Orionnnn/hub-stats huggingface dataset:
Advancing Speech Understanding in Speech-Aware Language Models with GRPO
Authors: name: Avishai Elmakies, name: Hagai Aronowitz, name: Nimrod Shabtay, name: Eli Schwartz, name: Ron Hoory, name: Avihu Dekel
Published: 2025-09-21 09:09:36

In this paper, we introduce a Group Relative Policy Optimization (GRPO)-based
method for training Speech-Aware Large Language Models (SALLMs) on open-format
speech understanding tasks, such as Spoken Question Answering and Automatic
Speech Translation. SALLMs have proven highly effective for speech
understanding tasks. GRPO has recently gained traction for its efficiency in
training LLMs, and prior work has explored its application to SALLMs, primarily
in multiple-choice tasks. Building on this, we focus on open-format tasks that
better reflect the generative abilities of the models. Our approach leverages
GRPO with BLEU as the reward signal to optimize SALLMs, and we demonstrate
empirically that it surpasses standard SFT across several key metrics. Finally,
we explore the potential of incorporating off-policy samples within GRPO for
these tasks, highlighting avenues for further improvement and further research.
LLMs4All: A Review on Large Language Models for Research and
  Applications in Academic Disciplines
Authors: name: Yanfang, name: Ye, name: Zheyuan Zhang, name: Tianyi Ma, name: Zehong Wang, name: Yiyang Li, name: Shifu Hou, name: Weixiang Sun, name: Kaiwen Shi, name: Yijun Ma, name: Wei Song, name: Ahmed Abbasi, name: Ying Cheng, name: Jane Cleland-Huang, name: Steven Corcelli, name: Patricia Culligan, name: Robert Goulding, name: Ming Hu, name: Ting Hua, name: John Lalor, name: Fang Liu, name: Tengfei Luo, name: Ed Maginn, name: Nuno Moniz, name: Jason Rohr, name: Brett Savoie, name: Daniel Slate, name: Tom Stapleford, name: Matthew Webber, name: Olaf Wiest, name: Johnny Zhang, name: Nitesh Chawla
Published: 2025-09-23 21:09:24

Cutting-edge Artificial Intelligence (AI) techniques keep reshaping our view
of the world. For example, Large Language Models (LLMs) based applications such
as ChatGPT have shown the capability of generating human-like conversation on
extensive topics. Due to the impressive performance on a variety of
language-related tasks (e.g., open-domain question answering, translation, and
document summarization), one can envision the far-reaching impacts that can be
brought by the LLMs with broader real-world applications (e.g., customer
service, education and accessibility, and scientific discovery). Inspired by
their success, this paper will offer an overview of state-of-the-art LLMs and
their integration into a wide range of academic disciplines, including: (1)
arts, letters, and law (e.g., history, philosophy, political science, arts and
architecture, law), (2) economics and business (e.g., finance, economics,
accounting, marketing), and (3) science and engineering (e.g., mathematics,
physics and mechanical engineering, chemistry and chemical engineering, life
sciences and bioengineering, earth sciences and civil engineering, computer
science and electrical engineering). Integrating humanity and technology, in
this paper, we will explore how LLMs are shaping research and practice in these
fields, while also discussing key limitations, open challenges, and future
directions in the era of generative AI. The review of how LLMs are engaged
across disciplines-along with key observations and insights-can help
researchers and practitioners interested in exploiting LLMs to advance their
works in diverse real-world applications.
Lavida-O: Elastic Large Masked Diffusion Models for Unified Multimodal
  Understanding and Generation
Authors: name: Shufan Li, name: Jiuxiang Gu, name: Kangning Liu, name: Zhe Lin, name: Zijun Wei, name: Aditya Grover, name: Jason Kuen
Published: 2025-09-23 17:05:46

