Orionnnn/hub-stats huggingface dataset:
Zebra-CoT: A Dataset for Interleaved Vision Language Reasoning
Authors: name: Ang Li, name: Charles Wang, name: Kaiyu Yue, name: Zikui Cai, name: Ollie Liu, name: Deqing Fu, name: Peng Guo, name: Wang Bill Zhu, name: Vatsal Sharan, name: Robin Jia, name: Willie Neiswanger, name: Furong Huang, name: Tom Goldstein, name: Micah Goldblum
Published: 2025-07-22 16:35:36

Humans often use visual aids, for example diagrams or sketches, when solving
complex problems. Training multimodal models to do the same, known as Visual
Chain of Thought (Visual CoT), is challenging due to: (1) poor off-the-shelf
visual CoT performance, which hinders reinforcement learning, and (2) the lack
of high-quality visual CoT training data. We introduce Zebra-CoT, a
diverse large-scale dataset with 182,384 samples, containing logically coherent
interleaved text-image reasoning traces. We focus on four categories of tasks
where sketching or visual reasoning is especially natural, spanning scientific
questions such as geometry, physics, and algorithms; 2D visual reasoning tasks
like visual search and jigsaw puzzles; 3D reasoning tasks including 3D
multi-hop inference, embodied and robot planning; visual logic problems and
strategic games like chess. Fine-tuning the Anole-7B model on the Zebra-CoT
training corpus results in an improvement of +12% in our test-set accuracy and
yields up to +13% performance gain on standard VLM benchmark evaluations.
Fine-tuning Bagel-7B yields a model that generates high-quality interleaved
visual reasoning chains, underscoring Zebra-CoT's effectiveness for developing
multimodal reasoning abilities. We open-source our dataset and models to
support development and evaluation of visual CoT.
MegaScience: Pushing the Frontiers of Post-Training Datasets for Science
  Reasoning
Authors: name: Run-Ze Fan, name: Zengzhi Wang, name: Pengfei Liu
Published: 2025-07-22 17:59:03

Scientific reasoning is critical for developing AI scientists and supporting
human researchers in advancing the frontiers of natural science discovery.
However, the open-source community has primarily focused on mathematics and
coding while neglecting the scientific domain, largely due to the absence of
open, large-scale, high-quality, verifiable scientific reasoning datasets. To
bridge this gap, we first present TextbookReasoning, an open dataset featuring
truthful reference answers extracted from 12k university-level scientific
textbooks, comprising 650k reasoning questions spanning 7 scientific
disciplines. We further introduce MegaScience, a large-scale mixture of
high-quality open-source datasets totaling 1.25 million instances, developed
through systematic ablation studies that evaluate various data selection
methodologies to identify the optimal subset for each publicly available
scientific dataset. Meanwhile, we build a comprehensive evaluation system
covering diverse subjects and question types across 15 benchmarks,
incorporating comprehensive answer extraction strategies to ensure accurate
evaluation metrics. Our experiments demonstrate that our datasets achieve
superior performance and training efficiency with more concise response lengths
compared to existing open-source scientific datasets. Furthermore, we train
Llama3.1, Qwen2.5, and Qwen3 series base models on MegaScience, which
significantly outperform the corresponding official instruct models in average
performance. In addition, MegaScience exhibits greater effectiveness for larger
and stronger models, suggesting a scaling benefit for scientific tuning. We
release our data curation pipeline, evaluation system, datasets, and seven
trained models to the community to advance scientific reasoning research.
HOComp: Interaction-Aware Human-Object Composition
Authors: name: Dong Liang, name: Jinyuan Jia, name: Yuhao Liu, name: Rynson W. H. Lau
Published: 2025-07-22 17:59:21

