Current long‑horizon LLM agents burn tokens at a rate that makes billion‑step research infeasible. Their prompts grow as every intermediate result is replayed, leading to duplicated effort and runaway costs. Decoupling durable state from the model’s active context, as proposed by Stateless Language Agents (SLAs), flips this trend on its head.

Historically, agent designs have embedded the entire research history inside each model invocation, so every call re‑feeds an ever‑larger transcript. Benchmarks were consequently limited to short budgets where token blow‑up never surfaced. This coupling forced researchers to accept a trade‑off between horizon length and compute expense.

Stateless Language Agents reach the strongest kernel baseline’s final performance while using over 84 % fewer tokens. By storing candidate solutions and outcomes in an external harness, each agent receives only the concise evidence it needs for its next step.…