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An Yang, Anfeng Li, Baosong Yang +57 authors
Qwen3, a unified series of large language models, integrates thinking and non-thinking modes, reduces computational resources, and achieves state-of-the-art performance across various tasks and languages.
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Khalil Hennara, Muhammad Hreden, Mohamed Motaism Hamed +3 authors
Mutarjim is a compact Arabic-English translation model that outperforms larger models on established benchmarks and achieves state-of-the-art performance on a new comprehensive Tarjama-25 benchmark.
Andrew Zhao, Yiran Wu, Yang Yue +8 authors
Absolute Zero Reasoner (AZR) achieves state-of-the-art performance on coding and mathematical reasoning tasks through self-generated, verified learning without external data.
Yunxin Li, Zhenyu Liu, Zitao Li +19 authors
This survey outlines the development of multimodal reasoning models, progressing from task-specific modules to unified frameworks, and discusses emerging capabilities and challenges in integrating reasoning across diverse data types.
Dong Guo, Faming Wu, Feida Zhu +194 authors
Seed1.5-VL, a vision-language foundation model combining a vision encoder and a large MoE LLM, achieves state-of-the-art performance across various benchmarks and excels in multimodal reasoning tasks such as visual puzzles.
Xuyang Liu, Zichen Wen, Shaobo Wang +13 authors
The focus in AI research is shifting from model-centric to data-centric compression, with token compression identified as key to improving efficiency in handling long-context scenarios.
Bowen Zhang, Congchao Guo, Geng Yang +17 authors
MiniMax-Speech, an autoregressive Transformer-based TTS model, generates high-quality speech with a learnable speaker encoder that extracts reference speaker features without transcription, achieving SOTA results in voice cloning and supporting various extensions.
Chaorui Deng, Deyao Zhu, Kunchang Li +9 authors
BAGEL, an open-source foundational model trained on diverse multimodal data, significantly outperforms existing models in both generation and understanding tasks.
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.
NovelSeek Team, Bo Zhang, Shiyang Feng +22 authors
Artificial Intelligence (AI) is accelerating the transformation of scientific research paradigms, not only enhancing research efficiency but also driving innovation. We introduce NovelSeek, a unified closed-loop multi-agent framework to conduct Autonomous Scientific Research (ASR) across various scientific research fields, enabling researchers to tackle complicated problems in these fields with unprecedented speed and precision. NovelSeek highlights three key advantages: 1) Scalability: NovelSeek has demonstrated its versatility across 12 scientific research tasks, capable of generating innovative ideas to enhance the performance of baseline code. 2) Interactivity: NovelSeek provides an interface for human expert feedback and multi-agent interaction in automated end-to-end processes, allowing for the seamless integration of domain expert knowledge. 3) Efficiency: NovelSeek has achieved promising performance gains in several scientific fields with significantly less time cost compared to human efforts. For instance, in reaction yield prediction, it increased from 27.6% to 35.4% in just 12 hours; in enhancer activity prediction, accuracy rose from 0.52 to 0.79 with only 4 hours of processing; and in 2D semantic segmentation, precision advanced from 78.8% to 81.0% in a mere 30 hours.
Kaitao Song, Xiaohua Wang, Xu Tan +14 authors
A novel Chain-of-Model framework introduces hierarchical hidden state chains in Transformers to improve scaling efficiency and inference flexibility for language models.
Zhiyuan Hu, Yibo Wang, Hanze Dong +5 authors
Explicit alignment of large reasoning models with deduction, induction, and abduction through a three-stage pipeline improves scalability and reliability in reasoning tasks.
Alan Arazi, Eilam Shapira, Roi Reichart
TabSTAR, a tabular foundation model with semantically target-aware representations, achieves state-of-the-art performance in classification tasks with text features through transfer learning without dataset-specific parameters.
Wei Pang, Kevin Qinghong Lin, Xiangru Jian +2 authors
A benchmark and metric suite for poster generation evaluates visual quality, coherence, and content accuracy, leading to a multi-agent pipeline that outperforms existing models with reduced computational cost.
