Orionnnn/hub-stats huggingface dataset:
Cross-Domain Evaluation of Transformer-Based Vulnerability Detection on
  Open & Industry Data
Authors: name: Moritz Mock, name: Thomas Forrer, name: Barbara Russo
Published: 2025-09-11 09:58:43

Deep learning solutions for vulnerability detection proposed in academic
research are not always accessible to developers, and their applicability in
industrial settings is rarely addressed. Transferring such technologies from
academia to industry presents challenges related to trustworthiness, legacy
systems, limited digital literacy, and the gap between academic and industrial
expertise. For deep learning in particular, performance and integration into
existing workflows are additional concerns. In this work, we first evaluate the
performance of CodeBERT for detecting vulnerable functions in industrial and
open-source software. We analyse its cross-domain generalisation when
fine-tuned on open-source data and tested on industrial data, and vice versa,
also exploring strategies for handling class imbalance. Based on these results,
we develop AI-DO(Automating vulnerability detection Integration for Developers'
Operations), a Continuous Integration-Continuous Deployment (CI/CD)-integrated
recommender system that uses fine-tuned CodeBERT to detect and localise
vulnerabilities during code review without disrupting workflows. Finally, we
assess the tool's perceived usefulness through a survey with the company's IT
professionals. Our results show that models trained on industrial data detect
vulnerabilities accurately within the same domain but lose performance on
open-source code, while a deep learner fine-tuned on open data, with
appropriate undersampling techniques, improves the detection of
vulnerabilities.
All You Need Is A Fuzzing Brain: An LLM-Powered System for Automated
  Vulnerability Detection and Patching
Authors: name: Ze Sheng, name: Qingxiao Xu, name: Jianwei Huang, name: Matthew Woodcock, name: Heqing Huang, name: Alastair F. Donaldson, name: Guofei Gu, name: Jeff Huang
Published: 2025-09-08 21:08:01

Our team, All You Need Is A Fuzzing Brain, was one of seven finalists in
DARPA's Artificial Intelligence Cyber Challenge (AIxCC), placing fourth in the
final round. During the competition, we developed a Cyber Reasoning System
(CRS) that autonomously discovered 28 security vulnerabilities - including six
previously unknown zero-days - in real-world open-source C and Java projects,
and successfully patched 14 of them. The complete CRS is open source at
https://github.com/o2lab/afc-crs-all-you-need-is-a-fuzzing-brain. This paper
provides a detailed technical description of our CRS, with an emphasis on its
LLM-powered components and strategies. Building on AIxCC, we further introduce
a public leaderboard for benchmarking state-of-the-art LLMs on vulnerability
detection and patching tasks, derived from the AIxCC dataset. The leaderboard
is available at https://o2lab.github.io/FuzzingBrain-Leaderboard/.
Towards Better Dental AI: A Multimodal Benchmark and Instruction Dataset
  for Panoramic X-ray Analysis
Authors: name: Jing Hao, name: Yuxuan Fan, name: Yanpeng Sun, name: Kaixin Guo, name: Lizhuo Lin, name: Jinrong Yang, name: Qi Yong H. Ai, name: Lun M. Wong, name: Hao Tang, name: Kuo Feng Hung
Published: 2025-09-11 08:39:08

Recent advances in large vision-language models (LVLMs) have demonstrated
strong performance on general-purpose medical tasks. However, their
effectiveness in specialized domains such as dentistry remains underexplored.
In particular, panoramic X-rays, a widely used imaging modality in oral
radiology, pose interpretative challenges due to dense anatomical structures
and subtle pathological cues, which are not captured by existing medical
benchmarks or instruction datasets. To this end, we introduce MMOral, the first
large-scale multimodal instruction dataset and benchmark tailored for panoramic
X-ray interpretation. MMOral consists of 20,563 annotated images paired with
1.3 million instruction-following instances across diverse task types,
including attribute extraction, report generation, visual question answering,
and image-grounded dialogue. In addition, we present MMOral-Bench, a
comprehensive evaluation suite covering five key diagnostic dimensions in
dentistry. We evaluate 64 LVLMs on MMOral-Bench and find that even the
best-performing model, i.e., GPT-4o, only achieves 41.45% accuracy, revealing
significant limitations of current models in this domain. To promote the
progress of this specific domain, we also propose OralGPT, which conducts
supervised fine-tuning (SFT) upon Qwen2.5-VL-7B with our meticulously curated
MMOral instruction dataset. Remarkably, a single epoch of SFT yields
substantial performance enhancements for LVLMs, e.g., OralGPT demonstrates a
24.73% improvement. Both MMOral and OralGPT hold significant potential as a
critical foundation for intelligent dentistry and enable more clinically
impactful multimodal AI systems in the dental field. The dataset, model,
benchmark, and evaluation suite are available at
https://github.com/isbrycee/OralGPT.
mmBERT: A Modern Multilingual Encoder with Annealed Language Learning
Authors: name: Marc Marone, name: Orion Weller, name: William Fleshman, name: Eugene Yang, name: Dawn Lawrie, name: Benjamin Van Durme
Published: 2025-09-08 17:08:42

