Orionnnn/hub-stats huggingface dataset:
Decoupling Angles and Strength in Low-rank Adaptation
Authors: name: Massimo Bini, name: Leander Girrbach, name: Zeynep Akata
Published: 2025-03-23 22:00:56

Parameter-Efficient FineTuning (PEFT) methods have recently gained
significant popularity thanks to the widespread availability of large-scale
pretrained models. These methods allow for quick adaptation to downstream tasks
with minimal computational cost. However, popular finetuning methods such as
LoRA exhibit limited robustness when it comes to hyperparameter choices or
extended training regimes, preventing optimal out-of-the-box performance. In
contrast, bounded approaches, such as ETHER, provide greater robustness but are
limited to extremely low-rank adaptations and fixed-strength transformations,
reducing their adaptation expressive power. In this work, we propose Decoupled
Low-rank Adaptation (DeLoRA), a novel finetuning method that normalizes and
scales learnable low-rank matrices. By bounding the distance of the
transformation, DeLoRA effectively decouples the angular learning from the
adaptation strength, enhancing robustness without compromising performance.
Through evaluations on subject-driven image generation, natural language
understanding, and instruction tuning, we show that DeLoRA matches or surpasses
performance of competing PEFT methods, while exhibiting stronger robustness.
Code is available at https://github.com/ExplainableML/DeLoRA.
Easi3R: Estimating Disentangled Motion from DUSt3R Without Training
Authors: name: Xingyu Chen, name: Yue Chen, name: Yuliang Xiu, name: Andreas Geiger, name: Anpei Chen
Published: 2025-03-31 17:59:58

Recent advances in DUSt3R have enabled robust estimation of dense point
clouds and camera parameters of static scenes, leveraging Transformer network
architectures and direct supervision on large-scale 3D datasets. In contrast,
the limited scale and diversity of available 4D datasets present a major
bottleneck for training a highly generalizable 4D model. This constraint has
driven conventional 4D methods to fine-tune 3D models on scalable dynamic video
data with additional geometric priors such as optical flow and depths. In this
work, we take an opposite path and introduce Easi3R, a simple yet efficient
training-free method for 4D reconstruction. Our approach applies attention
adaptation during inference, eliminating the need for from-scratch pre-training
or network fine-tuning. We find that the attention layers in DUSt3R inherently
encode rich information about camera and object motion. By carefully
disentangling these attention maps, we achieve accurate dynamic region
segmentation, camera pose estimation, and 4D dense point map reconstruction.
Extensive experiments on real-world dynamic videos demonstrate that our
lightweight attention adaptation significantly outperforms previous
state-of-the-art methods that are trained or finetuned on extensive dynamic
datasets. Our code is publicly available for research purpose at
https://easi3r.github.io/
TokenHSI: Unified Synthesis of Physical Human-Scene Interactions through
  Task Tokenization
Authors: name: Liang Pan, name: Zeshi Yang, name: Zhiyang Dou, name: Wenjia Wang, name: Buzhen Huang, name: Bo Dai, name: Taku Komura, name: Jingbo Wang
Published: 2025-03-25 17:57:46

Synthesizing diverse and physically plausible Human-Scene Interactions (HSI)
is pivotal for both computer animation and embodied AI. Despite encouraging
progress, current methods mainly focus on developing separate controllers, each
specialized for a specific interaction task. This significantly hinders the
ability to tackle a wide variety of challenging HSI tasks that require the
integration of multiple skills, e.g., sitting down while carrying an object. To
address this issue, we present TokenHSI, a single, unified transformer-based
policy capable of multi-skill unification and flexible adaptation. The key
insight is to model the humanoid proprioception as a separate shared token and
combine it with distinct task tokens via a masking mechanism. Such a unified
policy enables effective knowledge sharing across skills, thereby facilitating
the multi-task training. Moreover, our policy architecture supports variable
length inputs, enabling flexible adaptation of learned skills to new scenarios.
By training additional task tokenizers, we can not only modify the geometries
of interaction targets but also coordinate multiple skills to address complex
tasks. The experiments demonstrate that our approach can significantly improve
versatility, adaptability, and extensibility in various HSI tasks. Website:
https://liangpan99.github.io/TokenHSI/
Progressive Rendering Distillation: Adapting Stable Diffusion for
  Instant Text-to-Mesh Generation without 3D Data
Authors: name: Zhiyuan Ma, name: Xinyue Liang, name: Rongyuan Wu, name: Xiangyu Zhu, name: Zhen Lei, name: Lei Zhang
Published: 2025-03-27 16:59:15

