Orionnnn/hub-stats huggingface dataset:
MI-Fuse: Label Fusion for Unsupervised Domain Adaptation with
  Closed-Source Large-Audio Language Model
Authors: name: Hsiao-Ying Huang, name: Yi-Cheng Lin, name: Hung-yi Lee
Published: 2025-09-25 03:16:32

Large audio-language models (LALMs) show strong zero-shot ability on speech
tasks, suggesting promise for speech emotion recognition (SER). However, SER in
real-world deployments often fails under domain mismatch, where source data are
unavailable and powerful LALMs are accessible only through an API. We ask:
given only unlabeled target-domain audio and an API-only LALM, can a student
model be adapted to outperform the LALM in the target domain? To this end, we
propose MI-Fuse, a denoised label fusion framework that supplements the LALM
with a source-domain trained SER classifier as an auxiliary teacher. The
framework draws multiple stochastic predictions from both teachers, weights
their mean distributions by mutual-information-based uncertainty, and
stabilizes training with an exponential moving average teacher. Experiments
across three public emotion datasets and six cross-domain transfers show
consistent gains, with the student surpassing the LALM and outperforming the
strongest baseline by 3.9%. This approach strengthens emotion-aware speech
systems without sharing source data, enabling realistic adaptation.
Blueprints of Trust: AI System Cards for End to End Transparency and
  Governance
Authors: name: Huzaifa Sidhpurwala, name: Emily Fox, name: Garth Mollett, name: Florencio Cano Gabarda, name: Roman Zhukov
Published: 2025-09-23 05:58:32

This paper introduces the Hazard-Aware System Card (HASC), a novel framework
designed to enhance transparency and accountability in the development and
deployment of AI systems. The HASC builds upon existing model card and system
card concepts by integrating a comprehensive, dynamic record of an AI system's
security and safety posture. The framework proposes a standardized system of
identifiers, including a novel AI Safety Hazard (ASH) ID, to complement
existing security identifiers like CVEs, allowing for clear and consistent
communication of fixed flaws. By providing a single, accessible source of
truth, the HASC empowers developers and stakeholders to make more informed
decisions about AI system safety throughout its lifecycle. Ultimately, we also
compare our proposed AI system cards with the ISO/IEC 42001:2023 standard and
discuss how they can be used to complement each other, providing greater
transparency and accountability for AI systems.
TrustJudge: Inconsistencies of LLM-as-a-Judge and How to Alleviate Them
Authors: name: Yidong Wang, name: Yunze Song, name: Tingyuan Zhu, name: Xuanwang Zhang, name: Zhuohao Yu, name: Hao Chen, name: Chiyu Song, name: Qiufeng Wang, name: Cunxiang Wang, name: Zhen Wu, name: Xinyu Dai, name: Yue Zhang, name: Wei Ye, name: Shikun Zhang
Published: 2025-09-25 13:04:29

The adoption of Large Language Models (LLMs) as automated evaluators
(LLM-as-a-judge) has revealed critical inconsistencies in current evaluation
frameworks. We identify two fundamental types of inconsistencies: (1)
Score-Comparison Inconsistency, where lower-rated responses outperform
higher-scored ones in pairwise comparisons, and (2) Pairwise Transitivity
Inconsistency, manifested through circular preference chains (A>B>C>A) and
equivalence contradictions (A=B=C\neq A). We argue that these issues come from
information loss in discrete rating systems and ambiguous tie judgments during
pairwise evaluation. We propose TrustJudge, a probabilistic framework that
addresses these limitations through two key innovations: 1)
distribution-sensitive scoring that computes continuous expectations from
discrete rating probabilities, preserving information entropy for more precise
scoring, and 2) likelihood-aware aggregation that resolves transitivity
violations using bidirectional preference probabilities or perplexity. We also
formalize the theoretical limitations of current LLM-as-a-judge frameworks and
demonstrate how TrustJudge's components overcome them. When evaluated with
Llama-3.1-70B-Instruct as judge using our dataset, TrustJudge reduces
Score-Comparison inconsistency by 8.43% (from 23.32% to 14.89%) and Pairwise
Transitivity inconsistency by 10.82% (from 15.22% to 4.40%), while maintaining
higher evaluation accuracy. Our work provides the first systematic analysis of
evaluation framework inconsistencies in LLM-as-a-judge paradigms, offering both
theoretical insights and practical solutions for reliable automated assessment.
The framework demonstrates consistent improvements across various model
architectures and scales, enabling more trustworthy LLM evaluation without
requiring additional training or human annotations. The codes can be found at
https://github.com/TrustJudge/TrustJudge.
V-GameGym: Visual Game Generation for Code Large Language Models
Authors: name: Wei Zhang, name: Jack Yang, name: Renshuai Tao, name: Lingzheng Chai, name: Shawn Guo, name: Jiajun Wu, name: Xiaoming Chen, name: Ganqu Cui, name: Ning Ding, name: Xander Xu, name: Hu Wei, name: Bowen Zhou
Published: 2025-09-24 14:01:18

