Orionnnn/hub-stats huggingface dataset:
CHURRO: Making History Readable with an Open-Weight Large
  Vision-Language Model for High-Accuracy, Low-Cost Historical Text Recognition
Authors: name: Sina J. Semnani, name: Han Zhang, name: Xinyan He, name: Merve Tekgürler, name: Monica S. Lam
Published: 2025-09-24 05:38:45

Accurate text recognition for historical documents can greatly advance the
study and preservation of cultural heritage. Existing vision-language models
(VLMs), however, are designed for modern, standardized texts and are not
equipped to read the diverse languages and scripts, irregular layouts, and
frequent degradation found in historical materials.
  This paper presents CHURRO, a 3B-parameter open-weight VLM specialized for
historical text recognition. The model is trained on CHURRO-DS, the largest
historical text recognition dataset to date. CHURRO-DS unifies 155 historical
corpora comprising 99,491 pages, spanning 22 centuries of textual heritage
across 46 language clusters, including historical variants and dead languages.
  We evaluate several open-weight and closed VLMs and optical character
recognition (OCR) systems on CHURRO-DS and find that CHURRO outperforms all
other VLMs. On the CHURRO-DS test set, CHURRO achieves 82.3% (printed) and
70.1% (handwritten) normalized Levenshtein similarity, surpassing the
second-best model, Gemini 2.5 Pro, by 1.4% and 6.5%, respectively, while being
15.5 times more cost-effective.
  By releasing the model and dataset, we aim to enable community-driven
research to improve the readability of historical texts and accelerate
scholarship.
MinerU2.5: A Decoupled Vision-Language Model for Efficient
  High-Resolution Document Parsing
Authors: name: Junbo Niu, name: Zheng Liu, name: Zhuangcheng Gu, name: Bin Wang, name: Linke Ouyang, name: Zhiyuan Zhao, name: Tao Chu, name: Tianyao He, name: Fan Wu, name: Qintong Zhang, name: Zhenjiang Jin, name: Guang Liang, name: Rui Zhang, name: Wenzheng Zhang, name: Yuan Qu, name: Zhifei Ren, name: Yuefeng Sun, name: Yuanhong Zheng, name: Dongsheng Ma, name: Zirui Tang, name: Boyu Niu, name: Ziyang Miao, name: Hejun Dong, name: Siyi Qian, name: Junyuan Zhang, name: Jingzhou Chen, name: Fangdong Wang, name: Xiaomeng Zhao, name: Liqun Wei, name: Wei Li, name: Shasha Wang, name: Ruiliang Xu, name: Yuanyuan Cao, name: Lu Chen, name: Qianqian Wu, name: Huaiyu Gu, name: Lindong Lu, name: Keming Wang, name: Dechen Lin, name: Guanlin Shen, name: Xuanhe Zhou, name: Linfeng Zhang, name: Yuhang Zang, name: Xiaoyi Dong, name: Jiaqi Wang, name: Bo Zhang, name: Lei Bai, name: Pei Chu, name: Weijia Li, name: Jiang Wu, name: Lijun Wu, name: Zhenxiang Li, name: Guangyu Wang, name: Zhongying Tu, name: Chao Xu, name: Kai Chen, name: Yu Qiao, name: Bowen Zhou, name: Dahua Lin, name: Wentao Zhang, name: Conghui He
Published: 2025-09-26 10:45:48

We introduce MinerU2.5, a 1.2B-parameter document parsing vision-language
model that achieves state-of-the-art recognition accuracy while maintaining
exceptional computational efficiency. Our approach employs a coarse-to-fine,
two-stage parsing strategy that decouples global layout analysis from local
content recognition. In the first stage, the model performs efficient layout
analysis on downsampled images to identify structural elements, circumventing
the computational overhead of processing high-resolution inputs. In the second
stage, guided by the global layout, it performs targeted content recognition on
native-resolution crops extracted from the original image, preserving
fine-grained details in dense text, complex formulas, and tables. To support
this strategy, we developed a comprehensive data engine that generates diverse,
large-scale training corpora for both pretraining and fine-tuning. Ultimately,
MinerU2.5 demonstrates strong document parsing ability, achieving
state-of-the-art performance on multiple benchmarks, surpassing both
general-purpose and domain-specific models across various recognition tasks,
while maintaining significantly lower computational overhead.
Where MLLMs Attend and What They Rely On: Explaining Autoregressive
  Token Generation
Authors: name: Ruoyu Chen, name: Xiaoqing Guo, name: Kangwei Liu, name: Siyuan Liang, name: Shiming Liu, name: Qunli Zhang, name: Hua Zhang, name: Xiaochun Cao
Published: 2025-09-26 15:38:42

