Orionnnn/hub-stats huggingface dataset:
CUDA-L1: Improving CUDA Optimization via Contrastive Reinforcement
  Learning
Authors: name: Xiaoya Li, name: Xiaofei Sun, name: Albert Wang, name: Jiwei Li, name: Chris Shum
Published: 2025-07-18 17:43:56

The exponential growth in demand for GPU computing resources, driven by the
rapid advancement of Large Language Models, has created an urgent need for
automated CUDA optimization strategies. While recent advances in LLMs show
promise for code generation, current SOTA models (e.g. R1, o1) achieve low
success rates in improving CUDA speed. In this paper, we introduce CUDA-L1, an
automated reinforcement learning framework for CUDA optimization.
  CUDA-L1 achieves performance improvements on the CUDA optimization task:
trained on NVIDIA A100, it delivers an average speedup of x17.7 across all 250
CUDA kernels of KernelBench, with peak speedups reaching x449. Furthermore, the
model also demonstrates excellent portability across GPU architectures,
achieving average speedups of x17.8 on H100, x19.0 on RTX 3090, x16.5 on L40,
x14.7 on H800, and x13.9 on H20 despite being optimized specifically for A100.
Beyond these benchmark results, CUDA-L1 demonstrates several remarkable
properties: 1) Discovers a variety of CUDA optimization techniques and learns
to combine them strategically to achieve optimal performance; 2) Uncovers
fundamental principles of CUDA optimization; 3) Identifies non-obvious
performance bottlenecks and rejects seemingly beneficial optimizations that
harm performance.
  The capabilities of CUDA-L1 demonstrate that reinforcement learning can
transform an initially poor-performing LLM into an effective CUDA optimizer
through speedup-based reward signals alone, without human expertise or domain
knowledge. More importantly, the trained RL model extend the acquired reasoning
abilities to new kernels. This paradigm opens possibilities for automated
optimization of CUDA operations, and holds promise to substantially promote GPU
efficiency and alleviate the rising pressure on GPU computing resources.
HunyuanWorld 1.0: Generating Immersive, Explorable, and Interactive 3D
  Worlds from Words or Pixels
Authors: name: HunyuanWorld Team, name: Zhenwei Wang, name: Yuhao Liu, name: Junta Wu, name: Zixiao Gu, name: Haoyuan Wang, name: Xuhui Zuo, name: Tianyu Huang, name: Wenhuan Li, name: Sheng Zhang, name: Yihang Lian, name: Yulin Tsai, name: Lifu Wang, name: Sicong Liu, name: Puhua Jiang, name: Xianghui Yang, name: Dongyuan Guo, name: Yixuan Tang, name: Xinyue Mao, name: Jiaao Yu, name: Junlin Yu, name: Jihong Zhang, name: Meng Chen, name: Liang Dong, name: Yiwen Jia, name: Chao Zhang, name: Yonghao Tan, name: Hao Zhang, name: Zheng Ye, name: Peng He, name: Runzhou Wu, name: Minghui Chen, name: Zhan Li, name: Wangchen Qin, name: Lei Wang, name: Yifu Sun, name: Lin Niu, name: Xiang Yuan, name: Xiaofeng Yang, name: Yingping He, name: Jie Xiao, name: Yangyu Tao, name: Jianchen Zhu, name: Jinbao Xue, name: Kai Liu, name: Chongqing Zhao, name: Xinming Wu, name: Tian Liu, name: Peng Chen, name: Di Wang, name: Yuhong Liu, name: Linus, name: Jie Jiang, name: Tengfei Wang, name: Chunchao Guo
Published: 2025-07-29 13:43:35

Creating immersive and playable 3D worlds from texts or images remains a
fundamental challenge in computer vision and graphics. Existing world
generation approaches typically fall into two categories: video-based methods
that offer rich diversity but lack 3D consistency and rendering efficiency, and
3D-based methods that provide geometric consistency but struggle with limited
training data and memory-inefficient representations. To address these
limitations, we present HunyuanWorld 1.0, a novel framework that combines the
best of both worlds for generating immersive, explorable, and interactive 3D
scenes from text and image conditions. Our approach features three key
advantages: 1) 360{\deg} immersive experiences via panoramic world proxies; 2)
mesh export capabilities for seamless compatibility with existing computer
graphics pipelines; 3) disentangled object representations for augmented
interactivity. The core of our framework is a semantically layered 3D mesh
representation that leverages panoramic images as 360{\deg} world proxies for
semantic-aware world decomposition and reconstruction, enabling the generation
of diverse 3D worlds. Extensive experiments demonstrate that our method
achieves state-of-the-art performance in generating coherent, explorable, and
interactive 3D worlds while enabling versatile applications in virtual reality,
physical simulation, game development, and interactive content creation.
AnimalClue: Recognizing Animals by their Traces
Authors: name: Risa Shinoda, name: Nakamasa Inoue, name: Iro Laina, name: Christian Rupprecht, name: Hirokatsu Kataoka
Published: 2025-07-27 11:48:03

