Orionnnn/hub-stats huggingface dataset:
LLaVA-OneVision-1.5: Fully Open Framework for Democratized Multimodal
  Training
Authors: name: Xiang An, name: Yin Xie, name: Kaicheng Yang, name: Wenkang Zhang, name: Xiuwei Zhao, name: Zheng Cheng, name: Yirui Wang, name: Songcen Xu, name: Changrui Chen, name: Chunsheng Wu, name: Huajie Tan, name: Chunyuan Li, name: Jing Yang, name: Jie Yu, name: Xiyao Wang, name: Bin Qin, name: Yumeng Wang, name: Zizhen Yan, name: Ziyong Feng, name: Ziwei Liu, name: Bo Li, name: Jiankang Deng
Published: 2025-09-28 05:52:55

We present LLaVA-OneVision-1.5, a novel family of Large Multimodal Models
(LMMs) that achieve state-of-the-art performance with significantly reduced
computational and financial costs. Different from the existing works,
LLaVA-OneVision-1.5 provides an open, efficient, and reproducible framework for
building high-quality vision-language models entirely from scratch. The
LLaVA-OneVision-1.5 release comprises three primary components: (1) Large-Scale
Curated Datasets: We construct an 85M concept-balanced pretraining dataset
LLaVA-OneVision-1.5-Mid-Traning and a meticulously curated 26M instruction
dataset LLaVA-OneVision-1.5-Instruct, collectively encompassing 64B compressed
multimodal tokens. (2) Efficient Training Framework: We develop a complete
end-to-end efficient training framework leveraging an offline parallel data
packing strategy to facilitate the training of LLaVA-OneVision-1.5 within a
$16,000 budget. (3) State-of-the-art Performance: Experimental results
demonstrate that LLaVA-OneVision1.5 yields exceptionally competitive
performance across a broad range of downstream tasks. Specifically,
LLaVA-OneVision-1.5-8B outperforms Qwen2.5-VL-7B on 18 of 27 benchmarks, and
LLaVA-OneVision-1.5-4B surpasses Qwen2.5-VL-3B on all 27 benchmarks. We
anticipate releasing LLaVA-OneVision-1.5-RL shortly and encourage the community
to await further updates.
RLBFF: Binary Flexible Feedback to bridge between Human Feedback &
  Verifiable Rewards
Authors: name: Zhilin Wang, name: Jiaqi Zeng, name: Olivier Delalleau, name: Ellie Evans, name: Daniel Egert, name: Hoo-Chang Shin, name: Felipe Soares, name: Yi Dong, name: Oleksii Kuchaiev
Published: 2025-09-25 16:19:06

Reinforcement Learning with Human Feedback (RLHF) and Reinforcement Learning
with Verifiable Rewards (RLVR) are the main RL paradigms used in LLM
post-training, each offering distinct advantages. However, RLHF struggles with
interpretability and reward hacking because it relies on human judgments that
usually lack explicit criteria, whereas RLVR is limited in scope by its focus
on correctness-based verifiers. We propose Reinforcement Learning with Binary
Flexible Feedback (RLBFF), which combines the versatility of human-driven
preferences with the precision of rule-based verification, enabling reward
models to capture nuanced aspects of response quality beyond mere correctness.
RLBFF extracts principles that can be answered in a binary fashion (e.g.
accuracy of information: yes, or code readability: no) from natural language
feedback. Such principles can then be used to ground Reward Model training as
an entailment task (response satisfies or does not satisfy an arbitrary
principle). We show that Reward Models trained in this manner can outperform
Bradley-Terry models when matched for data and achieve top performance on
RM-Bench (86.2%) and JudgeBench (81.4%, #1 on leaderboard as of September 24,
2025). Additionally, users can specify principles of interest at inference time
to customize the focus of our reward models, in contrast to Bradley-Terry
models. Finally, we present a fully open source recipe (including data) to
align Qwen3-32B using RLBFF and our Reward Model, to match or exceed the
performance of o3-mini and DeepSeek R1 on general alignment benchmarks of
MT-Bench, WildBench, and Arena Hard v2 (at <5% of the inference cost).
COSPADI: Compressing LLMs via Calibration-Guided Sparse Dictionary
  Learning
Authors: name: Dmitriy Shopkhoev, name: Denis Makhov, name: Magauiya Zhussip, name: Ammar Ali, name: Stamatios Lefkimmiatis
Published: 2025-09-26 08:55:09

