Orionnnn/hub-stats huggingface dataset:
Calligrapher: Freestyle Text Image Customization
Authors: name: Yue Ma, name: Qingyan Bai, name: Hao Ouyang, name: Ka Leong Cheng, name: Qiuyu Wang, name: Hongyu Liu, name: Zichen Liu, name: Haofan Wang, name: Jingye Chen, name: Yujun Shen, name: Qifeng Chen
Published: 2025-06-30 17:59:06

We introduce Calligrapher, a novel diffusion-based framework that
innovatively integrates advanced text customization with artistic typography
for digital calligraphy and design applications. Addressing the challenges of
precise style control and data dependency in typographic customization, our
framework incorporates three key technical contributions. First, we develop a
self-distillation mechanism that leverages the pre-trained text-to-image
generative model itself alongside the large language model to automatically
construct a style-centric typography benchmark. Second, we introduce a
localized style injection framework via a trainable style encoder, which
comprises both Qformer and linear layers, to extract robust style features from
reference images. An in-context generation mechanism is also employed to
directly embed reference images into the denoising process, further enhancing
the refined alignment of target styles. Extensive quantitative and qualitative
evaluations across diverse fonts and design contexts confirm Calligrapher's
accurate reproduction of intricate stylistic details and precise glyph
positioning. By automating high-quality, visually consistent typography,
Calligrapher surpasses traditional models, empowering creative practitioners in
digital art, branding, and contextual typographic design.
VMoBA: Mixture-of-Block Attention for Video Diffusion Models
Authors: name: Jianzong Wu, name: Liang Hou, name: Haotian Yang, name: Xin Tao, name: Ye Tian, name: Pengfei Wan, name: Di Zhang, name: Yunhai Tong
Published: 2025-06-30 13:52:31

The quadratic complexity of full attention mechanisms poses a significant
bottleneck for Video Diffusion Models (VDMs) aiming to generate long-duration,
high-resolution videos. While various sparse attention methods have been
proposed, many are designed as training-free inference accelerators or do not
optimally capture the unique spatio-temporal characteristics inherent in video
data when trained natively. This paper introduces Video Mixture of Block
Attention (VMoBA), a novel sparse attention mechanism specifically adapted for
VDMs. Motivated by an in-depth analysis of attention patterns within
pre-trained video transformers, which revealed strong spatio-temporal locality,
varying query importance, and head-specific concentration levels, VMoBA
enhances the original MoBA framework with three key modifications: (1) a
layer-wise recurrent block partition scheme (1D-2D-3D) to dynamically adapt to
diverse spatio-temporal attention patterns and improve efficiency; (2) global
block selection to prioritize the most salient query-key block interactions
across an entire attention head; and (3) threshold-based block selection to
dynamically determine the number of attended blocks based on their cumulative
similarity. Extensive experiments demonstrate that VMoBA significantly
accelerates the training of VDMs on longer sequences, achieving 2.92x FLOPs and
1.48x latency speedup, while attaining comparable or even superior generation
quality to full attention. Furthermore, VMoBA exhibits competitive performance
in training-free inference, offering 2.40x FLOPs and 1.35x latency speedup for
high-res video generation.
Aha Moment Revisited: Are VLMs Truly Capable of Self Verification in
  Inference-time Scaling?
Authors: name: Mingyuan Wu, name: Meitang Li, name: Jingcheng Yang, name: Jize Jiang, name: Kaizhuo Yan, name: Zhaoheng Li, name: Minjia Zhang, name: Klara Nahrstedt
Published: 2025-06-20 18:23:48

Recent advances in large language models (LLMs) have demonstrated that
inference-time computation techniques, such as decoding-time scaling and
self-refinement, can significantly enhance reasoning capabilities without
relying on external knowledge. A key driver of this success is the emergence of
self-correction and self-verification behaviors, often elicited through
reinforcement learning (RL). In this paper, we investigate whether these
inference-time techniques extend effectively to vision-language models (VLMs),
particularly those trained with RL. We find that while decoding strategies such
as majority voting and best-of-N selection with self-verification all improve
VLM reasoning performance, generation-reliant methods such as the former
achieve significantly higher gains versus verification-reliant methods such as
the latter. Additionally, the self-correction behavior often associated with
RL-tuned models, such as aha moment, does not lead to measurable gains. We show
via extensive experimentation within the inference-time scaling framework to
identify a key root cause: RL-trained VLMs still lack robust self-verification
capabilities across both visual and textual modalities.
Jan-nano Technical Report
Authors: name: Alan Dao, name: Dinh Bach Vu
Published: 2025-06-28 05:44:57

