Orionnnn/hub-stats huggingface dataset:
From One to More: Contextual Part Latents for 3D Generation
Authors: name: Shaocong Dong, name: Lihe Ding, name: Xiao Chen, name: Yaokun Li, name: Yuxin Wang, name: Yucheng Wang, name: Qi Wang, name: Jaehyeok Kim, name: Chenjian Gao, name: Zhanpeng Huang, name: Zibin Wang, name: Tianfan Xue, name: Dan Xu
Published: 2025-07-11 17:33:18

Recent advances in 3D generation have transitioned from multi-view 2D
rendering approaches to 3D-native latent diffusion frameworks that exploit
geometric priors in ground truth data. Despite progress, three key limitations
persist: (1) Single-latent representations fail to capture complex multi-part
geometries, causing detail degradation; (2) Holistic latent coding neglects
part independence and interrelationships critical for compositional design; (3)
Global conditioning mechanisms lack fine-grained controllability. Inspired by
human 3D design workflows, we propose CoPart - a part-aware diffusion framework
that decomposes 3D objects into contextual part latents for coherent multi-part
generation. This paradigm offers three advantages: i) Reduces encoding
complexity through part decomposition; ii) Enables explicit part relationship
modeling; iii) Supports part-level conditioning. We further develop a mutual
guidance strategy to fine-tune pre-trained diffusion models for joint part
latent denoising, ensuring both geometric coherence and foundation model
priors. To enable large-scale training, we construct Partverse - a novel 3D
part dataset derived from Objaverse through automated mesh segmentation and
human-verified annotations. Extensive experiments demonstrate CoPart's superior
capabilities in part-level editing, articulated object generation, and scene
composition with unprecedented controllability.
Test-Time Scaling with Reflective Generative Model
Authors: name: Zixiao Wang, name: Yuxin Wang, name: Xiaorui Wang, name: Mengting Xing, name: Jie Gao, name: Jianjun Xu, name: Guangcan Liu, name: Chenhui Jin, name: Zhuo Wang, name: Shengzhuo Zhang, name: Hongtao Xie
Published: 2025-07-02 17:58:01

We introduce our first reflective generative model MetaStone-S1, which
obtains OpenAI o3's performance via the self-supervised process reward model
(SPRM). Through sharing the backbone network and using task-specific heads for
next token prediction and process scoring respectively, SPRM successfully
integrates the policy model and process reward model(PRM) into a unified
interface without extra process annotation, reducing over 99% PRM parameters
for efficient reasoning. Equipped with SPRM, MetaStone-S1 is naturally suitable
for test time scaling (TTS), and we provide three reasoning effort modes (low,
medium, and high), based on the controllable thinking length. Moreover, we
empirically establish a scaling law that reveals the relationship between total
thinking computation and TTS performance. Experiments demonstrate that our
MetaStone-S1 achieves comparable performance to OpenAI-o3-mini's series with
only 32B parameter size. To support the research community, we have
open-sourced MetaStone-S1 at https://github.com/MetaStone-AI/MetaStone-S1.
CLiFT: Compressive Light-Field Tokens for Compute-Efficient and Adaptive
  Neural Rendering
Authors: name: Zhengqing Wang, name: Yuefan Wu, name: Jiacheng Chen, name: Fuyang Zhang, name: Yasutaka Furukawa
Published: 2025-07-11 17:38:52

This paper proposes a neural rendering approach that represents a scene as
"compressed light-field tokens (CLiFTs)", retaining rich appearance and
geometric information of a scene. CLiFT enables compute-efficient rendering by
compressed tokens, while being capable of changing the number of tokens to
represent a scene or render a novel view with one trained network. Concretely,
given a set of images, multi-view encoder tokenizes the images with the camera
poses. Latent-space K-means selects a reduced set of rays as cluster centroids
using the tokens. The multi-view ``condenser'' compresses the information of
all the tokens into the centroid tokens to construct CLiFTs. At test time,
given a target view and a compute budget (i.e., the number of CLiFTs), the
system collects the specified number of nearby tokens and synthesizes a novel
view using a compute-adaptive renderer. Extensive experiments on RealEstate10K
and DL3DV datasets quantitatively and qualitatively validate our approach,
achieving significant data reduction with comparable rendering quality and the
highest overall rendering score, while providing trade-offs of data size,
rendering quality, and rendering speed.
Dynamic Chunking for End-to-End Hierarchical Sequence Modeling
Authors: name: Sukjun Hwang, name: Brandon Wang, name: Albert Gu
Published: 2025-07-10 17:39:37

