Orionnnn/hub-stats huggingface dataset:
Gradient-Attention Guided Dual-Masking Synergetic Framework for Robust
  Text-based Person Retrieval
Authors: name: Tianlu Zheng, name: Yifan Zhang, name: Xiang An, name: Ziyong Feng, name: Kaicheng Yang, name: Qichuan Ding
Published: 2025-09-11 03:06:22

Although Contrastive Language-Image Pre-training (CLIP) exhibits strong
performance across diverse vision tasks, its application to person
representation learning faces two critical challenges: (i) the scarcity of
large-scale annotated vision-language data focused on person-centric images,
and (ii) the inherent limitations of global contrastive learning, which
struggles to maintain discriminative local features crucial for fine-grained
matching while remaining vulnerable to noisy text tokens. This work advances
CLIP for person representation learning through synergistic improvements in
data curation and model architecture. First, we develop a noise-resistant data
construction pipeline that leverages the in-context learning capabilities of
MLLMs to automatically filter and caption web-sourced images. This yields
WebPerson, a large-scale dataset of 5M high-quality person-centric image-text
pairs. Second, we introduce the GA-DMS (Gradient-Attention Guided Dual-Masking
Synergetic) framework, which improves cross-modal alignment by adaptively
masking noisy textual tokens based on the gradient-attention similarity score.
Additionally, we incorporate masked token prediction objectives that compel the
model to predict informative text tokens, enhancing fine-grained semantic
representation learning. Extensive experiments show that GA-DMS achieves
state-of-the-art performance across multiple benchmarks.
OmniEVA: Embodied Versatile Planner via Task-Adaptive 3D-Grounded and
  Embodiment-aware Reasoning
Authors: name: Yuecheng Liu, name: Dafeng Chi, name: Shiguang Wu, name: Zhanguang Zhang, name: Yuzheng Zhuang, name: Bowen Yang, name: He Zhu, name: Lingfeng Zhang, name: Pengwei Xie, name: David Gamaliel Arcos Bravo, name: Yingxue Zhang, name: Jianye Hao, name: Xingyue Quan
Published: 2025-09-11 10:32:22

Recent advances in multimodal large language models (MLLMs) have opened new
opportunities for embodied intelligence, enabling multimodal understanding,
reasoning, and interaction, as well as continuous spatial decision-making.
Nevertheless, current MLLM-based embodied systems face two critical
limitations. First, Geometric Adaptability Gap: models trained solely on 2D
inputs or with hard-coded 3D geometry injection suffer from either insufficient
spatial information or restricted 2D generalization, leading to poor
adaptability across tasks with diverse spatial demands. Second, Embodiment
Constraint Gap: prior work often neglects the physical constraints and
capacities of real robots, resulting in task plans that are theoretically valid
but practically infeasible.To address these gaps, we introduce OmniEVA -- an
embodied versatile planner that enables advanced embodied reasoning and task
planning through two pivotal innovations: (1) a Task-Adaptive 3D Grounding
mechanism, which introduces a gated router to perform explicit selective
regulation of 3D fusion based on contextual requirements, enabling
context-aware 3D grounding for diverse embodied tasks. (2) an Embodiment-Aware
Reasoning framework that jointly incorporates task goals and embodiment
constraints into the reasoning loop, resulting in planning decisions that are
both goal-directed and executable. Extensive experimental results demonstrate
that OmniEVA not only achieves state-of-the-art general embodied reasoning
performance, but also exhibits a strong ability across a wide range of
downstream scenarios. Evaluations of a suite of proposed embodied benchmarks,
including both primitive and composite tasks, confirm its robust and versatile
planning capabilities. Project page: https://omnieva.github.io
EchoX: Towards Mitigating Acoustic-Semantic Gap via Echo Training for
  Speech-to-Speech LLMs
Authors: name: Yuhao Zhang, name: Yuhao Du, name: Zhanchen Dai, name: Xiangnan Ma, name: Kaiqi Kou, name: Benyou Wang, name: Haizhou Li
Published: 2025-09-11 06:17:59