While existing image-guided composition methods may help insert a foreground
object onto a user-specified region of a background image, achieving natural
blending inside the region with the rest of the image unchanged, we observe
that these existing methods often struggle in synthesizing seamless
interaction-aware compositions when the task involves human-object
interactions. In this paper, we first propose HOComp, a novel approach for
compositing a foreground object onto a human-centric background image, while
ensuring harmonious interactions between the foreground object and the
background person and their consistent appearances. Our approach includes two
key designs: (1) MLLMs-driven Region-based Pose Guidance (MRPG), which utilizes
MLLMs to identify the interaction region as well as the interaction type (e.g.,
holding and lefting) to provide coarse-to-fine constraints to the generated
pose for the interaction while incorporating human pose landmarks to track
action variations and enforcing fine-grained pose constraints; and (2)
Detail-Consistent Appearance Preservation (DCAP), which unifies a shape-aware
attention modulation mechanism, a multi-view appearance loss, and a background
consistency loss to ensure consistent shapes/textures of the foreground and
faithful reproduction of the background human. We then propose the first
dataset, named Interaction-aware Human-Object Composition (IHOC), for the task.
Experimental results on our dataset show that HOComp effectively generates
harmonious human-object interactions with consistent appearances, and
outperforms relevant methods qualitatively and quantitatively.
The Serial Scaling Hypothesis
Authors: name: Yuxi Liu, name: Konpat Preechakul, name: Kananart Kuwaranancharoen, name: Yutong Bai
Published: 2025-07-16 18:01:26

While machine learning has advanced through massive parallelization, we
identify a critical blind spot: some problems are fundamentally sequential.
These "inherently serial" problems-from mathematical reasoning to physical
simulations to sequential decision-making-require dependent computational steps
that cannot be parallelized. Drawing from complexity theory, we formalize this
distinction and demonstrate that current parallel-centric architectures face
fundamental limitations on such tasks. We argue that recognizing the serial
nature of computation holds profound implications on machine learning, model
design, hardware development. As AI tackles increasingly complex reasoning,
deliberately scaling serial computation-not just parallel computation-is
essential for continued progress.
ParaStudent: Generating and Evaluating Realistic Student Code by
  Teaching LLMs to Struggle
Authors: name: Mihran Miroyan, name: Rose Niousha, name: Joseph E. Gonzalez, name: Gireeja Ranade, name: Narges Norouzi
Published: 2025-07-16 23:12:14

Large Language Models (LLMs) have shown strong performance on programming
tasks, but can they generate student-like code like real students - imperfect,
iterative, and stylistically diverse? We present ParaStudent, a systematic
study of LLM-based "student-like" code generation in an introductory
programming course setting. Using a dataset of timestamped student submissions
across multiple semesters, we design low- and high-resolution experiments to
model student progress and evaluate code outputs along semantic, functional,
and stylistic dimensions. Our results show that fine-tuning significantly
improves alignment with real student trajectories and captures error patterns,
incremental improvements, and stylistic variations more faithfully. This study
shows that modeling realistic student code requires capturing learning dynamics
through context-aware generation, temporal modeling, and multi-dimensional
evaluation. Code for experiments and evaluation is available at
https://github.com/mmiroyan/ParaStudent.
MCPEval: Automatic MCP-based Deep Evaluation for AI Agent Models
Authors: name: Zhiwei Liu, name: Jielin Qiu, name: Shiyu Wang, name: Jianguo Zhang, name: Zuxin Liu, name: Roshan Ram, name: Haolin Chen, name: Weiran Yao, name: Huan Wang, name: Shelby Heinecke, name: Silvio Savarese, name: Caiming Xiong
Published: 2025-07-17 05:46:27

The rapid rise of Large Language Models (LLMs)-based intelligent agents
underscores the need for robust, scalable evaluation frameworks. Existing
methods rely on static benchmarks and labor-intensive data collection, limiting
practical assessment. We introduce \oursystemname, an open-source Model Context
Protocol (MCP)-based framework that automates end-to-end task generation and
deep evaluation of LLM agents across diverse domains. MCPEval standardizes
metrics, seamlessly integrates with native agent tools, and eliminates manual
effort in building evaluation pipelines. Empirical results across five
real-world domains show its effectiveness in revealing nuanced, domain-specific
performance. We publicly release MCPEval
https://github.com/SalesforceAIResearch/MCPEval to promote reproducible and
standardized LLM agent evaluation.
PhysGym: Benchmarking LLMs in Interactive Physics Discovery with
  Controlled Priors
Authors: name: Yimeng Chen, name: Piotr Piȩkos, name: Mateusz Ostaszewski, name: Firas Laakom, name: Jürgen Schmidhuber
Published: 2025-07-21 12:28:10

