Tag
This paper presents a method for fine-grained identity tuning in text-to-image personalization models. It explores the latent space of a frozen encoder to enable localized, semantically coherent facial edits without additional training.
InternVLA-A1.5 integrates pretrained vision-language models with future prediction in latent space to enable efficient robot manipulation with compositional generalization and long-horizon execution, achieving state-of-the-art results on simulation benchmarks.
Micro-JEPA is a lightweight Python implementation of the Joint Embedding Predictive Architecture (JEPA), enabling an agent to learn environment representations, predict future states in latent space, and plan actions to avoid obstacles.
Introduces a framework called Latent Maps of Performance for generating counterfactual feedback in StarCraft II using a Guided Variational Autoencoder trained on professional replays, enabling improvement trajectories for amateur players.
This paper proposes using steering vectors for control over language model behavior and latent space-based calibrators to assess trustworthiness, aiming to demystify internal representations and build more reliable AI systems.
Introduces Cross-Space Distillation, a method to transfer knowledge from modern high-capacity diffusion models to compact student models with different latent spaces using a lightweight latent interface called Bridge, enabling quality improvements without modifying the student backbone.
A 5M-parameter model outperforms frontier LLMs on hard logical puzzles at a fraction of the inference cost by using continuous latent space test-time compute.
This opinion paper argues that large language models are a degenerate special case of world models, not a separate paradigm, and proposes a continuous spectrum from next-token prediction to latent-space architectures like JEPA, examining the data and architecture challenges along this path.
Introduces a semi-autoregressive framework that combines latent block diffusion with temporal point processes for generating asynchronous event sequences, reducing error accumulation while enabling variable-length output.
This paper introduces LatentMAS, a training-free framework for multi-agent systems that enables large language model agents to collaborate directly in continuous latent space via shared latent working memory, achieving up to 14.6% higher accuracy and 4x faster inference while reducing token usage by over 70%.
LatentMAS is a new multi-agent collaboration method where agents directly transfer reasoning states in latent space without text encoding/decoding, achieving a 13.3% accuracy improvement, 4.3x speed, and 83.7% reduction in token usage. It requires no extra training and can be plugged into existing LLMs. It has been accepted as an ICML 2026 Spotlight.
FLAT proposes a method to decode explicit triangle splats directly from video diffusion latents for geometrically accurate 3D scene generation. It introduces a ray-centered rotation parameterization and a product window function to improve gradient flow, achieving better geometric accuracy than prior feedforward methods while supporting real-time rendering.
An empirical study demonstrating that long, semantically dense, benign text can shift a model's latent space and bypass alignment, causing it to generate otherwise blocked critiques. The author, a non-expert, requests an audit of their metrics to distinguish genuine semantic hijacking from artifacts.
An empirical study investigating how long, semantically dense benign text can shift a model's latent space trajectory, diluting initial system prompts and bypassing post-training alignment constraints, as observed in both closed and open-source models.
This paper explores the use of variational autoencoders to learn latent representations of large-scale X-ray scattering data, enabling efficient data compression and analysis.
Introduces Parallel-Synthesis, a framework that enables direct consumption of KV caches from parallel worker agents, reducing time-to-first-token by 2.5x–11x while maintaining or improving performance on agentic tasks.
Sarah Guo's framework on open models, model labs vs agent labs, and the concept of 'untrainable' is discussed, emphasizing that applications win through unglamorous integration work and that intent is a scarce input.
Latent Memory introduces a compressed representation approach for external memory in question answering, reducing token consumption and storage requirements while maintaining competitive performance across text-only and multimodal benchmarks.
Light-WAM is a lightweight world action model for efficient robot manipulation that uses a compact video backbone and downsampled latent space for future-video supervision, achieving high performance with low inference latency.
Lumos-Nexus is a training-efficient video generation framework that uses a two-stage design with a lightweight generator for training and a high-capacity pretrained generator for inference, achieving enhanced visual fidelity through Unified Progressive Frequency Bridging.