Orionnnn/hub-stats huggingface dataset:
Processing and acquisition traces in visual encoders: What does CLIP
  know about your camera?
Authors: name: Ryan Ramos, name: Vladan Stojnić, name: Giorgos Kordopatis-Zilos, name: Yuta Nakashima, name: Giorgos Tolias, name: Noa Garcia
Published: 2025-08-14 13:34:13

Prior work has analyzed the robustness of visual encoders to image
transformations and corruptions, particularly in cases where such alterations
are not seen during training. When this occurs, they introduce a form of
distribution shift at test time, often leading to performance degradation. The
primary focus has been on severe corruptions that, when applied aggressively,
distort useful signals necessary for accurate semantic predictions.
  We take a different perspective by analyzing parameters of the image
acquisition process and transformations that may be subtle or even
imperceptible to the human eye. We find that such parameters are systematically
encoded in the learned visual representations and can be easily recovered. More
strikingly, their presence can have a profound impact, either positively or
negatively, on semantic predictions. This effect depends on whether there is a
strong correlation or anti-correlation between semantic labels and these
acquisition-based or processing-based labels. Our code and data are available
at: https://github.com/ryan-caesar-ramos/visual-encoder-traces
ToonComposer: Streamlining Cartoon Production with Generative
  Post-Keyframing
Authors: name: Lingen Li, name: Guangzhi Wang, name: Zhaoyang Zhang, name: Yaowei Li, name: Xiaoyu Li, name: Qi Dou, name: Jinwei Gu, name: Tianfan Xue, name: Ying Shan
Published: 2025-08-14 17:50:11

Traditional cartoon and anime production involves keyframing, inbetweening,
and colorization stages, which require intensive manual effort. Despite recent
advances in AI, existing methods often handle these stages separately, leading
to error accumulation and artifacts. For instance, inbetweening approaches
struggle with large motions, while colorization methods require dense per-frame
sketches. To address this, we introduce ToonComposer, a generative model that
unifies inbetweening and colorization into a single post-keyframing stage.
ToonComposer employs a sparse sketch injection mechanism to provide precise
control using keyframe sketches. Additionally, it uses a cartoon adaptation
method with the spatial low-rank adapter to tailor a modern video foundation
model to the cartoon domain while keeping its temporal prior intact. Requiring
as few as a single sketch and a colored reference frame, ToonComposer excels
with sparse inputs, while also supporting multiple sketches at any temporal
location for more precise motion control. This dual capability reduces manual
workload and improves flexibility, empowering artists in real-world scenarios.
To evaluate our model, we further created PKBench, a benchmark featuring
human-drawn sketches that simulate real-world use cases. Our evaluation
demonstrates that ToonComposer outperforms existing methods in visual quality,
motion consistency, and production efficiency, offering a superior and more
flexible solution for AI-assisted cartoon production.
Pass@k Training for Adaptively Balancing Exploration and Exploitation of
  Large Reasoning Models
Authors: name: Zhipeng Chen, name: Xiaobo Qin, name: Youbin Wu, name: Yue Ling, name: Qinghao Ye, name: Wayne Xin Zhao, name: Guang Shi
Published: 2025-08-14 15:34:47

Reinforcement learning with verifiable rewards (RLVR), which typically adopts
Pass@1 as the reward, has faced the issues in balancing exploration and
exploitation, causing policies to prefer conservative actions, converging to a
local optimum. Identifying an appropriate reward metric is therefore crucial.
Regarding the prior work, although Pass@k has been used in evaluation, its
connection to LLM exploration ability in RLVR remains largely overlooked. To
investigate this, we first use Pass@k as the reward to train the policy model
(i.e., Pass@k Training), and observe the improvement on its
exploration ability. Next, we derive an analytical solution for the advantage
of Pass@k Training, leading to an efficient and effective process. Building on
this, our analysis reveals that exploration and exploitation are not inherently
conflicting objectives, while they can mutually enhance each other. Moreover,
Pass@k Training with analytical derivation essentially involves directly
designing the advantage function. Inspired by this, we preliminarily explore
the advantage design for RLVR, showing promising results and highlighting a
potential future direction.
NextStep-1: Toward Autoregressive Image Generation with Continuous
  Tokens at Scale
Authors: name: NextStep Team, name: Chunrui Han, name: Guopeng Li, name: Jingwei Wu, name: Quan Sun, name: Yan Cai, name: Yuang Peng, name: Zheng Ge, name: Deyu Zhou, name: Haomiao Tang, name: Hongyu Zhou, name: Kenkun Liu, name: Ailin Huang, name: Bin Wang, name: Changxin Miao, name: Deshan Sun, name: En Yu, name: Fukun Yin, name: Gang Yu, name: Hao Nie, name: Haoran Lv, name: Hanpeng Hu, name: Jia Wang, name: Jian Zhou, name: Jianjian Sun, name: Kaijun Tan, name: Kang An, name: Kangheng Lin, name: Liang Zhao, name: Mei Chen, name: Peng Xing, name: Rui Wang, name: Shiyu Liu, name: Shutao Xia, name: Tianhao You, name: Wei Ji, name: Xianfang Zeng, name: Xin Han, name: Xuelin Zhang, name: Yana Wei, name: Yanming Xu, name: Yimin Jiang, name: Yingming Wang, name: Yu Zhou, name: Yucheng Han, name: Ziyang Meng, name: Binxing Jiao, name: Daxin Jiang, name: Xiangyu Zhang, name: Yibo Zhu
Published: 2025-08-14 14:54:22