Speech-to-speech large language models (SLLMs) are attracting increasing
attention. Derived from text-based large language models (LLMs), SLLMs often
exhibit degradation in knowledge and reasoning capabilities. We hypothesize
that this limitation arises because current training paradigms for SLLMs fail
to bridge the acoustic-semantic gap in the feature representation space. To
address this issue, we propose EchoX, which leverages semantic representations
and dynamically generates speech training targets. This approach integrates
both acoustic and semantic learning, enabling EchoX to preserve strong
reasoning abilities as a speech LLM. Experimental results demonstrate that
EchoX, with about six thousand hours of training data, achieves advanced
performance on multiple knowledge-based question-answering benchmarks. The
project is available at https://github.com/FreedomIntelligence/EchoX.
LoCoBench: A Benchmark for Long-Context Large Language Models in Complex
  Software Engineering
Authors: name: Jielin Qiu, name: Zuxin Liu, name: Zhiwei Liu, name: Rithesh Murthy, name: Jianguo Zhang, name: Haolin Chen, name: Shiyu Wang, name: Ming Zhu, name: Liangwei Yang, name: Juntao Tan, name: Zhepeng Cen, name: Cheng Qian, name: Shelby Heinecke, name: Weiran Yao, name: Silvio Savarese, name: Caiming Xiong, name: Huan Wang
Published: 2025-09-11 16:55:04

The emergence of long-context language models with context windows extending
to millions of tokens has created new opportunities for sophisticated code
understanding and software development evaluation. We propose LoCoBench, a
comprehensive benchmark specifically designed to evaluate long-context LLMs in
realistic, complex software development scenarios. Unlike existing code
evaluation benchmarks that focus on single-function completion or short-context
tasks, LoCoBench addresses the critical evaluation gap for long-context
capabilities that require understanding entire codebases, reasoning across
multiple files, and maintaining architectural consistency across large-scale
software systems. Our benchmark provides 8,000 evaluation scenarios
systematically generated across 10 programming languages, with context lengths
spanning 10K to 1M tokens, a 100x variation that enables precise assessment of
long-context performance degradation in realistic software development
settings. LoCoBench introduces 8 task categories that capture essential
long-context capabilities: architectural understanding, cross-file refactoring,
multi-session development, bug investigation, feature implementation, code
comprehension, integration testing, and security analysis. Through a 5-phase
pipeline, we create diverse, high-quality scenarios that challenge LLMs to
reason about complex codebases at unprecedented scale. We introduce a
comprehensive evaluation framework with 17 metrics across 4 dimensions,
including 8 new evaluation metrics, combined in a LoCoBench Score (LCBS). Our
evaluation of state-of-the-art long-context models reveals substantial
performance gaps, demonstrating that long-context understanding in complex
software development represents a significant unsolved challenge that demands
more attention. LoCoBench is released at:
https://github.com/SalesforceAIResearch/LoCoBench.
Harnessing Uncertainty: Entropy-Modulated Policy Gradients for
  Long-Horizon LLM Agents
Authors: name: Jiawei Wang, name: Jiacai Liu, name: Yuqian Fu, name: Yingru Li, name: Xintao Wang, name: Yuan Lin, name: Yu Yue, name: Lin Zhang, name: Yang Wang, name: Ke Wang
Published: 2025-09-11 08:50:01

In long-horizon tasks, recent agents based on Large Language Models (LLMs)
face a significant challenge that sparse, outcome-based rewards make it
difficult to assign credit to intermediate steps. Previous methods mainly focus
on creating dense reward signals to guide learning, either through traditional
reinforcement learning techniques like inverse reinforcement learning or by
using Process Reward Models for step-by-step feedback. In this paper, we
identify a fundamental problem in the learning dynamics of LLMs: the magnitude
of policy gradients is inherently coupled with the entropy, which leads to
inefficient small updates for confident correct actions and potentially
destabilizes large updates for uncertain ones. To resolve this, we propose
Entropy-Modulated Policy Gradients (EMPG), a framework that re-calibrates the
learning signal based on step-wise uncertainty and the final task outcome. EMPG
amplifies updates for confident correct actions, penalizes confident errors,
and attenuates updates from uncertain steps to stabilize exploration. We
further introduce a bonus term for future clarity that encourages agents to
find more predictable solution paths. Through comprehensive experiments on
three challenging agent tasks, WebShop, ALFWorld, and Deep Search, we
demonstrate that EMPG achieves substantial performance gains and significantly
outperforms strong policy gradient baselines. Project page is at
https://empgseed-seed.github.io/
SimpleVLA-RL: Scaling VLA Training via Reinforcement Learning
Authors: name: Haozhan Li, name: Yuxin Zuo, name: Jiale Yu, name: Yuhao Zhang, name: Zhaohui Yang, name: Kaiyan Zhang, name: Xuekai Zhu, name: Yuchen Zhang, name: Tianxing Chen, name: Ganqu Cui, name: Dehui Wang, name: Dingxiang Luo, name: Yuchen Fan, name: Youbang Sun, name: Jia Zeng, name: Jiangmiao Pang, name: Shanghang Zhang, name: Yu Wang, name: Yao Mu, name: Bowen Zhou, name: Ning Ding
Published: 2025-09-11 17:59:17

