Orionnnn/hub-stats huggingface dataset:
High-Resolution Visual Reasoning via Multi-Turn Grounding-Based
  Reinforcement Learning
Authors: name: Xinyu Huang, name: Yuhao Dong, name: Weiwei Tian, name: Bo Li, name: Rui Feng, name: Ziwei Liu
Published: 2025-07-08 12:05:05

State-of-the-art large multi-modal models (LMMs) face challenges when
processing high-resolution images, as these inputs are converted into enormous
visual tokens, many of which are irrelevant to the downstream task. In this
paper, we propose Multi-turn Grounding-based Policy Optimization (MGPO), an
end-to-end reinforcement learning (RL) framework that enables LMMs to
iteratively focus on key visual regions by automatically cropping sub-images,
based on model-predicted grounding coordinates within a multi-turn conversation
framework. Compared to supervised fine-tuning (SFT), which requires costly
additional grounding annotations, our approach highlights that LMMs can emerge
robust grounding abilities during the RL training process, leveraging only a
binary reward function derived from the correctness of the final answer.
Additionally, we observe that LMMs struggle to autonomously trigger visual
grounding during the rollout process. To address this cold start problem, we
design a multi-turn conversational template and restrict policy loss
computation to model outputs generated across multiple dialogue rounds, thereby
promoting stable optimization. Extensive experiments demonstrate that, when
trained on standard visual-question-short answering data without grounding
annotations, MGPO effectively elicits stronger grounding capabilities compared
to GRPO, leading to 5.4\% improvement on in-distribution MME-Realworld and
5.2\% improvement on the challenging out-of-distribution (OOD) V* Bench.
Notably, MGPO post-training on Qwen2.5-VL-7B with 21K samples surpasses
OpenAI's o1 and GPT-4o models on the OOD V* Bench. Codes are available at
https://github.com/EvolvingLMMs-Lab/MGPO.
OmniPart: Part-Aware 3D Generation with Semantic Decoupling and
  Structural Cohesion
Authors: name: Yunhan Yang, name: Yufan Zhou, name: Yuan-Chen Guo, name: Zi-Xin Zou, name: Yukun Huang, name: Ying-Tian Liu, name: Hao Xu, name: Ding Liang, name: Yan-Pei Cao, name: Xihui Liu
Published: 2025-07-08 16:46:15

The creation of 3D assets with explicit, editable part structures is crucial
for advancing interactive applications, yet most generative methods produce
only monolithic shapes, limiting their utility. We introduce OmniPart, a novel
framework for part-aware 3D object generation designed to achieve high semantic
decoupling among components while maintaining robust structural cohesion.
OmniPart uniquely decouples this complex task into two synergistic stages: (1)
an autoregressive structure planning module generates a controllable,
variable-length sequence of 3D part bounding boxes, critically guided by
flexible 2D part masks that allow for intuitive control over part decomposition
without requiring direct correspondences or semantic labels; and (2) a
spatially-conditioned rectified flow model, efficiently adapted from a
pre-trained holistic 3D generator, synthesizes all 3D parts simultaneously and
consistently within the planned layout. Our approach supports user-defined part
granularity, precise localization, and enables diverse downstream applications.
Extensive experiments demonstrate that OmniPart achieves state-of-the-art
performance, paving the way for more interpretable, editable, and versatile 3D
content.
How to Train Your LLM Web Agent: A Statistical Diagnosis
Authors: name: Dheeraj Vattikonda, name: Santhoshi Ravichandran, name: Emiliano Penaloza, name: Hadi Nekoei, name: Megh Thakkar, name: Thibault Le Sellier de Chezelles, name: Nicolas Gontier, name: Miguel Muñoz-Mármol, name: Sahar Omidi Shayegan, name: Stefania Raimondo, name: Xue Liu, name: Alexandre Drouin, name: Laurent Charlin, name: Alexandre Piché, name: Alexandre Lacoste, name: Massimo Caccia
Published: 2025-07-05 17:12:33

