Orionnnn/hub-stats huggingface dataset:
Singular Value Few-shot Adaptation of Vision-Language Models
Authors: name: Taha Koleilat, name: Hassan Rivaz, name: Yiming Xiao
Published: 2025-09-03 22:00:23

Vision-language models (VLMs) like CLIP have shown impressive zero-shot and
few-shot learning capabilities across diverse applications. However, adapting
these models to new fine-grained domains remains difficult due to reliance on
prompt engineering and the high cost of full model fine-tuning. Existing
adaptation approaches rely on augmented components, such as prompt tokens and
adapter modules, which could limit adaptation quality, destabilize the model,
and compromise the rich knowledge learned during pretraining. In this work, we
present CLIP-SVD, a novel multi-modal and
parameter-efficient adaptation technique that leverages Singular Value
Decomposition (SVD) to modify the internal parameter space of CLIP without
injecting additional modules. Specifically, we fine-tune only the singular
values of the CLIP parameter matrices to rescale the basis vectors for domain
adaptation while retaining the pretrained model. This design enables enhanced
adaptation performance using only 0.04\% of the model's total
parameters and better preservation of its generalization ability. CLIP-SVD
achieves state-of-the-art classification results on 11 natural and 10
biomedical datasets, outperforming previous methods in both accuracy and
generalization under few-shot settings. Additionally, we leverage a natural
language-based approach to analyze the effectiveness and dynamics of the CLIP
adaptation to allow interpretability of CLIP-SVD. The code is publicly
available at https://github.com/HealthX-Lab/CLIP-SVD.
Inpaint4Drag: Repurposing Inpainting Models for Drag-Based Image Editing
  via Bidirectional Warping
Authors: name: Jingyi Lu, name: Kai Han
Published: 2025-09-04 18:04:47

Drag-based image editing has emerged as a powerful paradigm for intuitive
image manipulation. However, existing approaches predominantly rely on
manipulating the latent space of generative models, leading to limited
precision, delayed feedback, and model-specific constraints. Accordingly, we
present Inpaint4Drag, a novel framework that decomposes drag-based editing into
pixel-space bidirectional warping and image inpainting. Inspired by elastic
object deformation in the physical world, we treat image regions as deformable
materials that maintain natural shape under user manipulation. Our method
achieves real-time warping previews (0.01s) and efficient inpainting (0.3s) at
512x512 resolution, significantly improving the interaction experience compared
to existing methods that require minutes per edit. By transforming drag inputs
directly into standard inpainting formats, our approach serves as a universal
adapter for any inpainting model without architecture modification,
automatically inheriting all future improvements in inpainting technology.
Extensive experiments demonstrate that our method achieves superior visual
quality and precise control while maintaining real-time performance. Project
page: https://visual-ai.github.io/inpaint4drag/
DCReg: Decoupled Characterization for Efficient Degenerate LiDAR
  Registration
Authors: name: Xiangcheng Hu, name: Xieyuanli Chen, name: Mingkai Jia, name: Jin Wu, name: Ping Tan, name: Steven L. Waslander
Published: 2025-09-08 02:12:54

LiDAR point cloud registration is fundamental to robotic perception and
navigation. However, in geometrically degenerate or narrow environments,
registration problems become ill-conditioned, leading to unstable solutions and
degraded accuracy. While existing approaches attempt to handle these issues,
they fail to address the core challenge: accurately detection, interpret, and
resolve this ill-conditioning, leading to missed detections or corrupted
solutions. In this study, we introduce DCReg, a principled framework that
systematically addresses the ill-conditioned registration problems through
three integrated innovations. First, DCReg achieves reliable ill-conditioning
detection by employing a Schur complement decomposition to the hessian matrix.
This technique decouples the registration problem into clean rotational and
translational subspaces, eliminating coupling effects that mask degeneracy
patterns in conventional analyses. Second, within these cleanly subspaces, we
develop quantitative characterization techniques that establish explicit
mappings between mathematical eigenspaces and physical motion directions,
providing actionable insights about which specific motions lack constraints.
Finally, leveraging this clean subspace, we design a targeted mitigation
strategy: a novel preconditioner that selectively stabilizes only the
identified ill-conditioned directions while preserving all well-constrained
information in observable space. This enables efficient and robust optimization
via the Preconditioned Conjugate Gradient method with a single physical
interpretable parameter. Extensive experiments demonstrate DCReg achieves at
least 20% - 50% improvement in localization accuracy and 5-100 times speedup
over state-of-the-art methods across diverse environments. Our implementation
will be available at https://github.com/JokerJohn/DCReg.
Guided Decoding and Its Critical Role in Retrieval-Augmented Generation
Authors: name: Özgür Uğur, name: Musa Yılmaz, name: Esra Şavirdi, name: Özay Ezerceli, name: Mahmut El Huseyni, name: Selva Taş, name: Reyhan Bayraktar
Published: 2025-09-08 12:51:40