Despite incredible progress in language models (LMs) in recent years, largely
resulting from moving away from specialized models designed for specific tasks
to general models based on powerful architectures (e.g. the Transformer) that
learn everything from raw data, pre-processing steps such as tokenization
remain a barrier to true end-to-end foundation models. We introduce a
collection of new techniques that enable a dynamic chunking mechanism which
automatically learns content -- and context -- dependent segmentation
strategies learned jointly with the rest of the model. Incorporating this into
an explicit hierarchical network (H-Net) allows replacing the (implicitly
hierarchical) tokenization-LM-detokenization pipeline with a single model
learned fully end-to-end. When compute- and data- matched, an H-Net with one
stage of hierarchy operating at the byte level outperforms a strong Transformer
language model operating over BPE tokens. Iterating the hierarchy to multiple
stages further increases its performance by modeling multiple levels of
abstraction, demonstrating significantly better scaling with data and matching
a token-based Transformer of twice its size. H-Nets pretrained on English show
significantly increased character-level robustness, and qualitatively learn
meaningful data-dependent chunking strategies without any heuristics or
explicit supervision. Finally, the H-Net's improvement over tokenized pipelines
is further increased in languages and modalities with weaker tokenization
heuristics, such as Chinese and code, or DNA sequences (nearly 4x improvement
in data efficiency over baselines), showing the potential of true end-to-end
models that learn and scale better from unprocessed data.
Re-Bottleneck: Latent Re-Structuring for Neural Audio Autoencoders
Authors: name: Dimitrios Bralios, name: Jonah Casebeer, name: Paris Smaragdis
Published: 2025-07-10 15:47:43

Neural audio codecs and autoencoders have emerged as versatile models for
audio compression, transmission, feature-extraction, and latent-space
generation. However, a key limitation is that most are trained to maximize
reconstruction fidelity, often neglecting the specific latent structure
necessary for optimal performance in diverse downstream applications. We
propose a simple, post-hoc framework to address this by modifying the
bottleneck of a pre-trained autoencoder. Our method introduces a
"Re-Bottleneck", an inner bottleneck trained exclusively through latent space
losses to instill user-defined structure. We demonstrate the framework's
effectiveness in three experiments. First, we enforce an ordering on latent
channels without sacrificing reconstruction quality. Second, we align latents
with semantic embeddings, analyzing the impact on downstream diffusion
modeling. Third, we introduce equivariance, ensuring that a filtering operation
on the input waveform directly corresponds to a specific transformation in the
latent space. Ultimately, our Re-Bottleneck framework offers a flexible and
efficient way to tailor representations of neural audio models, enabling them
to seamlessly meet the varied demands of different applications with minimal
additional training.
Emergent Semantics Beyond Token Embeddings: Transformer LMs with Frozen
  Visual Unicode Representations
Authors: name: A. Bochkov
Published: 2025-07-07 11:17:32

Understanding the locus of semantic representation in large language models
(LLMs) is crucial for interpretability and architectural innovation. The
dominant paradigm posits that trainable input embeddings serve as foundational
"meaning vectors." This paper challenges that view. We construct Transformer
models where the embedding layer is entirely frozen, with vectors derived not
from data, but from the visual structure of Unicode glyphs. These non-semantic,
precomputed visual embeddings are fixed throughout training. Our method is
compatible with any tokenizer, including a novel Unicode-centric tokenizer we
introduce to ensure universal text coverage. Despite the absence of trainable,
semantically initialized embeddings, our models converge, generate coherent
text, and, critically, outperform architecturally identical models with
trainable embeddings on the MMLU reasoning benchmark. We attribute this to
"representational interference" in conventional models, where the embedding
layer is burdened with learning both structural and semantic features. Our
results indicate that high-level semantics are not inherent to input embeddings
but are an emergent property of the Transformer's compositional architecture
and data scale. This reframes the role of embeddings from meaning containers to
structural primitives. We release all code and models to foster further
research.
Growing Transformers: Modular Composition and Layer-wise Expansion on a
  Frozen Substrate
Authors: name: A. Bochkov
Published: 2025-07-08 20:01:15

