Orionnnn/hub-stats huggingface dataset:
FACTORY: A Challenging Human-Verified Prompt Set for Long-Form
  Factuality
Authors: name: Mingda Chen, name: Yang Li, name: Xilun Chen, name: Adina Williams, name: Gargi Ghosh, name: Scott Yih
Published: 2025-07-31 19:00:11

Long-form factuality evaluation assesses the ability of models to generate
accurate, comprehensive responses to short prompts. Existing benchmarks often
lack human verification, leading to potential quality issues. To address this
limitation, we introduce FACTORY, a large-scale, human-verified prompt set.
Developed using a model-in-the-loop approach and refined by humans, FACTORY
includes challenging prompts that are fact-seeking, answerable, and
unambiguous. We conduct human evaluations on 6 state-of-the-art language models
using FACTORY and existing datasets. Our results show that FACTORY is a
challenging benchmark: approximately 40% of the claims made in the responses of
SOTA models are not factual, compared to only 10% for other datasets. Our
analysis identifies the strengths of FACTORY over prior benchmarks, emphasizing
its reliability and the necessity for models to reason across long-tailed
facts.
HarmonyGuard: Toward Safety and Utility in Web Agents via Adaptive
  Policy Enhancement and Dual-Objective Optimization
Authors: name: Yurun Chen, name: Xavier Hu, name: Yuhan Liu, name: Keting Yin, name: Juncheng Li, name: Zhuosheng Zhang, name: Shengyu Zhang
Published: 2025-08-06 01:49:32

Large language models enable agents to autonomously perform tasks in open web
environments. However, as hidden threats within the web evolve, web agents face
the challenge of balancing task performance with emerging risks during
long-sequence operations. Although this challenge is critical, current research
remains limited to single-objective optimization or single-turn scenarios,
lacking the capability for collaborative optimization of both safety and
utility in web environments. To address this gap, we propose HarmonyGuard, a
multi-agent collaborative framework that leverages policy enhancement and
objective optimization to jointly improve both utility and safety. HarmonyGuard
features a multi-agent architecture characterized by two fundamental
capabilities: (1) Adaptive Policy Enhancement: We introduce the Policy Agent
within HarmonyGuard, which automatically extracts and maintains structured
security policies from unstructured external documents, while continuously
updating policies in response to evolving threats. (2) Dual-Objective
Optimization: Based on the dual objectives of safety and utility, the Utility
Agent integrated within HarmonyGuard performs the Markovian real-time reasoning
to evaluate the objectives and utilizes metacognitive capabilities for their
optimization. Extensive evaluations on multiple benchmarks show that
HarmonyGuard improves policy compliance by up to 38% and task completion by up
to 20% over existing baselines, while achieving over 90% policy compliance
across all tasks. Our project is available here:
https://github.com/YurunChen/HarmonyGuard.
DiffSemanticFusion: Semantic Raster BEV Fusion for Autonomous Driving
  via Online HD Map Diffusion
Authors: name: Zhigang Sun, name: Yiru Wang, name: Anqing Jiang, name: Shuo Wang, name: Yu Gao, name: Yuwen Heng, name: Shouyi Zhang, name: An He, name: Hao Jiang, name: Jinhao Chai, name: Zichong Gu, name: Wang Jijun, name: Shichen Tang, name: Lavdim Halilaj, name: Juergen Luettin, name: Hao Sun
Published: 2025-08-03 14:32:05

Autonomous driving requires accurate scene understanding, including road
geometry, traffic agents, and their semantic relationships. In online HD map
generation scenarios, raster-based representations are well-suited to vision
models but lack geometric precision, while graph-based representations retain
structural detail but become unstable without precise maps. To harness the
complementary strengths of both, we propose DiffSemanticFusion -- a fusion
framework for multimodal trajectory prediction and planning. Our approach
reasons over a semantic raster-fused BEV space, enhanced by a map diffusion
module that improves both the stability and expressiveness of online HD map
representations. We validate our framework on two downstream tasks: trajectory
prediction and planning-oriented end-to-end autonomous driving. Experiments on
real-world autonomous driving benchmarks, nuScenes and NAVSIM, demonstrate
improved performance over several state-of-the-art methods. For the prediction
task on nuScenes, we integrate DiffSemanticFusion with the online HD map
informed QCNet, achieving a 5.1\% performance improvement. For end-to-end
autonomous driving in NAVSIM, DiffSemanticFusion achieves state-of-the-art
results, with a 15\% performance gain in NavHard scenarios. In addition,
extensive ablation and sensitivity studies show that our map diffusion module
can be seamlessly integrated into other vector-based approaches to enhance
performance. All artifacts are available at
https://github.com/SunZhigang7/DiffSemanticFusion.
StepFun-Formalizer: Unlocking the Autoformalization Potential of LLMs
  through Knowledge-Reasoning Fusion
Authors: name: Yutong Wu, name: Di Huang, name: Ruosi Wan, name: Yue Peng, name: Shijie Shang, name: Chenrui Cao, name: Lei Qi, name: Rui Zhang, name: Zidong Du, name: Jie Yan, name: Xing Hu
Published: 2025-08-06 13:28:22

