TL;DR
This paper proposes self-programmed execution (SPE), enabling language models to act as agents without fixed turn-based policies, using a novel Lisp-like language called Spell for self-editing and re-evaluation.
Contribution
It introduces SPE and Spell, allowing language models to self-orchestrate their actions, and demonstrates their effectiveness on challenging agentic tasks.
Findings
Frontier models can operate under SPE without training for it.
Models can perform complex agentic tasks using self-programmed execution.
Code implementation is available at the provided GitHub link.
Abstract
At the heart of existing language model agents is a fixed orchestrator program responsible for the state transition between consecutive turns. This paper introduces self-programmed execution (SPE), an agent architecture in which the model completion is itself the orchestrator program, and the harness evaluates this program but does not impose its own orchestration policy. I formalize this idea using agentic machines: an SPE state is one from which a model completion can load any state of an embedded copy of the machine, meaning that it is subject to no fixed turn-to-turn orchestration policy. Realizing SPE in practice is nontrivial because the same data is both model context and executable program. I therefore introduce Spell, a Lisp-based language in which programs can edit and re-evaluate themselves, and effectful expressions like model invocations are structured such that…
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