Orionnnn/hub-stats huggingface dataset:
From Virtual Games to Real-World Play
Authors: name: Wenqiang Sun, name: Fangyun Wei, name: Jinjing Zhao, name: Xi Chen, name: Zilong Chen, name: Hongyang Zhang, name: Jun Zhang, name: Yan Lu
Published: 2025-06-23 17:59:53

We introduce RealPlay, a neural network-based real-world game engine that
enables interactive video generation from user control signals. Unlike prior
works focused on game-style visuals, RealPlay aims to produce photorealistic,
temporally consistent video sequences that resemble real-world footage. It
operates in an interactive loop: users observe a generated scene, issue a
control command, and receive a short video chunk in response. To enable such
realistic and responsive generation, we address key challenges including
iterative chunk-wise prediction for low-latency feedback, temporal consistency
across iterations, and accurate control response. RealPlay is trained on a
combination of labeled game data and unlabeled real-world videos, without
requiring real-world action annotations. Notably, we observe two forms of
generalization: (1) control transfer-RealPlay effectively maps control signals
from virtual to real-world scenarios; and (2) entity transfer-although training
labels originate solely from a car racing game, RealPlay generalizes to control
diverse real-world entities, including bicycles and pedestrians, beyond
vehicles. Project page can be found: https://wenqsun.github.io/RealPlay/
FinCoT: Grounding Chain-of-Thought in Expert Financial Reasoning
Authors: name: Natapong Nitarach, name: Warit Sirichotedumrong, name: Panop Pitchayarthorn, name: Pittawat Taveekitworachai, name: Potsawee Manakul, name: Kunat Pipatanakul
Published: 2025-06-19 08:18:55

This paper presents FinCoT, a structured chain-of-thought (CoT) prompting
approach that incorporates insights from domain-specific expert financial
reasoning to guide the reasoning traces of large language models. We
investigate that there are three main prompting styles in FinNLP: (1) standard
prompting--zero-shot prompting; (2) unstructured CoT--CoT prompting without an
explicit reasoning structure, such as the use of tags; and (3) structured CoT
prompting--CoT prompting with explicit instructions or examples that define
structured reasoning steps. Previously, FinNLP has primarily focused on prompt
engineering with either standard or unstructured CoT prompting. However,
structured CoT prompting has received limited attention in prior work.
Furthermore, the design of reasoning structures in structured CoT prompting is
often based on heuristics from non-domain experts. In this study, we
investigate each prompting approach in FinNLP. We evaluate the three main
prompting styles and FinCoT on CFA-style questions spanning ten financial
domains. We observe that FinCoT improves performance from 63.2% to 80.5% and
Qwen-2.5-7B-Instruct from 69.7% to 74.2%, while reducing generated tokens
eight-fold compared to structured CoT prompting. Our findings show that
domain-aligned structured prompts not only improve performance and reduce
inference costs but also yield more interpretable and expert-aligned reasoning
traces.
A deep learning and machine learning approach to predict neonatal death
  in the context of São Paulo
Authors: name: Mohon Raihan, name: Plabon Kumar Saha, name: Rajan Das Gupta, name: A Z M Tahmidul Kabir, name: Afia Anjum Tamanna, name: Md. Harun-Ur-Rashid, name: Adnan Bin Abdus Salam, name: Md Tanvir Anjum, name: A Z M Ahteshamul Kabir
Published: 2025-06-20 11:44:48

Neonatal death is still a concerning reality for underdeveloped and even some
developed countries. Worldwide data indicate that 26.693 babies out of 1,000
births die, according to Macro Trades. To reduce this number, early prediction
of endangered babies is crucial. Such prediction enables the opportunity to
take ample care of the child and mother so that early child death can be
avoided. In this context, machine learning was used to determine whether a
newborn baby is at risk. To train the predictive model, historical data of 1.4
million newborns was used. Machine learning and deep learning techniques such
as logical regression, K-nearest neighbor, random forest classifier, extreme
gradient boosting (XGBoost), convolutional neural network, and long short-term
memory (LSTM) were implemented using the dataset to identify the most accurate
model for predicting neonatal mortality. Among the machine learning algorithms,
XGBoost and random forest classifier achieved the best accuracy with 94%, while
among the deep learning models, LSTM delivered the highest accuracy with 99%.
Therefore, using LSTM appears to be the most suitable approach to predict
whether precautionary measures for a child are necessary.
Robust Reward Modeling via Causal Rubrics
Authors: name: Pragya Srivastava, name: Harman Singh, name: Rahul Madhavan, name: Gandharv Patil, name: Sravanti Addepalli, name: Arun Suggala, name: Rengarajan Aravamudhan, name: Soumya Sharma, name: Anirban Laha, name: Aravindan Raghuveer, name: Karthikeyan Shanmugam, name: Doina Precup
Published: 2025-06-19 17:59:47