Autoformalization aims to translate natural-language mathematical statements
into a formal language. While LLMs have accelerated progress in this area,
existing methods still suffer from low accuracy. We identify two key abilities
for effective autoformalization: comprehensive mastery of formal-language
domain knowledge, and reasoning capability of natural language problem
understanding and informal-formal alignment. Without the former, a model cannot
identify the correct formal objects; without the latter, it struggles to
interpret real-world contexts and map them precisely into formal expressions.
To address these gaps, we introduce ThinkingF, a data synthesis and training
pipeline that improves both abilities. First, we construct two datasets: one by
distilling and selecting large-scale examples rich in formal knowledge, and
another by generating informal-to-formal reasoning trajectories guided by
expert-designed templates. We then apply SFT and RLVR with these datasets to
further fuse and refine the two abilities. The resulting 7B and 32B models
exhibit both comprehensive formal knowledge and strong informal-to-formal
reasoning. Notably, StepFun-Formalizer-32B achieves SOTA BEq@1 scores of 40.5%
on FormalMATH-Lite and 26.7% on ProverBench, surpassing all prior
general-purpose and specialized models.
C3D-AD: Toward Continual 3D Anomaly Detection via Kernel Attention with
  Learnable Advisor
Authors: name: Haoquan Lu, name: Hanzhe Liang, name: Jie Zhang, name: Chenxi Hu, name: Jinbao Wang, name: Can Gao
Published: 2025-08-02 10:54:55

3D Anomaly Detection (AD) has shown great potential in detecting anomalies or
defects of high-precision industrial products. However, existing methods are
typically trained in a class-specific manner and also lack the capability of
learning from emerging classes. In this study, we proposed a continual learning
framework named Continual 3D Anomaly Detection (C3D-AD), which can not only
learn generalized representations for multi-class point clouds but also handle
new classes emerging over time.Specifically, in the feature extraction module,
to extract generalized local features from diverse product types of different
tasks efficiently, Kernel Attention with random feature Layer (KAL) is
introduced, which normalizes the feature space. Then, to reconstruct data
correctly and continually, an efficient Kernel Attention with learnable Advisor
(KAA) mechanism is proposed, which learns the information from new categories
while discarding redundant old information within both the encoder and decoder.
Finally, to keep the representation consistency over tasks, a Reconstruction
with Parameter Perturbation (RPP) module is proposed by designing a
representation rehearsal loss function, which ensures that the model remembers
previous category information and returns category-adaptive
representation.Extensive experiments on three public datasets demonstrate the
effectiveness of the proposed method, achieving an average performance of
66.4%, 83.1%, and 63.4% AUROC on Real3D-AD, Anomaly-ShapeNet, and MulSen-AD,
respectively.
Sel3DCraft: Interactive Visual Prompts for User-Friendly Text-to-3D
  Generation
Authors: name: Nan Xiang, name: Tianyi Liang, name: Haiwen Huang, name: Shiqi Jiang, name: Hao Huang, name: Yifei Huang, name: Liangyu Chen, name: Changbo Wang, name: Chenhui Li
Published: 2025-08-01 08:36:15

