Orionnnn/hub-stats huggingface dataset:
Evolving Language Models without Labels: Majority Drives Selection,
  Novelty Promotes Variation
Authors: name: Yujun Zhou, name: Zhenwen Liang, name: Haolin Liu, name: Wenhao Yu, name: Kishan Panaganti, name: Linfeng Song, name: Dian Yu, name: Xiangliang Zhang, name: Haitao Mi, name: Dong Yu
Published: 2025-09-18 17:50:04

Large language models (LLMs) are increasingly trained with reinforcement
learning from verifiable rewards (RLVR), yet real-world deployment demands
models that can self-improve without labels or external judges. Existing
label-free methods, confidence minimization, self-consistency, or majority-vote
objectives, stabilize learning but steadily shrink exploration, causing an
entropy collapse: generations become shorter, less diverse, and brittle. Unlike
prior approaches such as Test-Time Reinforcement Learning (TTRL), which
primarily adapt models to the immediate unlabeled dataset at hand, our goal is
broader: to enable general improvements without sacrificing the model's
inherent exploration capacity and generalization ability, i.e., evolving. We
formalize this issue and propose EVolution-Oriented and Label-free
Reinforcement Learning (EVOL-RL), a simple rule that couples stability with
variation under a label-free setting. EVOL-RL keeps the majority-voted answer
as a stable anchor (selection) while adding a novelty-aware reward that favors
responses whose reasoning differs from what has already been produced
(variation), measured in semantic space. Implemented with GRPO, EVOL-RL also
uses asymmetric clipping to preserve strong signals and an entropy regularizer
to sustain search. This majority-for-selection + novelty-for-variation design
prevents collapse, maintains longer and more informative chains of thought, and
improves both pass@1 and pass@n. EVOL-RL consistently outperforms the
majority-only TTRL baseline; e.g., training on label-free AIME24 lifts
Qwen3-4B-Base AIME25 pass@1 from TTRL's 4.6% to 16.4%, and pass@16 from 18.5%
to 37.9%. EVOL-RL not only prevents diversity collapse but also unlocks
stronger generalization across domains (e.g., GPQA). Furthermore, we
demonstrate that EVOL-RL also boosts performance in the RLVR setting,
highlighting its broad applicability.
ScaleCUA: Scaling Open-Source Computer Use Agents with Cross-Platform
  Data
Authors: name: Zhaoyang Liu, name: JingJing Xie, name: Zichen Ding, name: Zehao Li, name: Bowen Yang, name: Zhenyu Wu, name: Xuehui Wang, name: Qiushi Sun, name: Shi Liu, name: Weiyun Wang, name: Shenglong Ye, name: Qingyun Li, name: Zeyue Tian, name: Gen Luo, name: Xiangyu Yue, name: Biqing Qi, name: Kai Chen, name: Bowen Zhou, name: Yu Qiao, name: Qifeng Chen, name: Wenhai Wang
Published: 2025-09-18 17:59:22

Vision-Language Models (VLMs) have enabled computer use agents (CUAs) that
operate GUIs autonomously, showing great potential, yet progress is limited by
the lack of large-scale, open-source computer use data and foundation models.
In this work, we introduce ScaleCUA, a step toward scaling open-source CUAs. It
offers a large-scale dataset spanning 6 operating systems and 3 task domains,
built via a closed-loop pipeline uniting automated agents with human experts.
Trained on this scaled-up data, ScaleCUA can operate seamlessly across
platforms. Specifically, it delivers strong gains over baselines (+26.6 on
WebArena-Lite-v2, +10.7 on ScreenSpot-Pro) and sets new state-of-the-art
results (94.4% on MMBench-GUI L1-Hard, 60.6% on OSWorld-G, 47.4% on
WebArena-Lite-v2). These findings underscore the power of data-driven scaling
for general-purpose computer use agents. We will release data, models, and code
to advance future research: https://github.com/OpenGVLab/ScaleCUA.
The Sum Leaks More Than Its Parts: Compositional Privacy Risks and
  Mitigations in Multi-Agent Collaboration
Authors: name: Vaidehi Patil, name: Elias Stengel-Eskin, name: Mohit Bansal
Published: 2025-09-16 16:57:25

