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Haotian Ma, Ruqi Yang, Philip Townsend
Spectral-Spatial Interpretation assigns localized channel importance at each spatial position via conditional Shapley values, yielding full spectral-spatial attribution matrices that outperform existing methods on multispectral image tasks.
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StreamEval Team
StreamEval benchmarks interactive streaming video generation across five tasks, revealing that reliable request execution and sustained state consistency remain major challenges despite high visual quality.
Enhan Zhao, Yongqi Tong, Xuejun Yu +6 authors
BizBench evaluates long-horizon business agents on intermediate execution validity using real-world payment procedures, revealing that frontier models frequently violate constraints despite achieving partial goal success.
SenseNova, Joint Research Team, Zhongang Cai +59 authors
GPT-6-Astra achieves high performance across diverse robotic tasks via in-context learning, yet real-world deployment reveals gaps in demonstration cost, execution time, and practical embodied intelligence.
Tianming Liang, Junyi Pan, Yuxi Li +5 authors
WeVisOne-VL unifies object-level and pixel-level visual perception via structured token prediction, supporting detection, localization, counting, OCR, segmentation, and matting with a lightweight pixel decoder and multi-stage training.
Zhuofan Josh Ying, Guanglong Sun, Nolan Ho +6 authors
Parameter-efficient fine-tuning with low-rank adapters enables efficient adaptation of very large multimodal backbones for brain encoding, improving performance and interpretability while reducing computational cost.
Songlin Yang, Zhe Wang, Xiaotong Zhao +8 authors
Factor-Aware Predictive Learning enables video diffusion transformers to leverage multimodal language model representations for sparse temporal inference and factor-aware cinematic control through staged grounding and optimization.
Jihai Zhang, Runjia Qian, Kaiqin Kong +15 authors
Matrix-Game 4.0 separates game logic from visual synthesis via an executable engine and autoregressive video renderer, achieving real-time interactive gameplay with low latency.
Wen Xue, Yong Liu, Xueheng Li +5 authors
LayerEditor unifies instruction-guided image editing with reusable RGBA layer generation via joint token modeling and edit-prior distillation, enabling precise local manipulation and layer reuse.
Xinhao Ji, Hui En Pang, Hao Li +12 authors
EgoXperience introduces a unified framework for joint reconstruction of camera motion, scene geometry, hands, and objects from egocentric video using automatically annotated large-scale data and a single-pass model.
Jiaming Zhang, Xuan Wang, Fuyao Zhang +3 authors
A decoder-free, action-conditioned world model predicts next-screen representations to verify GUI transitions and detect hijacked screens with high accuracy and low latency.
Xiaomin Yu, Hongliang Chen, Zhe Huang +22 authors
OpenDS-World introduces a live-web benchmark to evaluate autonomous multi-step deep search agents, revealing significant gaps between frontier models and human performance.
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.
Hao Li, Xiyuan Zheng, Yuanyuan Gao +7 authors
HOLI-WORLD is an integrated ecosystem that automates the construction of large-scale learning resources from egocentric recordings to improve machine understanding of dynamic scenes and interactions.
Zhaoxi Zhang, Hao Liang, Linxuan Wu +7 authors
SpatialScout evaluates vision-language agents on active 3D object mapping, revealing major gaps in precise metric map construction.
Pan Wang, Yihao Hu, Guangyuan Dong +8 authors
Progress-Aligned Recurrence trains looped language models to match intermediate states and stabilize recurrent norms, enabling strong length extrapolation on sequential tracking tasks.
Mingze Sun, Ziang Cao, Yuan Fei +8 authors
DONIC is a generalist bimanual tracker trained on diverse human-motion data that uses reinforcement learning with recycled demonstrations and per-hand rewards to achieve high success on unseen objects without fine-tuning.
Fuyao Zhang, Wenwen He, Jiaming Zhang +6 authors
FocusGUI is a lightweight adapter that dynamically selects relevant multimodal evidence for GUI agents to prevent compounding errors from irrelevant context, improving action accuracy without fine-tuning the backbone.
Tianyu Fu, Ruoxi Wang, Zihan Min +6 authors
AKV introduces cache engineering to manage LLM agent key-value caches without altering trajectories, improving efficiency and accuracy via drop and repos operations with a co-designed serving engine.
Seungone Kim, Jiayi Geng, Young-Jun Lee +25 authors
Researchers benchmarked AI agents on open scientific problems by having them draft follow-up papers from unpublished directions, finding that none met expert standards and most contained fabricated claims or contradictory evidence.
Tianqi Liu, Hanfei Zhao, Qifeng Liang +12 authors
Ropedia W0 is a world foundation model for cross-embodiment loco-manipulation that unifies human and robot data via kinematic representations and achieves state-of-the-art results across diverse benchmarks.
Zhiyu Li, Zeyu Zhang, Linyun Liu +71 authors
MemT-1.0 introduces a memory-native architecture that embeds continuous memory into foundation model computation, improving long-context efficiency and agentic task performance.
Yijiang Li, Bingyang Wang, Nuno Vasconcelos
ACT3D trains vision-language models via reinforcement learning to actively explore 3D environments from single first-person views, improving answer accuracy through adaptive, question-conditioned navigation without direct exploration supervision.
Chao Yu, Shu'ang Yu, Hao Lin +39 authors
Robots share execution experience across fleets to improve policies via failure-guided retrieval and asynchronous training, achieving high success without extra expert corrections.
Yuchen Lu, Julian McAuley, Lei Zhang
CarryOn organizes long-horizon agent context around reusable task objects rather than interaction histories, improving efficiency and performance on complex benchmarks.
Hongcheng Gao, Alex Zhou, Jay Zhu +13 authors
HexaShell is a coding agent that controls a dual-arm robot via vision and self-written tools, achieving state-of-the-art results on RoboDojo but struggling with contact-heavy tasks.
Jiaqi Su, Guibin Zhang, Jie-Jing Shao +5 authors
CoEvo-RSI reduces brain-body mismatch in recursive self-improving agents by using bidirectional historical inheritance to align model capabilities with evolving harness requirements, achieving strong results via supervised fine-tuning.
Yu Zhou, Cheng-Fu Yang, Rui Sun +8 authors
OpenVLHarness improves multimodal reasoning by integrating persistent memory, heterogeneous tool interfaces, and iterative evidence composition across 22 benchmarks.
Rui Gong, Ruoyu Wang, Yalun Dai +9 authors
S-Agent 2.0 improves spatial reasoning by integrating a persistent scene memory and plain-language interpretation into a coding agent with 3D perception tools.
Dingjie Song, Zhiling Yan, Yuxuan Zhang +1 authors
LEV is a looped decision model that increases reasoning depth by iterating its layers without generating tokens, improving accuracy on hard questions while allowing adaptive computation via a stopper.
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