Orionnnn/hub-stats huggingface dataset:
ARC-Hunyuan-Video-7B: Structured Video Comprehension of Real-World
  Shorts
Authors: name: Yuying Ge, name: Yixiao Ge, name: Chen Li, name: Teng Wang, name: Junfu Pu, name: Yizhuo Li, name: Lu Qiu, name: Jin Ma, name: Lisheng Duan, name: Xinyu Zuo, name: Jinwen Luo, name: Weibo Gu, name: Zexuan Li, name: Xiaojing Zhang, name: Yangyu Tao, name: Han Hu, name: Di Wang, name: Ying Shan
Published: 2025-07-28 15:52:36

Real-world user-generated short videos, especially those distributed on
platforms such as WeChat Channel and TikTok, dominate the mobile internet.
However, current large multimodal models lack essential temporally-structured,
detailed, and in-depth video comprehension capabilities, which are the
cornerstone of effective video search and recommendation, as well as emerging
video applications. Understanding real-world shorts is actually challenging due
to their complex visual elements, high information density in both visuals and
audio, and fast pacing that focuses on emotional expression and viewpoint
delivery. This requires advanced reasoning to effectively integrate multimodal
information, including visual, audio, and text. In this work, we introduce
ARC-Hunyuan-Video, a multimodal model that processes visual, audio, and textual
signals from raw video inputs end-to-end for structured comprehension. The
model is capable of multi-granularity timestamped video captioning and
summarization, open-ended video question answering, temporal video grounding,
and video reasoning. Leveraging high-quality data from an automated annotation
pipeline, our compact 7B-parameter model is trained through a comprehensive
regimen: pre-training, instruction fine-tuning, cold start, reinforcement
learning (RL) post-training, and final instruction fine-tuning. Quantitative
evaluations on our introduced benchmark ShortVid-Bench and qualitative
comparisons demonstrate its strong performance in real-world video
comprehension, and it supports zero-shot or fine-tuning with a few samples for
diverse downstream applications. The real-world production deployment of our
model has yielded tangible and measurable improvements in user engagement and
satisfaction, a success supported by its remarkable efficiency, with stress
tests indicating an inference time of just 10 seconds for a one-minute video on
H20 GPU.
JAM: A Tiny Flow-based Song Generator with Fine-grained Controllability
  and Aesthetic Alignment
Authors: name: Renhang Liu, name: Chia-Yu Hung, name: Navonil Majumder, name: Taylor Gautreaux, name: Amir Ali Bagherzadeh, name: Chuan Li, name: Dorien Herremans, name: Soujanya Poria
Published: 2025-07-28 14:34:02

Diffusion and flow-matching models have revolutionized automatic
text-to-audio generation in recent times. These models are increasingly capable
of generating high quality and faithful audio outputs capturing to speech and
acoustic events. However, there is still much room for improvement in creative
audio generation that primarily involves music and songs. Recent open
lyrics-to-song models, such as, DiffRhythm, ACE-Step, and LeVo, have set an
acceptable standard in automatic song generation for recreational use. However,
these models lack fine-grained word-level controllability often desired by
musicians in their workflows. To the best of our knowledge, our
flow-matching-based JAM is the first effort toward endowing word-level timing
and duration control in song generation, allowing fine-grained vocal control.
To enhance the quality of generated songs to better align with human
preferences, we implement aesthetic alignment through Direct Preference
Optimization, which iteratively refines the model using a synthetic dataset,
eliminating the need or manual data annotations. Furthermore, we aim to
standardize the evaluation of such lyrics-to-song models through our public
evaluation dataset JAME. We show that JAM outperforms the existing models in
terms of the music-specific attributes.
AFRDA: Attentive Feature Refinement for Domain Adaptive Semantic
  Segmentation
Authors: name: Md. Al-Masrur Khan, name: Durgakant Pushp, name: Lantao Liu
Published: 2025-07-23 22:02:17

