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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.
ReDeck is proposed, a step-level render-grounded refinement framework that decomposes slide revision into atomic edit actions and returns renderer-derived observations after each step, turning refinement into"one edit, one observation."
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.
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