Orionnnn/hub-stats huggingface dataset:
DuaShepherd: Integrating Stepwise Correctness and Potential Rewards for
  Mathematical Reasoning
Authors: name: Yuanhao Wu, name: Juntong Song, name: Hanning Zhang, name: Tong Zhang, name: Cheng Niu
Published: 2025-06-21 01:11:01

In this paper, we propose DuaShepherd, a novel reward modeling framework that
integrates two complementary reward signals, correctness and potential, to
enhance the mathematical reasoning capabilities of Large Language Models
(LLMs). While correctness-based signals emphasize identification of stepwise
errors, potential-based signals focus on the likelihood of reaching the correct
final answer. We developed an automated pipeline for constructing large-scale
reward modeling dataset with both signals. A unified, multi-head architecture
was explored to train the two reward models in a multi-task setup,
demonstrating benefits from learning both correctness and potential in
parallel. By combining these two signals into a compound probability, our model
achieves consistent performance improvements across multiple benchmarks.
Empirical evaluations on MATH500 and ProcessBench confirm that this combined
reward significantly outperforms models trained on either reward type alone,
achieving state-of-the-art performance under comparable resource constraints.
PhysRig: Differentiable Physics-Based Skinning and Rigging Framework for
  Realistic Articulated Object Modeling
Authors: name: Hao Zhang, name: Haolan Xu, name: Chun Feng, name: Varun Jampani, name: Narendra Ahuja
Published: 2025-06-26 01:58:09

Skinning and rigging are fundamental components in animation, articulated
object reconstruction, motion transfer, and 4D generation. Existing approaches
predominantly rely on Linear Blend Skinning (LBS), due to its simplicity and
differentiability. However, LBS introduces artifacts such as volume loss and
unnatural deformations, and it fails to model elastic materials like soft
tissues, fur, and flexible appendages (e.g., elephant trunks, ears, and fatty
tissues). In this work, we propose PhysRig: a differentiable physics-based
skinning and rigging framework that overcomes these limitations by embedding
the rigid skeleton into a volumetric representation (e.g., a tetrahedral mesh),
which is simulated as a deformable soft-body structure driven by the animated
skeleton. Our method leverages continuum mechanics and discretizes the object
as particles embedded in an Eulerian background grid to ensure
differentiability with respect to both material properties and skeletal motion.
Additionally, we introduce material prototypes, significantly reducing the
learning space while maintaining high expressiveness. To evaluate our
framework, we construct a comprehensive synthetic dataset using meshes from
Objaverse, The Amazing Animals Zoo, and MixaMo, covering diverse object
categories and motion patterns. Our method consistently outperforms traditional
LBS-based approaches, generating more realistic and physically plausible
results. Furthermore, we demonstrate the applicability of our framework in the
pose transfer task highlighting its versatility for articulated object
modeling.
DiLoCoX: A Low-Communication Large-Scale Training Framework for
  Decentralized Cluster
Authors: name: Ji Qi, name: WenPeng Zhu, name: Li Li, name: Ming Wu, name: YingJun Wu, name: Wu He, name: Xun Gao, name: Jason Zeng, name: Michael Heinrich
Published: 2025-06-26 13:45:04

The distributed training of foundation models, particularly large language
models (LLMs), demands a high level of communication. Consequently, it is
highly dependent on a centralized cluster with fast and reliable interconnects.
Can we conduct training on slow networks and thereby unleash the power of
decentralized clusters when dealing with models exceeding 100 billion
parameters? In this paper, we propose DiLoCoX, a low-communication large-scale
decentralized cluster training framework. It combines Pipeline Parallelism with
Dual Optimizer Policy, One-Step-Delay Overlap of Communication and Local
Training, and an Adaptive Gradient Compression Scheme. This combination
significantly improves the scale of parameters and the speed of model
pre-training. We justify the benefits of one-step-delay overlap of
communication and local training, as well as the adaptive gradient compression
scheme, through a theoretical analysis of convergence. Empirically, we
demonstrate that DiLoCoX is capable of pre-training a 107B foundation model
over a 1Gbps network. Compared to vanilla AllReduce, DiLoCoX can achieve a 357x
speedup in distributed training while maintaining negligible degradation in
model convergence. To the best of our knowledge, this is the first
decentralized training framework successfully applied to models with over 100
billion parameters.
FairyGen: Storied Cartoon Video from a Single Child-Drawn Character
Authors: name: Jiayi Zheng, name: Xiaodong Cun
Published: 2025-06-26 13:58:16

