Orionnnn/hub-stats huggingface dataset:
Breaking the Modality Barrier: Universal Embedding Learning with
  Multimodal LLMs
Authors: name: Tiancheng Gu, name: Kaicheng Yang, name: Ziyong Feng, name: Xingjun Wang, name: Yanzhao Zhang, name: Dingkun Long, name: Yingda Chen, name: Weidong Cai, name: Jiankang Deng
Published: 2025-04-24 10:51:52

The Contrastive Language-Image Pre-training (CLIP) framework has become a
widely used approach for multimodal representation learning, particularly in
image-text retrieval and clustering. However, its efficacy is constrained by
three key limitations: (1) text token truncation, (2) isolated image-text
encoding, and (3) deficient compositionality due to bag-of-words behavior.
While recent Multimodal Large Language Models (MLLMs) have demonstrated
significant advances in generalized vision-language understanding, their
potential for learning transferable multimodal representations remains
underexplored.In this work, we present UniME (Universal Multimodal Embedding),
a novel two-stage framework that leverages MLLMs to learn discriminative
representations for diverse downstream tasks. In the first stage, we perform
textual discriminative knowledge distillation from a powerful LLM-based teacher
model to enhance the embedding capability of the MLLM\'s language component. In
the second stage, we introduce hard negative enhanced instruction tuning to
further advance discriminative representation learning. Specifically, we
initially mitigate false negative contamination and then sample multiple hard
negatives per instance within each batch, forcing the model to focus on
challenging samples. This approach not only improves discriminative power but
also enhances instruction-following ability in downstream tasks. We conduct
extensive experiments on the MMEB benchmark and multiple retrieval tasks,
including short and long caption retrieval and compositional retrieval. Results
demonstrate that UniME achieves consistent performance improvement across all
tasks, exhibiting superior discriminative and compositional capabilities.
Perspective-Aware Reasoning in Vision-Language Models via Mental Imagery
  Simulation
Authors: name: Phillip Y. Lee, name: Jihyeon Je, name: Chanho Park, name: Mikaela Angelina Uy, name: Leonidas Guibas, name: Minhyuk Sung
Published: 2025-04-24 02:41:34

We present a framework for perspective-aware reasoning in vision-language
models (VLMs) through mental imagery simulation. Perspective-taking, the
ability to perceive an environment or situation from an alternative viewpoint,
is a key benchmark for human-level visual understanding, essential for
environmental interaction and collaboration with autonomous agents. Despite
advancements in spatial reasoning within VLMs, recent research has shown that
modern VLMs significantly lack perspective-aware reasoning capabilities and
exhibit a strong bias toward egocentric interpretations. To bridge the gap
between VLMs and human perception, we focus on the role of mental imagery,
where humans perceive the world through abstracted representations that
facilitate perspective shifts. Motivated by this, we propose a framework for
perspective-aware reasoning, named Abstract Perspective Change (APC), that
effectively leverages vision foundation models, such as object detection,
segmentation, and orientation estimation, to construct scene abstractions and
enable perspective transformations. Our experiments on synthetic and real-image
benchmarks, compared with various VLMs, demonstrate significant improvements in
perspective-aware reasoning with our framework, further outperforming
fine-tuned spatial reasoning models and novel-view-synthesis-based approaches.
Causal-Copilot: An Autonomous Causal Analysis Agent
Authors: name: Xinyue Wang, name: Kun Zhou, name: Wenyi Wu, name: Har Simrat Singh, name: Fang Nan, name: Songyao Jin, name: Aryan Philip, name: Saloni Patnaik, name: Hou Zhu, name: Shivam Singh, name: Parjanya Prashant, name: Qian Shen, name: Biwei Huang
Published: 2025-04-17 18:05:39

