Orionnnn/hub-stats huggingface dataset:
Parallel Test-Time Scaling for Latent Reasoning Models
Authors: name: Runyang You, name: Yongqi Li, name: Meng Liu, name: Wenjie Wang, name: Liqiang Nie, name: Wenjie Li
Published: 2025-10-09 03:33:00

Parallel test-time scaling (TTS) is a pivotal approach for enhancing large
language models (LLMs), typically by sampling multiple token-based
chains-of-thought in parallel and aggregating outcomes through voting or
search. Recent advances in latent reasoning, where intermediate reasoning
unfolds in continuous vector spaces, offer a more efficient alternative to
explicit Chain-of-Thought, yet whether such latent models can similarly benefit
from parallel TTS remains open, mainly due to the absence of sampling
mechanisms in continuous space, and the lack of probabilistic signals for
advanced trajectory aggregation. \ This work enables parallel TTS for latent
reasoning models by addressing the above issues. For sampling, we introduce two
uncertainty-inspired stochastic strategies: Monte Carlo Dropout and Additive
Gaussian Noise. For aggregation, we design a Latent Reward Model (LatentRM)
trained with step-wise contrastive objective to score and guide latent
reasoning. Extensive experiments and visualization analyses show that both
sampling strategies scale effectively with compute and exhibit distinct
exploration dynamics, while LatentRM enables effective trajectory selection.
Together, our explorations open a new direction for scalable inference in
continuous spaces. Code released at https://github.com/YRYangang/LatentTTS.
PhysToolBench: Benchmarking Physical Tool Understanding for MLLMs
Authors: name: Zixin Zhang, name: Kanghao Chen, name: Xingwang Lin, name: Lutao Jiang, name: Xu Zheng, name: Yuanhuiyi Lyu, name: Litao Guo, name: Yinchuan Li, name: Ying-Cong Chen
Published: 2025-10-10 16:10:45

The ability to use, understand, and create tools is a hallmark of human
intelligence, enabling sophisticated interaction with the physical world. For
any general-purpose intelligent agent to achieve true versatility, it must also
master these fundamental skills. While modern Multimodal Large Language Models
(MLLMs) leverage their extensive common knowledge for high-level planning in
embodied AI and in downstream Vision-Language-Action (VLA) models, the extent
of their true understanding of physical tools remains unquantified. To bridge
this gap, we present PhysToolBench, the first benchmark dedicated to evaluating
the comprehension of physical tools by MLLMs. Our benchmark is structured as a
Visual Question Answering (VQA) dataset comprising over 1,000 image-text pairs.
It assesses capabilities across three distinct difficulty levels: (1) Tool
Recognition: Requiring the recognition of a tool's primary function. (2) Tool
Understanding: Testing the ability to grasp the underlying principles of a
tool's operation. (3) Tool Creation: Challenging the model to fashion a new
tool from surrounding objects when conventional options are unavailable. Our
comprehensive evaluation of 32 MLLMs-spanning proprietary, open-source,
specialized embodied, and backbones in VLAs-reveals a significant deficiency in
tool understanding. Furthermore, we provide an in-depth analysis and propose
preliminary solutions. Code and dataset are publicly available.
Multimodal Prompt Optimization: Why Not Leverage Multiple Modalities for
  MLLMs
Authors: name: Yumin Choi, name: Dongki Kim, name: Jinheon Baek, name: Sung Ju Hwang
Published: 2025-10-10 09:41:25

Large Language Models (LLMs) have shown remarkable success, and their
multimodal expansions (MLLMs) further unlock capabilities spanning images,
videos, and other modalities beyond text. However, despite this shift, prompt
optimization approaches, designed to reduce the burden of manual prompt
crafting while maximizing performance, remain confined to text, ultimately
limiting the full potential of MLLMs. Motivated by this gap, we introduce the
new problem of multimodal prompt optimization, which expands the prior
definition of prompt optimization to the multimodal space defined by the pairs
of textual and non-textual prompts. To tackle this problem, we then propose the
Multimodal Prompt Optimizer (MPO), a unified framework that not only performs
the joint optimization of multimodal prompts through alignment-preserving
updates but also guides the selection process of candidate prompts by
leveraging earlier evaluations as priors in a Bayesian-based selection
strategy. Through extensive experiments across diverse modalities that go
beyond text, such as images, videos, and even molecules, we demonstrate that
MPO outperforms leading text-only optimization methods, establishing multimodal
prompt optimization as a crucial step to realizing the potential of MLLMs.
Thinking with Camera: A Unified Multimodal Model for Camera-Centric
  Understanding and Generation
Authors: name: Kang Liao, name: Size Wu, name: Zhonghua Wu, name: Linyi Jin, name: Chao Wang, name: Yikai Wang, name: Fei Wang, name: Wei Li, name: Chen Change Loy
Published: 2025-10-09 17:59:29

