Orionnnn/hub-stats huggingface dataset:
CMPhysBench: A Benchmark for Evaluating Large Language Models in
  Condensed Matter Physics
Authors: name: Weida Wang, name: Dongchen Huang, name: Jiatong Li, name: Tengchao Yang, name: Ziyang Zheng, name: Di Zhang, name: Dong Han, name: Benteng Chen, name: Binzhao Luo, name: Zhiyu Liu, name: Kunling Liu, name: Zhiyuan Gao, name: Shiqi Geng, name: Wei Ma, name: Jiaming Su, name: Xin Li, name: Shuchen Pu, name: Yuhan Shui, name: Qianjia Cheng, name: Zhihao Dou, name: Dongfei Cui, name: Changyong He, name: Jin Zeng, name: Zeke Xie, name: Mao Su, name: Dongzhan Zhou, name: Yuqiang Li, name: Wanli Ouyang, name: Yunqi Cai, name: Xi Dai, name: Shufei Zhang, name: Lei Bai, name: Jinguang Cheng, name: Zhong Fang, name: Hongming Weng
Published: 2025-08-25 15:32:22

We introduce CMPhysBench, designed to assess the proficiency of Large
Language Models (LLMs) in Condensed Matter Physics, as a novel Benchmark.
CMPhysBench is composed of more than 520 graduate-level meticulously curated
questions covering both representative subfields and foundational theoretical
frameworks of condensed matter physics, such as magnetism, superconductivity,
strongly correlated systems, etc. To ensure a deep understanding of the
problem-solving process,we focus exclusively on calculation problems, requiring
LLMs to independently generate comprehensive solutions. Meanwhile, leveraging
tree-based representations of expressions, we introduce the Scalable Expression
Edit Distance (SEED) score, which provides fine-grained (non-binary) partial
credit and yields a more accurate assessment of similarity between prediction
and ground-truth. Our results show that even the best models, Grok-4, reach
only 36 average SEED score and 28% accuracy on CMPhysBench, underscoring a
significant capability gap, especially for this practical and frontier domain
relative to traditional physics. The code anddataset are publicly available at
https://github.com/CMPhysBench/CMPhysBench.
TreePO: Bridging the Gap of Policy Optimization and Efficacy and
  Inference Efficiency with Heuristic Tree-based Modeling
Authors: name: Yizhi Li, name: Qingshui Gu, name: Zhoufutu Wen, name: Ziniu Li, name: Tianshun Xing, name: Shuyue Guo, name: Tianyu Zheng, name: Xin Zhou, name: Xingwei Qu, name: Wangchunshu Zhou, name: Zheng Zhang, name: Wei Shen, name: Qian Liu, name: Chenghua Lin, name: Jian Yang, name: Ge Zhang, name: Wenhao Huang
Published: 2025-08-24 16:52:37

Recent advancements in aligning large language models via reinforcement
learning have achieved remarkable gains in solving complex reasoning problems,
but at the cost of expensive on-policy rollouts and limited exploration of
diverse reasoning paths. In this work, we introduce TreePO, involving a
self-guided rollout algorithm that views sequence generation as a
tree-structured searching process. Composed of dynamic tree sampling policy and
fixed-length segment decoding, TreePO leverages local uncertainty to warrant
additional branches. By amortizing computation across common prefixes and
pruning low-value paths early, TreePO essentially reduces the per-update
compute burden while preserving or enhancing exploration diversity. Key
contributions include: (1) a segment-wise sampling algorithm that alleviates
the KV cache burden through contiguous segments and spawns new branches along
with an early-stop mechanism; (2) a tree-based segment-level advantage
estimation that considers both global and local proximal policy optimization.
and (3) analysis on the effectiveness of probability and quality-driven dynamic
divergence and fallback strategy. We empirically validate the performance gain
of TreePO on a set reasoning benchmarks and the efficiency saving of GPU hours
from 22\% up to 43\% of the sampling design for the trained models, meanwhile
showing up to 40\% reduction at trajectory-level and 35\% at token-level
sampling compute for the existing models. While offering a free lunch of
inference efficiency, TreePO reveals a practical path toward scaling RL-based
post-training with fewer samples and less compute. Home page locates at
https://m-a-p.ai/TreePO.
CineScale: Free Lunch in High-Resolution Cinematic Visual Generation
Authors: name: Haonan Qiu, name: Ning Yu, name: Ziqi Huang, name: Paul Debevec, name: Ziwei Liu
Published: 2025-08-21 17:59:57

