Orionnnn/hub-stats huggingface dataset:
Real-Time Object Detection Meets DINOv3
Authors: name: Shihua Huang, name: Yongjie Hou, name: Longfei Liu, name: Xuanlong Yu, name: Xi Shen
Published: 2025-09-25 06:14:00

Benefiting from the simplicity and effectiveness of Dense O2O and MAL, DEIM
has become the mainstream training framework for real-time DETRs, significantly
outperforming the YOLO series. In this work, we extend it with DINOv3 features,
resulting in DEIMv2. DEIMv2 spans eight model sizes from X to Atto, covering
GPU, edge, and mobile deployment. For the X, L, M, and S variants, we adopt
DINOv3-pretrained or distilled backbones and introduce a Spatial Tuning Adapter
(STA), which efficiently converts DINOv3's single-scale output into multi-scale
features and complements strong semantics with fine-grained details to enhance
detection. For ultra-lightweight models (Nano, Pico, Femto, and Atto), we
employ HGNetv2 with depth and width pruning to meet strict resource budgets.
Together with a simplified decoder and an upgraded Dense O2O, this unified
design enables DEIMv2 to achieve a superior performance-cost trade-off across
diverse scenarios, establishing new state-of-the-art results. Notably, our
largest model, DEIMv2-X, achieves 57.8 AP with only 50.3 million parameters,
surpassing prior X-scale models that require over 60 million parameters for
just 56.5 AP. On the compact side, DEIMv2-S is the first sub-10 million model
(9.71 million) to exceed the 50 AP milestone on COCO, reaching 50.9 AP. Even
the ultra-lightweight DEIMv2-Pico, with just 1.5 million parameters, delivers
38.5 AP, matching YOLOv10-Nano (2.3 million) with around 50 percent fewer
parameters. Our code and pre-trained models are available at
https://github.com/Intellindust-AI-Lab/DEIMv2
WoW: Towards a World omniscient World model Through Embodied Interaction
Authors: name: Xiaowei Chi, name: Peidong Jia, name: Chun-Kai Fan, name: Xiaozhu Ju, name: Weishi Mi, name: Kevin Zhang, name: Zhiyuan Qin, name: Wanxin Tian, name: Kuangzhi Ge, name: Hao Li, name: Zezhong Qian, name: Anthony Chen, name: Qiang Zhou, name: Yueru Jia, name: Jiaming Liu, name: Yong Dai, name: Qingpo Wuwu, name: Chengyu Bai, name: Yu-Kai Wang, name: Ying Li, name: Lizhang Chen, name: Yong Bao, name: Zhiyuan Jiang, name: Jiacheng Zhu, name: Kai Tang, name: Ruichuan An, name: Yulin Luo, name: Qiuxuan Feng, name: Siyuan Zhou, name: Chi-min Chan, name: Chengkai Hou, name: Wei Xue, name: Sirui Han, name: Yike Guo, name: Shanghang Zhang, name: Jian Tang
Published: 2025-09-26 17:59:07

Humans develop an understanding of intuitive physics through active
interaction with the world. This approach is in stark contrast to current video
models, such as Sora, which rely on passive observation and therefore struggle
with grasping physical causality. This observation leads to our central
hypothesis: authentic physical intuition of the world model must be grounded in
extensive, causally rich interactions with the real world. To test this
hypothesis, we present WoW, a 14-billion-parameter generative world model
trained on 2 million robot interaction trajectories. Our findings reveal that
the model's understanding of physics is a probabilistic distribution of
plausible outcomes, leading to stochastic instabilities and physical
hallucinations. Furthermore, we demonstrate that this emergent capability can
be actively constrained toward physical realism by SOPHIA, where
vision-language model agents evaluate the DiT-generated output and guide its
refinement by iteratively evolving the language instructions. In addition, a
co-trained Inverse Dynamics Model translates these refined plans into
executable robotic actions, thus closing the imagination-to-action loop. We
establish WoWBench, a new benchmark focused on physical consistency and causal
reasoning in video, where WoW achieves state-of-the-art performance in both
human and autonomous evaluation, demonstrating strong ability in physical
causality, collision dynamics, and object permanence. Our work provides
systematic evidence that large-scale, real-world interaction is a cornerstone
for developing physical intuition in AI. Models, data, and benchmarks will be
open-sourced.
RefAM: Attention Magnets for Zero-Shot Referral Segmentation
Authors: name: Anna Kukleva, name: Enis Simsar, name: Alessio Tonioni, name: Muhammad Ferjad Naeem, name: Federico Tombari, name: Jan Eric Lenssen, name: Bernt Schiele
Published: 2025-09-26 17:59:57

