Orionnnn/hub-stats huggingface dataset:
Machine Text Detectors are Membership Inference Attacks
Authors: name: Ryuto Koike, name: Liam Dugan, name: Masahiro Kaneko, name: Chris Callison-Burch, name: Naoaki Okazaki
Published: 2025-10-22 11:39:01

Although membership inference attacks (MIAs) and machine-generated text
detection target different goals, identifying training samples and synthetic
texts, their methods often exploit similar signals based on a language model's
probability distribution. Despite this shared methodological foundation, the
two tasks have been independently studied, which may lead to conclusions that
overlook stronger methods and valuable insights developed in the other task. In
this work, we theoretically and empirically investigate the transferability,
i.e., how well a method originally developed for one task performs on the
other, between MIAs and machine text detection. For our theoretical
contribution, we prove that the metric that achieves the asymptotically highest
performance on both tasks is the same. We unify a large proportion of the
existing literature in the context of this optimal metric and hypothesize that
the accuracy with which a given method approximates this metric is directly
correlated with its transferability. Our large-scale empirical experiments,
including 7 state-of-the-art MIA methods and 5 state-of-the-art machine text
detectors across 13 domains and 10 generators, demonstrate very strong rank
correlation (rho > 0.6) in cross-task performance. We notably find that
Binoculars, originally designed for machine text detection, achieves
state-of-the-art performance on MIA benchmarks as well, demonstrating the
practical impact of the transferability. Our findings highlight the need for
greater cross-task awareness and collaboration between the two research
communities. To facilitate cross-task developments and fair evaluations, we
introduce MINT, a unified evaluation suite for MIAs and machine-generated text
detection, with implementation of 15 recent methods from both tasks.
BAPO: Stabilizing Off-Policy Reinforcement Learning for LLMs via
  Balanced Policy Optimization with Adaptive Clipping
Authors: name: Zhiheng Xi, name: Xin Guo, name: Yang Nan, name: Enyu Zhou, name: Junrui Shen, name: Wenxiang Chen, name: Jiaqi Liu, name: Jixuan Huang, name: Zhihao Zhang, name: Honglin Guo, name: Xun Deng, name: Zhikai Lei, name: Miao Zheng, name: Guoteng Wang, name: Shuo Zhang, name: Peng Sun, name: Rui Zheng, name: Hang Yan, name: Tao Gui, name: Qi Zhang, name: Xuanjing Huang
Published: 2025-10-21 12:55:04

Reinforcement learning (RL) has recently become the core paradigm for
aligning and strengthening large language models (LLMs). Yet, applying RL in
off-policy settings--where stale data from past policies are used for
training--improves sample efficiency, but remains challenging: policy entropy
declines sharply, optimization often becomes unstable and may even collapse.
Through theoretical and empirical analysis, we identify two key insights: (i)
an imbalance in optimization, where negative-advantage samples dominate the
policy gradient, suppressing useful behaviors and risking gradient explosions;
and (ii) the derived Entropy-Clip Rule, which reveals that the fixed clipping
mechanism in PPO-like objectives systematically blocks entropy-increasing
updates, thereby driving the policy toward over-exploitation at the expense of
exploration. Building on these insights, we propose BAlanced Policy
Optimization with Adaptive Clipping (BAPO), a simple yet effective method that
dynamically adjusts clipping bounds to adaptively re-balance positive and
negative contributions, preserve entropy, and stabilize RL optimization. Across
diverse off-policy scenarios--including sample replay and partial rollout--BAPO
achieves fast, stable, and data-efficient training. On AIME 2024 and AIME 2025
benchmarks, our 7B BAPO model surpasses open-source counterparts such as
SkyWork-OR1-7B, while our 32B BAPO model not only achieves state-of-the-art
results among models of the same scale but also outperforms leading proprietary
systems like o3-mini and Gemini-2.5-Flash-Thinking.
ProfBench: Multi-Domain Rubrics requiring Professional Knowledge to
  Answer and Judge
Authors: name: Zhilin Wang, name: Jaehun Jung, name: Ximing Lu, name: Shizhe Diao, name: Ellie Evans, name: Jiaqi Zeng, name: Pavlo Molchanov, name: Yejin Choi, name: Jan Kautz, name: Yi Dong
Published: 2025-10-21 17:59:44