The prevailing paradigm for scaling large language models (LLMs) involves
monolithic, end-to-end training, a resource-intensive process that lacks
flexibility. This paper explores an alternative, constructive approach to model
development, built upon the foundation of non-trainable, deterministic input
embeddings. In prior [1], we established that high-level semantic reasoning can
emerge in Transformers using frozen embeddings derived from the visual
structure of Unicode glyphs. Here, we demonstrate that this fixed
representational substrate acts as a universal "docking port," enabling two
powerful and efficient scaling paradigms: seamless modular composition and
progressive layer-wise growth.
  First, we show that specialist models trained on disparate datasets (e.g.,
Russian and Chinese text) can be merged into a single, more capable
Mixture-of-Experts (MoE) model, post-training, with zero architectural
modification. This is achieved by simply averaging their output logits. The
resulting MoE model exhibits immediate performance improvements on reasoning
benchmarks like MMLU, surpassing its constituent experts without catastrophic
forgetting. Second, we introduce a layer-wise constructive training
methodology, where a deep Transformer is "grown" by progressively stacking and
training one layer at a time. This method demonstrates stable convergence and a
clear correlation between model depth and the emergence of complex reasoning
abilities, such as those required for SQuAD.
  Our findings suggest a paradigm shift from monolithic optimization towards a
more biological or constructive model of AI development, where complexity is
built incrementally and modules can be composed freely. This opens new avenues
for resource-efficient scaling, continual learning, and a more democratized
ecosystem for building powerful AI systems. We release all code and models to
facilitate further research.
Beyond the Linear Separability Ceiling
Authors: name: Enrico Vompa, name: Tanel Tammet, name: Mohit Vaishnav
Published: 2025-07-10 09:23:32

Most state-of-the-art Visual-Language Models (VLMs) are seemingly limited by
the linear separabilty of their visual embeddings on abstract reasoning tasks.
This work investigates this "linear reasoning bottleneck" by introducing the
Linear Separability Ceiling (LSC), the performance of a simple linear
classifier on a VLM's visual embeddings. We find this bottleneck is widespread
and stems not from poor perception, but from failures in the language model's
reasoning pathways. We demonstrate this is a solvable alignment issue. The
required intervention, however, is task-dependent: activating existing pathways
suffices for semantic concepts, while complex relational reasoning requires
adapting core model weights. Using postfix tuning as a methodological control,
we find strong evidence for powerful, dormant reasoning pathways within VLMs.
However, for complex relational tasks requiring deeper adaptation, explicitly
improving representation quality causes the model to fail on new prompt formats
despite its embeddings remaining well separated. Ultimately, this work provides
a new lens for VLM analysis, showing that robust reasoning is a matter of
targeted alignment, not simply improved representation learning.
T-LoRA: Single Image Diffusion Model Customization Without Overfitting
Authors: name: Vera Soboleva, name: Aibek Alanov, name: Andrey Kuznetsov, name: Konstantin Sobolev
Published: 2025-07-08 13:14:10