Visual diffusion models achieve remarkable progress, yet they are typically
trained at limited resolutions due to the lack of high-resolution data and
constrained computation resources, hampering their ability to generate
high-fidelity images or videos at higher resolutions. Recent efforts have
explored tuning-free strategies to exhibit the untapped potential
higher-resolution visual generation of pre-trained models. However, these
methods are still prone to producing low-quality visual content with repetitive
patterns. The key obstacle lies in the inevitable increase in high-frequency
information when the model generates visual content exceeding its training
resolution, leading to undesirable repetitive patterns deriving from the
accumulated errors. In this work, we propose CineScale, a novel inference
paradigm to enable higher-resolution visual generation. To tackle the various
issues introduced by the two types of video generation architectures, we
propose dedicated variants tailored to each. Unlike existing baseline methods
that are confined to high-resolution T2I and T2V generation, CineScale broadens
the scope by enabling high-resolution I2V and V2V synthesis, built atop
state-of-the-art open-source video generation frameworks. Extensive experiments
validate the superiority of our paradigm in extending the capabilities of
higher-resolution visual generation for both image and video models.
Remarkably, our approach enables 8k image generation without any fine-tuning,
and achieves 4k video generation with only minimal LoRA fine-tuning. Generated
video samples are available at our website:
https://eyeline-labs.github.io/CineScale/.
Training Language Model Agents to Find Vulnerabilities with CTF-Dojo
Authors: name: Terry Yue Zhuo, name: Dingmin Wang, name: Hantian Ding, name: Varun Kumar, name: Zijian Wang
Published: 2025-08-25 18:02:23

Large language models (LLMs) have demonstrated exceptional capabilities when
trained within executable runtime environments, notably excelling at software
engineering tasks through verified feedback loops. Yet, scalable and
generalizable execution-grounded environments remain scarce, limiting progress
in training more capable ML agents. We introduce CTF-Dojo, the first
large-scale executable runtime tailored for training LLMs with verifiable
feedback, featuring 658 fully functional Capture-The-Flag (CTF)-style
challenges containerized in Docker with guaranteed reproducibility. To enable
rapid scaling without manual intervention, we develop CTF-Forge, an automated
pipeline that transforms publicly available artifacts into ready-to-use
execution environments in minutes, eliminating weeks of expert configuration
traditionally required. We trained LLM-based agents on just 486 high-quality,
execution-verified trajectories from CTF-Dojo, achieving up to 11.6% absolute
gains over strong baselines across three competitive benchmarks: InterCode-CTF,
NYU CTF Bench, and Cybench. Our best-performing 32B model reaches 31.9% Pass@1,
establishing a new open-weight state-of-the-art that rivals frontier models
like DeepSeek-V3-0324 and Gemini-2.5-Flash. By framing CTF-style tasks as a
benchmark for executable-agent learning, CTF-Dojo demonstrates that
execution-grounded training signals are not only effective but pivotal in
advancing high-performance ML agents without dependence on costly proprietary
systems.
Wan-S2V: Audio-Driven Cinematic Video Generation
Authors: name: Xin Gao, name: Li Hu, name: Siqi Hu, name: Mingyang Huang, name: Chaonan Ji, name: Dechao Meng, name: Jinwei Qi, name: Penchong Qiao, name: Zhen Shen, name: Yafei Song, name: Ke Sun, name: Linrui Tian, name: Guangyuan Wang, name: Qi Wang, name: Zhongjian Wang, name: Jiayu Xiao, name: Sheng Xu, name: Bang Zhang, name: Peng Zhang, name: Xindi Zhang, name: Zhe Zhang, name: Jingren Zhou, name: Lian Zhuo
Published: 2025-08-26 02:51:31