Encoder-only languages models are frequently used for a variety of standard
machine learning tasks, including classification and retrieval. However, there
has been a lack of recent research for encoder models, especially with respect
to multilingual models. We introduce mmBERT, an encoder-only language model
pretrained on 3T tokens of multilingual text in over 1800 languages. To build
mmBERT we introduce several novel elements, including an inverse mask ratio
schedule and an inverse temperature sampling ratio. We add over 1700
low-resource languages to the data mix only during the decay phase, showing
that it boosts performance dramatically and maximizes the gains from the
relatively small amount of training data. Despite only including these
low-resource languages in the short decay phase we achieve similar
classification performance to models like OpenAI's o3 and Google's Gemini 2.5
Pro. Overall, we show that mmBERT significantly outperforms the previous
generation of models on classification and retrieval tasks -- on both high and
low-resource languages.
ObjectReact: Learning Object-Relative Control for Visual Navigation
Authors: name: Sourav Garg, name: Dustin Craggs, name: Vineeth Bhat, name: Lachlan Mares, name: Stefan Podgorski, name: Madhava Krishna, name: Feras Dayoub, name: Ian Reid
Published: 2025-09-11 16:34:17

Visual navigation using only a single camera and a topological map has
recently become an appealing alternative to methods that require additional
sensors and 3D maps. This is typically achieved through an "image-relative"
approach to estimating control from a given pair of current observation and
subgoal image. However, image-level representations of the world have
limitations because images are strictly tied to the agent's pose and
embodiment. In contrast, objects, being a property of the map, offer an
embodiment- and trajectory-invariant world representation. In this work, we
present a new paradigm of learning "object-relative" control that exhibits
several desirable characteristics: a) new routes can be traversed without
strictly requiring to imitate prior experience, b) the control prediction
problem can be decoupled from solving the image matching problem, and c) high
invariance can be achieved in cross-embodiment deployment for variations across
both training-testing and mapping-execution settings. We propose a topometric
map representation in the form of a "relative" 3D scene graph, which is used to
obtain more informative object-level global path planning costs. We train a
local controller, dubbed "ObjectReact", conditioned directly on a high-level
"WayObject Costmap" representation that eliminates the need for an explicit RGB
input. We demonstrate the advantages of learning object-relative control over
its image-relative counterpart across sensor height variations and multiple
navigation tasks that challenge the underlying spatial understanding
capability, e.g., navigating a map trajectory in the reverse direction. We
further show that our sim-only policy is able to generalize well to real-world
indoor environments. Code and supplementary material are accessible via project
page: https://object-react.github.io/
Modality Alignment with Multi-scale Bilateral Attention for Multimodal
  Recommendation
Authors: name: Kelin Ren, name: Chan-Yang Ju, name: Dong-Ho Lee
Published: 2025-09-11 02:52:26

