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Bobo Li, Hao Fei, Tianjie Ju +2 authors
OmniScientist is an end-to-end omni-modal AI scientist that performs multidisciplinary research directly from heterogeneous raw evidence using autonomous agents and lifecycle-wide perception, improving evidence-grounded discovery across diverse scientific modalities.
Jianhao Ruan, Zhihao Xu, Yiran Peng +8 authors
AOrchestra is a framework-agnostic agentic system that uses a tuple-based abstraction to dynamically create specialized task executors, achieving improved performance on complex benchmarks through automated agent creation and resource management.
Zonglin Yang, Lidong Bing
MOOSE-Star framework enables efficient training and inference for generative reasoning by addressing combinatorial complexity through decomposed subtasks, hierarchical search, and bounded composition.
Songhao Wu, Ang Lv, Ruobing Xie +1 authors
Researchers propose a novel router redesign for Mixture-of-Experts models that aligns router rows with the principal singular directions of expert matrices using Manifold Power Iteration to improve model effectiveness.
Wei Yu, Runjia Qian, Yumeng Li +8 authors
Video diffusion models use hybrid spatial memory to maintain consistency under camera motion and enable long-term scene editing and navigation.
Chen Tang, Yizhou Wang, Jianyu Wu +26 authors
SciReasoner is a multimodal scientific foundation model that enables interpretable structural reasoning across proteins, molecules, and crystals by discretizing structural elements into a unified vocabulary for enhanced prediction and scientific inference.
Jaewon Chu, Jinwoo Seo, Jaewon Cho +4 authors
AgentGrad improves multi-agent prompt optimization by identifying target agents through sequential intervention and clustering gradients semantically to avoid mixing unrelated errors.
Jian Chen, Yesheng Liang, Zhijian Liu
DFlash is a speculative decoding framework that uses a lightweight block diffusion model for parallel token drafting, achieving significant speedup over existing autoregressive methods while maintaining high-quality outputs.
Xiangyang Luo, Xiaozhe Xin, Tao Feng +3 authors
CoInteract presents an end-to-end framework for human-object interaction video synthesis using a Diffusion Transformer backbone with specialized modules for structural stability and physical plausibility.
Zimo Wen, Boxiu Li, Wanbo Zhang +11 authors
Unified multimodal models show mixed performance in generation-to-understanding tasks, with specific subtasks benefiting from enhanced spatial and reasoning capabilities while overall performance lags behind specialized vision-language models.
Haochen Shi, Xingdi Yuan, Bang Liu
Programmatic Skill Network enables continual skill acquisition through executable symbolic programs that evolve via reflection, progressive optimization, and structural refactoring mechanisms.
Guanting Dong, Junting Lu, Junjie Huang +17 authors
Agent-World introduces a self-evolving training framework that advances general agent intelligence through autonomous environment discovery and continuous learning across diverse real-world scenarios.
Letian Wang, Chuhan Zhang, Rishabh Kabra +9 authors
GenCeption uses a video generative diffusion backbone to build a generalist vision model that achieves strong performance across diverse perception tasks with high data efficiency and emergent generalization.
Haozhe Wang, Weijia Feng, Jinpeng Yu +8 authors
Researchers introduce benchmarks and a co-training framework to enable search-augmented visual generation that addresses open-ended world-knowledge gaps.
Yuanyuan Gao, Hao Li, Yifei Liu +14 authors
Holi-Spatial presents the first fully automated, large-scale, spatially-aware multimodal dataset constructed from raw video inputs, supporting multi-level spatial supervision for 3D scene understanding and spatial reasoning tasks.
Youngrok Park, Sangmin Bae, Hojung Jung +6 authors
On-Policy Reverse Distillation enables stronger models to exceed weak supervisors by amplifying verifier-supported policy gradients along the teacher's shift direction, accelerating optimization without imposing capacity limits.
Jingcheng Hu, Yinmin Zhang, Shijie Shang +17 authors
Parallel Coordinated Reasoning enables large-scale test-time compute scaling beyond sequential reasoning limitations through parallel exploration and message-passing architecture.
Johannes Kirmayr, Lukas Stappen, Elisabeth André
Current LLM agent benchmarks fail to evaluate reliability in real-world scenarios with uncertain user inputs, prompting the creation of CAR-bench to test consistency, uncertainty management, and capability awareness in in-car assistant applications.
Zefeng He, Siyuan Huang, Xiaoye Qu +4 authors
GEMS is an agent-native multimodal generation framework that enhances model capabilities through structured multi-agent optimization, persistent memory, and domain-specific skills across general and downstream tasks.
Daixuan Cheng, Shaohan Huang, Yuxian Gu +6 authors
LLM-in-Sandbox enables large language models to perform general intelligence tasks across diverse domains by allowing them to explore a code sandbox environment, achieving robust generalization without additional training.
Mengru Wang, Junfeng Fang, Shuofei Qiao +16 authors
Mechanist is an autonomous agentic system that uses AI to discover and control the mechanisms underlying model intelligence, generating hypotheses, performing causal interventions, and improving safety and performance.
Yilei Jiang, Jinyuan Hu, Qianyin Xiao +8 authors
OpenGame is an open-source agentic framework for end-to-end web game creation that uses specialized code models and evaluation benchmarks to overcome challenges in interactive application development.
Ang Li, Ben Liu, Bin Han +215 authors
Ling-2.6 and Ring-2.6 models are presented as scalable solutions for agentic intelligence, featuring architectural upgrades and specialized training methods to balance fast response times with advanced reasoning capabilities.
Zheng-Hui Huang, Guixu Lin, Jiacheng Lin +8 authors
A programmable world model separates explicit state evolution from video generation using executable rules and 3D bounding boxes to maintain persistent, controllable environments.
Runhui Huang, Qihui Zhang, Zhe Liu +3 authors
SpectraReward leverages pretrained multimodal language models as training-free reward models for image-generation reinforcement learning by measuring prompt recovery likelihood, with a self-improving variant that uses the policy's own understanding branch to close the loop.
Jiayi Tian, Shiao Liu, Yuting Xu +24 authors
ABot-AgentOS is a general robotic agent operating system that provides deliberative planning, isolated skill execution, verification, and persistent multi-modal memory for long-horizon embodied tasks, evaluated on the EmbodiedWorldBench benchmark.
Jane Luo, Chengyu Yin, Xin Zhang +10 authors
RPG-Encoder framework transforms repository comprehension and generation into a unified cycle by encoding code into high-fidelity Repository Planning Graph representations that improve understanding and reconstruction accuracy.
Zichen Wen, Boxue Yang, Shuang Chen +31 authors
Innovator-VL demonstrates that principled training design and transparent methodology can achieve strong scientific intelligence with reduced data requirements while maintaining general vision performance.
Junlin Liu, Jiangwang Chen, Zixin Song +7 authors
Multi-Agent Protocol Distillation improves agentic search by using structured, style-normalized protocols to transfer reasoning from proprietary teachers to smaller models via dense distillation combined with sparse reinforcement learning.
Xiang Hu, Xinyu Wei, Hao Gu +10 authors
Hierarchical Landmark Sparse Attention enables efficient long-context language modeling by learning chunk selection end-to-end, achieving performance comparable to full attention while extrapolating beyond training context lengths.
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