Wildlife observation plays an important role in biodiversity conservation,
necessitating robust methodologies for monitoring wildlife populations and
interspecies interactions. Recent advances in computer vision have
significantly contributed to automating fundamental wildlife observation tasks,
such as animal detection and species identification. However, accurately
identifying species from indirect evidence like footprints and feces remains
relatively underexplored, despite its importance in contributing to wildlife
monitoring. To bridge this gap, we introduce AnimalClue, the first large-scale
dataset for species identification from images of indirect evidence. Our
dataset consists of 159,605 bounding boxes encompassing five categories of
indirect clues: footprints, feces, eggs, bones, and feathers. It covers 968
species, 200 families, and 65 orders. Each image is annotated with
species-level labels, bounding boxes or segmentation masks, and fine-grained
trait information, including activity patterns and habitat preferences. Unlike
existing datasets primarily focused on direct visual features (e.g., animal
appearances), AnimalClue presents unique challenges for classification,
detection, and instance segmentation tasks due to the need for recognizing more
detailed and subtle visual features. In our experiments, we extensively
evaluate representative vision models and identify key challenges in animal
identification from their traces. Our dataset and code are available at
https://dahlian00.github.io/AnimalCluePage/
X-Omni: Reinforcement Learning Makes Discrete Autoregressive Image
  Generative Models Great Again
Authors: name: Zigang Geng, name: Yibing Wang, name: Yeyao Ma, name: Chen Li, name: Yongming Rao, name: Shuyang Gu, name: Zhao Zhong, name: Qinglin Lu, name: Han Hu, name: Xiaosong Zhang, name: Linus, name: Di Wang, name: Jie Jiang
Published: 2025-07-29 17:59:04

Numerous efforts have been made to extend the ``next token prediction''
paradigm to visual contents, aiming to create a unified approach for both image
generation and understanding. Nevertheless, attempts to generate images through
autoregressive modeling with discrete tokens have been plagued by issues such
as low visual fidelity, distorted outputs, and failure to adhere to complex
instructions when rendering intricate details. These shortcomings are likely
attributed to cumulative errors during autoregressive inference or information
loss incurred during the discretization process. Probably due to this
challenge, recent research has increasingly shifted toward jointly training
image generation with diffusion objectives and language generation with
autoregressive objectives, moving away from unified modeling approaches. In
this work, we demonstrate that reinforcement learning can effectively mitigate
artifacts and largely enhance the generation quality of a discrete
autoregressive modeling method, thereby enabling seamless integration of image
and language generation. Our framework comprises a semantic image tokenizer, a
unified autoregressive model for both language and images, and an offline
diffusion decoder for image generation, termed X-Omni. X-Omni achieves
state-of-the-art performance in image generation tasks using a 7B language
model, producing images with high aesthetic quality while exhibiting strong
capabilities in following instructions and rendering long texts.
Evaluating Deep Learning Models for African Wildlife Image
  Classification: From DenseNet to Vision Transformers
Authors: name: Lukman Jibril Aliyu, name: Umar Sani Muhammad, name: Bilqisu Ismail, name: Nasiru Muhammad, name: Almustapha A Wakili, name: Seid Muhie Yimam, name: Shamsuddeen Hassan Muhammad, name: Mustapha Abdullahi
Published: 2025-07-28 22:18:13

