Trending Research
Representing Long Volumetric Video with Temporal Gaussian Hierarchy
dendenxu/fast-gaussian-rasterization
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In addition, the tree-like structure of the Gaussian hierarchy allows us to efficiently represent the scene at a particular moment with a subset of Gaussian primitives, leading to nearly constant GPU memory usage during the training or rendering regardless of the video length.
self-prompting analogical reasoning for uav object detection
lnxwow/Analogical-Reasoning
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Proceedings of the AAAI Conference on Artificial Intelligence 2025
While for analogical reasoningmodule, graph nodes consist of category-level prompt nodes and pixel-level image feature nodes. Analogical inference is based on graph convolution.
Darwin Godel Machine: Open-Ended Evolution of Self-Improving Agents
The G\"odel machine proposed a theoretical alternative: a self-improving AI that repeatedly modifies itself in a provably beneficial manner.
EasyVolcap: Accelerating Neural Volumetric Video Research
Volumetric video is a technology that digitally records dynamic events such as artistic performances, sporting events, and remote conversations.
Direct3D-S2: Gigascale 3D Generation Made Easy with Spatial Sparse Attention
Generating high-resolution 3D shapes using volumetric representations such as Signed Distance Functions (SDFs) presents substantial computational and memory challenges.
Paper2Poster: Towards Multimodal Poster Automation from Scientific Papers
To address this challenge, we introduce the first benchmark and metric suite for poster generation, which pairs recent conference papers with author-designed posters and evaluates outputs on (i)Visual Quality-semantic alignment with human posters, (ii)Textual Coherence-language fluency, (iii)Holistic Assessment-six fine-grained aesthetic and informational criteria scored by a VLM-as-judge, and notably (iv)PaperQuiz-the poster's ability to convey core paper content as measured by VLMs answering generated quizzes.
R-KV: Redundancy-aware KV Cache Compression for Training-Free Reasoning Models Acceleration
To address this, we propose Redundancy-aware KV Cache Compression for Reasoning models (R-KV), a novel method specifically targeting redundant tokens in reasoning models.
RenderFormer: Transformer-based Neural Rendering of Triangle Meshes with Global Illumination
We present RenderFormer, a neural rendering pipeline that directly renders an image from a triangle-based representation of a scene with full global illumination effects and that does not require per-scene training or fine-tuning.
OmniAudio: Generating Spatial Audio from 360-Degree Video
To generate spatial audio from 360-degree video, we propose a novel framework OmniAudio, which leverages self-supervised pre-training using both spatial audio data (in FOA format) and large-scale non-spatial data.
Which Agent Causes Task Failures and When? On Automated Failure Attribution of LLM Multi-Agent Systems
mingyin1/agents_failure_attribution
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In this paper, we propose and formulate a new research area: automated failure attribution for LLM multi-agent systems.