In Unsupervised Domain Adaptive Semantic Segmentation (UDA-SS), a model is
trained on labeled source domain data (e.g., synthetic images) and adapted to
an unlabeled target domain (e.g., real-world images) without access to target
annotations. Existing UDA-SS methods often struggle to balance fine-grained
local details with global contextual information, leading to segmentation
errors in complex regions. To address this, we introduce the Adaptive Feature
Refinement (AFR) module, which enhances segmentation accuracy by refining
highresolution features using semantic priors from low-resolution logits. AFR
also integrates high-frequency components, which capture fine-grained
structures and provide crucial boundary information, improving object
delineation. Additionally, AFR adaptively balances local and global information
through uncertaintydriven attention, reducing misclassifications. Its
lightweight design allows seamless integration into HRDA-based UDA methods,
leading to state-of-the-art segmentation performance. Our approach improves
existing UDA-SS methods by 1.05% mIoU on GTA V --> Cityscapes and 1.04% mIoU on
Synthia-->Cityscapes. The implementation of our framework is available at:
https://github.com/Masrur02/AFRDA
When Tokens Talk Too Much: A Survey of Multimodal Long-Context Token
  Compression across Images, Videos, and Audios
Authors: name: Kele Shao, name: Keda Tao, name: Kejia Zhang, name: Sicheng Feng, name: Mu Cai, name: Yuzhang Shang, name: Haoxuan You, name: Can Qin, name: Yang Sui, name: Huan Wang
Published: 2025-07-27 09:33:56

Multimodal large language models (MLLMs) have made remarkable strides,
largely driven by their ability to process increasingly long and complex
contexts, such as high-resolution images, extended video sequences, and lengthy
audio input. While this ability significantly enhances MLLM capabilities, it
introduces substantial computational challenges, primarily due to the quadratic
complexity of self-attention mechanisms with numerous input tokens. To mitigate
these bottlenecks, token compression has emerged as an auspicious and critical
approach, efficiently reducing the number of tokens during both training and
inference. In this paper, we present the first systematic survey and synthesis
of the burgeoning field of multimodal long context token compression.
Recognizing that effective compression strategies are deeply tied to the unique
characteristics and redundancies of each modality, we categorize existing
approaches by their primary data focus, enabling researchers to quickly access
and learn methods tailored to their specific area of interest: (1)
image-centric compression, which addresses spatial redundancy in visual data;
(2) video-centric compression, which tackles spatio-temporal redundancy in
dynamic sequences; and (3) audio-centric compression, which handles temporal
and spectral redundancy in acoustic signals. Beyond this modality-driven
categorization, we further dissect methods based on their underlying
mechanisms, including transformation-based, similarity-based, attention-based,
and query-based approaches. By providing a comprehensive and structured
overview, this survey aims to consolidate current progress, identify key
challenges, and inspire future research directions in this rapidly evolving
domain. We also maintain a public repository to continuously track and update
the latest advances in this promising area.
GEPA: Reflective Prompt Evolution Can Outperform Reinforcement Learning
Authors: name: Lakshya A Agrawal, name: Shangyin Tan, name: Dilara Soylu, name: Noah Ziems, name: Rishi Khare, name: Krista Opsahl-Ong, name: Arnav Singhvi, name: Herumb Shandilya, name: Michael J Ryan, name: Meng Jiang, name: Christopher Potts, name: Koushik Sen, name: Alexandros G. Dimakis, name: Ion Stoica, name: Dan Klein, name: Matei Zaharia, name: Omar Khattab
Published: 2025-07-25 17:42:32

