Orionnnn/hub-stats huggingface dataset:
FlowDirector: Training-Free Flow Steering for Precise Text-to-Video
  Editing
Authors: name: Guangzhao Li, name: Yanming Yang, name: Chenxi Song, name: Chi Zhang
Published: 2025-06-05 13:54:40

Text-driven video editing aims to modify video content according to natural
language instructions. While recent training-free approaches have made progress
by leveraging pre-trained diffusion models, they typically rely on
inversion-based techniques that map input videos into the latent space, which
often leads to temporal inconsistencies and degraded structural fidelity. To
address this, we propose FlowDirector, a novel inversion-free video editing
framework. Our framework models the editing process as a direct evolution in
data space, guiding the video via an Ordinary Differential Equation (ODE) to
smoothly transition along its inherent spatiotemporal manifold, thereby
preserving temporal coherence and structural details. To achieve localized and
controllable edits, we introduce an attention-guided masking mechanism that
modulates the ODE velocity field, preserving non-target regions both spatially
and temporally. Furthermore, to address incomplete edits and enhance semantic
alignment with editing instructions, we present a guidance-enhanced editing
strategy inspired by Classifier-Free Guidance, which leverages differential
signals between multiple candidate flows to steer the editing trajectory toward
stronger semantic alignment without compromising structural consistency.
Extensive experiments across benchmarks demonstrate that FlowDirector achieves
state-of-the-art performance in instruction adherence, temporal consistency,
and background preservation, establishing a new paradigm for efficient and
coherent video editing without inversion.
Perceptual Decoupling for Scalable Multi-modal Reasoning via
  Reward-Optimized Captioning
Authors: name: Yunhao Gou, name: Kai Chen, name: Zhili Liu, name: Lanqing Hong, name: Xin Jin, name: Zhenguo Li, name: James T. Kwok, name: Yu Zhang
Published: 2025-06-05 02:28:07

Recent advances in slow-thinking language models (e.g., OpenAI-o1 and
DeepSeek-R1) have demonstrated remarkable abilities in complex reasoning tasks
by emulating human-like reflective cognition. However, extending such
capabilities to multi-modal large language models (MLLMs) remains challenging
due to the high cost of retraining vision-language alignments when upgrading
the underlying reasoner LLMs. A straightforward solution is to decouple
perception from reasoning, i.e., converting visual inputs into language
representations (e.g., captions) that are then passed to a powerful text-only
reasoner. However, this decoupling introduces a critical challenge: the visual
extractor must generate descriptions that are both faithful to the image and
informative enough to support accurate downstream reasoning. To address this,
we propose Reasoning-Aligned Perceptual Decoupling via Caption Reward
Optimization (RACRO) - a reasoning-guided reinforcement learning strategy that
aligns the extractor's captioning behavior with the reasoning objective. By
closing the perception-reasoning loop via reward-based optimization, RACRO
significantly enhances visual grounding and extracts reasoning-optimized
representations. Experiments on multi-modal math and science benchmarks show
that the proposed RACRO method achieves state-of-the-art average performance
while enabling superior scalability and plug-and-play adaptation to more
advanced reasoning LLMs without the necessity for costly multi-modal
re-alignment.
Search Arena: Analyzing Search-Augmented LLMs
Authors: name: Mihran Miroyan, name: Tsung-Han Wu, name: Logan King, name: Tianle Li, name: Jiayi Pan, name: Xinyan Hu, name: Wei-Lin Chiang, name: Anastasios N. Angelopoulos, name: Trevor Darrell, name: Narges Norouzi, name: Joseph E. Gonzalez
Published: 2025-06-05 17:59:26

Search-augmented language models combine web search with Large Language
Models (LLMs) to improve response groundedness and freshness. However,
analyzing these systems remains challenging: existing datasets are limited in
scale and narrow in scope, often constrained to static, single-turn,
fact-checking questions. In this work, we introduce Search Arena, a
crowd-sourced, large-scale, human-preference dataset of over 24,000 paired
multi-turn user interactions with search-augmented LLMs. The dataset spans
diverse intents and languages, and contains full system traces with around
12,000 human preference votes. Our analysis reveals that user preferences are
influenced by the number of citations, even when the cited content does not
directly support the attributed claims, uncovering a gap between perceived and
actual credibility. Furthermore, user preferences vary across cited sources,
revealing that community-driven platforms are generally preferred and static
encyclopedic sources are not always appropriate and reliable. To assess
performance across different settings, we conduct cross-arena analyses by
testing search-augmented LLMs in a general-purpose chat environment and
conventional LLMs in search-intensive settings. We find that web search does
not degrade and may even improve performance in non-search settings; however,
the quality in search settings is significantly affected if solely relying on
the model's parametric knowledge. We open-sourced the dataset to support future
research in this direction. Our dataset and code are available at:
https://github.com/lmarena/search-arena.
MARBLE: Material Recomposition and Blending in CLIP-Space
Authors: name: Ta-Ying Cheng, name: Prafull Sharma, name: Mark Boss, name: Varun Jampani
Published: 2025-06-05 17:55:16