Evaluating progress in large language models (LLMs) is often constrained by
the challenge of verifying responses, limiting assessments to tasks like
mathematics, programming, and short-form question-answering. However, many
real-world applications require evaluating LLMs in processing professional
documents, synthesizing information, and generating comprehensive reports in
response to user queries. We introduce ProfBench: a set of over 7000
response-criterion pairs as evaluated by human-experts with professional
knowledge across Physics PhD, Chemistry PhD, Finance MBA and Consulting MBA. We
build robust and affordable LLM-Judges to evaluate ProfBench rubrics, by
mitigating self-enhancement bias and reducing the cost of evaluation by 2-3
orders of magnitude, to make it fair and accessible to the broader community.
Our findings reveal that ProfBench poses significant challenges even for
state-of-the-art LLMs, with top-performing models like GPT-5-high achieving
only 65.9\% overall performance. Furthermore, we identify notable performance
disparities between proprietary and open-weight models and provide insights
into the role that extended thinking plays in addressing complex,
professional-domain tasks. Data:
https://huggingface.co/datasets/nvidia/ProfBench and Code:
https://github.com/NVlabs/ProfBench
NeuroAda: Activating Each Neuron's Potential for Parameter-Efficient
  Fine-Tuning
Authors: name: Zhi Zhang, name: Yixian Shen, name: Congfeng Cao, name: Ekaterina Shutova
Published: 2025-10-21 17:59:24

Existing parameter-efficient fine-tuning (PEFT) methods primarily fall into
two categories: addition-based and selective in-situ adaptation. The former,
such as LoRA, introduce additional modules to adapt the model to downstream
tasks, offering strong memory efficiency. However, their representational
capacity is often limited, making them less suitable for fine-grained
adaptation. In contrast, the latter directly fine-tunes a carefully chosen
subset of the original model parameters, allowing for more precise and
effective adaptation, but at the cost of significantly increased memory
consumption. To reconcile this trade-off, we propose NeuroAda, a novel PEFT
method that enables fine-grained model finetuning while maintaining high memory
efficiency. Our approach first identifies important parameters (i.e.,
connections within the network) as in selective adaptation, and then introduces
bypass connections for these selected parameters. During finetuning, only the
bypass connections are updated, leaving the original model parameters frozen.
Empirical results on 23+ tasks spanning both natural language generation and
understanding demonstrate that NeuroAda achieves state-of-the-art performance
with as little as leq 0.02% trainable parameters, while reducing
CUDA memory usage by up to 60%. We release our code here:
https://github.com/FightingFighting/NeuroAda.git.
RIR-Mega: a large-scale simulated room impulse response dataset for
  machine learning and room acoustics modeling
Authors: name: Mandip Goswami
Published: 2025-10-21 06:53:14

Room impulse responses are a core resource for dereverberation, robust speech
recognition, source localization, and room acoustics estimation. We present
RIR-Mega, a large collection of simulated RIRs described by a compact, machine
friendly metadata schema and distributed with simple tools for validation and
reuse. The dataset ships with a Hugging Face Datasets loader, scripts for
metadata checks and checksums, and a reference regression baseline that
predicts RT60 like targets from waveforms. On a train and validation split of
36,000 and 4,000 examples, a small Random Forest on lightweight time and
spectral features reaches a mean absolute error near 0.013 s and a root mean
square error near 0.022 s. We host a subset with 1,000 linear array RIRs and
3,000 circular array RIRs on Hugging Face for streaming and quick tests, and
preserve the complete 50,000 RIR archive on Zenodo. The dataset and code are
public to support reproducible studies.
AlphaOPT: Formulating Optimization Programs with Self-Improving LLM
  Experience Library
Authors: name: Minwei Kong, name: Ao Qu, name: Xiaotong Guo, name: Wenbin Ouyang, name: Chonghe Jiang, name: Han Zheng, name: Yining Ma, name: Dingyi Zhuang, name: Yuhan Tang, name: Junyi Li, name: Hai Wang, name: Cathy Wu, name: Jinhua Zhao
Published: 2025-10-21 09:03:26

Optimization modeling enables critical decisions across industries but
remains difficult to automate: informal language must be mapped to precise
mathematical formulations and executable solver code. Prior LLM approaches
either rely on brittle prompting or costly retraining with limited
generalization. We present AlphaOPT, a self-improving experience library that
enables an LLM to learn from limited demonstrations (even answers alone,
without gold-standard programs) and solver feedback - without annotated
reasoning traces or parameter updates. AlphaOPT operates in a continual
two-phase cycle: (i) a Library Learning phase that reflects on failed attempts,
extracting solver-verified, structured insights as {taxonomy, condition,
explanation, example}; and (ii) a Library Evolution phase that diagnoses
retrieval misalignments and refines the applicability conditions of stored
insights, improving transfer across tasks. This design (1) learns efficiently
from limited demonstrations without curated rationales, (2) expands continually
without costly retraining by updating the library rather than model weights,
and (3) makes knowledge explicit and interpretable for human inspection and
intervention. Experiments show that AlphaOPT steadily improves with more data
(65% to 72% from 100 to 300 training items) and surpasses the strongest
baseline by 7.7% on the out-of-distribution OptiBench dataset when trained only
on answers. Code and data are available at:
https://github.com/Minw913/AlphaOPT.
Unified Reinforcement and Imitation Learning for Vision-Language Models
Authors: name: Byung-Kwan Lee, name: Ryo Hachiuma, name: Yong Man Ro, name: Yu-Chiang Frank Wang, name: Yueh-Hua Wu
Published: 2025-10-22 07:12:14