LLM-based web agents have recently made significant progress, but much of it
has occurred in closed-source systems, widening the gap with open-source
alternatives. Progress has been held back by two key challenges: first, a
narrow focus on single-step tasks that overlooks the complexity of multi-step
web interactions; and second, the high compute costs required to post-train
LLM-based web agents. To address this, we present the first statistically
grounded study on compute allocation for LLM web-agent post-training. Our
approach uses a two-stage pipeline, training a Llama 3.1 8B student to imitate
a Llama 3.3 70B teacher via supervised fine-tuning (SFT), followed by on-policy
reinforcement learning. We find this process highly sensitive to hyperparameter
choices, making exhaustive sweeps impractical. To spare others from expensive
trial-and-error, we sample 1,370 configurations and use bootstrapping to
estimate effective hyperparameters. Our results show that combining SFT with
on-policy RL consistently outperforms either approach alone on both WorkArena
and MiniWob++. Further, this strategy requires only 55% of the compute to match
the peak performance of pure SFT on MiniWob++, effectively pushing the
compute-performance Pareto frontier, and is the only strategy that can close
the gap with closed-source models.
A Survey on Latent Reasoning
Authors: name: Rui-Jie Zhu, name: Tianhao Peng, name: Tianhao Cheng, name: Xingwei Qu, name: Jinfa Huang, name: Dawei Zhu, name: Hao Wang, name: Kaiwen Xue, name: Xuanliang Zhang, name: Yong Shan, name: Tianle Cai, name: Taylor Kergan, name: Assel Kembay, name: Andrew Smith, name: Chenghua Lin, name: Binh Nguyen, name: Yuqi Pan, name: Yuhong Chou, name: Zefan Cai, name: Zhenhe Wu, name: Yongchi Zhao, name: Tianyu Liu, name: Jian Yang, name: Wangchunshu Zhou, name: Chujie Zheng, name: Chongxuan Li, name: Yuyin Zhou, name: Zhoujun Li, name: Zhaoxiang Zhang, name: Jiaheng Liu, name: Ge Zhang, name: Wenhao Huang, name: Jason Eshraghian
Published: 2025-07-08 17:29:07

Large Language Models (LLMs) have demonstrated impressive reasoning
capabilities, especially when guided by explicit chain-of-thought (CoT)
reasoning that verbalizes intermediate steps. While CoT improves both
interpretability and accuracy, its dependence on natural language reasoning
limits the model's expressive bandwidth. Latent reasoning tackles this
bottleneck by performing multi-step inference entirely in the model's
continuous hidden state, eliminating token-level supervision. To advance latent
reasoning research, this survey provides a comprehensive overview of the
emerging field of latent reasoning. We begin by examining the foundational role
of neural network layers as the computational substrate for reasoning,
highlighting how hierarchical representations support complex transformations.
Next, we explore diverse latent reasoning methodologies, including
activation-based recurrence, hidden state propagation, and fine-tuning
strategies that compress or internalize explicit reasoning traces. Finally, we
discuss advanced paradigms such as infinite-depth latent reasoning via masked
diffusion models, which enable globally consistent and reversible reasoning
processes. By unifying these perspectives, we aim to clarify the conceptual
landscape of latent reasoning and chart future directions for research at the
frontier of LLM cognition. An associated GitHub repository collecting the
latest papers and repos is available at:
https://github.com/multimodal-art-projection/LatentCoT-Horizon/.
Tora2: Motion and Appearance Customized Diffusion Transformer for
  Multi-Entity Video Generation
Authors: name: Zhenghao Zhang, name: Junchao Liao, name: Xiangyu Meng, name: Long Qin, name: Weizhi Wang
Published: 2025-07-08 13:11:40

Recent advances in diffusion transformer models for motion-guided video
generation, such as Tora, have shown significant progress. In this paper, we
present Tora2, an enhanced version of Tora, which introduces several design
improvements to expand its capabilities in both appearance and motion
customization. Specifically, we introduce a decoupled personalization extractor
that generates comprehensive personalization embeddings for multiple open-set
entities, better preserving fine-grained visual details compared to previous
methods. Building on this, we design a gated self-attention mechanism to
integrate trajectory, textual description, and visual information for each
entity. This innovation significantly reduces misalignment in multimodal
conditioning during training. Moreover, we introduce a contrastive loss that
jointly optimizes trajectory dynamics and entity consistency through explicit
mapping between motion and personalization embeddings. Tora2 is, to our best
knowledge, the first method to achieve simultaneous multi-entity customization
of appearance and motion for video generation. Experimental results demonstrate
that Tora2 achieves competitive performance with state-of-the-art customization
methods while providing advanced motion control capabilities, which marks a
critical advancement in multi-condition video generation. Project page:
https://github.com/alibaba/Tora .
MedGen: Unlocking Medical Video Generation by Scaling
  Granularly-annotated Medical Videos
Authors: name: Rongsheng Wang, name: Junying Chen, name: Ke Ji, name: Zhenyang Cai, name: Shunian Chen, name: Yunjin Yang, name: Benyou Wang
Published: 2025-07-08 04:58:36