Camera-centric understanding and generation are two cornerstones of spatial
intelligence, yet they are typically studied in isolation. We present Puffin, a
unified camera-centric multimodal model that extends spatial awareness along
the camera dimension. Puffin integrates language regression and diffusion-based
generation to interpret and create scenes from arbitrary viewpoints. To bridge
the modality gap between cameras and vision-language, we introduce a novel
paradigm that treats camera as language, enabling thinking with camera. This
guides the model to align spatially grounded visual cues with photographic
terminology while reasoning across geometric context. Puffin is trained on
Puffin-4M, a large-scale dataset of 4 million vision-language-camera triplets.
We incorporate both global camera parameters and pixel-wise camera maps,
yielding flexible and reliable spatial generation. Experiments demonstrate
Puffin superior performance over specialized models for camera-centric
generation and understanding. With instruction tuning, Puffin generalizes to
diverse cross-view tasks such as spatial imagination, world exploration, and
photography guidance. We will release the code, models, dataset pipeline, and
benchmark to advance multimodal spatial intelligence research.
R-Horizon: How Far Can Your Large Reasoning Model Really Go in Breadth
  and Depth?
Authors: name: Yi Lu, name: Jianing Wang, name: Linsen Guo, name: Wei He, name: Hongyin Tang, name: Tao Gui, name: Xuanjing Huang, name: Xuezhi Cao, name: Wei Wang, name: Xunliang Cai
Published: 2025-10-09 13:16:22

Recent trends in test-time scaling for reasoning models (e.g., OpenAI o1,
DeepSeek-R1) have led to remarkable improvements through long Chain-of-Thought
(CoT). However, existing benchmarks mainly focus on immediate, single-horizon
tasks, failing to adequately evaluate models' ability to understand and respond
to complex, long-horizon scenarios. To address this incomplete evaluation of
Large Reasoning Models (LRMs), we propose R-HORIZON, a method designed to
stimulate long-horizon reasoning behaviors in LRMs through query composition.
Based on R-HORIZON, we construct a long-horizon reasoning benchmark, comprising
complex multi-step reasoning tasks with interdependent problems that span long
reasoning horizons. Through comprehensive evaluation of LRMs using the
R-HORIZON benchmark, we find that even the most advanced LRMs suffer
significant performance degradation. Our analysis reveals that LRMs exhibit
limited effective reasoning length and struggle to allocate thinking budget
across multiple problems appropriately. Recognizing these limitations, we use
R-HORIZON to construct long-horizon reasoning data for reinforcement learning
with verified rewards (RLVR). Compared to training with single-horizon data,
RLVR with R-HORIZON not only substantially improves performance on the
multi-horizon reasoning tasks, but also promotes accuracy on standard reasoning
tasks, with an increase of 7.5 on AIME2024. These results position R-HORIZON as
a scalable, controllable, and low-cost paradigm for enhancing and evaluating
the long-horizon reasoning capabilities of LRMs.
TC-LoRA: Temporally Modulated Conditional LoRA for Adaptive Diffusion
  Control
Authors: name: Minkyoung Cho, name: Ruben Ohana, name: Christian Jacobsen, name: Adityan Jothi, name: Min-Hung Chen, name: Z. Morley Mao, name: Ethem Can
Published: 2025-10-10 17:13:02

