Orionnnn/hub-stats huggingface dataset:
RICO: Improving Accuracy and Completeness in Image Recaptioning via
  Visual Reconstruction
Authors: name: Yuchi Wang, name: Yishuo Cai, name: Shuhuai Ren, name: Sihan Yang, name: Linli Yao, name: Yuanxin Liu, name: Yuanxing Zhang, name: Pengfei Wan, name: Xu Sun
Published: 2025-05-28 17:29:34

Image recaptioning is widely used to generate training datasets with enhanced
quality for various multimodal tasks. Existing recaptioning methods typically
rely on powerful multimodal large language models (MLLMs) to enhance textual
descriptions, but often suffer from inaccuracies due to hallucinations and
incompleteness caused by missing fine-grained details. To address these
limitations, we propose RICO, a novel framework that refines captions through
visual reconstruction. Specifically, we leverage a text-to-image model to
reconstruct a caption into a reference image, and prompt an MLLM to identify
discrepancies between the original and reconstructed images to refine the
caption. This process is performed iteratively, further progressively promoting
the generation of more faithful and comprehensive descriptions. To mitigate the
additional computational cost induced by the iterative process, we introduce
RICO-Flash, which learns to generate captions like RICO using DPO. Extensive
experiments demonstrate that our approach significantly improves caption
accuracy and completeness, outperforms most baselines by approximately 10% on
both CapsBench and CompreCap. Code released at
https://github.com/wangyuchi369/RICO.
EPiC: Efficient Video Camera Control Learning with Precise Anchor-Video
  Guidance
Authors: name: Zun Wang, name: Jaemin Cho, name: Jialu Li, name: Han Lin, name: Jaehong Yoon, name: Yue Zhang, name: Mohit Bansal
Published: 2025-05-28 01:45:26

Recent approaches on 3D camera control in video diffusion models (VDMs) often
create anchor videos to guide diffusion models as a structured prior by
rendering from estimated point clouds following annotated camera trajectories.
However, errors inherent in point cloud estimation often lead to inaccurate
anchor videos. Moreover, the requirement for extensive camera trajectory
annotations further increases resource demands. To address these limitations,
we introduce EPiC, an efficient and precise camera control learning framework
that automatically constructs high-quality anchor videos without expensive
camera trajectory annotations. Concretely, we create highly precise anchor
videos for training by masking source videos based on first-frame visibility.
This approach ensures high alignment, eliminates the need for camera trajectory
annotations, and thus can be readily applied to any in-the-wild video to
generate image-to-video (I2V) training pairs. Furthermore, we introduce
Anchor-ControlNet, a lightweight conditioning module that integrates anchor
video guidance in visible regions to pretrained VDMs, with less than 1% of
backbone model parameters. By combining the proposed anchor video data and
ControlNet module, EPiC achieves efficient training with substantially fewer
parameters, training steps, and less data, without requiring modifications to
the diffusion model backbone typically needed to mitigate rendering
misalignments. Although being trained on masking-based anchor videos, our
method generalizes robustly to anchor videos made with point clouds during
inference, enabling precise 3D-informed camera control. EPiC achieves SOTA
performance on RealEstate10K and MiraData for I2V camera control task,
demonstrating precise and robust camera control ability both quantitatively and
qualitatively. Notably, EPiC also exhibits strong zero-shot generalization to
video-to-video scenarios.
The Entropy Mechanism of Reinforcement Learning for Reasoning Language
  Models
Authors: name: Ganqu Cui, name: Yuchen Zhang, name: Jiacheng Chen, name: Lifan Yuan, name: Zhi Wang, name: Yuxin Zuo, name: Haozhan Li, name: Yuchen Fan, name: Huayu Chen, name: Weize Chen, name: Zhiyuan Liu, name: Hao Peng, name: Lei Bai, name: Wanli Ouyang, name: Yu Cheng, name: Bowen Zhou, name: Ning Ding
Published: 2025-05-28 17:38:45

