Trending Research
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.
IndexTTS: An Industrial-Level Controllable and Efficient Zero-Shot Text-To-Speech System
Recently, large language model (LLM) based text-to-speech (TTS) systems have gradually become the mainstream in the industry due to their high naturalness and powerful zero-shot voice cloning capabilities. Here, we introduce the IndexTTS system, which is mainly based on the XTTS and Tortoise model.
Training Long-Context LLMs Efficiently via Chunk-wise Optimization
While long-context large language models (LLMs) exhibit remarkable document processing capabilities, their prohibitively high training costs often hinder customized applications.
MiniCPM4: Ultra-Efficient LLMs on End Devices
openbmb/minicpm
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Specifically, in terms of model architecture, we propose InfLLM v2, a trainable sparse attention mechanism that accelerates both prefilling and decoding phases for long-context processing.
DeltaProduct: Improving State-Tracking in Linear RNNs via Householder Products
sustcsonglin/flash-linear-attention
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Linear Recurrent Neural Networks (linear RNNs) have emerged as competitive alternatives to Transformers for sequence modeling, offering efficient training and linear-time inference.
MASKSEARCH: A Universal Pre-Training Framework to Enhance Agentic Search Capability
In the pre-training stage, we introduce the Retrieval Augmented Mask Prediction (RAMP) task, where the model learns to leverage search tools to fill masked spans on a large number of pre-training data, thus acquiring universal retrieval and reasoning capabilities for LLMs.
Digital Player: Evaluating Large Language Models based Human-like Agent in Games
With the rapid advancement of Large Language Models (LLMs), LLM-based autonomous agents have shown the potential to function as digital employees, such as digital analysts, teachers, and programmers.
Unifying Appearance Codes and Bilateral Grids for Driving Scene Gaussian Splatting
bigcileng/bilateral-driving
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In this paper, we propose a novel multi-scale bilateral grid that unifies appearance codes and bilateral grids.
AlphaEvolve: A Learning Framework to Discover Novel Alphas in Quantitative Investment
In this paper, we introduce a new class of alphas to model scalar, vector, and matrix features which possess the strengths of these two existing classes.
VRAG-RL: Empower Vision-Perception-Based RAG for Visually Rich Information Understanding via Iterative Reasoning with Reinforcement Learning
alibaba-nlp/vrag
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As RL has been proven to be beneficial for model reasoning, we introduce VRAG-RL, a novel RL framework tailored for complex reasoning across visually rich information.