Causal analysis plays a foundational role in scientific discovery and
reliable decision-making, yet it remains largely inaccessible to domain experts
due to its conceptual and algorithmic complexity. This disconnect between
causal methodology and practical usability presents a dual challenge: domain
experts are unable to leverage recent advances in causal learning, while causal
researchers lack broad, real-world deployment to test and refine their methods.
To address this, we introduce Causal-Copilot, an autonomous agent that
operationalizes expert-level causal analysis within a large language model
framework. Causal-Copilot automates the full pipeline of causal analysis for
both tabular and time-series data -- including causal discovery, causal
inference, algorithm selection, hyperparameter optimization, result
interpretation, and generation of actionable insights. It supports interactive
refinement through natural language, lowering the barrier for non-specialists
while preserving methodological rigor. By integrating over 20 state-of-the-art
causal analysis techniques, our system fosters a virtuous cycle -- expanding
access to advanced causal methods for domain experts while generating rich,
real-world applications that inform and advance causal theory. Empirical
evaluations demonstrate that Causal-Copilot achieves superior performance
compared to existing baselines, offering a reliable, scalable, and extensible
solution that bridges the gap between theoretical sophistication and real-world
applicability in causal analysis. A live interactive demo of Causal-Copilot is
available at https://causalcopilot.com/.
AIMO-2 Winning Solution: Building State-of-the-Art Mathematical
  Reasoning Models with OpenMathReasoning dataset
Authors: name: Ivan Moshkov, name: Darragh Hanley, name: Ivan Sorokin, name: Shubham Toshniwal, name: Christof Henkel, name: Benedikt Schifferer, name: Wei Du, name: Igor Gitman
Published: 2025-04-23 17:13:04

This paper presents our winning submission to the AI Mathematical Olympiad -
Progress Prize 2 (AIMO-2) competition. Our recipe for building state-of-the-art
mathematical reasoning models relies on three key pillars. First, we create a
large-scale dataset comprising 540K unique high-quality math problems,
including olympiad-level problems, and their 3.2M long-reasoning solutions.
Second, we develop a novel method to integrate code execution with long
reasoning models through iterative training, generation, and quality filtering,
resulting in 1.7M high-quality Tool-Integrated Reasoning solutions. Third, we
create a pipeline to train models to select the most promising solution from
many candidates. We show that such generative solution selection (GenSelect)
can significantly improve upon majority voting baseline. Combining these ideas,
we train a series of models that achieve state-of-the-art results on
mathematical reasoning benchmarks. To facilitate further research, we release
our code, models, and the complete OpenMathReasoning dataset under a
commercially permissive license.
PHYBench: Holistic Evaluation of Physical Perception and Reasoning in
  Large Language Models
Authors: name: Shi Qiu, name: Shaoyang Guo, name: Zhuo-Yang Song, name: Yunbo Sun, name: Zeyu Cai, name: Jiashen Wei, name: Tianyu Luo, name: Yixuan Yin, name: Haoxu Zhang, name: Yi Hu, name: Chenyang Wang, name: Chencheng Tang, name: Haoling Chang, name: Qi Liu, name: Ziheng Zhou, name: Tianyu Zhang, name: Jingtian Zhang, name: Zhangyi Liu, name: Minghao Li, name: Yuku Zhang, name: Boxuan Jing, name: Xianqi Yin, name: Yutong Ren, name: Zizhuo Fu, name: Weike Wang, name: Xudong Tian, name: Anqi Lv, name: Laifu Man, name: Jianxiang Li, name: Feiyu Tao, name: Qihua Sun, name: Zhou Liang, name: Yushu Mu, name: Zhongxuan Li, name: Jing-Jun Zhang, name: Shutao Zhang, name: Xiaotian Li, name: Xingqi Xia, name: Jiawei Lin, name: Zheyu Shen, name: Jiahang Chen, name: Qiuhao Xiong, name: Binran Wang, name: Fengyuan Wang, name: Ziyang Ni, name: Bohan Zhang, name: Fan Cui, name: Changkun Shao, name: Qing-Hong Cao, name: Ming-xing Luo, name: Muhan Zhang, name: Hua Xing Zhu
Published: 2025-04-22 17:53:29

We introduce PHYBench, a novel, high-quality benchmark designed for
evaluating reasoning capabilities of large language models (LLMs) in physical
contexts. PHYBench consists of 500 meticulously curated physics problems based
on real-world physical scenarios, designed to assess the ability of models to
understand and reason about realistic physical processes. Covering mechanics,
electromagnetism, thermodynamics, optics, modern physics, and advanced physics,
the benchmark spans difficulty levels from high school exercises to
undergraduate problems and Physics Olympiad challenges. Additionally, we
propose the Expression Edit Distance (EED) Score, a novel evaluation metric
based on the edit distance between mathematical expressions, which effectively
captures differences in model reasoning processes and results beyond
traditional binary scoring methods. We evaluate various LLMs on PHYBench and
compare their performance with human experts. Our results reveal that even
state-of-the-art reasoning models significantly lag behind human experts,
highlighting their limitations and the need for improvement in complex physical
reasoning scenarios. Our benchmark results and dataset are publicly available
at https://phybench-official.github.io/phybench-demo/.
Progressive Language-guided Visual Learning for Multi-Task Visual
  Grounding
Authors: name: Jingchao Wang, name: Hong Wang, name: Wenlong Zhang, name: Kunhua Ji, name: Dingjiang Huang, name: Yefeng Zheng
Published: 2025-04-22 12:48:12