This paper aims to overcome a major obstacle in scaling RL for reasoning with
LLMs, namely the collapse of policy entropy. Such phenomenon is consistently
observed across vast RL runs without entropy intervention, where the policy
entropy dropped sharply at the early training stage, this diminished
exploratory ability is always accompanied with the saturation of policy
performance. In practice, we establish a transformation equation R=-a*e^H+b
between entropy H and downstream performance R. This empirical law strongly
indicates that, the policy performance is traded from policy entropy, thus
bottlenecked by its exhaustion, and the ceiling is fully predictable H=0,
R=-a+b. Our finding necessitates entropy management for continuous exploration
toward scaling compute for RL. To this end, we investigate entropy dynamics
both theoretically and empirically. Our derivation highlights that, the change
in policy entropy is driven by the covariance between action probability and
the change in logits, which is proportional to its advantage when using Policy
Gradient-like algorithms. Empirical study shows that, the values of covariance
term and entropy differences matched exactly, supporting the theoretical
conclusion. Moreover, the covariance term stays mostly positive throughout
training, further explaining why policy entropy would decrease monotonically.
Through understanding the mechanism behind entropy dynamics, we motivate to
control entropy by restricting the update of high-covariance tokens.
Specifically, we propose two simple yet effective techniques, namely Clip-Cov
and KL-Cov, which clip and apply KL penalty to tokens with high covariances
respectively. Experiments show that these methods encourage exploration, thus
helping policy escape entropy collapse and achieve better downstream
performance.
Safe-Sora: Safe Text-to-Video Generation via Graphical Watermarking
Authors: name: Zihan Su, name: Xuerui Qiu, name: Hongbin Xu, name: Tangyu Jiang, name: Junhao Zhuang, name: Chun Yuan, name: Ming Li, name: Shengfeng He, name: Fei Richard Yu
Published: 2025-05-19 03:31:31

The explosive growth of generative video models has amplified the demand for
reliable copyright preservation of AI-generated content. Despite its popularity
in image synthesis, invisible generative watermarking remains largely
underexplored in video generation. To address this gap, we propose Safe-Sora,
the first framework to embed graphical watermarks directly into the video
generation process. Motivated by the observation that watermarking performance
is closely tied to the visual similarity between the watermark and cover
content, we introduce a hierarchical coarse-to-fine adaptive matching
mechanism. Specifically, the watermark image is divided into patches, each
assigned to the most visually similar video frame, and further localized to the
optimal spatial region for seamless embedding. To enable spatiotemporal fusion
of watermark patches across video frames, we develop a 3D wavelet
transform-enhanced Mamba architecture with a novel spatiotemporal local
scanning strategy, effectively modeling long-range dependencies during
watermark embedding and retrieval. To the best of our knowledge, this is the
first attempt to apply state space models to watermarking, opening new avenues
for efficient and robust watermark protection. Extensive experiments
demonstrate that Safe-Sora achieves state-of-the-art performance in terms of
video quality, watermark fidelity, and robustness, which is largely attributed
to our proposals. We will release our code upon publication.
GRE Suite: Geo-localization Inference via Fine-Tuned Vision-Language
  Models and Enhanced Reasoning Chains
Authors: name: Chun Wang, name: Xiaoran Pan, name: Zihao Pan, name: Haofan Wang, name: Yiren Song
Published: 2025-05-24 13:48:57

Recent advances in Visual Language Models (VLMs) have demonstrated
exceptional performance in visual reasoning tasks. However, geo-localization
presents unique challenges, requiring the extraction of multigranular visual
cues from images and their integration with external world knowledge for
systematic reasoning. Current approaches to geo-localization tasks often lack
robust reasoning mechanisms and explainability, limiting their effectiveness.
To address these limitations, we propose the Geo Reason Enhancement (GRE)
Suite, a novel framework that augments VLMs with structured reasoning chains
for accurate and interpretable location inference. The GRE Suite is
systematically developed across three key dimensions: dataset, model, and
benchmark. First, we introduce GRE30K, a high-quality geo-localization
reasoning dataset designed to facilitate fine-grained visual and contextual
analysis. Next, we present the GRE model, which employs a multi-stage reasoning
strategy to progressively infer scene attributes, local details, and semantic
features, thereby narrowing down potential geographic regions with enhanced
precision. Finally, we construct the Geo Reason Evaluation Benchmark
(GREval-Bench), a comprehensive evaluation framework that assesses VLMs across
diverse urban, natural, and landmark scenes to measure both coarse-grained
(e.g., country, continent) and fine-grained (e.g., city, street) localization
performance. Experimental results demonstrate that GRE significantly
outperforms existing methods across all granularities of geo-localization
tasks, underscoring the efficacy of reasoning-augmented VLMs in complex
geographic inference. Code and data will be released at
https://github.com/Thorin215/GRE.
SageAttention2++: A More Efficient Implementation of SageAttention2
Authors: name: Jintao Zhang, name: Xiaoming Xu, name: Jia Wei, name: Haofeng Huang, name: Pengle Zhang, name: Chendong Xiang, name: Jun Zhu, name: Jianfei Chen
Published: 2025-05-27 12:50:36

