Orionnnn/hub-stats huggingface dataset:
GeoDistill: Geometry-Guided Self-Distillation for Weakly Supervised
  Cross-View Localization
Authors: name: Shaowen Tong, name: Zimin Xia, name: Alexandre Alahi, name: Xuming He, name: Yujiao Shi
Published: 2025-07-15 03:00:15

Cross-view localization, the task of estimating a camera's
3-degrees-of-freedom (3-DoF) pose by aligning ground-level images with
satellite images, is crucial for large-scale outdoor applications like
autonomous navigation and augmented reality. Existing methods often rely on
fully supervised learning, which requires costly ground-truth pose annotations.
In this work, we propose GeoDistill, a Geometry guided weakly supervised self
distillation framework that uses teacher-student learning with Field-of-View
(FoV)-based masking to enhance local feature learning for robust cross-view
localization. In GeoDistill, the teacher model localizes a panoramic image,
while the student model predicts locations from a limited FoV counterpart
created by FoV-based masking. By aligning the student's predictions with those
of the teacher, the student focuses on key features like lane lines and ignores
textureless regions, such as roads. This results in more accurate predictions
and reduced uncertainty, regardless of whether the query images are panoramas
or limited FoV images. Our experiments show that GeoDistill significantly
improves localization performance across different frameworks. Additionally, we
introduce a novel orientation estimation network that predicts relative
orientation without requiring precise planar position ground truth. GeoDistill
provides a scalable and efficient solution for real-world cross-view
localization challenges. Code and model can be found at
https://github.com/tongshw/GeoDistill.
Data Mixing Agent: Learning to Re-weight Domains for Continual
  Pre-training
Authors: name: Kailai Yang, name: Xiao Liu, name: Lei Ji, name: Hao Li, name: Yeyun Gong, name: Peng Cheng, name: Mao Yang
Published: 2025-07-21 14:01:54

Continual pre-training on small-scale task-specific data is an effective
method for improving large language models in new target fields, yet it risks
catastrophic forgetting of their original capabilities. A common solution is to
re-weight training data mixtures from source and target fields on a domain
space to achieve balanced performance. Previous domain reweighting strategies
rely on manual designation with certain heuristics based on human intuition or
empirical results. In this work, we prove that more general heuristics can be
parameterized by proposing Data Mixing Agent, the first model-based, end-to-end
framework that learns to re-weight domains. The agent learns generalizable
heuristics through reinforcement learning on large quantities of data mixing
trajectories with corresponding feedback from an evaluation environment.
Experiments in continual pre-training on math reasoning show that Data Mixing
Agent outperforms strong baselines in achieving balanced performance across
source and target field benchmarks. Furthermore, it generalizes well across
unseen source fields, target models, and domain spaces without retraining.
Direct application to the code generation field also indicates its adaptability
across target domains. Further analysis showcases the agents' well-aligned
heuristics with human intuitions and their efficiency in achieving superior
model performance with less source-field data.
SeC: Advancing Complex Video Object Segmentation via Progressive Concept
  Construction
Authors: name: Zhixiong Zhang, name: Shuangrui Ding, name: Xiaoyi Dong, name: Songxin He, name: Jianfan Lin, name: Junsong Tang, name: Yuhang Zang, name: Yuhang Cao, name: Dahua Lin, name: Jiaqi Wang
Published: 2025-07-21 17:59:02