Wildlife populations in Africa face severe threats, with vertebrate numbers
declining by over 65% in the past five decades. In response, image
classification using deep learning has emerged as a promising tool for
biodiversity monitoring and conservation. This paper presents a comparative
study of deep learning models for automatically classifying African wildlife
images, focusing on transfer learning with frozen feature extractors. Using a
public dataset of four species: buffalo, elephant, rhinoceros, and zebra; we
evaluate the performance of DenseNet-201, ResNet-152, EfficientNet-B4, and
Vision Transformer ViT-H/14. DenseNet-201 achieved the best performance among
convolutional networks (67% accuracy), while ViT-H/14 achieved the highest
overall accuracy (99%), but with significantly higher computational cost,
raising deployment concerns. Our experiments highlight the trade-offs between
accuracy, resource requirements, and deployability. The best-performing CNN
(DenseNet-201) was integrated into a Hugging Face Gradio Space for real-time
field use, demonstrating the feasibility of deploying lightweight models in
conservation settings. This work contributes to African-grounded AI research by
offering practical insights into model selection, dataset preparation, and
responsible deployment of deep learning tools for wildlife conservation.
MaPPO: Maximum a Posteriori Preference Optimization with Prior Knowledge
Authors: name: Guangchen Lan, name: Sipeng Zhang, name: Tianle Wang, name: Yuwei Zhang, name: Daoan Zhang, name: Xinpeng Wei, name: Xiaoman Pan, name: Hongming Zhang, name: Dong-Jun Han, name: Christopher G. Brinton
Published: 2025-07-27 05:26:50

As the era of large language models (LLMs) on behalf of users unfolds,
Preference Optimization (PO) methods have become a central approach to aligning
LLMs with human preferences and improving performance. We propose Maximum a
Posteriori Preference Optimization (MaPPO), a framework for learning from
preferences that explicitly incorporates prior reward knowledge into the
optimization objective. While existing methods such as Direct Preference
Optimization (DPO) and its variants treat preference learning as a Maximum
Likelihood Estimation (MLE) problem, MaPPO extends this paradigm by integrating
prior reward estimates into a principled Maximum a Posteriori (MaP) objective.
This not only generalizes DPO and its variants, but also enhances alignment by
mitigating the oversimplified binary classification of responses. More
importantly, MaPPO introduces no additional hyperparameter, and supports
preference optimization in both offline and online settings. In addition, MaPPO
can be used as a plugin with consistent improvement on DPO variants, including
widely used SimPO, IPO, and CPO. Extensive empirical evaluations of different
model sizes and model series on three standard benchmarks, including MT-Bench,
AlpacaEval 2.0, and Arena-Hard, demonstrate consistent improvements in
alignment performance without sacrificing computational efficiency.
MOVE: Motion-Guided Few-Shot Video Object Segmentation
Authors: name: Kaining Ying, name: Hengrui Hu, name: Henghui Ding
Published: 2025-07-29 17:59:35

This work addresses motion-guided few-shot video object segmentation (FSVOS),
which aims to segment dynamic objects in videos based on a few annotated
examples with the same motion patterns. Existing FSVOS datasets and methods
typically focus on object categories, which are static attributes that ignore
the rich temporal dynamics in videos, limiting their application in scenarios
requiring motion understanding. To fill this gap, we introduce MOVE, a
large-scale dataset specifically designed for motion-guided FSVOS. Based on
MOVE, we comprehensively evaluate 6 state-of-the-art methods from 3 different
related tasks across 2 experimental settings. Our results reveal that current
methods struggle to address motion-guided FSVOS, prompting us to analyze the
associated challenges and propose a baseline method, Decoupled Motion
Appearance Network (DMA). Experiments demonstrate that our approach achieves
superior performance in few shot motion understanding, establishing a solid
foundation for future research in this direction.
EDGE-GRPO: Entropy-Driven GRPO with Guided Error Correction for
  Advantage Diversity
Authors: name: Xingjian Zhang, name: Siwei Wen, name: Wenjun Wu, name: Lei Huang
Published: 2025-07-29 14:23:58

Large Language Models (LLMs) have made remarkable progress in enhancing
step-by-step reasoning through reinforcement learning. However, the Group
Relative Policy Optimization (GRPO) algorithm, which relies on sparse reward
rules, often encounters the issue of identical rewards within groups, leading
to the advantage collapse problem. Existing works typically address this
challenge from two perspectives: enforcing model reflection to enhance response
diversity, and introducing internal feedback to augment the training signal
(advantage). In this work, we begin by analyzing the limitations of model
reflection and investigating the policy entropy of responses at the
fine-grained sample level. Based on our experimental findings, we propose the
EDGE-GRPO algorithm, which adopts Entropy-Driven Advantage
and Guided Error Correction to effectively mitigate the
problem of advantage collapse. Extensive experiments on several main reasoning
benchmarks demonstrate the effectiveness and superiority of our approach. It is
available at https://github.com/ZhangXJ199/EDGE-GRPO.
Running in CIRCLE? A Simple Benchmark for LLM Code Interpreter Security
Authors: name: Gabriel Chua
Published: 2025-07-25 16:06:16

