Orionnnn/hub-stats huggingface dataset:
Elevating 3D Models: High-Quality Texture and Geometry Refinement from a
  Low-Quality Model
Authors: name: Nuri Ryu, name: Jiyun Won, name: Jooeun Son, name: Minsu Gong, name: Joo-Haeng Lee, name: Sunghyun Cho
Published: 2025-07-15 16:36:20

High-quality 3D assets are essential for various applications in computer
graphics and 3D vision but remain scarce due to significant acquisition costs.
To address this shortage, we introduce Elevate3D, a novel framework that
transforms readily accessible low-quality 3D assets into higher quality. At the
core of Elevate3D is HFS-SDEdit, a specialized texture enhancement method that
significantly improves texture quality while preserving the appearance and
geometry while fixing its degradations. Furthermore, Elevate3D operates in a
view-by-view manner, alternating between texture and geometry refinement.
Unlike previous methods that have largely overlooked geometry refinement, our
framework leverages geometric cues from images refined with HFS-SDEdit by
employing state-of-the-art monocular geometry predictors. This approach ensures
detailed and accurate geometry that aligns seamlessly with the enhanced
texture. Elevate3D outperforms recent competitors by achieving state-of-the-art
quality in 3D model refinement, effectively addressing the scarcity of
high-quality open-source 3D assets.
Ultra3D: Efficient and High-Fidelity 3D Generation with Part Attention
Authors: name: Yiwen Chen, name: Zhihao Li, name: Yikai Wang, name: Hu Zhang, name: Qin Li, name: Chi Zhang, name: Guosheng Lin
Published: 2025-07-23 17:57:16

Recent advances in sparse voxel representations have significantly improved
the quality of 3D content generation, enabling high-resolution modeling with
fine-grained geometry. However, existing frameworks suffer from severe
computational inefficiencies due to the quadratic complexity of attention
mechanisms in their two-stage diffusion pipelines. In this work, we propose
Ultra3D, an efficient 3D generation framework that significantly accelerates
sparse voxel modeling without compromising quality. Our method leverages the
compact VecSet representation to efficiently generate a coarse object layout in
the first stage, reducing token count and accelerating voxel coordinate
prediction. To refine per-voxel latent features in the second stage, we
introduce Part Attention, a geometry-aware localized attention mechanism that
restricts attention computation within semantically consistent part regions.
This design preserves structural continuity while avoiding unnecessary global
attention, achieving up to 6.7x speed-up in latent generation. To support this
mechanism, we construct a scalable part annotation pipeline that converts raw
meshes into part-labeled sparse voxels. Extensive experiments demonstrate that
Ultra3D supports high-resolution 3D generation at 1024 resolution and achieves
state-of-the-art performance in both visual fidelity and user preference.
Can One Domain Help Others? A Data-Centric Study on Multi-Domain
  Reasoning via Reinforcement Learning
Authors: name: Yu Li, name: Zhuoshi Pan, name: Honglin Lin, name: Mengyuan Sun, name: Conghui He, name: Lijun Wu
Published: 2025-07-23 13:51:04

Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a
powerful paradigm for enhancing the reasoning capabilities of LLMs. Existing
research has predominantly concentrated on isolated reasoning domains such as
mathematical problem-solving, coding tasks, or logical reasoning. However, real
world reasoning scenarios inherently demand an integrated application of
multiple cognitive skills. Despite this, the interplay among these reasoning
skills under reinforcement learning remains poorly understood. To bridge this
gap, we present a systematic investigation of multi-domain reasoning within the
RLVR framework, explicitly focusing on three primary domains: mathematical
reasoning, code generation, and logical puzzle solving. We conduct a
comprehensive study comprising four key components: (1) Leveraging the GRPO
algorithm and the Qwen-2.5-7B model family, our study thoroughly evaluates the
models' in-domain improvements and cross-domain generalization capabilities
when trained on single-domain datasets. (2) Additionally, we examine the
intricate interactions including mutual enhancements and conflicts that emerge
during combined cross-domain training. (3) To further understand the influence
of SFT on RL, we also analyze and compare performance differences between base
and instruct models under identical RL configurations. (4) Furthermore, we
delve into critical RL training details, systematically exploring the impacts
of curriculum learning strategies, variations in reward design, and
language-specific factors. Through extensive experiments, our results offer
significant insights into the dynamics governing domain interactions, revealing
key factors influencing both specialized and generalizable reasoning
performance. These findings provide valuable guidance for optimizing RL
methodologies to foster comprehensive, multi-domain reasoning capabilities in
LLMs.
PrefPalette: Personalized Preference Modeling with Latent Attributes
Authors: name: Shuyue Stella Li, name: Melanie Sclar, name: Hunter Lang, name: Ansong Ni, name: Jacqueline He, name: Puxin Xu, name: Andrew Cohen, name: Chan Young Park, name: Yulia Tsvetkov, name: Asli Celikyilmaz
Published: 2025-07-17 21:21:54

Personalizing AI systems requires understanding not just what users prefer,
but the reasons that underlie those preferences - yet current preference models
typically treat human judgment as a black box. We introduce PrefPalette, a
framework that decomposes preferences into attribute dimensions and tailors its
preference prediction to distinct social community values in a
human-interpretable manner. PrefPalette operationalizes a cognitive science
principle known as multi-attribute decision making in two ways: (1) a scalable
counterfactual attribute synthesis step that involves generating synthetic
training data to isolate for individual attribute effects (e.g., formality,
humor, cultural values), and (2) attention-based preference modeling that
learns how different social communities dynamically weight these attributes.
This approach moves beyond aggregate preference modeling to capture the diverse
evaluation frameworks that drive human judgment. When evaluated on 45 social
communities from the online platform Reddit, PrefPalette outperforms GPT-4o by
46.6% in average prediction accuracy. Beyond raw predictive improvements,
PrefPalette also shed light on intuitive, community-specific profiles:
scholarly communities prioritize verbosity and stimulation, conflict-oriented
communities value sarcasm and directness, and support-based communities
emphasize empathy. By modeling the attribute-mediated structure of human
judgment, PrefPalette delivers both superior preference modeling and
transparent, interpretable insights, and serves as a first step toward more
trustworthy, value-aware personalized applications.
Task-Specific Zero-shot Quantization-Aware Training for Object Detection
Authors: name: Changhao Li, name: Xinrui Chen, name: Ji Wang, name: Kang Zhao, name: Jianfei Chen
Published: 2025-07-22 17:28:29

Quantization is a key technique to reduce network size and computational
complexity by representing the network parameters with a lower precision.
Traditional quantization methods rely on access to original training data,
which is often restricted due to privacy concerns or security challenges.
Zero-shot Quantization (ZSQ) addresses this by using synthetic data generated
from pre-trained models, eliminating the need for real training data. Recently,
ZSQ has been extended to object detection. However, existing methods use
unlabeled task-agnostic synthetic images that lack the specific information
required for object detection, leading to suboptimal performance. In this
paper, we propose a novel task-specific ZSQ framework for object detection
networks, which consists of two main stages. First, we introduce a bounding box
and category sampling strategy to synthesize a task-specific calibration set
from the pre-trained network, reconstructing object locations, sizes, and
category distributions without any prior knowledge. Second, we integrate
task-specific training into the knowledge distillation process to restore the
performance of quantized detection networks. Extensive experiments conducted on
the MS-COCO and Pascal VOC datasets demonstrate the efficiency and
state-of-the-art performance of our method. Our code is publicly available at:
https://github.com/DFQ-Dojo/dfq-toolkit .
Experience is the Best Teacher: Grounding VLMs for Robotics through
  Self-Generated Memory
Authors: name: Guowei Lan, name: Kaixian Qu, name: René Zurbrügg, name: Changan Chen, name: Christopher E. Mower, name: Haitham Bou-Ammar, name: Marco Hutter
Published: 2025-07-22 15:48:49