We propose Lavida-O, a unified Masked Diffusion Model (MDM) for multimodal
understanding and generation. Unlike existing multimodal MDMs such as MMaDa and
Muddit which only support simple image-level understanding tasks and
low-resolution image generation, Lavida-O presents a single framework that
enables image-level understanding, object grounding, image editing, and
high-resolution (1024px) text-to-image synthesis. Lavida-O incorporates a novel
Elastic Mixture-of-Transformers (Elastic-MoT) architecture that couples a
lightweight generation branch with a larger understanding branch, supported by
token compression, universal text conditioning and stratified sampling for
efficient and high-quality generation. Lavida-O further incorporates planning
and iterative self-reflection in image generation and editing tasks, seamlessly
boosting generation quality with its understanding capabilities. Lavida-O
achieves state-of-the-art performance on a wide range of benchmarks including
RefCOCO object grounding, GenEval text-to-image generation, and ImgEdit image
editing, outperforming existing autoregressive models and continuous diffusion
models such as Qwen2.5-VL and FluxKontext-dev, while offering considerable
speedup at inference. These advances establish Lavida-O as a new paradigm for
scalable multimodal reasoning and generation.
SIM-CoT: Supervised Implicit Chain-of-Thought
Authors: name: Xilin Wei, name: Xiaoran Liu, name: Yuhang Zang, name: Xiaoyi Dong, name: Yuhang Cao, name: Jiaqi Wang, name: Xipeng Qiu, name: Dahua Lin
Published: 2025-09-24 17:01:32

Implicit Chain-of-Thought (CoT) methods present a promising, token-efficient
alternative to explicit CoT reasoning in Large Language Models (LLMs), but a
persistent performance gap has limited the application of implicit CoT. We
identify a core latent instability issue by scaling the computational budget of
implicit CoT approaches: as we increase the number of implicit reasoning tokens
to enhance performance, the training process often becomes unstable and
collapses. Our analysis reveals that this instability arises from the latent
representations becoming homogeneous and losing their semantic diversity, a
failure caused by insufficient step-level supervision in existing implicit CoT
approaches. To address this issue, we propose SIM-CoT, a plug-and-play training
module that introduces step-level supervision to stabilize and enrich the
latent reasoning space. Specifically, SIM-CoT employs an auxiliary decoder
during training to align each implicit token with its corresponding explicit
reasoning step, ensuring that latent states capture distinct and meaningful
information. The proposed auxiliary decoder is removed during inference,
preserving the computational efficiency of implicit CoT methods with no added
overhead. In addition, the auxiliary decoder affords interpretability of
implicit reasoning by projecting each latent token onto an explicit reasoning
vocabulary, enabling per-step visualization of semantic roles and diagnosis.
SIM-CoT significantly enhances both the in-domain accuracy and out-of-domain
stability of various implicit CoT methods, boosting baselines like Coconut by
+8.2% on GPT-2 and CODI by +3.0% on LLaMA-3.1 8B. Demonstrating strong
scalability, SIM-CoT also surpasses the explicit CoT baseline on GPT-2 by 2.1%
with 2.3\times greater token efficiency, while substantially closing the
performance gap on larger models like LLaMA-3.1 8B.
Logics-Parsing Technical Report
Authors: name: Xiangyang Chen, name: Shuzhao Li, name: Xiuwen Zhu, name: Yongfan Chen, name: Fan Yang, name: Cheng Fang, name: Lin Qu, name: Xiaoxiao Xu, name: Hu Wei, name: Minggang Wu
Published: 2025-09-24 04:54:37

Recent advances in Large Vision-Language models (LVLM) have spurred
significant progress in document parsing task. Compared to traditional
pipeline-based methods, end-to-end paradigms have shown their excellence in
converting PDF images into structured outputs through integrated Optical
Character Recognition (OCR), table recognition, mathematical formula
recognition and so on. However, the absence of explicit analytical stages for
document layouts and reading orders limits the LVLM's capability in handling
complex document types such as multi-column newspapers or posters. To address
this limitation, we propose in this report Logics-Parsing: an end-to-end
LVLM-based model augmented with reinforcement learning. Our model incorporates
meticulously designed reward mechanisms to optimize complex layout analysis and
reading order inference. In addition, we expand the model's versatility by
incorporating diverse data types such as chemical formulas and handwritten
Chinese characters into supervised fine-tuning. Finally, to enable rigorous
evaluation of our approach, we introduce LogicsParsingBench, a curated set of
1,078 page-level PDF images spanning nine major categories and over twenty
sub-categories, which will be released later. Comprehensive experiments
conducted on LogicsParsingBench have validated the efficacy and
State-of-the-art (SOTA) performance of our proposed model across diverse
document analysis scenarios. Project Page:
https://github.com/alibaba/Logics-Parsing
On the Use of Agentic Coding: An Empirical Study of Pull Requests on
  GitHub
Authors: name: Miku Watanabe, name: Hao Li, name: Yutaro Kashiwa, name: Brittany Reid, name: Hajimu Iida, name: Ahmed E. Hassan
Published: 2025-09-18 08:48:32