Current controllable diffusion models typically rely on fixed architectures
that modify intermediate activations to inject guidance conditioned on a new
modality. This approach uses a static conditioning strategy for a dynamic,
multi-stage denoising process, limiting the model's ability to adapt its
response as the generation evolves from coarse structure to fine detail. We
introduce TC-LoRA (Temporally Modulated Conditional LoRA), a new paradigm that
enables dynamic, context-aware control by conditioning the model's weights
directly. Our framework uses a hypernetwork to generate LoRA adapters
on-the-fly, tailoring weight modifications for the frozen backbone at each
diffusion step based on time and the user's condition. This mechanism enables
the model to learn and execute an explicit, adaptive strategy for applying
conditional guidance throughout the entire generation process. Through
experiments on various data domains, we demonstrate that this dynamic,
parametric control significantly enhances generative fidelity and adherence to
spatial conditions compared to static, activation-based methods. TC-LoRA
establishes an alternative approach in which the model's conditioning strategy
is modified through a deeper functional adaptation of its weights, allowing
control to align with the dynamic demands of the task and generative stage.
Temporal Prompting Matters: Rethinking Referring Video Object
  Segmentation
Authors: name: Ci-Siang Lin, name: Min-Hung Chen, name: I-Jieh Liu, name: Chien-Yi Wang, name: Sifei Liu, name: Yu-Chiang Frank Wang
Published: 2025-10-08 17:59:57

Referring Video Object Segmentation (RVOS) aims to segment the object
referred to by the query sentence in the video. Most existing methods require
end-to-end training with dense mask annotations, which could be
computation-consuming and less scalable. In this work, we rethink the RVOS
problem and aim to investigate the key to this task. Based on existing
foundation segmentation models, we decompose the RVOS task into referring,
video, and segmentation factors, and propose a Temporal Prompt Generation and
Selection (Tenet) framework to address the referring and video factors while
leaving the segmentation problem to foundation models. To efficiently adapt
image-based foundation segmentation models to referring video object
segmentation, we leverage off-the-shelf object detectors and trackers to
produce temporal prompts associated with the referring sentence. While
high-quality temporal prompts could be produced, they can not be easily
identified from confidence scores. To tackle this issue, we propose Prompt
Preference Learning to evaluate the quality of the produced temporal prompts.
By taking such prompts to instruct image-based foundation segmentation models,
we would be able to produce high-quality masks for the referred object,
enabling efficient model adaptation to referring video object segmentation.
Experiments on RVOS benchmarks demonstrate the effectiveness of the Tenet
framework.
Instant4D: 4D Gaussian Splatting in Minutes
Authors: name: Zhanpeng Luo, name: Haoxi Ran, name: Li Lu
Published: 2025-10-01 17:07:21

Dynamic view synthesis has seen significant advances, yet reconstructing
scenes from uncalibrated, casual video remains challenging due to slow
optimization and complex parameter estimation. In this work, we present
Instant4D, a monocular reconstruction system that leverages native 4D
representation to efficiently process casual video sequences within minutes,
without calibrated cameras or depth sensors. Our method begins with geometric
recovery through deep visual SLAM, followed by grid pruning to optimize scene
representation. Our design significantly reduces redundancy while maintaining
geometric integrity, cutting model size to under 10% of its original footprint.
To handle temporal dynamics efficiently, we introduce a streamlined 4D Gaussian
representation, achieving a 30x speed-up and reducing training time to within
two minutes, while maintaining competitive performance across several
benchmarks. Our method reconstruct a single video within 10 minutes on the
Dycheck dataset or for a typical 200-frame video. We further apply our model to
in-the-wild videos, showcasing its generalizability. Our project website is
published at https://instant4d.github.io/.
BigCodeArena: Unveiling More Reliable Human Preferences in Code
  Generation via Execution
Authors: name: Terry Yue Zhuo, name: Xiaolong Jin, name: Hange Liu, name: Juyong Jiang, name: Tianyang Liu, name: Chen Gong, name: Bhupesh Bishnoi, name: Vaisakhi Mishra, name: Marek Suppa, name: Noah Ziems, name: Saiteja Utpala, name: Ming Xu, name: Guangyu Song, name: Kaixin Li, name: Yuhan Cao, name: Bo Liu, name: Zheng Liu, name: Sabina Abdurakhmanova, name: Wenhao Yu, name: Mengzhao Jia, name: Jihan Yao, name: Kenneth Hamilton, name: Kumar Shridhar, name: Minh Chien Vu, name: Dingmin Wang, name: Jiawei Liu, name: Zijian Wang, name: Qian Liu, name: Binyuan Hui, name: Meg Risdal, name: Ahsen Khaliq, name: Atin Sood, name: Zhenchang Xing, name: Wasi Uddin Ahmad, name: John Grundy, name: David Lo, name: Banghua Zhu, name: Xiaoning Du, name: Torsten Scholak, name: Leandro von Werra
Published: 2025-10-09 18:01:47

