Orionnnn/hub-stats huggingface dataset:
P3-SAM: Native 3D Part Segmentation
Authors: name: Changfeng Ma, name: Yang Li, name: Xinhao Yan, name: Jiachen Xu, name: Yunhan Yang, name: Chunshi Wang, name: Zibo Zhao, name: Yanwen Guo, name: Zhuo Chen, name: Chunchao Guo
Published: 2025-09-08 15:12:17

Segmenting 3D assets into their constituent parts is crucial for enhancing 3D
understanding, facilitating model reuse, and supporting various applications
such as part generation. However, current methods face limitations such as poor
robustness when dealing with complex objects and cannot fully automate the
process. In this paper, we propose a native 3D point-promptable part
segmentation model termed P3-SAM, designed to fully automate the segmentation
of any 3D objects into components. Inspired by SAM, P3-SAM consists of a
feature extractor, multiple segmentation heads, and an IoU predictor, enabling
interactive segmentation for users. We also propose an algorithm to
automatically select and merge masks predicted by our model for part instance
segmentation. Our model is trained on a newly built dataset containing nearly
3.7 million models with reasonable segmentation labels. Comparisons show that
our method achieves precise segmentation results and strong robustness on any
complex objects, attaining state-of-the-art performance. Our code will be
released soon.
Hunyuan-MT Technical Report
Authors: name: Mao Zheng, name: Zheng Li, name: Bingxin Qu, name: Mingyang Song, name: Yang Du, name: Mingrui Sun, name: Di Wang
Published: 2025-09-05 16:11:05

In this report, we introduce Hunyuan-MT-7B, our first open-source
multilingual translation model, which supports bidirectional translation across
33 major languages and places a special emphasis on translation between
Mandarin and several ethnic minority languages as well as dialects.
Furthermore, to serve and address diverse translation scenarios and enhance
model performance at test time, we introduce Hunyuan-MT-Chimera-7B, a
translation model inspired by the slow thinking mode. This model integrates
multiple outputs generated by the Hunyuan-MT-7B model under varying parameter
settings, thereby achieving performance superior to that of conventional
slow-thinking models based on Chain-of-Thought (CoT). The development of our
models follows a holistic training process specifically engineered for
multilingual translation, which begins with general and MT-oriented
pre-training to build foundational capabilities, proceeds to Supervised
Fine-Tuning (SFT) for task-specific adaptation, and culminates in advanced
alignment through Reinforcement Learning (RL) and weak-to-strong RL. Through
comprehensive experimentation, we demonstrate that both Hunyuan-MT-7B and
Hunyuan-MT-Chimera-7B significantly outperform all translation-specific models
of comparable parameter size and most of the SOTA large models, particularly on
the task of translation between Mandarin and minority languages as well as
dialects. In the WMT2025 shared task (General Machine Translation), our models
demonstrate state-of-the-art performance, ranking first in 30 out of 31
language pairs. This result highlights the robustness of our models across a
diverse linguistic spectrum, encompassing high-resource languages such as
Chinese, English, and Japanese, as well as low-resource languages including
Czech, Marathi, Estonian, and Icelandic.
EnvX: Agentize Everything with Agentic AI
Authors: name: Linyao Chen, name: Zimian Peng, name: Yingxuan Yang, name: Yikun Wang, name: Wenzheng Tom Tang, name: Hiroki H. Kobayashi, name: Weinan Zhang
Published: 2025-09-09 18:51:36

The widespread availability of open-source repositories has led to a vast
collection of reusable software components, yet their utilization remains
manual, error-prone, and disconnected. Developers must navigate documentation,
understand APIs, and write integration code, creating significant barriers to
efficient software reuse. To address this, we present EnvX, a framework that
leverages Agentic AI to agentize GitHub repositories, transforming them into
intelligent, autonomous agents capable of natural language interaction and
inter-agent collaboration. Unlike existing approaches that treat repositories
as static code resources, EnvX reimagines them as active agents through a
three-phase process: (1) TODO-guided environment initialization, which sets up
the necessary dependencies, data, and validation datasets; (2) human-aligned
agentic automation, allowing repository-specific agents to autonomously perform
real-world tasks; and (3) Agent-to-Agent (A2A) protocol, enabling multiple
agents to collaborate. By combining large language model capabilities with
structured tool integration, EnvX automates not just code generation, but the
entire process of understanding, initializing, and operationalizing repository
functionality. We evaluate EnvX on the GitTaskBench benchmark, using 18
repositories across domains such as image processing, speech recognition,
document analysis, and video manipulation. Our results show that EnvX achieves
a 74.07% execution completion rate and 51.85% task pass rate, outperforming
existing frameworks. Case studies further demonstrate EnvX's ability to enable
multi-repository collaboration via the A2A protocol. This work marks a shift
from treating repositories as passive code resources to intelligent,
interactive agents, fostering greater accessibility and collaboration within
the open-source ecosystem.
HumanAgencyBench: Scalable Evaluation of Human Agency Support in AI
  Assistants
Authors: name: Benjamin Sturgeon, name: Daniel Samuelson, name: Jacob Haimes, name: Jacy Reese Anthis
Published: 2025-09-10 11:10:10

