Orionnnn/hub-stats huggingface dataset:
Scaling Agents via Continual Pre-training
Authors: name: Liangcai Su, name: Zhen Zhang, name: Guangyu Li, name: Zhuo Chen, name: Chenxi Wang, name: Maojia Song, name: Xinyu Wang, name: Kuan Li, name: Jialong Wu, name: Xuanzhong Chen, name: Zile Qiao, name: Zhongwang Zhang, name: Huifeng Yin, name: Shihao Cai, name: Runnan Fang, name: Zhengwei Tao, name: Wenbiao Yin, name: Chenxiong Qian, name: Yong Jiang, name: Pengjun Xie, name: Fei Huang, name: Jingren Zhou
Published: 2025-09-16 17:57:19

Large language models (LLMs) have evolved into agentic systems capable of
autonomous tool use and multi-step reasoning for complex problem-solving.
However, post-training approaches building upon general-purpose foundation
models consistently underperform in agentic tasks, particularly in open-source
implementations. We identify the root cause: the absence of robust agentic
foundation models forces models during post-training to simultaneously learn
diverse agentic behaviors while aligning them to expert demonstrations, thereby
creating fundamental optimization tensions. To this end, we are the first to
propose incorporating Agentic Continual Pre-training (Agentic CPT) into the
deep research agents training pipeline to build powerful agentic foundational
models. Based on this approach, we develop a deep research agent model named
AgentFounder. We evaluate our AgentFounder-30B on 10 benchmarks and achieve
state-of-the-art performance while retains strong tool-use ability, notably
39.9% on BrowseComp-en, 43.3% on BrowseComp-zh, and 31.5% Pass@1 on HLE.
ReSum: Unlocking Long-Horizon Search Intelligence via Context
  Summarization
Authors: name: Xixi Wu, name: Kuan Li, name: Yida Zhao, name: Liwen Zhang, name: Litu Ou, name: Huifeng Yin, name: Zhongwang Zhang, name: Yong Jiang, name: Pengjun Xie, name: Fei Huang, name: Minhao Cheng, name: Shuai Wang, name: Hong Cheng, name: Jingren Zhou
Published: 2025-09-16 17:57:22

Large Language Model (LLM)-based web agents demonstrate strong performance on
knowledge-intensive tasks but are hindered by context window limitations in
paradigms like ReAct. Complex queries involving multiple entities, intertwined
relationships, and high uncertainty demand extensive search cycles that rapidly
exhaust context budgets before reaching complete solutions. To overcome this
challenge, we introduce ReSum, a novel paradigm that enables indefinite
exploration through periodic context summarization. ReSum converts growing
interaction histories into compact reasoning states, maintaining awareness of
prior discoveries while bypassing context constraints. For paradigm adaptation,
we propose ReSum-GRPO, integrating GRPO with segmented trajectory training and
advantage broadcasting to familiarize agents with summary-conditioned
reasoning. Extensive experiments on web agents of varying scales across three
benchmarks demonstrate that ReSum delivers an average absolute improvement of
4.5\% over ReAct, with further gains of up to 8.2\% following ReSum-GRPO
training. Notably, with only 1K training samples, our WebResummer-30B (a
ReSum-GRPO-trained version of WebSailor-30B) achieves 33.3\% Pass@1 on
BrowseComp-zh and 18.3\% on BrowseComp-en, surpassing existing open-source web
agents.
WebSailor-V2: Bridging the Chasm to Proprietary Agents via Synthetic
  Data and Scalable Reinforcement Learning
Authors: name: Kuan Li, name: Zhongwang Zhang, name: Huifeng Yin, name: Rui Ye, name: Yida Zhao, name: Liwen Zhang, name: Litu Ou, name: Dingchu Zhang, name: Xixi Wu, name: Jialong Wu, name: Xinyu Wang, name: Zile Qiao, name: Zhen Zhang, name: Yong Jiang, name: Pengjun Xie, name: Fei Huang, name: Jingren Zhou
Published: 2025-09-16 17:57:03

