Orionnnn/hub-stats huggingface dataset:
PEEK: Guiding and Minimal Image Representations for Zero-Shot
  Generalization of Robot Manipulation Policies
Authors: name: Jesse Zhang, name: Marius Memmel, name: Kevin Kim, name: Dieter Fox, name: Jesse Thomason, name: Fabio Ramos, name: Erdem Bıyık, name: Abhishek Gupta, name: Anqi Li
Published: 2025-09-22 18:10:14

Robotic manipulation policies often fail to generalize because they must
simultaneously learn where to attend, what actions to take, and how to execute
them. We argue that high-level reasoning about where and what can be offloaded
to vision-language models (VLMs), leaving policies to specialize in how to act.
We present PEEK (Policy-agnostic Extraction of Essential Keypoints), which
fine-tunes VLMs to predict a unified point-based intermediate representation:
1. end-effector paths specifying what actions to take, and 2. task-relevant
masks indicating where to focus. These annotations are directly overlaid onto
robot observations, making the representation policy-agnostic and transferable
across architectures. To enable scalable training, we introduce an automatic
annotation pipeline, generating labeled data across 20+ robot datasets spanning
9 embodiments. In real-world evaluations, PEEK consistently boosts zero-shot
generalization, including a 41.4x real-world improvement for a 3D policy
trained only in simulation, and 2-3.5x gains for both large VLAs and small
manipulation policies. By letting VLMs absorb semantic and visual complexity,
PEEK equips manipulation policies with the minimal cues they need--where, what,
and how. Website at https://peek-robot.github.io/.
Soft Tokens, Hard Truths
Authors: name: Natasha Butt, name: Ariel Kwiatkowski, name: Ismail Labiad, name: Julia Kempe, name: Yann Ollivier
Published: 2025-09-23 15:43:47

The use of continuous instead of discrete tokens during the Chain-of-Thought
(CoT) phase of reasoning LLMs has garnered attention recently, based on the
intuition that a continuous mixture of discrete tokens could simulate a
superposition of several reasoning paths simultaneously. Theoretical results
have formally proven that continuous tokens have much greater expressivity and
can solve specific problems more efficiently. However, practical use of
continuous tokens has been limited by strong training difficulties: previous
works either just use continuous tokens at inference time on a pre-trained
discrete-token model, or must distill the continuous CoT from ground-truth
discrete CoTs and face computational costs that limit the CoT to very few
tokens.
  This is the first work introducing a scalable method to learn continuous CoTs
via reinforcement learning (RL), without distilling from reference discrete
CoTs. We use "soft" tokens: mixtures of tokens together with noise on the input
embedding to provide RL exploration. Computational overhead is minimal,
enabling us to learn continuous CoTs with hundreds of tokens. On math reasoning
benchmarks with Llama and Qwen models up to 8B, training with continuous CoTs
match discrete-token CoTs for pass@1 and surpass them for pass@32, showing
greater CoT diversity. In systematic comparisons, the best-performing scenario
is to train with continuous CoT tokens then use discrete tokens for inference,
meaning the "soft" models can be deployed in a standard way. Finally, we show
continuous CoT RL training better preserves the predictions of the base model
on out-of-domain tasks, thus providing a softer touch to the base model.
CommonForms: A Large, Diverse Dataset for Form Field Detection
Authors: name: Joe Barrow
Published: 2025-09-20 02:55:40

This paper introduces CommonForms, a web-scale dataset for form field
detection. It casts the problem of form field detection as object detection:
given an image of a page, predict the location and type (Text Input, Choice
Button, Signature) of form fields. The dataset is constructed by filtering
Common Crawl to find PDFs that have fillable elements. Starting with 8 million
documents, the filtering process is used to arrive at a final dataset of
roughly 55k documents that have over 450k pages. Analysis shows that the
dataset contains a diverse mixture of languages and domains; one third of the
pages are non-English, and among the 14 classified domains, no domain makes up
more than 25% of the dataset.
  In addition, this paper presents a family of form field detectors,
FFDNet-Small and FFDNet-Large, which attain a very high average precision on
the CommonForms test set. Each model cost less than $500 to train. Ablation
results show that high-resolution inputs are crucial for high-quality form
field detection, and that the cleaning process improves data efficiency over
using all PDFs that have fillable fields in Common Crawl. A qualitative
analysis shows that they outperform a popular, commercially available PDF
reader that can prepare forms. Unlike the most popular commercially available
solutions, FFDNet can predict checkboxes in addition to text and signature
fields. This is, to our knowledge, the first large scale dataset released for
form field detection, as well as the first open source models. The dataset,
models, and code will be released at https://github.com/jbarrow/commonforms
Better Late Than Never: Evaluation of Latency Metrics for Simultaneous
  Speech-to-Text Translation
Authors: name: Peter Polák, name: Sara Papi, name: Luisa Bentivogli, name: Ondřej Bojar
Published: 2025-09-22 04:21:19