Large language models (LLMs) are increasingly being integrated into software
development processes. The ability to generate code and submit pull requests
with minimal human intervention, through the use of autonomous AI agents, is
poised to become a standard practice. However, little is known about the
practical usefulness of these pull requests and the extent to which their
contributions are accepted in real-world projects. In this paper, we
empirically study 567 GitHub pull requests (PRs) generated using Claude Code,
an agentic coding tool, across 157 diverse open-source projects. Our analysis
reveals that developers tend to rely on agents for tasks such as refactoring,
documentation, and testing. The results indicate that 83.8% of these
agent-assisted PRs are eventually accepted and merged by project maintainers,
with 54.9% of the merged PRs are integrated without further modification. The
remaining 45.1% require additional changes benefit from human revisions,
especially for bug fixes, documentation, and adherence to project-specific
standards. These findings suggest that while agent-assisted PRs are largely
acceptable, they still benefit from human oversight and refinement.
Video models are zero-shot learners and reasoners
Authors: name: Thaddäus Wiedemer, name: Yuxuan Li, name: Paul Vicol, name: Shixiang Shane Gu, name: Nick Matarese, name: Kevin Swersky, name: Been Kim, name: Priyank Jaini, name: Robert Geirhos
Published: 2025-09-24 17:17:27

The remarkable zero-shot capabilities of Large Language Models (LLMs) have
propelled natural language processing from task-specific models to unified,
generalist foundation models. This transformation emerged from simple
primitives: large, generative models trained on web-scale data. Curiously, the
same primitives apply to today's generative video models. Could video models be
on a trajectory towards general-purpose vision understanding, much like LLMs
developed general-purpose language understanding? We demonstrate that Veo 3 can
solve a broad variety of tasks it wasn't explicitly trained for: segmenting
objects, detecting edges, editing images, understanding physical properties,
recognizing object affordances, simulating tool use, and more. These abilities
to perceive, model, and manipulate the visual world enable early forms of
visual reasoning like maze and symmetry solving. Veo's emergent zero-shot
capabilities indicate that video models are on a path to becoming unified,
generalist vision foundation models.
PhysCtrl: Generative Physics for Controllable and Physics-Grounded Video
  Generation
Authors: name: Chen Wang, name: Chuhao Chen, name: Yiming Huang, name: Zhiyang Dou, name: Yuan Liu, name: Jiatao Gu, name: Lingjie Liu
Published: 2025-09-24 17:58:04

Existing video generation models excel at producing photo-realistic videos
from text or images, but often lack physical plausibility and 3D
controllability. To overcome these limitations, we introduce PhysCtrl, a novel
framework for physics-grounded image-to-video generation with physical
parameters and force control. At its core is a generative physics network that
learns the distribution of physical dynamics across four materials (elastic,
sand, plasticine, and rigid) via a diffusion model conditioned on physics
parameters and applied forces. We represent physical dynamics as 3D point
trajectories and train on a large-scale synthetic dataset of 550K animations
generated by physics simulators. We enhance the diffusion model with a novel
spatiotemporal attention block that emulates particle interactions and
incorporates physics-based constraints during training to enforce physical
plausibility. Experiments show that PhysCtrl generates realistic,
physics-grounded motion trajectories which, when used to drive image-to-video
models, yield high-fidelity, controllable videos that outperform existing
methods in both visual quality and physical plausibility. Project Page:
https://cwchenwang.github.io/physctrl
EditVerse: Unifying Image and Video Editing and Generation with
  In-Context Learning
Authors: name: Xuan Ju, name: Tianyu Wang, name: Yuqian Zhou, name: He Zhang, name: Qing Liu, name: Nanxuan Zhao, name: Zhifei Zhang, name: Yijun Li, name: Yuanhao Cai, name: Shaoteng Liu, name: Daniil Pakhomov, name: Zhe Lin, name: Soo Ye Kim, name: Qiang Xu
Published: 2025-09-24 17:59:30