Vision-Language-Action (VLA) models have recently emerged as a powerful
paradigm for robotic manipulation. Despite substantial progress enabled by
large-scale pretraining and supervised fine-tuning (SFT), these models face two
fundamental challenges: (i) the scarcity and high cost of large-scale
human-operated robotic trajectories required for SFT scaling, and (ii) limited
generalization to tasks involving distribution shift. Recent breakthroughs in
Large Reasoning Models (LRMs) demonstrate that reinforcement learning (RL) can
dramatically enhance step-by-step reasoning capabilities, raising a natural
question: Can RL similarly improve the long-horizon step-by-step action
planning of VLA? In this work, we introduce SimpleVLA-RL, an efficient RL
framework tailored for VLA models. Building upon veRL, we introduce
VLA-specific trajectory sampling, scalable parallelization, multi-environment
rendering, and optimized loss computation. When applied to OpenVLA-OFT,
SimpleVLA-RL achieves SoTA performance on LIBERO and even outperforms pi_0
on RoboTwin 1.0\&2.0 with the exploration-enhancing strategies we introduce.
SimpleVLA-RL not only reduces dependence on large-scale data and enables robust
generalization, but also remarkably surpasses SFT in real-world tasks.
Moreover, we identify a novel phenomenon ``pushcut'' during RL training,
wherein the policy discovers previously unseen patterns beyond those seen in
the previous training process. Github: https://github.com/PRIME-RL/SimpleVLA-RL
Kling-Avatar: Grounding Multimodal Instructions for Cascaded
  Long-Duration Avatar Animation Synthesis
Authors: name: Yikang Ding, name: Jiwen Liu, name: Wenyuan Zhang, name: Zekun Wang, name: Wentao Hu, name: Liyuan Cui, name: Mingming Lao, name: Yingchao Shao, name: Hui Liu, name: Xiaohan Li, name: Ming Chen, name: Xiaoqiang Liu, name: Yu-Shen Liu, name: Pengfei Wan
Published: 2025-09-11 16:34:57

Recent advances in audio-driven avatar video generation have significantly
enhanced audio-visual realism. However, existing methods treat instruction
conditioning merely as low-level tracking driven by acoustic or visual cues,
without modeling the communicative purpose conveyed by the instructions. This
limitation compromises their narrative coherence and character expressiveness.
To bridge this gap, we introduce Kling-Avatar, a novel cascaded framework that
unifies multimodal instruction understanding with photorealistic portrait
generation. Our approach adopts a two-stage pipeline. In the first stage, we
design a multimodal large language model (MLLM) director that produces a
blueprint video conditioned on diverse instruction signals, thereby governing
high-level semantics such as character motion and emotions. In the second
stage, guided by blueprint keyframes, we generate multiple sub-clips in
parallel using a first-last frame strategy. This global-to-local framework
preserves fine-grained details while faithfully encoding the high-level intent
behind multimodal instructions. Our parallel architecture also enables fast and
stable generation of long-duration videos, making it suitable for real-world
applications such as digital human livestreaming and vlogging. To
comprehensively evaluate our method, we construct a benchmark of 375 curated
samples covering diverse instructions and challenging scenarios. Extensive
experiments demonstrate that Kling-Avatar is capable of generating vivid,
fluent, long-duration videos at up to 1080p and 48 fps, achieving superior
performance in lip synchronization accuracy, emotion and dynamic
expressiveness, instruction controllability, identity preservation, and
cross-domain generalization. These results establish Kling-Avatar as a new
benchmark for semantically grounded, high-fidelity audio-driven avatar
synthesis.
SpatialVID: A Large-Scale Video Dataset with Spatial Annotations
Authors: name: Jiahao Wang, name: Yufeng Yuan, name: Rujie Zheng, name: Youtian Lin, name: Jian Gao, name: Lin-Zhuo Chen, name: Yajie Bao, name: Yi Zhang, name: Chang Zeng, name: Yanxi Zhou, name: Xiaoxiao Long, name: Hao Zhu, name: Zhaoxiang Zhang, name: Xun Cao, name: Yao Yao
Published: 2025-09-11 17:59:31

