Trends · 研究趋势
数据来自 Hugging Face 论文的 AI 提取关键词,按月统计研究方向的增长与热度。
Hongyuan Adam Lu, Z. L. Victor Wei, Qun Zhang +28 authors
Looped World Models introduce iterative latent state refinement through shared transformer blocks, achieving 100x parameter efficiency while adapting computational depth to prediction complexity.
Fangfu Liu, Kai He, Tianchang Shen +7 authors
A generative multi-agent world model is presented that uses simplex rotary agent encoding and sparse hub attention to enable scalable, permutation-symmetric interaction between multiple agents in interactive video generation.
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.
Pengfei Zhou, Hexin Wang, Zhengfeiyang Zhang +5 authors
Game engines provide executable verification and long-horizon trajectories for reinforcement learning post-training of spatial world models, motivating a human-engine verification paradigm.
Ruopeng Gao, Yukun Huang, Jiarui Hu +4 authors
Future Chunk Prediction improves autoregressive video diffusion by enforcing future-chunk supervision, yielding more physically plausible video generation with reduced training cost.
Meng Luo, Yanlin Li, Hao Li +7 authors
AI applications across the game lifecycle are organized into six roles, highlighting transferable capabilities and setting-specific limitations that require targeted validation.
Yuanyang Yin, Gongxuan Wang, Yifan Zhan +3 authors
Evoke is an interactive world model that uses external persistent memory and a redesigned long-horizon teacher to enable responsive, open-ended video generation with bounded context and low latency.
Haotian Zhang, Fengyuan Yu, Dezhi Luo +28 authors
Researchers created a cognitive-science-inspired dataset and benchmark to evaluate and improve object permanence reasoning in video generation world models.
Yuandong Pu, Le Zhuo, Sayak Paul +11 authors
The study formalizes probabilistic alignment for world models, introduces PAWBench and PAWEval to evaluate video generators as stochastic samplers, and finds current models fail to match reference behavior distributions.
Siyin Wang, Zhaoye Fei, Qinyuan Cheng +4 authors
Dual Preference Optimization (D$^2$PO) enhances embodied task planning in large vision-language models by jointly optimizing state prediction and action selection using preference learning and tree search for efficient trajectory collection.
Tong Wu, Shuai Yang, Ryan Po +4 authors
A new framework enhances video world models' long-term consistency by integrating a geometry-grounded long-term spatial memory mechanism.
Siyu Zhou, Tianyi Zhou, Yijun Yang +4 authors
A neurosymbolic approach aligns large language models with their environment through rule learning, improving model-based agent performance in open-world tasks.
GigaWorld Team, Angyuan Ma, Boyuan Wang +24 authors
World models for robotic policy evaluation are systematically studied through a new benchmark, revealing that long-horizon rollout consistency and robot-specific controllability are more important than short-term visual realism for reliable policy assessment.
Eloi Alonso, Adam Jelley, Vincent Micheli +4 authors
DIAMOND, a reinforcement learning agent using a diffusion world model, achieves top performance on Atari 100k by capturing visual details effectively.
Lionel Wong, Gabriel Grand, Alexander K. Lew +4 authors
A framework combining large language models and probabilistic programs translates language into a symbolic language of thought for rational, commonsense reasoning across multiple cognitive domains.
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