Text-to-3D (T23D) generation has transformed digital content creation, yet
remains bottlenecked by blind trial-and-error prompting processes that yield
unpredictable results. While visual prompt engineering has advanced in
text-to-image domains, its application to 3D generation presents unique
challenges requiring multi-view consistency evaluation and spatial
understanding. We present Sel3DCraft, a visual prompt engineering system for
T23D that transforms unstructured exploration into a guided visual process. Our
approach introduces three key innovations: a dual-branch structure combining
retrieval and generation for diverse candidate exploration; a multi-view hybrid
scoring approach that leverages MLLMs with innovative high-level metrics to
assess 3D models with human-expert consistency; and a prompt-driven visual
analytics suite that enables intuitive defect identification and refinement.
Extensive testing and user studies demonstrate that Sel3DCraft surpasses other
T23D systems in supporting creativity for designers.
Light-IF: Endowing LLMs with Generalizable Reasoning via Preview and
  Self-Checking for Complex Instruction Following
Authors: name: Chenyang Wang, name: Liang Wen, name: Shousheng Jia, name: Xiangzheng Zhang, name: Liang Xu
Published: 2025-08-05 07:42:00

While advancements in the reasoning abilities of LLMs have significantly
enhanced their performance in solving mathematical problems, coding tasks, and
general puzzles, their effectiveness in accurately adhering to instructions
remains inconsistent, particularly with more complex directives. Our
investigation identifies lazy reasoning during the thinking stage as the
primary factor contributing to poor instruction adherence. To mitigate this
issue, we propose a comprehensive framework designed to enable rigorous
reasoning processes involving preview and self-checking, essential for
satisfying strict instruction constraints. Specifically, we first generate
instructions with complex constraints and apply a filtering process to obtain
valid prompts, resulting in three distinct prompt datasets categorized as hard,
easy, and pass. Then, we employ rejection sampling on the pass prompts to
curate a small yet high-quality dataset, enabling a cold-start initialization
of the model and facilitating its adaptation to effective reasoning patterns.
Subsequently, we employ an entropy-preserving supervised fine-tuning
(Entropy-SFT) strategy coupled with token-wise entropy-adaptive (TEA-RL)
reinforcement learning guided by rule-based dense rewards. This approach
encourages the model to transform its reasoning mechanism, ultimately fostering
generalizable reasoning abilities that encompass preview and self-checking.
Extensive experiments conducted on instruction-following benchmarks demonstrate
remarkable performance improvements across various model scales. Notably, our
Light-IF-32B model surpasses both larger open-source models such as DeepSeek-R1
and closed-source models like Doubao-1.6.
Training Long-Context, Multi-Turn Software Engineering Agents with
  Reinforcement Learning
Authors: name: Alexander Golubev, name: Maria Trofimova, name: Sergei Polezhaev, name: Ibragim Badertdinov, name: Maksim Nekrashevich, name: Anton Shevtsov, name: Simon Karasik, name: Sergey Abramov, name: Andrei Andriushchenko, name: Filipp Fisin, name: Sergei Skvortsov, name: Boris Yangel
Published: 2025-08-05 14:30:47

Research on applications of Reinforcement Learning (RL) to Large Language
Models (LLMs) has mostly been focused on single-turn problems, such as
mathematical reasoning or single-shot code generation. While these problems can
be viewed as token-level multi-turn MDPs, this view corresponds to a degenerate
case of multi-turn interaction where the environment provides no feedback. This
contrasts with many real-world domains, such as software engineering (SWE),
which require rich multi-turn interactions with a stateful environment that
responds to each action with a non-trivial observation.
  To bridge this gap, we demonstrate the successful application of RL to this
general regime. Using a modified Decoupled Advantage Policy Optimization (DAPO)
algorithm, we train an agent based on Qwen2.5-72B-Instruct to solve real-world
software engineering tasks. Our approach increases the agent's success rate on
the SWE-bench Verified benchmark from a 20% rejection fine-tuned baseline to
39%, without relying on any teacher models. On SWE-rebench, our agent matches
or outperforms leading open-weight models such as DeepSeek-V3-0324 and
Qwen3-235B-A22B using an identical scaffolding, offering a viable path toward
building more capable autonomous agents for complex real-world problems based
on open models.
IFDECORATOR: Wrapping Instruction Following Reinforcement Learning with
  Verifiable Rewards
Authors: name: Xu Guo, name: Tianyi Liang, name: Tong Jian, name: Xiaogui Yang, name: Ling-I Wu, name: Chenhui Li, name: Zhihui Lu, name: Qipeng Guo, name: Kai Chen
Published: 2025-08-06 17:00:54

