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Matching papers

StreamEval: Evaluating Interactive Streaming Video Generation Models

292 upvotes · 20 OCT 2026 · StreamEval Team

This paper introduces a benchmark, StreamEval, to evaluate interactive streaming video generation models, which is essential for enabling real-time interaction with generated video in applications like streaming services. Practitioners caring about the development of interactive video generation systems will find this paper useful.

interactive video generationreal-time systemsmultimodal modelsbenchmarkingevaluation metrics

Metis: Memory Foundation Model

256 upvotes · 29 JUL 2026 · Zeyu Zhang, Ziliang Guo, Yihang Sun et al.

This paper introduces a new type of AI model called memory foundation models, which allows the model to learn and retain information internally, rather than relying on external memory modules. This could be useful for practitioners who want to build more efficient and flexible AI agents.

memory foundation modelsmultimodal modelsnative memorymodel compressionattention mechanismscontinual learning

SenseNova-U1.5: Towards Native Unified Visual Intelligence

248 upvotes · 10 SEP 2026 · Haiwen Diao, Jiahao Wang, Chenjing Ding et al.

This paper introduces SenseNova-U1.5, a powerful AI model that can understand, reason about, and generate visual content without needing a separate text-to-image model. Practitioners might care because this model can be used to create complex visual content, such as images and videos, with high fidelity and accuracy.

multimodal modelsvisual intelligenceencoder-free architectureVAE-freepatch reconstructiongenerative models

Scaling Parameter-Efficient Fine-Tuning for Brain Encoding

230 upvotes · 19 OCT 2026 · Zhuofan Josh Ying, Guanglong Sun, Nolan Ho et al.

This paper develops a method to efficiently fine-tune large pretrained models for brain encoding, allowing for the adaptation of models with up to 104B parameters. Practitioners might care because this approach enables better performance and interpretability for brain encoding tasks.

brain encodingcontinual learningneural dataparameter-efficient fine-tuningmultimodal models

UniEvo-VL: An On-policy Self-Distillation Training Recipe for Multimodal Model Self-improvement

179 upvotes · 30 SEP 2026 · Fang Wu, Da Xing, Yanjie Huang et al.

This paper introduces UniEvo-VL, a self-improvement framework for multimodal models that uses their own feedback to learn and improve, without relying on an external teacher. Practitioners might care because it can lead to better image generation capabilities and more efficient models.

multimodal modelsself-distillationcontinual learninggenerative modelsself-supervised learning