Post-training compression of large language models (LLMs) largely relies on
low-rank weight approximation, which represents each column of a weight matrix
in a shared low-dimensional subspace. While this is a computationally efficient
strategy, the imposed structural constraint is rigid and can lead to a
noticeable model accuracy drop. In this work, we propose CoSpaDi (Compression
via Sparse Dictionary Learning), a novel training-free compression framework
that replaces low-rank decomposition with a more flexible structured sparse
factorization in which each weight matrix is represented with a dense
dictionary and a column-sparse coefficient matrix. This formulation enables a
union-of-subspaces representation: different columns of the original weight
matrix are approximated in distinct subspaces spanned by adaptively selected
dictionary atoms, offering greater expressiveness than a single invariant
basis. Crucially, CoSpaDi leverages a small calibration dataset to optimize the
factorization such that the output activations of compressed projection layers
closely match those of the original ones, thereby minimizing functional
reconstruction error rather than mere weight approximation. This data-aware
strategy preserves better model fidelity without any fine-tuning under
reasonable compression ratios. Moreover, the resulting structured sparsity
allows efficient sparse-dense matrix multiplication and is compatible with
post-training quantization for further memory and latency gains. We evaluate
CoSpaDi across multiple Llama and Qwen models under per-layer and per-group
settings at 20-50\% compression ratios, demonstrating consistent superiority
over state-of-the-art data-aware low-rank methods both in accuracy and
perplexity. Our results establish structured sparse dictionary learning as a
powerful alternative to conventional low-rank approaches for efficient LLM
deployment.
The role of synthetic data in Multilingual, Multi-cultural AI systems:
  Lessons from Indic Languages
Authors: name: Pranjal A. Chitale, name: Varun Gumma, name: Sanchit Ahuja, name: Prashant Kodali, name: Manan Uppadhyay, name: Deepthi Sudharsan, name: Sunayana Sitaram
Published: 2025-09-25 15:13:00

Developing AI systems that operate effectively across languages while
remaining culturally grounded is a long-standing challenge, particularly in
low-resource settings. Synthetic data provides a promising avenue, yet its
effectiveness in multilingual and multicultural contexts remains underexplored.
We investigate the creation and impact of synthetic, culturally contextualized
datasets for Indian languages through a bottom-up generation strategy that
prompts large open-source LLMs (>= 235B parameters) to ground data generation
in language-specific Wikipedia content. This approach complements the dominant
top-down paradigm of translating synthetic datasets from high-resource
languages such as English. We introduce Updesh, a high-quality large-scale
synthetic instruction-following dataset comprising 9.5M data points across 13
Indian languages, encompassing diverse reasoning and generative tasks with an
emphasis on long-context, multi-turn capabilities, and alignment with Indian
cultural contexts. A comprehensive evaluation incorporating both automated
metrics and human annotation across 10k assessments indicates that generated
data is high quality; though, human evaluation highlights areas for further
improvement. Additionally, we perform downstream evaluations by fine-tuning
models on our dataset and assessing the performance across 15 diverse
multilingual datasets. Models trained on Updesh consistently achieve
significant gains on generative tasks and remain competitive on multiple-choice
style NLU tasks. Notably, relative improvements are most pronounced in low and
medium-resource languages, narrowing their gap with high-resource languages.
These findings provide empirical evidence that effective multilingual AI
requires multi-faceted data curation and generation strategies that incorporate
context-aware, culturally grounded methodologies.
Scale-Wise VAR is Secretly Discrete Diffusion
Authors: name: Amandeep Kumar, name: Nithin Gopalakrishnan Nair, name: Vishal M. Patel
Published: 2025-09-26 17:58:04

