Orionnnn/hub-stats huggingface dataset:
LLMalMorph: On The Feasibility of Generating Variant Malware using
  Large-Language-Models
Authors: name: Md Ajwad Akil, name: Adrian Shuai Li, name: Imtiaz Karim, name: Arun Iyengar, name: Ashish Kundu, name: Vinny Parla, name: Elisa Bertino
Published: 2025-07-12 22:11:10

Large Language Models (LLMs) have transformed software development and
automated code generation. Motivated by these advancements, this paper explores
the feasibility of LLMs in modifying malware source code to generate variants.
We introduce LLMalMorph, a semi-automated framework that leverages semantical
and syntactical code comprehension by LLMs to generate new malware variants.
LLMalMorph extracts function-level information from the malware source code and
employs custom-engineered prompts coupled with strategically defined code
transformations to guide the LLM in generating variants without
resource-intensive fine-tuning. To evaluate LLMalMorph, we collected 10 diverse
Windows malware samples of varying types, complexity and functionality and
generated 618 variants. Our thorough experiments demonstrate that it is
possible to reduce the detection rates of antivirus engines of these malware
variants to some extent while preserving malware functionalities. In addition,
despite not optimizing against any Machine Learning (ML)-based malware
detectors, several variants also achieved notable attack success rates against
an ML-based malware classifier. We also discuss the limitations of current LLM
capabilities in generating malware variants from source code and assess where
this emerging technology stands in the broader context of malware variant
generation.
Can Multimodal Foundation Models Understand Schematic Diagrams? An
  Empirical Study on Information-Seeking QA over Scientific Papers
Authors: name: Yilun Zhao, name: Chengye Wang, name: Chuhan Li, name: Arman Cohan
Published: 2025-07-14 20:35:25

This paper introduces MISS-QA, the first benchmark specifically designed to
evaluate the ability of models to interpret schematic diagrams within
scientific literature. MISS-QA comprises 1,500 expert-annotated examples over
465 scientific papers. In this benchmark, models are tasked with interpreting
schematic diagrams that illustrate research overviews and answering
corresponding information-seeking questions based on the broader context of the
paper. We assess the performance of 18 frontier multimodal foundation models,
including o4-mini, Gemini-2.5-Flash, and Qwen2.5-VL. We reveal a significant
performance gap between these models and human experts on MISS-QA. Our analysis
of model performance on unanswerable questions and our detailed error analysis
further highlight the strengths and limitations of current models, offering key
insights to enhance models in comprehending multimodal scientific literature.
Subject-Consistent and Pose-Diverse Text-to-Image Generation
Authors: name: Zhanxin Gao, name: Beier Zhu, name: Liang Yao, name: Jian Yang, name: Ying Tai
Published: 2025-07-11 08:15:56

Subject-consistent generation (SCG)-aiming to maintain a consistent subject
identity across diverse scenes-remains a challenge for text-to-image (T2I)
models. Existing training-free SCG methods often achieve consistency at the
cost of layout and pose diversity, hindering expressive visual storytelling. To
address the limitation, we propose subject-Consistent and pose-Diverse T2I
framework, dubbed as CoDi, that enables consistent subject generation with
diverse pose and layout. Motivated by the progressive nature of diffusion,
where coarse structures emerge early and fine details are refined later, CoDi
adopts a two-stage strategy: Identity Transport (IT) and Identity Refinement
(IR). IT operates in the early denoising steps, using optimal transport to
transfer identity features to each target image in a pose-aware manner. This
promotes subject consistency while preserving pose diversity. IR is applied in
the later denoising steps, selecting the most salient identity features to
further refine subject details. Extensive qualitative and quantitative results
on subject consistency, pose diversity, and prompt fidelity demonstrate that
CoDi achieves both better visual perception and stronger performance across all
metrics. The code is provided in https://github.com/NJU-PCALab/CoDi.
Hashed Watermark as a Filter: Defeating Forging and Overwriting Attacks
  in Weight-based Neural Network Watermarking
Authors: name: Yuan Yao, name: Jin Song, name: Jian Jin
Published: 2025-07-15 09:38:11