As large language models (LLMs) increasingly integrate native code
interpreters, they enable powerful real-time execution capabilities,
substantially expanding their utility. However, such integrations introduce
potential system-level cybersecurity threats, fundamentally different from
prompt-based vulnerabilities. To systematically evaluate these
interpreter-specific risks, we propose CIRCLE (Code-Interpreter Resilience
Check for LLM Exploits), a simple benchmark comprising 1,260 prompts targeting
CPU, memory, and disk resource exhaustion. Each risk category includes
explicitly malicious ("direct") and plausibly benign ("indirect") prompt
variants. Our automated evaluation framework assesses not only whether LLMs
refuse or generates risky code, but also executes the generated code within the
interpreter environment to evaluate code correctness, simplifications made by
the LLM to make the code safe, or execution timeouts. Evaluating 7 commercially
available models from OpenAI and Google, we uncover significant and
inconsistent vulnerabilities. For instance, evaluations show substantial
disparities even within providers - OpenAI's o4-mini correctly refuses risky
requests at 7.1%, notably higher rates compared to GPT-4.1 at 0.5%. Results
particularly underscore that indirect, socially-engineered prompts
substantially weaken model defenses. This highlights an urgent need for
interpreter-specific cybersecurity benchmarks, dedicated mitigation tools
(e.g., guardrails), and clear industry standards to guide safe and responsible
deployment of LLM interpreter integrations. The benchmark dataset and
evaluation code are publicly released to foster further research.
Goal Alignment in LLM-Based User Simulators for Conversational AI
Authors: name: Shuhaib Mehri, name: Xiaocheng Yang, name: Takyoung Kim, name: Gokhan Tur, name: Shikib Mehri, name: Dilek Hakkani-Tür
Published: 2025-07-27 07:07:12

User simulators are essential to conversational AI, enabling scalable agent
development and evaluation through simulated interactions. While current Large
Language Models (LLMs) have advanced user simulation capabilities, we reveal
that they struggle to consistently demonstrate goal-oriented behavior across
multi-turn conversations--a critical limitation that compromises their
reliability in downstream applications. We introduce User Goal State Tracking
(UGST), a novel framework that tracks user goal progression throughout
conversations. Leveraging UGST, we present a three-stage methodology for
developing user simulators that can autonomously track goal progression and
reason to generate goal-aligned responses. Moreover, we establish comprehensive
evaluation metrics for measuring goal alignment in user simulators, and
demonstrate that our approach yields substantial improvements across two
benchmarks (MultiWOZ 2.4 and {\tau}-Bench). Our contributions address a
critical gap in conversational AI and establish UGST as an essential framework
for developing goal-aligned user simulators.
SAND-Math: Using LLMs to Generate Novel, Difficult and Useful
  Mathematics Questions and Answers
Authors: name: Chaitanya Manem, name: Pratik Prabhanjan Brahma, name: Prakamya Mishra, name: Zicheng Liu, name: Emad Barsoum
Published: 2025-07-28 05:17:48

The demand for Large Language Models (LLMs) capable of sophisticated
mathematical reasoning is growing across industries. However, the development
of performant mathematical LLMs is critically bottlenecked by the scarcity of
difficult, novel training data. We introduce SAND-Math (Synthetic
Augmented Novel and Difficult Mathematics problems and solutions), a pipeline
that addresses this by first generating high-quality problems from scratch and
then systematically elevating their complexity via a new Difficulty
Hiking step. We demonstrate the effectiveness of our approach through two key
findings. First, augmenting a strong baseline with SAND-Math data significantly
boosts performance, outperforming the next-best synthetic dataset by
uparrow 17.85 absolute points on the AIME25 benchmark. Second, in a
dedicated ablation study, we show our Difficulty Hiking process is highly
effective: by increasing average problem difficulty from 5.02 to 5.98, this
step lifts AIME25 performance from 46.38\% to 49.23\%. The full generation
pipeline, final dataset, and a fine-tuned model form a practical and scalable
toolkit for building more capable and efficient mathematical reasoning LLMs.
SAND-Math dataset is released here:
https://huggingface.co/datasets/amd/SAND-MATH{https://huggingface.co/datasets/amd/SAND-MATH}
Diversity-Enhanced Reasoning for Subjective Questions
Authors: name: Yumeng Wang, name: Zhiyuan Fan, name: Jiayu Liu, name: Yi R. Fung
Published: 2025-07-27 09:07:42