Editing materials of objects in images based on exemplar images is an active
area of research in computer vision and graphics. We propose MARBLE, a method
for performing material blending and recomposing fine-grained material
properties by finding material embeddings in CLIP-space and using that to
control pre-trained text-to-image models. We improve exemplar-based material
editing by finding a block in the denoising UNet responsible for material
attribution. Given two material exemplar-images, we find directions in the
CLIP-space for blending the materials. Further, we can achieve parametric
control over fine-grained material attributes such as roughness, metallic,
transparency, and glow using a shallow network to predict the direction for the
desired material attribute change. We perform qualitative and quantitative
analysis to demonstrate the efficacy of our proposed method. We also present
the ability of our method to perform multiple edits in a single forward pass
and applicability to painting.
  Project Page: https://marblecontrol.github.io/
Watermarking Degrades Alignment in Language Models: Analysis and
  Mitigation
Authors: name: Apurv Verma, name: NhatHai Phan, name: Shubhendu Trivedi
Published: 2025-06-04 21:29:07

Watermarking techniques for large language models (LLMs) can significantly
impact output quality, yet their effects on truthfulness, safety, and
helpfulness remain critically underexamined. This paper presents a systematic
analysis of how two popular watermarking approaches-Gumbel and KGW-affect these
core alignment properties across four aligned LLMs. Our experiments reveal two
distinct degradation patterns: guard attenuation, where enhanced helpfulness
undermines model safety, and guard amplification, where excessive caution
reduces model helpfulness. These patterns emerge from watermark-induced shifts
in token distribution, surfacing the fundamental tension that exists between
alignment objectives.
  To mitigate these degradations, we propose Alignment Resampling (AR), an
inference-time sampling method that uses an external reward model to restore
alignment. We establish a theoretical lower bound on the improvement in
expected reward score as the sample size is increased and empirically
demonstrate that sampling just 2-4 watermarked generations effectively recovers
or surpasses baseline (unwatermarked) alignment scores. To overcome the limited
response diversity of standard Gumbel watermarking, our modified implementation
sacrifices strict distortion-freeness while maintaining robust detectability,
ensuring compatibility with AR. Experimental results confirm that AR
successfully recovers baseline alignment in both watermarking approaches, while
maintaining strong watermark detectability. This work reveals the critical
balance between watermark strength and model alignment, providing a simple
inference-time solution to responsibly deploy watermarked LLMs in practice.
SViMo: Synchronized Diffusion for Video and Motion Generation in
  Hand-object Interaction Scenarios
Authors: name: Lingwei Dang, name: Ruizhi Shao, name: Hongwen Zhang, name: Wei Min, name: Yebin Liu, name: Qingyao Wu
Published: 2025-06-03 05:04:29

Hand-Object Interaction (HOI) generation has significant application
potential. However, current 3D HOI motion generation approaches heavily rely on
predefined 3D object models and lab-captured motion data, limiting
generalization capabilities. Meanwhile, HOI video generation methods prioritize
pixel-level visual fidelity, often sacrificing physical plausibility.
Recognizing that visual appearance and motion patterns share fundamental
physical laws in the real world, we propose a novel framework that combines
visual priors and dynamic constraints within a synchronized diffusion process
to generate the HOI video and motion simultaneously. To integrate the
heterogeneous semantics, appearance, and motion features, our method implements
tri-modal adaptive modulation for feature aligning, coupled with 3D
full-attention for modeling inter- and intra-modal dependencies. Furthermore,
we introduce a vision-aware 3D interaction diffusion model that generates
explicit 3D interaction sequences directly from the synchronized diffusion
outputs, then feeds them back to establish a closed-loop feedback cycle. This
architecture eliminates dependencies on predefined object models or explicit
pose guidance while significantly enhancing video-motion consistency.
Experimental results demonstrate our method's superiority over state-of-the-art
approaches in generating high-fidelity, dynamically plausible HOI sequences,
with notable generalization capabilities in unseen real-world scenarios.
Project page at https://github.com/Droliven/SViMo\_project.
FEAT: Full-Dimensional Efficient Attention Transformer for Medical Video
  Generation
Authors: name: Huihan Wang, name: Zhiwen Yang, name: Hui Zhang, name: Dan Zhao, name: Bingzheng Wei, name: Yan Xu
Published: 2025-06-05 12:31:02