The integration of Large Language Models (LLMs) into various applications has
driven the need for structured and reliable responses. A key challenge in
Retrieval-Augmented Generation (RAG) systems is ensuring that outputs align
with expected formats while minimizing hallucinations. This study examines the
role of guided decoding in RAG systems, comparing three methods, Outlines,
XGrammar, and LM Format Enforcer, across different multi-turn prompting setups
(0-turn, 1-turn, and 2-turn). By evaluating success rates, hallucination rates,
and output quality, we provide insights into their performance and
applicability. Our findings reveal how multi-turn interactions influence guided
decoding, uncovering unexpected performance variations that can inform method
selection for specific use cases. This work advances the understanding of
structured output generation in RAG systems, offering both theoretical insights
and practical guidance for LLM deployment.
Test-Time Scaling in Reasoning Models Is Not Effective for
  Knowledge-Intensive Tasks Yet
Authors: name: James Xu Zhao, name: Bryan Hooi, name: See-Kiong Ng
Published: 2025-09-08 16:28:25

Test-time scaling increases inference-time computation by allowing models to
generate long reasoning chains, and has shown strong performance across many
domains. However, in this work, we show that this approach is not yet effective
for knowledge-intensive tasks, where high factual accuracy and low
hallucination rates are essential. We conduct a comprehensive evaluation of
test-time scaling using 12 reasoning models on two knowledge-intensive
benchmarks. Our results reveal that increasing test-time computation does not
consistently improve accuracy and, in many cases, it even leads to more
hallucinations. We then analyze how extended reasoning affects hallucination
behavior. We find that reduced hallucinations often result from the model
choosing to abstain after thinking more, rather than from improved factual
recall. Conversely, for some models, longer reasoning encourages attempts on
previously unanswered questions, many of which result in hallucinations. Case
studies show that extended reasoning can induce confirmation bias, leading to
overconfident hallucinations. Despite these limitations, we observe that
compared to non-thinking, enabling thinking remains beneficial. Code and data
are available at https://github.com/XuZhao0/tts-knowledge
Saturation-Driven Dataset Generation for LLM Mathematical Reasoning in
  the TPTP Ecosystem
Authors: name: Valentin Quesnel, name: Damien Sileo
Published: 2025-09-08 15:43:29

The scarcity of high-quality, logically sound data is a critical bottleneck
for advancing the mathematical reasoning of Large Language Models (LLMs). Our
work confronts this challenge by turning decades of automated theorem proving
research into a scalable data engine. Rather than relying on error-prone LLMs
or complex proof-assistant syntax like Lean and Isabelle, our framework
leverages E-prover's saturation capabilities on the vast TPTP axiom library to
derive a massive, guaranteed-valid corpus of theorems. Our pipeline is
principled and simple: saturate axioms, filter for "interesting" theorems, and
generate tasks. With no LLMs in the loop, we eliminate factual errors by
construction. This purely symbolic data is then transformed into three
difficulty-controlled challenges: entailment verification, premise selection,
and proof reconstruction. Our zero-shot experiments on frontier models reveal a
clear weakness: performance collapses on tasks requiring deep, structural
reasoning. Our framework provides both the diagnostic tool to measure this gap
and a scalable source of symbolic training data to address it. We make the code
and data publicly available.
  https://github.com/sileod/reasoning_core
https://hf.co/datasets/reasoning-core/rc1
WebExplorer: Explore and Evolve for Training Long-Horizon Web Agents
Authors: name: Junteng Liu, name: Yunji Li, name: Chi Zhang, name: Jingyang Li, name: Aili Chen, name: Ke Ji, name: Weiyu Cheng, name: Zijia Wu, name: Chengyu Du, name: Qidi Xu, name: Jiayuan Song, name: Zhengmao Zhu, name: Wenhu Chen, name: Pengyu Zhao, name: Junxian He
Published: 2025-09-08 10:07:03