Most existing approaches to referring segmentation achieve strong performance
only through fine-tuning or by composing multiple pre-trained models, often at
the cost of additional training and architectural modifications. Meanwhile,
large-scale generative diffusion models encode rich semantic information,
making them attractive as general-purpose feature extractors. In this work, we
introduce a new method that directly exploits features, attention scores, from
diffusion transformers for downstream tasks, requiring neither architectural
modifications nor additional training. To systematically evaluate these
features, we extend benchmarks with vision-language grounding tasks spanning
both images and videos. Our key insight is that stop words act as attention
magnets: they accumulate surplus attention and can be filtered to reduce noise.
Moreover, we identify global attention sinks (GAS) emerging in deeper layers
and show that they can be safely suppressed or redirected onto auxiliary
tokens, leading to sharper and more accurate grounding maps. We further propose
an attention redistribution strategy, where appended stop words partition
background activations into smaller clusters, yielding sharper and more
localized heatmaps. Building on these findings, we develop RefAM, a simple
training-free grounding framework that combines cross-attention maps, GAS
handling, and redistribution. Across zero-shot referring image and video
segmentation benchmarks, our approach consistently outperforms prior methods,
establishing a new state of the art without fine-tuning or additional
components.
See, Point, Fly: A Learning-Free VLM Framework for Universal Unmanned
  Aerial Navigation
Authors: name: Chih Yao Hu, name: Yang-Sen Lin, name: Yuna Lee, name: Chih-Hai Su, name: Jie-Ying Lee, name: Shr-Ruei Tsai, name: Chin-Yang Lin, name: Kuan-Wen Chen, name: Tsung-Wei Ke, name: Yu-Lun Liu
Published: 2025-09-26 17:59:59

We present See, Point, Fly (SPF), a training-free aerial vision-and-language
navigation (AVLN) framework built atop vision-language models (VLMs). SPF is
capable of navigating to any goal based on any type of free-form instructions
in any kind of environment. In contrast to existing VLM-based approaches that
treat action prediction as a text generation task, our key insight is to
consider action prediction for AVLN as a 2D spatial grounding task. SPF
harnesses VLMs to decompose vague language instructions into iterative
annotation of 2D waypoints on the input image. Along with the predicted
traveling distance, SPF transforms predicted 2D waypoints into 3D displacement
vectors as action commands for UAVs. Moreover, SPF also adaptively adjusts the
traveling distance to facilitate more efficient navigation. Notably, SPF
performs navigation in a closed-loop control manner, enabling UAVs to follow
dynamic targets in dynamic environments. SPF sets a new state of the art in DRL
simulation benchmark, outperforming the previous best method by an absolute
margin of 63%. In extensive real-world evaluations, SPF outperforms strong
baselines by a large margin. We also conduct comprehensive ablation studies to
highlight the effectiveness of our design choice. Lastly, SPF shows remarkable
generalization to different VLMs. Project page: https://spf-web.pages.dev
X-CoT: Explainable Text-to-Video Retrieval via LLM-based
  Chain-of-Thought Reasoning
Authors: name: Prasanna Reddy Pulakurthi, name: Jiamian Wang, name: Majid Rabbani, name: Sohail Dianat, name: Raghuveer Rao, name: Zhiqiang Tao
Published: 2025-09-25 20:39:45