We propose FairyGen, an automatic system for generating story-driven cartoon
videos from a single child's drawing, while faithfully preserving its unique
artistic style. Unlike previous storytelling methods that primarily focus on
character consistency and basic motion, FairyGen explicitly disentangles
character modeling from stylized background generation and incorporates
cinematic shot design to support expressive and coherent storytelling. Given a
single character sketch, we first employ an MLLM to generate a structured
storyboard with shot-level descriptions that specify environment settings,
character actions, and camera perspectives. To ensure visual consistency, we
introduce a style propagation adapter that captures the character's visual
style and applies it to the background, faithfully retaining the character's
full visual identity while synthesizing style-consistent scenes. A shot design
module further enhances visual diversity and cinematic quality through frame
cropping and multi-view synthesis based on the storyboard. To animate the
story, we reconstruct a 3D proxy of the character to derive physically
plausible motion sequences, which are then used to fine-tune an MMDiT-based
image-to-video diffusion model. We further propose a two-stage motion
customization adapter: the first stage learns appearance features from
temporally unordered frames, disentangling identity from motion; the second
stage models temporal dynamics using a timestep-shift strategy with frozen
identity weights. Once trained, FairyGen directly renders diverse and coherent
video scenes aligned with the storyboard. Extensive experiments demonstrate
that our system produces animations that are stylistically faithful,
narratively structured natural motion, highlighting its potential for
personalized and engaging story animation. The code will be available at
https://github.com/GVCLab/FairyGen
Learning to Skip the Middle Layers of Transformers
Authors: name: Tim Lawson, name: Laurence Aitchison
Published: 2025-06-26 09:01:19

Conditional computation is a popular strategy to make Transformers more
efficient. Existing methods often target individual modules (e.g.,
mixture-of-experts layers) or skip layers independently of one another.
However, interpretability research has demonstrated that the middle layers of
Transformers exhibit greater redundancy, and that early layers aggregate
information into token positions. Guided by these insights, we propose a novel
architecture that dynamically skips a variable number of layers from the middle
outward. In particular, a learned gating mechanism determines whether to bypass
a symmetric span of central blocks based on the input, and a gated attention
mechanism prevents subsequent tokens from attending to skipped token positions.
Residual norms are controlled with a 'sandwich' or 'perilayernorm' scheme and
gate sparsity with an adaptive regularization loss. We had aimed to reduce
compute requirements for 'simpler' tokens and potentially foster an emergent
multi-level representational hierarchy but, at the scales investigated, our
approach does not achieve improvements in the trade-off between validation
cross-entropy and estimated FLOPs compared to dense baselines with fewer
layers. We release our code at https://github.com/tim-lawson/skip-middle.
MADrive: Memory-Augmented Driving Scene Modeling
Authors: name: Polina Karpikova, name: Daniil Selikhanovych, name: Kirill Struminsky, name: Ruslan Musaev, name: Maria Golitsyna, name: Dmitry Baranchuk
Published: 2025-06-26 17:41:07

Recent advances in scene reconstruction have pushed toward highly realistic
modeling of autonomous driving (AD) environments using 3D Gaussian splatting.
However, the resulting reconstructions remain closely tied to the original
observations and struggle to support photorealistic synthesis of significantly
altered or novel driving scenarios. This work introduces MADrive, a
memory-augmented reconstruction framework designed to extend the capabilities
of existing scene reconstruction methods by replacing observed vehicles with
visually similar 3D assets retrieved from a large-scale external memory bank.
Specifically, we release MAD-Cars, a curated dataset of {sim}70K 360{\deg}
car videos captured in the wild and present a retrieval module that finds the
most similar car instances in the memory bank, reconstructs the corresponding
3D assets from video, and integrates them into the target scene through
orientation alignment and relighting. The resulting replacements provide
complete multi-view representations of vehicles in the scene, enabling
photorealistic synthesis of substantially altered configurations, as
demonstrated in our experiments. Project page:
https://yandex-research.github.io/madrive/
MuseControlLite: Multifunctional Music Generation with Lightweight
  Conditioners
Authors: name: Fang-Duo Tsai, name: Shih-Lun Wu, name: Weijaw Lee, name: Sheng-Ping Yang, name: Bo-Rui Chen, name: Hao-Chung Cheng, name: Yi-Hsuan Yang
Published: 2025-06-23 15:08:03

