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Kristian Kuznetsov, Laida Kushnareva, Polina Druzhinina +5 authors
Sparse Autoencoders enhance interpretability in Artificial Text Detection by extracting distinctive features from LLM outputs, providing insights into differences from human-written texts.
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Jin Xu, Zhifang Guo, Jinzheng He +11 authors
Qwen2.5-Omni is a multimodal model that processes text, images, audio, and video in a streaming fashion and generates text and speech using a dual-track architecture, achieving state-of-the-art performance on multimodal benchmarks.
Ahmed Nassar, Andres Marafioti, Matteo Omenetti +10 authors
SmolDocling is a compact vision-language model that performs end-to-end document conversion with robust performance across various document types using 256M parameters and a new markup format.
Jiachen Zhu, Xinlei Chen, Kaiming He +2 authors
Dynamic Tanh (DyT) replaces normalization layers in Transformers, achieving equivalent or superior performance without hyperparameter tuning across various tasks.
Bo Peng, Ruichong Zhang, Daniel Goldstein +12 authors
RWKV-7 "Goose" achieves state-of-the-art performance in multilingual tasks with optimal memory and inference efficiency, exceeding Transformer capabilities in complexity.
Qiying Yu, Zheng Zhang, Ruofei Zhu +32 authors
The DAPO algorithm, which includes decoupled clip and dynamic sampling policy optimization, enables open-source, high-performance reinforcement learning training for large-scale language models, enhancing reproducibility in the field.
Jianhong Bai, Menghan Xia, Xiao Fu +8 authors
ReCamMaster uses pre-trained text-to-video models to render dynamic scenes of input videos from novel camera trajectories, leveraging a unique video conditioning mechanism and a custom dataset created with Unreal Engine 5.
Runze Zhang, Guoguang Du, Xiaochuan Li +10 authors
The DropletVideo model preserves spatio-temporal coherence in generated videos by considering both camera movements and plot developments, leveraging a large annotated dataset.
Aleksandr Nesterov, Andrey Sakhovskiy, Ivan Sviridov +5 authors
Experiments on a new Russian-language ICD coding dataset using models like BERT, LLaMA with LoRA, and RAG show significant accuracy improvements in automated clinical coding compared to manual annotations.
Yibin Wang, Yuhang Zang, Hao Li +2 authors
A unified reward model for multimodal understanding and generation improves the assessment and preference alignment in image and video tasks through joint learning and direct preference optimization.
Andrey Galichin, Alexey Dontsov, Polina Druzhinina +4 authors
A study uses Sparse Autoencoders to identify and enhance reasoning features in Large Language Models, providing insights into the mechanisms driving their reasoning capabilities.
Chengpeng Li, Mingfeng Xue, Zhenru Zhang +7 authors
START integrates external tools into large reasoning models to enhance capabilities, using techniques like Hint-infer and Hint Rejection Sampling Fine-Tuning, achieving high performance across various benchmarks.
Samuel Cahyawijaya, Holy Lovenia, Joel Ruben Antony Moniz +89 authors
SEA-VL is an open-source project aimed at creating a large, culturally relevant dataset for Southeast Asian languages to improve AI inclusivity.
Asaf Yehudai, Lilach Eden, Alan Li +5 authors
This survey analyzes evaluation methodologies for large language model-based agents, covering fundamental capabilities, application-specific benchmarks, and generalist agents, highlighting trends and gaps in the field.
Jindong Jiang, Xiuyu Li, Zhijian Liu +13 authors
STORM integrates a temporal encoder using the Mamba State Space Model to enhance video understanding and reduce computational costs in multimodal large language models.
Daniil Selikhanovych, David Li, Aleksei Leonov +6 authors
RSD is a distillation method for ResShift, a top diffusion-based super-resolution model, that achieves high-quality results with fewer parameters and GPU memory compared to other methods.
Eliahu Horwitz, Nitzan Kurer, Jonathan Kahana +2 authors
An atlas of Hugging Face's model repository provides visualizations and analysis, with methods for mapping undocumented areas based on structural priors.
Abdelrahman Abouelenin, Atabak Ashfaq, Adam Atkinson +70 authors
Phi-4-Mini and Phi-4-Multimodal are compact yet highly capable language and multimodal models, featuring advanced attention mechanisms and LoRA adapters for efficient and powerful reasoning across various tasks.
Fangfu Liu, Hanyang Wang, Yimo Cai +3 authors
Test-Time Scaling (TTS) in video generation improves video quality by adaptively sampling from noise space with feedback mechanisms, particularly demonstrated with the Tree-of-Frames method.
Yingzhe Peng, Gongrui Zhang, Miaosen Zhang +7 authors
A two-stage framework enhances reasoning in large multimodal models by first strengthening text-only reasoning and then generalizing it to multimodal tasks.
Kwanyoung Kim, Byeongsu Sim
PLADIS leverages sparse attention in cross-attention layers to enhance pre-trained text-to-image diffusion models, improving text alignment and human preference without additional training.
Ziyu Liu, Zeyi Sun, Yuhang Zang +5 authors
Visual Reinforcement Fine-Tuning (Visual-RFT) enhances Large Vision-Language Models (LVLMs) through reinforcement learning with visual perception verifiable rewards, achieving competitive performance in various visual and reasoning tasks with limited data.
Junyu Luo, Weizhi Zhang, Ye Yuan +23 authors
A survey of large language model agents provides a structured taxonomy, exploring architectural principles, collaboration, evolution, evaluation, and applications, highlighting promising research directions.
Nicolas Boizard, Hippolyte Gisserot-Boukhlef, Duarte M. Alves +16 authors
EuroBERT, a family of multilingual encoders covering European and global languages, outperforms existing models across various tasks and supports long sequences, surpassing traditional bidirectional encoders.
Kaituo Feng, Kaixiong Gong, Bohao Li +5 authors
Video-R1, leveraging rule-based reinforcement learning and temporal information, enhances video reasoning in multimodal large language models using a combination of video and image data.
Advait Gupta, NandaKiran Velaga, Dang Nguyen +1 authors
A three-stage approach, CoSTA*, combines LLMs and A* search to efficiently find cost-effective tool paths for multi-turn text-to-image editing, outperforming state-of-the-art models in cost and quality.
Yang Sui, Yu-Neng Chuang, Guanchu Wang +9 authors
This survey examines progress and methods for improving efficient reasoning in Large Language Models (LLMs), focusing on model, output, and input prompt optimizations, as well as the use of efficient data and small models.
Marianne Arriola, Aaron Gokaslan, Justin T Chiu +5 authors
Block diffusion language models improve generation efficiency and sequence length flexibility compared to autoregressive and discrete denoising diffusion models.
Itay Nakash, Nitay Calderon, Eyal Ben David +2 authors
AdaptiVocab, a novel vocabulary adaptation method, enhances the efficiency of Large Language Models in domain-specific applications without sacrificing performance by reducing token usage.
Ruibin Yuan, Hanfeng Lin, Shuyue Guo +54 authors
YuE, a family of open foundation models based on LLaMA2, can generate long-form music with aligned lyrics, coherent structure, and appropriate accompaniment using innovative techniques in next-token prediction, conditioning, and pre-training.
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