Significant progress has been made in spatial intelligence, spanning both
spatial reconstruction and world exploration. However, the scalability and
real-world fidelity of current models remain severely constrained by the
scarcity of large-scale, high-quality training data. While several datasets
provide camera pose information, they are typically limited in scale,
diversity, and annotation richness, particularly for real-world dynamic scenes
with ground-truth camera motion. To this end, we collect SpatialVID, a
dataset consists of a large corpus of in-the-wild videos with diverse scenes,
camera movements and dense 3D annotations such as per-frame camera poses,
depth, and motion instructions. Specifically, we collect more than 21,000 hours
of raw video, and process them into 2.7 million clips through a hierarchical
filtering pipeline, totaling 7,089 hours of dynamic content. A subsequent
annotation pipeline enriches these clips with detailed spatial and semantic
information, including camera poses, depth maps, dynamic masks, structured
captions, and serialized motion instructions. Analysis of SpatialVID's data
statistics reveals a richness and diversity that directly foster improved model
generalization and performance, establishing it as a key asset for the video
and 3D vision research community.
FLUX-Reason-6M & PRISM-Bench: A Million-Scale Text-to-Image Reasoning
  Dataset and Comprehensive Benchmark
Authors: name: Rongyao Fang, name: Aldrich Yu, name: Chengqi Duan, name: Linjiang Huang, name: Shuai Bai, name: Yuxuan Cai, name: Kun Wang, name: Si Liu, name: Xihui Liu, name: Hongsheng Li
Published: 2025-09-11 17:59:59

The advancement of open-source text-to-image (T2I) models has been hindered
by the absence of large-scale, reasoning-focused datasets and comprehensive
evaluation benchmarks, resulting in a performance gap compared to leading
closed-source systems. To address this challenge, We introduce FLUX-Reason-6M
and PRISM-Bench (Precise and Robust Image Synthesis Measurement Benchmark).
FLUX-Reason-6M is a massive dataset consisting of 6 million high-quality
FLUX-generated images and 20 million bilingual (English and Chinese)
descriptions specifically designed to teach complex reasoning. The image are
organized according to six key characteristics: Imagination, Entity, Text
rendering, Style, Affection, and Composition, and design explicit Generation
Chain-of-Thought (GCoT) to provide detailed breakdowns of image generation
steps. The whole data curation takes 15,000 A100 GPU days, providing the
community with a resource previously unattainable outside of large industrial
labs. PRISM-Bench offers a novel evaluation standard with seven distinct
tracks, including a formidable Long Text challenge using GCoT. Through
carefully designed prompts, it utilizes advanced vision-language models for
nuanced human-aligned assessment of prompt-image alignment and image
aesthetics. Our extensive evaluation of 19 leading models on PRISM-Bench
reveals critical performance gaps and highlights specific areas requiring
improvement. Our dataset, benchmark, and evaluation code are released to
catalyze the next wave of reasoning-oriented T2I generation. Project page:
https://flux-reason-6m.github.io/ .
CDE: Curiosity-Driven Exploration for Efficient Reinforcement Learning
  in Large Language Models
Authors: name: Runpeng Dai, name: Linfeng Song, name: Haolin Liu, name: Zhenwen Liang, name: Dian Yu, name: Haitao Mi, name: Zhaopeng Tu, name: Rui Liu, name: Tong Zheng, name: Hongtu Zhu, name: Dong Yu
Published: 2025-09-11 17:59:17

Reinforcement Learning with Verifiable Rewards (RLVR) is a powerful paradigm
for enhancing the reasoning ability of Large Language Models (LLMs). Yet
current RLVR methods often explore poorly, leading to premature convergence and
entropy collapse. To address this challenge, we introduce Curiosity-Driven
Exploration (CDE), a framework that leverages the model's own intrinsic sense
of curiosity to guide exploration. We formalize curiosity with signals from
both the actor and the critic: for the actor, we use perplexity over its
generated response, and for the critic, we use the variance of value estimates
from a multi-head architecture. Both signals serve as an exploration bonus
within the RLVR framework to guide the model. Our theoretical analysis shows
that the actor-wise bonus inherently penalizes overconfident errors and
promotes diversity among correct responses; moreover, we connect the
critic-wise bonus to the well-established count-based exploration bonus in RL.
Empirically, our method achieves an approximate +3 point improvement over
standard RLVR using GRPO/PPO on AIME benchmarks. Further analysis identifies a
calibration collapse mechanism within RLVR, shedding light on common LLM
failure modes.
Statistical Methods in Generative AI
Authors: name: Edgar Dobriban
Published: 2025-09-08 17:42:59