Code large language models have demonstrated remarkable capabilities in
programming tasks, yet current benchmarks primarily focus on single modality
rather than visual game development. Most existing code-related benchmarks
evaluate syntax correctness and execution accuracy, overlooking critical
game-specific metrics such as playability, visual aesthetics, and user
engagement that are essential for real-world deployment. To address the gap
between current LLM capabilities in algorithmic problem-solving and competitive
programming versus the comprehensive requirements of practical game
development, we present V-GameGym, a comprehensive benchmark comprising 2,219
high-quality samples across 100 thematic clusters derived from real-world
repositories, adopting a novel clustering-based curation methodology to ensure
both diversity and structural completeness. Further, we introduce a multimodal
evaluation framework with an automated LLM-driven pipeline for visual code
synthesis using complete UI sandbox environments. Our extensive analysis
reveals that V-GameGym effectively bridges the gap between code generation
accuracy and practical game development workflows, providing quantifiable
quality metrics for visual programming and interactive element generation.
Discrete Diffusion for Reflective Vision-Language-Action Models in
  Autonomous Driving
Authors: name: Pengxiang Li, name: Yinan Zheng, name: Yue Wang, name: Huimin Wang, name: Hang Zhao, name: Jingjing Liu, name: Xianyuan Zhan, name: Kun Zhan, name: Xianpeng Lang
Published: 2025-09-24 13:35:15

End-to-End (E2E) solutions have emerged as a mainstream approach for
autonomous driving systems, with Vision-Language-Action (VLA) models
representing a new paradigm that leverages pre-trained multimodal knowledge
from Vision-Language Models (VLMs) to interpret and interact with complex
real-world environments. However, these methods remain constrained by the
limitations of imitation learning, which struggles to inherently encode
physical rules during training. Existing approaches often rely on complex
rule-based post-refinement, employ reinforcement learning that remains largely
limited to simulation, or utilize diffusion guidance that requires
computationally expensive gradient calculations. To address these challenges,
we introduce ReflectDrive, a novel learning-based framework that integrates a
reflection mechanism for safe trajectory generation via discrete diffusion. We
first discretize the two-dimensional driving space to construct an action
codebook, enabling the use of pre-trained Diffusion Language Models for
planning tasks through fine-tuning. Central to our approach is a safety-aware
reflection mechanism that performs iterative self-correction without gradient
computation. Our method begins with goal-conditioned trajectory generation to
model multi-modal driving behaviors. Based on this, we apply local search
methods to identify unsafe tokens and determine feasible solutions, which then
serve as safe anchors for inpainting-based regeneration. Evaluated on the
NAVSIM benchmark, ReflectDrive demonstrates significant advantages in
safety-critical trajectory generation, offering a scalable and reliable
solution for autonomous driving systems.
Does FLUX Already Know How to Perform Physically Plausible Image
  Composition?
Authors: name: Shilin Lu, name: Zhuming Lian, name: Zihan Zhou, name: Shaocong Zhang, name: Chen Zhao, name: Adams Wai-Kin Kong
Published: 2025-09-25 15:01:49

