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Recast is an efficient and scalable framework for synthesizing datasets where each example incorporates far more constraints than those in existing benchmarks, aiming to challenge and extend the boundaries of models'ability to follow complex instructions.
This work shows that an agent's reliance on memory can be modeled as an explicit and user-controllable dimension, and proposes a framework that allows users to dynamically regulate memory reliance, ranging from a fresh-start mode that promotes innovation to a high-fidelity mode that closely follows interaction history.
Experimental results demonstrate that the ToolVerse framework significantly strengthens LLMs'capabilities in long-horizon tool use, achieving a marked performance boost and showcasing robust reasoning within dynamic environments.
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