Video Object Segmentation (VOS) is a core task in computer vision, requiring
models to track and segment target objects across video frames. Despite notable
advances with recent efforts, current techniques still lag behind human
capabilities in handling drastic visual variations, occlusions, and complex
scene changes. This limitation arises from their reliance on appearance
matching, neglecting the human-like conceptual understanding of objects that
enables robust identification across temporal dynamics. Motivated by this gap,
we propose Segment Concept (SeC), a concept-driven segmentation framework that
shifts from conventional feature matching to the progressive construction and
utilization of high-level, object-centric representations. SeC employs Large
Vision-Language Models (LVLMs) to integrate visual cues across diverse frames,
constructing robust conceptual priors. During inference, SeC forms a
comprehensive semantic representation of the target based on processed frames,
realizing robust segmentation of follow-up frames. Furthermore, SeC adaptively
balances LVLM-based semantic reasoning with enhanced feature matching,
dynamically adjusting computational efforts based on scene complexity. To
rigorously assess VOS methods in scenarios demanding high-level conceptual
reasoning and robust semantic understanding, we introduce the Semantic Complex
Scenarios Video Object Segmentation benchmark (SeCVOS). SeCVOS comprises 160
manually annotated multi-scenario videos designed to challenge models with
substantial appearance variations and dynamic scene transformations. In
particular, SeC achieves an 11.8-point improvement over SAM 2.1 on SeCVOS,
establishing a new state-of-the-art in concept-aware video object segmentation.
Gaussian Splatting with Discretized SDF for Relightable Assets
Authors: name: Zuo-Liang Zhu, name: Jian Yang, name: Beibei Wang
Published: 2025-07-21 13:52:33

3D Gaussian splatting (3DGS) has shown its detailed expressive ability and
highly efficient rendering speed in the novel view synthesis (NVS) task. The
application to inverse rendering still faces several challenges, as the
discrete nature of Gaussian primitives makes it difficult to apply geometry
constraints. Recent works introduce the signed distance field (SDF) as an extra
continuous representation to regularize the geometry defined by Gaussian
primitives. It improves the decomposition quality, at the cost of increasing
memory usage and complicating training. Unlike these works, we introduce a
discretized SDF to represent the continuous SDF in a discrete manner by
encoding it within each Gaussian using a sampled value. This approach allows us
to link the SDF with the Gaussian opacity through an SDF-to-opacity
transformation, enabling rendering the SDF via splatting and avoiding the
computational cost of ray marching.The key challenge is to regularize the
discrete samples to be consistent with the underlying SDF, as the discrete
representation can hardly apply the gradient-based constraints (\eg Eikonal
loss). For this, we project Gaussians onto the zero-level set of SDF and
enforce alignment with the surface from splatting, namely a projection-based
consistency loss. Thanks to the discretized SDF, our method achieves higher
relighting quality, while requiring no extra memory beyond GS and avoiding
complex manually designed optimization. The experiments reveal that our method
outperforms existing Gaussian-based inverse rendering methods. Our code is
available at https://github.com/NK-CS-ZZL/DiscretizedSDF.
The Invisible Leash: Why RLVR May Not Escape Its Origin
Authors: name: Fang Wu, name: Weihao Xuan, name: Ximing Lu, name: Zaid Harchaoui, name: Yejin Choi
Published: 2025-07-20 07:04:08

Recent advances in large reasoning models highlight Reinforcement Learning
with Verifiable Rewards (RLVR) as a promising method for enhancing AI's
capabilities, particularly in solving complex logical tasks. However, it
remains unclear whether RLVR truly expands a model's reasoning boundary or
merely amplifies high-reward outputs that the base model already knows for
improved precision. This study presents a theoretical and empirical
investigation that provides fresh insights into the potential limits of RLVR.
First, we offer a new theoretical perspective that RLVR is constrained by the
base model's support-unable to sample solutions with zero initial
probability-and operates as a conservative reweighting mechanism that may
restrict the discovery of entirely original solutions. We also identify an
entropy-reward tradeoff: while RLVR reliably enhances precision, it may
progressively narrow exploration and potentially overlook correct yet
underrepresented solutions. Extensive empirical experiments validate that while
RLVR consistently improves pass@1, the shrinkage of empirical support generally
outweighs the expansion of empirical support under larger sampling budgets,
failing to recover correct answers that were previously accessible to the base
model. Interestingly, we also observe that while RLVR sometimes increases
token-level entropy, resulting in greater uncertainty at each generation step,
answer-level entropy declines, indicating that these seemingly more uncertain
paths ultimately converge onto a smaller set of distinct answers. Taken
together, these findings reveal potential limits of RLVR in extending reasoning
horizons. Breaking this invisible leash may require future algorithmic
innovations such as explicit exploration mechanisms or hybrid strategies that
seed probability mass into underrepresented solution regions.
Latent Denoising Makes Good Visual Tokenizers
Authors: name: Jiawei Yang, name: Tianhong Li, name: Lijie Fan, name: Yonglong Tian, name: Yue Wang
Published: 2025-07-21 17:59:56

