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cs.AI2026

Can Agents Design Libraries for Agents?

Gabriel Orlanski, Alex L. Zhang, Avi Trost +4

Agents increasingly build on code written by other agents, and they reimplement rather than reuse, growing the codebases later agents must work in. To measure how well agents desig…

cs.AI2026

MAGS: Multi-agent Auto-formalization Guarantees Safety for Agentic Outputs

Albert Wu, Nicholas Roberts, Tzu-Heng Huang +5

LLM coding agents now generate complex programs at a scale that makes thorough human review increasingly difficult, raising the risk of safety and security failures. Common approac…

cs.AI2026

OSWorld 2.0: Benchmarking Computer Use Agents on Long-Horizon Real-World Tasks

Mengqi Yuan, Zilong Zhou, Xinzhuang Xiong +33

Existing computer-use benchmarks fail to capture the realism, complexity, and long-horizon demands of real-world computer use, limiting their ability to reveal the limitations of f…

cs.AI2026

Continual Learning Bench: Evaluating Frontier AI Systems in Real-World Stateful Environments

Parth Asawa, Christopher M. Glaze, Gabriel Orlanski +7

Continual learning, the ability of AI systems to improve through sequential experience, has attracted substantial interest, but no high-quality benchmark exists to evaluate it. We…

cs.AI2026

Can Generalist Agents Automate Data Curation?

Feiyang Kang, Hanze Li, Adam Nguyen +5

Curating training data is among the most consequential yet labor-intensive parts of modern AI development: practitioners iteratively propose, implement, evaluate, and revise data p…

cs.AI2026

The Hitchhikers Guide to Rubric Quality Understanding and Enrichment

Zhengyang Qi, Ankit Aich, Charles Dickens +7

Rubrics distill notions of expert quality and measure agent performance. However, the quality of rubrics themselves have not been systematically measured and are often left to down…

cs.AI2026

SkillOrchestra: Learning to Route Agents via Skill Transfer

Jiayu Wang, Yifei Ming, Zixuan Ke +3

Compound AI systems promise capabilities beyond those of individual models, yet their success depends critically on effective orchestration. Existing routing approaches face two li…

cs.AI2025

LiveResearchBench: A Live Benchmark for User-Centric Deep Research in the Wild

Jiayu Wang, Yifei Ming, Riya Dulepet +7

Deep research -- producing comprehensive, citation-grounded reports by searching and synthesizing information from hundreds of live web sources -- marks an important frontier for a…

cs.AI2025

Beyond Accuracy: Dissecting Mathematical Reasoning for LLMs Under Reinforcement Learning

Jiayu Wang, Yifei Ming, Zixuan Ke +4

Reinforcement learning (RL) has become the dominant paradigm for improving the performance of language models on complex reasoning tasks. Despite the substantial empirical gains de…