Evaluating the scientific discovery capabilities of large language model
based agents, particularly how they cope with varying environmental complexity
and utilize prior knowledge, requires specialized benchmarks currently lacking
in the landscape. To address this gap, we introduce PhysGym, a novel benchmark
suite and simulation platform for rigorously assessing LLM-based scientific
reasoning in interactive physics environments. PhysGym's primary contribution
lies in its sophisticated control over the level of prior knowledge provided to
the agent. This allows researchers to dissect agent performance along axes
including the complexity of the problem and the prior knowledge levels. The
benchmark comprises a suite of interactive simulations, where agents must
actively probe environments, gather data sequentially under constraints and
formulate hypotheses about underlying physical laws. PhysGym provides
standardized evaluation protocols and metrics for assessing hypothesis accuracy
and model fidelity. We demonstrate the benchmark's utility by presenting
results from baseline LLMs, showcasing its ability to differentiate
capabilities based on varying priors and task complexity.
TokensGen: Harnessing Condensed Tokens for Long Video Generation
Authors: name: Wenqi Ouyang, name: Zeqi Xiao, name: Danni Yang, name: Yifan Zhou, name: Shuai Yang, name: Lei Yang, name: Jianlou Si, name: Xingang Pan
Published: 2025-07-21 15:37:33

Generating consistent long videos is a complex challenge: while
diffusion-based generative models generate visually impressive short clips,
extending them to longer durations often leads to memory bottlenecks and
long-term inconsistency. In this paper, we propose TokensGen, a novel two-stage
framework that leverages condensed tokens to address these issues. Our method
decomposes long video generation into three core tasks: (1) inner-clip semantic
control, (2) long-term consistency control, and (3) inter-clip smooth
transition. First, we train To2V (Token-to-Video), a short video diffusion
model guided by text and video tokens, with a Video Tokenizer that condenses
short clips into semantically rich tokens. Second, we introduce T2To
(Text-to-Token), a video token diffusion transformer that generates all tokens
at once, ensuring global consistency across clips. Finally, during inference,
an adaptive FIFO-Diffusion strategy seamlessly connects adjacent clips,
reducing boundary artifacts and enhancing smooth transitions. Experimental
results demonstrate that our approach significantly enhances long-term temporal
and content coherence without incurring prohibitive computational overhead. By
leveraging condensed tokens and pre-trained short video models, our method
provides a scalable, modular solution for long video generation, opening new
possibilities for storytelling, cinematic production, and immersive
simulations. Please see our project page at
https://vicky0522.github.io/tokensgen-webpage/ .
Inverse Scaling in Test-Time Compute
Authors: name: Aryo Pradipta Gema, name: Alexander Hägele, name: Runjin Chen, name: Andy Arditi, name: Jacob Goldman-Wetzler, name: Kit Fraser-Taliente, name: Henry Sleight, name: Linda Petrini, name: Julian Michael, name: Beatrice Alex, name: Pasquale Minervini, name: Yanda Chen, name: Joe Benton, name: Ethan Perez
Published: 2025-07-19 00:06:13

We construct evaluation tasks where extending the reasoning length of Large
Reasoning Models (LRMs) deteriorates performance, exhibiting an inverse scaling
relationship between test-time compute and accuracy. Our evaluation tasks span
four categories: simple counting tasks with distractors, regression tasks with
spurious features, deduction tasks with constraint tracking, and advanced AI
risks. We identify five distinct failure modes when models reason for longer:
1) Claude models become increasingly distracted by irrelevant information; 2)
OpenAI o-series models resist distractors but overfit to problem framings; 3)
models shift from reasonable priors to spurious correlations; 4) all models
show difficulties in maintaining focus on complex deductive tasks; and 5)
extended reasoning may amplify concerning behaviors, with Claude Sonnet 4
showing increased expressions of self-preservation. These findings suggest that
while test-time compute scaling remains promising for improving model
capabilities, it may inadvertently reinforce problematic reasoning patterns.
Our results demonstrate the importance of evaluating models across diverse
reasoning lengths to identify and address these failure modes in LRMs.
UGPL: Uncertainty-Guided Progressive Learning for Evidence-Based
  Classification in Computed Tomography
Authors: name: Shravan Venkatraman, name: Pavan Kumar S, name: Rakesh Raj Madavan, name: Chandrakala S
Published: 2025-07-18 17:30:56

