Orionnnn/hub-stats huggingface dataset:
TF1-EN-3M: Three Million Synthetic Moral Fables for Training Small, Open
  Language Models
Authors: name: Mihai Nadas, name: Laura Diosan, name: Andrei Piscoran, name: Andreea Tomescu
Published: 2025-04-29 10:15:28

Moral stories are a time-tested vehicle for transmitting values, yet modern
NLP lacks a large, structured corpus that couples coherent narratives with
explicit ethical lessons. We close this gap with TF1-EN-3M, the first open
dataset of three million English-language fables generated exclusively by
instruction-tuned models no larger than 8B parameters. Each story follows a
six-slot scaffold (character -> trait -> setting -> conflict -> resolution ->
moral), produced through a combinatorial prompt engine that guarantees genre
fidelity while covering a broad thematic space.
  A hybrid evaluation pipeline blends (i) a GPT-based critic that scores
grammar, creativity, moral clarity, and template adherence with (ii)
reference-free diversity and readability metrics. Among ten open-weight
candidates, an 8B-parameter Llama-3 variant delivers the best quality-speed
trade-off, producing high-scoring fables on a single consumer GPU (<24 GB VRAM)
at approximately 13.5 cents per 1,000 fables.
  We release the dataset, generation code, evaluation scripts, and full
metadata under a permissive license, enabling exact reproducibility and cost
benchmarking. TF1-EN-3M opens avenues for research in instruction following,
narrative intelligence, value alignment, and child-friendly educational AI,
demonstrating that large-scale moral storytelling no longer requires
proprietary giant models.
MediAug: Exploring Visual Augmentation in Medical Imaging
Authors: name: Xuyin Qi, name: Zeyu Zhang, name: Canxuan Gang, name: Hao Zhang, name: Lei Zhang, name: Zhiwei Zhang, name: Yang Zhao
Published: 2025-04-26 17:56:56

Data augmentation is essential in medical imaging for improving
classification accuracy, lesion detection, and organ segmentation under limited
data conditions. However, two significant challenges remain. First, a
pronounced domain gap between natural photographs and medical images can
distort critical disease features. Second, augmentation studies in medical
imaging are fragmented and limited to single tasks or architectures, leaving
the benefits of advanced mix-based strategies unclear. To address these
challenges, we propose a unified evaluation framework with six mix-based
augmentation methods integrated with both convolutional and transformer
backbones on brain tumour MRI and eye disease fundus datasets. Our
contributions are threefold. (1) We introduce MediAug, a comprehensive and
reproducible benchmark for advanced data augmentation in medical imaging. (2)
We systematically evaluate MixUp, YOCO, CropMix, CutMix, AugMix, and SnapMix
with ResNet-50 and ViT-B backbones. (3) We demonstrate through extensive
experiments that MixUp yields the greatest improvement on the brain tumor
classification task for ResNet-50 with 79.19% accuracy and SnapMix yields the
greatest improvement for ViT-B with 99.44% accuracy, and that YOCO yields the
greatest improvement on the eye disease classification task for ResNet-50 with
91.60% accuracy and CutMix yields the greatest improvement for ViT-B with
97.94% accuracy. Code will be available at
https://github.com/AIGeeksGroup/MediAug.
A Survey of Interactive Generative Video
Authors: name: Jiwen Yu, name: Yiran Qin, name: Haoxuan Che, name: Quande Liu, name: Xintao Wang, name: Pengfei Wan, name: Di Zhang, name: Kun Gai, name: Hao Chen, name: Xihui Liu
Published: 2025-04-30 17:59:02

