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Pengqi Lu
Deep diffusion transformers face structural instability at extreme depths due to mean-dominated collapse triggered by mean mode screaming, which is mitigated through mean-variance split residuals that maintain stable training while preserving performance.
Chaoyou Fu, Haozhi Yuan, Yuhao Dong +16 authors
Video-MME-v2 presents a comprehensive benchmark for evaluating video understanding models through a progressive hierarchy and group-based evaluation to assess robustness and faithfulness.
Ruhan Wang, Yucheng Shi, Zongxia Li +7 authors
The Harness Handbook and Behavior-Guided Progressive Disclosure automatically map agent behaviors to source code, improving localization and edit planning for evolving AI harnesses.
Shih-Yang Liu, Xin Dong, Ximing Lu +10 authors
Multi-reward reinforcement learning suffers from reward normalization collapse in GRPO, which GDPO addresses by decoupling reward normalization for improved training stability and performance across reasoning tasks.
Chuyan Chen, Haoxing Chen, Kun Chen +27 authors
LLaDA-Image unifies a 6B diffusion transformer with a frozen vision-language module, using image-only pre-training and a Muon optimizer to generate photorealistic images with precise editing, and is distilled into a fast 2-4 step variant that achieves state-of-the-art open-source results.
Meng Chu, Xuan Billy Zhang, Kevin Qinghong Lin +39 authors
World models are categorized into three capability levels and four law regimes to better understand and develop predictive environment models for AI agents across diverse domains.
Yuhao Wu, Jingyuan Zhang, Jiajun Shi +18 authors
ASPIRE introduces a benchmark for self-evolving LLM agents from vague natural-language goals, revealing challenges in goal interpretation, data selection, and stable weight-level improvement.
Dawei Zhu, Rui Meng, Yale Song +4 authors
_paperbanana is an agentic framework that automates the creation of publication-ready academic illustrations using advanced vision-language models and image generation techniques.
Xuying Ning, Katherine Tieu, Dongqi Fu +39 authors
Large language models are increasingly used as operational substrates for agent reasoning and execution in agentic systems, with code serving as a unified infrastructure layer across multiple domains and applications.
Mind Lab, Song Cao, Vic Cao +59 authors
MinT is a managed infrastructure system that enables efficient low-rank adaptation training and serving by keeping base models resident and moving lightweight adapter revisions, scaling across multiple dimensions including large model architectures, reduced storage requirements, and distributed policy management.
Zihao Li, Jiaru Zou, Feihao Fang +6 authors
Eywa is a heterogeneous agentic framework that extends language-centric systems to scientific foundation models by integrating domain-specific models with language-based reasoning interfaces for improved performance across diverse scientific domains.
Guobin Shen, Chenxiao Zhao, Xiang Cheng +2 authors
VESPO addresses training instability in LLM reinforcement learning by using variational formulation with variance reduction to correct policy divergence without length normalization.
Yijiang Li, Bingyang Wang, Yijun Liang +3 authors
Unsupervised on-policy self-distillation improves large language models by using internal consistency and majority-vote pseudo-solutions to correct confident errors without external supervision.
Dingyu Yao, Junhao Zhou, Chenxu Yang +12 authors
A vision-language model operates continuously in real-time, making autonomous decisions about when to respond or delegate, enabling interactive systems that perceive and act upon environmental changes without user prompting.
Chengwen Liu, Xiaomin Yu, Zhuoyue Chang +13 authors
VideoDR benchmark enables video question answering by combining cross-frame visual extraction, web retrieval, and multi-hop reasoning in open-domain settings.
Changhai Zhou, Kieran Liu, Yuhua Zhou +17 authors
LongStraw is an execution framework that enables million-token reinforcement learning post-training with fixed GPU resources by reducing live training graphs through replay and selective state retention.
Kaijie Zhu, Yuzhou Nie, Yijiang Li +10 authors
TermiGen introduces a pipeline for generating verifiable terminal environments and resilient trajectories to improve open-weight LLMs' ability to execute complex tasks and recover from runtime errors.
Lei Huang, Xiang Cheng, Chenxiao Zhao +6 authors
Language feedback is leveraged in reinforcement learning to improve exploration efficiency and sample utilization through grouped critique aggregation and joint generation-refinement optimization.
Jian Yang, Shawn Guo, Wei Zhang +16 authors
Parallel loop Transformers achieve better code generation performance with two loops due to refined representations, while additional loops cause diminishing returns and increased positional mismatch costs.
Ziyang Ma, Zhikang Niu, Wenming Tu +30 authors
AuK is an open-source foundational model that unifies speech generation and editing via natural-language instructions and audio context, using a multimodal language model, joint VAE, hybrid rectified-flow Transformer, and efficient distillation for fast inference.
Kaiyi Zhang, Wei Wu, Yankai Lin
Reinforcement learning from verifiable rewards is enhanced through a discriminative token credit assignment method that improves reward-based training by amplifying distinctive token-gradient directions and reducing noise from shared patterns.
Apodex Team, B. An, B. Li +68 authors
Apodex 1.1 improves sustained, verifiable progress on complex real-world tasks by scaling executable environments and training agents to coordinate long-horizon work with state maintenance and recovery.
Tianxin Wei, Ting-Wei Li, Zhining Liu +26 authors
Agentic reasoning redefines large language models as autonomous agents capable of planning, acting, and learning through continuous interaction in dynamic environments across single-agent and multi-agent frameworks.
Jingxuan Wei, Siyuan Li, Yuhang Xu +21 authors
World Models require three consistency principles—modal, spatial, and temporal—for general artificial intelligence, with a proposed benchmark evaluating multimodal learning systems.
DataFlow Team, Bohan Zeng, Daili Hua +38 authors
OpenWorldLib presents a standardized framework for advanced world models that integrate perception, interaction, and long-term memory capabilities for comprehensive world understanding and prediction.
Yuhao Zheng, Li'an Zhong, Yi Wang +6 authors
Code2World enables autonomous GUI agents to predict next visual states through renderable code generation, achieving high visual fidelity and structural controllability while improving navigation performance.
Liang Chen, Weichu Xie, Yiyan Liang +26 authors
Current multimodal large language models exhibit significant gaps in fundamental visual understanding compared to human children, as demonstrated by the BabyVision benchmark.
Ailin Huang, Ang Li, Aobo Kong +212 authors
Step 3.5 Flash is a sparse Mixture-of-Experts model that achieves frontier-level agentic intelligence through efficient parameter utilization and optimized attention mechanisms, demonstrating strong performance across multiple benchmarks.
Zhixia Zhang, Zixuan Huang, Xin Xia +7 authors
HACRL enables collaborative reinforcement learning where heterogeneous agents share verified rollouts during training to improve collectively while maintaining independent operation at inference time, with HACPO achieving superior performance through efficient sample utilization and cross-agent knowledge transfer.
Kehan Li, Bohan Hou, Minghao Zhu +27 authors
RynnBrain 1.1 is a family of embodied foundation models that integrates spatio-temporal and physical grounding with contact-point prediction, 3D grounding, and cross-embodiment action spaces to improve robot perception, reasoning, and manipulation.
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