Transcending human cognitive limitations represents a critical frontier in
LLM training. Proprietary agentic systems like DeepResearch have demonstrated
superhuman capabilities on extremely complex information-seeking benchmarks
such as BrowseComp, a feat previously unattainable. We posit that their success
hinges on a sophisticated reasoning pattern absent in open-source models: the
ability to systematically reduce extreme uncertainty when navigating vast
information landscapes. Based on this insight, we introduce WebSailor, a
complete post-training methodology designed to instill this crucial capability.
Our approach involves generating novel, high-uncertainty tasks through
structured sampling and information obfuscation, RFT cold start, and an
efficient agentic RL training algorithm, Duplicating Sampling Policy
Optimization (DUPO). With this integrated pipeline, WebSailor significantly
outperforms all open-source agents in complex information-seeking tasks,
matching proprietary agents' performance and closing the capability gap.
EconProver: Towards More Economical Test-Time Scaling for Automated
  Theorem Proving
Authors: name: Mukai Li, name: Linfeng Song, name: Zhenwen Liang, name: Jiahao Xu, name: Shansan Gong, name: Qi Liu, name: Haitao Mi, name: Dong Yu
Published: 2025-09-16 03:00:13

Large Language Models (LLMs) have recently advanced the field of Automated
Theorem Proving (ATP), attaining substantial performance gains through widely
adopted test-time scaling strategies, notably reflective Chain-of-Thought (CoT)
reasoning and increased sampling passes. However, they both introduce
significant computational overhead for inference. Moreover, existing cost
analyses typically regulate only the number of sampling passes, while
neglecting the substantial disparities in sampling costs introduced by
different scaling strategies. In this paper, we systematically compare the
efficiency of different test-time scaling strategies for ATP models and
demonstrate the inefficiency of the current state-of-the-art (SOTA) open-source
approaches. We then investigate approaches to significantly reduce token usage
and sample passes while maintaining the original performance. Specifically, we
propose two complementary methods that can be integrated into a unified EconRL
pipeline for amplified benefits: (1) a dynamic Chain-of-Thought (CoT) switching
mechanism designed to mitigate unnecessary token consumption, and (2) Diverse
parallel-scaled reinforcement learning (RL) with trainable prefixes to enhance
pass rates under constrained sampling passes. Experiments on miniF2F and
ProofNet demonstrate that our EconProver achieves comparable performance to
baseline methods with only 12% of the computational cost. This work provides
actionable insights for deploying lightweight ATP models without sacrificing
performance.
Exact Coset Sampling for Quantum Lattice Algorithms
Authors: name: Yifan Zhang
Published: 2025-09-15 18:10:28

We give a simple, fully correct, and assumption-light replacement for the
contested "domain-extension" in Step 9 of a recent windowed-QFT lattice
algorithm with complex-Gaussian windows~chen2024quantum. The published
Step~9 suffers from a periodicity/support mismatch. We present a pair-shift
difference construction that coherently cancels all unknown offsets, produces
an exact uniform CRT-coset state over Z_{P}, and then uses the QFT
to enforce the intended modular linear relation. The unitary is reversible,
uses poly(log M_2) gates, and preserves the algorithm's
asymptotics. Project Page: https://github.com/yifanzhang-pro/quantum-lattice.
Multimodal Reasoning for Science: Technical Report and 1st Place
  Solution to the ICML 2025 SeePhys Challenge
Authors: name: Hao Liang, name: Ruitao Wu, name: Bohan Zeng, name: Junbo Niu, name: Wentao Zhang, name: Bin Dong
Published: 2025-09-07 14:47:32