Current state-of-the-art (SOTA) methods for audio-driven character animation
demonstrate promising performance for scenarios primarily involving speech and
singing. However, they often fall short in more complex film and television
productions, which demand sophisticated elements such as nuanced character
interactions, realistic body movements, and dynamic camera work. To address
this long-standing challenge of achieving film-level character animation, we
propose an audio-driven model, which we refere to as Wan-S2V, built upon Wan.
Our model achieves significantly enhanced expressiveness and fidelity in
cinematic contexts compared to existing approaches. We conducted extensive
experiments, benchmarking our method against cutting-edge models such as
Hunyuan-Avatar and Omnihuman. The experimental results consistently demonstrate
that our approach significantly outperforms these existing solutions.
Additionally, we explore the versatility of our method through its applications
in long-form video generation and precise video lip-sync editing.
ThinkDial: An Open Recipe for Controlling Reasoning Effort in Large
  Language Models
Authors: name: Qianyu He, name: Siyu Yuan, name: Xuefeng Li, name: Mingxuan Wang, name: Jiangjie Chen
Published: 2025-08-26 07:57:28

Large language models (LLMs) with chain-of-thought reasoning have
demonstrated remarkable problem-solving capabilities, but controlling their
computational effort remains a significant challenge for practical deployment.
Recent proprietary systems like OpenAI's gpt-oss series have introduced
discrete operational modes for intuitive reasoning control, but the open-source
community has largely failed to achieve such capabilities. In this paper, we
introduce ThinkDial, the first open-recipe end-to-end framework that
successfully implements gpt-oss-style controllable reasoning through discrete
operational modes. Our system enables seamless switching between three distinct
reasoning regimes: High mode (full reasoning capability), Medium mode (50
percent token reduction with <10 percent performance degradation), and Low mode
(75 percent token reduction with <15 percent performance degradation). We
achieve this through an end-to-end training paradigm that integrates
budget-mode control throughout the entire pipeline: budget-mode supervised
fine-tuning that embeds controllable reasoning capabilities directly into the
learning process, and two-phase budget-aware reinforcement learning with
adaptive reward shaping. Extensive experiments demonstrate that ThinkDial
achieves target compression-performance trade-offs with clear response length
reductions while maintaining performance thresholds. The framework also
exhibits strong generalization capabilities on out-of-distribution tasks.
UltraMemV2: Memory Networks Scaling to 120B Parameters with Superior
  Long-Context Learning
Authors: name: Zihao Huang, name: Yu Bao, name: Qiyang Min, name: Siyan Chen, name: Ran Guo, name: Hongzhi Huang, name: Defa Zhu, name: Yutao Zeng, name: Banggu Wu, name: Xun Zhou, name: Siyuan Qiao
Published: 2025-08-26 07:33:11

While Mixture of Experts (MoE) models achieve remarkable efficiency by
activating only subsets of parameters, they suffer from high memory access
costs during inference. Memory-layer architectures offer an appealing
alternative with very few memory access, but previous attempts like UltraMem
have only matched the performance of 2-expert MoE models, falling significantly
short of state-of-the-art 8-expert configurations. We present UltraMemV2, a
redesigned memory-layer architecture that closes this performance gap. Our
approach introduces five key improvements: integrating memory layers into every
transformer block, simplifying value expansion with single linear projections,
adopting FFN-based value processing from PEER, implementing principled
parameter initialization, and rebalancing memory-to-FFN computation ratios.
Through extensive evaluation, we demonstrate that UltraMemV2 achieves
performance parity with 8-expert MoE models under same computation and
parameters but significantly low memory access. Notably, UltraMemV2 shows
superior performance on memory-intensive tasks, with improvements of +1.6
points on long-context memorization, +6.2 points on multi-round memorization,
and +7.9 points on in-context learning. We validate our approach at scale with
models up to 2.5B activated parameters from 120B total parameters, and
establish that activation density has greater impact on performance than total
sparse parameter count. Our work brings memory-layer architectures to
performance parity with state-of-the-art MoE models, presenting a compelling
alternative for efficient sparse computation.
Pixie: Fast and Generalizable Supervised Learning of 3D Physics from
  Pixels
Authors: name: Long Le, name: Ryan Lucas, name: Chen Wang, name: Chuhao Chen, name: Dinesh Jayaraman, name: Eric Eaton, name: Lingjie Liu
Published: 2025-08-20 19:24:04