Recent advances in video generation have shown remarkable progress in
open-domain settings, yet medical video generation remains largely
underexplored. Medical videos are critical for applications such as clinical
training, education, and simulation, requiring not only high visual fidelity
but also strict medical accuracy. However, current models often produce
unrealistic or erroneous content when applied to medical prompts, largely due
to the lack of large-scale, high-quality datasets tailored to the medical
domain. To address this gap, we introduce MedVideoCap-55K, the first
large-scale, diverse, and caption-rich dataset for medical video generation. It
comprises over 55,000 curated clips spanning real-world medical scenarios,
providing a strong foundation for training generalist medical video generation
models. Built upon this dataset, we develop MedGen, which achieves leading
performance among open-source models and rivals commercial systems across
multiple benchmarks in both visual quality and medical accuracy. We hope our
dataset and model can serve as a valuable resource and help catalyze further
research in medical video generation. Our code and data is available at
https://github.com/FreedomIntelligence/MedGen
Coding Triangle: How Does Large Language Model Understand Code?
Authors: name: Taolin Zhang, name: Zihan Ma, name: Maosong Cao, name: Junnan Liu, name: Songyang Zhang, name: Kai Chen
Published: 2025-07-08 16:20:43

Large language models (LLMs) have achieved remarkable progress in code
generation, yet their true programming competence remains underexplored. We
introduce the Code Triangle framework, which systematically evaluates LLMs
across three fundamental dimensions: editorial analysis, code implementation,
and test case generation. Through extensive experiments on competitive
programming benchmarks, we reveal that while LLMs can form a self-consistent
system across these dimensions, their solutions often lack the diversity and
robustness of human programmers. We identify a significant distribution shift
between model cognition and human expertise, with model errors tending to
cluster due to training data biases and limited reasoning transfer. Our study
demonstrates that incorporating human-generated editorials, solutions, and
diverse test cases, as well as leveraging model mixtures, can substantially
enhance both the performance and robustness of LLMs. Furthermore, we reveal
both the consistency and inconsistency in the cognition of LLMs that may
facilitate self-reflection and self-improvement, providing a potential
direction for developing more powerful coding models.
RLVER: Reinforcement Learning with Verifiable Emotion Rewards for
  Empathetic Agents
Authors: name: Peisong Wang, name: Ruotian Ma, name: Bang Zhang, name: Xingyu Chen, name: Zhiwei He, name: Kang Luo, name: Qingsong Lv, name: Qingxuan Jiang, name: Zheng Xie, name: Shanyi Wang, name: Yuan Li, name: Fanghua Ye, name: Jian Li, name: Yifan Yang, name: Zhaopeng Tu, name: Xiaolong Li
Published: 2025-07-03 18:33:18

Large language models (LLMs) excel at logical and algorithmic reasoning, yet
their emotional intelligence (EQ) still lags far behind their cognitive
prowess. While reinforcement learning from verifiable rewards (RLVR) has
advanced in other domains, its application to dialogue-especially for emotional
intelligence-remains underexplored. In this work, we introduce RLVER, the first
end-to-end reinforcement learning framework that leverages verifiable emotion
rewards from simulated users to cultivate higher-order empathetic abilities in
LLMs. Within this framework, self-consistent affective simulated users engage
in dialogue rollouts and produce deterministic emotion scores during
conversations, serving as reward signals to guide the LLM's learning.
Fine-tuning publicly available Qwen2.5-7B-Instruct model with PPO boosts its
Sentient-Benchmark score from 13.3 to 79.2 while largely preserving
mathematical and coding competence. Extensive experiments reveal that: (i)
RLVER consistently improves multiple dialogue capabilities; (ii) Thinking and
non-thinking models show distinct trends--thinking models excel in empathy and
insight, while non-thinking models favor action; (iii) GRPO often yields stable
gains, while PPO can push certain capabilities to a higher ceiling; (iv) More
challenging environments are not always better-moderate ones can yield stronger
outcomes. Our results show that RLVER is a practical route toward emotionally
intelligent and broadly capable language agents.
PRING: Rethinking Protein-Protein Interaction Prediction from Pairs to
  Graphs
Authors: name: Xinzhe Zheng, name: Hao Du, name: Fanding Xu, name: Jinzhe Li, name: Zhiyuan Liu, name: Wenkang Wang, name: Tao Chen, name: Wanli Ouyang, name: Stan Z. Li, name: Yan Lu, name: Nanqing Dong, name: Yang Zhang
Published: 2025-07-07 15:21:05