We propose MuseControlLite, a lightweight mechanism designed to fine-tune
text-to-music generation models for precise conditioning using various
time-varying musical attributes and reference audio signals. The key finding is
that positional embeddings, which have been seldom used by text-to-music
generation models in the conditioner for text conditions, are critical when the
condition of interest is a function of time. Using melody control as an
example, our experiments show that simply adding rotary positional embeddings
to the decoupled cross-attention layers increases control accuracy from 56.6%
to 61.1%, while requiring 6.75 times fewer trainable parameters than
state-of-the-art fine-tuning mechanisms, using the same pre-trained diffusion
Transformer model of Stable Audio Open. We evaluate various forms of musical
attribute control, audio inpainting, and audio outpainting, demonstrating
improved controllability over MusicGen-Large and Stable Audio Open ControlNet
at a significantly lower fine-tuning cost, with only 85M trainble parameters.
Source code, model checkpoints, and demo examples are available at:
https://musecontrollite.github.io/web/.
HeurAgenix: Leveraging LLMs for Solving Complex Combinatorial
  Optimization Challenges
Authors: name: Xianliang Yang, name: Ling Zhang, name: Haolong Qian, name: Lei Song, name: Jiang Bian
Published: 2025-06-18 07:20:01

Heuristic algorithms play a vital role in solving combinatorial optimization
(CO) problems, yet traditional designs depend heavily on manual expertise and
struggle to generalize across diverse instances. We introduce
HeurAgenix, a two-stage hyper-heuristic framework powered by large
language models (LLMs) that first evolves heuristics and then selects among
them automatically. In the heuristic evolution phase, HeurAgenix leverages an
LLM to compare seed heuristic solutions with higher-quality solutions and
extract reusable evolution strategies. During problem solving, it dynamically
picks the most promising heuristic for each problem state, guided by the LLM's
perception ability. For flexibility, this selector can be either a
state-of-the-art LLM or a fine-tuned lightweight model with lower inference
cost. To mitigate the scarcity of reliable supervision caused by CO complexity,
we fine-tune the lightweight heuristic selector with a dual-reward mechanism
that jointly exploits singals from selection preferences and state perception,
enabling robust selection under noisy annotations. Extensive experiments on
canonical benchmarks show that HeurAgenix not only outperforms existing
LLM-based hyper-heuristics but also matches or exceeds specialized solvers.
Code is available at https://github.com/microsoft/HeurAgenix.
FaSTA^*: Fast-Slow Toolpath Agent with Subroutine Mining for Efficient
  Multi-turn Image Editing
Authors: name: Advait Gupta, name: Rishie Raj, name: Dang Nguyen, name: Tianyi Zhou
Published: 2025-06-26 00:33:43

We develop a cost-efficient neurosymbolic agent to address challenging
multi-turn image editing tasks such as "Detect the bench in the image while
recoloring it to pink. Also, remove the cat for a clearer view and recolor the
wall to yellow.'' It combines the fast, high-level subtask planning by large
language models (LLMs) with the slow, accurate, tool-use, and local A^*
search per subtask to find a cost-efficient toolpath -- a sequence of calls to
AI tools. To save the cost of A^* on similar subtasks, we perform inductive
reasoning on previously successful toolpaths via LLMs to continuously
extract/refine frequently used subroutines and reuse them as new tools for
future tasks in an adaptive fast-slow planning, where the higher-level
subroutines are explored first, and only when they fail, the low-level A^*
search is activated. The reusable symbolic subroutines considerably save
exploration cost on the same types of subtasks applied to similar images,
yielding a human-like fast-slow toolpath agent "FaSTA^*'': fast subtask
planning followed by rule-based subroutine selection per subtask is attempted
by LLMs at first, which is expected to cover most tasks, while slow A^*
search is only triggered for novel and challenging subtasks. By comparing with
recent image editing approaches, we demonstrate FaSTA^* is significantly more
computationally efficient while remaining competitive with the state-of-the-art
baseline in terms of success rate.
Whole-Body Conditioned Egocentric Video Prediction
Authors: name: Yutong Bai, name: Danny Tran, name: Amir Bar, name: Yann LeCun, name: Trevor Darrell, name: Jitendra Malik
Published: 2025-06-26 17:59:59