Prevailing autoregressive (AR) models for text-to-image generation either
rely on heavy, computationally-intensive diffusion models to process continuous
image tokens, or employ vector quantization (VQ) to obtain discrete tokens with
quantization loss. In this paper, we push the autoregressive paradigm forward
with NextStep-1, a 14B autoregressive model paired with a 157M flow matching
head, training on discrete text tokens and continuous image tokens with
next-token prediction objectives. NextStep-1 achieves state-of-the-art
performance for autoregressive models in text-to-image generation tasks,
exhibiting strong capabilities in high-fidelity image synthesis. Furthermore,
our method shows strong performance in image editing, highlighting the power
and versatility of our unified approach. To facilitate open research, we will
release our code and models to the community.
UI-Venus Technical Report: Building High-performance UI Agents with RFT
Authors: name: Zhangxuan Gu, name: Zhengwen Zeng, name: Zhenyu Xu, name: Xingran Zhou, name: Shuheng Shen, name: Yunfei Liu, name: Beitong Zhou, name: Changhua Meng, name: Tianyu Xia, name: Weizhi Chen, name: Yue Wen, name: Jingya Dou, name: Fei Tang, name: Jinzhen Lin, name: Yulin Liu, name: Zhenlin Guo, name: Yichen Gong, name: Heng Jia, name: Changlong Gao, name: Yuan Guo, name: Yong Deng, name: Zhenyu Guo, name: Liang Chen, name: Weiqiang Wang
Published: 2025-08-14 16:58:07

We present UI-Venus, a native UI agent that takes only screenshots as input
based on a multimodal large language model. UI-Venus achieves SOTA performance
on both UI grounding and navigation tasks using only several hundred thousand
high-quality training samples through reinforcement finetune (RFT) based on
Qwen2.5-VL. Specifically, the 7B and 72B variants of UI-Venus obtain 94.1% /
50.8% and 95.3% / 61.9% on the standard grounding benchmarks, i.e.,
Screenspot-V2 / Pro, surpassing the previous SOTA baselines including
open-source GTA1 and closed-source UI-TARS-1.5.To show UI-Venus's summary and
planing ability, we also evaluate it on the AndroidWorld, an online UI
navigation arena, on which our 7B and 72B variants achieve 49.1% and 65.9%
success rate, also beating existing models.To achieve this, we introduce
carefully designed reward functions for both UI grounding and navigation tasks
and corresponding efficient data cleaning strategies.To further boost
navigation performance, we propose Self-Evolving Trajectory History Alignment
\& Sparse Action Enhancement that refine historical reasoning traces and
balances the distribution of sparse but critical actions, leading to more
coherent planning and better generalization in complex UI tasks. Our
contributions include the publish of SOTA open-source UI agents, comprehensive
data cleaning protocols and a novel self-evolving framework for improving
navigation performance, which encourage further research and development in the
community. Code is available at https://github.com/antgroup/UI-Venus.
HumanSense: From Multimodal Perception to Empathetic Context-Aware
  Responses through Reasoning MLLMs
Authors: name: Zheng Qin, name: Ruobing Zheng, name: Yabing Wang, name: Tianqi Li, name: Yi Yuan, name: Jingdong Chen, name: Le Wang
Published: 2025-08-14 12:14:15