Synthesizing high-quality dynamic medical videos remains a significant
challenge due to the need for modeling both spatial consistency and temporal
dynamics. Existing Transformer-based approaches face critical limitations,
including insufficient channel interactions, high computational complexity from
self-attention, and coarse denoising guidance from timestep embeddings when
handling varying noise levels. In this work, we propose FEAT, a
full-dimensional efficient attention Transformer, which addresses these issues
through three key innovations: (1) a unified paradigm with sequential
spatial-temporal-channel attention mechanisms to capture global dependencies
across all dimensions, (2) a linear-complexity design for attention mechanisms
in each dimension, utilizing weighted key-value attention and global channel
attention, and (3) a residual value guidance module that provides fine-grained
pixel-level guidance to adapt to different noise levels. We evaluate FEAT on
standard benchmarks and downstream tasks, demonstrating that FEAT-S, with only
23\% of the parameters of the state-of-the-art model Endora, achieves
comparable or even superior performance. Furthermore, FEAT-L surpasses all
comparison methods across multiple datasets, showcasing both superior
effectiveness and scalability. Code is available at
https://github.com/Yaziwel/FEAT.
Scaling Laws for Robust Comparison of Open Foundation Language-Vision
  Models and Datasets
Authors: name: Marianna Nezhurina, name: Tomer Porian, name: Giovanni Pucceti, name: Tommie Kerssies, name: Romain Beaumont, name: Mehdi Cherti, name: Jenia Jitsev
Published: 2025-06-05 03:35:59

In studies of transferable learning, scaling laws are obtained for various
important foundation models to predict their properties and performance at
larger scales. We show here how scaling law derivation can also be used for
model and dataset comparison, allowing to decide which procedure is to be
preferred for pre-training. For the first time, full scaling laws based on
dense measurements across a wide span of model and samples seen scales are
derived for two important language-vision learning procedures, CLIP and MaMMUT,
that use either contrastive only or contrastive and captioning text generative
loss. Ensuring sufficient prediction accuracy for held out points, we use
derived scaling laws to compare both models, obtaining evidence for MaMMUT's
stronger improvement with scale and better sample efficiency than standard
CLIP. To strengthen validity of the comparison, we show scaling laws for
various downstream tasks, classification, retrieval, and segmentation, and for
different open datasets, DataComp, DFN and Re-LAION, observing consistently the
same trends. We show that comparison can also be performed when deriving
scaling laws with a constant learning rate schedule, reducing compute cost.
Accurate derivation of scaling laws provides thus means to perform model and
dataset comparison across scale spans, avoiding misleading conclusions based on
measurements from single reference scales only, paving the road for systematic
comparison and improvement of open foundation models and datasets for their
creation. We release all the pre-trained models with their intermediate
checkpoints, including openMaMMUT-L/14, which achieves 80.3% zero-shot
ImageNet-1k accuracy, trained on 12.8B samples from DataComp-1.4B. Code for
reproducing experiments in the paper and raw experiments data can be found at
https://github.com/LAION-AI/scaling-laws-for-comparison.
Images are Worth Variable Length of Representations
Authors: name: Lingjun Mao, name: Rodolfo Corona, name: Xin Liang, name: Wenhao Yan, name: Zineng Tang
Published: 2025-06-04 07:40:33