Inferring the physical properties of 3D scenes from visual information is a
critical yet challenging task for creating interactive and realistic virtual
worlds. While humans intuitively grasp material characteristics such as
elasticity or stiffness, existing methods often rely on slow, per-scene
optimization, limiting their generalizability and application. To address this
problem, we introduce PIXIE, a novel method that trains a generalizable neural
network to predict physical properties across multiple scenes from 3D visual
features purely using supervised losses. Once trained, our feed-forward network
can perform fast inference of plausible material fields, which coupled with a
learned static scene representation like Gaussian Splatting enables realistic
physics simulation under external forces. To facilitate this research, we also
collected PIXIEVERSE, one of the largest known datasets of paired 3D assets and
physic material annotations. Extensive evaluations demonstrate that PIXIE is
about 1.46-4.39x better and orders of magnitude faster than test-time
optimization methods. By leveraging pretrained visual features like CLIP, our
method can also zero-shot generalize to real-world scenes despite only ever
been trained on synthetic data. https://pixie-3d.github.io/
FastMesh:Efficient Artistic Mesh Generation via Component Decoupling
Authors: name: Jeonghwan Kim, name: Yushi Lan, name: Armando Fortes, name: Yongwei Chen, name: Xingang Pan
Published: 2025-08-26 16:51:02

Recent mesh generation approaches typically tokenize triangle meshes into
sequences of tokens and train autoregressive models to generate these tokens
sequentially. Despite substantial progress, such token sequences inevitably
reuse vertices multiple times to fully represent manifold meshes, as each
vertex is shared by multiple faces. This redundancy leads to excessively long
token sequences and inefficient generation processes. In this paper, we propose
an efficient framework that generates artistic meshes by treating vertices and
faces separately, significantly reducing redundancy. We employ an
autoregressive model solely for vertex generation, decreasing the token count
to approximately 23\% of that required by the most compact existing tokenizer.
Next, we leverage a bidirectional transformer to complete the mesh in a single
step by capturing inter-vertex relationships and constructing the adjacency
matrix that defines the mesh faces. To further improve the generation quality,
we introduce a fidelity enhancer to refine vertex positioning into more natural
arrangements and propose a post-processing framework to remove undesirable edge
connections. Experimental results show that our method achieves more than
8times faster speed on mesh generation compared to state-of-the-art
approaches, while producing higher mesh quality.
OmniHuman-1.5: Instilling an Active Mind in Avatars via Cognitive
  Simulation
Authors: name: Jianwen Jiang, name: Weihong Zeng, name: Zerong Zheng, name: Jiaqi Yang, name: Chao Liang, name: Wang Liao, name: Han Liang, name: Yuan Zhang, name: Mingyuan Gao
Published: 2025-08-26 17:15:26

Existing video avatar models can produce fluid human animations, yet they
struggle to move beyond mere physical likeness to capture a character's
authentic essence. Their motions typically synchronize with low-level cues like
audio rhythm, lacking a deeper semantic understanding of emotion, intent, or
context. To bridge this gap, we propose a framework designed to
generate character animations that are not only physically plausible but also
semantically coherent and expressive. Our model, OmniHuman-1.5, is
built upon two key technical contributions. First, we leverage Multimodal Large
Language Models to synthesize a structured textual representation of conditions
that provides high-level semantic guidance. This guidance steers our motion
generator beyond simplistic rhythmic synchronization, enabling the production
of actions that are contextually and emotionally resonant. Second, to ensure
the effective fusion of these multimodal inputs and mitigate inter-modality
conflicts, we introduce a specialized Multimodal DiT architecture with a novel
Pseudo Last Frame design. The synergy of these components allows our model to
accurately interpret the joint semantics of audio, images, and text, thereby
generating motions that are deeply coherent with the character, scene, and
linguistic content. Extensive experiments demonstrate that our model achieves
leading performance across a comprehensive set of metrics, including lip-sync
accuracy, video quality, motion naturalness and semantic consistency with
textual prompts. Furthermore, our approach shows remarkable extensibility to
complex scenarios, such as those involving multi-person and non-human subjects.
Homepage: https://omnihuman-lab.github.io/v1_5/
Unraveling the cognitive patterns of Large Language Models through
  module communities
Authors: name: Kushal Raj Bhandari, name: Pin-Yu Chen, name: Jianxi Gao
Published: 2025-08-25 16:49:38