The paradigm of Large Language Models (LLMs) has increasingly shifted toward
agentic applications, where web browsing capabilities are fundamental for
retrieving information from diverse online sources. However, existing
open-source web agents either demonstrate limited information-seeking abilities
on complex tasks or lack transparent implementations. In this work, we identify
that the key challenge lies in the scarcity of challenging data for information
seeking. To address this limitation, we introduce WebExplorer: a systematic
data generation approach using model-based exploration and iterative,
long-to-short query evolution. This method creates challenging query-answer
pairs that require multi-step reasoning and complex web navigation. By
leveraging our curated high-quality dataset, we successfully develop advanced
web agent WebExplorer-8B through supervised fine-tuning followed by
reinforcement learning. Our model supports 128K context length and up to 100
tool calling turns, enabling long-horizon problem solving. Across diverse
information-seeking benchmarks, WebExplorer-8B achieves the state-of-the-art
performance at its scale. Notably, as an 8B-sized model, WebExplorer-8B is able
to effectively search over an average of 16 turns after RL training, achieving
higher accuracy than WebSailor-72B on BrowseComp-en/zh and attaining the best
performance among models up to 100B parameters on WebWalkerQA and FRAMES.
Beyond these information-seeking tasks, our model also achieves strong
generalization on the HLE benchmark even though it is only trained on
knowledge-intensive QA data. These results highlight our approach as a
practical path toward long-horizon web agents.
Interleaving Reasoning for Better Text-to-Image Generation
Authors: name: Wenxuan Huang, name: Shuang Chen, name: Zheyong Xie, name: Shaosheng Cao, name: Shixiang Tang, name: Yufan Shen, name: Qingyu Yin, name: Wenbo Hu, name: Xiaoman Wang, name: Yuntian Tang, name: Junbo Qiao, name: Yue Guo, name: Yao Hu, name: Zhenfei Yin, name: Philip Torr, name: Yu Cheng, name: Wanli Ouyang, name: Shaohui Lin
Published: 2025-09-08 17:56:23

Unified multimodal understanding and generation models recently have achieve
significant improvement in image generation capability, yet a large gap remains
in instruction following and detail preservation compared to systems that
tightly couple comprehension with generation such as GPT-4o. Motivated by
recent advances in interleaving reasoning, we explore whether such reasoning
can further improve Text-to-Image (T2I) generation. We introduce Interleaving
Reasoning Generation (IRG), a framework that alternates between text-based
thinking and image synthesis: the model first produces a text-based thinking to
guide an initial image, then reflects on the result to refine fine-grained
details, visual quality, and aesthetics while preserving semantics. To train
IRG effectively, we propose Interleaving Reasoning Generation Learning (IRGL),
which targets two sub-goals: (1) strengthening the initial think-and-generate
stage to establish core content and base quality, and (2) enabling high-quality
textual reflection and faithful implementation of those refinements in a
subsequent image. We curate IRGL-300K, a dataset organized into six decomposed
learning modes that jointly cover learning text-based thinking, and full
thinking-image trajectories. Starting from a unified foundation model that
natively emits interleaved text-image outputs, our two-stage training first
builds robust thinking and reflection, then efficiently tunes the IRG pipeline
in the full thinking-image trajectory data. Extensive experiments show SoTA
performance, yielding absolute gains of 5-10 points on GenEval, WISE, TIIF,
GenAI-Bench, and OneIG-EN, alongside substantial improvements in visual quality
and fine-grained fidelity. The code, model weights and datasets will be
released in: https://github.com/Osilly/Interleaving-Reasoning-Generation .
Focusing by Contrastive Attention: Enhancing VLMs' Visual Reasoning
Authors: name: Yuyao Ge, name: Shenghua Liu, name: Yiwei Wang, name: Lingrui Mei, name: Baolong Bi, name: Xuanshan Zhou, name: Jiayu Yao, name: Jiafeng Guo, name: Xueqi Cheng
Published: 2025-09-08 09:20:04