Multimodal reasoning remains a fundamental challenge in artificial
intelligence. Despite substantial advances in text-based reasoning, even
state-of-the-art models such as GPT-o3 struggle to maintain strong performance
in multimodal scenarios. To address this gap, we introduce a caption-assisted
reasoning framework that effectively bridges visual and textual modalities. Our
approach achieved 1st place in the ICML 2025 AI for Math Workshop \& Challenge
2: SeePhys, highlighting its effectiveness and robustness. Furthermore, we
validate its generalization on the MathVerse benchmark for geometric reasoning,
demonstrating the versatility of our method. Our code is publicly available at
https://github.com/OpenDCAI/SciReasoner.
Hunyuan3D Studio: End-to-End AI Pipeline for Game-Ready 3D Asset
  Generation
Authors: name: Biwen Lei, name: Yang Li, name: Xinhai Liu, name: Shuhui Yang, name: Lixin Xu, name: Jingwei Huang, name: Ruining Tang, name: Haohan Weng, name: Jian Liu, name: Jing Xu, name: Zhen Zhou, name: Yiling Zhu, name: Jiankai Xing, name: Jiachen Xu, name: Changfeng Ma, name: Xinhao Yan, name: Yunhan Yang, name: Chunshi Wang, name: Duoteng Xu, name: Xueqi Ma, name: Yuguang Chen, name: Jing Li, name: Mingxin Yang, name: Sheng Zhang, name: Yifei Feng, name: Xin Huang, name: Di Luo, name: Zebin He, name: Puhua Jiang, name: Changrong Hu, name: Zihan Qin, name: Shiwei Miao, name: Haolin Liu, name: Yunfei Zhao, name: Zeqiang Lai, name: Qingxiang Lin, name: Zibo Zhao, name: Kunhong Li, name: Xianghui Yang, name: Huiwen Shi, name: Xin Yang, name: Yuxuan Wang, name: Zebin Yao, name: Yihang Lian, name: Sicong Liu, name: Xintong Han, name: Wangchen Qin, name: Caisheng Ouyang, name: Jianyin Liu, name: Tianwen Yuan, name: Shuai Jiang, name: Hong Duan, name: Yanqi Niu, name: Wencong Lin, name: Yifu Sun, name: Shirui Huang, name: Lin Niu, name: Gu Gong, name: Guojian Xiao, name: Bojian Zheng, name: Xiang Yuan, name: Qi Chen, name: Jie Xiao, name: Dongyang Zheng, name: Xiaofeng Yang, name: Kai Liu, name: Jianchen Zhu, name: Lifu Wang, name: Qinglin Lu, name: Jie Liu, name: Liang Dong, name: Fan Jiang, name: Ruibin Chen, name: Lei Wang, name: Chao Zhang, name: Jiaxin Lin, name: Hao Zhang, name: Zheng Ye, name: Peng He, name: Runzhou Wu, name: Yinhe Wu, name: Jiayao Du, name: Jupeng Chen, name: Xinyue Mao, name: Dongyuan Guo, name: Yixuan Tang, name: Yulin Tsai, name: Yonghao Tan, name: Jiaao Yu, name: Junlin Yu, name: Keren Zhang, name: Yifan Li, name: Peng Chen, name: Tian Liu, name: Di Wang, name: Yuhong Liu, name: Linus, name: Jie Jiang, name: Zhuo Chen, name: Chunchao Guo
Published: 2025-09-16 08:33:03

The creation of high-quality 3D assets, a cornerstone of modern game
development, has long been characterized by labor-intensive and specialized
workflows. This paper presents Hunyuan3D Studio, an end-to-end AI-powered
content creation platform designed to revolutionize the game production
pipeline by automating and streamlining the generation of game-ready 3D assets.
At its core, Hunyuan3D Studio integrates a suite of advanced neural modules
(such as Part-level 3D Generation, Polygon Generation, Semantic UV, etc.) into
a cohesive and user-friendly system. This unified framework allows for the
rapid transformation of a single concept image or textual description into a
fully-realized, production-quality 3D model complete with optimized geometry
and high-fidelity PBR textures. We demonstrate that assets generated by
Hunyuan3D Studio are not only visually compelling but also adhere to the
stringent technical requirements of contemporary game engines, significantly
reducing iteration time and lowering the barrier to entry for 3D content
creation. By providing a seamless bridge from creative intent to technical
asset, Hunyuan3D Studio represents a significant leap forward for AI-assisted
workflows in game development and interactive media.
Single-stream Policy Optimization
Authors: name: Zhongwen Xu, name: Zihan Ding
Published: 2025-09-16 16:39:11