Multimodal large language models (MLLMs) have demonstrated remarkable
capabilities in aligning visual inputs with natural language outputs. Yet, the
extent to which generated tokens depend on visual modalities remains poorly
understood, limiting interpretability and reliability. In this work, we present
EAGLE, a lightweight black-box framework for explaining autoregressive token
generation in MLLMs. EAGLE attributes any selected tokens to compact perceptual
regions while quantifying the relative influence of language priors and
perceptual evidence. The framework introduces an objective function that
unifies sufficiency (insight score) and indispensability (necessity score),
optimized via greedy search over sparsified image regions for faithful and
efficient attribution. Beyond spatial attribution, EAGLE performs
modality-aware analysis that disentangles what tokens rely on, providing
fine-grained interpretability of model decisions. Extensive experiments across
open-source MLLMs show that EAGLE consistently outperforms existing methods in
faithfulness, localization, and hallucination diagnosis, while requiring
substantially less GPU memory. These results highlight its effectiveness and
practicality for advancing the interpretability of MLLMs. The code is available
at https://github.com/RuoyuChen10/EAGLE.
UniVid: Unifying Vision Tasks with Pre-trained Video Generation Models
Authors: name: Lan Chen, name: Yuchao Gu, name: Qi Mao
Published: 2025-09-26 01:43:40

Large language models, trained on extensive corpora, successfully unify
diverse linguistic tasks within a single generative framework. Inspired by
this, recent works like Large Vision Model (LVM) extend this paradigm to vision
by organizing tasks into sequential visual sentences, where visual prompts
serve as the context to guide outputs. However, such modeling requires
task-specific pre-training across modalities and sources, which is costly and
limits scalability to unseen tasks. Given that pre-trained video generation
models inherently capture temporal sequence dependencies, we explore a more
unified and scalable alternative: can a pre-trained video generation model
adapt to diverse image and video tasks? To answer this, we propose UniVid, a
framework that fine-tunes a video diffusion transformer to handle various
vision tasks without task-specific modifications. Tasks are represented as
visual sentences, where the context sequence defines both the task and the
expected output modality. We evaluate the generalization of UniVid from two
perspectives: (1) cross-modal inference with contexts composed of both images
and videos, extending beyond LVM's uni-modal setting; (2) cross-source tasks
from natural to annotated data, without multi-source pre-training. Despite
being trained solely on natural video data, UniVid generalizes well in both
settings. Notably, understanding and generation tasks can easily switch by
simply reversing the visual sentence order in this paradigm. These findings
highlight the potential of pre-trained video generation models to serve as a
scalable and unified foundation for vision modeling. Our code will be released
at https://github.com/CUC-MIPG/UniVid.
OverLayBench: A Benchmark for Layout-to-Image Generation with Dense
  Overlaps
Authors: name: Bingnan Li, name: Chen-Yu Wang, name: Haiyang Xu, name: Xiang Zhang, name: Ethan Armand, name: Divyansh Srivastava, name: Xiaojun Shan, name: Zeyuan Chen, name: Jianwen Xie, name: Zhuowen Tu
Published: 2025-09-23 17:50:00

Despite steady progress in layout-to-image generation, current methods still
struggle with layouts containing significant overlap between bounding boxes. We
identify two primary challenges: (1) large overlapping regions and (2)
overlapping instances with minimal semantic distinction. Through both
qualitative examples and quantitative analysis, we demonstrate how these
factors degrade generation quality. To systematically assess this issue, we
introduce OverLayScore, a novel metric that quantifies the complexity of
overlapping bounding boxes. Our analysis reveals that existing benchmarks are
biased toward simpler cases with low OverLayScore values, limiting their
effectiveness in evaluating model performance under more challenging
conditions. To bridge this gap, we present OverLayBench, a new benchmark
featuring high-quality annotations and a balanced distribution across different
levels of OverLayScore. As an initial step toward improving performance on
complex overlaps, we also propose CreatiLayout-AM, a model fine-tuned on a
curated amodal mask dataset. Together, our contributions lay the groundwork for
more robust layout-to-image generation under realistic and challenging
scenarios. Project link: https://mlpc-ucsd.github.io/OverLayBench.
UserRL: Training Interactive User-Centric Agent via Reinforcement
  Learning
Authors: name: Cheng Qian, name: Zuxin Liu, name: Akshara Prabhakar, name: Jielin Qiu, name: Zhiwei Liu, name: Haolin Chen, name: Shirley Kokane, name: Heng Ji, name: Weiran Yao, name: Shelby Heinecke, name: Silvio Savarese, name: Caiming Xiong, name: Huan Wang
Published: 2025-09-24 03:33:20