Simultaneous speech-to-text translation (SimulST) systems have to balance
translation quality with latency--the delay between speech input and the
translated output. While quality evaluation is well established, accurate
latency measurement remains a challenge. Existing metrics often produce
inconsistent or misleading results, especially in the widely used short-form
setting, where speech is artificially presegmented. In this paper, we present
the first comprehensive analysis of SimulST latency metrics across language
pairs, systems, and both short- and long-form regimes. We uncover a structural
bias in current metrics related to segmentation that undermines fair and
meaningful comparisons. To address this, we introduce YAAL (Yet Another Average
Lagging), a refined latency metric that delivers more accurate evaluations in
the short-form regime. We extend YAAL to LongYAAL for unsegmented audio and
propose SoftSegmenter, a novel resegmentation tool based on word-level
alignment. Our experiments show that YAAL and LongYAAL outperform popular
latency metrics, while SoftSegmenter enhances alignment quality in long-form
evaluation, together enabling more reliable assessments of SimulST systems.
DRISHTIKON: A Multimodal Multilingual Benchmark for Testing Language
  Models' Understanding on Indian Culture
Authors: name: Arijit Maji, name: Raghvendra Kumar, name: Akash Ghosh, name: Anushka, name: Nemil Shah, name: Abhilekh Borah, name: Vanshika Shah, name: Nishant Mishra, name: Sriparna Saha
Published: 2025-09-23 17:40:43

We introduce DRISHTIKON, a first-of-its-kind multimodal and multilingual
benchmark centered exclusively on Indian culture, designed to evaluate the
cultural understanding of generative AI systems. Unlike existing benchmarks
with a generic or global scope, DRISHTIKON offers deep, fine-grained coverage
across India's diverse regions, spanning 15 languages, covering all states and
union territories, and incorporating over 64,000 aligned text-image pairs. The
dataset captures rich cultural themes including festivals, attire, cuisines,
art forms, and historical heritage amongst many more. We evaluate a wide range
of vision-language models (VLMs), including open-source small and large models,
proprietary systems, reasoning-specialized VLMs, and Indic-focused models,
across zero-shot and chain-of-thought settings. Our results expose key
limitations in current models' ability to reason over culturally grounded,
multimodal inputs, particularly for low-resource languages and less-documented
traditions. DRISHTIKON fills a vital gap in inclusive AI research, offering a
robust testbed to advance culturally aware, multimodally competent language
technologies.
GeoSVR: Taming Sparse Voxels for Geometrically Accurate Surface
  Reconstruction
Authors: name: Jiahe Li, name: Jiawei Zhang, name: Youmin Zhang, name: Xiao Bai, name: Jin Zheng, name: Xiaohan Yu, name: Lin Gu
Published: 2025-09-22 17:58:48

Reconstructing accurate surfaces with radiance fields has achieved remarkable
progress in recent years. However, prevailing approaches, primarily based on
Gaussian Splatting, are increasingly constrained by representational
bottlenecks. In this paper, we introduce GeoSVR, an explicit voxel-based
framework that explores and extends the under-investigated potential of sparse
voxels for achieving accurate, detailed, and complete surface reconstruction.
As strengths, sparse voxels support preserving the coverage completeness and
geometric clarity, while corresponding challenges also arise from absent scene
constraints and locality in surface refinement. To ensure correct scene
convergence, we first propose a Voxel-Uncertainty Depth Constraint that
maximizes the effect of monocular depth cues while presenting a voxel-oriented
uncertainty to avoid quality degradation, enabling effective and robust scene
constraints yet preserving highly accurate geometries. Subsequently, Sparse
Voxel Surface Regularization is designed to enhance geometric consistency for
tiny voxels and facilitate the voxel-based formation of sharp and accurate
surfaces. Extensive experiments demonstrate our superior performance compared
to existing methods across diverse challenging scenarios, excelling in
geometric accuracy, detail preservation, and reconstruction completeness while
maintaining high efficiency. Code is available at
https://github.com/Fictionarry/GeoSVR.
Large Language Models Discriminate Against Speakers of German Dialects
Authors: name: Minh Duc Bui, name: Carolin Holtermann, name: Valentin Hofmann, name: Anne Lauscher, name: Katharina von der Wense
Published: 2025-09-17 09:05:37

