arXiv Machine Learning July 14, 2026 By Tung Dang, The Hung Phung, Son Lam Nguyen, Tu Nguyen

Trustworthy synthetic data for campaign decision support: strategy simulation fidelity and the PolicySynth framework

Read the original on arXiv Machine Learning →

arXiv:2607. 11269v1 Announce Type: new Abstract: Decision support systems (DSS) increasingly run retention what-if analysis on synthetic customer populations, because privacy constraints preclude unrestricted use of real data.

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

safety

arXiv Machine Learning

Jul 30

Lottery Tickets Are Not Deployment Tickets

arXiv:2607. 27031v1 Announce Type: new Abstract: Reports on how sparsification, compression, and lottery tickets change model behavior have been mixed in the prior literature, with beneficial effects observed in some studies and adverse effects in others.

By Bum Jun Kim

efficiency

arXiv AI

Jul 22

Distribution-First Population Simulation: Collapse, Calibration, and Recall in Non-WEIRD LLM Persona Modeling

arXiv:2607. 18310v1 Announce Type: cross Abstract: Synthetic-population tools increasingly run every individual as an independent large language model (LLM) agent.

By Gurkan Ozkan

llms agents

arXiv AI

Jul 22

SFGA: A Statistics-First Gating Architecture with Adjudicative Escalation for Trustworthy SFT Data Procurement

arXiv:2607. 18960v1 Announce Type: cross Abstract: Procuring supervised fine-tuning (SFT) data forces a buyer to decide, before any downstream training, whether a candidate corpus is worth acquiring.

By Arther Tian, Alex Ding, Simon Wu, Aaron Chan

llms fine-tuning benchmarks

arXiv AI

Jul 16

Operational Evidence Gaps for LLMs in Fraud Detection and Trust-and-Safety Workflows

arXiv:2607. 13078v1 Announce Type: cross Abstract: LLMs are now proposed for fraud detection, scam investigation, content moderation, and other trust-and-safety workflows.

By Keyur Gabani

llms agents benchmarks safety

arXiv AI

1d ago

Calibrated Trust, Not Sharper Prediction: An Empirical Test of Uncertainty Fusion

arXiv:2608. 14617v1 Announce Type: cross Abstract: A recurring proposal in legal AI is to improve case-outcome prediction by fusing uncertainty tools (evidence graphs with belief propagation, sequential Bayesian odds updating, Dempster-Shafer combination, and conformal prediction) into one pipeline.

By Surya Saka

llms

Hugging Face Trending Papers

Jul 21

SFGA: A Statistics-First Gating Architecture with Adjudicative Escalation for Trustworthy SFT Data Procurement

Procuring supervised fine-tuning (SFT) data forces a buyer to decide, before any downstream training, whether a candidate corpus is worth acquiring. We present \sys{}, a statistics-first gating architecture that treats procurement as a cost-aware routing problem over three intrinsic quality axes -- diversity, utility, and redundancy.

llms fine-tuning benchmarks

Related stories

Lottery Tickets Are Not Deployment Tickets

Distribution-First Population Simulation: Collapse, Calibration, and Recall in Non-WEIRD LLM Persona Modeling

SFGA: A Statistics-First Gating Architecture with Adjudicative Escalation for Trustworthy SFT Data Procurement

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Calibrated Trust, Not Sharper Prediction: An Empirical Test of Uncertainty Fusion

SFGA: A Statistics-First Gating Architecture with Adjudicative Escalation for Trustworthy SFT Data Procurement