While Multimodal Large Language Models (MLLMs) show immense promise for
achieving truly human-like interactions, progress is hindered by the lack of
fine-grained evaluation frameworks for human-centered scenarios, encompassing
both the understanding of complex human intentions and the provision of
empathetic, context-aware responses. Here we introduce HumanSense, a
comprehensive benchmark designed to evaluate the human-centered perception and
interaction capabilities of MLLMs, with a particular focus on deep
understanding of extended multimodal contexts and the formulation of rational
feedback. Our evaluation reveals that leading MLLMs still have considerable
room for improvement, particularly for advanced interaction-oriented tasks.
Supplementing visual input with audio and text information yields substantial
improvements, and Omni-modal models show advantages on these tasks.
Furthermore, we argue that appropriate feedback stems from a contextual
analysis of the interlocutor's needs and emotions, with reasoning ability
serving as the key to unlocking it. Accordingly, we employ a multi-stage,
modality-progressive reinforcement learning to enhance the reasoning abilities
of an Omni model, achieving substantial gains on evaluation results.
Additionally, we observe that successful reasoning processes exhibit highly
consistent thought patterns. By designing corresponding prompts, we also
enhance the performance of non-reasoning models in a training-free manner.
Project page:
brightpinkhttps://digital-avatar.github.io/ai/HumanSense/
PRELUDE: A Benchmark Designed to Require Global Comprehension and
  Reasoning over Long Contexts
Authors: name: Mo Yu, name: Tsz Ting Chung, name: Chulun Zhou, name: Tong Li, name: Rui Lu, name: Jiangnan Li, name: Liyan Xu, name: Haoshu Lu, name: Ning Zhang, name: Jing Li, name: Jie Zhou
Published: 2025-08-13 14:28:25

We introduce PRELUDE, a benchmark for evaluating long-context understanding
through the task of determining whether a character's prequel story is
consistent with the canonical narrative of the original book. Our task poses a
stronger demand for global comprehension and deep reasoning than existing
benchmarks -- as the prequels are not part of the original story, assessing
their plausibility typically requires searching and integrating information
that is only indirectly related. Empirically, 88% of instances require evidence
from multiple parts of the narrative. Experimental results highlight the
challenge of our task: in-context learning, RAG and in-domain training with
state-of-the-art LLMs, and commercial DeepResearch services, lag behind humans
by >15%. A further human study reveals that models often produce correct
answers with flawed reasoning, leading to an over 30% gap in reasoning accuracy
compared to humans. These findings underscore the substantial room for
improvement in long-context understanding and reasoning.
We-Math 2.0: A Versatile MathBook System for Incentivizing Visual
  Mathematical Reasoning
Authors: name: Runqi Qiao, name: Qiuna Tan, name: Peiqing Yang, name: Yanzi Wang, name: Xiaowan Wang, name: Enhui Wan, name: Sitong Zhou, name: Guanting Dong, name: Yuchen Zeng, name: Yida Xu, name: Jie Wang, name: Chong Sun, name: Chen Li, name: Honggang Zhang
Published: 2025-08-14 08:15:41

Multimodal Large Language Models (MLLMs) have demonstrated impressive
capabilities across various tasks, but still struggle with complex mathematical
reasoning. Existing research primarily focuses on dataset construction and
method optimization, often overlooking two critical aspects: comprehensive
knowledge-driven design and model-centric data space modeling. In this paper,
we introduce We-Math 2.0, a unified system that integrates a structured
mathematical knowledge system, model-centric data space modeling, and a
reinforcement learning (RL)-based training paradigm to comprehensively enhance
the mathematical reasoning abilities of MLLMs. The key contributions of We-Math
2.0 are fourfold: (1) MathBook Knowledge System: We construct a five-level
hierarchical system encompassing 491 knowledge points and 1,819 fundamental
principles. (2) MathBook-Standard & Pro: We develop MathBook-Standard, a
dataset that ensures broad conceptual coverage and flexibility through dual
expansion. Additionally, we define a three-dimensional difficulty space and
generate 7 progressive variants per problem to build MathBook-Pro, a
challenging dataset for robust training. (3) MathBook-RL: We propose a
two-stage RL framework comprising: (i) Cold-Start Fine-tuning, which aligns the
model with knowledge-oriented chain-of-thought reasoning; and (ii) Progressive
Alignment RL, leveraging average-reward learning and dynamic data scheduling to
achieve progressive alignment across difficulty levels. (4) MathBookEval: We
introduce a comprehensive benchmark covering all 491 knowledge points with
diverse reasoning step distributions. Experimental results show that
MathBook-RL performs competitively with existing baselines on four widely-used
benchmarks and achieves strong results on MathBookEval, suggesting promising
generalization in mathematical reasoning.
From Black Box to Transparency: Enhancing Automated Interpreting
  Assessment with Explainable AI in College Classrooms
Authors: name: Zhaokun Jiang, name: Ziyin Zhang
Published: 2025-08-14 17:31:18