As humans delegate more tasks and decisions to artificial intelligence (AI),
we risk losing control of our individual and collective futures. Relatively
simple algorithmic systems already steer human decision-making, such as social
media feed algorithms that lead people to unintentionally and absent-mindedly
scroll through engagement-optimized content. In this paper, we develop the idea
of human agency by integrating philosophical and scientific theories of agency
with AI-assisted evaluation methods: using large language models (LLMs) to
simulate and validate user queries and to evaluate AI responses. We develop
HumanAgencyBench (HAB), a scalable and adaptive benchmark with six dimensions
of human agency based on typical AI use cases. HAB measures the tendency of an
AI assistant or agent to Ask Clarifying Questions, Avoid Value Manipulation,
Correct Misinformation, Defer Important Decisions, Encourage Learning, and
Maintain Social Boundaries. We find low-to-moderate agency support in
contemporary LLM-based assistants and substantial variation across system
developers and dimensions. For example, while Anthropic LLMs most support human
agency overall, they are the least supportive LLMs in terms of Avoid Value
Manipulation. Agency support does not appear to consistently result from
increasing LLM capabilities or instruction-following behavior (e.g., RLHF), and
we encourage a shift towards more robust safety and alignment targets.
AgentGym-RL: Training LLM Agents for Long-Horizon Decision Making
  through Multi-Turn Reinforcement Learning
Authors: name: Zhiheng Xi, name: Jixuan Huang, name: Chenyang Liao, name: Baodai Huang, name: Honglin Guo, name: Jiaqi Liu, name: Rui Zheng, name: Junjie Ye, name: Jiazheng Zhang, name: Wenxiang Chen, name: Wei He, name: Yiwen Ding, name: Guanyu Li, name: Zehui Chen, name: Zhengyin Du, name: Xuesong Yao, name: Yufei Xu, name: Jiecao Chen, name: Tao Gui, name: Zuxuan Wu, name: Qi Zhang, name: Xuanjing Huang, name: Yu-Gang Jiang
Published: 2025-09-10 16:46:11

Developing autonomous LLM agents capable of making a series of intelligent
decisions to solve complex, real-world tasks is a fast-evolving frontier. Like
human cognitive development, agents are expected to acquire knowledge and
skills through exploration and interaction with the environment. Despite
advances, the community still lacks a unified, interactive reinforcement
learning (RL) framework that can effectively train such agents from scratch --
without relying on supervised fine-tuning (SFT) -- across diverse and realistic
environments. To bridge this gap, we introduce AgentGym-RL, a new framework to
train LLM agents for multi-turn interactive decision-making through RL. The
framework features a modular and decoupled architecture, ensuring high
flexibility and extensibility. It encompasses a wide variety of real-world
scenarios, and supports mainstream RL algorithms. Furthermore, we propose
ScalingInter-RL, a training approach designed for exploration-exploitation
balance and stable RL optimization. In early stages, it emphasizes exploitation
by restricting the number of interactions, and gradually shifts towards
exploration with larger horizons to encourage diverse problem-solving
strategies. In this way, the agent develops more diverse behaviors and is less
prone to collapse under long horizons. We perform extensive experiments to
validate the stability and effectiveness of both the AgentGym-RL framework and
the ScalingInter-RL approach. Our agents match or surpass commercial models on
27 tasks across diverse environments. We offer key insights and will
open-source the complete AgentGym-RL framework -- including code and datasets
-- to empower the research community in developing the next generation of
intelligent agents.
A Survey of Reinforcement Learning for Large Reasoning Models
Authors: name: Kaiyan Zhang, name: Yuxin Zuo, name: Bingxiang He, name: Youbang Sun, name: Runze Liu, name: Che Jiang, name: Yuchen Fan, name: Kai Tian, name: Guoli Jia, name: Pengfei Li, name: Yu Fu, name: Xingtai Lv, name: Yuchen Zhang, name: Sihang Zeng, name: Shang Qu, name: Haozhan Li, name: Shijie Wang, name: Yuru Wang, name: Xinwei Long, name: Fangfu Liu, name: Xiang Xu, name: Jiaze Ma, name: Xuekai Zhu, name: Ermo Hua, name: Yihao Liu, name: Zonglin Li, name: Huayu Chen, name: Xiaoye Qu, name: Yafu Li, name: Weize Chen, name: Zhenzhao Yuan, name: Junqi Gao, name: Dong Li, name: Zhiyuan Ma, name: Ganqu Cui, name: Zhiyuan Liu, name: Biqing Qi, name: Ning Ding, name: Bowen Zhou
Published: 2025-09-10 17:59:43