Deep learning-based computational methods have achieved promising results in
predicting protein-protein interactions (PPIs). However, existing benchmarks
predominantly focus on isolated pairwise evaluations, overlooking a model's
capability to reconstruct biologically meaningful PPI networks, which is
crucial for biology research. To address this gap, we introduce PRING, the
first comprehensive benchmark that evaluates protein-protein interaction
prediction from a graph-level perspective. PRING curates a high-quality,
multi-species PPI network dataset comprising 21,484 proteins and 186,818
interactions, with well-designed strategies to address both data redundancy and
leakage. Building on this golden-standard dataset, we establish two
complementary evaluation paradigms: (1) topology-oriented tasks, which assess
intra and cross-species PPI network construction, and (2) function-oriented
tasks, including protein complex pathway prediction, GO module analysis, and
essential protein justification. These evaluations not only reflect the model's
capability to understand the network topology but also facilitate protein
function annotation, biological module detection, and even disease mechanism
analysis. Extensive experiments on four representative model categories,
consisting of sequence similarity-based, naive sequence-based, protein language
model-based, and structure-based approaches, demonstrate that current PPI
models have potential limitations in recovering both structural and functional
properties of PPI networks, highlighting the gap in supporting real-world
biological applications. We believe PRING provides a reliable platform to guide
the development of more effective PPI prediction models for the community. The
dataset and source code of PRING are available at
https://github.com/SophieSarceau/PRING.
CriticLean: Critic-Guided Reinforcement Learning for Mathematical
  Formalization
Authors: name: Zhongyuan Peng, name: Yifan Yao, name: Kaijing Ma, name: Shuyue Guo, name: Yizhe Li, name: Yichi Zhang, name: Chenchen Zhang, name: Yifan Zhang, name: Zhouliang Yu, name: Luming Li, name: Minghao Liu, name: Yihang Xia, name: Jiawei Shen, name: Yuchen Wu, name: Yixin Cao, name: Zhaoxiang Zhang, name: Wenhao Huang, name: Jiaheng Liu, name: Ge Zhang
Published: 2025-07-08 17:03:39

Translating natural language mathematical statements into formal, executable
code is a fundamental challenge in automated theorem proving. While prior work
has focused on generation and compilation success, little attention has been
paid to the critic phase-the evaluation of whether generated formalizations
truly capture the semantic intent of the original problem. In this paper, we
introduce CriticLean, a novel critic-guided reinforcement learning framework
that elevates the role of the critic from a passive validator to an active
learning component. Specifically, first, we propose the CriticLeanGPT, trained
via supervised fine-tuning and reinforcement learning, to rigorously assess the
semantic fidelity of Lean 4 formalizations. Then, we introduce CriticLeanBench,
a benchmark designed to measure models' ability to distinguish semantically
correct from incorrect formalizations, and demonstrate that our trained
CriticLeanGPT models can significantly outperform strong open- and
closed-source baselines. Building on the CriticLean framework, we construct
FineLeanCorpus, a dataset comprising over 285K problems that exhibits rich
domain diversity, broad difficulty coverage, and high correctness based on
human evaluation. Overall, our findings highlight that optimizing the critic
phase is essential for producing reliable formalizations, and we hope our
CriticLean will provide valuable insights for future advances in formal
mathematical reasoning.
any4: Learned 4-bit Numeric Representation for LLMs
Authors: name: Mostafa Elhoushi, name: Jeff Johnson
Published: 2025-07-07 01:59:47