Generative Artificial Intelligence is emerging as an important technology,
promising to be transformative in many areas. At the same time, generative AI
techniques are based on sampling from probabilistic models, and by default,
they come with no guarantees about correctness, safety, fairness, or other
properties. Statistical methods offer a promising potential approach to improve
the reliability of generative AI techniques. In addition, statistical methods
are also promising for improving the quality and efficiency of AI evaluation,
as well as for designing interventions and experiments in AI.
  In this paper, we review some of the existing work on these topics,
explaining both the general statistical techniques used, as well as their
applications to generative AI. We also discuss limitations and potential future
directions.
The Majority is not always right: RL training for solution aggregation
Authors: name: Wenting Zhao, name: Pranjal Aggarwal, name: Swarnadeep Saha, name: Asli Celikyilmaz, name: Jason Weston, name: Ilia Kulikov
Published: 2025-09-08 16:39:38

Scaling up test-time compute, by generating multiple independent solutions
and selecting or aggregating among them, has become a central paradigm for
improving large language models (LLMs) on challenging reasoning tasks. While
most prior work relies on simple majority voting or reward model ranking to
aggregate solutions, these approaches may only yield limited benefits. In this
work, we propose to learn aggregation as an explicit reasoning skill: given a
set of candidate solutions, we train an aggregator model to review, reconcile,
and synthesize a final, correct answer using reinforcement learning from
verifiable rewards. A key ingredient is careful balancing of easy and hard
training examples, allowing the model to learn both to recover
minority-but-correct answers as well as easy majority-correct answers.
Empirically, we find our method, AggLM, outperforms both strong rule-based and
reward-model baselines, across multiple benchmarks. Furthermore, it generalizes
effectively to solutions from differing models, including stronger ones than
contained in the training data, all while requiring substantially fewer tokens
than majority voting with larger numbers of solutions.
<think> So let's replace this phrase with insult... </think> Lessons
  learned from generation of toxic texts with LLMs
Authors: name: Sergey Pletenev, name: Daniil Moskovskiy, name: Alexander Panchenko
Published: 2025-09-10 07:48:24

Modern Large Language Models (LLMs) are excellent at generating synthetic
data. However, their performance in sensitive domains such as text
detoxification has not received proper attention from the scientific community.
This paper explores the possibility of using LLM-generated synthetic toxic data
as an alternative to human-generated data for training models for
detoxification. Using Llama 3 and Qwen activation-patched models, we generated
synthetic toxic counterparts for neutral texts from ParaDetox and SST-2
datasets. Our experiments show that models fine-tuned on synthetic data
consistently perform worse than those trained on human data, with a drop in
performance of up to 30% in joint metrics. The root cause is identified as a
critical lexical diversity gap: LLMs generate toxic content using a small,
repetitive vocabulary of insults that fails to capture the nuances and variety
of human toxicity. These findings highlight the limitations of current LLMs in
this domain and emphasize the continued importance of diverse, human-annotated
data for building robust detoxification systems.
3D and 4D World Modeling: A Survey
Authors: name: Lingdong Kong, name: Wesley Yang, name: Jianbiao Mei, name: Youquan Liu, name: Ao Liang, name: Dekai Zhu, name: Dongyue Lu, name: Wei Yin, name: Xiaotao Hu, name: Mingkai Jia, name: Junyuan Deng, name: Kaiwen Zhang, name: Yang Wu, name: Tianyi Yan, name: Shenyuan Gao, name: Song Wang, name: Linfeng Li, name: Liang Pan, name: Yong Liu, name: Jianke Zhu, name: Wei Tsang Ooi, name: Steven C. H. Hoi, name: Ziwei Liu
Published: 2025-09-04 17:59:58

World modeling has become a cornerstone in AI research, enabling agents to
understand, represent, and predict the dynamic environments they inhabit. While
prior work largely emphasizes generative methods for 2D image and video data,
they overlook the rapidly growing body of work that leverages native 3D and 4D
representations such as RGB-D imagery, occupancy grids, and LiDAR point clouds
for large-scale scene modeling. At the same time, the absence of a standardized
definition and taxonomy for ``world models'' has led to fragmented and
sometimes inconsistent claims in the literature. This survey addresses these
gaps by presenting the first comprehensive review explicitly dedicated to 3D
and 4D world modeling and generation. We establish precise definitions,
introduce a structured taxonomy spanning video-based (VideoGen),
occupancy-based (OccGen), and LiDAR-based (LiDARGen) approaches, and
systematically summarize datasets and evaluation metrics tailored to 3D/4D
settings. We further discuss practical applications, identify open challenges,
and highlight promising research directions, aiming to provide a coherent and
foundational reference for advancing the field. A systematic summary of
existing literature is available at https://github.com/worldbench/survey