It is highly desirable to obtain a model that can generate high-quality 3D
meshes from text prompts in just seconds. While recent attempts have adapted
pre-trained text-to-image diffusion models, such as Stable Diffusion (SD), into
generators of 3D representations (e.g., Triplane), they often suffer from poor
quality due to the lack of sufficient high-quality 3D training data. Aiming at
overcoming the data shortage, we propose a novel training scheme, termed as
Progressive Rendering Distillation (PRD), eliminating the need for 3D
ground-truths by distilling multi-view diffusion models and adapting SD into a
native 3D generator. In each iteration of training, PRD uses the U-Net to
progressively denoise the latent from random noise for a few steps, and in each
step it decodes the denoised latent into 3D output. Multi-view diffusion
models, including MVDream and RichDreamer, are used in joint with SD to distill
text-consistent textures and geometries into the 3D outputs through score
distillation. Since PRD supports training without 3D ground-truths, we can
easily scale up the training data and improve generation quality for
challenging text prompts with creative concepts. Meanwhile, PRD can accelerate
the inference speed of the generation model in just a few steps. With PRD, we
train a Triplane generator, namely TriplaneTurbo, which adds only 2.5%
trainable parameters to adapt SD for Triplane generation. TriplaneTurbo
outperforms previous text-to-3D generators in both efficiency and quality.
Specifically, it can produce high-quality 3D meshes in 1.2 seconds and
generalize well for challenging text input. The code is available at
https://github.com/theEricMa/TriplaneTurbo.
UPME: An Unsupervised Peer Review Framework for Multimodal Large
  Language Model Evaluation
Authors: name: Qihui Zhang, name: Munan Ning, name: Zheyuan Liu, name: Yanbo Wang, name: Jiayi Ye, name: Yue Huang, name: Shuo Yang, name: Xiao Chen, name: Yibing Song, name: Li Yuan
Published: 2025-03-19 07:15:41

Multimodal Large Language Models (MLLMs) have emerged to tackle the
challenges of Visual Question Answering (VQA), sparking a new research focus on
conducting objective evaluations of these models. Existing evaluation methods
face limitations due to the significant human workload required to design Q&A
pairs for visual images, which inherently restricts the scale and scope of
evaluations. Although automated MLLM-as-judge approaches attempt to reduce the
human workload through automatic evaluations, they often introduce biases. To
address these problems, we propose an Unsupervised Peer review MLLM Evaluation
framework. It utilizes only image data, allowing models to automatically
generate questions and conduct peer review assessments of answers from other
models, effectively alleviating the reliance on human workload. Additionally,
we introduce the vision-language scoring system to mitigate the bias issues,
which focuses on three aspects: (i) response correctness; (ii) visual
understanding and reasoning; and (iii) image-text correlation. Experimental
results demonstrate that UPME achieves a Pearson correlation of 0.944 with
human evaluations on the MMstar dataset and 0.814 on the ScienceQA dataset,
indicating that our framework closely aligns with human-designed benchmarks and
inherent human preferences.
Open-Reasoner-Zero: An Open Source Approach to Scaling Up Reinforcement
  Learning on the Base Model
Authors: name: Jingcheng Hu, name: Yinmin Zhang, name: Qi Han, name: Daxin Jiang, name: Xiangyu Zhang, name: Heung-Yeung Shum
Published: 2025-03-31 16:36:05

We introduce Open-Reasoner-Zero, the first open source implementation of
large-scale reasoning-oriented RL training focusing on scalability, simplicity
and accessibility. Through extensive experiments, we demonstrate that a
minimalist approach, vanilla PPO with GAE (lambda=1, gamma=1) and
straightforward rule-based rewards, without any KL regularization, is
sufficient to scale up both response length and benchmark performance, similar
to the phenomenon observed in DeepSeek-R1-Zero. Using the same base model as
DeepSeek-R1-Zero-Qwen-32B, our implementation achieves superior performance on
AIME2024, MATH500, and the GPQA Diamond benchmark while demonstrating
remarkable efficiency -- requiring only a tenth of the training steps, compared
to DeepSeek-R1-Zero pipeline. In the spirit of open source, we release our
source code, parameter settings, training data, and model weights across
various sizes.
Expanding RL with Verifiable Rewards Across Diverse Domains
Authors: name: Yi Su, name: Dian Yu, name: Linfeng Song, name: Juntao Li, name: Haitao Mi, name: Zhaopeng Tu, name: Min Zhang, name: Dong Yu
Published: 2025-03-31 08:22:49

