Depth Anything
A robust monocular depth estimator designed to handle "any image under any circumstance" by scaling self-training on unlabeled data rather than hunting for bigger labeled sets.
Knowledge Fusion of LLMs (FuseLLM)
FuseLLM proposes fusing the capabilities of multiple existing LLMs into a single target model by distilling their output distributions rather than retraining from scratch.
MambaByte
MambaByte adapts the Mamba state-space architecture to learn directly from raw bytes, bypassing tokenization and all its well-known failure modes.
Diffuse to Choose
Amazon's Diffuse to Choose is a diffusion-based image-conditioned inpainting model built for "virtual try-on" scenarios where product images must be placed naturally into user scenes.
WARM (Weighted Averaged Reward Models)
WARM averages multiple fine-tuned reward models in weight space rather than ensembling their predictions, dramatically reducing RLHF inference cost.
Resource-efficient LLMs & Multimodal Foundation Models
A wide-ranging survey of efficiency techniques for LLMs and multimodal foundation models, spanning architecture, algorithms, and system design.
Red Teaming Visual Language Models
Introduces the first dedicated red-teaming benchmark for VLMs, covering vulnerabilities unique to multimodal inputs.
Lumiere
Google's Lumiere is a space-time diffusion model for text-to-video that generates the entire video duration in a single forward pass rather than cascading short clips.
Medusa
Medusa accelerates LLM inference by bolting on multiple decoding heads that predict several future tokens in parallel, dramatically reducing decoding steps.
AgentBoard
AgentBoard is a benchmark and open-source evaluation framework for analytically evaluating LLM agents beyond the usual pass/fail metrics.
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