Prevalent text-to-video retrieval systems mainly adopt embedding models for
feature extraction and compute cosine similarities for ranking. However, this
design presents two limitations. Low-quality text-video data pairs could
compromise the retrieval, yet are hard to identify and examine. Cosine
similarity alone provides no explanation for the ranking results, limiting the
interpretability. We ask that can we interpret the ranking results, so as to
assess the retrieval models and examine the text-video data? This work proposes
X-CoT, an explainable retrieval framework upon LLM CoT reasoning in place of
the embedding model-based similarity ranking. We first expand the existing
benchmarks with additional video annotations to support semantic understanding
and reduce data bias. We also devise a retrieval CoT consisting of pairwise
comparison steps, yielding detailed reasoning and complete ranking. X-CoT
empirically improves the retrieval performance and produces detailed
rationales. It also facilitates the model behavior and data quality analysis.
Code and data are available at: https://github.com/PrasannaPulakurthi/X-CoT.
No Prompt Left Behind: Exploiting Zero-Variance Prompts in LLM
  Reinforcement Learning via Entropy-Guided Advantage Shaping
Authors: name: Thanh-Long V. Le, name: Myeongho Jeon, name: Kim Vu, name: Viet Lai, name: Eunho Yang
Published: 2025-09-26 05:03:54

Reinforcement Learning with Verifiable Rewards (RLVR) is a powerful framework
for improving the reasoning abilities of Large Language Models (LLMs). However,
current methods such as GRPO rely only on problems where the model responses to
the same input differ in correctness, while ignoring those where all responses
receive the same reward - so-called zero-variance prompts. In this work, we
argue that such prompts are not useless but can, in fact, provide meaningful
feedback for policy optimization. To this end, we introduce RL with
Zero-Variance Prompts (RL-ZVP), a novel algorithm that extract learning signals
from zero-variance prompts. RL-ZVP directly rewards correctness and penalizes
errors even without contrasting responses, modulating feedback with token-level
characteristics to preserve informative, nuanced signals. Across six math
reasoning benchmarks, RL-ZVP achieves significant improvements of up to 8.61
points in accuracy and 7.77 points in pass rate over GRPO, while consistently
outperforming other baselines that filter out zero-variance prompts. These
results highlight the untapped potential of learning from zero-variance prompts
in RLVR.
Mind-the-Glitch: Visual Correspondence for Detecting Inconsistencies in
  Subject-Driven Generation
Authors: name: Abdelrahman Eldesokey, name: Aleksandar Cvejic, name: Bernard Ghanem, name: Peter Wonka
Published: 2025-09-26 07:11:55

We propose a novel approach for disentangling visual and semantic features
from the backbones of pre-trained diffusion models, enabling visual
correspondence in a manner analogous to the well-established semantic
correspondence. While diffusion model backbones are known to encode
semantically rich features, they must also contain visual features to support
their image synthesis capabilities. However, isolating these visual features is
challenging due to the absence of annotated datasets. To address this, we
introduce an automated pipeline that constructs image pairs with annotated
semantic and visual correspondences based on existing subject-driven image
generation datasets, and design a contrastive architecture to separate the two
feature types. Leveraging the disentangled representations, we propose a new
metric, Visual Semantic Matching (VSM), that quantifies visual inconsistencies
in subject-driven image generation. Empirical results show that our approach
outperforms global feature-based metrics such as CLIP, DINO, and
vision--language models in quantifying visual inconsistencies while also
enabling spatial localization of inconsistent regions. To our knowledge, this
is the first method that supports both quantification and localization of
inconsistencies in subject-driven generation, offering a valuable tool for
advancing this task. Project
Page:https://abdo-eldesokey.github.io/mind-the-glitch/
MesaTask: Towards Task-Driven Tabletop Scene Generation via 3D Spatial
  Reasoning
Authors: name: Jinkun Hao, name: Naifu Liang, name: Zhen Luo, name: Xudong Xu, name: Weipeng Zhong, name: Ran Yi, name: Yichen Jin, name: Zhaoyang Lyu, name: Feng Zheng, name: Lizhuang Ma, name: Jiangmiao Pang
Published: 2025-09-26 12:46:00

