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Weian Mao, Xi Lin, Wei Huang +5 authors
TriAttention addresses KV cache memory bottlenecks in LLMs by leveraging Q/K vector concentration in pre-RoPE space to improve key importance estimation and enable efficient long-context generation.
V Team, Wenyi Hong, Xiaotao Gu +75 authors
GLM-5V-Turbo integrates multimodal perception as a core reasoning component for agentic tasks, demonstrating strong performance in multimodal coding and visual tool use while maintaining text-only capabilities.
Yaxuan Li, Yuxin Zuo, Bingxiang He +8 authors
On-policy distillation dynamics in large language models depend on compatible thinking patterns between teacher and student models, with successful distillation characterized by alignment on high-probability tokens and requiring teachers to provide novel capabilities beyond student training data.
Zhengyang Sun, Yu Chen, Xin Zhou +4 authors
NUMINA enhances text-to-video diffusion models' numerical accuracy through a training-free identify-then-guide framework that uses attention heads to detect layout inconsistencies and refine object counts.
Haozhe Wang, Cong Wei, Weiming Ren +3 authors
Training reward models to generate multi-dimensional critiques improves visual generation through both enhanced reinforcement learning rewards and test-time refinement loops, achieving state-of-the-art performance with reduced training data requirements.
Junyao Gao, Sibo Liu, Jiaxing Li +6 authors
MegaStyle presents a scalable data curation pipeline for creating high-quality, style-consistent datasets using large generative models and proposes style-supervised contrastive learning for effective style representation extraction.
Zheng-Hui Huang, Zhixiang Wang, Jiaming Tan +6 authors
A large-scale dynamic dataset derived from AAA games is introduced to improve generative inverse and forward rendering, featuring high-resolution synchronized RGB and G-buffer data alongside a novel VLM-based evaluation method that correlates well with human judgment.
Zhengxi Lu, Zhiyuan Yao, Jinyang Wu +7 authors
SKILL0 enables LLM agents to internalize skills during training, allowing zero-shot autonomous behavior through a dynamic curriculum that reduces contextual overhead while improving task performance.
Linhao Yu, Tianmeng Yang, Siyu Ding +8 authors
KnowRL is a knowledge-guided reinforcement learning framework that improves reasoning in language models by optimizing compact, interaction-aware guidance subsets through constrained subset search and addressing pruning interaction paradoxes.
Patrice Bechard, Orlando Marquez Ayala, Emily Chen +5 authors
Simple terminal-based coding agents using programmatic interfaces and foundation models can effectively perform enterprise tasks comparable to or better than complex tool-augmented agents.
Xiangru Jian, Hao Xu, Wei Pang +13 authors
FORGE introduces a high-quality multimodal manufacturing dataset with fine-grained domain semantics to evaluate MLLMs on real-world tasks, revealing that domain-specific knowledge rather than visual grounding limits performance, and demonstrating that supervised fine-tuning on structured annotations significantly improves accuracy.
Jinghui Lu, Jiayi Guan, Zhijian Huang +47 authors
OneVL presents a unified vision-language-action framework that improves latent chain-of-thought reasoning for autonomous driving by integrating language and visual world model supervision for faster, more accurate trajectory prediction.
Chenkai Pan, Xinglong Xu, Yuhang Xu +6 authors
Training data can be structured as source code and evaluated through unit testing, enabling systematic debugging and improvement of language models' domain-specific capabilities.
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.
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.
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.
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.
Zunhai Su, Hengyuan Zhang, Wei Wu +17 authors
Transformers face challenges from Attention Sink phenomenon where excessive attention focuses on uninformative tokens, impacting interpretability and performance, necessitating comprehensive research survey addressing fundamental usage, mechanistic understanding, and strategic mitigation approaches.
Chanyoung Kim, Minwoo Kim, Minseok Kang +2 authors
Vision-Language-Action models show significant performance drops when handling paraphrased instructions due to surface-level matching rather than semantic understanding, highlighting the need for better linguistic generalization metrics.
Han Zhang, Guo-Hua Yuan, Chaohao Yuan +6 authors
Lingshu-Cell is a masked discrete diffusion model that learns transcriptomic state distributions and enables conditional simulation of cellular perturbations across diverse tissues and species.
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