Crowdsourced model evaluation platforms, such as Chatbot Arena, enable
real-time evaluation from human perspectives to assess the quality of model
responses. In the coding domain, manually examining the quality of
LLM-generated content is extremely challenging, as it requires understanding
long chunks of raw code and deliberately simulating code execution. To this
end, we introduce BigCodeArena, an open human evaluation platform for code
generation backed by a comprehensive and on-the-fly execution environment.
Built on top of Chatbot Arena, BigCodeArena enables the execution of
LLM-generated code and allows humans to interact with the execution process and
outcomes. We collected over 14,000 raw code-centric conversation sessions
across 10 widely used LLMs, spanning 10 languages and 8 types of execution
environments. Among these conversations, we identified more than 4,700
multi-turn samples with pairwise human preferences. Further analysis uncovers
underexplored preferences of LLMs in fine-grained domains characterized by
tasks, languages, and frameworks. To systematically examine code understanding
and generation capabilities of frontier LLMs, we curated two benchmarks based
on the collected data, namely BigCodeReward and AutoCodeArena. For
BigCodeReward, we post-processed the 4,700 conversations and evaluated the
consistency between reward models and human preferences. The evaluation shows
that most LLMs have superior performance in judging coding preferences when the
execution results are available. Inspired by these findings, we propose
AutoCodeArena, an automatic Elo rating benchmark designed to assess the coding
quality of LLMs without human involvement. We find that proprietary LLMs like
GPT-5, Claude-Sonnet-4, and Claude-Opus-4 still lead in code generation
performance among recent emerging models.
Progressive Gaussian Transformer with Anisotropy-aware Sampling for Open
  Vocabulary Occupancy Prediction
Authors: name: Chi Yan, name: Dan Xu
Published: 2025-10-06 12:36:07

The 3D occupancy prediction task has witnessed remarkable progress in recent
years, playing a crucial role in vision-based autonomous driving systems. While
traditional methods are limited to fixed semantic categories, recent approaches
have moved towards predicting text-aligned features to enable open-vocabulary
text queries in real-world scenes. However, there exists a trade-off in
text-aligned scene modeling: sparse Gaussian representation struggles to
capture small objects in the scene, while dense representation incurs
significant computational overhead. To address these limitations, we present
PG-Occ, an innovative Progressive Gaussian Transformer Framework that enables
open-vocabulary 3D occupancy prediction. Our framework employs progressive
online densification, a feed-forward strategy that gradually enhances the 3D
Gaussian representation to capture fine-grained scene details. By iteratively
enhancing the representation, the framework achieves increasingly precise and
detailed scene understanding. Another key contribution is the introduction of
an anisotropy-aware sampling strategy with spatio-temporal fusion, which
adaptively assigns receptive fields to Gaussians at different scales and
stages, enabling more effective feature aggregation and richer scene
information capture. Through extensive evaluations, we demonstrate that PG-Occ
achieves state-of-the-art performance with a relative 14.3% mIoU improvement
over the previous best performing method. Code and pretrained models will be
released upon publication on our project page:
https://yanchi-3dv.github.io/PG-Occ
Mind-Paced Speaking: A Dual-Brain Approach to Real-Time Reasoning in
  Spoken Language Models
Authors: name: Donghang Wu, name: Haoyang Zhang, name: Jun Chen, name: Xiangyu, name: Zhang, name: Hexin Liu, name: Eng Siong Chng, name: Fei Tian, name: Xuerui Yang, name: Xiangyu Zhang, name: Daxin Jiang, name: Gang Yu
Published: 2025-10-10 17:50:59