Recent advances in foundation models highlight a clear trend toward
unification and scaling, showing emergent capabilities across diverse domains.
While image generation and editing have rapidly transitioned from task-specific
to unified frameworks, video generation and editing remain fragmented due to
architectural limitations and data scarcity. In this work, we introduce
EditVerse, a unified framework for image and video generation and editing
within a single model. By representing all modalities, i.e., text, image, and
video, as a unified token sequence, EditVerse leverages self-attention to
achieve robust in-context learning, natural cross-modal knowledge transfer, and
flexible handling of inputs and outputs with arbitrary resolutions and
durations. To address the lack of video editing training data, we design a
scalable data pipeline that curates 232K video editing samples and combines
them with large-scale image and video datasets for joint training. Furthermore,
we present EditVerseBench, the first benchmark for instruction-based video
editing covering diverse tasks and resolutions. Extensive experiments and user
studies demonstrate that EditVerse achieves state-of-the-art performance,
surpassing existing open-source and commercial models, while exhibiting
emergent editing and generation abilities across modalities.
EmbeddingGemma: Powerful and Lightweight Text Representations
Authors: name: Henrique Schechter Vera, name: Sahil Dua, name: Biao Zhang, name: Daniel Salz, name: Ryan Mullins, name: Sindhu Raghuram Panyam, name: Sara Smoot, name: Iftekhar Naim, name: Joe Zou, name: Feiyang Chen, name: Daniel Cer, name: Alice Lisak, name: Min Choi, name: Lucas Gonzalez, name: Omar Sanseviero, name: Glenn Cameron, name: Ian Ballantyne, name: Kat Black, name: Kaifeng Chen, name: Weiyi Wang, name: Zhe Li, name: Gus Martins, name: Jinhyuk Lee, name: Mark Sherwood, name: Juyeong Ji, name: Renjie Wu, name: Jingxiao Zheng, name: Jyotinder Singh, name: Abheesht Sharma, name: Divya Sreepat, name: Aashi Jain, name: Adham Elarabawy, name: AJ Co, name: Andreas Doumanoglou, name: Babak Samari, name: Ben Hora, name: Brian Potetz, name: Dahun Kim, name: Enrique Alfonseca, name: Fedor Moiseev, name: Feng Han, name: Frank Palma Gomez, name: Gustavo Hernández Ábrego, name: Hesen Zhang, name: Hui Hui, name: Jay Han, name: Karan Gill, name: Ke Chen, name: Koert Chen, name: Madhuri Shanbhogue, name: Michael Boratko, name: Paul Suganthan, name: Sai Meher Karthik Duddu, name: Sandeep Mariserla, name: Setareh Ariafar, name: Shanfeng Zhang, name: Shijie Zhang, name: Simon Baumgartner, name: Sonam Goenka, name: Steve Qiu, name: Tanmaya Dabral, name: Trevor Walker, name: Vikram Rao, name: Waleed Khawaja, name: Wenlei Zhou, name: Xiaoqi Ren, name: Ye Xia, name: Yichang Chen, name: Yi-Ting Chen, name: Zhe Dong, name: Zhongli Ding, name: Francesco Visin, name: Gaël Liu, name: Jiageng Zhang, name: Kathleen Kenealy, name: Michelle Casbon, name: Ravin Kumar, name: Thomas Mesnard, name: Zach Gleicher, name: Cormac Brick, name: Olivier Lacombe, name: Adam Roberts, name: Yunhsuan Sung, name: Raphael Hoffmann, name: Tris Warkentin, name: Armand Joulin, name: Tom Duerig, name: Mojtaba Seyedhosseini
Published: 2025-09-24 17:56:51

We introduce EmbeddingGemma, a new lightweight, open text embedding model
based on the Gemma 3 language model family. Our innovative training recipe
strategically captures knowledge from larger models via encoder-decoder
initialization and geometric embedding distillation. We improve model
robustness and expressiveness with a spread-out regularizer, and ensure
generalizability by merging checkpoints from varied, optimized mixtures.
Evaluated on the Massive Text Embedding Benchmark (MTEB) across multilingual,
English, and code domains, EmbeddingGemma (300M) achieves state-of-the-art
results. Notably, it outperforms prior top models, both proprietary and open,
with fewer than 500M parameters, and provides performance comparable to models
double its size, offering an exceptional performance-to-cost ratio. Remarkably,
this lead persists when quantizing model weights or truncating embedding
outputs. This makes EmbeddingGemma particularly well-suited for low-latency and
high-throughput use cases such as on-device applications. We provide ablation
studies exploring our key design choices. We release EmbeddingGemma to the
community to promote further research.
How Far are VLMs from Visual Spatial Intelligence? A Benchmark-Driven
  Perspective
Authors: name: Songsong Yu, name: Yuxin Chen, name: Hao Ju, name: Lianjie Jia, name: Fuxi Zhang, name: Shaofei Huang, name: Yuhan Wu, name: Rundi Cui, name: Binghao Ran, name: Zaibin Zhang, name: Zhedong Zheng, name: Zhipeng Zhang, name: Yifan Wang, name: Lin Song, name: Lijun Wang, name: Yanwei Li, name: Ying Shan, name: Huchuan Lu
Published: 2025-09-23 12:00:14