Dialects represent a significant component of human culture and are found
across all regions of the world. In Germany, more than 40% of the population
speaks a regional dialect (Adler and Hansen, 2022). However, despite cultural
importance, individuals speaking dialects often face negative societal
stereotypes. We examine whether such stereotypes are mirrored by large language
models (LLMs). We draw on the sociolinguistic literature on dialect perception
to analyze traits commonly associated with dialect speakers. Based on these
traits, we assess the dialect naming bias and dialect usage bias expressed by
LLMs in two tasks: an association task and a decision task. To assess a model's
dialect usage bias, we construct a novel evaluation corpus that pairs sentences
from seven regional German dialects (e.g., Alemannic and Bavarian) with their
standard German counterparts. We find that: (1) in the association task, all
evaluated LLMs exhibit significant dialect naming and dialect usage bias
against German dialect speakers, reflected in negative adjective associations;
(2) all models reproduce these dialect naming and dialect usage biases in their
decision making; and (3) contrary to prior work showing minimal bias with
explicit demographic mentions, we find that explicitly labeling linguistic
demographics--German dialect speakers--amplifies bias more than implicit cues
like dialect usage.
VIR-Bench: Evaluating Geospatial and Temporal Understanding of MLLMs via
  Travel Video Itinerary Reconstruction
Authors: name: Hao Wang, name: Eiki Murata, name: Lingfang Zhang, name: Ayako Sato, name: So Fukuda, name: Ziqi Yin, name: Wentao Hu, name: Keisuke Nakao, name: Yusuke Nakamura, name: Sebastian Zwirner, name: Yi-Chia Chen, name: Hiroyuki Otomo, name: Hiroki Ouchi, name: Daisuke Kawahara
Published: 2025-09-23 13:46:31

Recent advances in multimodal large language models (MLLMs) have
significantly enhanced video understanding capabilities, opening new
possibilities for practical applications. Yet current video benchmarks focus
largely on indoor scenes or short-range outdoor activities, leaving the
challenges associated with long-distance travel largely unexplored. Mastering
extended geospatial-temporal trajectories is critical for next-generation
MLLMs, underpinning real-world tasks such as embodied-AI planning and
navigation. To bridge this gap, we present VIR-Bench, a novel benchmark
consisting of 200 travel videos that frames itinerary reconstruction as a
challenging task designed to evaluate and push forward MLLMs'
geospatial-temporal intelligence. Experimental results reveal that
state-of-the-art MLLMs, including proprietary ones, struggle to achieve high
scores, underscoring the difficulty of handling videos that span extended
spatial and temporal scales. Moreover, we conduct an in-depth case study in
which we develop a prototype travel-planning agent that leverages the insights
gained from VIR-Bench. The agent's markedly improved itinerary recommendations
verify that our evaluation protocol not only benchmarks models effectively but
also translates into concrete performance gains in user-facing applications.
Baseer: A Vision-Language Model for Arabic Document-to-Markdown OCR
Authors: name: Khalil Hennara, name: Muhammad Hreden, name: Mohamed Motasim Hamed, name: Ahmad Bastati, name: Zeina Aldallal, name: Sara Chrouf, name: Safwan AlModhayan
Published: 2025-09-17 15:07:29

Arabic document OCR remains a challenging task due to the language's cursive
script, diverse fonts, diacritics, and right-to-left orientation. While modern
Multimodal Large Language Models (MLLMs) have advanced document understanding
for high-resource languages, their performance on Arabic remains limited. In
this work, we introduce Baseer, a vision-language model fine- tuned
specifically for Arabic document OCR. Leveraging a large-scale dataset
combining synthetic and real-world documents, Baseer is trained using a
decoder-only fine-tuning strategy to adapt a pre-trained MLLM while preserving
general visual features. We also present Misraj-DocOCR, a high-quality,
expert-verified benchmark designed for rigorous evaluation of Arabic OCR
systems. Our experiments show that Baseer significantly outperforms existing
open-source and commercial solutions, achieving a WER of 0.25 and establishing
a new state-of-the-art in the domain of Arabic document OCR. Our results
highlight the benefits of domain-specific adaptation of general-purpose MLLMs
and establish a strong baseline for high-accuracy OCR on morphologically rich
languages like Arabic.
Do You Need Proprioceptive States in Visuomotor Policies?
Authors: name: Juntu Zhao, name: Wenbo Lu, name: Di Zhang, name: Yufeng Liu, name: Yushen Liang, name: Tianluo Zhang, name: Yifeng Cao, name: Junyuan Xie, name: Yingdong Hu, name: Shengjie Wang, name: Junliang Guo, name: Dequan Wang, name: Yang Gao
Published: 2025-09-23 04:56:59