Vision-Language Models (VLMs) have demonstrated remarkable success across
diverse visual tasks, yet their performance degrades in complex visual
environments. While existing enhancement approaches require additional
training, rely on external segmentation tools, or operate at coarse-grained
levels, they overlook the innate ability within VLMs. To bridge this gap, we
investigate VLMs' attention patterns and discover that: (1) visual complexity
strongly correlates with attention entropy, negatively impacting reasoning
performance; (2) attention progressively refines from global scanning in
shallow layers to focused convergence in deeper layers, with convergence degree
determined by visual complexity. (3) Theoretically, we prove that the contrast
of attention maps between general queries and task-specific queries enables the
decomposition of visual signal into semantic signals and visual noise
components. Building on these insights, we propose Contrastive Attention
Refinement for Visual Enhancement (CARVE), a training-free method that extracts
task-relevant visual signals through attention contrasting at the pixel level.
Extensive experiments demonstrate that CARVE consistently enhances performance,
achieving up to 75% improvement on open-source models. Our work provides
critical insights into the interplay between visual complexity and attention
mechanisms, offering an efficient pathway for improving visual reasoning with
contrasting attention.
MAS-Bench: A Unified Benchmark for Shortcut-Augmented Hybrid Mobile GUI
  Agents
Authors: name: Pengxiang Zhao, name: Guangyi Liu, name: Yaozhen Liang, name: Weiqing He, name: Zhengxi Lu, name: Yuehao Huang, name: Yaxuan Guo, name: Kexin Zhang, name: Hao Wang, name: Liang Liu, name: Yong Liu
Published: 2025-09-08 09:43:48

To enhance the efficiency of GUI agents on various platforms like smartphones
and computers, a hybrid paradigm that combines flexible GUI operations with
efficient shortcuts (e.g., API, deep links) is emerging as a promising
direction. However, a framework for systematically benchmarking these hybrid
agents is still underexplored. To take the first step in bridging this gap, we
introduce MAS-Bench, a benchmark that pioneers the evaluation of GUI-shortcut
hybrid agents with a specific focus on the mobile domain. Beyond merely using
predefined shortcuts, MAS-Bench assesses an agent's capability to autonomously
generate shortcuts by discovering and creating reusable, low-cost workflows. It
features 139 complex tasks across 11 real-world applications, a knowledge base
of 88 predefined shortcuts (APIs, deep-links, RPA scripts), and 7 evaluation
metrics. The tasks are designed to be solvable via GUI-only operations, but can
be significantly accelerated by intelligently embedding shortcuts. Experiments
show that hybrid agents achieve significantly higher success rates and
efficiency than their GUI-only counterparts. This result also demonstrates the
effectiveness of our method for evaluating an agent's shortcut generation
capabilities. MAS-Bench fills a critical evaluation gap, providing a
foundational platform for future advancements in creating more efficient and
robust intelligent agents.
D-HUMOR: Dark Humor Understanding via Multimodal Open-ended Reasoning
Authors: name: Sai Kartheek Reddy Kasu, name: Mohammad Zia Ur Rehman, name: Shahid Shafi Dar, name: Rishi Bharat Junghare, name: Dhanvin Sanjay Namboodiri, name: Nagendra Kumar
Published: 2025-09-08 14:55:16

Dark humor in online memes poses unique challenges due to its reliance on
implicit, sensitive, and culturally contextual cues. To address the lack of
resources and methods for detecting dark humor in multimodal content, we
introduce a novel dataset of 4,379 Reddit memes annotated for dark humor,
target category (gender, mental health, violence, race, disability, and other),
and a three-level intensity rating (mild, moderate, severe). Building on this
resource, we propose a reasoning-augmented framework that first generates
structured explanations for each meme using a Large Vision-Language Model
(VLM). Through a Role-Reversal Self-Loop, VLM adopts the author's perspective
to iteratively refine its explanations, ensuring completeness and alignment. We
then extract textual features from both the OCR transcript and the self-refined
reasoning via a text encoder, while visual features are obtained using a vision
transformer. A Tri-stream Cross-Reasoning Network (TCRNet) fuses these three
streams, text, image, and reasoning, via pairwise attention mechanisms,
producing a unified representation for classification. Experimental results
demonstrate that our approach outperforms strong baselines across three tasks:
dark humor detection, target identification, and intensity prediction. The
dataset, annotations, and code are released to facilitate further research in
multimodal humor understanding and content moderation. Code and Dataset are
available at:
https://github.com/Sai-Kartheek-Reddy/D-Humor-Dark-Humor-Understanding-via-Multimodal-Open-ended-Reasoning
Does DINOv3 Set a New Medical Vision Standard?
Authors: name: Che Liu, name: Yinda Chen, name: Haoyuan Shi, name: Jinpeng Lu, name: Bailiang Jian, name: Jiazhen Pan, name: Linghan Cai, name: Jiayi Wang, name: Yundi Zhang, name: Jun Li, name: Cosmin I. Bercea, name: Cheng Ouyang, name: Chen Chen, name: Zhiwei Xiong, name: Benedikt Wiestler, name: Christian Wachinger, name: Daniel Rueckert, name: Wenjia Bai, name: Rossella Arcucci
Published: 2025-09-08 09:28:57

