A new study from researchers at Northwestern, Stanford, Cornell, and All Hands AI reveals that frontier LLM agents waste 28–64% of tokens on tasks they are doomed to fail. The paper, BAGEN: Are LLM Ag…
Microsoft Research's ARTIST framework trains LLM agents to discover when and how to call tools through reinforcement learning, using only outcome-based rewards rather than step-by-step annotations. At…
Claude Code hooks are scripts that attach to lifecycle events (like PreToolUse or PostToolUse) to enforce deterministic rules, such as blocking dangerous shell commands or automatically formatting fil…
ARES (Adaptive Reasoning Effort Selection) is a framework that reduces the cost of multi-step LLM agent tasks by using a lightweight router to predict the minimum reasoning effort needed for each step…
Traditional unit tests are insufficient for LLM applications, which require evaluation tools to catch regressions in non-deterministic outputs like JSON with incorrect business logic. It introduces Br…