Despite their fundamental role, it remains unclear what properties could make
visual tokenizers more effective for generative modeling. We observe that
modern generative models share a conceptually similar training objective --
reconstructing clean signals from corrupted inputs such as Gaussian noise or
masking -- a process we term denoising. Motivated by this insight, we propose
aligning tokenizer embeddings directly with the downstream denoising objective,
encouraging latent embeddings to be more easily reconstructed even when heavily
corrupted. To achieve this, we introduce the Latent Denoising Tokenizer
(l-DeTok), a simple yet effective tokenizer trained to reconstruct clean images
from latent embeddings corrupted by interpolative noise and random masking.
Extensive experiments on ImageNet 256x256 demonstrate that our tokenizer
consistently outperforms standard tokenizers across six representative
generative models. Our findings highlight denoising as a fundamental design
principle for tokenizer development, and we hope it could motivate new
perspectives for future tokenizer design.
Robust 3D-Masked Part-level Editing in 3D Gaussian Splatting with
  Regularized Score Distillation Sampling
Authors: name: Hayeon Kim, name: Ji Ha Jang, name: Se Young Chun
Published: 2025-07-15 07:54:11

Recent advances in 3D neural representations and instance-level editing
models have enabled the efficient creation of high-quality 3D content. However,
achieving precise local 3D edits remains challenging, especially for Gaussian
Splatting, due to inconsistent multi-view 2D part segmentations and inherently
ambiguous nature of Score Distillation Sampling (SDS) loss. To address these
limitations, we propose RoMaP, a novel local 3D Gaussian editing framework that
enables precise and drastic part-level modifications. First, we introduce a
robust 3D mask generation module with our 3D-Geometry Aware Label Prediction
(3D-GALP), which uses spherical harmonics (SH) coefficients to model
view-dependent label variations and soft-label property, yielding accurate and
consistent part segmentations across viewpoints. Second, we propose a
regularized SDS loss that combines the standard SDS loss with additional
regularizers. In particular, an L1 anchor loss is introduced via our Scheduled
Latent Mixing and Part (SLaMP) editing method, which generates high-quality
part-edited 2D images and confines modifications only to the target region
while preserving contextual coherence. Additional regularizers, such as
Gaussian prior removal, further improve flexibility by allowing changes beyond
the existing context, and robust 3D masking prevents unintended edits.
Experimental results demonstrate that our RoMaP achieves state-of-the-art local
3D editing on both reconstructed and generated Gaussian scenes and objects
qualitatively and quantitatively, making it possible for more robust and
flexible part-level 3D Gaussian editing. Code is available at
https://janeyeon.github.io/romap.
Towards Video Thinking Test: A Holistic Benchmark for Advanced Video
  Reasoning and Understanding
Authors: name: Yuanhan Zhang, name: Yunice Chew, name: Yuhao Dong, name: Aria Leo, name: Bo Hu, name: Ziwei Liu
Published: 2025-07-20 16:30:33

Human intelligence requires correctness and robustness, with the former being
foundational for the latter. In video understanding, correctness ensures the
accurate interpretation of visual content, and robustness maintains consistent
performance in challenging conditions. Despite advances in video large language
models (video LLMs), existing benchmarks inadequately reflect the gap between
these models and human intelligence in maintaining correctness and robustness
in video interpretation. We introduce the Video Thinking Test (Video-TT), to
assess if video LLMs can interpret real-world videos as effectively as humans.
Video-TT reflects genuine gaps in understanding complex visual narratives, and
evaluates robustness against natural adversarial questions. Video-TT comprises
1,000 YouTube Shorts videos, each with one open-ended question and four
adversarial questions that probe visual and narrative complexity. Our
evaluation shows a significant gap between video LLMs and human performance.
GR-3 Technical Report
Authors: name: Chilam Cheang, name: Sijin Chen, name: Zhongren Cui, name: Yingdong Hu, name: Liqun Huang, name: Tao Kong, name: Hang Li, name: Yifeng Li, name: Yuxiao Liu, name: Xiao Ma, name: Hao Niu, name: Wenxuan Ou, name: Wanli Peng, name: Zeyu Ren, name: Haixin Shi, name: Jiawen Tian, name: Hongtao Wu, name: Xin Xiao, name: Yuyang Xiao, name: Jiafeng Xu, name: Yichu Yang
Published: 2025-07-21 10:54:13