We present any4, a learned 4-bit weight quantization solution for large
language models (LLMs) providing arbitrary numeric representations without
requiring pre-processing of weights or activations. any4 yields higher accuracy
compared to other related 4-bit numeric representation types: int4, fp4 and
nf4, as evaluated on a range of model sizes, generations and families (Llama 2,
Llama 3, Mistral and Mixtral). While any4 does not require preprocessing of
weights or activations, it is also competitive with orthogonal techniques that
require such preprocessing (e.g., AWQ and GPTQ). We also experiment with any3
and any2 and show competitiveness at lower bits. Additionally, we show that we
can calibrate using a single curated diverse sample rather than hundreds of
samples from a dataset as done in most quantization approaches. We also open
source tinygemm, a latency optimized GPU matrix multiplication library for
LLMs, that implements any4 using a GPU-efficient lookup table strategy along
with other common quantization methods. We open source our code at
https://github.com/facebookresearch/any4 .
Is Diversity All You Need for Scalable Robotic Manipulation?
Authors: name: Modi Shi, name: Li Chen, name: Jin Chen, name: Yuxiang Lu, name: Chiming Liu, name: Guanghui Ren, name: Ping Luo, name: Di Huang, name: Maoqing Yao, name: Hongyang Li
Published: 2025-07-08 17:52:44

Data scaling has driven remarkable success in foundation models for Natural
Language Processing (NLP) and Computer Vision (CV), yet the principles of
effective data scaling in robotic manipulation remain insufficiently
understood. In this work, we investigate the nuanced role of data diversity in
robot learning by examining three critical dimensions-task (what to do),
embodiment (which robot to use), and expert (who demonstrates)-challenging the
conventional intuition of "more diverse is better". Throughout extensive
experiments on various robot platforms, we reveal that (1) task diversity
proves more critical than per-task demonstration quantity, benefiting transfer
from diverse pre-training tasks to novel downstream scenarios; (2)
multi-embodiment pre-training data is optional for cross-embodiment
transfer-models trained on high-quality single-embodiment data can efficiently
transfer to different platforms, showing more desirable scaling property during
fine-tuning than multi-embodiment pre-trained models; and (3) expert diversity,
arising from individual operational preferences and stochastic variations in
human demonstrations, can be confounding to policy learning, with velocity
multimodality emerging as a key contributing factor. Based on this insight, we
propose a distribution debiasing method to mitigate velocity ambiguity, the
yielding GO-1-Pro achieves substantial performance gains of 15%, equivalent to
using 2.5 times pre-training data. Collectively, these findings provide new
perspectives and offer practical guidance on how to scale robotic manipulation
datasets effectively.
GTA1: GUI Test-time Scaling Agent
Authors: name: Yan Yang, name: Dongxu Li, name: Yutong Dai, name: Yuhao Yang, name: Ziyang Luo, name: Zirui Zhao, name: Zhiyuan Hu, name: Junzhe Huang, name: Amrita Saha, name: Zeyuan Chen, name: Ran Xu, name: Liyuan Pan, name: Caiming Xiong, name: Junnan Li
Published: 2025-07-08 08:52:18

Graphical user interface (GUI) agents autonomously operate across platforms
(e.g., Linux) to complete tasks by interacting with visual elements.
Specifically, a user instruction is decomposed into a sequence of action
proposals, each corresponding to an interaction with the GUI. After each
action, the agent observes the updated GUI environment to plan the next step.
However, two main challenges arise: i) resolving ambiguity in task planning
(i.e., the action proposal sequence), where selecting an appropriate plan is
non-trivial, as many valid ones may exist; ii) accurately grounding actions in
complex and high-resolution interfaces, i.e., precisely interacting with visual
targets.
  This paper investigates the two aforementioned challenges with our GUI
Test-time Scaling Agent, namely GTA1. First, to select the most appropriate
action proposal, we introduce a test-time scaling method. At each step, we
sample multiple candidate action proposals and leverage a judge model to
evaluate and select the most suitable one. It trades off computation for better
decision quality by concurrent sampling, shortening task execution steps, and
improving overall performance. Second, we propose a model that achieves
improved accuracy when grounding the selected action proposal to its
corresponding visual elements. Our key insight is that reinforcement learning
(RL) facilitates visual grounding through inherent objective alignments,
rewarding successful clicks on interface elements.
  Experimentally, our method establishes state-of-the-art performance across
diverse benchmarks. For example, GTA1-7B achieves 50.1%, 92.4%, and 67.7%
accuracies on Screenspot-Pro, Screenspot-V2, and OSWorld-G, respectively. When
paired with a planner applying our test-time scaling strategy, it exhibits
state-of-the-art agentic performance (e.g., 45.2% task success rate on
OSWorld). We open-source our code and models here.