Reinforcement learning (RL) has shown promise in training agentic models that
move beyond static benchmarks to engage in dynamic, multi-turn interactions.
Yet, the ultimate value of such agents lies in their ability to assist users, a
setting where diversity and dynamics of user interaction pose challenges. In
this work, we propose UserRL, a unified framework for training and evaluating
user-centric abilities through standardized gym environments paired with
simulated users. We systematically vary turn-level reward assignment and
trajectory-level score calculation to analyze how different formulations affect
learning under the GRPO algorithm. Our experiments across Qwen3 models reveal
three key findings: (i) SFT cold start is critical for unlocking initial
interaction ability and enabling sustained RL improvements; (ii) deliberate
trajectory scoring yields more efficient and effective multi-turn interactions;
and (iii) while stronger simulated users (e.g., GPT-4o) facilitates training,
open-source simulators (e.g., Qwen3-32B) remain a cost-effective and
transferable option. Together, these results highlight that careful design of
reward shaping and user simulation choice is as crucial as model scale, and
establish UserRL as a practical pathway for developing robust user-centric
agentic models. All codes and data are public for future research.
CompLLM: Compression for Long Context Q&A
Authors: name: Gabriele Berton, name: Jayakrishnan Unnikrishnan, name: Son Tran, name: Mubarak Shah
Published: 2025-09-23 16:49:43

Large Language Models (LLMs) face significant computational challenges when
processing long contexts due to the quadratic complexity of self-attention.
While soft context compression methods, which map input text to smaller latent
representations, have shown promise, their real-world adoption is limited.
Existing techniques typically compress the context as a single unit, which
leads to quadratic compression complexity and an inability to reuse
computations across queries with overlapping contexts. In this work, we
introduce CompLLM, a soft compression technique designed for practical
deployment. Instead of processing the context holistically, CompLLM divides it
into segments and compresses each one independently. This simple design choice
yields three critical properties: efficiency, as the compression step scales
linearly with the context length; scalability, enabling models trained on short
sequences (e.g., 1k tokens) to generalize to contexts of 100k tokens; and
reusability, allowing compressed segments to be cached and reused across
different queries. Our experiments show that with a 2x compression rate, at
high context lengths CompLLM speeds up Time To First Token (TTFT) by up to 4x
and reduces the KV cache size by 50%. Furthermore, CompLLM achieves performance
comparable to that obtained with the uncompressed context, and even surpasses
it on very long sequences, demonstrating its effectiveness and practical
utility.
Evaluating Large Language Models for Detecting Antisemitism
Authors: name: Jay Patel, name: Hrudayangam Mehta, name: Jeremy Blackburn
Published: 2025-09-22 18:23:21

Detecting hateful content is a challenging and important problem. Automated
tools, like machine-learning models, can help, but they require continuous
training to adapt to the ever-changing landscape of social media. In this work,
we evaluate eight open-source LLMs' capability to detect antisemitic content,
specifically leveraging in-context definition as a policy guideline. We explore
various prompting techniques and design a new CoT-like prompt, Guided-CoT.
Guided-CoT handles the in-context policy well, increasing performance across
all evaluated models, regardless of decoding configuration, model sizes, or
reasoning capability. Notably, Llama 3.1 70B outperforms fine-tuned GPT-3.5.
Additionally, we examine LLM errors and introduce metrics to quantify semantic
divergence in model-generated rationales, revealing notable differences and
paradoxical behaviors among LLMs. Our experiments highlight the differences
observed across LLMs' utility, explainability, and reliability.
Thinking While Listening: Simple Test Time Scaling For Audio
  Classification
Authors: name: Prateek Verma, name: Mert Pilanci
Published: 2025-09-24 01:17:24

