arXiv AI June 2, 2026 By Samuele Marro, Jialin Yu, Emanuele La Malfa, Oishi Deb, Jiawei Li, Yibo Yang, Ebey Abraham, Sunando Sengupta, Eric Sommerlade, Michael Wooldridge, Philip Torr

Benchmarking at the Edge of Comprehension

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arXiv:2602. 14307v4 Announce Type: replace Abstract: As frontier Large Language Models (LLMs) increasingly saturate new benchmarks shortly after they are published, benchmarking itself is at a juncture: if frontier models keep improving, it will become increasingly hard for humans to generate discriminative tasks, provide accurate ground-truth answers, or evaluate complex solutions.

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

arXiv AI

Jul 16

AIMO Interpretability Challenge

arXiv:2607. 13899v1 Announce Type: new Abstract: We propose the AIMO Interpretability Challenge, a competition on distinguishing robust from spurious reasoning in frontier mathematical language models based on the models' internal mechanisms.

By Michal \v{S}tef\'anik, Philipp Mondorf, Andreas Waldis, Qianying Liu, Chuan Yang, Michal Spiegel, Josef Kucha\v{r}, Marek Kadl\v{c}\'ik, Adam Vawda-Oomerjee, Chaoran Liu, Simon Frieder, Barbara Plank, Fazl Barez, Pontus Stenetorp

llms benchmarks safety

arXiv AI

Aug 10

Ask-E: An Environment for Calibrated Question Generation

arXiv:2608. 06933v1 Announce Type: cross Abstract: Today, we improve models by training and evaluating them on problems at the frontier of their abilities.

By Sarah Pratt, Jae Sung Park, Scott Geng, Ali Farhadi

llms benchmarks

arXiv AI

Jul 7

CAP-CoT: Cycle Adversarial Prompt for Improving Chain of Thoughts in LLM Reasoning

arXiv:2604. 23270v2 Announce Type: replace Abstract: Chain-of-Thought (CoT) prompting has emerged as a simple and effective way to elicit step-by-step solutions from large language models (LLMs).

By Shuxu Chen, Yitian Zhou, Jiaquan Zhang, Haoyu Bian, Wenrui Hu, Aming Wu, Sungyoung Lee, Chaoning Zhang, Hyundong Shin

llms agents benchmarks safety

arXiv AI

Jul 22

A Red Teaming Framework for Large Language Models: A Case Study on Faithfulness Evaluation

arXiv:2606. 25476v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have demonstrated remarkable performance across natural language processing tasks, yet their deployment in high-stakes applications raises critical concerns regarding reliability, safety, and trustworthiness.

By Abrar Alotaibi, Raed Mughus, Moataz Ahmed

llms nlp safety

arXiv AI

Jul 24

StabilityBench: Benchmarking Instability in LLMs

arXiv:2607. 20558v1 Announce Type: cross Abstract: AI Assistants are increasingly deployed in high-stakes settings, such as healthcare or government services.

By Emma Kondrup, Zachary Yang, Anne Imouza, Reihaneh Rabbany

llms benchmarks safety

arXiv AI

1d ago

The Synthetic Web: Adversarially-Curated Mini-Internets for Diagnosing Epistemic Weaknesses of Language Agents

arXiv:2603. 00801v2 Announce Type: replace Abstract: Language agents increasingly act as web-enabled systems that search, browse, and synthesize information from diverse sources.

By Shrey Shah, Levent Ozgur

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AIMO Interpretability Challenge

Ask-E: An Environment for Calibrated Question Generation

CAP-CoT: Cycle Adversarial Prompt for Improving Chain of Thoughts in LLM Reasoning

A Red Teaming Framework for Large Language Models: A Case Study on Faithfulness Evaluation

StabilityBench: Benchmarking Instability in LLMs

The Synthetic Web: Adversarially-Curated Mini-Internets for Diagnosing Epistemic Weaknesses of Language Agents