The ability of robots to interpret human instructions and execute
manipulation tasks necessitates the availability of task-relevant tabletop
scenes for training. However, traditional methods for creating these scenes
rely on time-consuming manual layout design or purely randomized layouts, which
are limited in terms of plausibility or alignment with the tasks. In this
paper, we formulate a novel task, namely task-oriented tabletop scene
generation, which poses significant challenges due to the substantial gap
between high-level task instructions and the tabletop scenes. To support
research on such a challenging task, we introduce MesaTask-10K, a large-scale
dataset comprising approximately 10,700 synthetic tabletop scenes with manually
crafted layouts that ensure realistic layouts and intricate inter-object
relations. To bridge the gap between tasks and scenes, we propose a Spatial
Reasoning Chain that decomposes the generation process into object inference,
spatial interrelation reasoning, and scene graph construction for the final 3D
layout. We present MesaTask, an LLM-based framework that utilizes this
reasoning chain and is further enhanced with DPO algorithms to generate
physically plausible tabletop scenes that align well with given task
descriptions. Exhaustive experiments demonstrate the superior performance of
MesaTask compared to baselines in generating task-conforming tabletop scenes
with realistic layouts. Project page is at https://mesatask.github.io/
Chasing the Tail: Effective Rubric-based Reward Modeling for Large
  Language Model Post-Training
Authors: name: Junkai Zhang, name: Zihao Wang, name: Lin Gui, name: Swarnashree Mysore Sathyendra, name: Jaehwan Jeong, name: Victor Veitch, name: Wei Wang, name: Yunzhong He, name: Bing Liu, name: Lifeng Jin
Published: 2025-09-25 19:57:39

Reinforcement fine-tuning (RFT) often suffers from reward
over-optimization, where a policy model hacks the reward signals to achieve
high scores while producing low-quality outputs. Our theoretical analysis shows
that the key lies in reward misspecification at the high-reward tail: the
inability to reliably distinguish Excellent responses from merely Great ones.
This motivate us to focus on the high-reward region. However, such tail
examples are scarce under the base LLM. While off-policy exemplars (e.g. from
stronger models or rewrites) are easier to obtain, naively training on them
yields a misspecified reward for the policy we aim to align. To address this,
we study rubric-based rewards. By design, rubrics can leverage off-policy
examples while remaining insensitive to their artifacts. To elicit rubrics that
capture the high-reward tail, we highlight the importance of distinguishing
among great and diverse responses, and introduce a workflow to implement this
idea. We empirically demonstrate that rubric-based rewards substantially
mitigate reward over-optimization and deliver effective LLM post-training
improvements. Our code can be accessed at
https://github.com/Jun-Kai-Zhang/rubrics.git .
StateX: Enhancing RNN Recall via Post-training State Expansion
Authors: name: Xingyu Shen, name: Yingfa Chen, name: Zhen Leng Thai, name: Xu Han, name: Zhiyuan Liu, name: Maosong Sun
Published: 2025-09-26 17:55:22

While Transformer-based models have demonstrated remarkable language modeling
performance, their high complexities result in high costs when processing long
contexts. In contrast, recurrent neural networks (RNNs) such as linear
attention and state space models have gained popularity due to their constant
per-token complexities. However, these recurrent models struggle with tasks
that require accurate recall of contextual information from long contexts,
because all contextual information is compressed into a constant-size recurrent
state. Previous works have shown that recall ability is positively correlated
with the recurrent state size, yet directly training RNNs with larger recurrent
states results in high training costs. In this paper, we introduce StateX, a
training pipeline for efficiently expanding the states of pre-trained RNNs
through post-training. For two popular classes of RNNs, linear attention and
state space models, we design post-training architectural modifications to
scale up the state size with no or negligible increase in model parameters.
Experiments on models up to 1.3B parameters demonstrate that StateX efficiently
enhances the recall and in-context learning ability of RNNs without incurring
high post-training costs or compromising other capabilities.
SPARK: Synergistic Policy And Reward Co-Evolving Framework
Authors: name: Ziyu Liu, name: Yuhang Zang, name: Shengyuan Ding, name: Yuhang Cao, name: Xiaoyi Dong, name: Haodong Duan, name: Dahua Lin, name: Jiaqi Wang
Published: 2025-09-26 17:50:12