Autoregressive (AR) transformers have emerged as a powerful paradigm for
visual generation, largely due to their scalability, computational efficiency
and unified architecture with language and vision. Among them, next scale
prediction Visual Autoregressive Generation (VAR) has recently demonstrated
remarkable performance, even surpassing diffusion-based models. In this work,
we revisit VAR and uncover a theoretical insight: when equipped with a
Markovian attention mask, VAR is mathematically equivalent to a discrete
diffusion. We term this reinterpretation as Scalable Visual Refinement with
Discrete Diffusion (SRDD), establishing a principled bridge between AR
transformers and diffusion models. Leveraging this new perspective, we show how
one can directly import the advantages of diffusion such as iterative
refinement and reduce architectural inefficiencies into VAR, yielding faster
convergence, lower inference cost, and improved zero-shot reconstruction.
Across multiple datasets, we show that the diffusion based perspective of VAR
leads to consistent gains in efficiency and generation.
CAD-Tokenizer: Towards Text-based CAD Prototyping via Modality-Specific
  Tokenization
Authors: name: Ruiyu Wang, name: Shizhao Sun, name: Weijian Ma, name: Jiang Bian
Published: 2025-09-25 13:38:36

Computer-Aided Design (CAD) is a foundational component of industrial
prototyping, where models are defined not by raw coordinates but by
construction sequences such as sketches and extrusions. This sequential
structure enables both efficient prototype initialization and subsequent
editing. Text-guided CAD prototyping, which unifies Text-to-CAD generation and
CAD editing, has the potential to streamline the entire design pipeline.
However, prior work has not explored this setting, largely because standard
large language model (LLM) tokenizers decompose CAD sequences into
natural-language word pieces, failing to capture primitive-level CAD semantics
and hindering attention modules from modeling geometric structure. We
conjecture that a multimodal tokenization strategy, aligned with CAD's
primitive and structural nature, can provide more effective representations. To
this end, we propose CAD-Tokenizer, a framework that represents CAD data with
modality-specific tokens using a sequence-based VQ-VAE with primitive-level
pooling and constrained decoding. This design produces compact, primitive-aware
representations that align with CAD's structural nature. Applied to unified
text-guided CAD prototyping, CAD-Tokenizer significantly improves instruction
following and generation quality, achieving better quantitative and qualitative
performance over both general-purpose LLMs and task-specific baselines.
Fine-tuning Done Right in Model Editing
Authors: name: Wanli Yang, name: Fei Sun, name: Rui Tang, name: Hongyu Zang, name: Du Su, name: Qi Cao, name: Jingang Wang, name: Huawei Shen, name: Xueqi Cheng
Published: 2025-09-26 08:53:13

Fine-tuning, a foundational method for adapting large language models, has
long been considered ineffective for model editing. Here, we challenge this
belief, arguing that the reported failure arises not from the inherent
limitation of fine-tuning itself, but from adapting it to the sequential nature
of the editing task, a single-pass depth-first pipeline that optimizes each
sample to convergence before moving on. While intuitive, this depth-first
pipeline coupled with sample-wise updating over-optimizes each edit and induces
interference across edits. Our controlled experiments reveal that simply
restoring fine-tuning to the standard breadth-first (i.e., epoch-based)
pipeline with mini-batch optimization substantially improves its effectiveness
for model editing. Moreover, fine-tuning in editing also suffers from
suboptimal tuning parameter locations inherited from prior methods. Through
systematic analysis of tuning locations, we derive LocFT-BF, a simple and
effective localized editing method built on the restored fine-tuning framework.
Extensive experiments across diverse LLMs and datasets demonstrate that
LocFT-BF outperforms state-of-the-art methods by large margins. Notably, to our
knowledge, it is the first to sustain 100K edits and 72B-parameter models,10 x
beyond prior practice, without sacrificing general capabilities. By clarifying
a long-standing misconception and introducing a principled localized tuning
strategy, we advance fine-tuning from an underestimated baseline to a leading
method for model editing, establishing a solid foundation for future research.
Finding 3D Positions of Distant Objects from Noisy Camera Movement and
  Semantic Segmentation Sequences
Authors: name: Julius Pesonen, name: Arno Solin, name: Eija Honkavaara
Published: 2025-09-25 08:46:37

