The challenge is not to choose a side, but to create feedback loops that mediate between the pace of AI-accelerated development and the requirements for reliability and maintainability.
Real business environments with actual money, inventory and customers reveal AI capabilities and risks that classic benchmarks miss, ranging from price-fixing to deception to legal misinterpretations.
CHERRL enables reproducible analysis of reward hacking mechanisms through controlled bias injection and automatic detection of exploitation onset in LLM-based training.
ThoughtFold identifies and removes redundant exploration steps in reasoning chains, reducing token consumption by 56% for DeepSeek-R1-Distill-Qwen-7B while maintaining state-of-the-art accuracy.
Long-horizon iterative improvement, not single high-quality responses, is the critical capability for autonomous AI agents tackling real-world engineering tasks.
MemTrain enhances memory capabilities of LLM agents through self-supervised pretraining based on two complementary reconstruction tasks, without requiring costly annotated data.
GitHub passed unscoped OAuth tokens to the VSCode browser instance, allowing attackers to access all private repositories of a developer via manipulated Jupyter Notebook extensions.