We train models to Predict Ego-centric Video from human Actions (PEVA), given
the past video and an action represented by the relative 3D body pose. By
conditioning on kinematic pose trajectories, structured by the joint hierarchy
of the body, our model learns to simulate how physical human actions shape the
environment from a first-person point of view. We train an auto-regressive
conditional diffusion transformer on Nymeria, a large-scale dataset of
real-world egocentric video and body pose capture. We further design a
hierarchical evaluation protocol with increasingly challenging tasks, enabling
a comprehensive analysis of the model's embodied prediction and control
abilities. Our work represents an initial attempt to tackle the challenges of
modeling complex real-world environments and embodied agent behaviors with
video prediction from the perspective of a human.
Arch-Router: Aligning LLM Routing with Human Preferences
Authors: name: Co Tran, name: Salman Paracha, name: Adil Hafeez, name: Shuguang Chen
Published: 2025-06-19 23:57:41

With the rapid proliferation of large language models (LLMs) -- each
optimized for different strengths, style, or latency/cost profile -- routing
has become an essential technique to operationalize the use of different
models. However, existing LLM routing approaches are limited in two key ways:
they evaluate performance using benchmarks that often fail to capture human
preferences driven by subjective evaluation criteria, and they typically select
from a limited pool of models. In this work, we propose a preference-aligned
routing framework that guides model selection by matching queries to
user-defined domains (e.g., travel) or action types (e.g., image editing) --
offering a practical mechanism to encode preferences in routing decisions.
Specifically, we introduce Arch-Router, a compact 1.5B model that
learns to map queries to domain-action preferences for model routing decisions.
Our approach also supports seamlessly adding new models for routing without
requiring retraining or architectural modifications. Experiments on
conversational datasets demonstrate that our approach achieves state-of-the-art
(SOTA) results in matching queries with human preferences, outperforming top
proprietary models. Our approach captures subjective evaluation criteria and
makes routing decisions more transparent and flexible. Our model is available
at: https://huggingface.co/katanemo/Arch-Router-1.5B.
Where to find Grokking in LLM Pretraining? Monitor
  Memorization-to-Generalization without Test
Authors: name: Ziyue Li, name: Chenrui Fan, name: Tianyi Zhou
Published: 2025-06-26 17:59:58

Grokking, i.e., test performance keeps improving long after training loss
converged, has been recently witnessed in neural network training, making the
mechanism of generalization and other emerging capabilities such as reasoning
mysterious. While prior studies usually train small models on a few toy or
highly-specific tasks for thousands of epochs, we conduct the first study of
grokking on checkpoints during one-pass pretraining of a 7B large language
model (LLM), i.e., OLMoE. We compute the training loss and evaluate
generalization on diverse benchmark tasks, including math reasoning, code
generation, and commonsense/domain-specific knowledge retrieval tasks.
  Our study, for the first time, verifies that grokking still happens in the
pretraining of large-scale foundation models, though different data may enter
grokking stages asynchronously. We further demystify grokking's "emergence of
generalization" by investigating LLM internal dynamics. Specifically, we find
that training samples' pathways (i.e., expert choices across layers) evolve
from random, instance-specific to more structured and shareable between samples
during grokking. Also, the complexity of a sample's pathway reduces despite the
converged loss. These indicate a memorization-to-generalization conversion,
providing a mechanistic explanation of delayed generalization. In the study, we
develop two novel metrics to quantify pathway distance and the complexity of a
single pathway. We show their ability to predict the generalization improvement
on diverse downstream tasks. They are efficient, simple to compute and solely
dependent on training data. Hence, they have practical value for pretraining,
enabling us to monitor the generalization performance without finetuning and
test. Theoretically, we show that more structured pathways reduce model
complexity and improve the generalization bound.
An Agentic System for Rare Disease Diagnosis with Traceable Reasoning
Authors: name: Weike Zhao, name: Chaoyi Wu, name: Yanjie Fan, name: Xiaoman Zhang, name: Pengcheng Qiu, name: Yuze Sun, name: Xiao Zhou, name: Yanfeng Wang, name: Ya Zhang, name: Yongguo Yu, name: Kun Sun, name: Weidi Xie
Published: 2025-06-25 13:42:26