3D object localisation based on a sequence of camera measurements is
essential for safety-critical surveillance tasks, such as drone-based wildfire
monitoring. Localisation of objects detected with a camera can typically be
solved with dense depth estimation or 3D scene reconstruction. However, in the
context of distant objects or tasks limited by the amount of available
computational resources, neither solution is feasible. In this paper, we show
that the task can be solved using particle filters for both single and multiple
target scenarios. The method was studied using a 3D simulation and a
drone-based image segmentation sequence with global navigation satellite system
(GNSS)-based camera pose estimates. The results showed that a particle filter
can be used to solve practical localisation tasks based on camera poses and
image segments in these situations where other solutions fail. The particle
filter is independent of the detection method, making it flexible for new
tasks. The study also demonstrates that drone-based wildfire monitoring can be
conducted using the proposed method paired with a pre-existing image
segmentation model.
TUN3D: Towards Real-World Scene Understanding from Unposed Images
Authors: name: Anton Konushin, name: Nikita Drozdov, name: Bulat Gabdullin, name: Alexey Zakharov, name: Anna Vorontsova, name: Danila Rukhovich, name: Maksim Kolodiazhnyi
Published: 2025-09-23 20:24:07

Layout estimation and 3D object detection are two fundamental tasks in indoor
scene understanding. When combined, they enable the creation of a compact yet
semantically rich spatial representation of a scene. Existing approaches
typically rely on point cloud input, which poses a major limitation since most
consumer cameras lack depth sensors and visual-only data remains far more
common. We address this issue with TUN3D, the first method that tackles joint
layout estimation and 3D object detection in real scans, given multi-view
images as input, and does not require ground-truth camera poses or depth
supervision. Our approach builds on a lightweight sparse-convolutional backbone
and employs two dedicated heads: one for 3D object detection and one for layout
estimation, leveraging a novel and effective parametric wall representation.
Extensive experiments show that TUN3D achieves state-of-the-art performance
across three challenging scene understanding benchmarks: (i) using ground-truth
point clouds, (ii) using posed images, and (iii) using unposed images. While
performing on par with specialized 3D object detection methods, TUN3D
significantly advances layout estimation, setting a new benchmark in holistic
indoor scene understanding. Code is available at
https://github.com/col14m/tun3d .
ERGO: Efficient High-Resolution Visual Understanding for Vision-Language
  Models
Authors: name: Jewon Lee, name: Wooksu Shin, name: Seungmin Yang, name: Ki-Ung Song, name: DongUk Lim, name: Jaeyeon Kim, name: Tae-Ho Kim, name: Bo-Kyeong Kim
Published: 2025-09-26 07:15:19

Efficient processing of high-resolution images is crucial for real-world
vision-language applications. However, existing Large Vision-Language Models
(LVLMs) incur substantial computational overhead due to the large number of
vision tokens. With the advent of "thinking with images" models, reasoning now
extends beyond text to the visual domain. This capability motivates our
two-stage "coarse-to-fine" reasoning pipeline: first, a downsampled image is
analyzed to identify task-relevant regions; then, only these regions are
cropped at full resolution and processed in a subsequent reasoning stage. This
approach reduces computational cost while preserving fine-grained visual
details where necessary. A major challenge lies in inferring which regions are
truly relevant to a given query. Recent related methods often fail in the first
stage after input-image downsampling, due to perception-driven reasoning, where
clear visual information is required for effective reasoning. To address this
issue, we propose ERGO (Efficient Reasoning & Guided Observation) that performs
reasoning-driven perception-leveraging multimodal context to determine where to
focus. Our model can account for perceptual uncertainty, expanding the cropped
region to cover visually ambiguous areas for answering questions. To this end,
we develop simple yet effective reward components in a reinforcement learning
framework for coarse-to-fine perception. Across multiple datasets, our approach
delivers higher accuracy than the original model and competitive methods, with
greater efficiency. For instance, ERGO surpasses Qwen2.5-VL-7B on the V*
benchmark by 4.7 points while using only 23% of the vision tokens, achieving a
3x inference speedup. The code and models can be found at:
https://github.com/nota-github/ERGO.
PromptCoT 2.0: Scaling Prompt Synthesis for Large Language Model
  Reasoning
Authors: name: Xueliang Zhao, name: Wei Wu, name: Jian Guan, name: Zhuocheng Gong, name: Lingpeng Kong
Published: 2025-09-24 08:46:29

