HuggingFace
MEND: RL For Flow Models via Proximal Velocity Matching
Reward post-training of flow models either reweights the model's own samples under a KL penalty or a frozen reference...
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HuggingFace
Towards In-Parameter Memory Augmentation for Large Language Models
Recently Large Language Models (LLMs) and LLM-based agents increasingly need to incorporate knowledge acquired after ...
HuggingFace
JLD: Perceptual Distance Through A Jacobian Lens
Image compression, restoration, and generation all require a way to measure how different two images look to a person...
HuggingFace
MiniCorp: The Last Mile of the AI Agent Firm
The last mile toward enterprise AGI is a company that runs itself. Training and adapting such agents require longitud...
HuggingFace
ConEx: Human-Interpretable Saliency Maps via Concept-Aware Attribution
Many visual explanation methods in computer vision highlight pixel importance but struggle to link these low-level cu...
HuggingFace
Taming VLAs under Robot Execution Errors: Self-Compensation and Stress Testing
Vision-language-action (VLA) policies often fail when a robot's executed motion deviates from their commanded action....
HuggingFace
HLA: Expressive Hybrid Linear Attention via Chunk-Wise Dynamic Mixing
Linear attention enables efficient long-context autoregressive decoding by compressing history into recurrent states,...
HuggingFace
NeMo-DCR: Bit-Exact Delta-Compressed Refit for Scalable Agentic RL at Trillion-Parameter Scale
Agentic reinforcement learning (RL) disaggregates training from rollout, so each policy update must reach the rollout...
HuggingFace
Adaptive Latent Capacity for World Models
We introduce Adaptive LeWorldModel (ALeWM), a world model based on a joint-embedding predictive architecture (JEPA) t...
HuggingFace
Harness-Aware Distillation for Small Language Model Agents
Language model agents are deployed with a harness, the software around the model that manages its context, tools, and...
HuggingFace
Learning Discriminative Geometry for Drifting Models
Recently proposed Drifting Models shift iterative distribution refinement from inference to training, enabling effect...
HuggingFace
SlimWise: Decoupling Expert Pruning Across Prefill and Decode for Efficient MoE Serving
Mixture-of-experts (MoE) models activate few experts per token, yet batched decoding can access nearly the entire exp...