Most language models face a fundamental tradeoff where powerful capabilities
require substantial computational resources. We shatter this constraint with
Jan-nano, a 4B parameter language model that redefines efficiency through
radical specialization: instead of trying to know everything, it masters the
art of finding anything instantly. Fine-tuned from Qwen3-4B using our novel
multi-stage RLVR system that completely eliminates reliance on next token
prediction training (SFT), Jan-nano achieves 83.2% on SimpleQA benchmark with
MCP integration while running on consumer hardware. With 128K context length,
Jan-nano proves that intelligence isn't about scale, it's about strategy.
Fractional Reasoning via Latent Steering Vectors Improves Inference Time
  Compute
Authors: name: Sheng Liu, name: Tianlang Chen, name: Pan Lu, name: Haotian Ye, name: Yizheng Chen, name: Lei Xing, name: James Zou
Published: 2025-06-18 21:15:59

Test-time compute has emerged as a powerful paradigm for improving the
performance of large language models (LLMs), where generating multiple outputs
or refining individual chains can significantly boost answer accuracy. However,
existing methods like Best-of-N, majority voting, and self-reflection typically
apply reasoning in a uniform way across inputs, overlooking the fact that
different problems may require different levels of reasoning depth. In this
work, we propose Fractional Reasoning, a training-free and model-agnostic
framework that enables continuous control over reasoning intensity at inference
time, going beyond the limitations of fixed instructional prompts. Our method
operates by extracting the latent steering vector associated with deeper
reasoning and reapplying it with a tunable scaling factor, allowing the model
to tailor its reasoning process to the complexity of each input. This supports
two key modes of test-time scaling: (1) improving output quality in
breadth-based strategies (e.g., Best-of-N, majority voting), and (2) enhancing
the correctness of individual reasoning chains in depth-based strategies (e.g.,
self-reflection). Experiments on GSM8K, MATH500, and GPQA demonstrate that
Fractional Reasoning consistently improves performance across diverse reasoning
tasks and models.
Adaptive Domain Modeling with Language Models: A Multi-Agent Approach to
  Task Planning
Authors: name: Harisankar Babu, name: Philipp Schillinger, name: Tamim Asfour
Published: 2025-06-24 13:02:06

We introduce TAPAS (Task-based Adaptation and Planning using AgentS), a
multi-agent framework that integrates Large Language Models (LLMs) with
symbolic planning to solve complex tasks without the need for manually defined
environment models. TAPAS employs specialized LLM-based agents that
collaboratively generate and adapt domain models, initial states, and goal
specifications as needed using structured tool-calling mechanisms. Through this
tool-based interaction, downstream agents can request modifications from
upstream agents, enabling adaptation to novel attributes and constraints
without manual domain redefinition. A ReAct (Reason+Act)-style execution agent,
coupled with natural language plan translation, bridges the gap between
dynamically generated plans and real-world robot capabilities. TAPAS
demonstrates strong performance in benchmark planning domains and in the
VirtualHome simulated real-world environment.
Do Vision-Language Models Have Internal World Models? Towards an Atomic
  Evaluation
Authors: name: Qiyue Gao, name: Xinyu Pi, name: Kevin Liu, name: Junrong Chen, name: Ruolan Yang, name: Xinqi Huang, name: Xinyu Fang, name: Lu Sun, name: Gautham Kishore, name: Bo Ai, name: Stone Tao, name: Mengyang Liu, name: Jiaxi Yang, name: Chao-Jung Lai, name: Chuanyang Jin, name: Jiannan Xiang, name: Benhao Huang, name: Zeming Chen, name: David Danks, name: Hao Su, name: Tianmin Shu, name: Ziqiao Ma, name: Lianhui Qin, name: Zhiting Hu
Published: 2025-06-27 03:24:29

