arXiv AI July 15, 2026 By Rahul Gupta, Abhinav Mohanty, Payal Motwani, Venkatesh Saligrama, Satyapriya Krishna, Connor Harris, Gary Anthony Ackerman, Brandon Behlendorf, Tom Hobson, Theodore Wilson, Spyros Matsoukas

A Threshold Exceedance Framework for CBRN Uplift Evaluation in Frontier Language Models

Read the original on arXiv AI →

arXiv:2607. 12200v1 Announce Type: new Abstract: As frontier language models advance, policymakers and model developers need methods for assessing whether model access materially increases a non-expert actor's ability to plan high-consequence Chemical, Biological, Radiological, or Nuclear (CBRN) misuse relative to public tools alone.

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

llms

arXiv AI

Jun 29

CalBrief: A Pilot Diagnostic Benchmark for Evidence-Calibrated Scientific Briefing with Large Language Models

arXiv:2606. 27383v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used as research assistants, yet it remains unclear whether they can calibrate research takeaways to the strength and scope of the supporting evidence.

By Yu Fu, Yongqi Kang, Yong Zhao

llms benchmarks

arXiv AI

Jul 16

Protective Capacity Hallucination: When Large Language Models Claim Nonexistent Capabilities

arXiv:2607. 13596v1 Announce Type: cross Abstract: When cast as the protector of a vulnerable user yet given no explicit capability boundary, a large language model (LLM) may respond not by acknowledging its limits but by claiming to have taken -- or to be taking -- a real-world protective action it cannot perform, such as contacting emergency services or administering care.

By Eunna Lee, Jungpyo Nam, Sunjun Hwang

llms safety

arXiv AI

Jul 22

SciHazard: A Benchmark for Measuring Scientific Safety Risks with Decomposed Harm Scoring

arXiv:2607. 18665v1 Announce Type: new Abstract: Large language models (LLMs) increasingly support science, but they can also convert hazardous scientific knowledge into actionable misuse guidance.

By Chunxiao Li, Yuan Xiong, Lijun Li, Tianyi Du, Wenlong Zhang, Lei Bai, Jing Shao

llms rag agents benchmarks safety

arXiv AI

Jul 20

CRAFT: Clustering Rubrics to Diagnose Weak LLM Capabilities and Generate Targeted Fine-Tuning Data

arXiv:2607. 16122v1 Announce Type: new Abstract: Evaluations should do more than measure a models current performance.

By Vipul Gupta, Zihao Wang, Razvan-Gabriel Dumitru, MohammadHossein Rezaei, Aakash Sabharwal, Yunzhong He

llms fine-tuning benchmarks

Hugging Face Trending Papers

Jul 21

SciHazard: A Benchmark for Measuring Scientific Safety Risks with Decomposed Harm Scoring

Large language models (LLMs) increasingly support science, but they can also convert hazardous scientific knowledge into actionable misuse guidance. Existing benchmarks often rely on templated queries disconnected from real-world hazards, and employ LLM-as-a-Judge paradigms without domain grounding.

llms rag agents benchmarks safety

arXiv Machine Learning

Jun 10

From Observation to Intervention: A Causal Audit of Expert Importance in Mixture-of-Experts Models

arXiv:2606. 10703v1 Announce Type: new Abstract: Interpretability methods routinely use population-level summary statistics over observed model behaviour to license claims about the effects of targeted interventions on specific computations; in Pearl's terms, they treat rung-1 associational evidence as if it supported rung-2 interventional conclusions, a move whose validity is rarely tested.

By Leonard Engmann, Christian Medeiros Adriano, Holger Giese

efficiency safety

Related stories

CalBrief: A Pilot Diagnostic Benchmark for Evidence-Calibrated Scientific Briefing with Large Language Models

Protective Capacity Hallucination: When Large Language Models Claim Nonexistent Capabilities

SciHazard: A Benchmark for Measuring Scientific Safety Risks with Decomposed Harm Scoring

CRAFT: Clustering Rubrics to Diagnose Weak LLM Capabilities and Generate Targeted Fine-Tuning Data

SciHazard: A Benchmark for Measuring Scientific Safety Risks with Decomposed Harm Scoring

From Observation to Intervention: A Causal Audit of Expert Importance in Mixture-of-Experts Models