In this paper, we survey recent advances in Reinforcement Learning (RL) for
reasoning with Large Language Models (LLMs). RL has achieved remarkable success
in advancing the frontier of LLM capabilities, particularly in addressing
complex logical tasks such as mathematics and coding. As a result, RL has
emerged as a foundational methodology for transforming LLMs into LRMs. With the
rapid progress of the field, further scaling of RL for LRMs now faces
foundational challenges not only in computational resources but also in
algorithm design, training data, and infrastructure. To this end, it is timely
to revisit the development of this domain, reassess its trajectory, and explore
strategies to enhance the scalability of RL toward Artificial SuperIntelligence
(ASI). In particular, we examine research applying RL to LLMs and LRMs for
reasoning abilities, especially since the release of DeepSeek-R1, including
foundational components, core problems, training resources, and downstream
applications, to identify future opportunities and directions for this rapidly
evolving area. We hope this review will promote future research on RL for
broader reasoning models. Github:
https://github.com/TsinghuaC3I/Awesome-RL-for-LRMs
RewardDance: Reward Scaling in Visual Generation
Authors: name: Jie Wu, name: Yu Gao, name: Zilyu Ye, name: Ming Li, name: Liang Li, name: Hanzhong Guo, name: Jie Liu, name: Zeyue Xue, name: Xiaoxia Hou, name: Wei Liu, name: Yan Zeng, name: Weilin Huang
Published: 2025-09-10 17:59:31

Reward Models (RMs) are critical for improving generation models via
Reinforcement Learning (RL), yet the RM scaling paradigm in visual generation
remains largely unexplored. It primarily due to fundamental limitations in
existing approaches: CLIP-based RMs suffer from architectural and input
modality constraints, while prevalent Bradley-Terry losses are fundamentally
misaligned with the next-token prediction mechanism of Vision-Language Models
(VLMs), hindering effective scaling. More critically, the RLHF optimization
process is plagued by Reward Hacking issue, where models exploit flaws in the
reward signal without improving true quality. To address these challenges, we
introduce RewardDance, a scalable reward modeling framework that overcomes
these barriers through a novel generative reward paradigm. By reformulating the
reward score as the model's probability of predicting a "yes" token, indicating
that the generated image outperforms a reference image according to specific
criteria, RewardDance intrinsically aligns reward objectives with VLM
architectures. This alignment unlocks scaling across two dimensions: (1) Model
Scaling: Systematic scaling of RMs up to 26 billion parameters; (2) Context
Scaling: Integration of task-specific instructions, reference examples, and
chain-of-thought (CoT) reasoning. Extensive experiments demonstrate that
RewardDance significantly surpasses state-of-the-art methods in text-to-image,
text-to-video, and image-to-video generation. Crucially, we resolve the
persistent challenge of "reward hacking": Our large-scale RMs exhibit and
maintain high reward variance during RL fine-tuning, proving their resistance
to hacking and ability to produce diverse, high-quality outputs. It greatly
relieves the mode collapse problem that plagues smaller models.
Sharing is Caring: Efficient LM Post-Training with Collective RL
  Experience Sharing
Authors: name: Jeffrey Amico, name: Gabriel Passamani Andrade, name: John Donaghy, name: Ben Fielding, name: Tristin Forbus, name: Harry Grieve, name: Semih Kara, name: Jari Kolehmainen, name: Yihua Lou, name: Christopher Nies, name: Edward Phillip Flores Nuño, name: Diogo Ortega, name: Shikhar Rastogi, name: Austin Virts, name: Matthew J. Wright
Published: 2025-09-10 16:14:20