Most existing vision encoders map images into a fixed-length sequence of
tokens, overlooking the fact that different images contain varying amounts of
information. For example, a visually complex image (e.g., a cluttered room)
inherently carries more information and thus deserves more tokens than a simple
image (e.g., a blank wall). To address this inefficiency, we propose DOVE, a
dynamic vision encoder that produces a variable number of visual tokens (i.e.,
continuous representation vectors) to reconstruct each image. Our results show
that DOVE significantly reduces the average number of tokens while maintaining
high reconstruction quality. In several linear probing and downstream
multimodal tasks, it outperforms existing autoencoder-based tokenization
methods when using far fewer tokens, capturing more expressive semantic
features compared to fixed-length encoding. We further extend DOVE with
query-conditioned tokenization. By guiding the model to focus on query-relevant
regions, it achieves more efficient and targeted semantic extraction. Our code
and checkpoints are available at https://dove-encoder.github.io/dove-encoder.
What do self-supervised speech models know about Dutch? Analyzing
  advantages of language-specific pre-training
Authors: name: Marianne de Heer Kloots, name: Hosein Mohebbi, name: Charlotte Pouw, name: Gaofei Shen, name: Willem Zuidema, name: Martijn Bentum
Published: 2025-06-01 12:25:13

How language-specific are speech representations learned by self-supervised
models? Existing work has shown that a range of linguistic features can be
successfully decoded from end-to-end models trained only on speech recordings.
However, it's less clear to what extent pre-training on specific languages
improves language-specific linguistic information. Here we test the encoding of
Dutch phonetic and lexical information in internal representations of
self-supervised Wav2Vec2 models. Pre-training exclusively on Dutch improves the
representation of Dutch linguistic features as compared to pre-training on
similar amounts of English or larger amounts of multilingual data. This
language-specific advantage is well-detected by trained clustering or
classification probes, and partially observable using zero-shot metrics.
Furthermore, the language-specific benefit on linguistic feature encoding
aligns with downstream performance on Automatic Speech Recognition.
Diagonal Batching Unlocks Parallelism in Recurrent Memory Transformers
  for Long Contexts
Authors: name: Danil Sivtsov, name: Ivan Rodkin, name: Gleb Kuzmin, name: Yuri Kuratov, name: Ivan Oseledets
Published: 2025-06-05 16:43:48

Transformer models struggle with long-context inference due to their
quadratic time and linear memory complexity. Recurrent Memory Transformers
(RMTs) offer a solution by reducing the asymptotic cost to linear time and
constant memory usage. However, their memory update mechanism leads to
sequential execution, causing a performance bottleneck.
  We introduce Diagonal Batching, a scheduling scheme that unlocks parallelism
across segments in RMTs while preserving exact recurrence. This approach
eliminates the sequential constraint, enabling efficient GPU inference even for
single long-context inputs without complex batching and pipelining techniques.
Because the technique is purely a run-time computation reordering, existing RMT
models adopt it with no retraining.
  Applied to a LLaMA-1B ARMT model, Diagonal Batching yields a 3.3x speedup
over standard full-attention LLaMA-1B and a 1.8x speedup over the sequential
RMT implementation on 131,072-token sequences. By removing sequential
bottleneck, Diagonal Batching reduces inference cost and latency, thereby
strengthening RMTs as a practical solution for real-world, long-context
applications.
Diffusion-Based Generative Models for 3D Occupancy Prediction in
  Autonomous Driving
Authors: name: Yunshen Wang, name: Yicheng Liu, name: Tianyuan Yuan, name: Yucheng Mao, name: Yingshi Liang, name: Xiuyu Yang, name: Honggang Zhang, name: Hang Zhao
Published: 2025-05-29 05:34:22

Accurately predicting 3D occupancy grids from visual inputs is critical for
autonomous driving, but current discriminative methods struggle with noisy
data, incomplete observations, and the complex structures inherent in 3D
scenes. In this work, we reframe 3D occupancy prediction as a generative
modeling task using diffusion models, which learn the underlying data
distribution and incorporate 3D scene priors. This approach enhances prediction
consistency, noise robustness, and better handles the intricacies of 3D spatial
structures. Our extensive experiments show that diffusion-based generative
models outperform state-of-the-art discriminative approaches, delivering more
realistic and accurate occupancy predictions, especially in occluded or
low-visibility regions. Moreover, the improved predictions significantly
benefit downstream planning tasks, highlighting the practical advantages of our
method for real-world autonomous driving applications.
FreeTimeGS: Free Gaussians at Anytime and Anywhere for Dynamic Scene
  Reconstruction
Authors: name: Yifan Wang, name: Peishan Yang, name: Zhen Xu, name: Jiaming Sun, name: Zhanhua Zhang, name: Yong Chen, name: Hujun Bao, name: Sida Peng, name: Xiaowei Zhou
Published: 2025-06-05 17:59:57