Large language models (LLMs) are increasingly adapted to downstream tasks via
reinforcement learning (RL) methods like Group Relative Policy Optimization
(GRPO), which often require thousands of rollouts to learn new tasks. We argue
that the interpretable nature of language can often provide a much richer
learning medium for LLMs, compared with policy gradients derived from sparse,
scalar rewards. To test this, we introduce GEPA (Genetic-Pareto), a prompt
optimizer that thoroughly incorporates natural language reflection to learn
high-level rules from trial and error. Given any AI system containing one or
more LLM prompts, GEPA samples system-level trajectories (e.g., reasoning, tool
calls, and tool outputs) and reflects on them in natural language to diagnose
problems, propose and test prompt updates, and combine complementary lessons
from the Pareto frontier of its own attempts. As a result of GEPA's design, it
can often turn even just a few rollouts into a large quality gain. Across four
tasks, GEPA outperforms GRPO by 10% on average and by up to 20%, while using up
to 35x fewer rollouts. GEPA also outperforms the leading prompt optimizer,
MIPROv2, by over 10% across two LLMs, and demonstrates promising results as an
inference-time search strategy for code optimization.
Frontier AI Risk Management Framework in Practice: A Risk Analysis
  Technical Report
Authors: name: Shanghai AI Lab, name: Xiaoyang Chen, name: Yunhao Chen, name: Zeren Chen, name: Zhiyun Chen, name: Hanyun Cui, name: Yawen Duan, name: Jiaxuan Guo, name: Qi Guo, name: Xuhao Hu, name: Hong Huang, name: Lige Huang, name: Chunxiao Li, name: Juncheng Li, name: Qihao Lin, name: Dongrui Liu, name: Xinmin Liu, name: Zicheng Liu, name: Chaochao Lu, name: Xiaoya Lu, name: Jingjing Qu, name: Qibing Ren, name: Jing Shao, name: Jingwei Shi, name: Jingwei Sun, name: Peng Wang, name: Weibing Wang, name: Jia Xu, name: Lewen Yan, name: Xiao Yu, name: Yi Yu, name: Boxuan Zhang, name: Jie Zhang, name: Weichen Zhang, name: Zhijie Zheng, name: Tianyi Zhou, name: Bowen Zhou
Published: 2025-07-22 12:44:38

To understand and identify the unprecedented risks posed by rapidly advancing
artificial intelligence (AI) models, this report presents a comprehensive
assessment of their frontier risks. Drawing on the E-T-C analysis (deployment
environment, threat source, enabling capability) from the Frontier AI Risk
Management Framework (v1.0) (SafeWork-F1-Framework), we identify critical risks
in seven areas: cyber offense, biological and chemical risks, persuasion and
manipulation, uncontrolled autonomous AI R\&D, strategic deception and
scheming, self-replication, and collusion. Guided by the "AI-45^circ Law,"
we evaluate these risks using "red lines" (intolerable thresholds) and "yellow
lines" (early warning indicators) to define risk zones: green (manageable risk
for routine deployment and continuous monitoring), yellow (requiring
strengthened mitigations and controlled deployment), and red (necessitating
suspension of development and/or deployment). Experimental results show that
all recent frontier AI models reside in green and yellow zones, without
crossing red lines. Specifically, no evaluated models cross the yellow line for
cyber offense or uncontrolled AI R\&D risks. For self-replication, and
strategic deception and scheming, most models remain in the green zone, except
for certain reasoning models in the yellow zone. In persuasion and
manipulation, most models are in the yellow zone due to their effective
influence on humans. For biological and chemical risks, we are unable to rule
out the possibility of most models residing in the yellow zone, although
detailed threat modeling and in-depth assessment are required to make further
claims. This work reflects our current understanding of AI frontier risks and
urges collective action to mitigate these challenges.
CLEAR: Error Analysis via LLM-as-a-Judge Made Easy
Authors: name: Asaf Yehudai, name: Lilach Eden, name: Yotam Perlitz, name: Roy Bar-Haim, name: Michal Shmueli-Scheuer
Published: 2025-07-24 13:15:21

The evaluation of Large Language Models (LLMs) increasingly relies on other
LLMs acting as judges. However, current evaluation paradigms typically yield a
single score or ranking, answering which model is better but not why. While
essential for benchmarking, these top-level scores obscure the specific,
actionable reasons behind a model's performance. To bridge this gap, we
introduce CLEAR, an interactive, open-source package for LLM-based error
analysis. CLEAR first generates per-instance textual feedback, then it creates
a set of system-level error issues, and quantifies the prevalence of each
identified issue. Our package also provides users with an interactive dashboard
that allows for a comprehensive error analysis through aggregate
visualizations, applies interactive filters to isolate specific issues or score
ranges, and drills down to the individual instances that exemplify a particular
behavioral pattern. We demonstrate CLEAR analysis for RAG and Math benchmarks,
and showcase its utility through a user case study.
MMBench-GUI: Hierarchical Multi-Platform Evaluation Framework for GUI
  Agents
Authors: name: Xuehui Wang, name: Zhenyu Wu, name: JingJing Xie, name: Zichen Ding, name: Bowen Yang, name: Zehao Li, name: Zhaoyang Liu, name: Qingyun Li, name: Xuan Dong, name: Zhe Chen, name: Weiyun Wang, name: Xiangyu Zhao, name: Jixuan Chen, name: Haodong Duan, name: Tianbao Xie, name: Chenyu Yang, name: Shiqian Su, name: Yue Yu, name: Yuan Huang, name: Yiqian Liu, name: Xiao Zhang, name: Yanting Zhang, name: Xiangyu Yue, name: Weijie Su, name: Xizhou Zhu, name: Wei Shen, name: Jifeng Dai, name: Wenhai Wang
Published: 2025-07-25 17:59:26