Imitation-learning-based visuomotor policies have been widely used in robot
manipulation, where both visual observations and proprioceptive states are
typically adopted together for precise control. However, in this study, we find
that this common practice makes the policy overly reliant on the proprioceptive
state input, which causes overfitting to the training trajectories and results
in poor spatial generalization. On the contrary, we propose the State-free
Policy, removing the proprioceptive state input and predicting actions only
conditioned on visual observations. The State-free Policy is built in the
relative end-effector action space, and should ensure the full task-relevant
visual observations, here provided by dual wide-angle wrist cameras. Empirical
results demonstrate that the State-free policy achieves significantly stronger
spatial generalization than the state-based policy: in real-world tasks such as
pick-and-place, challenging shirt-folding, and complex whole-body manipulation,
spanning multiple robot embodiments, the average success rate improves from 0\%
to 85\% in height generalization and from 6\% to 64\% in horizontal
generalization. Furthermore, they also show advantages in data efficiency and
cross-embodiment adaptation, enhancing their practicality for real-world
deployment.
OpenGVL - Benchmarking Visual Temporal Progress for Data Curation
Authors: name: Paweł Budzianowski, name: Emilia Wiśnios, name: Gracjan Góral, name: Igor Kulakov, name: Viktor Petrenko, name: Krzysztof Walas
Published: 2025-09-22 02:52:55

Data scarcity remains one of the most limiting factors in driving progress in
robotics. However, the amount of available robotics data in the wild is growing
exponentially, creating new opportunities for large-scale data utilization.
Reliable temporal task completion prediction could help automatically annotate
and curate this data at scale. The Generative Value Learning (GVL) approach was
recently proposed, leveraging the knowledge embedded in vision-language models
(VLMs) to predict task progress from visual observations. Building upon GVL, we
propose OpenGVL, a comprehensive benchmark for estimating task progress across
diverse challenging manipulation tasks involving both robotic and human
embodiments. We evaluate the capabilities of publicly available open-source
foundation models, showing that open-source model families significantly
underperform closed-source counterparts, achieving only approximately 70% of
their performance on temporal progress prediction tasks. Furthermore, we
demonstrate how OpenGVL can serve as a practical tool for automated data
curation and filtering, enabling efficient quality assessment of large-scale
robotics datasets. We release the benchmark along with the complete codebase at
github.com/budzianowski/opengvl{OpenGVL}.
CAR-Flow: Condition-Aware Reparameterization Aligns Source and Target
  for Better Flow Matching
Authors: name: Chen Chen, name: Pengsheng Guo, name: Liangchen Song, name: Jiasen Lu, name: Rui Qian, name: Xinze Wang, name: Tsu-Jui Fu, name: Wei Liu, name: Yinfei Yang, name: Alex Schwing
Published: 2025-09-23 17:59:31

Conditional generative modeling aims to learn a conditional data distribution
from samples containing data-condition pairs. For this, diffusion and
flow-based methods have attained compelling results. These methods use a
learned (flow) model to transport an initial standard Gaussian noise that
ignores the condition to the conditional data distribution. The model is hence
required to learn both mass transport and conditional injection. To ease the
demand on the model, we propose Condition-Aware Reparameterization for Flow
Matching (CAR-Flow) -- a lightweight, learned shift that conditions the source,
the target, or both distributions. By relocating these distributions, CAR-Flow
shortens the probability path the model must learn, leading to faster training
in practice. On low-dimensional synthetic data, we visualize and quantify the
effects of CAR. On higher-dimensional natural image data (ImageNet-256),
equipping SiT-XL/2 with CAR-Flow reduces FID from 2.07 to 1.68, while
introducing less than 0.6% additional parameters.
MAPO: Mixed Advantage Policy Optimization
Authors: name: Wenke Huang, name: Quan Zhang, name: Yiyang Fang, name: Jian Liang, name: Xuankun Rong, name: Huanjin Yao, name: Guancheng Wan, name: Ke Liang, name: Wenwen He, name: Mingjun Li, name: Leszek Rutkowski, name: Mang Ye, name: Bo Du, name: Dacheng Tao
Published: 2025-09-23 09:37:16