The efficiency of attention is critical because its time complexity grows
quadratically with sequence length. SageAttention2 addresses this by utilizing
quantization to accelerate matrix multiplications (Matmul) in attention. To
further accelerate SageAttention2, we propose to utilize the faster instruction
of FP8 Matmul accumulated in FP16. The instruction is 2x faster than the FP8
Matmul used in SageAttention2. Our experiments show that SageAttention2++
achieves a 3.9x speedup over FlashAttention while maintaining the same
attention accuracy as SageAttention2. This means SageAttention2++ effectively
accelerates various models, including those for language, image, and video
generation, with negligible end-to-end metrics loss. The code will be available
at https://github.com/thu-ml/SageAttention.
Towards Dynamic Theory of Mind: Evaluating LLM Adaptation to Temporal
  Evolution of Human States
Authors: name: Yang Xiao, name: Jiashuo Wang, name: Qiancheng Xu, name: Changhe Song, name: Chunpu Xu, name: Yi Cheng, name: Wenjie Li, name: Pengfei Liu
Published: 2025-05-23 09:27:40

As Large Language Models (LLMs) increasingly participate in human-AI
interactions, evaluating their Theory of Mind (ToM) capabilities - particularly
their ability to track dynamic mental states - becomes crucial. While existing
benchmarks assess basic ToM abilities, they predominantly focus on static
snapshots of mental states, overlooking the temporal evolution that
characterizes real-world social interactions. We present DynToM, a
novel benchmark specifically designed to evaluate LLMs' ability to understand
and track the temporal progression of mental states across interconnected
scenarios. Through a systematic four-step framework, we generate 1,100 social
contexts encompassing 5,500 scenarios and 78,100 questions, each validated for
realism and quality. Our comprehensive evaluation of ten state-of-the-art LLMs
reveals that their average performance underperforms humans by 44.7\%, with
performance degrading significantly when tracking and reasoning about the shift
of mental states. This performance gap highlights fundamental limitations in
current LLMs' ability to model the dynamic nature of human mental states.
LIMOPro: Reasoning Refinement for Efficient and Effective Test-time
  Scaling
Authors: name: Yang Xiao, name: Jiashuo Wang, name: Ruifeng Yuan, name: Chunpu Xu, name: Kaishuai Xu, name: Wenjie Li, name: Pengfei Liu
Published: 2025-05-25 15:17:57

Large language models (LLMs) have demonstrated remarkable reasoning
capabilities through test-time scaling approaches, particularly when fine-tuned
with chain-of-thought (CoT) data distilled from more powerful large reasoning
models (LRMs). However, these reasoning chains often contain verbose elements
that mirror human problem-solving, categorized as progressive reasoning (the
essential solution development path) and functional elements (verification
processes, alternative solution approaches, and error corrections). While
progressive reasoning is crucial, the functional elements significantly
increase computational demands during test-time inference. We introduce PIR
(Perplexity-based Importance Refinement), a principled framework that
quantitatively evaluates the importance of each reasoning step based on its
impact on answer prediction confidence. PIR systematically identifies and
selectively prunes only low-importance functional steps while preserving
progressive reasoning components, creating optimized training data that
maintains the integrity of the core solution path while reducing verbosity.
Models fine-tuned on PIR-optimized data exhibit superior test-time scaling
properties, generating more concise reasoning chains while achieving improved
accuracy (+0.9\% to +6.6\%) with significantly reduced token usage (-3\% to
-41\%) across challenging reasoning benchmarks (AIME, AMC, and GPQA Diamond).
Our approach demonstrates strong generalizability across different model sizes,
data sources, and token budgets, offering a practical solution for deploying
reasoning-capable LLMs in scenarios where efficient test-time scaling, response
time, and computational efficiency are valuable constraints.
Text2Grad: Reinforcement Learning from Natural Language Feedback
Authors: name: Hanyang Wang, name: Lu Wang, name: Chaoyun Zhang, name: Tianjun Mao, name: Si Qin, name: Qingwei Lin, name: Saravan Rajmohan, name: Dongmei Zhang
Published: 2025-05-28 13:23:49