Image composition aims to seamlessly insert a user-specified object into a
new scene, but existing models struggle with complex lighting (e.g., accurate
shadows, water reflections) and diverse, high-resolution inputs. Modern
text-to-image diffusion models (e.g., SD3.5, FLUX) already encode essential
physical and resolution priors, yet lack a framework to unleash them without
resorting to latent inversion, which often locks object poses into contextually
inappropriate orientations, or brittle attention surgery. We propose SHINE, a
training-free framework for Seamless, High-fidelity Insertion with Neutralized
Errors. SHINE introduces manifold-steered anchor loss, leveraging pretrained
customization adapters (e.g., IP-Adapter) to guide latents for faithful subject
representation while preserving background integrity. Degradation-suppression
guidance and adaptive background blending are proposed to further eliminate
low-quality outputs and visible seams. To address the lack of rigorous
benchmarks, we introduce ComplexCompo, featuring diverse resolutions and
challenging conditions such as low lighting, strong illumination, intricate
shadows, and reflective surfaces. Experiments on ComplexCompo and
DreamEditBench show state-of-the-art performance on standard metrics (e.g.,
DINOv2) and human-aligned scores (e.g., DreamSim, ImageReward, VisionReward).
Code and benchmark will be publicly available upon publication.
Behind RoPE: How Does Causal Mask Encode Positional Information?
Authors: name: Junu Kim, name: Xiao Liu, name: Zhenghao Lin, name: Lei Ji, name: Yeyun Gong, name: Edward Choi
Published: 2025-09-25 11:48:24

While explicit positional encodings such as RoPE are a primary source of
positional information in Transformer decoders, the causal mask also provides
positional information. In this work, we prove that the causal mask can induce
position-dependent patterns in attention scores, even without parameters or
causal dependency in the input. Our theoretical analysis indicates that the
induced attention pattern tends to favor nearby query-key pairs, mirroring the
behavior of common positional encodings. Empirical analysis confirms that
trained models exhibit the same behavior, with learned parameters further
amplifying these patterns. Notably, we found that the interaction of causal
mask and RoPE distorts RoPE's relative attention score patterns into
non-relative ones. We consistently observed this effect in modern large
language models, suggesting the importance of considering the causal mask as a
source of positional information alongside explicit positional encodings.
The Unanticipated Asymmetry Between Perceptual Optimization and
  Assessment
Authors: name: Jiabei Zhang, name: Qi Wang, name: Siyu Wu, name: Du Chen, name: Tianhe Wu
Published: 2025-09-25 08:08:26

Perceptual optimization is primarily driven by the fidelity objective, which
enforces both semantic consistency and overall visual realism, while the
adversarial objective provides complementary refinement by enhancing perceptual
sharpness and fine-grained detail. Despite their central role, the correlation
between their effectiveness as optimization objectives and their capability as
image quality assessment (IQA) metrics remains underexplored. In this work, we
conduct a systematic analysis and reveal an unanticipated asymmetry between
perceptual optimization and assessment: fidelity metrics that excel in IQA are
not necessarily effective for perceptual optimization, with this misalignment
emerging more distinctly under adversarial training. In addition, while
discriminators effectively suppress artifacts during optimization, their
learned representations offer only limited benefits when reused as backbone
initializations for IQA models. Beyond this asymmetry, our findings further
demonstrate that discriminator design plays a decisive role in shaping
optimization, with patch-level and convolutional architectures providing more
faithful detail reconstruction than vanilla or Transformer-based alternatives.
These insights advance the understanding of loss function design and its
connection to IQA transferability, paving the way for more principled
approaches to perceptual optimization.
MOSS-ChatV: Reinforcement Learning with Process Reasoning Reward for
  Video Temporal Reasoning
Authors: name: Sicheng Tao, name: Jungang Li, name: Yibo Yan, name: Junyan Zhang, name: Yubo Gao, name: Hanqian Li, name: ShuHang Xun, name: Yuxuan Fan, name: Hong Chen, name: Jianxiang He, name: Xuming Hu
Published: 2025-09-25 12:59:13

