AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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LoLBench is a multilingual benchmark of 100 tasks across 29 software systems in five domains that shows that both perception and implementation remain central challenges for coding agents in practical modular development on large software systems.
This research provides novel insights for the development of highly active and low-toxic phenamacril derivatives, and lays the foundation for achieving a closed-loop research and development process involving structure-activity relationships, toxicity prediction, iterative optimization, and re-evaluation.
TraceProbe is presented, a trajectory-diagnostic framework that recovers what resolve rate hides and adds auditable diagnostic context to outcomes by localizing inspection targets, suggesting failure hypotheses, and prioritizing runs for review.
SWE-RPG is introduced, a repository-level benchmark that combines executable patch evaluation with validated ground-truth references (GTs) for Requirement Clarification and Implementation Planning, and suggests implicit-requirement recovery as a key candidate direction for improving coding agents.
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