Interactive Generative Video (IGV) has emerged as a crucial technology in
response to the growing demand for high-quality, interactive video content
across various domains. In this paper, we define IGV as a technology that
combines generative capabilities to produce diverse high-quality video content
with interactive features that enable user engagement through control signals
and responsive feedback. We survey the current landscape of IGV applications,
focusing on three major domains: 1) gaming, where IGV enables infinite
exploration in virtual worlds; 2) embodied AI, where IGV serves as a
physics-aware environment synthesizer for training agents in multimodal
interaction with dynamically evolving scenes; and 3) autonomous driving, where
IGV provides closed-loop simulation capabilities for safety-critical testing
and validation. To guide future development, we propose a comprehensive
framework that decomposes an ideal IGV system into five essential modules:
Generation, Control, Memory, Dynamics, and Intelligence. Furthermore, we
systematically analyze the technical challenges and future directions in
realizing each component for an ideal IGV system, such as achieving real-time
generation, enabling open-domain control, maintaining long-term coherence,
simulating accurate physics, and integrating causal reasoning. We believe that
this systematic analysis will facilitate future research and development in the
field of IGV, ultimately advancing the technology toward more sophisticated and
practical applications.
DeepCritic: Deliberate Critique with Large Language Models
Authors: name: Wenkai Yang, name: Jingwen Chen, name: Yankai Lin, name: Ji-Rong Wen
Published: 2025-05-01 17:03:17

As Large Language Models (LLMs) are rapidly evolving, providing accurate
feedback and scalable oversight on their outputs becomes an urgent and critical
problem. Leveraging LLMs as critique models to achieve automated supervision is
a promising solution. In this work, we focus on studying and enhancing the math
critique ability of LLMs. Current LLM critics provide critiques that are too
shallow and superficial on each step, leading to low judgment accuracy and
struggling to offer sufficient feedback for the LLM generator to correct
mistakes. To tackle this issue, we propose a novel and effective two-stage
framework to develop LLM critics that are capable of deliberately critiquing on
each reasoning step of math solutions. In the first stage, we utilize
Qwen2.5-72B-Instruct to generate 4.5K long-form critiques as seed data for
supervised fine-tuning. Each seed critique consists of deliberate step-wise
critiques that includes multi-perspective verifications as well as in-depth
critiques of initial critiques for each reasoning step. Then, we perform
reinforcement learning on the fine-tuned model with either existing
human-labeled data from PRM800K or our automatically annotated data obtained
via Monte Carlo sampling-based correctness estimation, to further incentivize
its critique ability. Our developed critique model built on Qwen2.5-7B-Instruct
not only significantly outperforms existing LLM critics (including the
same-sized DeepSeek-R1-distill models and GPT-4o) on various error
identification benchmarks, but also more effectively helps the LLM generator
refine erroneous steps through more detailed feedback.
AdaR1: From Long-CoT to Hybrid-CoT via Bi-Level Adaptive Reasoning
  Optimization
Authors: name: Haotian Luo, name: Haiying He, name: Yibo Wang, name: Jinluan Yang, name: Rui Liu, name: Naiqiang Tan, name: Xiaochun Cao, name: Dacheng Tao, name: Li Shen
Published: 2025-04-30 14:01:45

Recently, long-thought reasoning models achieve strong performance on complex
reasoning tasks, but often incur substantial inference overhead, making
efficiency a critical concern. Our empirical analysis reveals that the benefit
of using Long-CoT varies across problems: while some problems require elaborate
reasoning, others show no improvement, or even degraded accuracy. This
motivates adaptive reasoning strategies that tailor reasoning depth to the
input. However, prior work primarily reduces redundancy within long reasoning
paths, limiting exploration of more efficient strategies beyond the Long-CoT
paradigm. To address this, we propose a novel two-stage framework for adaptive
and efficient reasoning. First, we construct a hybrid reasoning model by
merging long and short CoT models to enable diverse reasoning styles. Second,
we apply bi-level preference training to guide the model to select suitable
reasoning styles (group-level), and prefer concise and correct reasoning within
each style group (instance-level). Experiments demonstrate that our method
significantly reduces inference costs compared to other baseline approaches,
while maintaining performance. Notably, on five mathematical datasets, the
average length of reasoning is reduced by more than 50%, highlighting the
potential of adaptive strategies to optimize reasoning efficiency in large
language models. Our code is coming soon at https://github.com/StarDewXXX/AdaR1
T2I-R1: Reinforcing Image Generation with Collaborative Semantic-level
  and Token-level CoT
Authors: name: Dongzhi Jiang, name: Ziyu Guo, name: Renrui Zhang, name: Zhuofan Zong, name: Hao Li, name: Le Zhuo, name: Shilin Yan, name: Pheng-Ann Heng, name: Hongsheng Li
Published: 2025-05-01 17:59:46