Vision-Language Models (VLMs) have achieved remarkable progress, yet their
large scale often renders them impractical for resource-constrained
environments. This paper introduces Unified Reinforcement and Imitation
Learning (RIL), a novel and efficient training algorithm designed to create
powerful, lightweight VLMs. RIL distinctively combines the strengths of
reinforcement learning with adversarial imitation learning. This enables
smaller student VLMs not only to mimic the sophisticated text generation of
large teacher models but also to systematically improve their generative
capabilities through reinforcement signals. Key to our imitation framework is
an LLM-based discriminator that adeptly distinguishes between student and
teacher outputs, complemented by guidance from multiple large teacher VLMs to
ensure diverse learning. This unified learning strategy, leveraging both
reinforcement and imitation, empowers student models to achieve significant
performance gains, making them competitive with leading closed-source VLMs.
Extensive experiments on diverse vision-language benchmarks demonstrate that
RIL significantly narrows the performance gap with state-of-the-art open- and
closed-source VLMs and, in several instances, surpasses them.
FinSight: Towards Real-World Financial Deep Research
Authors: name: Jiajie Jin, name: Yuyao Zhang, name: Yimeng Xu, name: Hongjin Qian, name: Yutao Zhu, name: Zhicheng Dou
Published: 2025-10-19 14:05:35

Generating professional financial reports is a labor-intensive and
intellectually demanding process that current AI systems struggle to fully
automate. To address this challenge, we introduce FinSight (Financial InSight),
a novel multi agent framework for producing high-quality, multimodal financial
reports. The foundation of FinSight is the Code Agent with Variable Memory
(CAVM) architecture, which unifies external data, designed tools, and agents
into a programmable variable space, enabling flexible data collection, analysis
and report generation through executable code. To ensure professional-grade
visualization, we propose an Iterative Vision-Enhanced Mechanism that
progressively refines raw visual outputs into polished financial charts.
Furthermore, a two stage Writing Framework expands concise Chain-of-Analysis
segments into coherent, citation-aware, and multimodal reports, ensuring both
analytical depth and structural consistency. Experiments on various company and
industry-level tasks demonstrate that FinSight significantly outperforms all
baselines, including leading deep research systems in terms of factual
accuracy, analytical depth, and presentation quality, demonstrating a clear
path toward generating reports that approach human-expert quality.
MINED: Probing and Updating with Multimodal Time-Sensitive Knowledge for Large Multimodal Models
Authors: name: Kailin Jiang, name: Ning Jiang, name: Yuntao Du, name: Yuchen Ren, name: Yuchen Li, name: Yifan Gao, name: Jinhe Bi, name: Yunpu Ma, name: Qingqing Liu, name: Xianhao Wang, name: Yifan Jia, name: Hongbo Jiang, name: Yaocong Hu, name: Bin Li, name: Lei Liu
Published: 2025-10-22 10:41:57

Large Multimodal Models (LMMs) encode rich factual knowledge via cross-modal pre-training, yet their static representations struggle to maintain an accurate understanding of time-sensitive factual knowledge. Existing benchmarks remain constrained by static designs, inadequately evaluating LMMs' ability to understand time-sensitive knowledge. To address this gap, we propose MINED, a comprehensive benchmark that evaluates temporal awareness along 6 key dimensions and 11 challenging tasks: cognition, awareness, trustworthiness, understanding, reasoning, and robustness. MINED is constructed from Wikipedia by two professional annotators, containing 2,104 time-sensitive knowledge samples spanning six knowledge types. Evaluating 15 widely used LMMs on MINED shows that Gemini-2.5-Pro achieves the highest average CEM score of 63.07, while most open-source LMMs still lack time understanding ability. Meanwhile, LMMs perform best on organization knowledge, whereas their performance is weakest on sport. To address these challenges, we investigate the feasibility of updating time-sensitive knowledge in LMMs through knowledge editing methods and observe that LMMs can effectively update knowledge via knowledge editing methods in single editing scenarios.
Every Attention Matters: An Efficient Hybrid Architecture for
  Long-Context Reasoning
Authors: name: Ling Team, name: Bin Han, name: Caizhi Tang, name: Chen Liang, name: Donghao Zhang, name: Fan Yuan, name: Feng Zhu, name: Jie Gao, name: Jingyu Hu, name: Longfei Li, name: Meng Li, name: Mingyang Zhang, name: Peijie Jiang, name: Peng Jiao, name: Qian Zhao, name: Qingyuan Yang, name: Wenbo Shen, name: Xinxing Yang, name: Yalin Zhang, name: Yankun Ren, name: Yao Zhao, name: Yibo Cao, name: Yixuan Sun, name: Yue Zhang, name: Yuchen Fang, name: Zibin Lin, name: Zixuan Cheng, name: Jun Zhou
Published: 2025-10-22 07:59:38