Multimodal recommendation systems are increasingly becoming foundational
technologies for e-commerce and content platforms, enabling personalized
services by jointly modeling users' historical behaviors and the multimodal
features of items (e.g., visual and textual). However, most existing methods
rely on either static fusion strategies or graph-based local interaction
modeling, facing two critical limitations: (1) insufficient ability to model
fine-grained cross-modal associations, leading to suboptimal fusion quality;
and (2) a lack of global distribution-level consistency, causing
representational bias. To address these, we propose MambaRec, a novel framework
that integrates local feature alignment and global distribution regularization
via attention-guided learning. At its core, we introduce the Dilated Refinement
Attention Module (DREAM), which uses multi-scale dilated convolutions with
channel-wise and spatial attention to align fine-grained semantic patterns
between visual and textual modalities. This module captures hierarchical
relationships and context-aware associations, improving cross-modal semantic
modeling. Additionally, we apply Maximum Mean Discrepancy (MMD) and contrastive
loss functions to constrain global modality alignment, enhancing semantic
consistency. This dual regularization reduces mode-specific deviations and
boosts robustness. To improve scalability, MambaRec employs a dimensionality
reduction strategy to lower the computational cost of high-dimensional
multimodal features. Extensive experiments on real-world e-commerce datasets
show that MambaRec outperforms existing methods in fusion quality,
generalization, and efficiency. Our code has been made publicly available at
https://github.com/rkl71/MambaRec.
Reasoning Introduces New Poisoning Attacks Yet Makes Them More
  Complicated
Authors: name: Hanna Foerster, name: Ilia Shumailov, name: Yiren Zhao, name: Harsh Chaudhari, name: Jamie Hayes, name: Robert Mullins, name: Yarin Gal
Published: 2025-09-06 15:06:18

Early research into data poisoning attacks against Large Language Models
(LLMs) demonstrated the ease with which backdoors could be injected. More
recent LLMs add step-by-step reasoning, expanding the attack surface to include
the intermediate chain-of-thought (CoT) and its inherent trait of decomposing
problems into subproblems. Using these vectors for more stealthy poisoning, we
introduce ``decomposed reasoning poison'', in which the attacker modifies only
the reasoning path, leaving prompts and final answers clean, and splits the
trigger across multiple, individually harmless components.
  Fascinatingly, while it remains possible to inject these decomposed poisons,
reliably activating them to change final answers (rather than just the CoT) is
surprisingly difficult. This difficulty arises because the models can often
recover from backdoors that are activated within their thought processes.
Ultimately, it appears that an emergent form of backdoor robustness is
originating from the reasoning capabilities of these advanced LLMs, as well as
from the architectural separation between reasoning and final answer
generation.
VLA-Adapter: An Effective Paradigm for Tiny-Scale Vision-Language-Action
  Model
Authors: name: Yihao Wang, name: Pengxiang Ding, name: Lingxiao Li, name: Can Cui, name: Zirui Ge, name: Xinyang Tong, name: Wenxuan Song, name: Han Zhao, name: Wei Zhao, name: Pengxu Hou, name: Siteng Huang, name: Yifan Tang, name: Wenhui Wang, name: Ru Zhang, name: Jianyi Liu, name: Donglin Wang
Published: 2025-09-11 11:42:21

Vision-Language-Action (VLA) models typically bridge the gap between
perceptual and action spaces by pre-training a large-scale Vision-Language
Model (VLM) on robotic data. While this approach greatly enhances performance,
it also incurs significant training costs. In this paper, we investigate how to
effectively bridge vision-language (VL) representations to action (A). We
introduce VLA-Adapter, a novel paradigm designed to reduce the reliance of VLA
models on large-scale VLMs and extensive pre-training. To this end, we first
systematically analyze the effectiveness of various VL conditions and present
key findings on which conditions are essential for bridging perception and
action spaces. Based on these insights, we propose a lightweight Policy module
with Bridge Attention, which autonomously injects the optimal condition into
the action space. In this way, our method achieves high performance using only
a 0.5B-parameter backbone, without any robotic data pre-training. Extensive
experiments on both simulated and real-world robotic benchmarks demonstrate
that VLA-Adapter not only achieves state-of-the-art level performance, but also
offers the fast inference speed reported to date. Furthermore, thanks to the
proposed advanced bridging paradigm, VLA-Adapter enables the training of a
powerful VLA model in just 8 hours on a single consumer-grade GPU, greatly
lowering the barrier to deploying the VLA model. Project page:
https://vla-adapter.github.io/.
Can Understanding and Generation Truly Benefit Together -- or Just
  Coexist?
Authors: name: Zhiyuan Yan, name: Kaiqing Lin, name: Zongjian Li, name: Junyan Ye, name: Hui Han, name: Zhendong Wang, name: Hao Liu, name: Bin Lin, name: Hao Li, name: Xue Xu, name: Xinyan Xiao, name: Jingdong Wang, name: Haifeng Wang, name: Li Yuan
Published: 2025-09-11 17:57:59