Reward models (RMs) are fundamental to aligning Large Language Models (LLMs)
via human feedback, yet they often suffer from reward hacking. They tend to
latch on to superficial or spurious attributes, such as response length or
formatting, mistaking these cues learned from correlations in training data for
the true causal drivers of quality (e.g., factuality, relevance). This occurs
because standard training objectives struggle to disentangle these factors,
leading to brittle RMs and misaligned policies. We introduce Crome (Causally
Robust Reward Modeling), a novel framework grounded in an explicit causal model
designed to mitigate reward hacking. Crome employs the following synthetic
targeted augmentations during training: (1) Causal Augmentations, which are
pairs that differ along specific causal attributes, to enforce sensitivity
along each causal attribute individually, and (2) Neutral Augmentations, which
are tie-label pairs varying primarily in spurious attributes, to enforce
invariance along spurious attributes. Notably, our augmentations are produced
without any knowledge of spurious factors, via answer interventions only along
causal rubrics, that are identified by querying an oracle LLM. Empirically,
Crome significantly outperforms standard baselines on RewardBench, improving
average accuracy by up to 5.4% and achieving gains of up to 13.2% and 7.2% in
specific categories. The robustness of Crome is further testified by the
consistent gains obtained in a Best-of-N inference setting across increasing N,
across various benchmarks, including the popular RewardBench (covering chat,
chat-hard, safety, and reasoning tasks), the safety-focused WildGuardTest, and
the reasoning-specific GSM8k.
ViDAR: Video Diffusion-Aware 4D Reconstruction From Monocular Inputs
Authors: name: Michal Nazarczuk, name: Sibi Catley-Chandar, name: Thomas Tanay, name: Zhensong Zhang, name: Gregory Slabaugh, name: Eduardo Pérez-Pellitero
Published: 2025-06-23 16:01:15

Dynamic Novel View Synthesis aims to generate photorealistic views of moving
subjects from arbitrary viewpoints. This task is particularly challenging when
relying on monocular video, where disentangling structure from motion is
ill-posed and supervision is scarce. We introduce Video Diffusion-Aware
Reconstruction (ViDAR), a novel 4D reconstruction framework that leverages
personalised diffusion models to synthesise a pseudo multi-view supervision
signal for training a Gaussian splatting representation. By conditioning on
scene-specific features, ViDAR recovers fine-grained appearance details while
mitigating artefacts introduced by monocular ambiguity. To address the
spatio-temporal inconsistency of diffusion-based supervision, we propose a
diffusion-aware loss function and a camera pose optimisation strategy that
aligns synthetic views with the underlying scene geometry. Experiments on
DyCheck, a challenging benchmark with extreme viewpoint variation, show that
ViDAR outperforms all state-of-the-art baselines in visual quality and
geometric consistency. We further highlight ViDAR's strong improvement over
baselines on dynamic regions and provide a new benchmark to compare performance
in reconstructing motion-rich parts of the scene. Project page:
https://vidar-4d.github.io
I Know Which LLM Wrote Your Code Last Summer: LLM generated Code
  Stylometry for Authorship Attribution
Authors: name: Tamas Bisztray, name: Bilel Cherif, name: Richard A. Dubniczky, name: Nils Gruschka, name: Bertalan Borsos, name: Mohamed Amine Ferrag, name: Attila Kovacs, name: Vasileios Mavroeidis, name: Norbert Tihanyi
Published: 2025-06-18 19:49:41