Video reasoning has emerged as a critical capability for multimodal large
language models (MLLMs), requiring models to move beyond static perception
toward coherent understanding of temporal dynamics in complex scenes. Yet
existing MLLMs often exhibit process inconsistency, where intermediate
reasoning drifts from video dynamics even when the final answer is correct,
undermining interpretability and robustness. To address this issue, we
introduce MOSS-ChatV, a reinforcement learning framework with a Dynamic Time
Warping (DTW)-based process reward. This rule-based reward aligns reasoning
traces with temporally grounded references, enabling efficient process
supervision without auxiliary reward models. We further identify dynamic state
prediction as a key measure of video reasoning and construct MOSS-Video, a
benchmark with annotated reasoning traces, where the training split is used to
fine-tune MOSS-ChatV and the held-out split is reserved for evaluation.
MOSS-ChatV achieves 87.2\% on MOSS-Video (test) and improves performance on
general video benchmarks such as MVBench and MMVU. The framework consistently
yields gains across different architectures, including Qwen2.5-VL and Phi-2,
confirming its broad applicability. Evaluations with GPT-4o-as-judge further
show that MOSS-ChatV produces more consistent and stable reasoning traces.
VCRL: Variance-based Curriculum Reinforcement Learning for Large
  Language Models
Authors: name: Guochao Jiang, name: Wenfeng Feng, name: Guofeng Quan, name: Chuzhan Hao, name: Yuewei Zhang, name: Guohua Liu, name: Hao Wang
Published: 2025-09-24 06:38:58

Policy-based reinforcement learning currently plays an important role in
improving LLMs on mathematical reasoning tasks. However, existing rollout-based
reinforcement learning methods (GRPO, DAPO, GSPO, etc.) fail to explicitly
consider LLMs' learning ability for samples of different difficulty levels,
which is contrary to the human cognitive process of mathematical reasoning
tasks from easy to difficult. Intuitively, we find that the variance of the
rollout group's reward in RLVR partly reflects the difficulty of the current
sample for LLMs. Samples that are too easy or too difficult have a lower
variance, while samples with moderate difficulty have a higher variance. Based
on this, we propose VCRL, a curriculum reinforcement learning framework that
dynamically controls the difficulty of training samples based on the variance
of group rewards. Experiments on five mathematical benchmarks and two models
reveal the advantages of VCRL over the current LLM RL baselines.
Tree Search for LLM Agent Reinforcement Learning
Authors: name: Yuxiang Ji, name: Ziyu Ma, name: Yong Wang, name: Guanhua Chen, name: Xiangxiang Chu, name: Liaoni Wu
Published: 2025-09-25 14:37:09

Recent advances in reinforcement learning (RL) have significantly enhanced
the agentic capabilities of large language models (LLMs). In long-term and
multi-turn agent tasks, existing approaches driven solely by outcome rewards
often suffer from the problem of sparse supervision. To address the challenge,
we propose Tree-based Group Relative Policy Optimization (Tree-GRPO), a grouped
agent RL method based on tree search, where each tree node represents the
complete agent interaction step. By sharing common prefixes, the tree search
sampling increases the number of rollouts achievable within a fixed budget of
tokens or tool calls. Moreover, we find that the tree-structured trajectory
naturally allows the construction of step-wise process supervised signals even
using only the outcome reward. Based on this, Tree-GRPO estimates the grouped
relative advantages both on intra-tree and inter-tree levels. Through
theoretical analysis, we demonstrate that the objective of intra-tree level
group relative policy optimization is equivalent to that of step-level direct
preference learning. Experiments across 11 datasets and 3 types of QA tasks
demonstrate the superiority of the proposed tree-based RL over the chain-based
RL method.
CHARM: Control-point-based 3D Anime Hairstyle Auto-Regressive Modeling
Authors: name: Yuze He, name: Yanning Zhou, name: Wang Zhao, name: Jingwen Ye, name: Yushi Bai, name: Kaiwen Xiao, name: Yong-Jin Liu, name: Zhongqian Sun, name: Wei Yang
Published: 2025-09-25 13:00:38