As valuable digital assets, deep neural networks necessitate robust ownership
protection, positioning neural network watermarking (NNW) as a promising
solution. Among various NNW approaches, weight-based methods are favored for
their simplicity and practicality; however, they remain vulnerable to forging
and overwriting attacks. To address those challenges, we propose NeuralMark, a
robust method built around a hashed watermark filter. Specifically, we utilize
a hash function to generate an irreversible binary watermark from a secret key,
which is then used as a filter to select the model parameters for embedding.
This design cleverly intertwines the embedding parameters with the hashed
watermark, providing a robust defense against both forging and overwriting
attacks. An average pooling is also incorporated to resist fine-tuning and
pruning attacks. Furthermore, it can be seamlessly integrated into various
neural network architectures, ensuring broad applicability. Theoretically, we
analyze its security boundary. Empirically, we verify its effectiveness and
robustness across 13 distinct Convolutional and Transformer architectures,
covering five image classification tasks and one text generation task. The
source codes are available at https://github.com/AIResearch-Group/NeuralMark.
Sound and Complete Neuro-symbolic Reasoning with LLM-Grounded
  Interpretations
Authors: name: Bradley P. Allen, name: Prateek Chhikara, name: Thomas Macaulay Ferguson, name: Filip Ilievski, name: Paul Groth
Published: 2025-07-13 19:05:43

Large language models (LLMs) have demonstrated impressive capabilities in
natural language understanding and generation, but they exhibit problems with
logical consistency in the output they generate. How can we harness LLMs'
broad-coverage parametric knowledge in formal reasoning despite their
inconsistency? We present a method for directly integrating an LLM into the
interpretation function of the formal semantics for a paraconsistent logic. We
provide experimental evidence for the feasibility of the method by evaluating
the function using datasets created from several short-form factuality
benchmarks. Unlike prior work, our method offers a theoretical framework for
neuro-symbolic reasoning that leverages an LLM's knowledge while preserving the
underlying logic's soundness and completeness properties.
DreamPoster: A Unified Framework for Image-Conditioned Generative Poster
  Design
Authors: name: Xiwei Hu, name: Haokun Chen, name: Zhongqi Qi, name: Hui Zhang, name: Dexiang Hong, name: Jie Shao, name: Xinglong Wu
Published: 2025-07-06 03:06:45

We present DreamPoster, a Text-to-Image generation framework that
intelligently synthesizes high-quality posters from user-provided images and
text prompts while maintaining content fidelity and supporting flexible
resolution and layout outputs. Specifically, DreamPoster is built upon our T2I
model, Seedream3.0 to uniformly process different poster generating types. For
dataset construction, we propose a systematic data annotation pipeline that
precisely annotates textual content and typographic hierarchy information
within poster images, while employing comprehensive methodologies to construct
paired datasets comprising source materials (e.g., raw graphics/text) and their
corresponding final poster outputs. Additionally, we implement a progressive
training strategy that enables the model to hierarchically acquire multi-task
generation capabilities while maintaining high-quality generation. Evaluations
on our testing benchmarks demonstrate DreamPoster's superiority over existing
methods, achieving a high usability rate of 88.55\%, compared to GPT-4o
(47.56\%) and SeedEdit3.0 (25.96\%). DreamPoster will be online in Jimeng and
other Bytedance Apps.
Favicon Trojans: Executable Steganography Via Ico Alpha Channel
  Exploitation
Authors: name: David Noever, name: Forrest McKee
Published: 2025-07-11 23:29:04

This paper presents a novel method of executable steganography using the
alpha transparency layer of ICO image files to embed and deliver
self-decompressing JavaScript payloads within web browsers. By targeting the
least significant bit (LSB) of non-transparent alpha layer image values, the
proposed method successfully conceals compressed JavaScript code inside a
favicon image without affecting visual fidelity. Global web traffic loads 294
billion favicons daily and consume 0.9 petabytes of network bandwidth. A
proof-of-concept implementation demonstrates that a 64x64 ICO image can embed
up to 512 bytes uncompressed, or 0.8 kilobyte when using lightweight two-fold
compression. On page load, a browser fetches the favicon as part of standard
behavior, allowing an embedded loader script to extract and execute the payload
entirely in memory using native JavaScript APIs and canvas pixel access. This
creates a two-stage covert channel requiring no additional network or user
requests. Testing across multiple browsers in both desktop and mobile
environments confirms successful and silent execution of the embedded script.
We evaluate the threat model, relate it to polymorphic phishing attacks that
evade favicon-based detection, and analyze evasion of content security policies
and antivirus scanners. We map nine example MITRE ATT&CK Framework objectives
to single line JavaScript to execute arbitrarily in ICO files. Existing
steganalysis and sanitization defenses are discussed, highlighting limitations
in detecting or neutralizing alpha-channel exploits. The results demonstrate a
stealthy and reusable attack surface that blurs traditional boundaries between
static images and executable content. Because modern browsers report silent
errors when developers specifically fail to load ICO files, this attack surface
offers an interesting example of required web behaviors that in turn compromise
security.
A Practical Two-Stage Recipe for Mathematical LLMs: Maximizing Accuracy
  with SFT and Efficiency with Reinforcement Learning
Authors: name: Hiroshi Yoshihara, name: Taiki Yamaguchi, name: Yuichi Inoue
Published: 2025-07-11 02:26:01