We revisit policy-gradient optimization for Large Language Models (LLMs) from
a single-stream perspective. Prevailing group-based methods like GRPO reduce
variance with on-the-fly baselines but suffer from critical flaws: frequent
degenerate groups erase learning signals, and synchronization barriers hinder
scalability. We introduce Single-stream Policy Optimization (SPO), which
eliminates these issues by design. SPO replaces per-group baselines with a
persistent, KL-adaptive value tracker and normalizes advantages globally across
the batch, providing a stable, low-variance learning signal for every sample.
Being group-free, SPO enables higher throughput and scales effectively in
long-horizon or tool-integrated settings where generation times vary.
Furthermore, the persistent value tracker naturally enables an adaptive
curriculum via prioritized sampling. Experiments using Qwen3-8B show that SPO
converges more smoothly and attains higher accuracy than GRPO, while
eliminating computation wasted on degenerate groups. Ablation studies confirm
that SPO's gains stem from its principled approach to baseline estimation and
advantage normalization, offering a more robust and efficient path for LLM
reasoning. Across five hard math benchmarks with Qwen3 8B, SPO improves the
average maj@32 by +3.4 percentage points (pp) over GRPO, driven by substantial
absolute point gains on challenging datasets, including +7.3 pp on BRUMO 25,
+4.4 pp on AIME 25, +3.3 pp on HMMT 25, and achieves consistent relative gain
in pass@k across the evaluated k values. SPO's success challenges the
prevailing trend of adding incidental complexity to RL algorithms, highlighting
a path where fundamental principles, not architectural workarounds, drive the
next wave of progress in LLM reasoning.
3D Aware Region Prompted Vision Language Model
Authors: name: An-Chieh Cheng, name: Yang Fu, name: Yukang Chen, name: Zhijian Liu, name: Xiaolong Li, name: Subhashree Radhakrishnan, name: Song Han, name: Yao Lu, name: Jan Kautz, name: Pavlo Molchanov, name: Hongxu Yin, name: Xiaolong Wang, name: Sifei Liu
Published: 2025-09-16 17:59:06

We present Spatial Region 3D (SR-3D) aware vision-language model that
connects single-view 2D images and multi-view 3D data through a shared visual
token space. SR-3D supports flexible region prompting, allowing users to
annotate regions with bounding boxes, segmentation masks on any frame, or
directly in 3D, without the need for exhaustive multi-frame labeling. We
achieve this by enriching 2D visual features with 3D positional embeddings,
which allows the 3D model to draw upon strong 2D priors for more accurate
spatial reasoning across frames, even when objects of interest do not co-occur
within the same view. Extensive experiments on both general 2D vision language
and specialized 3D spatial benchmarks demonstrate that SR-3D achieves
state-of-the-art performance, underscoring its effectiveness for unifying 2D
and 3D representation space on scene understanding. Moreover, we observe
applicability to in-the-wild videos without sensory 3D inputs or ground-truth
3D annotations, where SR-3D accurately infers spatial relationships and metric
measurements.
WebResearcher: Unleashing unbounded reasoning capability in Long-Horizon
  Agents
Authors: name: Zile Qiao, name: Guoxin Chen, name: Xuanzhong Chen, name: Donglei Yu, name: Wenbiao Yin, name: Xinyu Wang, name: Zhen Zhang, name: Baixuan Li, name: Huifeng Yin, name: Kuan Li, name: Rui Min, name: Minpeng Liao, name: Yong Jiang, name: Pengjun Xie, name: Fei Huang, name: Jingren Zhou
Published: 2025-09-16 17:57:17