Reinforcement Learning with Verifiable Rewards (RLVR) improves instruction
following capabilities of large language models (LLMs), but suffers from
training inefficiency due to inadequate difficulty assessment. Moreover, RLVR
is prone to over-optimization, where LLMs exploit verification shortcuts
without aligning to the actual intent of user instructions. We introduce
Instruction Following Decorator (IFDecorator}, a framework that wraps RLVR
training into a robust and sample-efficient pipeline. It consists of three
components: (1) a cooperative-adversarial data flywheel that co-evolves
instructions and hybrid verifications, generating progressively more
challenging instruction-verification pairs; (2) IntentCheck, a bypass module
enforcing intent alignment; and (3) trip wires, a diagnostic mechanism that
detects reward hacking via trap instructions, which trigger and capture
shortcut exploitation behaviors. Our Qwen2.5-32B-Instruct-IFDecorator achieves
87.43% accuracy on IFEval, outperforming larger proprietary models such as
GPT-4o. Additionally, we demonstrate substantial improvements on FollowBench
while preserving general capabilities. Our trip wires show significant
reductions in reward hacking rates. We will release models, code, and data for
future research.
MiDashengLM: Efficient Audio Understanding with General Audio Captions
Authors: name: Heinrich Dinkel, name: Gang Li, name: Jizhong Liu, name: Jian Luan, name: Yadong Niu, name: Xingwei Sun, name: Tianzi Wang, name: Qiyang Xiao, name: Junbo Zhang, name: Jiahao Zhou
Published: 2025-08-06 00:30:19

Current approaches for large audio language models (LALMs) often rely on
closed data sources or proprietary models, limiting their generalization and
accessibility. This paper introduces MiDashengLM, a novel open audio-language
model designed for efficient and comprehensive audio understanding through the
use of general audio captions using our novel ACAVCaps training dataset.
MiDashengLM exclusively relies on publicly available pretraining and supervised
fine-tuning (SFT) datasets, ensuring full transparency and reproducibility. At
its core, MiDashengLM integrates Dasheng, an open-source audio encoder,
specifically engineered to process diverse auditory information effectively.
Unlike previous works primarily focused on Automatic Speech Recognition (ASR)
based audio-text alignment, our strategy centers on general audio captions,
fusing speech, sound and music information into one textual representation,
enabling a holistic textual representation of complex audio scenes. Lastly,
MiDashengLM provides an up to 4x speedup in terms of time-to-first-token (TTFT)
and up to 20x higher throughput than comparable models. Checkpoints are
available online at https://huggingface.co/mispeech/midashenglm-7b and
https://github.com/xiaomi-research/dasheng-lm.
Reasoning Language Models for Root Cause Analysis in 5G Wireless
  Networks
Authors: name: Mohamed Sana, name: Nicola Piovesan, name: Antonio De Domenico, name: Yibin Kang, name: Haozhe Zhang, name: Merouane Debbah, name: Fadhel Ayed
Published: 2025-07-29 16:21:42

Root Cause Analysis (RCA) in mobile networks remains a challenging task due
to the need for interpretability, domain expertise, and causal reasoning. In
this work, we propose a lightweight framework that leverages Large Language
Models (LLMs) for RCA. To do so, we introduce TeleLogs, a curated dataset of
annotated troubleshooting problems designed to benchmark RCA capabilities. Our
evaluation reveals that existing open-source reasoning LLMs struggle with these
problems, underscoring the need for domain-specific adaptation. To address this
issue, we propose a two-stage training methodology that combines supervised
fine-tuning with reinforcement learning to improve the accuracy and reasoning
quality of LLMs. The proposed approach fine-tunes a series of RCA models to
integrate domain knowledge and generate structured, multi-step diagnostic
explanations, improving both interpretability and effectiveness. Extensive
experiments across multiple LLM sizes show significant performance gains over
state-of-the-art reasoning and non-reasoning models, including strong
generalization to randomized test variants. These results demonstrate the
promise of domain-adapted, reasoning-enhanced LLMs for practical and
explainable RCA in network operation and management.
Enhancing Vision-Language Model Training with Reinforcement Learning in
  Synthetic Worlds for Real-World Success
Authors: name: George Bredis, name: Stanislav Dereka, name: Viacheslav Sinii, name: Ruslan Rakhimov, name: Daniil Gavrilov
Published: 2025-08-06 10:08:48