We report our recent progress towards building generalist robot policies, the
development of GR-3. GR-3 is a large-scale vision-language-action (VLA) model.
It showcases exceptional capabilities in generalizing to novel objects,
environments, and instructions involving abstract concepts. Furthermore, it can
be efficiently fine-tuned with minimal human trajectory data, enabling rapid
and cost-effective adaptation to new settings. GR-3 also excels in handling
long-horizon and dexterous tasks, including those requiring bi-manual
manipulation and mobile movement, showcasing robust and reliable performance.
These capabilities are achieved through a multi-faceted training recipe that
includes co-training with web-scale vision-language data, efficient fine-tuning
from human trajectory data collected via VR devices, and effective imitation
learning with robot trajectory data. In addition, we introduce ByteMini, a
versatile bi-manual mobile robot designed with exceptional flexibility and
reliability, capable of accomplishing a wide range of tasks when integrated
with GR-3. Through extensive real-world experiments, we show GR-3 surpasses the
state-of-the-art baseline method, pi_0, on a wide variety of challenging
tasks. We hope GR-3 can serve as a step towards building generalist robots
capable of assisting humans in daily life.
STITCH: Simultaneous Thinking and Talking with Chunked Reasoning for
  Spoken Language Models
Authors: name: Cheng-Han Chiang, name: Xiaofei Wang, name: Linjie Li, name: Chung-Ching Lin, name: Kevin Lin, name: Shujie Liu, name: Zhendong Wang, name: Zhengyuan Yang, name: Hung-yi Lee, name: Lijuan Wang
Published: 2025-07-21 08:30:03

Spoken Language Models (SLMs) are designed to take speech inputs and produce
spoken responses. However, current SLMs lack the ability to perform an
internal, unspoken thinking process before responding. In contrast, humans
typically engage in complex mental reasoning internally, enabling them to
communicate ideas clearly and concisely. Thus, integrating an unspoken thought
process into SLMs is highly desirable. While naively generating a complete
chain-of-thought (CoT) reasoning before starting to talk can enable thinking
for SLMs, this induces additional latency for the speech response, as the CoT
reasoning can be arbitrarily long. To solve this issue, we propose Stitch, a
novel generation method that alternates between the generation of unspoken
reasoning chunks and spoken response chunks. Since the audio duration of a
chunk of spoken response is much longer than the time to generate the tokens in
a chunk of spoken response, we use the remaining free time to generate the
unspoken reasoning tokens. When a chunk of audio is played to the user, the
model continues to generate the next unspoken reasoning chunk, achieving
simultaneous thinking and talking. Remarkably, Stitch matches the latency of
baselines that cannot generate unspoken CoT by design while outperforming those
baselines by 15% on math reasoning datasets; Stitch also performs equally well
on non-reasoning datasets as those baseline models. Some animations and
demonstrations are on the project page: https://d223302.github.io/STITCH.
MiroMind-M1: An Open-Source Advancement in Mathematical Reasoning via
  Context-Aware Multi-Stage Policy Optimization
Authors: name: Xingxuan Li, name: Yao Xiao, name: Dianwen Ng, name: Hai Ye, name: Yue Deng, name: Xiang Lin, name: Bin Wang, name: Zhanfeng Mo, name: Chong Zhang, name: Yueyi Zhang, name: Zonglin Yang, name: Ruilin Li, name: Lei Lei, name: Shihao Xu, name: Han Zhao, name: Weiling Chen, name: Feng Ji, name: Lidong Bing
Published: 2025-07-19 16:21:23