We introduce MMBench-GUI, a hierarchical benchmark for evaluating GUI
automation agents across Windows, macOS, Linux, iOS, Android, and Web
platforms. It comprises four levels: GUI Content Understanding, Element
Grounding, Task Automation, and Task Collaboration, covering essential skills
for GUI agents. In addition, we propose a novel Efficiency-Quality Area (EQA)
metric to assess GUI agent execution efficiency in online automation scenarios.
Through MMBench-GUI, we identify accurate visual grounding as a critical
determinant of overall task success, emphasizing the substantial benefits of
modular frameworks that integrate specialized grounding modules. Furthermore,
to achieve reliable GUI automation, an agent requires strong task planning and
cross-platform generalization abilities, with long-context memory, a broad
action space, and long-term reasoning playing a critical role. More important,
task efficiency remains a critically underexplored dimension, and all models
suffer from substantial inefficiencies, with excessive redundant steps even
when tasks are ultimately completed. The integration of precise localization,
effective planning, and early stopping strategies is indispensable to enable
truly efficient and scalable GUI automation. Our benchmark code, evaluation
data, and running environment will be publicly available at
https://github.com/open-compass/MMBench-GUI.
The Geometry of LLM Quantization: GPTQ as Babai's Nearest Plane
  Algorithm
Authors: name: Jiale Chen, name: Torsten Hoefler, name: Dan Alistarh
Published: 2025-07-24 16:22:18

Quantizing the weights of large language models (LLMs) from 16-bit to lower
bitwidth is the de facto approach to deploy massive transformers onto more
affordable accelerators. GPTQ emerged as one of the standard methods for
one-shot post-training quantization at LLM scale. Yet, its inner workings are
described as a sequence of ad-hoc algebraic updates that obscure any geometric
meaning or worst-case guarantees. In this work, we show that, when executed
back-to-front (from the last to first dimension) for a linear layer, GPTQ is
mathematically identical to Babai's nearest plane algorithm for the classical
closest vector problem (CVP) on a lattice defined by the Hessian matrix of the
layer's inputs. This equivalence is based on a sophisticated mathematical
argument, and has two analytical consequences: (i) the GPTQ error propagation
step gains an intuitive geometric interpretation; (ii) GPTQ inherits the error
upper bound of Babai's algorithm under the no-clipping condition. Taken
together, these results place GPTQ on firm theoretical footing and open the
door to importing decades of progress in lattice algorithms towards the design
of future quantization algorithms for billion-parameter models.
Specification Self-Correction: Mitigating In-Context Reward Hacking
  Through Test-Time Refinement
Authors: name: Víctor Gallego
Published: 2025-07-24 18:44:28

Language models (LMs) are susceptible to in-context reward hacking, where
they exploit flaws in tainted or faulty written specifications or rubrics to
achieve high scores without fulfilling the user's true intent. We introduce
Specification Self-Correction (SSC), a novel, test-time framework that enables
an LM to identify and correct flaws within its own guiding specification. SSC
employs a multi-step inference process where the model first generates a
response based on a potentially tainted specification, critiques its output,
and then revises the specification itself to remove the exploitable loophole. A
final, more robust response is then generated using this self-corrected
specification. Across experiments spanning creative writing and agentic coding
tasks with several LMs, we demonstrate that while models initially game tainted
specifications in 50-70\% of cases, the SSC process reduces this vulnerability
by over 90\%. This dynamic repair occurs at inference time, requires no weight
modification, and leads to more robustly aligned model behavior. Code at
https://github.com/vicgalle/specification-self-correction .
Deep Researcher with Test-Time Diffusion
Authors: name: Rujun Han, name: Yanfei Chen, name: Zoey CuiZhu, name: Lesly Miculicich, name: Guan Sun, name: Yuanjun Bi, name: Weiming Wen, name: Hui Wan, name: Chunfeng Wen, name: Solène Maître, name: George Lee, name: Vishy Tirumalashetty, name: Emily Xue, name: Zizhao Zhang, name: Salem Haykal, name: Burak Gokturk, name: Tomas Pfister, name: Chen-Yu Lee
Published: 2025-07-21 21:23:21