Accurate classification of computed tomography (CT) images is essential for
diagnosis and treatment planning, but existing methods often struggle with the
subtle and spatially diverse nature of pathological features. Current
approaches typically process images uniformly, limiting their ability to detect
localized abnormalities that require focused analysis. We introduce UGPL, an
uncertainty-guided progressive learning framework that performs a
global-to-local analysis by first identifying regions of diagnostic ambiguity
and then conducting detailed examination of these critical areas. Our approach
employs evidential deep learning to quantify predictive uncertainty, guiding
the extraction of informative patches through a non-maximum suppression
mechanism that maintains spatial diversity. This progressive refinement
strategy, combined with an adaptive fusion mechanism, enables UGPL to integrate
both contextual information and fine-grained details. Experiments across three
CT datasets demonstrate that UGPL consistently outperforms state-of-the-art
methods, achieving improvements of 3.29%, 2.46%, and 8.08% in accuracy for
kidney abnormality, lung cancer, and COVID-19 detection, respectively. Our
analysis shows that the uncertainty-guided component provides substantial
benefits, with performance dramatically increasing when the full progressive
learning pipeline is implemented. Our code is available at:
https://github.com/shravan-18/UGPL
A Simple "Try Again" Can Elicit Multi-Turn LLM Reasoning
Authors: name: Licheng Liu, name: Zihan Wang, name: Linjie Li, name: Chenwei Xu, name: Yiping Lu, name: Han Liu, name: Avirup Sil, name: Manling Li
Published: 2025-07-18 18:07:38

Multi-turn problem solving is critical yet challenging for Large Reasoning
Models (LRMs) to reflect on their reasoning and revise from feedback. Existing
Reinforcement Learning (RL) methods train large reasoning models on a
single-turn paradigm with verifiable rewards. However, we observe that models
trained with existing RL paradigms often lose their ability to solve problems
across multiple turns and struggle to revise answers based on contextual
feedback, leading to repetitive responses. We ask: can LRMs learn to reflect
their answers in a multi-turn context? In this work, we find that training
models with multi-turn RL using only unary feedback (e.g., "Let's try again")
after wrong answers can improve both single-turn performance and multi-turn
reasoning. We introduce Unary Feedback as Observation (UFO) for reinforcement
learning, which uses minimal yet common unary user feedback during iterative
problem solving. It can be easily applied to existing single-turn RL training
setups. Experimental results show that RL training with UFO keeps single-turn
performance and improves multi-turn reasoning accuracy by up to 14%, enabling
language models to better react to feedback in multi-turn problem solving. To
further minimize the number of turns needed for a correct answer while
encouraging diverse reasoning when mistakes occur, we design reward structures
that guide models to produce careful and deliberate answers in each turn. Code:
https://github.com/lichengliu03/unary-feedback
NoHumansRequired: Autonomous High-Quality Image Editing Triplet Mining
Authors: name: Maksim Kuprashevich, name: Grigorii Alekseenko, name: Irina Tolstykh, name: Georgii Fedorov, name: Bulat Suleimanov, name: Vladimir Dokholyan, name: Aleksandr Gordeev
Published: 2025-07-18 17:50:00