In this technical report, we present the Ring-linear model series,
specifically including Ring-mini-linear-2.0 and Ring-flash-linear-2.0.
Ring-mini-linear-2.0 comprises 16B parameters and 957M activations, while
Ring-flash-linear-2.0 contains 104B parameters and 6.1B activations. Both
models adopt a hybrid architecture that effectively integrates linear attention
and softmax attention, significantly reducing I/O and computational overhead in
long-context inference scenarios. Compared to a 32 billion parameter dense
model, this series reduces inference cost to 1/10, and compared to the original
Ring series, the cost is also reduced by over 50%. Furthermore, through
systematic exploration of the ratio between different attention mechanisms in
the hybrid architecture, we have identified the currently optimal model
structure. Additionally, by leveraging our self-developed high-performance FP8
operator library-linghe, overall training efficiency has been improved by 50%.
Benefiting from the high alignment between the training and inference engine
operators, the models can undergo long-term, stable, and highly efficient
optimization during the reinforcement learning phase, consistently maintaining
SOTA performance across multiple challenging complex reasoning benchmarks.
KORE: Enhancing Knowledge Injection for Large Multimodal Models via
  Knowledge-Oriented Augmentations and Constraints
Authors: name: Kailin Jiang, name: Hongbo Jiang, name: Ning Jiang, name: Zhi Gao, name: Jinhe Bi, name: Yuchen Ren, name: Bin Li, name: Yuntao Du, name: Lei Liu, name: Qing Li
Published: 2025-10-22 07:26:55

Large Multimodal Models encode extensive factual knowledge in their
pre-trained weights. However, its knowledge remains static and limited, unable
to keep pace with real-world developments, which hinders continuous knowledge
acquisition. Effective knowledge injection thus becomes critical, involving two
goals: knowledge adaptation (injecting new knowledge) and knowledge retention
(preserving old knowledge). Existing methods often struggle to learn new
knowledge and suffer from catastrophic forgetting. To address this, we propose
KORE, a synergistic method of KnOwledge-oRientEd augmentations and constraints
for injecting new knowledge into large multimodal models while preserving old
knowledge. Unlike general text or image data augmentation, KORE automatically
converts individual knowledge items into structured and comprehensive knowledge
to ensure that the model accurately learns new knowledge, enabling accurate
adaptation. Meanwhile, KORE stores previous knowledge in the covariance matrix
of LMM's linear layer activations and initializes the adapter by projecting the
original weights into the matrix's null space, defining a fine-tuning direction
that minimizes interference with previous knowledge, enabling powerful
retention. Extensive experiments on various LMMs, including LLaVA-v1.5-7B,
LLaVA-v1.5-13B, and Qwen2.5-VL-7B, show that KORE achieves superior new
knowledge injection performance and effectively mitigates catastrophic
forgetting.
OmniNWM: Omniscient Driving Navigation World Models
Authors: name: Bohan Li, name: Zhuang Ma, name: Dalong Du, name: Baorui Peng, name: Zhujin Liang, name: Zhenqiang Liu, name: Chao Ma, name: Yueming Jin, name: Hao Zhao, name: Wenjun Zeng, name: Xin Jin
Published: 2025-10-21 05:49:01