Deep research agents, powered by Large Language Models (LLMs), are rapidly
advancing; yet, their performance often plateaus when generating complex,
long-form research reports using generic test-time scaling algorithms. Drawing
inspiration from the iterative nature of human research, which involves cycles
of searching, reasoning, and revision, we propose the Test-Time Diffusion Deep
Researcher (TTD-DR). This novel framework conceptualizes research report
generation as a diffusion process. TTD-DR initiates this process with a
preliminary draft, an updatable skeleton that serves as an evolving foundation
to guide the research direction. The draft is then iteratively refined through
a "denoising" process, which is dynamically informed by a retrieval mechanism
that incorporates external information at each step. The core process is
further enhanced by a self-evolutionary algorithm applied to each component of
the agentic workflow, ensuring the generation of high-quality context for the
diffusion process. This draft-centric design makes the report writing process
more timely and coherent while reducing information loss during the iterative
search process. We demonstrate that our TTD-DR achieves state-of-the-art
results on a wide array of benchmarks that require intensive search and
multi-hop reasoning, significantly outperforming existing deep research agents.
PRIX: Learning to Plan from Raw Pixels for End-to-End Autonomous Driving
Authors: name: Maciej K. Wozniak, name: Lianhang Liu, name: Yixi Cai, name: Patric Jensfelt
Published: 2025-07-23 15:28:23

While end-to-end autonomous driving models show promising results, their
practical deployment is often hindered by large model sizes, a reliance on
expensive LiDAR sensors and computationally intensive BEV feature
representations. This limits their scalability, especially for mass-market
vehicles equipped only with cameras. To address these challenges, we propose
PRIX (Plan from Raw Pixels). Our novel and efficient end-to-end driving
architecture operates using only camera data, without explicit BEV
representation and forgoing the need for LiDAR. PRIX leverages a visual feature
extractor coupled with a generative planning head to predict safe trajectories
from raw pixel inputs directly. A core component of our architecture is the
Context-aware Recalibration Transformer (CaRT), a novel module designed to
effectively enhance multi-level visual features for more robust planning. We
demonstrate through comprehensive experiments that PRIX achieves
state-of-the-art performance on the NavSim and nuScenes benchmarks, matching
the capabilities of larger, multimodal diffusion planners while being
significantly more efficient in terms of inference speed and model size, making
it a practical solution for real-world deployment. Our work is open-source and
the code will be at https://maxiuw.github.io/prix.
Chat with AI: The Surprising Turn of Real-time Video Communication from
  Human to AI
Authors: name: Jiangkai Wu, name: Zhiyuan Ren, name: Liming Liu, name: Xinggong Zhang
Published: 2025-07-14 17:34:49

AI Video Chat emerges as a new paradigm for Real-time Communication (RTC),
where one peer is not a human, but a Multimodal Large Language Model (MLLM).
This makes interaction between humans and AI more intuitive, as if chatting
face-to-face with a real person. However, this poses significant challenges to
latency, because the MLLM inference takes up most of the response time, leaving
very little time for video streaming. Due to network uncertainty and
instability, transmission latency becomes a critical bottleneck preventing AI
from being like a real person. To address this, we propose Artic, an
AI-oriented Real-time Communication framework, exploring the network
requirement shift from "humans watching video" to "AI understanding video". To
reduce bitrate dramatically while maintaining MLLM accuracy, we propose
Context-Aware Video Streaming that recognizes the importance of each video
region for chat and allocates bitrate almost exclusively to chat-important
regions. To avoid packet retransmission, we propose Loss-Resilient Adaptive
Frame Rate that leverages previous frames to substitute for lost/delayed frames
while avoiding bitrate waste. To evaluate the impact of video streaming quality
on MLLM accuracy, we build the first benchmark, named Degraded Video
Understanding Benchmark (DeViBench). Finally, we discuss some open questions
and ongoing solutions for AI Video Chat.