As large language models (LLMs) become integral to multi-agent systems, new
privacy risks emerge that extend beyond memorization, direct inference, or
single-turn evaluations. In particular, seemingly innocuous responses, when
composed across interactions, can cumulatively enable adversaries to recover
sensitive information, a phenomenon we term compositional privacy leakage. We
present the first systematic study of such compositional privacy leaks and
possible mitigation methods in multi-agent LLM systems. First, we develop a
framework that models how auxiliary knowledge and agent interactions jointly
amplify privacy risks, even when each response is benign in isolation. Next, to
mitigate this, we propose and evaluate two defense strategies: (1)
Theory-of-Mind defense (ToM), where defender agents infer a questioner's intent
by anticipating how their outputs may be exploited by adversaries, and (2)
Collaborative Consensus Defense (CoDef), where responder agents collaborate
with peers who vote based on a shared aggregated state to restrict sensitive
information spread. Crucially, we balance our evaluation across compositions
that expose sensitive information and compositions that yield benign
inferences. Our experiments quantify how these defense strategies differ in
balancing the privacy-utility trade-off. We find that while chain-of-thought
alone offers limited protection to leakage (~39% sensitive blocking rate), our
ToM defense substantially improves sensitive query blocking (up to 97%) but can
reduce benign task success. CoDef achieves the best balance, yielding the
highest Balanced Outcome (79.8%), highlighting the benefit of combining
explicit reasoning with defender collaboration. Together, our results expose a
new class of risks in collaborative LLM deployments and provide actionable
insights for designing safeguards against compositional, context-driven privacy
leakage.
WildSmoke: Ready-to-Use Dynamic 3D Smoke Assets from a Single Video in
  the Wild
Authors: name: Yuqiu Liu, name: Jialin Song, name: Manolis Savva, name: Wuyang Chen
Published: 2025-09-14 06:06:42

We propose a pipeline to extract and reconstruct dynamic 3D smoke assets from
a single in-the-wild video, and further integrate interactive simulation for
smoke design and editing. Recent developments in 3D vision have significantly
improved reconstructing and rendering fluid dynamics, supporting realistic and
temporally consistent view synthesis. However, current fluid reconstructions
rely heavily on carefully controlled clean lab environments, whereas real-world
videos captured in the wild are largely underexplored. We pinpoint three key
challenges of reconstructing smoke in real-world videos and design targeted
techniques, including smoke extraction with background removal, initialization
of smoke particles and camera poses, and inferring multi-view videos. Our
method not only outperforms previous reconstruction and generation methods with
high-quality smoke reconstructions (+2.22 average PSNR on wild videos), but
also enables diverse and realistic editing of fluid dynamics by simulating our
smoke assets. We provide our models, data, and 4D smoke assets at
[https://autumnyq.github.io/WildSmoke](https://autumnyq.github.io/WildSmoke).
Image Tokenizer Needs Post-Training
Authors: name: Kai Qiu, name: Xiang Li, name: Hao Chen, name: Jason Kuen, name: Xiaohao Xu, name: Jiuxiang Gu, name: Yinyi Luo, name: Bhiksha Raj, name: Zhe Lin, name: Marios Savvides
Published: 2025-09-15 21:38:03

Recent image generative models typically capture the image distribution in a
pre-constructed latent space, relying on a frozen image tokenizer. However,
there exists a significant discrepancy between the reconstruction and
generation distribution, where current tokenizers only prioritize the
reconstruction task that happens before generative training without considering
the generation errors during sampling. In this paper, we comprehensively
analyze the reason for this discrepancy in a discrete latent space, and, from
which, we propose a novel tokenizer training scheme including both
main-training and post-training, focusing on improving latent space
construction and decoding respectively. During the main training, a latent
perturbation strategy is proposed to simulate sampling noises, \ie, the
unexpected tokens generated in generative inference. Specifically, we propose a
plug-and-play tokenizer training scheme, which significantly enhances the
robustness of tokenizer, thus boosting the generation quality and convergence
speed, and a novel tokenizer evaluation metric, \ie, pFID, which successfully
correlates the tokenizer performance to generation quality. During
post-training, we further optimize the tokenizer decoder regarding a
well-trained generative model to mitigate the distribution difference between
generated and reconstructed tokens. With a sim400M generator, a discrete
tokenizer trained with our proposed main training achieves a notable 1.60 gFID
and further obtains 1.36 gFID with the additional post-training. Further
experiments are conducted to broadly validate the effectiveness of our
post-training strategy on off-the-shelf discrete and continuous tokenizers,
coupled with autoregressive and diffusion-based generators.
Synthesizing Behaviorally-Grounded Reasoning Chains: A Data-Generation
  Framework for Personal Finance LLMs
Authors: name: Akhil Theerthala
Published: 2025-09-17 17:12:38