While diffusion model fine-tuning offers a powerful approach for customizing
pre-trained models to generate specific objects, it frequently suffers from
overfitting when training samples are limited, compromising both generalization
capability and output diversity. This paper tackles the challenging yet most
impactful task of adapting a diffusion model using just a single concept image,
as single-image customization holds the greatest practical potential. We
introduce T-LoRA, a Timestep-Dependent Low-Rank Adaptation framework
specifically designed for diffusion model personalization. In our work we show
that higher diffusion timesteps are more prone to overfitting than lower ones,
necessitating a timestep-sensitive fine-tuning strategy. T-LoRA incorporates
two key innovations: (1) a dynamic fine-tuning strategy that adjusts
rank-constrained updates based on diffusion timesteps, and (2) a weight
parametrization technique that ensures independence between adapter components
through orthogonal initialization. Extensive experiments show that T-LoRA and
its individual components outperform standard LoRA and other diffusion model
personalization techniques. They achieve a superior balance between concept
fidelity and text alignment, highlighting the potential of T-LoRA in
data-limited and resource-constrained scenarios. Code is available at
https://github.com/ControlGenAI/T-LoRA.
Multi-Granular Spatio-Temporal Token Merging for Training-Free
  Acceleration of Video LLMs
Authors: name: Jeongseok Hyun, name: Sukjun Hwang, name: Su Ho Han, name: Taeoh Kim, name: Inwoong Lee, name: Dongyoon Wee, name: Joon-Young Lee, name: Seon Joo Kim, name: Minho Shim
Published: 2025-07-10 17:59:02

Video large language models (LLMs) achieve strong video understanding by
leveraging a large number of spatio-temporal tokens, but suffer from quadratic
computational scaling with token count. To address this, we propose a
training-free spatio-temporal token merging method, named STTM. Our key insight
is to exploit local spatial and temporal redundancy in video data which has
been overlooked in prior work. STTM first transforms each frame into
multi-granular spatial tokens using a coarse-to-fine search over a quadtree
structure, then performs directed pairwise merging across the temporal
dimension. This decomposed merging approach outperforms existing token
reduction methods across six video QA benchmarks. Notably, STTM achieves a
2times speed-up with only a 0.5% accuracy drop under a 50% token budget, and
a 3times speed-up with just a 2% drop under a 30% budget. Moreover, STTM is
query-agnostic, allowing KV cache reuse across different questions for the same
video. The project page is available at https://www.jshyun.me/projects/sttm.
SciMaster: Towards General-Purpose Scientific AI Agents, Part I.
  X-Master as Foundation: Can We Lead on Humanity's Last Exam?
Authors: name: Jingyi Chai, name: Shuo Tang, name: Rui Ye, name: Yuwen Du, name: Xinyu Zhu, name: Mengcheng Zhou, name: Yanfeng Wang, name: Weinan E, name: Yuzhi Zhang, name: Linfeng Zhang, name: Siheng Chen
Published: 2025-07-07 17:50:52

The rapid advancements of AI agents have ignited the long-held ambition of
leveraging them to accelerate scientific discovery. Achieving this goal
requires a deep understanding of the frontiers of human knowledge. As such,
Humanity's Last Exam (HLE) provides an exceptionally challenging touchstone for
evaluating scientific AI agents. In this work, we aim to construct the
foundational architecture for general-purpose agents and validate the
capabilities through leading performance on HLE. To achieve this, we introduce
X-Master, a tool-augmented reasoning agent designed to emulate human
researchers by interacting flexibly with external tools during its reasoning
process. This agent, guided by the conceptualization of code as an interaction
language, can flexibly leverage built-in Python libraries and our customized
tools to augment the reasoning. We further scale its capabilities through
X-Masters, a scattered-and-stacked agentic workflow that systematically
enhances breadth and depth of reasoning. Our open-source solution, X-Masters,
sets a new state-of-the-art record on HLE with a score of 32.1%, surpassing
OpenAI's and Google's Deep Research (26.6% and 26.9%) and becoming the first to
exceed the 30% threshold. This work allows us to gain a deeper understanding of
complex task-solving and accumulates valuable experience that can inform future
advancements, guiding subsequent model training.
Token Bottleneck: One Token to Remember Dynamics
Authors: name: Taekyung Kim, name: Dongyoon Han, name: Byeongho Heo, name: Jeongeun Park, name: Sangdoo Yun
Published: 2025-07-09 04:57:29