Large reasoning models (LRM) with long chain-of-thought (CoT) capabilities
have shown strong performance on objective tasks, such as math reasoning and
coding. However, their effectiveness on subjective questions that may have
different responses from different perspectives is still limited by a tendency
towards homogeneous reasoning, introduced by the reliance on a single ground
truth in supervised fine-tuning and verifiable reward in reinforcement
learning. Motivated by the finding that increasing role perspectives
consistently improves performance, we propose MultiRole-R1, a
diversity-enhanced framework with multiple role perspectives, to improve the
accuracy and diversity in subjective reasoning tasks. MultiRole-R1 features an
unsupervised data construction pipeline that generates reasoning chains that
incorporate diverse role perspectives. We further employ reinforcement learning
via Group Relative Policy Optimization (GRPO) with reward shaping, by taking
diversity as a reward signal in addition to the verifiable reward. With
specially designed reward functions, we successfully promote perspective
diversity and lexical diversity, uncovering a positive relation between
reasoning diversity and accuracy. Our experiment on six benchmarks demonstrates
MultiRole-R1's effectiveness and generalizability in enhancing both subjective
and objective reasoning, showcasing the potential of diversity-enhanced
training in LRMs.
Beyond Binary Rewards: Training LMs to Reason About Their Uncertainty
Authors: name: Mehul Damani, name: Isha Puri, name: Stewart Slocum, name: Idan Shenfeld, name: Leshem Choshen, name: Yoon Kim, name: Jacob Andreas
Published: 2025-07-22 17:56:01

When language models (LMs) are trained via reinforcement learning (RL) to
generate natural language "reasoning chains", their performance improves on a
variety of difficult question answering tasks. Today, almost all successful
applications of RL for reasoning use binary reward functions that evaluate the
correctness of LM outputs. Because such reward functions do not penalize
guessing or low-confidence outputs, they often have the unintended side-effect
of degrading calibration and increasing the rate at which LMs generate
incorrect responses (or "hallucinate") in other problem domains. This paper
describes RLCR (Reinforcement Learning with Calibration Rewards), an approach
to training reasoning models that jointly improves accuracy and calibrated
confidence estimation. During RLCR, LMs generate both predictions and numerical
confidence estimates after reasoning. They are trained to optimize a reward
function that augments a binary correctness score with a Brier score -- a
scoring rule for confidence estimates that incentivizes calibrated prediction.
We first prove that this reward function (or any analogous reward function that
uses a bounded, proper scoring rule) yields models whose predictions are both
accurate and well-calibrated. We next show that across diverse datasets, RLCR
substantially improves calibration with no loss in accuracy, on both in-domain
and out-of-domain evaluations -- outperforming both ordinary RL training and
classifiers trained to assign post-hoc confidence scores. While ordinary RL
hurts calibration, RLCR improves it. Finally, we demonstrate that verbalized
confidence can be leveraged at test time to improve accuracy and calibration
via confidence-weighted scaling methods. Our results show that explicitly
optimizing for calibration can produce more generally reliable reasoning
models.
Met^2Net: A Decoupled Two-Stage Spatio-Temporal Forecasting Model for
  Complex Meteorological Systems
Authors: name: Shaohan Li, name: Hao Yang, name: Min Chen, name: Xiaolin Qin
Published: 2025-07-23 04:26:56

The increasing frequency of extreme weather events due to global climate
change urges accurate weather prediction. Recently, great advances have been
made by the end-to-end methods, thanks to deep learning techniques,
but they face limitations of representation inconsistency in
multivariable integration and struggle to effectively capture the dependency
between variables, which is required in complex weather systems. Treating
different variables as distinct modalities and applying a two-stage
training approach from multimodal models can partially alleviate this issue,
but due to the inconformity in training tasks between the two stages, the
results are often suboptimal. To address these challenges, we propose an
implicit two-stage training method, configuring separate encoders and decoders
for each variable. In detailed, in the first stage, the Translator is frozen
while the Encoders and Decoders learn a shared latent space, in the second
stage, the Encoders and Decoders are frozen, and the Translator captures
inter-variable interactions for prediction. Besides, by introducing a
self-attention mechanism for multivariable fusion in the latent space, the
performance achieves further improvements. Empirically, extensive experiments
show the state-of-the-art performance of our method. Specifically, it reduces
the MSE for near-surface air temperature and relative humidity predictions by
28.82\% and 23.39\%, respectively. The source code is available at
https://github.com/ShremG/Met2Net.