Personalized financial advice requires consideration of user goals,
constraints, risk tolerance, and jurisdiction. Prior LLM work has focused on
support systems for investors and financial planners. Simultaneously, numerous
recent studies examine broader personal finance tasks, including budgeting,
debt management, retirement, and estate planning, through agentic pipelines
that incur high maintenance costs, yielding less than 25% of their expected
financial returns. In this study, we introduce a novel and reproducible
framework that integrates relevant financial context with behavioral finance
studies to construct supervision data for end-to-end advisors. Using this
framework, we create a 19k sample reasoning dataset and conduct a comprehensive
fine-tuning of the Qwen-3-8B model on the dataset. Through a held-out test
split and a blind LLM-jury study, we demonstrate that through careful data
curation and behavioral integration, our 8B model achieves performance
comparable to significantly larger baselines (14-32B parameters) across factual
accuracy, fluency, and personalization metrics while incurring 80% lower costs
than the larger counterparts.
LLM-I: LLMs are Naturally Interleaved Multimodal Creators
Authors: name: Zirun Guo, name: Feng Zhang, name: Kai Jia, name: Tao Jin
Published: 2025-09-17 02:33:29

We propose LLM-Interleaved (LLM-I), a flexible and dynamic framework that
reframes interleaved image-text generation as a tool-use problem. LLM-I is
designed to overcome the "one-tool" bottleneck of current unified models, which
are limited to synthetic imagery and struggle with tasks requiring factual
grounding or programmatic precision. Our framework empowers a central LLM or
MLLM agent to intelligently orchestrate a diverse toolkit of specialized visual
tools, including online image search, diffusion-based generation, code
execution, and image editing. The agent is trained to select and apply these
tools proficiently via a Reinforcement Learning (RL) framework that features a
hybrid reward system combining rule-based logic with judgments from LLM and
MLLM evaluators. Trained on a diverse new dataset using four different model
backbones, LLM-I demonstrates state-of-the-art performance, outperforming
existing methods by a large margin across four benchmarks. We also introduce a
novel test-time scaling strategy that provides further performance gains.
Project Page: https://github.com/ByteDance-BandAI/LLM-I.
Hybrid Quantum-Classical Model for Image Classification
Authors: name: Muhammad Adnan Shahzad
Published: 2025-09-14 09:55:00

This study presents a systematic comparison between hybrid quantum-classical
neural networks and purely classical models across three benchmark datasets
(MNIST, CIFAR100, and STL10) to evaluate their performance, efficiency, and
robustness. The hybrid models integrate parameterized quantum circuits with
classical deep learning architectures, while the classical counterparts use
conventional convolutional neural networks (CNNs). Experiments were conducted
over 50 training epochs for each dataset, with evaluations on validation
accuracy, test accuracy, training time, computational resource usage, and
adversarial robustness (tested with epsilon=0.1 perturbations).Key findings
demonstrate that hybrid models consistently outperform classical models in
final accuracy, achieving {99.38\% (MNIST), 41.69\% (CIFAR100), and 74.05\%
(STL10) validation accuracy, compared to classical benchmarks of 98.21\%,
32.25\%, and 63.76\%, respectively. Notably, the hybrid advantage scales with
dataset complexity, showing the most significant gains on CIFAR100 (+9.44\%)
and STL10 (+10.29\%). Hybrid models also train 5--12times faster (e.g.,
21.23s vs. 108.44s per epoch on MNIST) and use 6--32\% fewer parameters} while
maintaining superior generalization to unseen test data.Adversarial robustness
tests reveal that hybrid models are significantly more resilient on simpler
datasets (e.g., 45.27\% robust accuracy on MNIST vs. 10.80\% for classical) but
show comparable fragility on complex datasets like CIFAR100 (sim1\%
robustness for both). Resource efficiency analyses indicate that hybrid models
consume less memory (4--5GB vs. 5--6GB for classical) and lower CPU utilization
(9.5\% vs. 23.2\% on average).These results suggest that hybrid
quantum-classical architectures offer compelling advantages in accuracy,
training efficiency, and parameter scalability, particularly for complex vision
tasks.
Quantum Variational Activation Functions Empower Kolmogorov-Arnold
  Networks
Authors: name: Jiun-Cheng Jiang, name: Morris Yu-Chao Huang, name: Tianlong Chen, name: Hsi-Sheng Goan
Published: 2025-09-17 14:28:42