Large language models (LLMs) are evolving from conversational systems into
strong reasoners for tasks such as Olympiad mathematics and competitive
programming. While scaling parameters and test-time computation has driven
progress, a key bottleneck is the lack of high-quality training problems:
human-curated datasets are costly and limited, while existing synthetic corpora
are often too easy or narrow. PromptCoT 1.0 showed that injecting rationales
into prompt synthesis increases problem difficulty. Building on this, we
present PromptCoT 2.0, a scalable framework that replaces hand-crafted
heuristics with an expectation-maximization (EM) loop, where rationales are
iteratively refined to guide prompt construction. This produces problems that
are both harder and more diverse than prior corpora. The synthetic prompts
support two post-training regimes: (1) Self-Play, where strong models improve
autonomously via verifiable feedback without stronger teachers; and (2)
Supervised Fine-Tuning (SFT), where weaker models learn from teacher-distilled
traces. Extensive experiments demonstrate the effectiveness of this approach.
In self-play, applying PromptCoT 2.0 to Qwen3-30B-A3B-Thinking-2507 sets new
state-of-the-art results at the 30B scale, with +4.4, +4.8, and +5.3 on AIME
24/25 and HMMT 25, +6.1 and +5.0 on LiveCodeBench v5/v6, and +35 Elo on
Codeforces. In SFT, training Qwen2.5-7B-Instruct solely on synthetic prompts
boosts accuracy to 73.1 (AIME 24), 65.6 (AIME 25), and 53.4 (LiveCodeBench v5),
surpassing models trained on human or hybrid data. Analyses further confirm
that PromptCoT 2.0 yields fundamentally harder and distributionally distinct
problems. These results establish prompt synthesis as a new axis for scaling
reasoning and position PromptCoT 2.0 as a scalable foundation for future
open-source models. The implementation is available at
https://github.com/inclusionAI/PromptCoT.
Instruction-Following Evaluation in Function Calling for Large Language
  Models
Authors: name: Nikolai Skripko
Published: 2025-09-22 21:04:39

Function calling is a core capability of large language models, essential for
AI agents. Existing benchmarks such as the Berkeley Function Calling
Leaderboard (BFCL), tau^2-Bench (arXiv:2506.07982), and ACEBench
(arXiv:2501.12851) evaluate argument correctness but do not test adherence to
format instructions embedded in parameter descriptions, such as enclosing
values in double quotes or using ISO date formats.
  We introduce IFEval-FC, a benchmark inspired by IFEval (arXiv:2311.07911)
that assesses precise instruction following in function calling. IFEval-FC
encodes verifiable formats directly within JSON schema descriptions, for
example specifying that a value must not contain punctuation. It includes 750
test cases, each consisting of a function with an embedded format for one of
its input parameters and a corresponding user query. Evaluation is fully
algorithmic, ensuring objectivity, reproducibility, and scalability.
  Our results show that even state-of-the-art proprietary models, including
GPT-5 and Claude 4.1 Opus, frequently fail to follow basic formatting rules,
highlighting a practical limitation for real-world agent systems. The complete
codebase and data are publicly available at
https://github.com/Skripkon/IFEval-FC.
HiGS: History-Guided Sampling for Plug-and-Play Enhancement of Diffusion
  Models
Authors: name: Seyedmorteza Sadat, name: Farnood Salehi, name: Romann M. Weber
Published: 2025-09-26 13:01:10

While diffusion models have made remarkable progress in image generation,
their outputs can still appear unrealistic and lack fine details, especially
when using fewer number of neural function evaluations (NFEs) or lower guidance
scales. To address this issue, we propose a novel momentum-based sampling
technique, termed history-guided sampling (HiGS), which enhances quality and
efficiency of diffusion sampling by integrating recent model predictions into
each inference step. Specifically, HiGS leverages the difference between the
current prediction and a weighted average of past predictions to steer the
sampling process toward more realistic outputs with better details and
structure. Our approach introduces practically no additional computation and
integrates seamlessly into existing diffusion frameworks, requiring neither
extra training nor fine-tuning. Extensive experiments show that HiGS
consistently improves image quality across diverse models and architectures and
under varying sampling budgets and guidance scales. Moreover, using a
pretrained SiT model, HiGS achieves a new state-of-the-art FID of 1.61 for
unguided ImageNet generation at 256times256 with only 30 sampling steps
(instead of the standard 250). We thus present HiGS as a plug-and-play
enhancement to standard diffusion sampling that enables faster generation with
higher fidelity.