AI Research

As intelligent machines move into real-world environments, researchers reveal that adversaries can manipulate robot behavior through compromised AI models, wireless exploits, and runtime perception attacks.

Researchers propose a mathematical fix that makes reinforcement learning more reliable when training and inference engines differ.

New analysis reveals how data bias fundamentally reshapes how much information neural networks can store reliably.

Researchers discover how to combine automated prompt engineering with targeted fine-tuning to improve cost-effective AI agents on enterprise tasks.

Researchers solve a fundamental problem that has blocked reinforcement learning approaches from improving code generation models at deployment time.

Justice Department files legal brief supporting fair-use doctrine for LLM training, reshaping AI industry litigation landscape.

Researchers deploy a lightweight mixture-of-experts vision model that rivals much larger systems while maintaining strict privacy and cost constraints.

Researchers achieve supervised performance without ground-truth data by using asymmetric training during inference, potentially unlocking faster AI model improvements.

Researchers propose SPO++ to accelerate reinforcement learning for language models using tools, eliminating costly waiting periods during training.

New diagnostic tool reveals that AI-trained robot simulators fail to properly follow non-expert commands, threatening the reliability of policy learning systems.

A new generation of companion robots prioritizes persistent presence and emotional engagement over functionality, targeting isolated elderly adults, remote-parented children, and urban professionals.

A new vision-language system trained on expert assessments could dramatically expand safety inspections in resource-constrained regions.

Combine keyword and semantic retrieval to boost RAG recall. Implementation patterns, tuning strategies, and when hybrid beats pure vector search.

Researchers address a fundamental mismatch between how AI learns from teachers and what actually solves complex problems.

Researchers propose BATON, a method that breaks long-horizon robot tasks into independently learnable subtasks, improving success rates by up to 15%.

A technical guide to understanding what AI model benchmarks test, their blind spots, and how to evaluate responsibly.

The search giant's latest model prioritizes faster responses without sacrificing reasoning capability, signaling a shift toward practical deployment.

A new sandbox environment lets scientists observe language model knowledge acquisition with unprecedented precision and control.