Post-training language models (LMs) with reinforcement learning (RL) can
enhance their complex reasoning capabilities without supervised fine-tuning, as
demonstrated by DeepSeek-R1-Zero. However, effectively utilizing RL for LMs
requires significant parallelization to scale-up inference, which introduces
non-trivial technical challenges (e.g. latency, memory, and reliability)
alongside ever-growing financial costs. We present Swarm sAmpling Policy
Optimization (SAPO), a fully decentralized and asynchronous RL post-training
algorithm. SAPO is designed for decentralized networks of heterogenous compute
nodes, where each node manages its own policy model(s) while "sharing" rollouts
with others in the network; no explicit assumptions about latency, model
homogeneity, or hardware are required and nodes can operate in silo if desired.
As a result, the algorithm avoids common bottlenecks in scaling RL
post-training while also allowing (and even encouraging) new possibilities. By
sampling rollouts "shared" across the network, it enables "Aha moments" to
propagate, thereby bootstrapping the learning process. In this paper we show
SAPO achieved cumulative reward gains of up to 94% in controlled experiments.
We also share insights from tests on a network with thousands of nodes
contributed by Gensyn community members running the algorithm on diverse
hardware and models during an open-source demo.
From Noise to Narrative: Tracing the Origins of Hallucinations in
  Transformers
Authors: name: Praneet Suresh, name: Jack Stanley, name: Sonia Joseph, name: Luca Scimeca, name: Danilo Bzdok
Published: 2025-09-08 17:50:45

As generative AI systems become competent and democratized in science,
business, and government, deeper insight into their failure modes now poses an
acute need. The occasional volatility in their behavior, such as the propensity
of transformer models to hallucinate, impedes trust and adoption of emerging AI
solutions in high-stakes areas. In the present work, we establish how and when
hallucinations arise in pre-trained transformer models through concept
representations captured by sparse autoencoders, under scenarios with
experimentally controlled uncertainty in the input space. Our systematic
experiments reveal that the number of semantic concepts used by the transformer
model grows as the input information becomes increasingly unstructured. In the
face of growing uncertainty in the input space, the transformer model becomes
prone to activate coherent yet input-insensitive semantic features, leading to
hallucinated output. At its extreme, for pure-noise inputs, we identify a wide
variety of robustly triggered and meaningful concepts in the intermediate
activations of pre-trained transformer models, whose functional integrity we
confirm through targeted steering. We also show that hallucinations in the
output of a transformer model can be reliably predicted from the concept
patterns embedded in transformer layer activations. This collection of insights
on transformer internal processing mechanics has immediate consequences for
aligning AI models with human values, AI safety, opening the attack surface for
potential adversarial attacks, and providing a basis for automatic
quantification of a model's hallucination risk.
Benchmarking Information Retrieval Models on Complex Retrieval Tasks
Authors: name: Julian Killingback, name: Hamed Zamani
Published: 2025-09-08 22:11:10

Large language models (LLMs) are incredible and versatile tools for
text-based tasks that have enabled countless, previously unimaginable,
applications. Retrieval models, in contrast, have not yet seen such capable
general-purpose models emerge. To achieve this goal, retrieval models must be
able to perform complex retrieval tasks, where queries contain multiple parts,
constraints, or requirements in natural language. These tasks represent a
natural progression from the simple, single-aspect queries that are used in the
vast majority of existing, commonly used evaluation sets. Complex queries
naturally arise as people expect search systems to handle more specific and
often ambitious information requests, as is demonstrated by how people use
LLM-based information systems. Despite the growing desire for retrieval models
to expand their capabilities in complex retrieval tasks, there exist limited
resources to assess the ability of retrieval models on a comprehensive set of
diverse complex tasks. The few resources that do exist feature a limited scope
and often lack realistic settings making it hard to know the true capabilities
of retrieval models on complex real-world retrieval tasks. To address this
shortcoming and spur innovation in next-generation retrieval models, we
construct a diverse and realistic set of complex retrieval tasks and benchmark
a representative set of state-of-the-art retrieval models. Additionally, we
explore the impact of LLM-based query expansion and rewriting on retrieval
quality. Our results show that even the best models struggle to produce
high-quality retrieval results with the highest average nDCG@10 of only 0.346
and R@100 of only 0.587 across all tasks. Although LLM augmentation can help
weaker models, the strongest model has decreased performance across all metrics
with all rewriting techniques.
Emergent Hierarchical Reasoning in LLMs through Reinforcement Learning
Authors: name: Haozhe Wang, name: Qixin Xu, name: Che Liu, name: Junhong Wu, name: Fangzhen Lin, name: Wenhu Chen
Published: 2025-09-03 18:52:49

