Trending Research
olmOCR: Unlocking Trillions of Tokens in PDFs with Vision Language Models
PDF documents have the potential to provide trillions of novel, high-quality tokens for training language models.
LHM: Large Animatable Human Reconstruction Model from a Single Image in Seconds
aigc3d/LHM
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Animatable 3D human reconstruction from a single image is a challenging problem due to the ambiguity in decoupling geometry, appearance, and deformation.
Zep: A Temporal Knowledge Graph Architecture for Agent Memory
We introduce Zep, a novel memory layer service for AI agents that outperforms the current state-of-the-art system, MemGPT, in the Deep Memory Retrieval (DMR) benchmark.
Olympus: A Universal Task Router for Computer Vision Tasks
We introduce Olympus, a new approach that transforms Multimodal Large Language Models (MLLMs) into a unified framework capable of handling a wide array of computer vision tasks.
3DGUT: Enabling Distorted Cameras and Secondary Rays in Gaussian Splatting
nv-tlabs/3dgrut
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3D Gaussian Splatting (3DGS) enables efficient reconstruction and high-fidelity real-time rendering of complex scenes on consumer hardware.
CHORDONOMICON: A Dataset of 666,000 Songs and their Chord Progressions
Chord progressions encapsulate important information about music, pertaining to its structure and conveyed emotions.
What Kind of Visual Tokens Do We Need? Training-free Visual Token Pruning for Multi-modal Large Language Models from the Perspective of Graph
jytmelon/g-prune
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Recent Multimodal Large Language Models(MLLMs) often use a large number of visual tokens to compensate their visual shortcoming, leading to excessive computation and obvious visual redundancy.
Agent S2: A Compositional Generalist-Specialist Framework for Computer Use Agents
Computer use agents automate digital tasks by directly interacting with graphical user interfaces (GUIs) on computers and mobile devices, offering significant potential to enhance human productivity by completing an open-ended space of user queries.
Large Language Model Agent: A Survey on Methodology, Applications and Challenges
The era of intelligent agents is upon us, driven by revolutionary advancements in large language models.
Sleep-time Compute: Beyond Inference Scaling at Test-time
Scaling test-time compute has emerged as a key ingredient for enabling large language models (LLMs) to solve difficult problems, but comes with high latency and inference cost.
