Routing Small Models With Internal Confidence Signals
Examines routing in small LLMs using internal confidence signals to choose answering, search, document retrieval, or refusal.
Examines routing in small LLMs using internal confidence signals to choose answering, search, document retrieval, or refusal.
Fara-1.5 highlights why scalable data pipelines and verifiers, not just models, matter for computer-use agent training.
View LLM agents as runtime-adaptive computation graphs to optimize accuracy, cost, latency, debugging, and control.
Defines skills as executable function code and manages them online via create-run-update-on-fail-save-on-success loops.
A practical look at memory admission control for LLM agents, reducing long-term memory pollution while improving auditability and metrics.
A field report from running a community bot: what automation can do, and what still requires human operational control.
Analyze how OpenAI's Responses API and MCP reduce AI agent latency and improve cache efficiency through server-side state management.