We present CHARM, a novel parametric representation and generative framework
for anime hairstyle modeling. While traditional hair modeling methods focus on
realistic hair using strand-based or volumetric representations, anime
hairstyle exhibits highly stylized, piecewise-structured geometry that
challenges existing techniques. Existing works often rely on dense mesh
modeling or hand-crafted spline curves, making them inefficient for editing and
unsuitable for scalable learning. CHARM introduces a compact, invertible
control-point-based parameterization, where a sequence of control points
represents each hair card, and each point is encoded with only five geometric
parameters. This efficient and accurate representation supports both
artist-friendly design and learning-based generation. Built upon this
representation, CHARM introduces an autoregressive generative framework that
effectively generates anime hairstyles from input images or point clouds. By
interpreting anime hairstyles as a sequential "hair language", our
autoregressive transformer captures both local geometry and global hairstyle
topology, resulting in high-fidelity anime hairstyle creation. To facilitate
both training and evaluation of anime hairstyle generation, we construct
AnimeHair, a large-scale dataset of 37K high-quality anime hairstyles with
separated hair cards and processed mesh data. Extensive experiments demonstrate
state-of-the-art performance of CHARM in both reconstruction accuracy and
generation quality, offering an expressive and scalable solution for anime
hairstyle modeling. Project page: https://hyzcluster.github.io/charm/
Thinking Augmented Pre-training
Authors: name: Liang Wang, name: Nan Yang, name: Shaohan Huang, name: Li Dong, name: Furu Wei
Published: 2025-09-24 14:45:13

This paper introduces a simple and scalable approach to improve the data
efficiency of large language model (LLM) training by augmenting existing text
data with thinking trajectories. The compute for pre-training LLMs has been
growing at an unprecedented rate, while the availability of high-quality data
remains limited. Consequently, maximizing the utility of available data
constitutes a significant research challenge. A primary impediment is that
certain high-quality tokens are difficult to learn given a fixed model
capacity, as the underlying rationale for a single token can be exceptionally
complex and deep. To address this issue, we propose Thinking augmented
Pre-Training (TPT), a universal methodology that augments text with
automatically generated thinking trajectories. Such augmentation effectively
increases the volume of the training data and makes high-quality tokens more
learnable through step-by-step reasoning and decomposition. We apply TPT across
diverse training configurations up to 100B tokens, encompassing pre-training
with both constrained and abundant data, as well as mid-training from strong
open-source checkpoints. Experimental results indicate that our method
substantially improves the performance of LLMs across various model sizes and
families. Notably, TPT enhances the data efficiency of LLM pre-training by a
factor of 3. For a 3B parameter model, it improves the post-training
performance by over 10% on several challenging reasoning benchmarks.
ScaleDiff: Scaling Difficult Problems for Advanced Mathematical
  Reasoning
Authors: name: Qizhi Pei, name: Zhuoshi Pan, name: Honglin Lin, name: Xin Gao, name: Yu Li, name: Zinan Tang, name: Conghui He, name: Rui Yan, name: Lijun Wu
Published: 2025-09-25 12:22:44

Large Reasoning Models (LRMs) have shown impressive capabilities in complex
problem-solving, often benefiting from training on difficult mathematical
problems that stimulate intricate reasoning. Recent efforts have explored
automated synthesis of mathematical problems by prompting proprietary models or
large-scale open-source models from seed data or inherent mathematical
concepts. However, scaling up these methods remains challenging due to their
high computational/API cost, complexity of prompting, and limited difficulty
level of the generated problems. To overcome these limitations, we propose
ScaleDiff, a simple yet effective pipeline designed to scale the creation of
difficult problems. We efficiently identify difficult problems from existing
datasets with only a single forward pass using an adaptive thinking model,
which can perceive problem difficulty and automatically switch between
"Thinking" and "NoThinking" modes. We then train a specialized difficult
problem generator (DiffGen-8B) on this filtered difficult data, which can
produce new difficult problems in large scale, eliminating the need for
complex, per-instance prompting and its associated high API costs. Fine-tuning
Qwen2.5-Math-7B-Instruct on the ScaleDiff-Math dataset yields a substantial
performance increase of 11.3% compared to the original dataset and achieves a
65.9% average accuracy on AIME'24, AIME'25, HMMT-Feb'25, BRUMO'25, and MATH500,
outperforming recent strong LRMs like OpenThinker3. Notably, this performance
is achieved using the cost-efficient Qwen3-8B model as a teacher, demonstrating
that our pipeline can effectively transfer advanced reasoning capabilities
without relying on larger, more expensive teacher models. Furthermore, we
observe a clear scaling phenomenon in model performance on difficult benchmarks
as the quantity of difficult problems increases. Code:
https://github.com/QizhiPei/ScaleDiff.