Internal world models (WMs) enable agents to understand the world's state and
predict transitions, serving as the basis for advanced deliberative reasoning.
Recent large Vision-Language Models (VLMs), such as OpenAI o3, GPT-4o and
Gemini, exhibit potential as general-purpose WMs. While the latest studies have
evaluated and shown limitations in specific capabilities such as visual
understanding, a systematic evaluation of VLMs' fundamental WM abilities
remains absent. Drawing on comparative psychology and cognitive science, we
propose a two-stage framework that assesses Perception (visual, spatial,
temporal, quantitative, and motion) and Prediction (mechanistic simulation,
transitive inference, compositional inference) to provide an atomic evaluation
of VLMs as WMs. Guided by this framework, we introduce WM-ABench, a large-scale
benchmark comprising 23 fine-grained evaluation dimensions across 6 diverse
simulated environments with controlled counterfactual simulations. Through 660
experiments on 15 latest commercial and open-source VLMs, we find that these
models exhibit striking limitations in basic world modeling abilities. For
instance, almost all models perform at near-random accuracy when distinguishing
motion trajectories. Additionally, they lack disentangled understanding --
e.g., some models tend to believe blue objects move faster than green ones.
More rich results and analyses reveal significant gaps between VLMs and
human-level world modeling.
Spatial Mental Modeling from Limited Views
Authors: name: Baiqiao Yin, name: Qineng Wang, name: Pingyue Zhang, name: Jianshu Zhang, name: Kangrui Wang, name: Zihan Wang, name: Jieyu Zhang, name: Keshigeyan Chandrasegaran, name: Han Liu, name: Ranjay Krishna, name: Saining Xie, name: Manling Li, name: Jiajun Wu, name: Li Fei-Fei
Published: 2025-06-26 16:38:19

Can Vision Language Models (VLMs) imagine the full scene from just a few
views, like humans do? Humans form spatial mental models, internal
representations of unseen space, to reason about layout, perspective, and
motion. Our new MindCube benchmark with 21,154 questions across 3,268 images
exposes this critical gap, where existing VLMs exhibit near-random performance.
Using MindCube, we systematically evaluate how well VLMs build robust spatial
mental models through representing positions (cognitive mapping), orientations
(perspective-taking), and dynamics (mental simulation for "what-if" movements).
We then explore three approaches to help VLMs approximate spatial mental
models, including unseen intermediate views, natural language reasoning chains,
and cognitive maps. The significant improvement comes from a synergistic
approach, "map-then-reason", that jointly trains the model to first generate a
cognitive map and then reason upon it. By training models to reason over these
internal maps, we boosted accuracy from 37.8% to 60.8% (+23.0%). Adding
reinforcement learning pushed performance even further to 70.7% (+32.9%). Our
key insight is that such scaffolding of spatial mental models, actively
constructing and utilizing internal structured spatial representations with
flexible reasoning processes, significantly improves understanding of
unobservable space.
SMMILE: An Expert-Driven Benchmark for Multimodal Medical In-Context
  Learning
Authors: name: Melanie Rieff, name: Maya Varma, name: Ossian Rabow, name: Subathra Adithan, name: Julie Kim, name: Ken Chang, name: Hannah Lee, name: Nidhi Rohatgi, name: Christian Bluethgen, name: Mohamed S. Muneer, name: Jean-Benoit Delbrouck, name: Michael Moor
Published: 2025-06-26 15:08:18

Multimodal in-context learning (ICL) remains underexplored despite
significant potential for domains such as medicine. Clinicians routinely
encounter diverse, specialized tasks requiring adaptation from limited
examples, such as drawing insights from a few relevant prior cases or
considering a constrained set of differential diagnoses. While multimodal large
language models (MLLMs) have shown advances in medical visual question
answering (VQA), their ability to learn multimodal tasks from context is
largely unknown. We introduce SMMILE, the first expert-driven multimodal ICL
benchmark for medical tasks. Eleven medical experts curated problems, each
including a multimodal query and multimodal in-context examples as task
demonstrations. SMMILE encompasses 111 problems (517 question-image-answer
triplets) covering 6 medical specialties and 13 imaging modalities. We further
introduce SMMILE++, an augmented variant with 1038 permuted problems. A
comprehensive evaluation of 15 MLLMs demonstrates that most models exhibit
moderate to poor multimodal ICL ability in medical tasks. In open-ended
evaluations, ICL contributes only 8% average improvement over zero-shot on
SMMILE and 9.4% on SMMILE++. We observe a susceptibility for irrelevant
in-context examples: even a single noisy or irrelevant example can degrade
performance by up to 9.5%. Moreover, example ordering exhibits a recency bias,
i.e., placing the most relevant example last can lead to substantial
performance improvements by up to 71%. Our findings highlight critical
limitations and biases in current MLLMs when learning multimodal medical tasks
from context.
Shape-for-Motion: Precise and Consistent Video Editing with 3D Proxy
Authors: name: Yuhao Liu, name: Tengfei Wang, name: Fang Liu, name: Zhenwei Wang, name: Rynson W. H. Lau
Published: 2025-06-27 17:59:01