Real-time Spoken Language Models (SLMs) struggle to leverage Chain-of-Thought
(CoT) reasoning due to the prohibitive latency of generating the entire thought
process sequentially. Enabling SLMs to think while speaking, similar to humans,
is attracting increasing attention. We present, for the first time, Mind-Paced
Speaking (MPS), a brain-inspired framework that enables high-fidelity,
real-time reasoning. Similar to how humans utilize distinct brain regions for
thinking and responding, we propose a novel dual-brain approach, employing a
"Formulation Brain" for high-level reasoning to pace and guide a separate
"Articulation Brain" for fluent speech generation. This division of labor
eliminates mode-switching, preserving the integrity of the reasoning process.
Experiments show that MPS significantly outperforms existing
think-while-speaking methods and achieves reasoning performance comparable to
models that pre-compute the full CoT before speaking, while drastically
reducing latency. Under a zero-latency configuration, the proposed method
achieves an accuracy of 92.8% on the mathematical reasoning task Spoken-MQA and
attains a score of 82.5 on the speech conversation task URO-Bench. Our work
effectively bridges the gap between high-quality reasoning and real-time
interaction.
SpaceVista: All-Scale Visual Spatial Reasoning from mm to km
Authors: name: Peiwen Sun, name: Shiqiang Lang, name: Dongming Wu, name: Yi Ding, name: Kaituo Feng, name: Huadai Liu, name: Zhen Ye, name: Rui Liu, name: Yun-Hui Liu, name: Jianan Wang, name: Xiangyu Yue
Published: 2025-10-10 17:59:46

With the current surge in spatial reasoning explorations, researchers have
made significant progress in understanding indoor scenes, but still struggle
with diverse applications such as robotics and autonomous driving. This paper
aims to advance all-scale spatial reasoning across diverse scenarios by
tackling two key challenges: 1) the heavy reliance on indoor 3D scans and
labor-intensive manual annotations for dataset curation; 2) the absence of
effective all-scale scene modeling, which often leads to overfitting to
individual scenes. In this paper, we introduce a holistic solution that
integrates a structured spatial reasoning knowledge system, scale-aware
modeling, and a progressive training paradigm, as the first attempt to broaden
the all-scale spatial intelligence of MLLMs to the best of our knowledge. Using
a task-specific, specialist-driven automated pipeline, we curate over 38K video
scenes across 5 spatial scales to create SpaceVista-1M, a dataset comprising
approximately 1M spatial QA pairs spanning 19 diverse task types. While
specialist models can inject useful domain knowledge, they are not reliable for
evaluation. We then build an all-scale benchmark with precise annotations by
manually recording, retrieving, and assembling video-based data. However, naive
training with SpaceVista-1M often yields suboptimal results due to the
potential knowledge conflict. Accordingly, we introduce SpaceVista-7B, a
spatial reasoning model that accepts dense inputs beyond semantics and uses
scale as an anchor for scale-aware experts and progressive rewards. Finally,
extensive evaluations across 5 benchmarks, including our SpaceVista-Bench,
demonstrate competitive performance, showcasing strong generalization across
all scales and scenarios. Our dataset, model, and benchmark will be released on
https://peiwensun2000.github.io/mm2km .
StreamingVLM: Real-Time Understanding for Infinite Video Streams
Authors: name: Ruyi Xu, name: Guangxuan Xiao, name: Yukang Chen, name: Liuning He, name: Kelly Peng, name: Yao Lu, name: Song Han
Published: 2025-10-10 17:59:58

Vision-language models (VLMs) could power real-time assistants and autonomous
agents, but they face a critical challenge: understanding near-infinite video
streams without escalating latency and memory usage. Processing entire videos
with full attention leads to quadratic computational costs and poor performance
on long videos. Meanwhile, simple sliding window methods are also flawed, as
they either break coherence or suffer from high latency due to redundant
recomputation. In this paper, we introduce StreamingVLM, a model designed for
real-time, stable understanding of infinite visual input. Our approach is a
unified framework that aligns training with streaming inference. During
inference, we maintain a compact KV cache by reusing states of attention sinks,
a short window of recent vision tokens, and a long window of recent text
tokens. This streaming ability is instilled via a simple supervised fine-tuning
(SFT) strategy that applies full attention on short, overlapped video chunks,
which effectively mimics the inference-time attention pattern without training
on prohibitively long contexts. For evaluation, we build Inf-Streams-Eval, a
new benchmark with videos averaging over two hours that requires dense,
per-second alignment between frames and text. On Inf-Streams-Eval, StreamingVLM
achieves a 66.18% win rate against GPT-4O mini and maintains stable, real-time
performance at up to 8 FPS on a single NVIDIA H100. Notably, our SFT strategy
also enhances general VQA abilities without any VQA-specific fine-tuning,
improving performance on LongVideoBench by +4.30 and OVOBench Realtime by
+5.96. Code is available at https://github.com/mit-han-lab/streaming-vlm.