Detecting AI-generated code, deepfakes, and other synthetic content is an
emerging research challenge. As code generated by Large Language Models (LLMs)
becomes more common, identifying the specific model behind each sample is
increasingly important. This paper presents the first systematic study of LLM
authorship attribution for C programs. We released CodeT5-Authorship, a novel
model that uses only the encoder layers from the original CodeT5
encoder-decoder architecture, discarding the decoder to focus on
classification. Our model's encoder output (first token) is passed through a
two-layer classification head with GELU activation and dropout, producing a
probability distribution over possible authors. To evaluate our approach, we
introduce LLM-AuthorBench, a benchmark of 32,000 compilable C programs
generated by eight state-of-the-art LLMs across diverse tasks. We compare our
model to seven traditional ML classifiers and eight fine-tuned transformer
models, including BERT, RoBERTa, CodeBERT, ModernBERT, DistilBERT, DeBERTa-V3,
Longformer, and LoRA-fine-tuned Qwen2-1.5B. In binary classification, our model
achieves 97.56% accuracy in distinguishing C programs generated by closely
related models such as GPT-4.1 and GPT-4o, and 95.40% accuracy for multi-class
attribution among five leading LLMs (Gemini 2.5 Flash, Claude 3.5 Haiku,
GPT-4.1, Llama 3.3, and DeepSeek-V3). To support open science, we release the
CodeT5-Authorship architecture, the LLM-AuthorBench benchmark, and all relevant
Google Colab scripts on GitHub: https://github.com/LLMauthorbench/.
FaithfulSAE: Towards Capturing Faithful Features with Sparse
  Autoencoders without External Dataset Dependencies
Authors: name: Seonglae Cho, name: Harryn Oh, name: Donghyun Lee, name: Luis Eduardo Rodrigues Vieira, name: Andrew Bermingham, name: Ziad El Sayed
Published: 2025-06-21 10:18:25

Sparse Autoencoders (SAEs) have emerged as a promising solution for
decomposing large language model representations into interpretable features.
However, Paulo and Belrose (2025) have highlighted instability across different
initialization seeds, and Heap et al. (2025) have pointed out that SAEs may not
capture model-internal features. These problems likely stem from training SAEs
on external datasets - either collected from the Web or generated by another
model - which may contain out-of-distribution (OOD) data beyond the model's
generalisation capabilities. This can result in hallucinated SAE features,
which we term "Fake Features", that misrepresent the model's internal
activations. To address these issues, we propose FaithfulSAE, a method that
trains SAEs on the model's own synthetic dataset. Using FaithfulSAEs, we
demonstrate that training SAEs on less-OOD instruction datasets results in SAEs
being more stable across seeds. Notably, FaithfulSAEs outperform SAEs trained
on web-based datasets in the SAE probing task and exhibit a lower Fake Feature
Ratio in 5 out of 7 models. Overall, our approach eliminates the dependency on
external datasets, advancing interpretability by better capturing
model-internal features while highlighting the often neglected importance of
SAE training datasets.
OAgents: An Empirical Study of Building Effective Agents
Authors: name: He Zhu, name: Tianrui Qin, name: King Zhu, name: Heyuan Huang, name: Yeyi Guan, name: Jinxiang Xia, name: Yi Yao, name: Hanhao Li, name: Ningning Wang, name: Pai Liu, name: Tianhao Peng, name: Xin Gui, name: Xiaowan Li, name: Yuhui Liu, name: Yuchen Eleanor Jiang, name: Jun Wang, name: Changwang Zhang, name: Xiangru Tang, name: Ge Zhang, name: Jian Yang, name: Minghao Liu, name: Xitong Gao, name: Jiaheng Liu, name: Wangchunshu Zhou
Published: 2025-06-17 17:59:02

Recently, Agentic AI has become an increasingly popular research field.
However, we argue that current agent research practices lack standardization
and scientific rigor, making it hard to conduct fair comparisons among methods.
As a result, it is still unclear how different design choices in agent
frameworks affect effectiveness, and measuring their progress remains
challenging. In this work, we conduct a systematic empirical study on GAIA
benchmark and BrowseComp to examine the impact of popular design choices in key
agent components in a fair and rigorous manner. We find that the lack of a
standard evaluation protocol makes previous works, even open-sourced ones,
non-reproducible, with significant variance between random runs. Therefore, we
introduce a more robust evaluation protocol to stabilize comparisons. Our study
reveals which components and designs are crucial for effective agents, while
others are redundant, despite seeming logical. Based on our findings, we build
and open-source OAgents, a new foundation agent framework that achieves
state-of-the-art performance among open-source projects. OAgents offers a
modular design for various agent components, promoting future research in
Agentic AI.
RLPR: Extrapolating RLVR to General Domains without Verifiers
Authors: name: Tianyu Yu, name: Bo Ji, name: Shouli Wang, name: Shu Yao, name: Zefan Wang, name: Ganqu Cui, name: Lifan Yuan, name: Ning Ding, name: Yuan Yao, name: Zhiyuan Liu, name: Maosong Sun, name: Tat-Seng Chua
Published: 2025-06-23 02:56:36