Hyungjoo Chae, Sunghwan Kim, Junhee Cho +18 authors
The paper introduces Web-Shepherd, a process reward model for web navigation, which improves accuracy and cost-effectiveness in step-level trajectory assessment compared to existing multimodal large language models.
Jiuhai Chen, Zhiyang Xu, Xichen Pan +10 authors
A diffusion transformer is used in a unified multimodal model framework to improve image generation while maintaining image understanding capabilities.
Ling Yang, Ye Tian, Bowen Li +4 authors
MMaDA, a multimodal diffusion foundation model, achieves superior performance through a unified architecture, mixed long chain-of-thought fine-tuning, and a unified policy-gradient-based RL algorithm.
Ibragim Badertdinov, Alexander Golubev, Maksim Nekrashevich +6 authors
A novel pipeline extracts real-world, interactive software engineering tasks from GitHub to create SWE-rebench, improving the evaluation of reinforcement learning models in SWE.
Roman Abramov, Felix Steinbauer, Gjergji Kasneci
Grokking with synthetic data augmentation in knowledge graphs enhances Transformer models' multi-step factual reasoning on real-world tasks, achieving high accuracy on benchmarks.
Yibin Wang, Zhimin Li, Yuhang Zang +4 authors
A unified multimodal reward model that incorporates long chains of thought reasoning improves reliability and accuracy in vision reward tasks through exploration-driven reinforcement learning.
Zheyuan Yang, Lyuhao Chen, Arman Cohan +1 authors
Two post-training strategies, distillation and RLVR, enable inference-time scaling in table reasoning tasks, resulting in a model (Table-R1-Zero) that matches GPT-4.1's performance using fewer parameters and shows strong generalization.
Fanqi Wan, Weizhou Shen, Shengyi Liao +7 authors
A framework called QwenLong-L1 enhances large reasoning models for long-context reasoning through reinforcement learning, achieving leading performance on document question-answering benchmarks.
Jie Liu, Gongye Liu, Jiajun Liang +6 authors
Flow-GRPO combines online reinforcement learning with flow matching models through an ODE-to-SDE conversion and denoising reduction, improving sampling efficiency and performance across text-to-image tasks.
Xiaomi LLM-Core Team, Bingquan Xia, Bowen Shen +61 authors
MiMo-7B, a large language model optimized for reasoning tasks, enhances pre-training with data mixing and Multi-Token Prediction, and post-training with reinforcement learning on math and programming problems, achieving superior performance over larger models.
Valerii Startsev, Alexander Ustyuzhanin, Alexey Kirillov +2 authors
A new method using a pre-trained generative model helps construct a high-impact SFT dataset, Alchemist, which improves the generative quality of text-to-image models while maintaining diversity.
Yemin Shi, Yu Shu, Siwei Dong +4 authors
Voila is an end-to-end voice-language model that enables low-latency, emotionally expressive voice interactions and supports multiple applications including ASR, TTS, and multilingual speech translation.
Hao Fei, Yuan Zhou, Juncheng Li +29 authors
A General-Level evaluation framework is introduced to assess multimodal generalist performance in LLMs, using Synergy to measure consistent capabilities across comprehension and generation.
Jiakang Yuan, Tianshuo Peng, Yilei Jiang +8 authors
MME-Reasoning evaluates the logical reasoning capabilities of multimodal large language models, revealing significant limitations and performance imbalances across inductive, deductive, and abductive reasoning types.
Jiajie Zhang, Nianyi Lin, Lei Hou +2 authors
AdaptThink, an RL algorithm, enhances reasoning models' efficiency and performance by adaptively selecting the optimal thinking mode based on problem difficulty.
Mouxiang Chen, Binyuan Hui, Zeyu Cui +5 authors
Parallel scaling (ParScale) improves inference efficiency by reusing existing parameters and executing multiple transformations in parallel, offering superior performance with reduced memory and latency compared to parameter scaling.
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