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This paper demonstrates that diffusion language models (DLMs) internally represent denoising progress as a latent timestep signal in their residual streams, which can be probed and steered to modulate model confidence and entropy.
BAAI released the Orca paper describing a multimodal latent world model that learns a unified world representation first, then decodes into text, images, or actions using frozen backbones and tiny decoders, with weights coming soon.
This paper introduces the concept of the audit gap between behavioral safety and representation-level robustness in LLMs, proposing an intervention-based evaluation framework and the Latent Vulnerability Score (LVS) to measure hidden vulnerabilities.
Introduces Thoughts-as-Planning, a framework that models chain-of-thought optimization as sequential decision-making using latent world models and reinforcement learning, outperforming existing methods in efficiency and generalization.
Latte introduces a framework that represents personalization as forecasting a peer-anchored relative preference state using latent trajectories, injecting a soft token into a frozen LLM to achieve personalized generation. It outperforms existing personalization methods on Amazon Reviews 2023 and MemoryCD datasets.
ATLAS presents a visual reasoning framework that combines agentic operations and latent representations using functional tokens, enabling efficient training via next-token prediction and reinforcement learning while avoiding intermediate image generation.
This paper establishes nonparametric identifiability guarantees for extracting task-relevant representations from generalist models, proving that task structure is identifiable across time steps and latent representations are identifiable within each step under sparsity regularization.