arXiv Machine Learning June 9, 2026 By Hongyu Chen, David Simchi-Levi, Ruoxuan Xiong

Partial Identification under Missing Data Using Weak Shadow Variables from Pretrained Models

Read the original on arXiv Machine Learning →

arXiv:2602. 16061v2 Announce Type: replace-cross Abstract: Estimating population quantities such as mean outcomes from user feedback is fundamental to platform evaluation and social science, yet feedback is often missing not at random (MNAR): users with stronger opinions are more likely to respond, so standard estimators are biased and the estimand is not identified without additional assumptions.

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

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arXiv AI

Jun 30

Using Large Language Models as Low-Cost Statistical Estimators for Human-Response Data

arXiv:2606. 30372v1 Announce Type: new Abstract: Quantitative research across the social and behavioral sciences depends on human subject experiments that are expensive, slow, and subject to sampling bias.

By Haobo Yang

llms safety

arXiv AI

Jul 22

Estimating Rare Events in Language Models with Proper Evaluation

arXiv:2607. 18454v1 Announce Type: cross Abstract: Quantifying the risk of rare failures in language models, such as those triggered by adversarial distribution shifts or very large-scale deployments, requires estimating probabilities far too small for random sampling.

By Nikita Y. Parulekar, Anqi Liu

llms safety

arXiv Machine Learning

Jul 7

How Much is Left? LLMs Linearly Encode Their Remaining Output Length

arXiv:2607. 05316v1 Announce Type: cross Abstract: Large language models generate one token at a time, yet their responses show remarkably consistent length structure: step-by-step solutions converge in predictable token counts, retrievals stop after a few sentences, retractions extend responses by measurable amounts.

By Mohamed Amine Merzouk, Dmitri Carpov, Mirko Bronzi, Damiano Fornasiere, Adam Oberman

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arXiv AI

Jul 13

Rectification Difficulty and Optimal Sample Allocation in LLM-Augmented Surveys

arXiv:2604. 17267v2 Announce Type: replace Abstract: Large Language Models can generate synthetic survey responses at low cost, but their accuracy varies unpredictably across questions.

By Zikun Ye, Hema Yoganarasimhan

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arXiv Machine Learning

Jun 4

STRIDE: Training Data Attribution via Sparse Recovery from Subset Perturbations

arXiv:2606. 05165v1 Announce Type: new Abstract: Training Data Attribution (TDA) seeks to trace a model's predictions back to its training data.

By Rishit Dagli, Abir Harrasse, Luke Zhang, Florent Draye, Amirali Abdullah, Bernhard Sch\"olkopf, Zhijing Jin

llms benchmarks

arXiv AI

Aug 12

On Solomonoff Induction in Large Language Models and the Limits of Self-Improving: The Singularity Is Not Near Without Symbolic Model Synthesis

arXiv:2601. 05280v3 Announce Type: replace-cross Abstract: On the one hand, the question of whether large language models (LLMs) are Solomonoff induction estimators has become an explicit question at the intersection of Algorithmic Information Theory (AIT) and Machine Learning (ML) of great interest.

By Hector Zenil

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Related stories

Using Large Language Models as Low-Cost Statistical Estimators for Human-Response Data

Estimating Rare Events in Language Models with Proper Evaluation

How Much is Left? LLMs Linearly Encode Their Remaining Output Length

Rectification Difficulty and Optimal Sample Allocation in LLM-Augmented Surveys

STRIDE: Training Data Attribution via Sparse Recovery from Subset Perturbations

On Solomonoff Induction in Large Language Models and the Limits of Self-Improving: The Singularity Is Not Near Without Symbolic Model Synthesis