Recent advances in deep-research systems have demonstrated the potential for
AI agents to autonomously discover and synthesize knowledge from external
sources. In this paper, we introduce WebResearcher, a novel framework for
building such agents through two key components: (1) WebResearcher, an
iterative deep-research paradigm that reformulates deep research as a Markov
Decision Process, where agents periodically consolidate findings into evolving
reports while maintaining focused workspaces, overcoming the context
suffocation and noise contamination that plague existing mono-contextual
approaches; and (2) WebFrontier, a scalable data synthesis engine that
generates high-quality training data through tool-augmented complexity
escalation, enabling systematic creation of research tasks that bridge the gap
between passive knowledge recall and active knowledge construction. Notably, we
find that the training data from our paradigm significantly enhances tool-use
capabilities even for traditional mono-contextual methods. Furthermore, our
paradigm naturally scales through parallel thinking, enabling concurrent
multi-agent exploration for more comprehensive conclusions. Extensive
experiments across 6 challenging benchmarks demonstrate that WebResearcher
achieves state-of-the-art performance, even surpassing frontier proprietary
systems.
Towards General Agentic Intelligence via Environment Scaling
Authors: name: Runnan Fang, name: Shihao Cai, name: Baixuan Li, name: Jialong Wu, name: Guangyu Li, name: Wenbiao Yin, name: Xinyu Wang, name: Xiaobin Wang, name: Liangcai Su, name: Zhen Zhang, name: Shibin Wu, name: Zhengwei Tao, name: Yong Jiang, name: Pengjun Xie, name: Fei Huang, name: Jingren Zhou
Published: 2025-09-16 17:57:20

Advanced agentic intelligence is a prerequisite for deploying Large Language
Models in practical, real-world applications. Diverse real-world APIs demand
precise, robust function-calling intelligence, which needs agents to develop
these capabilities through interaction in varied environments. The breadth of
function-calling competence is closely tied to the diversity of environments in
which agents are trained. In this work, we scale up environments as a step
towards advancing general agentic intelligence. This gives rise to two central
challenges: (i) how to scale environments in a principled manner, and (ii) how
to effectively train agentic capabilities from experiences derived through
interactions with these environments. To address these, we design a scalable
framework that automatically constructs heterogeneous environments that are
fully simulated, systematically broadening the space of function-calling
scenarios. We further adapt a two-phase agent fine-tuning strategy: first
endowing agents with fundamental agentic capabilities, then specializing them
for domain-specific contexts. Extensive experiments on agentic benchmarks,
tau-bench, tau2-Bench, and ACEBench, demonstrate that our trained model,
AgentScaler, significantly enhances the function-calling capability of models.
WebWeaver: Structuring Web-Scale Evidence with Dynamic Outlines for
  Open-Ended Deep Research
Authors: name: Zijian Li, name: Xin Guan, name: Bo Zhang, name: Shen Huang, name: Houquan Zhou, name: Shaopeng Lai, name: Ming Yan, name: Yong Jiang, name: Pengjun Xie, name: Fei Huang, name: Jun Zhang, name: Jingren Zhou
Published: 2025-09-16 17:57:21

This paper tackles open-ended deep research (OEDR), a complex challenge where
AI agents must synthesize vast web-scale information into insightful reports.
Current approaches are plagued by dual-fold limitations: static research
pipelines that decouple planning from evidence acquisition and one-shot
generation paradigms that easily suffer from long-context failure issues like
"loss in the middle" and hallucinations. To address these challenges, we
introduce WebWeaver, a novel dual-agent framework that emulates the human
research process. The planner operates in a dynamic cycle, iteratively
interleaving evidence acquisition with outline optimization to produce a
comprehensive, source-grounded outline linking to a memory bank of evidence.
The writer then executes a hierarchical retrieval and writing process,
composing the report section by section. By performing targeted retrieval of
only the necessary evidence from the memory bank for each part, it effectively
mitigates long-context issues. Our framework establishes a new state-of-the-art
across major OEDR benchmarks, including DeepResearch Bench, DeepConsult, and
DeepResearchGym. These results validate our human-centric, iterative
methodology, demonstrating that adaptive planning and focused synthesis are
crucial for producing high-quality, reliable, and well-structured reports.
Multiple Instance Learning Framework with Masked Hard Instance Mining
  for Gigapixel Histopathology Image Analysis
Authors: name: Wenhao Tang, name: Sheng Huang, name: Heng Fang, name: Fengtao Zhou, name: Bo Liu, name: Qingshan Liu
Published: 2025-09-15 02:31:33

