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Qizheng Zhang, Changran Hu, Shubhangi Upasani +10 authors
ACE, a framework for adaptive context engineering, enhances LLM applications by preserving detailed knowledge through structured updates, outperforming baselines in agent and domain-specific tasks with reduced adaptation costs.
Ruihang Chu, Yefei He, Zhekai Chen +10 authors
Wan-Move enhances motion control in video generative models by integrating motion-aware features into latent space, enabling high-quality and scalable video synthesis.
Khalil Hennara, Muhammad Hreden, Mohamed Motasim Hamed +4 authors
Baseer, a vision-language model fine-tuned for Arabic document OCR, achieves state-of-the-art performance using a decoder-only strategy and a large-scale dataset, outperforming existing solutions with a WER of 0.25.
Aleksandr Nesterov, Andrey Sakhovskiy, Ivan Sviridov +5 authors
Experiments on a new Russian-language ICD coding dataset using models like BERT, LLaMA with LoRA, and RAG show significant accuracy improvements in automated clinical coding compared to manual annotations.
Chengsong Huang, Wenhao Yu, Xiaoyang Wang +6 authors
R-Zero is a self-evolving framework that autonomously generates and learns from its own training data, improving reasoning capabilities in LLMs without human-curated tasks.
Zhiyuan Liang, Dongwen Tang, Yuhao Zhou +11 authors
Drag-and-Drop LLMs generate task-specific parameters through prompt-conditioned parameter generation, achieving significant efficiency gains and cross-domain generalization without per-task training.
Kimi Team, Yu Zhang, Zongyu Lin +57 authors
Kimi Linear, a hybrid linear attention architecture, outperforms full attention in various scenarios with improved efficiency and performance, using Kimi Delta Attention and Multi-Head Latent Attention.
Kwai Keye Team, Biao Yang, Bin Wen +57 authors
Kwai Keye-VL, an 8-billion-parameter multimodal model, excels in short-video understanding and general vision-language tasks through a comprehensive pre-training and post-training process, including a five-mode data mixture and reinforcement learning.
Guowei Xu, Peng Jin, Ziang Wu +5 authors
LLaVA-CoT is a vision-language model that achieves improved reasoning performance through structured multistage processing and test-time scaling, outperforming larger models with a smaller training dataset.
Ganqu Cui, Yuchen Zhang, Jiacheng Chen +14 authors
Entropy dynamics in reinforcement learning with large language models are investigated to prevent policy entropy collapse and improve exploration.
Kiran Kamble, Melisa Russak, Dmytro Mozolevskyi +3 authors
FailSafeQA evaluates the robustness and context-awareness of large language models in financial applications through domain expertise, query completeness, linguistic accuracy, and document relevance challenges.
Jinjie Ni, Qian Liu, Longxu Dou +5 authors
Diffusion language models outperform autoregressive models in low-data settings due to any-order modeling, iterative bidirectional denoising, and Monte Carlo augmentation, and maintain advantages even at scale.
Aleksandr Razin, Danil Kazantsev, Ilya Makarov
LUA is a lightweight module that performs super-resolution directly in the latent space of diffusion models, improving efficiency without compromising image quality.
Team Seawead, Ceyuan Yang, Zhijie Lin +51 authors
A mid-sized video generation model with 7 billion parameters, trained efficiently with substantial GPU hours, achieves competitive performance and strong generalization across various applications.
Liyang Chen, Tianxiang Ma, Jiawei Liu +7 authors
HuMo is a unified framework for human-centric video generation that addresses challenges in multimodal control through a two-stage training paradigm and novel strategies for subject preservation and audio-visual synchronization.
Kimi Team, Angang Du, Bofei Gao +91 authors
A multi-modal LLM trained with reinforcement learning achieves state-of-the-art reasoning performance across various benchmarks by utilizing long context scaling and effective policy optimization methods.
Jingyu Liu, Xin Dong, Zhifan Ye +6 authors
TiDAR combines diffusion and autoregressive models to achieve high throughput and quality in language generation.
Jane Luo, Xin Zhang, Steven Liu +11 authors
A graph-driven framework called ZeroRepo uses the Repository Planning Graph (RPG) to generate complete software repositories from scratch, significantly outperforming existing baselines in terms of code size, functional coverage, and test pass rate.
Weizhen Li, Jianbo Lin, Zhuosong Jiang +27 authors
Chain-of-Agents (CoA) paradigm enables end-to-end complex problem-solving in LLMs through dynamic agent activation, improving performance via multi-agent distillation and agentic reinforcement learning.
Jiaru Zou, Xiyuan Yang, Ruizhong Qiu +10 authors
LatentMAS enables efficient, lossless collaboration among LLM agents in latent space, improving performance and reducing computational costs compared to text-based methods.
Cheng Cui, Ting Sun, Suyin Liang +15 authors
PaddleOCR-VL, a vision-language model combining NaViT-style dynamic resolution and ERNIE, achieves state-of-the-art performance in document parsing and element recognition with high efficiency.
Haoming Wang, Haoyang Zou, Huatong Song +103 authors
UI-TARS-2, a native GUI-centered agent model, addresses challenges in data scalability, multi-turn reinforcement learning, and environment stability, achieving significant improvements over its predecessor and strong baselines across various benchmarks.
Hongjin Su, Shizhe Diao, Ximing Lu +13 authors
A small orchestrator using ToolOrchestra method coordinates various intelligent tools with reinforcement learning, achieving higher accuracy and efficiency in solving complex tasks like Humanity's Last Exam compared to larger models.
Kang Liao, Size Wu, Zhonghua Wu +6 authors
Puffin, a unified multimodal model, integrates language regression and diffusion-based generation to enhance camera-centric spatial understanding and generation by treating camera parameters as language.
Shen Nie, Fengqi Zhu, Zebin You +7 authors
LLaDA, a diffusion model trained from scratch, outperforms autoregressive models in benchmarks and demonstrates strong instruction-following capabilities, challenging the dominance of ARMs in LLMs.
Kuan Li, Zhongwang Zhang, Huifeng Yin +16 authors
WebSailor, a post-training methodology, enhances open-source LLMs with sophisticated reasoning to match proprietary systems in complex information-seeking tasks.
Dmitrii Mikhailov, Aleksey Letunovskiy, Maria Kovaleva +6 authors
NABLA, a Neighborhood Adaptive Block-Level Attention mechanism, enhances video diffusion transformers by reducing computational overhead without significantly impacting generative quality or visual fidelity.
Niklas Muennighoff, Zitong Yang, Weijia Shi +7 authors
The use of budget forcing during test time improves reasoning performance in language models, as demonstrated by the s1 model which outperforms the o1-preview on competition math questions.
Tianzhe Chu, Yuexiang Zhai, Jihan Yang +6 authors
Reinforcement learning demonstrates superior generalization compared to supervised fine-tuning across textual and visual domains, while supervised fine-tuning stabilizes the model output format essential for RL performance.
Hongyin Luo, Nathaniel Morgan, Tina Li +7 authors
The Thread Inference Model (TIM) and TIMRUN enable long-horizon reasoning in large language models by using reasoning trees and a rule-based subtask-pruning mechanism to manage working memory and GPU resources efficiently.
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