, from 1.5B to 7B

arXiv:2607. 28576v1 Announce Type: cross Abstract: Methods that make a language model plan, criticise and rewrite its own answer, reflect on mistakes, pick the best of several attempts, or debate with copies of itself nearly all make it generate far more text than a single chain of thought.

By Iliya Mirzaei

llms benchmarks

Related stories

Certifying Compressed Language Models: An Audit and a Statistical Toolkit

Heckman-Corrected Epistemic Uncertainty: Selection on Unobservables Defeats Importance Weighting

Calibration, Not Compilation: Detecting and Repairing Misspecified Probabilistic Programs Written by Language Models

UpliftBench: Revealing Outcome-Regime and Objective Mismatch in Uplift Evaluation

Which Decisions Low-Bit Quantization Breaks, and How to Predict Them

Sample More, Reflect Less: Self-Refine and Reflexion Lose to Repeated Sampling at Equal Token Cost, from 1.5B to 7B