We propose a framework that enables neural models to "think while listening"
to everyday sounds, thereby enhancing audio classification performance.
Motivated by recent advances in the reasoning capabilities of large language
models, we address two central questions: (i) how can thinking be incorporated
into existing audio classification pipelines to enable reasoning in the
category space and improve performance, and (ii) can a new architecture be
designed from the ground up to support both thinking and test-time scaling? We
demonstrate that in both settings, our models exhibit improved classification
accuracy. Leveraging test-time scaling, we observe consistent gains as the
number of sampled traces increases. Furthermore, we evaluate two open-source
reasoning models, GPT-OSS-20B and Qwen3-14B, showing that while such models are
capable of zero-shot reasoning, a lightweight approach--retraining only the
embedding matrix of a frozen, smaller model like GPT-2--can surpass the
performance of billion-parameter text-based reasoning models.
BESPOKE: Benchmark for Search-Augmented Large Language Model
  Personalization via Diagnostic Feedback
Authors: name: Hyunseo Kim, name: Sangam Lee, name: Kwangwook Seo, name: Dongha Lee
Published: 2025-09-25 12:53:07

Search-augmented large language models (LLMs) have advanced
information-seeking tasks by integrating retrieval into generation, reducing
users' cognitive burden compared to traditional search systems. Yet they remain
insufficient for fully addressing diverse user needs, which requires
recognizing how the same query can reflect different intents across users and
delivering information in preferred forms. While recent systems such as ChatGPT
and Gemini attempt personalization by leveraging user histories, systematic
evaluation of such personalization is under-explored. To address this gap, we
propose BESPOKE, the realistic benchmark for evaluating personalization in
search-augmented LLMs. BESPOKE is designed to be both realistic, by collecting
authentic chat and search histories directly from humans, and diagnostic, by
pairing responses with fine-grained preference scores and feedback. The
benchmark is constructed through long-term, deeply engaged human annotation,
where human annotators contributed their own histories, authored queries with
detailed information needs, and evaluated responses with scores and diagnostic
feedback. Leveraging BESPOKE, we conduct systematic analyses that reveal key
requirements for effective personalization in information-seeking tasks,
providing a foundation for fine-grained evaluation of personalized
search-augmented LLMs. Our code and data are available at
https://augustinlib.github.io/BESPOKE/.
When Judgment Becomes Noise: How Design Failures in LLM Judge Benchmarks
  Silently Undermine Validity
Authors: name: Benjamin Feuer, name: Chiung-Yi Tseng, name: Astitwa Sarthak Lathe, name: Oussama Elachqar, name: John P Dickerson
Published: 2025-09-24 16:26:47

LLM-judged benchmarks are increasingly used to evaluate complex model
behaviors, yet their design introduces failure modes absent in conventional
ground-truth based benchmarks. We argue that without tight objectives and
verifiable constructions, benchmark rankings can produce high-confidence
rankings that are in fact largely noise. We introduce two mechanisms to
diagnose these issues. Schematic adherence quantifies how much of a judge's
overall verdict is explained by the explicit evaluation schema, revealing
unexplained variance when judges deviate from their own rubric. Psychometric
validity aggregates internal consistency and discriminant validity signals to
quantify irreducible uncertainty in any benchmarking run. Applying these tools
to Arena-Hard Auto, we find severe schema incoherence and factor collapse
across popular judges: for example, unexplained variance exceeding 90 percent
for DeepSeek-R1-32B and factor correlations above 0.93 for most criteria. We
also show that the ELO-style aggregation used by Arena-Hard Auto collapses and
masks genuine ranking uncertainty. Our results highlight design failures that
undermine validity and offer actionable principles for building better-scoped,
reliability-aware LLM-judged benchmarks. We release our code at
https://anonymous.4open.science/r/judgment-to-noise-947D/README.md
Recon-Act: A Self-Evolving Multi-Agent Browser-Use System via Web
  Reconnaissance, Tool Generation, and Task Execution
Authors: name: Kaiwen He, name: Zhiwei Wang, name: Chenyi Zhuang, name: Jinjie Gu
Published: 2025-09-25 12:23:49

Recent years, multimodal models have made remarkable strides and pave the way
for intelligent browser use agents. However, when solving tasks on real world
webpages in multi-turn, long-horizon trajectories, current agents still suffer
from disordered action sequencing and excessive trial and error during
execution. This paper introduces Recon-Act, a self-evolving multi-agent
framework grounded in Reconnaissance-Action behavioral paradigm. The system
comprises a Reconnaissance Team and an Action Team: the former conducts
comparative analysis and tool generation, while the latter handles intent
decomposition, tool orchestration, and execution. By contrasting the erroneous
trajectories with successful ones, the Reconnaissance Team infers remedies, and
abstracts them into a unified notion of generalized tools, either expressed as
hints or as rule-based codes, and register to the tool archive in real time.
The Action Team reinference the process empowered with these targeting tools,
thus establishing a closed-loop training pipeline of
data-tools-action-feedback. Following the 6 level implementation roadmap
proposed in this work, we have currently reached Level 3 (with limited
human-in-the-loop intervention). Leveraging generalized tools obtained through
reconnaissance, Recon-Act substantially improves adaptability to unseen
websites and solvability on long-horizon tasks, and achieves state-of-the-art
performance on the challenging VisualWebArena dataset.
AutoIntent: AutoML for Text Classification
Authors: name: Ilya Alekseev, name: Roman Solomatin, name: Darina Rustamova, name: Denis Kuznetsov
Published: 2025-09-25 13:27:52

