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Chujie Zheng, Shixuan Liu, Mingze Li +9 authors
Group Sequence Policy Optimization (GSPO) is a reinforcement learning algorithm that improves training efficiency and performance of large language models by using sequence-level importance ratios and operations.
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Lingrui Mei, Jiayu Yao, Yuyao Ge +12 authors
Context Engineering systematically optimizes information payloads for Large Language Models, addressing gaps in generating sophisticated, long-form outputs.
Wenyi Hong, Wenmeng Yu, Xiaotao Gu +74 authors
A vision-language model (VLM) named GLM-4.1V-Thinking, developed with a reasoning-centric training framework, achieves state-of-the-art performance across various tasks, including STEM problem solving, video understanding, and long document understanding, outperforming larger models on many benchmarks.
Zhiyu Li, Shichao Song, Chenyang Xi +36 authors
MemOS, a memory operating system for Large Language Models, addresses memory management challenges by unifying plaintext, activation-based, and parameter-level memories, enabling efficient storage, retrieval, and continual learning.
Guanting Dong, Hangyu Mao, Kai Ma +11 authors
Agentic Reinforced Policy Optimization (ARPO) enhances multi-turn reasoning in large language models by balancing long-horizon capabilities and tool interactions, using entropy-based adaptive rollouts and advantage attribution.
Yukang Chen, Wei Huang, Baifeng Shi +11 authors
A framework scales vision-language models for long video reasoning using reinforcement learning, achieving strong performance on benchmarks and demonstrating consistent gains with increased video frames.
HunyuanWorld Team, Zhenwei Wang, Yuhao Liu +52 authors
HunyuanWorld 1.0 generates immersive 3D scenes from text and images using a semantically layered 3D mesh representation with panoramic world proxies, achieving state-of-the-art performance in coherence, exploration, and interactivity.
Xingxuan Li, Yao Xiao, Dianwen Ng +15 authors
The MiroMind-M1 series, built on Qwen-2.5, introduces fully open-source reasoning language models with state-of-the-art performance in mathematical reasoning through Context-Aware Multi-Stage Policy Optimization.
Fei Tang, Zhangxuan Gu, Zhengxi Lu +9 authors
A new reward framework, GUI-G$^2$, models GUI elements as continuous Gaussian distributions to improve autonomous interaction through dense gradient signals, outperforming existing methods in spatial reasoning tasks.
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.
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.
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.
Vera Soboleva, Aibek Alanov, Andrey Kuznetsov +1 authors
T-LoRA, a timestep-dependent low-rank adaptation framework, enhances diffusion model personalization with a dynamic fine-tuning strategy and orthogonal initialization, improving concept fidelity and text alignment in data-limited settings.
David Bensaïd, Noam Rotstein, Roy Velich +2 authors
SingLoRA, a reformulated low-rank adaptation method, enhances parameter-efficient fine-tuning by learning a single low-rank matrix and its transpose, ensuring stable optimization and reducing parameter count.
Zixiao Wang, Yuxin Wang, Xiaorui Wang +8 authors
MetaStone-S1, a reflective generative model using a self-supervised process reward model, achieves high performance with reduced parameters and supports test time scaling.
Yushen Zuo, Qi Zheng, Mingyang Wu +10 authors
4KAgent, a unified agentic super-resolution system, enhances low-resolution images to 4K using a profiling module, perception agent, and restoration agent, achieving state-of-the-art performance across various imaging domains.
Yilei Jiang, Yaozhi Zheng, Yuxuan Wan +4 authors
A modular multi-agent framework named ScreenCoder decomposes UI design-to-code translation into grounding, planning, and generation stages, achieving superior layout accuracy and code correctness through specialized agents and fine-tuned multimodal models.
Rui-Jie Zhu, Tianhao Peng, Tianhao Cheng +30 authors
Latent reasoning in Large Language Models (LLMs) performs multi-step inference in continuous hidden states, enhancing reasoning capabilities without token-level supervision, and includes methodologies like activation-based recurrence and infinite-depth reasoning via masked diffusion models.
Xiaofeng Mao, Shaoheng Lin, Zhen Li +7 authors
A framework for generating and exploring interactive, high-fidelity video worlds from images using a Masked Video Diffusion Transformer, advanced sampling techniques, and model acceleration.
Mingqi Wu, Zhihao Zhang, Qiaole Dong +9 authors
Research on enhancing reasoning capabilities in large language models using reinforcement learning reveals that accurate reward signals are crucial for performance improvement, and current benchmarks may be unreliable due to data contamination.
Zhaochen Su, Peng Xia, Hangyu Guo +12 authors
The survey outlines the evolution of multimodal AI from treating vision as static context to integrating it dynamically into the reasoning process, highlighting three stages and key challenges.
Yangning Li, Weizhi Zhang, Yuyao Yang +17 authors
This survey integrates reasoning and retrieval in Large Language Models to improve factuality and multi-step inference, highlighting Synergized RAG-Reasoning frameworks and outlining future research directions.
Huan-ang Gao, Jiayi Geng, Wenyue Hua +24 authors
This survey reviews self-evolving agents, focusing on mechanisms for adaptation in components like models, memory, and architecture, and discusses challenges in safety, scalability, and co-evolutionary dynamics.
Fang Wu, Weihao Xuan, Ximing Lu +2 authors
Reinforcement Learning with Verifiable Rewards (RLVR) enhances precision but may limit exploration and discovery of new solutions, suggesting potential limits to its effectiveness in expanding reasoning capabilities.
Luke Rivard, Sun Sun, Hongyu Guo +2 authors
NeuralOS, a neural framework combining RNNs and diffusion-based rendering, simulates realistic GUIs by predicting screen frames in response to user inputs.
Hippolyte Gisserot-Boukhlef, Nicolas Boizard, Manuel Faysse +5 authors
A biphasic training strategy combining Causal Language Modeling and Masked Language Modeling yields optimal text representation performance, especially when initialized with pretrained CLM models.
Senqiao Yang, Junyi Li, Xin Lai +3 authors
VisionThink dynamically adjusts image resolution and visual token processing for efficient and effective vision-language tasks, improving performance and reducing computational cost.
Yu Wang, Xi Chen
MIRIX, a modular multi-agent memory system, enhances language models' memory capabilities by integrating diverse memory types and a dynamic framework, achieving superior performance in multimodal and long-form conversation benchmarks.
Maggie Huan, Yuetai Li, Tuney Zheng +6 authors
Reinforcement learning-tuned models generalize better across domains compared to supervised fine-tuned models in reasoning tasks, indicating a need to reconsider standard training methods.
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