Reinforcement learning (RL) with verifiable rewards (RLVR) has shown
promising results in mathematical reasoning and coding tasks where
well-structured reference answers are available. However, its applicability to
broader domains remains underexplored. In this work, we study the extension of
RLVR to more diverse domains such as medicine, chemistry, psychology, and
economics. We observe high agreement in binary judgments across different large
language models (LLMs) when objective reference answers exist, which challenges
the necessity of large-scale annotation for training domain-specific reward
models. To address the limitations of binary rewards when handling unstructured
reference answers, we further incorporate model-based soft scoring into RLVR to
improve its flexibility. Our experiments show that a distilled generative
reward model can serve as an effective cross-domain verifier, providing
reliable reward signals for RL without requiring domain-specific annotations.
By fine-tuning a base 7B model using various RL algorithms against our reward
model, we obtain policies that outperform state-of-the-art open-source aligned
LLMs such as Qwen2.5-72B-Instruct and DeepSeek-R1-Distill-Qwen-32B by a large
margin, across domains in free-form answer settings. This also strengthens
RLVR's robustness and scalability, highlighting its potential for real-world
applications with noisy or weak labels.
Query and Conquer: Execution-Guided SQL Generation
Authors: name: Łukasz Borchmann, name: Marek Wydmuch
Published: 2025-03-31 17:43:36

We propose a novel approach for generating complex outputs that significantly
improves accuracy in text-to-SQL tasks. Our method leverages execution results
to select the most semantically consistent query from multiple candidates,
enabling smaller, cost-effective models to surpass computationally intensive
reasoning methods such as o1, o3-mini, and DeepSeek R1 while reducing inference
cost by as much as 30 times. It integrates effortlessly with existing models,
offering a practical and scalable pathway to state-of-the-art SQL generation.
SketchVideo: Sketch-based Video Generation and Editing
Authors: name: Feng-Lin Liu, name: Hongbo Fu, name: Xintao Wang, name: Weicai Ye, name: Pengfei Wan, name: Di Zhang, name: Lin Gao
Published: 2025-03-30 02:44:09

Video generation and editing conditioned on text prompts or images have
undergone significant advancements. However, challenges remain in accurately
controlling global layout and geometry details solely by texts, and supporting
motion control and local modification through images. In this paper, we aim to
achieve sketch-based spatial and motion control for video generation and
support fine-grained editing of real or synthetic videos. Based on the DiT
video generation model, we propose a memory-efficient control structure with
sketch control blocks that predict residual features of skipped DiT blocks.
Sketches are drawn on one or two keyframes (at arbitrary time points) for easy
interaction. To propagate such temporally sparse sketch conditions across all
frames, we propose an inter-frame attention mechanism to analyze the
relationship between the keyframes and each video frame. For sketch-based video
editing, we design an additional video insertion module that maintains
consistency between the newly edited content and the original video's spatial
feature and dynamic motion. During inference, we use latent fusion for the
accurate preservation of unedited regions. Extensive experiments demonstrate
that our SketchVideo achieves superior performance in controllable video
generation and editing.
TextCrafter: Accurately Rendering Multiple Texts in Complex Visual
  Scenes
Authors: name: Nikai Du, name: Zhennan Chen, name: Zhizhou Chen, name: Shan Gao, name: Xi Chen, name: Zhengkai Jiang, name: Jian Yang, name: Ying Tai
Published: 2025-03-30 14:36:55

