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The paper explores how looped language models, which use iterative latent computation, improve compositional tool calling in agentic systems, showing benefits for multi-step API interactions.
Looped language models enhance compositional tool calling by leveraging recurrent computation, improving accuracy on multi-step tasks while adaptive inference optimizes the balance between performance and compute cost. The study suggests these models are promising for reliable agentic systems.
This paper introduces Learned Relay Representations (Relay), a method that allows masked diffusion models to propagate latent information across denoising steps, overcoming the hard reset problem and improving performance-latency trade-offs. The method is shown to outperform standard supervised finetuning on coding tasks while reducing inference latency by up to 32%.