Variational quantum circuits (VQCs) are central to quantum machine learning,
while recent progress in Kolmogorov-Arnold networks (KANs) highlights the power
of learnable activation functions. We unify these directions by introducing
quantum variational activation functions (QVAFs), realized through single-qubit
data re-uploading circuits called DatA Re-Uploading ActivatioNs (DARUANs). We
show that DARUAN with trainable weights in data pre-processing possesses an
exponentially growing frequency spectrum with data repetitions, enabling an
exponential reduction in parameter size compared with Fourier-based activations
without loss of expressivity. Embedding DARUAN into KANs yields
quantum-inspired KANs (QKANs), which retain the interpretability of KANs while
improving their parameter efficiency, expressivity, and generalization. We
further introduce two novel techniques to enhance scalability, feasibility and
computational efficiency, such as layer extension and hybrid QKANs (HQKANs) as
drop-in replacements of multi-layer perceptrons (MLPs) for feed-forward
networks in large-scale models. We provide theoretical analysis and extensive
experiments on function regression, image classification, and autoregressive
generative language modeling, demonstrating the efficiency and scalability of
QKANs. DARUANs and QKANs offer a promising direction for advancing quantum
machine learning on both noisy intermediate-scale quantum (NISQ) hardware and
classical quantum simulators.
MedReseacher-R1: Expert-Level Medical Deep Researcher via A
  Knowledge-Informed Trajectory Synthesis Framework
Authors: name: Ailing Yu, name: Lan Yao, name: Jingnan Liu, name: Zhe Chen, name: Jiajun Yin, name: Yuan Wang, name: Xinhao Liao, name: Zhiling Ye, name: Ji Li, name: Yun Yue, name: Hansong Xiao, name: Hualei Zhou, name: Chunxiao Guo, name: Peng Wei, name: Jinjie Gu
Published: 2025-08-20 17:51:20

Recent developments in Large Language Model (LLM)-based agents have shown
impressive capabilities spanning multiple domains, exemplified by deep research
systems that demonstrate superior performance on complex information-seeking
and synthesis tasks. While general-purpose deep research agents have shown
impressive capabilities, they struggle significantly with medical domain
challenges, as evidenced by leading proprietary systems achieving limited
accuracy on complex medical benchmarks. The key limitations are: (1) the model
lacks sufficient dense medical knowledge for clinical reasoning, and (2) the
framework is constrained by the absence of specialized retrieval tools tailored
for medical contexts.We present a medical deep research agent that addresses
these challenges through two core innovations. First, we develop a novel data
synthesis framework using medical knowledge graphs, extracting the longest
chains from subgraphs around rare medical entities to generate complex
multi-hop question-answer pairs. Second, we integrate a custom-built private
medical retrieval engine alongside general-purpose tools, enabling accurate
medical information synthesis. Our approach generates 2100+ diverse
trajectories across 12 medical specialties, each averaging 4.2 tool
interactions.Through a two-stage training paradigm combining supervised
fine-tuning and online reinforcement learning with composite rewards, our
MedResearcher-R1-32B model demonstrates exceptional performance, establishing
new state-of-the-art results on medical benchmarks while maintaining
competitive performance on general deep research tasks. Our work demonstrates
that strategic domain-specific innovations in architecture, tool design, and
training data construction can enable smaller open-source models to outperform
much larger proprietary systems in specialized domains.
AERIS: Argonne Earth Systems Model for Reliable and Skillful Predictions
Authors: name: Väinö Hatanpää, name: Eugene Ku, name: Jason Stock, name: Murali Emani, name: Sam Foreman, name: Chunyong Jung, name: Sandeep Madireddy, name: Tung Nguyen, name: Varuni Sastry, name: Ray A. O. Sinurat, name: Sam Wheeler, name: Huihuo Zheng, name: Troy Arcomano, name: Venkatram Vishwanath, name: Rao Kotamarthi
Published: 2025-09-16 20:38:29

