Output compression reduces inference costs by 1.4–3x, while input compression increases them by an average of 1.15x because models respond to imprecise prompts with longer answers.
JSON schema constraints compile tool-call tokens into unreachable regions of token space, causing models to suppress function calls despite both functions working in isolation.
iLLaDA demonstrates that fully bidirectional diffusion training from scratch can be a competitive path to strong language models, even without autoregressive training.
AI agents can be trained as data scientists to automatically generate high-quality synthetic training data, which continuously improves through meta-optimization.
Agentic Overlays are thin wrapper layers that convert REST-APIs into A2A-capable agents without code duplication, eliminating the need for parallel infrastructures.
Geopolitical tensions and the use of AI are significantly intensifying the threat landscape, necessitating a rethink of defensive strategies beyond traditional perimeter security.
Governance for agentic AI requires access control at every level – from tool discovery through query execution to response synthesis – not just at a single central checkpoint like in RAG.
Chaplin enables Operations teams to autonomously analyze AWS Health Events through AI agents without waiting for TAM support, by exposing AWS Health APIs via the Model Context Protocol as tools for Claude and other MCP clients.