Autonomous driving world models are expected to work effectively across three
core dimensions: state, action, and reward. Existing models, however, are
typically restricted to limited state modalities, short video sequences,
imprecise action control, and a lack of reward awareness. In this paper, we
introduce OmniNWM, an omniscient panoramic navigation world model that
addresses all three dimensions within a unified framework. For state, OmniNWM
jointly generates panoramic videos of RGB, semantics, metric depth, and 3D
occupancy. A flexible forcing strategy enables high-quality long-horizon
auto-regressive generation. For action, we introduce a normalized panoramic
Plucker ray-map representation that encodes input trajectories into pixel-level
signals, enabling highly precise and generalizable control over panoramic video
generation. Regarding reward, we move beyond learning reward functions with
external image-based models: instead, we leverage the generated 3D occupancy to
directly define rule-based dense rewards for driving compliance and safety.
Extensive experiments demonstrate that OmniNWM achieves state-of-the-art
performance in video generation, control accuracy, and long-horizon stability,
while providing a reliable closed-loop evaluation framework through
occupancy-grounded rewards. Project page is available at
https://github.com/Arlo0o/OmniNWM.
ColorAgent: Building A Robust, Personalized, and Interactive OS Agent
Authors: name: Ning Li, name: Qiqiang Lin, name: Zheng Wu, name: Xiaoyun Mo, name: Weiming Zhang, name: Yin Zhao, name: Xiangmou Qu, name: Jiamu Zhou, name: Jun Wang, name: Congmin Zheng, name: Yuanyi Song, name: Hongjiang Chen, name: Heyuan Huang, name: Jihong Wang, name: Jiaxin Yin, name: Jingwei Yu, name: Junwei Liao, name: Qiuying Peng, name: Xingyu Lou, name: Jun Wang, name: Weiwen Liu, name: Zhuosheng Zhang, name: Weinan Zhang
Published: 2025-10-22 09:02:48

With the advancements in hardware, software, and large language model
technologies, the interaction between humans and operating systems has evolved
from the command-line interface to the rapidly emerging AI agent interactions.
Building an operating system (OS) agent capable of executing user instructions
and faithfully following user desires is becoming a reality. In this technical
report, we present ColorAgent, an OS agent designed to engage in long-horizon,
robust interactions with the environment while also enabling personalized and
proactive user interaction. To enable long-horizon interactions with the
environment, we enhance the model's capabilities through step-wise
reinforcement learning and self-evolving training, while also developing a
tailored multi-agent framework that ensures generality, consistency, and
robustness. In terms of user interaction, we explore personalized user intent
recognition and proactive engagement, positioning the OS agent not merely as an
automation tool but as a warm, collaborative partner. We evaluate ColorAgent on
the AndroidWorld and AndroidLab benchmarks, achieving success rates of 77.2%
and 50.7%, respectively, establishing a new state of the art. Nonetheless, we
note that current benchmarks are insufficient for a comprehensive evaluation of
OS agents and propose further exploring directions in future work, particularly
in the areas of evaluation paradigms, agent collaboration, and security. Our
code is available at https://github.com/MadeAgents/mobile-use.
DaMo: Data Mixing Optimizer in Fine-tuning Multimodal LLMs for Mobile
  Phone Agents
Authors: name: Kai Shi, name: Jun Yang, name: Ni Yang, name: Binqiang Pan, name: Qingsong Xie, name: Chao Zhang, name: Zhenyu Yang, name: Tianhuang Su, name: Haonan Lu
Published: 2025-10-22 07:57:59

Mobile Phone Agents (MPAs) have emerged as a promising research direction due
to their broad applicability across diverse scenarios. While Multimodal Large
Language Models (MLLMs) serve as the foundation for MPAs, their effectiveness
in handling multiple mobile phone tasks simultaneously remains limited.
Although multitask supervised fine-tuning (SFT) is widely adopted for multitask
learning, existing approaches struggle to determine optimal training data
compositions for peak performance. To address this challenge, we propose DaMo
(Data Mixture Optimizer) - a novel solution employing a trainable network that
predicts optimal data mixtures by forecasting downstream task performance for
any given dataset ratio. To support comprehensive evaluation, we introduce
PhoneAgentBench, the first specialized benchmark to evaluate MLLMs on
multimodal mobile phone tasks, comprising 1235 QA pairs spanning diverse
real-world industrial mobile application scenarios. Demonstrating strong
predictive capability (R^2=0.81) in small-scale pilot experiments, DaMo
efficiently extrapolates optimal data mixing configurations. Our results show
DaMo achieves a 3.38% performance improvement on PhoneAgentBench compared to
alternative methods. Furthermore, extensive experiments across established
benchmarks including BFCL-v3, MME-Reasoning, MME-Perception, and OCRBench
reveal DaMo's superior generalization, outperforming other approaches by 2.57%
in terms of average score. When used solely for MLLM optimization on the
BFCL-v3 task, DaMo improves the metrics by 12.47% than other methods. Notably,
DaMo maintains robust scalability, preserving its effectiveness when applied to
other model architectures. The code and dataset are available at
https://github.com/OPPO-Mente-Lab/DaMo.git