Enhancing the mathematical reasoning of Large Language Models (LLMs) is a
pivotal challenge in advancing AI capabilities. While Supervised Fine-Tuning
(SFT) and Reinforcement Learning (RL) are the dominant training paradigms, a
systematic methodology for combining them to maximize both accuracy and
efficiency remains largely unexplored. This paper introduces a practical and
effective training recipe that strategically integrates extended SFT with RL
from online inference (GRPO). We posit that these methods play complementary,
not competing, roles: a prolonged SFT phase first pushes the model's accuracy
to its limits, after which a GRPO phase dramatically improves token efficiency
while preserving this peak performance. Our experiments reveal that extending
SFT for as many as 10 epochs is crucial for performance breakthroughs, and that
the primary role of GRPO in this framework is to optimize solution length. The
efficacy of our recipe is rigorously validated through top-tier performance on
challenging benchmarks, including a high rank among over 2,200 teams in the
strictly leak-free AI Mathematical Olympiad (AIMO). This work provides the
community with a battle-tested blueprint for developing state-of-the-art
mathematical reasoners that are both exceptionally accurate and practically
efficient. To ensure full reproducibility and empower future research, we will
open-source our entire framework, including all code, model checkpoints, and
training configurations at
https://github.com/analokmaus/kaggle-aimo2-fast-math-r1.
From KMMLU-Redux to KMMLU-Pro: A Professional Korean Benchmark Suite for
  LLM Evaluation
Authors: name: Seokhee Hong, name: Sunkyoung Kim, name: Guijin Son, name: Soyeon Kim, name: Yeonjung Hong, name: Jinsik Lee
Published: 2025-07-11 17:56:32

The development of Large Language Models (LLMs) requires robust benchmarks
that encompass not only academic domains but also industrial fields to
effectively evaluate their applicability in real-world scenarios. In this
paper, we introduce two Korean expert-level benchmarks. KMMLU-Redux,
reconstructed from the existing KMMLU, consists of questions from the Korean
National Technical Qualification exams, with critical errors removed to enhance
reliability. KMMLU-Pro is based on Korean National Professional Licensure exams
to reflect professional knowledge in Korea. Our experiments demonstrate that
these benchmarks comprehensively represent industrial knowledge in Korea. We
release our dataset publicly available.
REST: Stress Testing Large Reasoning Models by Asking Multiple Problems
  at Once
Authors: name: Zhuoshi Pan, name: Qizhi Pei, name: Yu Li, name: Qiyao Sun, name: Zinan Tang, name: H. Vicky Zhao, name: Conghui He, name: Lijun Wu
Published: 2025-07-14 17:58:47