Traditional RLHF optimizes language models with coarse, scalar rewards that
mask the fine-grained reasons behind success or failure, leading to slow and
opaque learning. Recent work augments RL with textual critiques through
prompting or reflection, improving interpretability but leaving model
parameters untouched. We introduce Text2Grad, a reinforcement-learning paradigm
that turns free-form textual feedback into span-level gradients. Given human
(or programmatic) critiques, Text2Grad aligns each feedback phrase with the
relevant token spans, converts these alignments into differentiable reward
signals, and performs gradient updates that directly refine the offending
portions of the model's policy. This yields precise, feedback-conditioned
adjustments instead of global nudges. Text2Grad is realized through three
components: (1) a high-quality feedback-annotation pipeline that pairs
critiques with token spans; (2) a fine-grained reward model that predicts
span-level reward on answer while generating explanatory critiques; and (3) a
span-level policy optimizer that back-propagates natural-language gradients.
Across summarization, code generation, and question answering, Text2Grad
consistently surpasses scalar-reward RL and prompt-only baselines, providing
both higher task metrics and richer interpretability. Our results demonstrate
that natural-language feedback, when converted to gradients, is a powerful
signal for fine-grained policy optimization. The code for our method is
available at https://github.com/microsoft/Text2Grad
Benchmarking Recommendation, Classification, and Tracing Based on
  Hugging Face Knowledge Graph
Authors: name: Qiaosheng Chen, name: Kaijia Huang, name: Xiao Zhou, name: Weiqing Luo, name: Yuanning Cui, name: Gong Cheng
Published: 2025-05-23 06:00:20

The rapid growth of open source machine learning (ML) resources, such as
models and datasets, has accelerated IR research. However, existing platforms
like Hugging Face do not explicitly utilize structured representations,
limiting advanced queries and analyses such as tracing model evolution and
recommending relevant datasets. To fill the gap, we construct HuggingKG, the
first large-scale knowledge graph built from the Hugging Face community for ML
resource management. With 2.6 million nodes and 6.2 million edges, HuggingKG
captures domain-specific relations and rich textual attributes. It enables us
to further present HuggingBench, a multi-task benchmark with three novel test
collections for IR tasks including resource recommendation, classification, and
tracing. Our experiments reveal unique characteristics of HuggingKG and the
derived tasks. Both resources are publicly available, expected to advance
research in open source resource sharing and management.
Skywork Open Reasoner 1 Technical Report
Authors: name: Jujie He, name: Jiacai Liu, name: Chris Yuhao Liu, name: Rui Yan, name: Chaojie Wang, name: Peng Cheng, name: Xiaoyu Zhang, name: Fuxiang Zhang, name: Jiacheng Xu, name: Wei Shen, name: Siyuan Li, name: Liang Zeng, name: Tianwen Wei, name: Cheng Cheng, name: Bo An, name: Yang Liu, name: Yahui Zhou
Published: 2025-05-28 12:56:04

The success of DeepSeek-R1 underscores the significant role of reinforcement
learning (RL) in enhancing the reasoning capabilities of large language models
(LLMs). In this work, we present Skywork-OR1, an effective and scalable RL
implementation for long Chain-of-Thought (CoT) models. Building on the
DeepSeek-R1-Distill model series, our RL approach achieves notable performance
gains, increasing average accuracy across AIME24, AIME25, and LiveCodeBench
from 57.8% to 72.8% (+15.0%) for the 32B model and from 43.6% to 57.5% (+13.9%)
for the 7B model. Our Skywork-OR1-32B model surpasses both DeepSeek-R1 and
Qwen3-32B on the AIME24 and AIME25 benchmarks, while achieving comparable
results on LiveCodeBench. The Skywork-OR1-7B and Skywork-OR1-Math-7B models
demonstrate competitive reasoning capabilities among models of similar size. We
perform comprehensive ablation studies on the core components of our training
pipeline to validate their effectiveness. Additionally, we thoroughly
investigate the phenomenon of entropy collapse, identify key factors affecting
entropy dynamics, and demonstrate that mitigating premature entropy collapse is
critical for improved test performance. To support community research, we fully
open-source our model weights, training code, and training datasets.
Advancing Multimodal Reasoning via Reinforcement Learning with Cold
  Start
Authors: name: Lai Wei, name: Yuting Li, name: Kaipeng Zheng, name: Chen Wang, name: Yue Wang, name: Linghe Kong, name: Lichao Sun, name: Weiran Huang
Published: 2025-05-28 13:21:38