Reinforcement Learning with Verifiable Rewards (RLVR) demonstrates promising
potential in advancing the reasoning capabilities of LLMs. However, its success
remains largely confined to mathematical and code domains. This primary
limitation stems from the heavy reliance on domain-specific verifiers, which
results in prohibitive complexity and limited scalability. To address the
challenge, our key observation is that LLM's intrinsic probability of
generating a correct free-form answer directly indicates its own evaluation of
the reasoning reward (i.e., how well the reasoning process leads to the correct
answer). Building on this insight, we propose RLPR, a simple verifier-free
framework that extrapolates RLVR to broader general domains. RLPR uses the
LLM's own token probability scores for reference answers as the reward signal
and maximizes the expected reward during training. We find that addressing the
high variance of this noisy probability reward is crucial to make it work, and
propose prob-to-reward and stabilizing methods to ensure a precise and stable
reward from LLM intrinsic probabilities. Comprehensive experiments in four
general-domain benchmarks and three mathematical benchmarks show that RLPR
consistently improves reasoning capabilities in both areas for Gemma, Llama,
and Qwen based models. Notably, RLPR outperforms concurrent VeriFree by 7.6
points on TheoremQA and 7.5 points on Minerva, and even surpasses strong
verifier-model-dependent approaches General-Reasoner by 1.6 average points
across seven benchmarks.
ConsumerBench: Benchmarking Generative AI Applications on End-User
  Devices
Authors: name: Yile Gu, name: Rohan Kadekodi, name: Hoang Nguyen, name: Keisuke Kamahori, name: Yiyu Liu, name: Baris Kasikci
Published: 2025-06-21 01:32:22

The recent shift in Generative AI (GenAI) applications from cloud-only
environments to end-user devices introduces new challenges in resource
management, system efficiency, and user experience. This paper presents
ConsumerBench, a comprehensive benchmarking framework designed to evaluate the
system efficiency and response time of GenAI models running on end-user
devices. Unlike existing benchmarks that assume exclusive model access on
dedicated GPUs, ConsumerBench simulates realistic multi-application scenarios
executing concurrently on constrained hardware. Furthermore, ConsumerBench
supports customizable workflows that simulate complex tasks requiring
coordination among multiple applications. ConsumerBench captures both
application-level metrics, including latency and Service Level Objective (SLO)
attainment, and system-level metrics like CPU/GPU utilization and memory
bandwidth. Through extensive experiments, ConsumerBench reveals inefficiencies
in resource sharing, unfair scheduling under greedy allocation, and performance
pitfalls of static model server configurations. The paper also provides
practical insights for model developers and system designers, highlighting the
benefits of custom kernels tailored to consumer-grade GPU architectures and the
value of implementing SLO-aware scheduling strategies.
LettinGo: Explore User Profile Generation for Recommendation System
Authors: name: Lu Wang, name: Di Zhang, name: Fangkai Yang, name: Pu Zhao, name: Jianfeng Liu, name: Yuefeng Zhan, name: Hao Sun, name: Qingwei Lin, name: Weiwei Deng, name: Dongmei Zhang, name: Feng Sun, name: Qi Zhang
Published: 2025-06-23 05:51:52

User profiling is pivotal for recommendation systems, as it transforms raw
user interaction data into concise and structured representations that drive
personalized recommendations. While traditional embedding-based profiles lack
interpretability and adaptability, recent advances with large language models
(LLMs) enable text-based profiles that are semantically richer and more
transparent. However, existing methods often adhere to fixed formats that limit
their ability to capture the full diversity of user behaviors. In this paper,
we introduce LettinGo, a novel framework for generating diverse and adaptive
user profiles. By leveraging the expressive power of LLMs and incorporating
direct feedback from downstream recommendation tasks, our approach avoids the
rigid constraints imposed by supervised fine-tuning (SFT). Instead, we employ
Direct Preference Optimization (DPO) to align the profile generator with
task-specific performance, ensuring that the profiles remain adaptive and
effective. LettinGo operates in three stages: (1) exploring diverse user
profiles via multiple LLMs, (2) evaluating profile quality based on their
impact in recommendation systems, and (3) aligning the profile generation
through pairwise preference data derived from task performance. Experimental
results demonstrate that our framework significantly enhances recommendation
accuracy, flexibility, and contextual awareness. This work enhances profile
generation as a key innovation for next-generation recommendation systems.
Auto-Regressively Generating Multi-View Consistent Images
Authors: name: JiaKui Hu, name: Yuxiao Yang, name: Jialun Liu, name: Jinbo Wu, name: Chen Zhao, name: Yanye Lu
Published: 2025-06-23 11:28:37

