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Unified Latent Diffusion Reasoner improves multimodal reasoning by weaving it into a single thread, where the model predicts successive thoughts in a common representation space.
Executable environments enable LLM agents to learn from the consequences of their actions. For embodied agents, those consequences extend beyond whether the current task succeeds: completing a delivery can consume the time, energy, or money needed for later work. Learning to plan therefore requires environments that pr...
This paper proposes Scaffolding Minds, a dedicated scaffolding encoder that provides an optimized target in latent space, and learns both the mean and variance of the RL sampler, and shows that these two improvements are complementary, together yielding substantial gains over strong baselines.
A novel training-free inference strategy for MLLMs that explicitly decouples perception and reasoning is presented, and a novel metric, the vision-to-text attention ratio, is proposed, to dynamically gauge the model's cognitive focus.
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