AutoIntent is an automated machine learning tool for text classification
tasks. Unlike existing solutions, AutoIntent offers end-to-end automation with
embedding model selection, classifier optimization, and decision threshold
tuning, all within a modular, sklearn-like interface. The framework is designed
to support multi-label classification and out-of-scope detection. AutoIntent
demonstrates superior performance compared to existing AutoML tools on standard
intent classification datasets and enables users to balance effectiveness and
resource consumption.
Quantized Visual Geometry Grounded Transformer
Authors: name: Weilun Feng, name: Haotong Qin, name: Mingqiang Wu, name: Chuanguang Yang, name: Yuqi Li, name: Xiangqi Li, name: Zhulin An, name: Libo Huang, name: Yulun Zhang, name: Michele Magno, name: Yongjun Xu
Published: 2025-09-25 15:17:11

Learning-based 3D reconstruction models, represented by Visual Geometry
Grounded Transformers (VGGTs), have made remarkable progress with the use of
large-scale transformers. Their prohibitive computational and memory costs
severely hinder real-world deployment. Post-Training Quantization (PTQ) has
become a common practice for compressing and accelerating models. However, we
empirically observe that PTQ faces unique obstacles when compressing
billion-scale VGGTs: the data-independent special tokens induce heavy-tailed
activation distributions, while the multi-view nature of 3D data makes
calibration sample selection highly unstable. This paper proposes the first
Quantization framework for VGGTs, namely QuantVGGT. This mainly relies on two
technical contributions: First, we introduce Dual-Smoothed Fine-Grained
Quantization, which integrates pre-global Hadamard rotation and post-local
channel smoothing to mitigate heavy-tailed distributions and inter-channel
variance robustly. Second, we design Noise-Filtered Diverse Sampling, which
filters outliers via deep-layer statistics and constructs frame-aware diverse
calibration clusters to ensure stable quantization ranges. Comprehensive
experiments demonstrate that QuantVGGT achieves the state-of-the-art results
across different benchmarks and bit-width, surpassing the previous
state-of-the-art generic quantization method with a great margin. We highlight
that our 4-bit QuantVGGT can deliver a 3.7times memory reduction and
2.5times acceleration in real-hardware inference, while maintaining
reconstruction accuracy above 98\% of its full-precision counterpart. This
demonstrates the vast advantages and practicality of QuantVGGT in
resource-constrained scenarios. Our code is released in
https://github.com/wlfeng0509/QuantVGGT.
Residual Off-Policy RL for Finetuning Behavior Cloning Policies
Authors: name: Lars Ankile, name: Zhenyu Jiang, name: Rocky Duan, name: Guanya Shi, name: Pieter Abbeel, name: Anusha Nagabandi
Published: 2025-09-23 17:59:46

Recent advances in behavior cloning (BC) have enabled impressive visuomotor
control policies. However, these approaches are limited by the quality of human
demonstrations, the manual effort required for data collection, and the
diminishing returns from increasing offline data. In comparison, reinforcement
learning (RL) trains an agent through autonomous interaction with the
environment and has shown remarkable success in various domains. Still,
training RL policies directly on real-world robots remains challenging due to
sample inefficiency, safety concerns, and the difficulty of learning from
sparse rewards for long-horizon tasks, especially for high-degree-of-freedom
(DoF) systems. We present a recipe that combines the benefits of BC and RL
through a residual learning framework. Our approach leverages BC policies as
black-box bases and learns lightweight per-step residual corrections via
sample-efficient off-policy RL. We demonstrate that our method requires only
sparse binary reward signals and can effectively improve manipulation policies
on high-degree-of-freedom (DoF) systems in both simulation and the real world.
In particular, we demonstrate, to the best of our knowledge, the first
successful real-world RL training on a humanoid robot with dexterous hands. Our
results demonstrate state-of-the-art performance in various vision-based tasks,
pointing towards a practical pathway for deploying RL in the real world.
Project website: https://residual-offpolicy-rl.github.io