Recent Large Reasoning Models (LRMs) have achieved remarkable progress on
task-specific benchmarks, yet their evaluation methods remain constrained by
isolated problem-solving paradigms. Existing benchmarks predominantly assess
single-question reasoning through sequential testing, resulting critical
limitations: (1) vulnerability to data contamination and less challenging
(e.g., DeepSeek-R1 achieves 97.0% on MATH500), forcing costly and perpetual
creation of new questions with large human efforts, (2) failure to evaluate
models under multi-context pressure, a key requirement for real-world
deployment. To bridge this gap, we present REST (Reasoning Evaluation through
Simultaneous Testing), a stress-testing framework that concurrently exposes
LRMs to multiple problems simultaneously. Beyond basic reasoning, REST
specifically evaluates several under-tested capabilities: contextual priority
allocation, cross-problem interference resistance, and dynamic cognitive load
management. Our evaluation reveals several striking findings: Even
state-of-the-art (SOTA) models like DeepSeek-R1 exhibit substantial performance
degradation under stress testing. Crucially, REST demonstrates stronger
discriminative power than existing benchmarks, revealing pronounced performance
differences among models that exhibit similar, near-ceiling performance under
single-question evaluations. Some key mechanistic insights emerge from our
analysis: (1) the "overthinking trap" is a critical factor contributing to the
performance degradation; (2) the models trained with "long2short" technique
preserve more accuracy of their single-problem performance under REST,
outperforming standard-trained counterparts. These results establish REST as a
cost-efficient, future-proof evaluation paradigm that better reflects
real-world reasoning demands while reducing reliance on continuous human
annotation.
Reasoning or Memorization? Unreliable Results of Reinforcement Learning
  Due to Data Contamination
Authors: name: Mingqi Wu, name: Zhihao Zhang, name: Qiaole Dong, name: Zhiheng Xi, name: Jun Zhao, name: Senjie Jin, name: Xiaoran Fan, name: Yuhao Zhou, name: Yanwei Fu, name: Qin Liu, name: Songyang Zhang, name: Qi Zhang
Published: 2025-07-14 17:55:15

The reasoning capabilities of large language models (LLMs) have been a
longstanding focus of research. Recent works have further enhanced these
capabilities using reinforcement learning (RL), with many new methods claiming
significant improvements with minimal or no external supervision. Surprisingly,
some studies even suggest that random or incorrect reward signals can enhance
reasoning performance. However, these breakthroughs are mostly reported on the
Qwen2.5 model family and evaluated on well-known benchmarks such as MATH-500,
AMC, and AIME, while failing to achieve similar gains on other models like
Llama, which warrants further investigation. Our analysis shows that although
Qwen2.5 achieves strong mathematical reasoning performance, its pretraining on
large-scale web corpora makes it vulnerable to data contamination in popular
benchmarks. As a result, results derived from these benchmarks may be
unreliable. To address this, we introduce a generator that produces fully
synthetic arithmetic problems of arbitrary length and difficulty, yielding a
clean dataset we call RandomCalculation. Using these leakage-free datasets, we
show that only accurate reward signals consistently improve performance, while
noisy or incorrect signals do not. We advocate for evaluating RL methods on
uncontaminated benchmarks and across diverse model families to ensure
trustworthy conclusions.
MoVieS: Motion-Aware 4D Dynamic View Synthesis in One Second
Authors: name: Chenguo Lin, name: Yuchen Lin, name: Panwang Pan, name: Yifan Yu, name: Honglei Yan, name: Katerina Fragkiadaki, name: Yadong Mu
Published: 2025-07-14 08:49:57

We present MoVieS, a novel feed-forward model that synthesizes 4D dynamic
novel views from monocular videos in one second. MoVieS represents dynamic 3D
scenes using pixel-aligned grids of Gaussian primitives, explicitly supervising
their time-varying motion. This allows, for the first time, the unified
modeling of appearance, geometry and motion, and enables view synthesis,
reconstruction and 3D point tracking within a single learning-based framework.
By bridging novel view synthesis with dynamic geometry reconstruction, MoVieS
enables large-scale training on diverse datasets with minimal dependence on
task-specific supervision. As a result, it also naturally supports a wide range
of zero-shot applications, such as scene flow estimation and moving object
segmentation. Extensive experiments validate the effectiveness and efficiency
of MoVieS across multiple tasks, achieving competitive performance while
offering several orders of magnitude speedups.
Mixture-of-Recursions: Learning Dynamic Recursive Depths for Adaptive
  Token-Level Computation
Authors: name: Sangmin Bae, name: Yujin Kim, name: Reza Bayat, name: Sungnyun Kim, name: Jiyoun Ha, name: Tal Schuster, name: Adam Fisch, name: Hrayr Harutyunyan, name: Ziwei Ji, name: Aaron Courville, name: Se-Young Yun
Published: 2025-07-14 17:49:00