In this paper, we introduce an insightful paradigm through the Auto-Encoder
lens-understanding as the encoder (I2T) that compresses images into text, and
generation as the decoder (T2I) that reconstructs images from that text. Using
reconstruction fidelity as the unified training objective, we enforce the
coherent bidirectional information flow between the understanding and
generation processes, bringing mutual gains. To implement this, we propose UAE,
a novel framework for unified multimodal learning. We begin by pre-training the
decoder with large-scale long-context image captions to capture fine-grained
semantic and complex spatial relationships. We then propose Unified-GRPO via
reinforcement learning (RL), which covers three stages: (1) A cold-start phase
to gently initialize both encoder and decoder with a semantic reconstruction
loss; (2) Generation for Understanding, where the encoder is trained to
generate informative captions that maximize the decoder's reconstruction
quality, enhancing its visual understanding; (3) Understanding for Generation,
where the decoder is refined to reconstruct from these captions, forcing it to
leverage every detail and improving its long-context instruction following and
generation fidelity. For evaluation, we introduce Unified-Bench, the first
benchmark tailored to assess the degree of unification of the UMMs. A
surprising "aha moment" arises within the multimodal learning domain: as RL
progresses, the encoder autonomously produces more descriptive captions, while
the decoder simultaneously demonstrates a profound ability to understand these
intricate descriptions, resulting in reconstructions of striking fidelity.
HuMo: Human-Centric Video Generation via Collaborative Multi-Modal
  Conditioning
Authors: name: Liyang Chen, name: Tianxiang Ma, name: Jiawei Liu, name: Bingchuan Li, name: Zhuowei Chen, name: Lijie Liu, name: Xu He, name: Gen Li, name: Qian He, name: Zhiyong Wu
Published: 2025-09-10 11:54:29

Human-Centric Video Generation (HCVG) methods seek to synthesize human videos
from multimodal inputs, including text, image, and audio. Existing methods
struggle to effectively coordinate these heterogeneous modalities due to two
challenges: the scarcity of training data with paired triplet conditions and
the difficulty of collaborating the sub-tasks of subject preservation and
audio-visual sync with multimodal inputs. In this work, we present HuMo, a
unified HCVG framework for collaborative multimodal control. For the first
challenge, we construct a high-quality dataset with diverse and paired text,
reference images, and audio. For the second challenge, we propose a two-stage
progressive multimodal training paradigm with task-specific strategies. For the
subject preservation task, to maintain the prompt following and visual
generation abilities of the foundation model, we adopt the minimal-invasive
image injection strategy. For the audio-visual sync task, besides the commonly
adopted audio cross-attention layer, we propose a focus-by-predicting strategy
that implicitly guides the model to associate audio with facial regions. For
joint learning of controllabilities across multimodal inputs, building on
previously acquired capabilities, we progressively incorporate the audio-visual
sync task. During inference, for flexible and fine-grained multimodal control,
we design a time-adaptive Classifier-Free Guidance strategy that dynamically
adjusts guidance weights across denoising steps. Extensive experimental results
demonstrate that HuMo surpasses specialized state-of-the-art methods in
sub-tasks, establishing a unified framework for collaborative
multimodal-conditioned HCVG. Project Page:
https://phantom-video.github.io/HuMo.
2D Gaussian Splatting with Semantic Alignment for Image Inpainting
Authors: name: Hongyu Li, name: Chaofeng Chen, name: Xiaoming Li, name: Guangming Lu
Published: 2025-09-02 05:12:52