Recent advancements in machine learning have spurred growing interests in
automated interpreting quality assessment. Nevertheless, existing research
suffers from insufficient examination of language use quality, unsatisfactory
modeling effectiveness due to data scarcity and imbalance, and a lack of
efforts to explain model predictions. To address these gaps, we propose a
multi-dimensional modeling framework that integrates feature engineering, data
augmentation, and explainable machine learning. This approach prioritizes
explainability over ``black box'' predictions by utilizing only
construct-relevant, transparent features and conducting Shapley Value (SHAP)
analysis. Our results demonstrate strong predictive performance on a novel
English-Chinese consecutive interpreting dataset, identifying BLEURT and
CometKiwi scores to be the strongest predictive features for fidelity,
pause-related features for fluency, and Chinese-specific phraseological
diversity metrics for language use. Overall, by placing particular emphasis on
explainability, we present a scalable, reliable, and transparent alternative to
traditional human evaluation, facilitating the provision of detailed diagnostic
feedback for learners and supporting self-regulated learning advantages not
afforded by automated scores in isolation.
The Surprising Effectiveness of Membership Inference with Simple N-Gram
  Coverage
Authors: name: Skyler Hallinan, name: Jaehun Jung, name: Melanie Sclar, name: Ximing Lu, name: Abhilasha Ravichander, name: Sahana Ramnath, name: Yejin Choi, name: Sai Praneeth Karimireddy, name: Niloofar Mireshghallah, name: Xiang Ren
Published: 2025-08-13 08:35:16

Membership inference attacks serves as useful tool for fair use of language
models, such as detecting potential copyright infringement and auditing data
leakage. However, many current state-of-the-art attacks require access to
models' hidden states or probability distribution, which prevents investigation
into more widely-used, API-access only models like GPT-4. In this work, we
introduce N-Gram Coverage Attack, a membership inference attack that relies
solely on text outputs from the target model, enabling attacks on completely
black-box models. We leverage the observation that models are more likely to
memorize and subsequently generate text patterns that were commonly observed in
their training data. Specifically, to make a prediction on a candidate member,
N-Gram Coverage Attack first obtains multiple model generations conditioned on
a prefix of the candidate. It then uses n-gram overlap metrics to compute and
aggregate the similarities of these outputs with the ground truth suffix; high
similarities indicate likely membership. We first demonstrate on a diverse set
of existing benchmarks that N-Gram Coverage Attack outperforms other black-box
methods while also impressively achieving comparable or even better performance
to state-of-the-art white-box attacks - despite having access to only text
outputs. Interestingly, we find that the success rate of our method scales with
the attack compute budget - as we increase the number of sequences generated
from the target model conditioned on the prefix, attack performance tends to
improve. Having verified the accuracy of our method, we use it to investigate
previously unstudied closed OpenAI models on multiple domains. We find that
more recent models, such as GPT-4o, exhibit increased robustness to membership
inference, suggesting an evolving trend toward improved privacy protections.
μ-Parametrization for Mixture of Experts
Authors: name: Jan Małaśnicki, name: Kamil Ciebiera, name: Mateusz Boruń, name: Maciej Pióro, name: Jan Ludziejewski, name: Maciej Stefaniak, name: Michał Krutul, name: Sebastian Jaszczur, name: Marek Cygan, name: Kamil Adamczewski, name: Jakub Krajewski
Published: 2025-08-13 12:31:27