Scaling language models unlocks impressive capabilities, but the accompanying
computational and memory demands make both training and deployment expensive.
Existing efficiency efforts typically target either parameter sharing or
adaptive computation, leaving open the question of how to attain both
simultaneously. We introduce Mixture-of-Recursions (MoR), a unified framework
that combines the two axes of efficiency inside a single Recursive Transformer.
MoR reuses a shared stack of layers across recursion steps to achieve parameter
efficiency, while lightweight routers enable adaptive token-level thinking by
dynamically assigning different recursion depths to individual tokens. This
allows MoR to focus quadratic attention computation only among tokens still
active at a given recursion depth, further improving memory access efficiency
by selectively caching only their key-value pairs. Beyond these core
mechanisms, we also propose a KV sharing variant that reuses KV pairs from the
first recursion, specifically designed to decrease prefill latency and memory
footprint. Across model scales ranging from 135M to 1.7B parameters, MoR forms
a new Pareto frontier: at equal training FLOPs and smaller model sizes, it
significantly lowers validation perplexity and improves few-shot accuracy,
while delivering higher throughput compared with vanilla and existing recursive
baselines. These gains demonstrate that MoR is an effective path towards
large-model quality without incurring large-model cost.
EmbRACE-3K: Embodied Reasoning and Action in Complex Environments
Authors: name: Mingxian Lin, name: Wei Huang, name: Yitang Li, name: Chengjie Jiang, name: Kui Wu, name: Fangwei Zhong, name: Shengju Qian, name: Xin Wang, name: Xiaojuan Qi
Published: 2025-07-14 17:59:46

Recent advanced vision-language models(VLMs) have demonstrated strong
performance on passive, offline image and video understanding tasks. However,
their effectiveness in embodied settings, which require online interaction and
active scene understanding remains limited. In such scenarios, an agent
perceives the environment from a first-person perspective, with each action
dynamically shaping subsequent observations. Even state-of-the-art models such
as GPT-4o, Claude 3.5 Sonnet, and Gemini 2.5 Pro struggle in open-environment
interactions, exhibiting clear limitations in spatial reasoning and
long-horizon planning. To address this gap, we introduce EmRACE-3K, a dataset
of over 3,000 language-guided tasks situated in diverse, photorealistic
environments constructed using Unreal Engine and the UnrealCV-Zoo framework.
The tasks encompass a wide range of embodied challenges, including navigation,
object manipulation, and multi-stage goal execution. Each task unfolds as a
multi-step trajectory, pairing first-person visual observations with high-level
instructions, grounded actions, and natural language rationales that express
the agent's intent at every step. Using EmRACE-3K, we establish a benchmark to
evaluate the embodied reasoning capabilities of VLMs across three key
dimensions: Exploration, Dynamic Spatial-Semantic Reasoning, and Multi-stage
Goal Execution. In zero-shot settings, all models achieve success rates below
20%, underscoring the challenge posed by our benchmark and the current
limitations of VLMs in interactive environments. To demonstrate the utility of
EmRACE-3K, we further fine-tune Qwen2.5-VL-7B using supervised learning
followed by reinforcement learning. This approach yields substantial
improvements across all three challenge categories, highlighting the dataset's
effectiveness in enabling the development of embodied reasoning capabilities.
CompassJudger-2: Towards Generalist Judge Model via Verifiable Rewards
Authors: name: Taolin Zhang, name: Maosong Cao, name: Alexander Lam, name: Songyang Zhang, name: Kai Chen
Published: 2025-07-12 01:34:24

Recently, the role of LLM-as-judge in evaluating large language models has
gained prominence. However, current judge models suffer from narrow
specialization and limited robustness, undermining their capacity for
comprehensive evaluations. In this work, we present CompassJudger-2, a novel
generalist judge model that overcomes these limitations via a task-driven,
multi-domain data curation strategy. Central to our approach is supervising
judgment tasks with verifiable rewards, guiding intrinsic critical reasoning
through rejection sampling to foster robust, generalizable judgment
capabilities. We introduce a refined learning objective with margin policy
gradient loss to enhance performance. Empirically, CompassJudger-2 achieves
superior results across multiple judge and reward benchmarks, and our 7B model
demonstrates competitive judgment accuracy with significantly larger models
like DeepSeek-V3 and Qwen3-235B-A22B. Additionally, we propose JudgerBenchV2, a
comprehensive benchmark evaluating cross-domain judgment accuracy and rank
consistency to standardize judge model evaluation. These contributions advance
robust, scalable LLM judgment and establish new performance and evaluation
standards.