Multi-task visual grounding (MTVG) includes two sub-tasks, i.e., Referring
Expression Comprehension (REC) and Referring Expression Segmentation (RES). The
existing representative approaches generally follow the research pipeline which
mainly consists of three core procedures, including independent feature
extraction for visual and linguistic modalities, respectively, cross-modal
interaction module, and independent prediction heads for different sub-tasks.
Albeit achieving remarkable performance, this research line has two
limitations: 1) The linguistic content has not been fully injected into the
entire visual backbone for boosting more effective visual feature extraction
and it needs an extra cross-modal interaction module; 2) The relationship
between REC and RES tasks is not effectively exploited to help the
collaborative prediction for more accurate output. To deal with these problems,
in this paper, we propose a Progressive Language-guided Visual Learning
framework for multi-task visual grounding, called PLVL, which not only finely
mine the inherent feature expression of the visual modality itself but also
progressively inject the language information to help learn linguistic-related
visual features. In this manner, our PLVL does not need additional cross-modal
fusion module while fully introducing the language guidance. Furthermore, we
analyze that the localization center for REC would help identify the
to-be-segmented object region for RES to some extent. Inspired by this
investigation, we design a multi-task head to accomplish collaborative
predictions for these two sub-tasks. Extensive experiments conducted on several
benchmark datasets comprehensively substantiate that our PLVL obviously
outperforms the representative methods in both REC and RES tasks.
https://github.com/jcwang0602/PLVL
Rethinking the Generation of High-Quality CoT Data from the Perspective
  of LLM-Adaptive Question Difficulty Grading
Authors: name: Qianjin Yu, name: Keyu Wu, name: Zihan Chen, name: Chushu Zhang, name: Manlin Mei, name: Lingjun Huang, name: Fang Tan, name: Yongsheng Du, name: Kunlin Liu, name: Yurui Zhu
Published: 2025-04-16 09:55:34

Recently, DeepSeek-R1 (671B) (DeepSeek-AIet al., 2025) has demonstrated its
excellent reasoning ability in complex tasks and has publiclyshared its
methodology. This provides potentially high-quality chain-of-thought (CoT) data
for stimulating the reasoning abilities of small-sized large language models
(LLMs). To generate high-quality CoT data for different LLMs, we seek an
efficient method for generating high-quality CoT data with LLM-Adaptive
questiondifficulty levels. First, we grade the difficulty of the questions
according to the reasoning ability of the LLMs themselves and construct a
LLM-Adaptive question database. Second, we sample the problem database based on
a distribution of difficulty levels of the questions and then use DeepSeek-R1
(671B) (DeepSeek-AI et al., 2025) to generate the corresponding high-quality
CoT data with correct answers. Thanks to the construction of CoT data with
LLM-Adaptive difficulty levels, we have significantly reduced the cost of data
generation and enhanced the efficiency of model supervised fine-tuning (SFT).
Finally, we have validated the effectiveness and generalizability of the
proposed method in the fields of complex mathematical competitions and code
generation tasks. Notably, with only 2k high-quality mathematical CoT data, our
ZMath-32B surpasses DeepSeek-Distill-32B in math reasoning task. Similarly,
with only 2k high-quality code CoT data, our ZCode-32B surpasses
DeepSeek-Distill-32B in code reasoning tasks.
RePOPE: Impact of Annotation Errors on the POPE Benchmark
Authors: name: Yannic Neuhaus, name: Matthias Hein
Published: 2025-04-22 08:47:59

Since data annotation is costly, benchmark datasets often incorporate labels
from established image datasets. In this work, we assess the impact of label
errors in MSCOCO on the frequently used object hallucination benchmark POPE. We
re-annotate the benchmark images and identify an imbalance in annotation errors
across different subsets. Evaluating multiple models on the revised labels,
which we denote as RePOPE, we observe notable shifts in model rankings,
highlighting the impact of label quality. Code and data are available at
https://github.com/YanNeu/RePOPE .
Tina: Tiny Reasoning Models via LoRA
Authors: name: Shangshang Wang, name: Julian Asilis, name: Ömer Faruk Akgül, name: Enes Burak Bilgin, name: Ollie Liu, name: Willie Neiswanger
Published: 2025-04-22 10:38:00