Interactive multimodal agents must convert raw visual observations into
coherent sequences of language-conditioned actions -- a capability that current
vision-language models (VLMs) still lack. Earlier reinforcement-learning (RL)
efforts could, in principle, endow VLMs with such skills, but they have seldom
tested whether the learned behaviours generalize beyond their training
simulators, and they depend either on brittle hyperparameter tuning or on
dense-reward environments with low state variability. We introduce
Vision-Language Decoupled Actor-Critic (VL-DAC), a lightweight,
hyperparameter-free RL algorithm. VL-DAC applies PPO updates to action tokens
while learning value only at the environment-step level: an arrangement, to our
knowledge, not previously explored for large VLMs or LLMs. This simple
decoupling removes unstable weighting terms and yields faster, more reliable
convergence. Training a single VLM with VL-DAC in one inexpensive simulator at
a time (MiniWorld, Gym-Cards, ALFWorld, or WebShop) already produces policies
that generalize widely: +50\% relative on BALROG (game-centric agentic
control), +5\% relative on the hardest part of VSI-Bench (spatial planning),
and +2\% on VisualWebBench (web navigation), all without degrading general
image understanding accuracy. These results provide the first evidence that a
simple RL algorithm can train VLMs entirely in cheap synthetic worlds while
delivering measurable gains on real-image agentic, spatial-reasoning, and
web-navigation benchmarks.
IAUNet: Instance-Aware U-Net
Authors: name: Yaroslav Prytula, name: Illia Tsiporenko, name: Ali Zeynalli, name: Dmytro Fishman
Published: 2025-08-03 21:36:20

Instance segmentation is critical in biomedical imaging to accurately
distinguish individual objects like cells, which often overlap and vary in
size. Recent query-based methods, where object queries guide segmentation, have
shown strong performance. While U-Net has been a go-to architecture in medical
image segmentation, its potential in query-based approaches remains largely
unexplored. In this work, we present IAUNet, a novel query-based U-Net
architecture. The core design features a full U-Net architecture, enhanced by a
novel lightweight convolutional Pixel decoder, making the model more efficient
and reducing the number of parameters. Additionally, we propose a Transformer
decoder that refines object-specific features across multiple scales. Finally,
we introduce the 2025 Revvity Full Cell Segmentation Dataset, a unique resource
with detailed annotations of overlapping cell cytoplasm in brightfield images,
setting a new benchmark for biomedical instance segmentation. Experiments on
multiple public datasets and our own show that IAUNet outperforms most
state-of-the-art fully convolutional, transformer-based, and query-based models
and cell segmentation-specific models, setting a strong baseline for cell
instance segmentation tasks. Code is available at
https://github.com/SlavkoPrytula/IAUNet
DreamVVT: Mastering Realistic Video Virtual Try-On in the Wild via a
  Stage-Wise Diffusion Transformer Framework
Authors: name: Tongchun Zuo, name: Zaiyu Huang, name: Shuliang Ning, name: Ente Lin, name: Chao Liang, name: Zerong Zheng, name: Jianwen Jiang, name: Yuan Zhang, name: Mingyuan Gao, name: Xin Dong
Published: 2025-08-04 18:27:55

Video virtual try-on (VVT) technology has garnered considerable academic
interest owing to its promising applications in e-commerce advertising and
entertainment. However, most existing end-to-end methods rely heavily on scarce
paired garment-centric datasets and fail to effectively leverage priors of
advanced visual models and test-time inputs, making it challenging to
accurately preserve fine-grained garment details and maintain temporal
consistency in unconstrained scenarios. To address these challenges, we propose
DreamVVT, a carefully designed two-stage framework built upon Diffusion
Transformers (DiTs), which is inherently capable of leveraging diverse unpaired
human-centric data to enhance adaptability in real-world scenarios. To further
leverage prior knowledge from pretrained models and test-time inputs, in the
first stage, we sample representative frames from the input video and utilize a
multi-frame try-on model integrated with a vision-language model (VLM), to
synthesize high-fidelity and semantically consistent keyframe try-on images.
These images serve as complementary appearance guidance for subsequent video
generation. In the second stage, skeleton maps together with
fine-grained motion and appearance descriptions are extracted from the input
content, and these along with the keyframe try-on images are then fed into a
pretrained video generation model enhanced with LoRA adapters. This ensures
long-term temporal coherence for unseen regions and enables highly plausible
dynamic motions. Extensive quantitative and qualitative experiments demonstrate
that DreamVVT surpasses existing methods in preserving detailed garment content
and temporal stability in real-world scenarios. Our project page
https://virtu-lab.github.io/