arXiv AI August 3, 2026 By Keigo Kusumegi, Xinyu Yang, Paul Ginsparg, Mathijs de Vaan, Toby Stuart, Yian Yin

A robust association between LLM use and scientific productivity: Assessing stopping-time selection

arXiv:2607. 28968v1 Announce Type: cross Abstract: Renault, Bergeaud, and Bosquet (hereafter RBB) argue that dating LLM adoption as the first month in which an author's abstract is flagged induces a stopping-time selection that can produce a positive event-study path even when there is no causal effect.

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

arXiv Machine Learning

Jul 22

HindsightBench: A Black-Box Behavioral Audit Protocol for Parametric Hindsight in Time-Indexed LLM Decision Tasks

arXiv:2607. 18867v1 Announce Type: new Abstract: Large language models leak parametric knowledge of realized outcomes into historical financial decision tasks.

By Haozhe Jia

llms robotics efficiency

arXiv Machine Learning

Jun 9

Benchmark Datasets for Lead-Lag Forecasting on Social Platforms

arXiv:2511. 03877v2 Announce Type: replace Abstract: Social and collaborative platforms emit multivariate time-series traces in which early interactions -- such as views, likes, or downloads -- are followed, sometimes months or years later, by higher impact like citations, sales, or reviews.

By Kimia Kazemian (Department of Computer Science, Cornell University), Zhenzhen Liu (Department of Computer Science, Cornell University), Yangfanyu Yang (Department of Information Science, Cornell University), Katie Luo (Department of Computer Science, Stanford University), Shuhan Gu (Department of Computer Science, Cornell University), Audrey Du (Department of Computer Science, Cornell University), Xinyu Yang (Department of Information Science, Cornell University), Jack Jansons (Department of Computer Science, Cornell University), Kilian Q. Weinberger (Department of Computer Science, Cornell University), John Thickstun (Department of Computer Science, Cornell University), Yian Yin (Department of Information Science, Cornell University), Sarah Dean (Department of Computer Science, Cornell University)

benchmarks safety

arXiv AI

Jun 15

The Shrinking Lifespan of LLMs in Science

arXiv:2604. 07530v2 Announce Type: replace-cross Abstract: Scaling laws describe how language model capabilities grow with compute and data, but say nothing about how long a model matters once released.

By Ana Tri\v{s}ovi\'c

llms

arXiv AI

Jul 31

Calibrate Globally, Measure Everywhere: Scaling LLM-Based Prevalence Measurement Across A/B Experiments

arXiv:2602. 16111v2 Announce Type: replace-cross Abstract: Online media platforms track the share of impressions associated with content attributes, or prevalence, to evaluate trade-offs and set guardrails in A/B experiments.

By Zehao Xu, Tony Paek, Kevin O'Sullivan, Attila Dobi

llms safety data-engineering

arXiv AI

Aug 7

Signal or Spurious Cue? A Randomized Audit of Survey-Country Metadata in LLM Social Inference

arXiv:2608. 06085v1 Announce Type: new Abstract: Survey-country metadata can improve an LLM's forecast of an individual response when informative, yet the same cue may redirect the forecast when assigned at random.

By Yifan Lyu, Xinran Li, Jiaqi Qiao, Xiujuan Xu

llms

arXiv AI

Jul 28

The Half-Lives of Generative-AI Evidence: A 40-Record Audit, a Claim-Currency Framework, and a Reflexive Case of Frontier-Model-Assisted Research

arXiv:2607. 24032v1 Announce Type: new Abstract: Generative-AI evaluations can become historical before publication, yet calendar age does not affect every conclusion equally.

By Carlo Iacono (Charles Sturt University, Australia)

llms

Related stories

HindsightBench: A Black-Box Behavioral Audit Protocol for Parametric Hindsight in Time-Indexed LLM Decision Tasks

Benchmark Datasets for Lead-Lag Forecasting on Social Platforms

The Shrinking Lifespan of LLMs in Science

Calibrate Globally, Measure Everywhere: Scaling LLM-Based Prevalence Measurement Across A/B Experiments

Signal or Spurious Cue? A Randomized Audit of Survey-Country Metadata in LLM Social Inference

The Half-Lives of Generative-AI Evidence: A 40-Record Audit, a Claim-Currency Framework, and a Reflexive Case of Frontier-Model-Assisted Research