Recent advancements in large language models (LLMs) have demonstrated
impressive chain-of-thought reasoning capabilities, with reinforcement learning
(RL) playing a crucial role in this progress. While "aha moment"
patterns--where models exhibit self-correction through reflection--are often
attributed to emergent properties from RL, we first demonstrate that these
patterns exist in multimodal LLMs (MLLMs) prior to RL training but may not
necessarily correlate with improved reasoning performance. Building on these
insights, we present a comprehensive study on enhancing multimodal reasoning
through a two-stage approach: (1) supervised fine-tuning (SFT) as a cold start
with structured chain-of-thought reasoning patterns, followed by (2)
reinforcement learning via GRPO to further refine these capabilities. Our
extensive experiments show that this combined approach consistently outperforms
both SFT-only and RL-only methods across challenging multimodal reasoning
benchmarks. The resulting models achieve state-of-the-art performance among
open-source MLLMs at both 3B and 7B scales, with our 7B model showing
substantial improvements over base models (e.g., 66.3 %rightarrow73.4 % on
MathVista, 62.9 %rightarrow70.4 % on We-Math) and our 3B model achieving
performance competitive with several 7B models. Overall, this work provides
practical guidance for building advanced multimodal reasoning models. Our code
is available at https://github.com/waltonfuture/RL-with-Cold-Start.
Unsupervised Post-Training for Multi-Modal LLM Reasoning via GRPO
Authors: name: Lai Wei, name: Yuting Li, name: Chen Wang, name: Yue Wang, name: Linghe Kong, name: Weiran Huang, name: Lichao Sun
Published: 2025-05-28 15:11:16

Improving Multi-modal Large Language Models (MLLMs) in the post-training
stage typically relies on supervised fine-tuning (SFT) or reinforcement
learning (RL). However, these supervised methods require expensive and manually
annotated multi-modal data--an ultimately unsustainable resource. While recent
efforts have explored unsupervised post-training, their methods are complex and
difficult to iterate. In this work, we are the first to investigate the use of
GRPO, a stable and scalable online RL algorithm, for enabling continual
self-improvement without any external supervision. We propose MM-UPT, a simple
yet effective framework for unsupervised post-training of MLLMs. MM-UPT builds
upon GRPO, replacing traditional reward signals with a self-rewarding mechanism
based on majority voting over multiple sampled responses. Our experiments
demonstrate that MM-UPT significantly improves the reasoning ability of
Qwen2.5-VL-7B (e.g., 66.3 %rightarrow72.9 % on MathVista, 62.9
%rightarrow68.7 % on We-Math), using standard dataset without ground truth
labels. MM-UPT also outperforms prior unsupervised baselines and even
approaches the results of supervised GRPO. Furthermore, we show that
incorporating synthetic questions, generated solely by MLLM itself, can boost
performance as well, highlighting a promising approach for scalable
self-improvement. Overall, MM-UPT offers a new paradigm for continual,
autonomous enhancement of MLLMs in the absence of external supervision. Our
code is available at https://github.com/waltonfuture/MM-UPT.
RenderFormer: Transformer-based Neural Rendering of Triangle Meshes with
  Global Illumination
Authors: name: Chong Zeng, name: Yue Dong, name: Pieter Peers, name: Hongzhi Wu, name: Xin Tong
Published: 2025-05-28 03:20:46

We present RenderFormer, a neural rendering pipeline that directly renders an
image from a triangle-based representation of a scene with full global
illumination effects and that does not require per-scene training or
fine-tuning. Instead of taking a physics-centric approach to rendering, we
formulate rendering as a sequence-to-sequence transformation where a sequence
of tokens representing triangles with reflectance properties is converted to a
sequence of output tokens representing small patches of pixels. RenderFormer
follows a two stage pipeline: a view-independent stage that models
triangle-to-triangle light transport, and a view-dependent stage that
transforms a token representing a bundle of rays to the corresponding pixel
values guided by the triangle-sequence from the view-independent stage. Both
stages are based on the transformer architecture and are learned with minimal
prior constraints. We demonstrate and evaluate RenderFormer on scenes with
varying complexity in shape and light transport.