Recent advances in deep generative modeling have unlocked unprecedented
opportunities for video synthesis. In real-world applications, however, users
often seek tools to faithfully realize their creative editing intentions with
precise and consistent control. Despite the progress achieved by existing
methods, ensuring fine-grained alignment with user intentions remains an open
and challenging problem. In this work, we present Shape-for-Motion, a novel
framework that incorporates a 3D proxy for precise and consistent video
editing. Shape-for-Motion achieves this by converting the target object in the
input video to a time-consistent mesh, i.e., a 3D proxy, allowing edits to be
performed directly on the proxy and then inferred back to the video frames. To
simplify the editing process, we design a novel Dual-Propagation Strategy that
allows users to perform edits on the 3D mesh of a single frame, and the edits
are then automatically propagated to the 3D meshes of the other frames. The 3D
meshes for different frames are further projected onto the 2D space to produce
the edited geometry and texture renderings, which serve as inputs to a
decoupled video diffusion model for generating edited results. Our framework
supports various precise and physically-consistent manipulations across the
video frames, including pose editing, rotation, scaling, translation, texture
modification, and object composition. Our approach marks a key step toward
high-quality, controllable video editing workflows. Extensive experiments
demonstrate the superiority and effectiveness of our approach. Project page:
https://shapeformotion.github.io/
Global and Local Entailment Learning for Natural World Imagery
Authors: name: Srikumar Sastry, name: Aayush Dhakal, name: Eric Xing, name: Subash Khanal, name: Nathan Jacobs
Published: 2025-06-26 17:05:06

Learning the hierarchical structure of data in vision-language models is a
significant challenge. Previous works have attempted to address this challenge
by employing entailment learning. However, these approaches fail to model the
transitive nature of entailment explicitly, which establishes the relationship
between order and semantics within a representation space. In this work, we
introduce Radial Cross-Modal Embeddings (RCME), a framework that enables the
explicit modeling of transitivity-enforced entailment. Our proposed framework
optimizes for the partial order of concepts within vision-language models. By
leveraging our framework, we develop a hierarchical vision-language foundation
model capable of representing the hierarchy in the Tree of Life. Our
experiments on hierarchical species classification and hierarchical retrieval
tasks demonstrate the enhanced performance of our models compared to the
existing state-of-the-art models. Our code and models are open-sourced at
https://vishu26.github.io/RCME/index.html.
Performance Prediction for Large Systems via Text-to-Text Regression
Authors: name: Yash Akhauri, name: Bryan Lewandowski, name: Cheng-Hsi Lin, name: Adrian N. Reyes, name: Grant C. Forbes, name: Arissa Wongpanich, name: Bangding Yang, name: Mohamed S. Abdelfattah, name: Sagi Perel, name: Xingyou Song
Published: 2025-06-26 19:10:08

In many industries, predicting metric outcomes of large systems is a
fundamental problem, driven largely by traditional tabular regression. However,
such methods struggle on complex systems data in the wild such as configuration
files or system logs, where feature engineering is often infeasible. We propose
text-to-text regression as a general, scalable alternative. For predicting
resource efficiency on Borg, Google's massive compute cluster scheduling
system, a 60M parameter encoder-decoder, trained from random initialization,
achieves up to a near perfect 0.99 (0.9 average) rank correlation across the
entire fleet, and 100x lower MSE than tabular approaches. The model also easily
adapts to new tasks in only 500 few-shot examples and captures the densities of
complex outcome distributions. Ablation studies highlight the importance of
using encoders, increasing sequence length, and the model's inherent
uncertainty quantification. These findings pave the way for universal
simulators of real-world outcomes.
GPAS: Accelerating Convergence of LLM Pretraining via
  Gradient-Preserving Activation Scaling
Authors: name: Tianhao Chen, name: Xin Xu, name: Zijing Liu, name: Pengxiang Li, name: Xinyuan Song, name: Ajay Kumar Jaiswal, name: Fan Zhang, name: Jishan Hu, name: Yang Wang, name: Hao Chen, name: Shizhe Diao, name: Shiwei Liu, name: Yu Li, name: Yin Lu, name: Can Yang
Published: 2025-06-27 09:45:15

