Trending Research
Recollection from Pensieve: Novel View Synthesis via Learning from Uncalibrated Videos
dwawayu/pensieve
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In the first stage, we learn to reconstruct the scene implicitly in a latent space without relying on any explicit 3D representation.
VTBench: Evaluating Visual Tokenizers for Autoregressive Image Generation
To address this gap, we introduce VTBench, a comprehensive benchmark that systematically evaluates VTs across three core tasks: Image Reconstruction, Detail Preservation, and Text Preservation, and covers a diverse range of evaluation scenarios.
Reservoir-enhanced Segment Anything Model for Subsurface Diagnosis
Urban roads and infrastructure, vital to city operations, face growing threats from subsurface anomalies like cracks and cavities.
Finetune-RAG: Fine-Tuning Language Models to Resist Hallucination in Retrieval-Augmented Generation
Pints-AI/Finetune-Bench-RAG
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Retrieval-Augmented Generation (RAG) has emerged as a powerful framework to improve factuality in large language models (LLMs) by grounding their outputs in retrieved documents.
qlib
microsoft/qlib
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Qlib is an AI-oriented quantitative investment platform that aims to realize the potential, empower research, and create value using AI technologies in quantitative investment, from exploring ideas to implementing productions.
Scaling Video-Language Models to 10K Frames via Hierarchical Differential Distillation
Based on this principle, we develop ViLAMP, a hierarchical video-language model that processes hour-long videos at "mixed precision" through two key mechanisms: (1) differential keyframe selection that maximizes query relevance while maintaining temporal distinctiveness at the frame level and (2) differential feature merging that preserves query-salient features in non-keyframes at the patch level.
Cosmos-Reason1: From Physical Common Sense To Embodied Reasoning
nvidia-cosmos/cosmos-reason1
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We begin by defining key capabilities for Physical AI reasoning, with a focus on physical common sense and embodied reasoning.
AlphaNet: Scaling Up Local-frame-based Atomistic Interatomic Potential
zmyybc/alphanet
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Molecular dynamics simulations demand an unprecedented combination of accuracy and scalability to tackle grand challenges in catalysis and materials design.
Do Large Language Models Need a Content Delivery Network?
lmcache/lmcache
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As the use of large language models (LLMs) expands rapidly, so does the range of knowledge needed to supplement various LLM queries.
Make LoRA Great Again: Boosting LoRA with Adaptive Singular Values and Mixture-of-Experts Optimization Alignment
While Low-Rank Adaptation (LoRA) enables parameter-efficient fine-tuning for Large Language Models (LLMs), its performance often falls short of Full Fine-Tuning (Full FT).