This paper explores the task of Complex Visual Text Generation (CVTG), which
centers on generating intricate textual content distributed across diverse
regions within visual images. In CVTG, image generation models often rendering
distorted and blurred visual text or missing some visual text. To tackle these
challenges, we propose TextCrafter, a novel multi-visual text rendering method.
TextCrafter employs a progressive strategy to decompose complex visual text
into distinct components while ensuring robust alignment between textual
content and its visual carrier. Additionally, it incorporates a token focus
enhancement mechanism to amplify the prominence of visual text during the
generation process. TextCrafter effectively addresses key challenges in CVTG
tasks, such as text confusion, omissions, and blurriness. Moreover, we present
a new benchmark dataset, CVTG-2K, tailored to rigorously evaluate the
performance of generative models on CVTG tasks. Extensive experiments
demonstrate that our method surpasses state-of-the-art approaches.
KOFFVQA: An Objectively Evaluated Free-form VQA Benchmark for Large
  Vision-Language Models in the Korean Language
Authors: name: Yoonshik Kim, name: Jaeyoon Jung
Published: 2025-03-31 05:04:25

The recent emergence of Large Vision-Language Models(VLMs) has resulted in a
variety of different benchmarks for evaluating such models. Despite this, we
observe that most existing evaluation methods suffer from the fact that they
either require the model to choose from pre-determined responses, sacrificing
open-endedness, or evaluate responses using a judge model, resulting in
subjective and unreliable evaluation. In addition, we observe a lack of
benchmarks for VLMs in the Korean language, which are necessary as a separate
metric from more common English language benchmarks, as the performance of
generative language models can differ significantly based on the language being
used. Therefore, we present KOFFVQA, a general-purpose free-form visual
question answering benchmark in the Korean language for the evaluation of VLMs.
Our benchmark consists of 275 carefully crafted questions each paired with an
image and grading criteria covering 10 different aspects of VLM performance.
The grading criteria eliminate the problem of unreliability by allowing the
judge model to grade each response based on a pre-determined set of rules. By
defining the evaluation criteria in an objective manner, even a small
open-source model can be used to evaluate models on our benchmark reliably. In
addition to evaluating a large number of existing VLMs on our benchmark, we
also experimentally verify that our method of using pre-existing grading
criteria for evaluation is much more reliable than existing methods. Our
evaluation code is available at https://github.com/maum-ai/KOFFVQA
Efficient Inference for Large Reasoning Models: A Survey
Authors: name: Yue Liu, name: Jiaying Wu, name: Yufei He, name: Hongcheng Gao, name: Hongyu Chen, name: Baolong Bi, name: Jiaheng Zhang, name: Zhiqi Huang, name: Bryan Hooi
Published: 2025-03-29 13:27:46

Large Reasoning Models (LRMs) significantly improve the reasoning ability of
Large Language Models (LLMs) by learning to reason, exhibiting promising
performance in complex task-solving. However, their deliberative reasoning
process leads to inefficiencies in token usage, memory consumption, and
inference time. Thus, this survey provides a review of efficient inference
methods designed specifically for LRMs, focusing on mitigating token
inefficiency while preserving the reasoning quality. First, we introduce a
taxonomy to group the recent methods into two main categories: (a) explicit
compact Chain-of-Thought (CoT), which reduces tokens while keeping the explicit
reasoning structure, and (b) implicit latent CoT, which encodes reasoning steps
within hidden representations instead of explicit tokens. Meanwhile, we discuss
their strengths and weaknesses. Then, we conduct empirical analyses on existing
methods from performance and efficiency aspects. Besides, we present open
challenges in this field, including human-centric controllable reasoning,
trade-off between interpretability and efficiency of reasoning, ensuring safety
of efficient reasoning, and broader applications of efficient reasoning. In
addition, we highlight key insights for enhancing LRMs' inference efficiency
via techniques such as model merging, new architectures, and agent routers. We
hope this work serves as a valuable guide, helping researchers overcome
challenges in this vibrant
fieldhttps://github.com/yueliu1999/Awesome-Efficient-Inference-for-LRMs.
What, How, Where, and How Well? A Survey on Test-Time Scaling in Large
  Language Models
Authors: name: Qiyuan Zhang, name: Fuyuan Lyu, name: Zexu Sun, name: Lei Wang, name: Weixu Zhang, name: Zhihan Guo, name: Yufei Wang, name: Irwin King, name: Xue Liu, name: Chen Ma
Published: 2025-03-31 15:46:15