Modern Large Language Models, such as the LLaMA, Qwen and DeepSeek series,
predominantly adopt the Pre-LayerNorm (Pre-LN) Transformer architecture. While
being stable during pretraining and scalable to large model sizes, Pre-LN
suffers from an exponential growth in activation variance across layers,
causing the residual path to dominate over sub-layer outputs and limiting the
learning capacity of deeper layers. To mitigate this issue, we propose
Gradient-Preserving Activation Scaling (GPAS), a simple technique that can be
used in combination with existing approaches. GPAS works by scaling down the
intermediate activations while keeping their gradients unchanged. This leaves
information in the activations intact, and avoids the gradient vanishing
problem associated with gradient downscaling. Extensive experiments across
various model sizes from 71M to 1B show that GPAS achieves consistent
performance gains. Beyond enhancing Pre-LN Transformers, GPAS also shows
promise in improving alternative architectures such as Sandwich-LN and
DeepNorm, demonstrating its versatility and potential for improving training
dynamics in a wide range of settings.
In-Context Learning Strategies Emerge Rationally
Authors: name: Daniel Wurgaft, name: Ekdeep Singh Lubana, name: Core Francisco Park, name: Hidenori Tanaka, name: Gautam Reddy, name: Noah D. Goodman
Published: 2025-06-21 23:49:08

Recent work analyzing in-context learning (ICL) has identified a broad set of
strategies that describe model behavior in different experimental conditions.
We aim to unify these findings by asking why a model learns these disparate
strategies in the first place. Specifically, we start with the observation that
when trained to learn a mixture of tasks, as is popular in the literature, the
strategies learned by a model for performing ICL can be captured by a family of
Bayesian predictors: a memorizing predictor, which assumes a discrete prior on
the set of seen tasks, and a generalizing predictor, where the prior matches
the underlying task distribution. Adopting the normative lens of rational
analysis, where a learner's behavior is explained as an optimal adaptation to
data given computational constraints, we develop a hierarchical Bayesian
framework that almost perfectly predicts Transformer next-token predictions
throughout training -- without assuming access to its weights. Under this
framework, pretraining is viewed as a process of updating the posterior
probability of different strategies, and inference-time behavior as a
posterior-weighted average over these strategies' predictions. Our framework
draws on common assumptions about neural network learning dynamics, which make
explicit a tradeoff between loss and complexity among candidate strategies:
beyond how well it explains the data, a model's preference towards implementing
a strategy is dictated by its complexity. This helps explain well-known ICL
phenomena, while offering novel predictions: e.g., we show a superlinear trend
in the timescale for transitioning from generalization to memorization as task
diversity increases. Overall, our work advances an explanatory and predictive
account of ICL grounded in tradeoffs between strategy loss and complexity.
Confucius3-Math: A Lightweight High-Performance Reasoning LLM for
  Chinese K-12 Mathematics Learning
Authors: name: Lixin Wu, name: Na Cai, name: Qiao Cheng, name: Jiachen Wang, name: Yitao Duan
Published: 2025-06-23 06:23:53

We introduce Confucius3-Math, an open-source large language model with 14B
parameters that (1) runs efficiently on a single consumer-grade GPU; (2)
achieves SOTA performances on a range of mathematical reasoning tasks,
outperforming many models with significantly larger sizes. In particular, as
part of our mission to enhancing education and knowledge dissemination with AI,
Confucius3-Math is specifically committed to mathematics learning for Chinese
K-12 students and educators. Built via post-training with large-scale
reinforcement learning (RL), Confucius3-Math aligns with national curriculum
and excels at solving main-stream Chinese K-12 mathematical problems with low
cost. In this report we share our development recipe, the challenges we
encounter and the techniques we develop to overcome them. In particular, we
introduce three technical innovations: Targeted Entropy Regularization, Recent
Sample Recovery and Policy-Specific Hardness Weighting. These innovations
encompass a new entropy regularization, a novel data scheduling policy, and an
improved group-relative advantage estimator. Collectively, they significantly
stabilize the RL training, improve data efficiency, and boost performance. Our
work demonstrates the feasibility of building strong reasoning models in a
particular domain at low cost. We open-source our model and code at
https://github.com/netease-youdao/Confucius3-Math.