This paper addresses the challenge of reconstructing dynamic 3D scenes with
complex motions. Some recent works define 3D Gaussian primitives in the
canonical space and use deformation fields to map canonical primitives to
observation spaces, achieving real-time dynamic view synthesis. However, these
methods often struggle to handle scenes with complex motions due to the
difficulty of optimizing deformation fields. To overcome this problem, we
propose FreeTimeGS, a novel 4D representation that allows Gaussian primitives
to appear at arbitrary time and locations. In contrast to canonical Gaussian
primitives, our representation possesses the strong flexibility, thus improving
the ability to model dynamic 3D scenes. In addition, we endow each Gaussian
primitive with an motion function, allowing it to move to neighboring regions
over time, which reduces the temporal redundancy. Experiments results on
several datasets show that the rendering quality of our method outperforms
recent methods by a large margin.
Surfer-H Meets Holo1: Cost-Efficient Web Agent Powered by Open Weights
Authors: name: Mathieu Andreux, name: Breno Baldas Skuk, name: Hamza Benchekroun, name: Emilien Biré, name: Antoine Bonnet, name: Riaz Bordie, name: Matthias Brunel, name: Pierre-Louis Cedoz, name: Antoine Chassang, name: Mickaël Chen, name: Alexandra D. Constantinou, name: Antoine d'Andigné, name: Hubert de La Jonquière, name: Aurélien Delfosse, name: Ludovic Denoyer, name: Alexis Deprez, name: Augustin Derupti, name: Michael Eickenberg, name: Mathïs Federico, name: Charles Kantor, name: Xavier Koegler, name: Yann Labbé, name: Matthew C. H. Lee, name: Erwan Le Jumeau de Kergaradec, name: Amir Mahla, name: Avshalom Manevich, name: Adrien Maret, name: Charles Masson, name: Rafaël Maurin, name: Arturo Mena, name: Philippe Modard, name: Axel Moyal, name: Axel Nguyen Kerbel, name: Julien Revelle, name: Mats L. Richter, name: María Santos, name: Laurent Sifre, name: Maxime Theillard, name: Marc Thibault, name: Louis Thiry, name: Léo Tronchon, name: Nicolas Usunier, name: Tony Wu
Published: 2025-06-03 13:29:03

We present Surfer-H, a cost-efficient web agent that integrates
Vision-Language Models (VLM) to perform user-defined tasks on the web. We pair
it with Holo1, a new open-weight collection of VLMs specialized in web
navigation and information extraction. Holo1 was trained on carefully curated
data sources, including open-access web content, synthetic examples, and
self-produced agentic data. Holo1 tops generalist User Interface (UI)
benchmarks as well as our new web UI localization benchmark, WebClick. When
powered by Holo1, Surfer-H achieves a 92.2% state-of-the-art performance on
WebVoyager, striking a Pareto-optimal balance between accuracy and
cost-efficiency. To accelerate research advancement in agentic systems, we are
open-sourcing both our WebClick evaluation dataset and the Holo1 model weights.
Inference-Time Hyper-Scaling with KV Cache Compression
Authors: name: Adrian Łańcucki, name: Konrad Staniszewski, name: Piotr Nawrot, name: Edoardo M. Ponti
Published: 2025-06-05 17:59:55

Inference-time scaling trades efficiency for increased reasoning accuracy by
generating longer or more parallel sequences. However, in Transformer LLMs,
generation cost is bottlenecked by the size of the key-value (KV) cache, rather
than the number of generated tokens. Hence, we explore inference-time
hyper-scaling: by compressing the KV cache, we can generate more tokens within
the same compute budget and further improve the accuracy of scaled inference.
The success of this approach, however, hinges on the ability of compression
methods to preserve accuracy even at high compression ratios. To make
hyper-scaling practical, we introduce Dynamic Memory Sparsification (DMS), a
novel method for sparsifying KV caches that only requires 1K training steps to
achieve 8times compression, while maintaining better accuracy than
training-free sparse attention. Instead of prematurely discarding cached
tokens, DMS delays token eviction, implicitly merging representations and
preserving critical information. We demonstrate the effectiveness of
inference-time hyper-scaling with DMS on multiple families of LLMs, showing
that it boosts accuracy for comparable inference runtime and memory load. For
instance, we enhance Qwen-R1 32B by an average of 9.1 points on AIME 24, 7.6 on
GPQA, and 9.6 on LiveCodeBench across compute budgets.