Gaussian Splatting (GS), a recent technique for converting discrete points
into continuous spatial representations, has shown promising results in 3D
scene modeling and 2D image super-resolution. In this paper, we explore its
untapped potential for image inpainting, which demands both locally coherent
pixel synthesis and globally consistent semantic restoration. We propose the
first image inpainting framework based on 2D Gaussian Splatting, which encodes
incomplete images into a continuous field of 2D Gaussian splat coefficients and
reconstructs the final image via a differentiable rasterization process. The
continuous rendering paradigm of GS inherently promotes pixel-level coherence
in the inpainted results. To improve efficiency and scalability, we introduce a
patch-wise rasterization strategy that reduces memory overhead and accelerates
inference. For global semantic consistency, we incorporate features from a
pretrained DINO model. We observe that DINO's global features are naturally
robust to small missing regions and can be effectively adapted to guide
semantic alignment in large-mask scenarios, ensuring that the inpainted content
remains contextually consistent with the surrounding scene. Extensive
experiments on standard benchmarks demonstrate that our method achieves
competitive performance in both quantitative metrics and perceptual quality,
establishing a new direction for applying Gaussian Splatting to 2D image
processing.
The Choice of Divergence: A Neglected Key to Mitigating Diversity
  Collapse in Reinforcement Learning with Verifiable Reward
Authors: name: Long Li, name: Jiaran Hao, name: Jason Klein Liu, name: Zhijian Zhou, name: Xiaoyu Tan, name: Wei Chu, name: Zhe Wang, name: Shirui Pan, name: Chao Qu, name: Yuan Qi
Published: 2025-09-09 06:34:32

A central paradox in fine-tuning Large Language Models (LLMs) with
Reinforcement Learning with Verifiable Reward (RLVR) is the frequent
degradation of multi-attempt performance (Pass@k) despite improvements in
single-attempt accuracy (Pass@1). This is often accompanied by catastrophic
forgetting, where models lose previously acquired skills. While various methods
have been proposed, the choice and function of the divergence term have been
surprisingly unexamined as a proactive solution. We argue that standard RLVR
objectives -- both those using the mode-seeking reverse KL-divergence and those
forgoing a divergence term entirely -- lack a crucial mechanism for knowledge
retention. The reverse-KL actively accelerates this decay by narrowing the
policy, while its absence provides no safeguard against the model drifting from
its diverse knowledge base. We propose a fundamental shift in perspective:
using the divergence term itself as the solution. Our framework,
Diversity-Preserving Hybrid RL (DPH-RL), leverages mass-covering f-divergences
(like forward-KL and JS-divergence) to function as a rehearsal mechanism. By
continuously referencing the initial policy, this approach forces the model to
maintain broad solution coverage. Extensive experiments on math and SQL
generation demonstrate that DPH-RL not only resolves the Pass@k degradation but
improves both Pass@1 and Pass@k in- and out-of-domain. Additionally, DPH-RL is
more training-efficient because it computes f-divergence using generator
functions, requiring only sampling from the initial policy and no online
reference model. Our work highlights a crucial, overlooked axis for improving
RLVR, demonstrating that the proper selection of a divergence measure is a
powerful tool for building more general and diverse reasoning models.
Visual Programmability: A Guide for Code-as-Thought in Chart
  Understanding
Authors: name: Bohao Tang, name: Yan Ma, name: Fei Zhang, name: Jiadi Su, name: Ethan Chern, name: Zhulin Hu, name: Zhixin Wang, name: Pengfei Liu, name: Ya Zhang
Published: 2025-09-11 09:22:16

Chart understanding presents a critical test to the reasoning capabilities of
Vision-Language Models (VLMs). Prior approaches face critical limitations: some
rely on external tools, making them brittle and constrained by a predefined
toolkit, while others fine-tune specialist models that often adopt a single
reasoning strategy, such as text-based chain-of-thought (CoT). The intermediate
steps of text-based reasoning are difficult to verify, which complicates the
use of reinforcement-learning signals that reward factual accuracy. To address
this, we propose a Code-as-Thought (CaT) approach to represent the visual
information of a chart in a verifiable, symbolic format. Our key insight is
that this strategy must be adaptive: a fixed, code-only implementation
consistently fails on complex charts where symbolic representation is
unsuitable. This finding leads us to introduce Visual Programmability: a
learnable property that determines if a chart-question pair is better solved
with code or direct visual analysis. We implement this concept in an adaptive
framework where a VLM learns to choose between the CaT pathway and a direct
visual reasoning pathway. The selection policy of the model is trained with
reinforcement learning using a novel dual-reward system. This system combines a
data-accuracy reward to ground the model in facts and prevent numerical
hallucination, with a decision reward that teaches the model when to use each
strategy, preventing it from defaulting to a single reasoning mode. Experiments
demonstrate strong and robust performance across diverse chart-understanding
benchmarks. Our work shows that VLMs can be taught not only to reason but also
how to reason, dynamically selecting the optimal reasoning pathway for each
task.