Recent advancements in large language models have demonstrated how
chain-of-thought (CoT) and reinforcement learning (RL) can improve performance.
However, applying such reasoning strategies to the visual generation domain
remains largely unexplored. In this paper, we present T2I-R1, a novel
reasoning-enhanced text-to-image generation model, powered by RL with a
bi-level CoT reasoning process. Specifically, we identify two levels of CoT
that can be utilized to enhance different stages of generation: (1) the
semantic-level CoT for high-level planning of the prompt and (2) the
token-level CoT for low-level pixel processing during patch-by-patch
generation. To better coordinate these two levels of CoT, we introduce
BiCoT-GRPO with an ensemble of generation rewards, which seamlessly optimizes
both generation CoTs within the same training step. By applying our reasoning
strategies to the baseline model, Janus-Pro, we achieve superior performance
with 13% improvement on T2I-CompBench and 19% improvement on the WISE
benchmark, even surpassing the state-of-the-art model FLUX.1. Code is available
at: https://github.com/CaraJ7/T2I-R1
100 Days After DeepSeek-R1: A Survey on Replication Studies and More
  Directions for Reasoning Language Models
Authors: name: Chong Zhang, name: Yue Deng, name: Xiang Lin, name: Bin Wang, name: Dianwen Ng, name: Hai Ye, name: Xingxuan Li, name: Yao Xiao, name: Zhanfeng Mo, name: Qi Zhang, name: Lidong Bing
Published: 2025-05-01 14:28:35

The recent development of reasoning language models (RLMs) represents a novel
evolution in large language models. In particular, the recent release of
DeepSeek-R1 has generated widespread social impact and sparked enthusiasm in
the research community for exploring the explicit reasoning paradigm of
language models. However, the implementation details of the released models
have not been fully open-sourced by DeepSeek, including DeepSeek-R1-Zero,
DeepSeek-R1, and the distilled small models. As a result, many replication
studies have emerged aiming to reproduce the strong performance achieved by
DeepSeek-R1, reaching comparable performance through similar training
procedures and fully open-source data resources. These works have investigated
feasible strategies for supervised fine-tuning (SFT) and reinforcement learning
from verifiable rewards (RLVR), focusing on data preparation and method design,
yielding various valuable insights. In this report, we provide a summary of
recent replication studies to inspire future research. We primarily focus on
SFT and RLVR as two main directions, introducing the details for data
construction, method design and training procedure of current replication
studies. Moreover, we conclude key findings from the implementation details and
experimental results reported by these studies, anticipating to inspire future
research. We also discuss additional techniques of enhancing RLMs, highlighting
the potential of expanding the application scope of these models, and
discussing the challenges in development. By this survey, we aim to help
researchers and developers of RLMs stay updated with the latest advancements,
and seek to inspire new ideas to further enhance RLMs.
Selecting Optimal Candidate Profiles in Adversarial Environments Using
  Conjoint Analysis and Machine Learning
Authors: name: Connor T. Jerzak, name: Priyanshi Chandra, name: Rishi Hazra
Published: 2025-04-26 22:35:58

Conjoint analysis, an application of factorial experimental design, is a
popular tool in social science research for studying multidimensional
preferences. In such experiments in the political analysis context, respondents
are asked to choose between two hypothetical political candidates with randomly
selected features, which can include partisanship, policy positions, gender and
race. We consider the problem of identifying optimal candidate profiles.
Because the number of unique feature combinations far exceeds the total number
of observations in a typical conjoint experiment, it is impossible to determine
the optimal profile exactly. To address this identification challenge, we
derive an optimal stochastic intervention that represents a probability
distribution of various attributes aimed at achieving the most favorable
average outcome. We first consider an environment where one political party
optimizes their candidate selection. We then move to the more realistic case
where two political parties optimize their own candidate selection
simultaneously and in opposition to each other. We apply the proposed
methodology to an existing candidate choice conjoint experiment concerning vote
choice for US president. We find that, in contrast to the non-adversarial
approach, expected outcomes in the adversarial regime fall within range of
historical electoral outcomes, with optimal strategies suggested by the method
more likely to match the actual observed candidates compared to strategies
derived from a non-adversarial approach. These findings indicate that
incorporating adversarial dynamics into conjoint analysis may yield unique
insight into social science data from experiments.
Generative AI for Character Animation: A Comprehensive Survey of
  Techniques, Applications, and Future Directions
Authors: name: Mohammad Mahdi Abootorabi, name: Omid Ghahroodi, name: Pardis Sadat Zahraei, name: Hossein Behzadasl, name: Alireza Mirrokni, name: Mobina Salimipanah, name: Arash Rasouli, name: Bahar Behzadipour, name: Sara Azarnoush, name: Benyamin Maleki, name: Erfan Sadraiye, name: Kiarash Kiani Feriz, name: Mahdi Teymouri Nahad, name: Ali Moghadasi, name: Abolfazl Eshagh Abianeh, name: Nizi Nazar, name: Hamid R. Rabiee, name: Mahdieh Soleymani Baghshah, name: Meisam Ahmadi, name: Ehsaneddin Asgari
Published: 2025-04-27 00:09:31