Generative machine learning offers new opportunities to better understand
complex Earth system dynamics. Recent diffusion-based methods address spectral
biases and improve ensemble calibration in weather forecasting compared to
deterministic methods, yet have so far proven difficult to scale stably at high
resolutions. We introduce AERIS, a 1.3 to 80B parameter pixel-level Swin
diffusion transformer to address this gap, and SWiPe, a generalizable technique
that composes window parallelism with sequence and pipeline parallelism to
shard window-based transformers without added communication cost or increased
global batch size. On Aurora (10,080 nodes), AERIS sustains 10.21 ExaFLOPS
(mixed precision) and a peak performance of 11.21 ExaFLOPS with 1 times 1
patch size on the 0.25{\deg} ERA5 dataset, achieving 95.5% weak scaling
efficiency, and 81.6% strong scaling efficiency. AERIS outperforms the IFS ENS
and remains stable on seasonal scales to 90 days, highlighting the potential of
billion-parameter diffusion models for weather and climate prediction.
Improving Context Fidelity via Native Retrieval-Augmented Reasoning
Authors: name: Suyuchen Wang, name: Jinlin Wang, name: Xinyu Wang, name: Shiqi Li, name: Xiangru Tang, name: Sirui Hong, name: Xiao-Wen Chang, name: Chenglin Wu, name: Bang Liu
Published: 2025-09-17 04:28:07

Large language models (LLMs) often struggle with context fidelity, producing
inconsistent answers when responding to questions based on provided
information. Existing approaches either rely on expensive supervised
fine-tuning to generate evidence post-answer or train models to perform web
searches without necessarily improving utilization of the given context. We
propose CARE, a novel native retrieval-augmented reasoning framework that
teaches LLMs to explicitly integrate in-context evidence within their reasoning
process with the model's own retrieval capabilities. Our method requires
limited labeled evidence data while significantly enhancing both retrieval
accuracy and answer generation performance through strategically retrieved
in-context tokens in the reasoning chain. Extensive experiments on multiple
real-world and counterfactual QA benchmarks demonstrate that our approach
substantially outperforms supervised fine-tuning, traditional
retrieval-augmented generation methods, and external retrieval solutions. This
work represents a fundamental advancement in making LLMs more accurate,
reliable, and efficient for knowledge-intensive tasks.
PANORAMA: The Rise of Omnidirectional Vision in the Embodied AI Era
Authors: name: Xu Zheng, name: Chenfei Liao, name: Ziqiao Weng, name: Kaiyu Lei, name: Zihao Dongfang, name: Haocong He, name: Yuanhuiyi Lyu, name: Lutao Jiang, name: Lu Qi, name: Li Chen, name: Danda Pani Paudel, name: Kailun Yang, name: Linfeng Zhang, name: Luc Van Gool, name: Xuming Hu
Published: 2025-09-16 11:54:37

Omnidirectional vision, using 360-degree vision to understand the
environment, has become increasingly critical across domains like robotics,
industrial inspection, and environmental monitoring. Compared to traditional
pinhole vision, omnidirectional vision provides holistic environmental
awareness, significantly enhancing the completeness of scene perception and the
reliability of decision-making. However, foundational research in this area has
historically lagged behind traditional pinhole vision. This talk presents an
emerging trend in the embodied AI era: the rapid development of omnidirectional
vision, driven by growing industrial demand and academic interest. We highlight
recent breakthroughs in omnidirectional generation, omnidirectional perception,
omnidirectional understanding, and related datasets. Drawing on insights from
both academia and industry, we propose an ideal panoramic system architecture
in the embodied AI era, PANORAMA, which consists of four key subsystems.
Moreover, we offer in-depth opinions related to emerging trends and
cross-community impacts at the intersection of panoramic vision and embodied
AI, along with the future roadmap and open challenges. This overview
synthesizes state-of-the-art advancements and outlines challenges and
opportunities for future research in building robust, general-purpose
omnidirectional AI systems in the embodied AI era.