How cost-effectively can strong reasoning abilities be achieved in language
models? Driven by this fundamental question, we present Tina, a family of tiny
reasoning models achieved with high cost-efficiency. Notably, Tina demonstrates
that substantial reasoning performance can be developed using only minimal
resources, by applying parameter-efficient updates during reinforcement
learning (RL), using low-rank adaptation (LoRA), to an already tiny 1.5B
parameter base model. This minimalist approach produces models that achieve
reasoning performance which is competitive with, and sometimes surpasses, SOTA
RL reasoning models built upon the same base model. Crucially, this is achieved
at a tiny fraction of the computational post-training cost employed by existing
SOTA models. In fact, the best Tina model achieves a >20\% reasoning
performance increase and 43.33\% Pass@1 accuracy on AIME24, at only \$9 USD
post-training and evaluation cost (i.e., an estimated 260x cost reduction). Our
work reveals the surprising effectiveness of efficient RL reasoning via LoRA.
We validate this across multiple open-source reasoning datasets and various
ablation settings starting with a single, fixed set of hyperparameters.
Furthermore, we hypothesize that this effectiveness and efficiency stem from
LoRA rapidly adapting the model to the structural format of reasoning rewarded
by RL, while largely preserving the base model's underlying knowledge. In
service of accessibility and open research, we fully open-source all code,
training logs, and model weights \& checkpoints.
DreamID: High-Fidelity and Fast diffusion-based Face Swapping via
  Triplet ID Group Learning
Authors: name: Fulong Ye, name: Miao Hua, name: Pengze Zhang, name: Xinghui Li, name: Qichao Sun, name: Songtao Zhao, name: Qian He, name: Xinglong Wu
Published: 2025-04-20 06:53:00

In this paper, we introduce DreamID, a diffusion-based face swapping model
that achieves high levels of ID similarity, attribute preservation, image
fidelity, and fast inference speed. Unlike the typical face swapping training
process, which often relies on implicit supervision and struggles to achieve
satisfactory results. DreamID establishes explicit supervision for face
swapping by constructing Triplet ID Group data, significantly enhancing
identity similarity and attribute preservation. The iterative nature of
diffusion models poses challenges for utilizing efficient image-space loss
functions, as performing time-consuming multi-step sampling to obtain the
generated image during training is impractical. To address this issue, we
leverage the accelerated diffusion model SD Turbo, reducing the inference steps
to a single iteration, enabling efficient pixel-level end-to-end training with
explicit Triplet ID Group supervision. Additionally, we propose an improved
diffusion-based model architecture comprising SwapNet, FaceNet, and ID Adapter.
This robust architecture fully unlocks the power of the Triplet ID Group
explicit supervision. Finally, to further extend our method, we explicitly
modify the Triplet ID Group data during training to fine-tune and preserve
specific attributes, such as glasses and face shape. Extensive experiments
demonstrate that DreamID outperforms state-of-the-art methods in terms of
identity similarity, pose and expression preservation, and image fidelity.
Overall, DreamID achieves high-quality face swapping results at 512*512
resolution in just 0.6 seconds and performs exceptionally well in challenging
scenarios such as complex lighting, large angles, and occlusions.
CRUST-Bench: A Comprehensive Benchmark for C-to-safe-Rust Transpilation
Authors: name: Anirudh Khatry, name: Robert Zhang, name: Jia Pan, name: Ziteng Wang, name: Qiaochu Chen, name: Greg Durrett, name: Isil Dillig
Published: 2025-04-21 17:33:33

C-to-Rust transpilation is essential for modernizing legacy C code while
enhancing safety and interoperability with modern Rust ecosystems. However, no
dataset currently exists for evaluating whether a system can transpile C into
safe Rust that passes a set of test cases. We introduce CRUST-Bench, a dataset
of 100 C repositories, each paired with manually-written interfaces in safe
Rust as well as test cases that can be used to validate correctness of the
transpilation. By considering entire repositories rather than isolated
functions, CRUST-Bench captures the challenges of translating complex projects
with dependencies across multiple files. The provided Rust interfaces provide
explicit specifications that ensure adherence to idiomatic, memory-safe Rust
patterns, while the accompanying test cases enforce functional correctness. We
evaluate state-of-the-art large language models (LLMs) on this task and find
that safe and idiomatic Rust generation is still a challenging problem for
various state-of-the-art methods and techniques. We also provide insights into
the errors LLMs usually make in transpiling code from C to safe Rust. The best
performing model, OpenAI o1, is able to solve only 15 tasks in a single-shot
setting. Improvements on CRUST-Bench would lead to improved transpilation
systems that can reason about complex scenarios and help in migrating legacy
codebases from C into languages like Rust that ensure memory safety. You can
find the dataset and code at https://github.com/anirudhkhatry/CRUST-bench.
Unchecked and Overlooked: Addressing the Checkbox Blind Spot in Large
  Language Models with CheckboxQA
Authors: name: Michał Turski, name: Mateusz Chiliński, name: Łukasz Borchmann
Published: 2025-04-14 17:06:59