The advent of large-scale vision foundation models, pre-trained on diverse
natural images, has marked a paradigm shift in computer vision. However, how
the frontier vision foundation models' efficacies transfer to specialized
domains remains such as medical imaging remains an open question. This report
investigates whether DINOv3, a state-of-the-art self-supervised vision
transformer (ViT) that features strong capability in dense prediction tasks,
can directly serve as a powerful, unified encoder for medical vision tasks
without domain-specific pre-training. To answer this, we benchmark DINOv3
across common medical vision tasks, including 2D/3D classification and
segmentation on a wide range of medical imaging modalities. We systematically
analyze its scalability by varying model sizes and input image resolutions. Our
findings reveal that DINOv3 shows impressive performance and establishes a
formidable new baseline. Remarkably, it can even outperform medical-specific
foundation models like BiomedCLIP and CT-Net on several tasks, despite being
trained solely on natural images. However, we identify clear limitations: The
model's features degrade in scenarios requiring deep domain specialization,
such as in Whole-Slide Pathological Images (WSIs), Electron Microscopy (EM),
and Positron Emission Tomography (PET). Furthermore, we observe that DINOv3
does not consistently obey scaling law in the medical domain; performance does
not reliably increase with larger models or finer feature resolutions, showing
diverse scaling behaviors across tasks. Ultimately, our work establishes DINOv3
as a strong baseline, whose powerful visual features can serve as a robust
prior for multiple complex medical tasks. This opens promising future
directions, such as leveraging its features to enforce multiview consistency in
3D reconstruction.
Scaling up Multi-Turn Off-Policy RL and Multi-Agent Tree Search for LLM
  Step-Provers
Authors: name: Ran Xin, name: Zeyu Zheng, name: Yanchen Nie, name: Kun Yuan, name: Xia Xiao
Published: 2025-09-08 09:54:18

The integration of Large Language Models (LLMs) into automated theorem
proving has shown immense promise, yet is fundamentally constrained by
challenges in scaling up both training-time reinforcement learning (RL) and
inference-time compute. This paper introduces BFS-Prover-V2, a system
designed to address this dual scaling problem. We present two primary
innovations. The first is a novel multi-turn off-policy RL framework for
continually improving the performance of LLM step-prover at training time. This
framework, inspired by the principles of AlphaZero, utilizes a multi-stage
expert iteration pipeline featuring adaptive tactic-level data filtering and
periodic retraining to surmount the performance plateaus that typically curtail
long-term RL in LLM-based agents. The second innovation is a planner-enhanced
multi-agent search architecture that scales reasoning capabilities at inference
time. This architecture employs a general reasoning model as a high-level
planner to iteratively decompose complex theorems into a sequence of simpler
subgoals. This hierarchical approach substantially reduces the search space,
enabling a team of parallel prover agents to collaborate efficiently by
leveraging a shared proof cache. We demonstrate that this dual approach to
scaling yields state-of-the-art results on established formal mathematics
benchmarks. BFS-Prover-V2 achieves 95.08\% and 41.4\% on the MiniF2F
and ProofNet test sets respectively. While demonstrated in the domain of formal
mathematics, the RL and inference techniques presented in this work are of
broader interest and may be applied to other domains requiring long-horizon
multi-turn reasoning and complex search.