Generative AI is reshaping art, gaming, and most notably animation. Recent
breakthroughs in foundation and diffusion models have reduced the time and cost
of producing animated content. Characters are central animation components,
involving motion, emotions, gestures, and facial expressions. The pace and
breadth of advances in recent months make it difficult to maintain a coherent
view of the field, motivating the need for an integrative review. Unlike
earlier overviews that treat avatars, gestures, or facial animation in
isolation, this survey offers a single, comprehensive perspective on all the
main generative AI applications for character animation. We begin by examining
the state-of-the-art in facial animation, expression rendering, image
synthesis, avatar creation, gesture modeling, motion synthesis, object
generation, and texture synthesis. We highlight leading research, practical
deployments, commonly used datasets, and emerging trends for each area. To
support newcomers, we also provide a comprehensive background section that
introduces foundational models and evaluation metrics, equipping readers with
the knowledge needed to enter the field. We discuss open challenges and map
future research directions, providing a roadmap to advance AI-driven
character-animation technologies. This survey is intended as a resource for
researchers and developers entering the field of generative AI animation or
adjacent fields. Resources are available at:
https://github.com/llm-lab-org/Generative-AI-for-Character-Animation-Survey.
UniBiomed: A Universal Foundation Model for Grounded Biomedical Image
  Interpretation
Authors: name: Linshan Wu, name: Yuxiang Nie, name: Sunan He, name: Jiaxin Zhuang, name: Hao Chen
Published: 2025-04-30 05:51:48

Multi-modal interpretation of biomedical images opens up novel opportunities
in biomedical image analysis. Conventional AI approaches typically rely on
disjointed training, i.e., Large Language Models (LLMs) for clinical text
generation and segmentation models for target extraction, which results in
inflexible real-world deployment and a failure to leverage holistic biomedical
information. To this end, we introduce UniBiomed, the first universal
foundation model for grounded biomedical image interpretation. UniBiomed is
based on a novel integration of Multi-modal Large Language Model (MLLM) and
Segment Anything Model (SAM), which effectively unifies the generation of
clinical texts and the segmentation of corresponding biomedical objects for
grounded interpretation. In this way, UniBiomed is capable of tackling a wide
range of biomedical tasks across ten diverse biomedical imaging modalities. To
develop UniBiomed, we curate a large-scale dataset comprising over 27 million
triplets of images, annotations, and text descriptions across ten imaging
modalities. Extensive validation on 84 internal and external datasets
demonstrated that UniBiomed achieves state-of-the-art performance in
segmentation, disease recognition, region-aware diagnosis, visual question
answering, and report generation. Moreover, unlike previous models that rely on
clinical experts to pre-diagnose images and manually craft precise textual or
visual prompts, UniBiomed can provide automated and end-to-end grounded
interpretation for biomedical image analysis. This represents a novel paradigm
shift in clinical workflows, which will significantly improve diagnostic
efficiency. In summary, UniBiomed represents a novel breakthrough in biomedical
AI, unlocking powerful grounded interpretation capabilities for more accurate
and efficient biomedical image analysis.
Beyond the Last Answer: Your Reasoning Trace Uncovers More than You
  Think
Authors: name: Hasan Abed Al Kader Hammoud, name: Hani Itani, name: Bernard Ghanem
Published: 2025-04-29 12:39:07