Checkboxes are critical in real-world document processing where the presence
or absence of ticks directly informs data extraction and decision-making
processes. Yet, despite the strong performance of Large Vision and Language
Models across a wide range of tasks, they struggle with interpreting checkable
content. This challenge becomes particularly pressing in industries where a
single overlooked checkbox may lead to costly regulatory or contractual
oversights. To address this gap, we introduce the CheckboxQA dataset, a
targeted resource designed to evaluate and improve model performance on
checkbox-related tasks. It reveals the limitations of current models and serves
as a valuable tool for advancing document comprehension systems, with
significant implications for applications in sectors such as legal tech and
finance.
  The dataset is publicly available at:
https://github.com/Snowflake-Labs/CheckboxQA
Decoupled Global-Local Alignment for Improving Compositional
  Understanding
Authors: name: Xiaoxing Hu, name: Kaicheng Yang, name: Jun Wang, name: Haoran Xu, name: Ziyong Feng, name: Yupei Wang
Published: 2025-04-23 15:20:53

Contrastive Language-Image Pre-training (CLIP) has achieved success on
multiple downstream tasks by aligning image and text modalities. However, the
nature of global contrastive learning limits CLIP's ability to comprehend
compositional concepts, such as relations and attributes. Although recent
studies employ global hard negative samples to improve compositional
understanding, these methods significantly compromise the model's inherent
general capabilities by forcibly distancing textual negative samples from
images in the embedding space. To overcome this limitation, we introduce a
Decoupled Global-Local Alignment (DeGLA) framework that improves compositional
understanding while substantially mitigating losses in general capabilities. To
optimize the retention of the model's inherent capabilities, we incorporate a
self-distillation mechanism within the global alignment process, aligning the
learnable image-text encoder with a frozen teacher model derived from an
exponential moving average. Under the constraint of self-distillation, it
effectively mitigates the catastrophic forgetting of pretrained knowledge
during fine-tuning. To improve compositional understanding, we first leverage
the in-context learning capability of Large Language Models (LLMs) to construct
about 2M high-quality negative captions across five types. Subsequently, we
propose the Image-Grounded Contrast (IGC) loss and Text-Grounded Contrast (TGC)
loss to enhance vision-language compositionally. Extensive experimental results
demonstrate the effectiveness of the DeGLA framework. Compared to previous
state-of-the-art methods, DeGLA achieves an average enhancement of 3.5% across
the VALSE, SugarCrepe, and ARO benchmarks. Concurrently, it obtains an average
performance improvement of 13.0% on zero-shot classification tasks across
eleven datasets. Our code will be released at
https://github.com/xiaoxing2001/DeGLA
I-Con: A Unifying Framework for Representation Learning
Authors: name: Shaden Alshammari, name: John Hershey, name: Axel Feldmann, name: William T. Freeman, name: Mark Hamilton
Published: 2025-04-23 17:59:01

As the field of representation learning grows, there has been a proliferation
of different loss functions to solve different classes of problems. We
introduce a single information-theoretic equation that generalizes a large
collection of modern loss functions in machine learning. In particular, we
introduce a framework that shows that several broad classes of machine learning
methods are precisely minimizing an integrated KL divergence between two
conditional distributions: the supervisory and learned representations. This
viewpoint exposes a hidden information geometry underlying clustering, spectral
methods, dimensionality reduction, contrastive learning, and supervised
learning. This framework enables the development of new loss functions by
combining successful techniques from across the literature. We not only present
a wide array of proofs, connecting over 23 different approaches, but we also
leverage these theoretical results to create state-of-the-art unsupervised
image classifiers that achieve a +8% improvement over the prior
state-of-the-art on unsupervised classification on ImageNet-1K. We also
demonstrate that I-Con can be used to derive principled debiasing methods which
improve contrastive representation learners.