Deriving compact and temporally aware visual representations from dynamic
scenes is essential for successful execution of sequential scene understanding
tasks such as visual tracking and robotic manipulation. In this paper, we
introduce Token Bottleneck (ToBo), a simple yet intuitive self-supervised
learning pipeline that squeezes a scene into a bottleneck token and predicts
the subsequent scene using minimal patches as hints. The ToBo pipeline
facilitates the learning of sequential scene representations by conservatively
encoding the reference scene into a compact bottleneck token during the squeeze
step. In the expansion step, we guide the model to capture temporal dynamics by
predicting the target scene using the bottleneck token along with few target
patches as hints. This design encourages the vision backbone to embed temporal
dependencies, thereby enabling understanding of dynamic transitions across
scenes. Extensive experiments in diverse sequential tasks, including video
label propagation and robot manipulation in simulated environments demonstrate
the superiority of ToBo over baselines. Moreover, deploying our pre-trained
model on physical robots confirms its robustness and effectiveness in
real-world environments. We further validate the scalability of ToBo across
different model scales.
OST-Bench: Evaluating the Capabilities of MLLMs in Online
  Spatio-temporal Scene Understanding
Authors: name: JingLi Lin, name: Chenming Zhu, name: Runsen Xu, name: Xiaohan Mao, name: Xihui Liu, name: Tai Wang, name: Jiangmiao Pang
Published: 2025-07-10 17:56:07

Recent advances in multimodal large language models (MLLMs) have shown
remarkable capabilities in integrating vision and language for complex
reasoning. While most existing benchmarks evaluate models under offline
settings with a fixed set of pre-recorded inputs, we introduce OST-Bench, a
benchmark designed to evaluate Online Spatio-Temporal understanding from the
perspective of an agent actively exploring a scene. The Online aspect
emphasizes the need to process and reason over incrementally acquired
observations, while the Spatio-Temporal component requires integrating current
visual inputs with historical memory to support dynamic spatial reasoning.
OST-Bench better reflects the challenges of real-world embodied perception.
Built on an efficient data collection pipeline, OST-Bench consists of 1.4k
scenes and 10k question-answer pairs collected from ScanNet, Matterport3D, and
ARKitScenes. We evaluate several leading MLLMs on OST-Bench and observe that
they fall short on tasks requiring complex spatio-temporal reasoning. Under the
online setting, their accuracy declines as the exploration horizon extends and
the memory grows. Through further experimental analysis, we identify common
error patterns across models and find that both complex clue-based spatial
reasoning demands and long-term memory retrieval requirements significantly
drop model performance along two separate axes, highlighting the core
challenges that must be addressed to improve online embodied reasoning. To
foster further research and development in the field, our codes, dataset, and
benchmark are available. Our project page is:
https://rbler1234.github.io/OSTBench.github.io/
Skip a Layer or Loop it? Test-Time Depth Adaptation of Pretrained LLMs
Authors: name: Ziyue Li, name: Yang Li, name: Tianyi Zhou
Published: 2025-07-10 17:59:53

Can a pretrained neural network adapt its architecture to different inputs
without any finetuning? Do we need all layers for simple tasks, and are they
adequate for challenging tasks? We found that the layers of a pretrained large
language model (LLM) can be manipulated as separate modules to build a better
and even shallower model customized for each test sample. In particular, each
layer from the pretrained model can be skipped/pruned or repeated multiple
times as recurrent neural networks (RNN), and stacked with others in arbitrary
orders, yielding a chain-of-layers (CoLa) per sample. This compositional space
greatly expands the scope of existing works on looped/recurrent pretrained
modules, layer pruning, or early-exit networks. We develop a Monte Carlo Tree
Search (MCTS) protocol to explore and identify the optimal CoLa for each sample
from math and commonsense reasoning benchmarks. Compared to a static model of a
fixed depth, CoLa allows shortcut paths (fast thinking), recurrence of the same
layer(s) (slow thinking), and combining both, offering more flexible, dynamic
architectures for different inputs. We conduct an extensive analysis of the
MCTS-optimized CoLa, which leads to two key findings: (1) For >75% of samples
with correct predictions by the original LLM, we can find shorter CoLa,
suggesting a large space for improving inference efficiency; (2) For >60% of
samples with originally incorrect predictions, we can identify CoLa achieving
correct predictions, suggesting a large space of performance enhancement. Our
results highlight the shortcomings of using a fixed architecture of pre-trained
LLMs for inference on different samples and pave the way to unlock the
generalization power of test-time depth adaptation.