Large Language Models (LLMs) have reshaped our world with significant
advancements in science, engineering, and society through applications ranging
from scientific discoveries and medical diagnostics to Chatbots. Despite their
ubiquity and utility, the underlying mechanisms of LLM remain concealed within
billions of parameters and complex structures, making their inner architecture
and cognitive processes challenging to comprehend. We address this gap by
adopting approaches to understanding emerging cognition in biology and
developing a network-based framework that links cognitive skills, LLM
architectures, and datasets, ushering in a paradigm shift in foundation model
analysis. The skill distribution in the module communities demonstrates that
while LLMs do not strictly parallel the focalized specialization observed in
specific biological systems, they exhibit unique communities of modules whose
emergent skill patterns partially mirror the distributed yet interconnected
cognitive organization seen in avian and small mammalian brains. Our numerical
results highlight a key divergence from biological systems to LLMs, where skill
acquisition benefits substantially from dynamic, cross-regional interactions
and neural plasticity. By integrating cognitive science principles with machine
learning, our framework provides new insights into LLM interpretability and
suggests that effective fine-tuning strategies should leverage distributed
learning dynamics rather than rigid modular interventions.
Spacer: Towards Engineered Scientific Inspiration
Authors: name: Minhyeong Lee, name: Suyoung Hwang, name: Seunghyun Moon, name: Geonho Nah, name: Donghyun Koh, name: Youngjun Cho, name: Johyun Park, name: Hojin Yoo, name: Jiho Park, name: Haneul Choi, name: Sungbin Moon, name: Taehoon Hwang, name: Seungwon Kim, name: Jaeyeong Kim, name: Seongjun Kim, name: Juneau Jung
Published: 2025-08-25 04:49:16

Recent advances in LLMs have made automated scientific research the next
frontline in the path to artificial superintelligence. However, these systems
are bound either to tasks of narrow scope or the limited creative capabilities
of LLMs. We propose Spacer, a scientific discovery system that develops
creative and factually grounded concepts without external intervention. Spacer
attempts to achieve this via 'deliberate decontextualization,' an approach that
disassembles information into atomic units - keywords - and draws creativity
from unexplored connections between them. Spacer consists of (i) Nuri, an
inspiration engine that builds keyword sets, and (ii) the Manifesting Pipeline
that refines these sets into elaborate scientific statements. Nuri extracts
novel, high-potential keyword sets from a keyword graph built with 180,000
academic publications in biological fields. The Manifesting Pipeline finds
links between keywords, analyzes their logical structure, validates their
plausibility, and ultimately drafts original scientific concepts. According to
our experiments, the evaluation metric of Nuri accurately classifies
high-impact publications with an AUROC score of 0.737. Our Manifesting Pipeline
also successfully reconstructs core concepts from the latest top-journal
articles solely from their keyword sets. An LLM-based scoring system estimates
that this reconstruction was sound for over 85% of the cases. Finally, our
embedding space analysis shows that outputs from Spacer are significantly more
similar to leading publications compared with those from SOTA LLMs.
VibeVoice Technical Report
Authors: name: Zhiliang Peng, name: Jianwei Yu, name: Wenhui Wang, name: Yaoyao Chang, name: Yutao Sun, name: Li Dong, name: Yi Zhu, name: Weijiang Xu, name: Hangbo Bao, name: Zehua Wang, name: Shaohan Huang, name: Yan Xia, name: Furu Wei
Published: 2025-08-26 17:09:12

This report presents VibeVoice, a novel model designed to synthesize
long-form speech with multiple speakers by employing next-token diffusion,
which is a unified method for modeling continuous data by autoregressively
generating latent vectors via diffusion. To enable this, we introduce a novel
continuous speech tokenizer that, when compared to the popular Encodec model,
improves data compression by 80 times while maintaining comparable performance.
The tokenizer effectively preserves audio fidelity while significantly boosting
computational efficiency for processing long sequences. Thus, VibeVoice can
synthesize long-form speech for up to 90 minutes (in a 64K context window
length) with a maximum of 4 speakers, capturing the authentic conversational
``vibe'' and surpassing open-source and proprietary dialogue models.