Rare diseases collectively affect over 300 million individuals worldwide, yet
timely and accurate diagnosis remains a pervasive challenge. This is largely
due to their clinical heterogeneity, low individual prevalence, and the limited
familiarity most clinicians have with rare conditions. Here, we introduce
DeepRare, the first rare disease diagnosis agentic system powered by a large
language model (LLM), capable of processing heterogeneous clinical inputs. The
system generates ranked diagnostic hypotheses for rare diseases, each
accompanied by a transparent chain of reasoning that links intermediate
analytic steps to verifiable medical evidence.
  DeepRare comprises three key components: a central host with a long-term
memory module; specialized agent servers responsible for domain-specific
analytical tasks integrating over 40 specialized tools and web-scale,
up-to-date medical knowledge sources, ensuring access to the most current
clinical information. This modular and scalable design enables complex
diagnostic reasoning while maintaining traceability and adaptability. We
evaluate DeepRare on eight datasets. The system demonstrates exceptional
diagnostic performance among 2,919 diseases, achieving 100% accuracy for 1013
diseases. In HPO-based evaluations, DeepRare significantly outperforms other 15
methods, like traditional bioinformatics diagnostic tools, LLMs, and other
agentic systems, achieving an average Recall@1 score of 57.18% and surpassing
the second-best method (Reasoning LLM) by a substantial margin of 23.79
percentage points. For multi-modal input scenarios, DeepRare achieves 70.60% at
Recall@1 compared to Exomiser's 53.20% in 109 cases. Manual verification of
reasoning chains by clinical experts achieves 95.40% agreements. Furthermore,
the DeepRare system has been implemented as a user-friendly web application
http://raredx.cn/doctor.
WorldVLA: Towards Autoregressive Action World Model
Authors: name: Jun Cen, name: Chaohui Yu, name: Hangjie Yuan, name: Yuming Jiang, name: Siteng Huang, name: Jiayan Guo, name: Xin Li, name: Yibing Song, name: Hao Luo, name: Fan Wang, name: Deli Zhao, name: Hao Chen
Published: 2025-06-26 17:55:40

We present WorldVLA, an autoregressive action world model that unifies action
and image understanding and generation. Our WorldVLA intergrates
Vision-Language-Action (VLA) model and world model in one single framework. The
world model predicts future images by leveraging both action and image
understanding, with the purpose of learning the underlying physics of the
environment to improve action generation. Meanwhile, the action model generates
the subsequent actions based on image observations, aiding in visual
understanding and in turn helps visual generation of the world model. We
demonstrate that WorldVLA outperforms standalone action and world models,
highlighting the mutual enhancement between the world model and the action
model. In addition, we find that the performance of the action model
deteriorates when generating sequences of actions in an autoregressive manner.
This phenomenon can be attributed to the model's limited generalization
capability for action prediction, leading to the propagation of errors from
earlier actions to subsequent ones. To address this issue, we propose an
attention mask strategy that selectively masks prior actions during the
generation of the current action, which shows significant performance
improvement in the action chunk generation task.
SAM4D: Segment Anything in Camera and LiDAR Streams
Authors: name: Jianyun Xu, name: Song Wang, name: Ziqian Ni, name: Chunyong Hu, name: Sheng Yang, name: Jianke Zhu, name: Qiang Li
Published: 2025-06-26 17:59:14

We present SAM4D, a multi-modal and temporal foundation model designed for
promptable segmentation across camera and LiDAR streams. Unified Multi-modal
Positional Encoding (UMPE) is introduced to align camera and LiDAR features in
a shared 3D space, enabling seamless cross-modal prompting and interaction.
Additionally, we propose Motion-aware Cross-modal Memory Attention (MCMA),
which leverages ego-motion compensation to enhance temporal consistency and
long-horizon feature retrieval, ensuring robust segmentation across dynamically
changing autonomous driving scenes. To avoid annotation bottlenecks, we develop
a multi-modal automated data engine that synergizes VFM-driven video masklets,
spatiotemporal 4D reconstruction, and cross-modal masklet fusion. This
framework generates camera-LiDAR aligned pseudo-labels at a speed orders of
magnitude faster than human annotation while preserving VFM-derived semantic
fidelity in point cloud representations. We conduct extensive experiments on
the constructed Waymo-4DSeg, which demonstrate the powerful cross-modal
segmentation ability and great potential in data annotation of proposed SAM4D.