Generating multi-view images from human instructions is crucial for 3D
content creation. The primary challenges involve maintaining consistency across
multiple views and effectively synthesizing shapes and textures under diverse
conditions. In this paper, we propose the Multi-View Auto-Regressive (MV-AR)
method, which leverages an auto-regressive model to progressively generate
consistent multi-view images from arbitrary prompts. Firstly, the
next-token-prediction capability of the AR model significantly enhances its
effectiveness in facilitating progressive multi-view synthesis. When generating
widely-separated views, MV-AR can utilize all its preceding views to extract
effective reference information. Subsequently, we propose a unified model that
accommodates various prompts via architecture designing and training
strategies. To address multiple conditions, we introduce condition injection
modules for text, camera pose, image, and shape. To manage multi-modal
conditions simultaneously, a progressive training strategy is employed. This
strategy initially adopts the text-to-multi-view (t2mv) model as a baseline to
enhance the development of a comprehensive X-to-multi-view (X2mv) model through
the randomly dropping and combining conditions. Finally, to alleviate the
overfitting problem caused by limited high-quality data, we propose the
"Shuffle View" data augmentation technique, thus significantly expanding the
training data by several magnitudes. Experiments demonstrate the performance
and versatility of our MV-AR, which consistently generates consistent
multi-view images across a range of conditions and performs on par with leading
diffusion-based multi-view image generation models. Code and models will be
released at https://github.com/MILab-PKU/MVAR.
Light of Normals: Unified Feature Representation for Universal
  Photometric Stereo
Authors: name: Hong Li, name: Houyuan Chen, name: Chongjie Ye, name: Zhaoxi Chen, name: Bohan Li, name: Shaocong Xu, name: Xianda Guo, name: Xuhui Liu, name: Yikai Wang, name: Baochang Zhang, name: Satoshi Ikehata, name: Boxin Shi, name: Anyi Rao, name: Hao Zhao
Published: 2025-06-23 17:53:11

Universal photometric stereo (PS) aims to recover high-quality surface
normals from objects under arbitrary lighting conditions without relying on
specific illumination models. Despite recent advances such as SDM-UniPS and Uni
MS-PS, two fundamental challenges persist: 1) the deep coupling between varying
illumination and surface normal features, where ambiguity in observed intensity
makes it difficult to determine whether brightness variations stem from
lighting changes or surface orientation; and 2) the preservation of
high-frequency geometric details in complex surfaces, where intricate
geometries create self-shadowing, inter-reflections, and subtle normal
variations that conventional feature processing operations struggle to capture
accurately.
Enhancing Step-by-Step and Verifiable Medical Reasoning in MLLMs
Authors: name: Haoran Sun, name: Yankai Jiang, name: Wenjie Lou, name: Yujie Zhang, name: Wenjie Li, name: Lilong Wang, name: Mianxin Liu, name: Lei Liu, name: Xiaosong Wang
Published: 2025-06-20 12:51:19

Multimodal large language models (MLLMs) have begun to demonstrate robust
reasoning capabilities on general tasks, yet their application in the medical
domain remains in its early stages. Constructing chain-of-thought (CoT)
training data is essential for bolstering the reasoning abilities of medical
MLLMs. However, existing approaches exhibit a deficiency in offering a
comprehensive framework for searching and evaluating effective reasoning paths
towards critical diagnosis. To address this challenge, we propose Mentor-Intern
Collaborative Search (MICS), a novel reasoning-path searching scheme to
generate rigorous and effective medical CoT data. MICS first leverages mentor
models to initialize the reasoning, one step at a time, then prompts each
intern model to continue the thinking along those initiated paths, and finally
selects the optimal reasoning path according to the overall reasoning
performance of multiple intern models. The reasoning performance is determined
by an MICS-Score, which assesses the quality of generated reasoning paths.
Eventually, we construct MMRP, a multi-task medical reasoning dataset with
ranked difficulty, and Chiron-o1, a new medical MLLM devised via a curriculum
learning strategy, with robust visual question-answering and generalizable
reasoning capabilities. Extensive experiments demonstrate that Chiron-o1,
trained on our CoT dataset constructed using MICS, achieves state-of-the-art
performance across a list of medical visual question answering and reasoning
benchmarks. Codes are available at GitHub - manglu097/Chiron-o1: Enhancing
Step-by-Step and Verifiable Medical Reasoning in MLLMs