Reinforcement Learning (RL) has proven highly effective at enhancing the
complex reasoning abilities of Large Language Models (LLMs), yet underlying
mechanisms driving this success remain largely opaque. Our analysis reveals
that puzzling phenomena like ``aha moments", ``length-scaling'' and entropy
dynamics are not disparate occurrences but hallmarks of an emergent reasoning
hierarchy, akin to the separation of high-level strategic planning from
low-level procedural execution in human cognition. We uncover a compelling
two-phase dynamic: initially, a model is constrained by procedural correctness
and must improve its low-level skills. The learning bottleneck then decisively
shifts, with performance gains being driven by the exploration and mastery of
high-level strategic planning. This insight exposes a core inefficiency in
prevailing RL algorithms like GRPO, which apply optimization pressure
agnostically and dilute the learning signal across all tokens. To address this,
we propose HIerarchy-Aware Credit Assignment (HICRA), an algorithm that
concentrates optimization efforts on high-impact planning tokens. HICRA
significantly outperforms strong baselines, demonstrating that focusing on this
strategic bottleneck is key to unlocking advanced reasoning. Furthermore, we
validate semantic entropy as a superior compass for measuring strategic
exploration over misleading metrics such as token-level entropy.
Directly Aligning the Full Diffusion Trajectory with Fine-Grained Human
  Preference
Authors: name: Xiangwei Shen, name: Zhimin Li, name: Zhantao Yang, name: Shiyi Zhang, name: Yingfang Zhang, name: Donghao Li, name: Chunyu Wang, name: Qinglin Lu, name: Yansong Tang
Published: 2025-09-08 17:54:08

Recent studies have demonstrated the effectiveness of directly aligning
diffusion models with human preferences using differentiable reward. However,
they exhibit two primary challenges: (1) they rely on multistep denoising with
gradient computation for reward scoring, which is computationally expensive,
thus restricting optimization to only a few diffusion steps; (2) they often
need continuous offline adaptation of reward models in order to achieve desired
aesthetic quality, such as photorealism or precise lighting effects. To address
the limitation of multistep denoising, we propose Direct-Align, a method that
predefines a noise prior to effectively recover original images from any time
steps via interpolation, leveraging the equation that diffusion states are
interpolations between noise and target images, which effectively avoids
over-optimization in late timesteps. Furthermore, we introduce Semantic
Relative Preference Optimization (SRPO), in which rewards are formulated as
text-conditioned signals. This approach enables online adjustment of rewards in
response to positive and negative prompt augmentation, thereby reducing the
reliance on offline reward fine-tuning. By fine-tuning the FLUX.1.dev model
with optimized denoising and online reward adjustment, we improve its
human-evaluated realism and aesthetic quality by over 3x.
ΔL Normalization: Rethink Loss Aggregation in RLVR
Authors: name: Zhiyuan He, name: Xufang Luo, name: Yike Zhang, name: Yuqing Yang, name: Lili Qiu
Published: 2025-09-09 09:52:34

We propose Delta L Normalization, a simple yet effective loss aggregation
method tailored to the characteristic of dynamic generation lengths in
Reinforcement Learning with Verifiable Rewards (RLVR). Recently, RLVR has
demonstrated strong potential in improving the reasoning capabilities of large
language models (LLMs), but a major challenge lies in the large variability of
response lengths during training, which leads to high gradient variance and
unstable optimization. Although previous methods such as GRPO, DAPO, and Dr.
GRPO introduce different loss normalization terms to address this issue, they
either produce biased estimates or still suffer from high gradient variance. By
analyzing the effect of varying lengths on policy loss both theoretically and
empirically, we reformulate the problem as finding a minimum-variance unbiased
estimator. Our proposed Delta L Normalization not only provides an unbiased
estimate of the true policy loss but also minimizes gradient variance in
theory. Extensive experiments show that it consistently achieves superior
results across different model sizes, maximum lengths, and tasks. Our code will
be made public at https://github.com/zerolllin/Delta-L-Normalization.