Recent Large Language Models (LLMs) and Large Vision-Language Models (LVLMs)
increasingly use Reinforcement Learning (RL) for post-pretraining, such as RL
with Verifiable Rewards (RLVR) for objective tasks and RL from Human Feedback
(RLHF) for subjective tasks. However, RLHF incurs high costs and potential
reward-policy mismatch due to reliance on human preferences, while RLVR still
wastes supervision by discarding rollouts and correctness signals after each
update. To address these challenges, we introduce the Synergistic Policy And
Reward Co-Evolving Framework (SPARK), an efficient, on-policy, and stable
method that builds on RLVR. Instead of discarding rollouts and correctness
data, SPARK recycles this valuable information to simultaneously train the
model itself as a generative reward model. This auxiliary training uses a mix
of objectives, such as pointwise reward score, pairwise comparison, and
evaluation conditioned on further-reflection responses, to teach the model to
evaluate and improve its own responses. Our process eliminates the need for a
separate reward model and costly human preference data. SPARK creates a
positive co-evolving feedback loop: improved reward accuracy yields better
policy gradients, which in turn produce higher-quality rollouts that further
refine the reward model. Our unified framework supports test-time scaling via
self-reflection without external reward models and their associated costs. We
show that SPARK achieves significant performance gains on multiple LLM and LVLM
models and multiple reasoning, reward models, and general benchmarks. For
example, SPARK-VL-7B achieves an average 9.7% gain on 7 reasoning benchmarks,
12.1% on 2 reward benchmarks, and 1.5% on 8 general benchmarks over the
baselines, demonstrating robustness and broad generalization.
ReviewScore: Misinformed Peer Review Detection with Large Language
  Models
Authors: name: Hyun Ryu, name: Doohyuk Jang, name: Hyemin S. Lee, name: Joonhyun Jeong, name: Gyeongman Kim, name: Donghyeon Cho, name: Gyouk Chu, name: Minyeong Hwang, name: Hyeongwon Jang, name: Changhun Kim, name: Haechan Kim, name: Jina Kim, name: Joowon Kim, name: Yoonjeon Kim, name: Kwanhyung Lee, name: Chanjae Park, name: Heecheol Yun, name: Gregor Betz, name: Eunho Yang
Published: 2025-09-25 22:55:05

Peer review serves as a backbone of academic research, but in most AI
conferences, the review quality is degrading as the number of submissions
explodes. To reliably detect low-quality reviews, we define misinformed review
points as either "weaknesses" in a review that contain incorrect premises, or
"questions" in a review that can be already answered by the paper. We verify
that 15.2% of weaknesses and 26.4% of questions are misinformed and introduce
ReviewScore indicating if a review point is misinformed. To evaluate the
factuality of each premise of weaknesses, we propose an automated engine that
reconstructs every explicit and implicit premise from a weakness. We build a
human expert-annotated ReviewScore dataset to check the ability of LLMs to
automate ReviewScore evaluation. Then, we measure human-model agreements on
ReviewScore using eight current state-of-the-art LLMs and verify moderate
agreements. We also prove that evaluating premise-level factuality shows
significantly higher agreements than evaluating weakness-level factuality. A
thorough disagreement analysis further supports a potential of fully automated
ReviewScore evaluation.
LucidFlux: Caption-Free Universal Image Restoration via a Large-Scale
  Diffusion Transformer
Authors: name: Song Fei, name: Tian Ye, name: Lujia Wang, name: Lei Zhu
Published: 2025-09-26 14:39:08

Universal image restoration (UIR) aims to recover images degraded by unknown
mixtures while preserving semantics -- conditions under which discriminative
restorers and UNet-based diffusion priors often oversmooth, hallucinate, or
drift. We present LucidFlux, a caption-free UIR framework that adapts a large
diffusion transformer (Flux.1) without image captions. LucidFlux introduces a
lightweight dual-branch conditioner that injects signals from the degraded
input and a lightly restored proxy to respectively anchor geometry and suppress
artifacts. Then, a timestep- and layer-adaptive modulation schedule is designed
to route these cues across the backbone's hierarchy, in order to yield
coarse-to-fine and context-aware updates that protect the global structure
while recovering texture. After that, to avoid the latency and instability of
text prompts or MLLM captions, we enforce caption-free semantic alignment via
SigLIP features extracted from the proxy. A scalable curation pipeline further
filters large-scale data for structure-rich supervision. Across synthetic and
in-the-wild benchmarks, LucidFlux consistently outperforms strong open-source
and commercial baselines, and ablation studies verify the necessity of each
component. LucidFlux shows that, for large DiTs, when, where, and what to
condition on -- rather than adding parameters or relying on text prompts -- is
the governing lever for robust and caption-free universal image restoration in
the wild.