Large Language Models (LLMs) leverage step-by-step reasoning to solve complex
problems. Standard evaluation practice involves generating a complete reasoning
trace and assessing the correctness of the final answer presented at its
conclusion. In this paper, we challenge the reliance on the final answer by
posing the following two questions: Does the final answer reliably represent
the model's optimal conclusion? Can alternative reasoning paths yield different
results? To answer these questions, we analyze intermediate reasoning steps,
termed subthoughts, and propose a method based on our findings. Our approach
involves segmenting a reasoning trace into sequential subthoughts based on
linguistic cues. We start by prompting the model to generate continuations from
the end-point of each intermediate subthought. We extract a potential answer
from every completed continuation originating from different subthoughts. We
find that aggregating these answers by selecting the most frequent one (the
mode) often yields significantly higher accuracy compared to relying solely on
the answer derived from the original complete trace. Analyzing the consistency
among the answers derived from different subthoughts reveals characteristics
that correlate with the model's confidence and correctness, suggesting
potential for identifying less reliable answers. Our experiments across various
LLMs and challenging mathematical reasoning datasets (AIME2024 and AIME2025)
show consistent accuracy improvements, with gains reaching up to 13\% and 10\%
respectively. Implementation is available at:
https://github.com/hammoudhasan/SubthoughtReasoner.
Sadeed: Advancing Arabic Diacritization Through Small Language Model
Authors: name: Zeina Aldallal, name: Sara Chrouf, name: Khalil Hennara, name: Mohamed Motaism Hamed, name: Muhammad Hreden, name: Safwan AlModhayan
Published: 2025-04-30 13:37:24

Arabic text diacritization remains a persistent challenge in natural language
processing due to the language's morphological richness. In this paper, we
introduce Sadeed, a novel approach based on a fine-tuned decoder-only language
model adapted from Kuwain 1.5B Hennara et al. [2025], a compact model
originally trained on diverse Arabic corpora. Sadeed is fine-tuned on carefully
curated, high-quality diacritized datasets, constructed through a rigorous
data-cleaning and normalization pipeline. Despite utilizing modest
computational resources, Sadeed achieves competitive results compared to
proprietary large language models and outperforms traditional models trained on
similar domains. Additionally, we highlight key limitations in current
benchmarking practices for Arabic diacritization. To address these issues, we
introduce SadeedDiac-25, a new benchmark designed to enable fairer and more
comprehensive evaluation across diverse text genres and complexity levels.
Together, Sadeed and SadeedDiac-25 provide a robust foundation for advancing
Arabic NLP applications, including machine translation, text-to-speech, and
language learning tools.
Llama-3.1-FoundationAI-SecurityLLM-Base-8B Technical Report
Authors: name: Paul Kassianik, name: Baturay Saglam, name: Alexander Chen, name: Blaine Nelson, name: Anu Vellore, name: Massimo Aufiero, name: Fraser Burch, name: Dhruv Kedia, name: Avi Zohary, name: Sajana Weerawardhena, name: Aman Priyanshu, name: Adam Swanda, name: Amy Chang, name: Hyrum Anderson, name: Kojin Oshiba, name: Omar Santos, name: Yaron Singer, name: Amin Karbasi
Published: 2025-04-28 08:41:12

As transformer-based large language models (LLMs) increasingly permeate
society, they have revolutionized domains such as software engineering,
creative writing, and digital arts. However, their adoption in cybersecurity
remains limited due to challenges like scarcity of specialized training data
and complexity of representing cybersecurity-specific knowledge. To address
these gaps, we present Foundation-Sec-8B, a cybersecurity-focused LLM built on
the Llama 3.1 architecture and enhanced through continued pretraining on a
carefully curated cybersecurity corpus. We evaluate Foundation-Sec-8B across
both established and new cybersecurity benchmarks, showing that it matches
Llama 3.1-70B and GPT-4o-mini in certain cybersecurity-specific tasks. By
releasing our model to the public, we aim to accelerate progress and adoption
of AI-driven tools in both public and private cybersecurity contexts.