Recent advances in generative modeling enable image editing assistants that
follow natural language instructions without additional user input. Their
supervised training requires millions of triplets: original image, instruction,
edited image. Yet mining pixel-accurate examples is hard. Each edit must affect
only prompt-specified regions, preserve stylistic coherence, respect physical
plausibility, and retain visual appeal. The lack of robust automated
edit-quality metrics hinders reliable automation at scale. We present an
automated, modular pipeline that mines high-fidelity triplets across domains,
resolutions, instruction complexities, and styles. Built on public generative
models and running without human intervention, our system uses a task-tuned
Gemini validator to score instruction adherence and aesthetics directly,
removing any need for segmentation or grounding models. Inversion and
compositional bootstrapping enlarge the mined set by approximately 2.2x,
enabling large-scale high-fidelity training data. By automating the most
repetitive annotation steps, the approach allows a new scale of training
without human labeling effort. To democratize research in this
resource-intensive area, we release NHR-Edit: an open dataset of 358k
high-quality triplets. In the largest cross-dataset evaluation, it surpasses
all public alternatives. We also release Bagel-NHR-Edit, an open-source
fine-tuned Bagel model, which achieves state-of-the-art metrics in our
experiments.
"PhyWorldBench": A Comprehensive Evaluation of Physical Realism in
  Text-to-Video Models
Authors: name: Jing Gu, name: Xian Liu, name: Yu Zeng, name: Ashwin Nagarajan, name: Fangrui Zhu, name: Daniel Hong, name: Yue Fan, name: Qianqi Yan, name: Kaiwen Zhou, name: Ming-Yu Liu, name: Xin Eric Wang
Published: 2025-07-17 17:54:09

Video generation models have achieved remarkable progress in creating
high-quality, photorealistic content. However, their ability to accurately
simulate physical phenomena remains a critical and unresolved challenge. This
paper presents PhyWorldBench, a comprehensive benchmark designed to evaluate
video generation models based on their adherence to the laws of physics. The
benchmark covers multiple levels of physical phenomena, ranging from
fundamental principles like object motion and energy conservation to more
complex scenarios involving rigid body interactions and human or animal motion.
Additionally, we introduce a novel ""Anti-Physics"" category, where prompts
intentionally violate real-world physics, enabling the assessment of whether
models can follow such instructions while maintaining logical consistency.
Besides large-scale human evaluation, we also design a simple yet effective
method that could utilize current MLLM to evaluate the physics realism in a
zero-shot fashion. We evaluate 12 state-of-the-art text-to-video generation
models, including five open-source and five proprietary models, with a detailed
comparison and analysis. we identify pivotal challenges models face in adhering
to real-world physics. Through systematic testing of their outputs across 1,050
curated prompts-spanning fundamental, composite, and anti-physics scenarios-we
identify pivotal challenges these models face in adhering to real-world
physics. We then rigorously examine their performance on diverse physical
phenomena with varying prompt types, deriving targeted recommendations for
crafting prompts that enhance fidelity to physical principles.
Being-H0: Vision-Language-Action Pretraining from Large-Scale Human
  Videos
Authors: name: Hao Luo, name: Yicheng Feng, name: Wanpeng Zhang, name: Sipeng Zheng, name: Ye Wang, name: Haoqi Yuan, name: Jiazheng Liu, name: Chaoyi Xu, name: Qin Jin, name: Zongqing Lu
Published: 2025-07-21 13:19:09

We introduce Being-H0, a dexterous Vision-Language-Action model (VLA) trained
on large-scale human videos. Existing VLAs struggle with complex manipulation
tasks requiring high dexterity and generalize poorly to novel scenarios and
tasks, primarily due to their reliance on synthetic data with significant
sim-to-real gaps or teleoperated demonstrations lacking scale and diversity. To
address this data bottleneck, we propose leveraging human hands as a foundation
manipulator, capitalizing on the rich dexterity and scalability present in web
data. Our approach centers on physical instruction tuning, a novel training
paradigm that combines large-scale VLA pretraining from human videos, physical
space alignment for 3D reasoning, and post-training adaptation for robotic
tasks. Additionally, we introduce a part-level motion tokenization method which
achieves millimeter-level reconstruction accuracy to model precise hand
trajectories for action learning. To support our proposed paradigm, we further
develop a comprehensive data curation pipeline that integrates heterogeneous
sources -- including motion capture, VR, and RGB-only videos -- into a
large-scale dataset with millions of motion-based instructional instances. We
empirically show the excellence of Being-H0 in hand motion generation and
instruction following, and it also scales well with model and data sizes.
Importantly, we observe the expected gains of Being-H0 in real-world robotic
manipulation as physical instruction tuning is applied. More details are
available at https://beingbeyond.github.io/Being-H0.