Visual Spatial Reasoning (VSR) is a core human cognitive ability and a
critical requirement for advancing embodied intelligence and autonomous
systems. Despite recent progress in Vision-Language Models (VLMs), achieving
human-level VSR remains highly challenging due to the complexity of
representing and reasoning over three-dimensional space. In this paper, we
present a systematic investigation of VSR in VLMs, encompassing a review of
existing methodologies across input modalities, model architectures, training
strategies, and reasoning mechanisms. Furthermore, we categorize spatial
intelligence into three levels of capability, ie, basic perception, spatial
understanding, spatial planning, and curate SIBench, a spatial intelligence
benchmark encompassing nearly 20 open-source datasets across 23 task settings.
Experiments with state-of-the-art VLMs reveal a pronounced gap between
perception and reasoning, as models show competence in basic perceptual tasks
but consistently underperform in understanding and planning tasks, particularly
in numerical estimation, multi-view reasoning, temporal dynamics, and spatial
imagination. These findings underscore the substantial challenges that remain
in achieving spatial intelligence, while providing both a systematic roadmap
and a comprehensive benchmark to drive future research in the field. The
related resources of this study are accessible at
https://sibench.github.io/Awesome-Visual-Spatial-Reasoning/.
SWE-QA: Can Language Models Answer Repository-level Code Questions?
Authors: name: Weihan Peng, name: Yuling Shi, name: Yuhang Wang, name: Xinyun Zhang, name: Beijun Shen, name: Xiaodong Gu
Published: 2025-09-18 05:25:32

Understanding and reasoning about entire software repositories is an
essential capability for intelligent software engineering tools. While existing
benchmarks such as CoSQA and CodeQA have advanced the field, they predominantly
focus on small, self-contained code snippets. These setups fail to capture the
complexity of real-world repositories, where effective understanding and
reasoning often require navigating multiple files, understanding software
architecture, and grounding answers in long-range code dependencies. In this
paper, we present SWE-QA, a repository-level code question answering (QA)
benchmark designed to facilitate research on automated QA systems in realistic
code environments. SWE-QA involves 576 high-quality question-answer pairs
spanning diverse categories, including intention understanding, cross-file
reasoning, and multi-hop dependency analysis. To construct SWE-QA, we first
crawled 77,100 GitHub issues from 11 popular repositories. Based on an analysis
of naturally occurring developer questions extracted from these issues, we
developed a two-level taxonomy of repository-level questions and constructed a
set of seed questions for each category. For each category, we manually curated
and validated questions and collected their corresponding answers. As a
prototype application, we further develop SWE-QA-Agent, an agentic framework in
which LLM agents reason and act to find answers automatically. We evaluate six
advanced LLMs on SWE-QA under various context augmentation strategies.
Experimental results highlight the promise of LLMs, particularly our
SWE-QA-Agent framework, in addressing repository-level QA, while also revealing
open challenges and pointing to future research directions.
RadEval: A framework for radiology text evaluation
Authors: name: Justin Xu, name: Xi Zhang, name: Javid Abderezaei, name: Julie Bauml, name: Roger Boodoo, name: Fatemeh Haghighi, name: Ali Ganjizadeh, name: Eric Brattain, name: Dave Van Veen, name: Zaiqiao Meng, name: David Eyre, name: Jean-Benoit Delbrouck
Published: 2025-09-22 17:03:48

We introduce RadEval, a unified, open-source framework for evaluating
radiology texts. RadEval consolidates a diverse range of metrics, from classic
n-gram overlap (BLEU, ROUGE) and contextual measures (BERTScore) to clinical
concept-based scores (F1CheXbert, F1RadGraph, RaTEScore, SRR-BERT,
TemporalEntityF1) and advanced LLM-based evaluators (GREEN). We refine and
standardize implementations, extend GREEN to support multiple imaging
modalities with a more lightweight model, and pretrain a domain-specific
radiology encoder, demonstrating strong zero-shot retrieval performance. We
also release a richly annotated expert dataset with over 450 clinically
significant error labels and show how different metrics correlate with
radiologist judgment. Finally, RadEval provides statistical testing tools and
baseline model evaluations across multiple publicly available datasets,
facilitating reproducibility and robust benchmarking in radiology report
generation.