Digitizing pathological images into gigapixel Whole Slide Images (WSIs) has
opened new avenues for Computational Pathology (CPath). As positive tissue
comprises only a small fraction of gigapixel WSIs, existing Multiple Instance
Learning (MIL) methods typically focus on identifying salient instances via
attention mechanisms. However, this leads to a bias towards easy-to-classify
instances while neglecting challenging ones. Recent studies have shown that
hard examples are crucial for accurately modeling discriminative boundaries.
Applying such an idea at the instance level, we elaborate a novel MIL framework
with masked hard instance mining (MHIM-MIL), which utilizes a Siamese structure
with a consistency constraint to explore the hard instances. Using a
class-aware instance probability, MHIM-MIL employs a momentum teacher to mask
salient instances and implicitly mine hard instances for training the student
model. To obtain diverse, non-redundant hard instances, we adopt large-scale
random masking while utilizing a global recycle network to mitigate the risk of
losing key features. Furthermore, the student updates the teacher using an
exponential moving average, which identifies new hard instances for subsequent
training iterations and stabilizes optimization. Experimental results on cancer
diagnosis, subtyping, survival analysis tasks, and 12 benchmarks demonstrate
that MHIM-MIL outperforms the latest methods in both performance and
efficiency. The code is available at: https://github.com/DearCaat/MHIM-MIL.
FuseCodec: Semantic-Contextual Fusion and Supervision for Neural Codecs
Authors: name: Md Mubtasim Ahasan, name: Rafat Hasan Khan, name: Tasnim Mohiuddin, name: Aman Chadha, name: Tariq Iqbal, name: M Ashraful Amin, name: Amin Ahsan Ali, name: Md Mofijul Islam, name: A K M Mahbubur Rahman
Published: 2025-09-14 20:35:36

Speech tokenization enables discrete representation and facilitates speech
language modeling. However, existing neural codecs capture low-level acoustic
features, overlooking the semantic and contextual cues inherent to human
speech. While recent efforts introduced semantic representations from
self-supervised speech models or incorporated contextual representations from
pre-trained language models, challenges remain in aligning and unifying the
semantic and contextual representations. We introduce FuseCodec, which unifies
acoustic, semantic, and contextual representations through strong cross-modal
alignment and globally informed supervision. We propose three complementary
techniques: (i) Latent Representation Fusion, integrating semantic and
contextual features directly into the encoder latent space for robust and
unified representation learning; (ii) Global Semantic-Contextual Supervision,
supervising discrete tokens with globally pooled and broadcasted
representations to enhance temporal consistency and cross-modal alignment; and
(iii) Temporally Aligned Contextual Supervision, strengthening alignment by
dynamically matching contextual and speech tokens within a local window for
fine-grained token-level supervision. We further introduce FuseCodec-TTS,
demonstrating our methodology's applicability to zero-shot speech synthesis.
Empirically, FuseCodec achieves state-of-the-art performance in LibriSpeech,
surpassing EnCodec, SpeechTokenizer, and DAC in transcription accuracy,
perceptual quality, intelligibility, and speaker similarity. Results highlight
the effectiveness of contextually and semantically guided tokenization for
speech tokenization and downstream tasks. Code and pretrained models are
available at https://github.com/mubtasimahasan/FuseCodec.