Agent与工具

Agentic AIGC 驱动广告素材修复

追踪代理 Harness 的隐性低效路径

An agent can finish a task and still take an inefficient path. A failed search can trigger another search. A truncated file read can lead to a command fetching the same content again. A correct final answer hides those extra steps, even though they increase latency and consume to

SCLATE: A Substrate for Continual-Learning Agent Training and Evaluation

Continual-learning agents are systems of models, harnesses, and memory operating over long multi-session horizons. Evaluating and training them requires interleaving tasks with agent-side events such as session stop and start, crons, and memory consolidation. Yet existing benchma

Is sandboxing sufficient to contain rogue agents?