As enthusiasm for scaling computation (data and parameters) in the
pretraining era gradually diminished, test-time scaling (TTS), also referred to
as ``test-time computing'' has emerged as a prominent research focus. Recent
studies demonstrate that TTS can further elicit the problem-solving
capabilities of large language models (LLMs), enabling significant
breakthroughs not only in specialized reasoning tasks, such as mathematics and
coding, but also in general tasks like open-ended Q&A. However, despite the
explosion of recent efforts in this area, there remains an urgent need for a
comprehensive survey offering a systemic understanding. To fill this gap, we
propose a unified, multidimensional framework structured along four core
dimensions of TTS research: what to scale, how to scale, where to scale, and
how well to scale. Building upon this taxonomy, we conduct an extensive review
of methods, application scenarios, and assessment aspects, and present an
organized decomposition that highlights the unique functional roles of
individual techniques within the broader TTS landscape. From this analysis, we
distill the major developmental trajectories of TTS to date and offer hands-on
guidelines for practical deployment. Furthermore, we identify several open
challenges and offer insights into promising future directions, including
further scaling, clarifying the functional essence of techniques, generalizing
to more tasks, and more attributions.
RIG: Synergizing Reasoning and Imagination in End-to-End Generalist
  Policy
Authors: name: Zhonghan Zhao, name: Wenwei Zhang, name: Haian Huang, name: Kuikun Liu, name: Jianfei Gao, name: Gaoang Wang, name: Kai Chen
Published: 2025-03-31 17:59:52

Reasoning before action and imagining potential outcomes (i.e., world models)
are essential for embodied agents operating in complex open-world environments.
Yet, prior work either incorporates only one of these abilities in an
end-to-end agent or integrates multiple specialized models into an agent
system, limiting the learning efficiency and generalization of the policy.
Thus, this paper makes the first attempt to synergize Reasoning and Imagination
in an end-to-end Generalist policy, termed RIG. To train RIG in an end-to-end
manner, we construct a data pipeline that progressively integrates and enriches
the content of imagination and reasoning in the trajectories collected from
existing agents. The joint learning of reasoning and next image generation
explicitly models the inherent correlation between reasoning, action, and
dynamics of environments, and thus exhibits more than 17times sample
efficiency improvements and generalization in comparison with previous works.
During inference, RIG first reasons about the next action, produces potential
action, and then predicts the action outcomes, which offers the agent a chance
to review and self-correct based on the imagination before taking real actions.
Experimental results show that the synergy of reasoning and imagination not
only improves the robustness, generalization, and interoperability of
generalist policy but also enables test-time scaling to enhance overall
performance.
TeleAntiFraud-28k: A Audio-Text Slow-Thinking Dataset for Telecom Fraud
  Detection
Authors: name: Zhiming Ma, name: Peidong Wang, name: Minhua Huang, name: Jingpeng Wang, name: Kai Wu, name: Xiangzhao Lv, name: Yachun Pang, name: Yin Yang, name: Wenjie Tang, name: Yuchen Kang
Published: 2025-03-31 14:06:17

The detection of telecom fraud faces significant challenges due to the lack
of high-quality multimodal training data that integrates audio signals with
reasoning-oriented textual analysis. To address this gap, we present
TeleAntiFraud-28k, the first open-source audio-text slow-thinking dataset
specifically designed for automated telecom fraud analysis. Our dataset is
constructed through three strategies: (1) Privacy-preserved text-truth sample
generation using automatically speech recognition (ASR)-transcribed call
recordings (with anonymized original audio), ensuring real-world consistency
through text-to-speech (TTS) model regeneration; (2) Semantic enhancement via
large language model (LLM)-based self-instruction sampling on authentic ASR
outputs to expand scenario coverage; (3) Multi-agent adversarial synthesis that
simulates emerging fraud tactics through predefined communication scenarios and
fraud typologies. The generated dataset contains 28,511 rigorously processed
speech-text pairs, complete with detailed annotations for fraud reasoning. The
dataset is divided into three tasks: scenario classification, fraud detection,
fraud type classification. Furthermore, we construct TeleAntiFraud-Bench, a
standardized evaluation benchmark comprising proportionally sampled instances
from the dataset, to facilitate systematic testing of model performance on
telecom fraud detection tasks. We also contribute a production-optimized
supervised fine-tuning (SFT) model trained on hybrid real/synthetic data, while
open-sourcing the data processing framework to enable community-driven dataset
expansion. This work establishes a foundational framework for multimodal
anti-fraud research while addressing critical challenges in data privacy and
scenario diversity. The project will be released at
https://github.com/JimmyMa99/TeleAntiFraud.