Recent advances in reinforcement learning for foundation models, such as
Group Relative Policy Optimization (GRPO), have significantly improved the
performance of foundation models on reasoning tasks. Notably, the advantage
function serves as a central mechanism in GRPO for ranking the trajectory
importance. However, existing explorations encounter both advantage reversion
and advantage mirror problems, which hinder the reasonable advantage allocation
across different query samples. In this work, we propose an easy but effective
GRPO strategy, Mixed Advantage Policy Optimization (MAPO). We reveal that the
trajectory appears with different certainty and propose the advantage percent
deviation for samples with high-certainty trajectories. Furthermore, we
dynamically reweight the advantage function for samples with varying trajectory
certainty, thereby adaptively configuring the advantage function to account for
sample-specific characteristics. Comparison with related state-of-the-art
methods, along with ablation studies on different advantage variants, validates
the effectiveness of our approach.
VolSplat: Rethinking Feed-Forward 3D Gaussian Splatting with
  Voxel-Aligned Prediction
Authors: name: Weijie Wang, name: Yeqing Chen, name: Zeyu Zhang, name: Hengyu Liu, name: Haoxiao Wang, name: Zhiyuan Feng, name: Wenkang Qin, name: Zheng Zhu, name: Donny Y. Chen, name: Bohan Zhuang
Published: 2025-09-23 17:59:02

Feed-forward 3D Gaussian Splatting (3DGS) has emerged as a highly effective
solution for novel view synthesis. Existing methods predominantly rely on a
pixel-aligned Gaussian prediction paradigm, where each 2D pixel is mapped to a
3D Gaussian. We rethink this widely adopted formulation and identify several
inherent limitations: it renders the reconstructed 3D models heavily dependent
on the number of input views, leads to view-biased density distributions, and
introduces alignment errors, particularly when source views contain occlusions
or low texture. To address these challenges, we introduce VolSplat, a new
multi-view feed-forward paradigm that replaces pixel alignment with
voxel-aligned Gaussians. By directly predicting Gaussians from a predicted 3D
voxel grid, it overcomes pixel alignment's reliance on error-prone 2D feature
matching, ensuring robust multi-view consistency. Furthermore, it enables
adaptive control over Gaussian density based on 3D scene complexity, yielding
more faithful Gaussian point clouds, improved geometric consistency, and
enhanced novel-view rendering quality. Experiments on widely used benchmarks
including RealEstate10K and ScanNet demonstrate that VolSplat achieves
state-of-the-art performance while producing more plausible and view-consistent
Gaussian reconstructions. In addition to superior results, our approach
establishes a more scalable framework for feed-forward 3D reconstruction with
denser and more robust representations, paving the way for further research in
wider communities. The video results, code and trained models are available on
our project page: https://lhmd.top/volsplat.
HyRF: Hybrid Radiance Fields for Memory-efficient and High-quality Novel
  View Synthesis
Authors: name: Zipeng Wang, name: Dan Xu
Published: 2025-09-21 13:59:26

Recently, 3D Gaussian Splatting (3DGS) has emerged as a powerful alternative
to NeRF-based approaches, enabling real-time, high-quality novel view synthesis
through explicit, optimizable 3D Gaussians. However, 3DGS suffers from
significant memory overhead due to its reliance on per-Gaussian parameters to
model view-dependent effects and anisotropic shapes. While recent works propose
compressing 3DGS with neural fields, these methods struggle to capture
high-frequency spatial variations in Gaussian properties, leading to degraded
reconstruction of fine details. We present Hybrid Radiance Fields (HyRF), a
novel scene representation that combines the strengths of explicit Gaussians
and neural fields. HyRF decomposes the scene into (1) a compact set of explicit
Gaussians storing only critical high-frequency parameters and (2) grid-based
neural fields that predict remaining properties. To enhance representational
capacity, we introduce a decoupled neural field architecture, separately
modeling geometry (scale, opacity, rotation) and view-dependent color.
Additionally, we propose a hybrid rendering scheme that composites Gaussian
splatting with a neural field-predicted background, addressing limitations in
distant scene representation. Experiments demonstrate that HyRF achieves
state-of-the-art rendering quality while reducing model size by over 20 times
compared to 3DGS and maintaining real-time performance. Our project page is
available at https://wzpscott.github.io/hyrf/.