Large language models have recently evolved from fluent text generation to
advanced reasoning across diverse domains, giving rise to reasoning language
models. Among these domains, mathematical reasoning serves as a representative
benchmark as it requires precise multi-step logic and abstract reasoning, which
can be generalized to other tasks. While closed-source RLMs such as GPT-o3
demonstrate impressive reasoning capabilities, their proprietary nature limits
transparency and reproducibility. Although many open-source projects aim to
close this gap, most of them lack sufficient openness by omitting critical
resources such as datasets and detailed training configurations, which hinders
reproducibility. To contribute toward greater transparency in RLM development,
we introduce the MiroMind-M1 series, a set of fully open-source RLMs built on
the Qwen-2.5 backbone that match or exceed the performance of existing
open-source RLMs. Specifically, our models are trained in two stages: SFT on a
carefully curated corpus of 719K math-reasoning problems with verified CoT
trajectories, followed by RLVR on 62K challenging and verifiable problems. To
enhance the robustness and efficiency of the RLVR process, we introduce
Context-Aware Multi-Stage Policy Optimization, an algorithm that integrates
length-progressive training with an adaptive repetition penalty to encourage
context-aware RL training. Our model achieves state-of-the-art or competitive
performance and superior token efficiency among Qwen-2.5-based open-source 7B
and 32B models on the AIME24, AIME25, and MATH benchmarks. To facilitate
reproducibility, we release the complete stack: models (MiroMind-M1-SFT-7B,
MiroMind-M1-RL-7B, MiroMind-M1-RL-32B); datasets (MiroMind-M1-SFT-719K,
MiroMind-M1-RL-62K); and all training and evaluation configurations. We hope
these resources will support further research and foster community advancement.
Streaming 4D Visual Geometry Transformer
Authors: name: Dong Zhuo, name: Wenzhao Zheng, name: Jiahe Guo, name: Yuqi Wu, name: Jie Zhou, name: Jiwen Lu
Published: 2025-07-15 17:59:57

Perceiving and reconstructing 4D spatial-temporal geometry from videos is a
fundamental yet challenging computer vision task. To facilitate interactive and
real-time applications, we propose a streaming 4D visual geometry transformer
that shares a similar philosophy with autoregressive large language models. We
explore a simple and efficient design and employ a causal transformer
architecture to process the input sequence in an online manner. We use temporal
causal attention and cache the historical keys and values as implicit memory to
enable efficient streaming long-term 4D reconstruction. This design can handle
real-time 4D reconstruction by incrementally integrating historical information
while maintaining high-quality spatial consistency. For efficient training, we
propose to distill knowledge from the dense bidirectional visual geometry
grounded transformer (VGGT) to our causal model. For inference, our model
supports the migration of optimized efficient attention operator (e.g.,
FlashAttention) from the field of large language models. Extensive experiments
on various 4D geometry perception benchmarks demonstrate that our model
increases the inference speed in online scenarios while maintaining competitive
performance, paving the way for scalable and interactive 4D vision systems.
Code is available at: https://github.com/wzzheng/StreamVGGT.
LLM Economist: Large Population Models and Mechanism Design in
  Multi-Agent Generative Simulacra
Authors: name: Seth Karten, name: Wenzhe Li, name: Zihan Ding, name: Samuel Kleiner, name: Yu Bai, name: Chi Jin
Published: 2025-07-21 17:21:14

We present the LLM Economist, a novel framework that uses agent-based
modeling to design and assess economic policies in strategic environments with
hierarchical decision-making. At the lower level, bounded rational worker
agents -- instantiated as persona-conditioned prompts sampled from U.S.
Census-calibrated income and demographic statistics -- choose labor supply to
maximize text-based utility functions learned in-context. At the upper level, a
planner agent employs in-context reinforcement learning to propose
piecewise-linear marginal tax schedules anchored to the current U.S. federal
brackets. This construction endows economic simulacra with three capabilities
requisite for credible fiscal experimentation: (i) optimization of
heterogeneous utilities, (ii) principled generation of large, demographically
realistic agent populations, and (iii) mechanism design -- the ultimate nudging
problem -- expressed entirely in natural language. Experiments with populations
of up to one hundred interacting agents show that the planner converges near
Stackelberg equilibria that improve aggregate social welfare relative to Saez
solutions, while a periodic, persona-level voting procedure furthers these
gains under decentralized governance. These results demonstrate that large
language model-based agents can jointly model, simulate, and govern complex
economic systems, providing a tractable test bed for policy evaluation at the
societal scale to help build better civilizations.