Recent years have seen a growing interest and adoption of LLMs, with
muTransfer becoming a key technique for tuning hyperparameters in
large-scale training. Meanwhile, Mixture-of-Experts (MoE) has emerged as a
leading architecture in extremely large models. However, the intersection of
these two advancements has remained unexplored. In this work, we derive a
mu-Parameterization (muP) for MoE, providing theoretical guarantees for
feature learning across model widths in both the router and experts. We
empirically validate our parameterization and further investigate how scaling
the number of experts and granularity affects the optimal learning rate.
Sample More to Think Less: Group Filtered Policy Optimization for
  Concise Reasoning
Authors: name: Vaishnavi Shrivastava, name: Ahmed Awadallah, name: Vidhisha Balachandran, name: Shivam Garg, name: Harkirat Behl, name: Dimitris Papailiopoulos
Published: 2025-08-13 11:43:49

Large language models trained with reinforcement learning with verifiable
rewards tend to trade accuracy for length--inflating response lengths to
achieve gains in accuracy. While longer answers may be warranted for harder
problems, many tokens are merely "filler": repetitive, verbose text that makes
no real progress. We introduce GFPO (Group Filtered Policy Optimization), which
curbs this length explosion by sampling larger groups per problem during
training and filtering responses to train on based on two key metrics: (1)
response length and (2) token efficiency: reward per token ratio. By sampling
more at training time, we teach models to think less at inference time. On the
Phi-4-reasoning model, GFPO cuts GRPO's length inflation by 46-71% across
challenging STEM and coding benchmarks (AIME 24/25, GPQA, Omni-MATH,
LiveCodeBench) while maintaining accuracy. Optimizing for reward per token
further increases reductions in length inflation to 71-85%. We also propose
Adaptive Difficulty GFPO, which dynamically allocates more training resources
to harder problems based on real-time difficulty estimates, improving the
balance between computational efficiency and accuracy especially on difficult
questions. GFPO demonstrates that increased training-time compute directly
translates to reduced test-time compute--a simple yet effective trade-off for
efficient reasoning.
ObfusQAte: A Proposed Framework to Evaluate LLM Robustness on Obfuscated
  Factual Question Answering
Authors: name: Shubhra Ghosh, name: Abhilekh Borah, name: Aditya Kumar Guru, name: Kripabandhu Ghosh
Published: 2025-08-10 12:27:52

The rapid proliferation of Large Language Models (LLMs) has significantly
contributed to the development of equitable AI systems capable of factual
question-answering (QA). However, no known study tests the LLMs' robustness
when presented with obfuscated versions of questions. To systematically
evaluate these limitations, we propose a novel technique, ObfusQAte and,
leveraging the same, introduce ObfusQA, a comprehensive, first of its kind,
framework with multi-tiered obfuscation levels designed to examine LLM
capabilities across three distinct dimensions: (i) Named-Entity Indirection,
(ii) Distractor Indirection, and (iii) Contextual Overload. By capturing these
fine-grained distinctions in language, ObfusQA provides a comprehensive
benchmark for evaluating LLM robustness and adaptability. Our study observes
that LLMs exhibit a tendency to fail or generate hallucinated responses when
confronted with these increasingly nuanced variations. To foster research in
this direction, we make ObfusQAte publicly available.
ASM-UNet: Adaptive Scan Mamba Integrating Group Commonalities and
  Individual Variations for Fine-Grained Segmentation
Authors: name: Bo Wang, name: Mengyuan Xu, name: Yue Yan, name: Yuqun Yang, name: Kechen Shu, name: Wei Ping, name: Xu Tang, name: Wei Jiang, name: Zheng You
Published: 2025-08-10 08:33:03

Precise lesion resection depends on accurately identifying fine-grained
anatomical structures. While many coarse-grained segmentation (CGS) methods
have been successful in large-scale segmentation (e.g., organs), they fall
short in clinical scenarios requiring fine-grained segmentation (FGS), which
remains challenging due to frequent individual variations in small-scale
anatomical structures. Although recent Mamba-based models have advanced medical
image segmentation, they often rely on fixed manually-defined scanning orders,
which limit their adaptability to individual variations in FGS. To address
this, we propose ASM-UNet, a novel Mamba-based architecture for FGS. It
introduces adaptive scan scores to dynamically guide the scanning order,
generated by combining group-level commonalities and individual-level
variations. Experiments on two public datasets (ACDC and Synapse) and a newly
proposed challenging biliary tract FGS dataset, namely BTMS, demonstrate that
ASM-UNet achieves superior performance in both CGS and FGS tasks. Our code and
dataset are available at https://github.com/YqunYang/ASM-UNet.