Vision-language models (VLMs) have been widely adopted in robotics to enable
autonomous planning. However, grounding VLMs, originally trained on internet
data, to diverse real-world robots remains a challenge. This paper presents
ExpTeach, a framework that grounds VLMs to physical robots by building a
self-generated memory of real-world experiences. In ExpTeach, the VLM
autonomously plans actions, verifies outcomes, reflects on failures, and adapts
robot behaviors in a closed loop. The self-generated experiences during this
process are then summarized into a long-term memory, enabling retrieval of
learned knowledge to guide future tasks via retrieval-augmented generation
(RAG). Additionally, ExpTeach enhances the spatial understanding of VLMs with
an on-demand image annotation module. In experiments, we show that reflection
improves success rates from 36% to 84% on four challenging robotic tasks and
observe the emergence of intelligent object interactions, including creative
tool use. Across extensive tests on 12 real-world scenarios (including eight
unseen ones), we find that grounding with long-term memory boosts single-trial
success rates from 22% to 80%, demonstrating the effectiveness and
generalizability of ExpTeach.
Upsample What Matters: Region-Adaptive Latent Sampling for Accelerated
  Diffusion Transformers
Authors: name: Wongi Jeong, name: Kyungryeol Lee, name: Hoigi Seo, name: Se Young Chun
Published: 2025-07-11 09:07:43

Diffusion transformers have emerged as an alternative to U-net-based
diffusion models for high-fidelity image and video generation, offering
superior scalability. However, their heavy computation remains a major obstacle
to real-world deployment. Existing acceleration methods primarily exploit the
temporal dimension such as reusing cached features across diffusion timesteps.
Here, we propose Region-Adaptive Latent Upsampling (RALU), a training-free
framework that accelerates inference along spatial dimension. RALU performs
mixed-resolution sampling across three stages: 1) low-resolution denoising
latent diffusion to efficiently capture global semantic structure, 2)
region-adaptive upsampling on specific regions prone to artifacts at
full-resolution, and 3) all latent upsampling at full-resolution for detail
refinement. To stabilize generations across resolution transitions, we leverage
noise-timestep rescheduling to adapt the noise level across varying
resolutions. Our method significantly reduces computation while preserving
image quality by achieving up to 7.0times speed-up on FLUX and 3.0times
on Stable Diffusion 3 with minimal degradation. Furthermore, RALU is
complementary to existing temporal accelerations such as caching methods, thus
can be seamlessly integrated to further reduce inference latency without
compromising generation quality.
Steering Out-of-Distribution Generalization with Concept Ablation
  Fine-Tuning
Authors: name: Helena Casademunt, name: Caden Juang, name: Adam Karvonen, name: Samuel Marks, name: Senthooran Rajamanoharan, name: Neel Nanda
Published: 2025-07-22 17:45:04

Fine-tuning large language models (LLMs) can lead to unintended
out-of-distribution generalization. Standard approaches to this problem rely on
modifying training data, for example by adding data that better specify the
intended generalization. However, this is not always practical. We introduce
Concept Ablation Fine-Tuning (CAFT), a technique that leverages
interpretability tools to control how LLMs generalize from fine-tuning, without
needing to modify the training data or otherwise use data from the target
distribution. Given a set of directions in an LLM's latent space corresponding
to undesired concepts, CAFT works by ablating these concepts with linear
projections during fine-tuning, steering the model away from unintended
generalizations. We successfully apply CAFT to three fine-tuning tasks,
including emergent misalignment, a phenomenon where LLMs fine-tuned on a narrow
task generalize to give egregiously misaligned responses to general questions.
Without any changes to the fine-tuning data, CAFT reduces misaligned responses
by 10x without degrading performance on the training distribution. Overall,
CAFT represents a novel approach for steering LLM generalization without
modifying training data.
Beyond Context Limits: Subconscious Threads for Long-Horizon Reasoning
Authors: name: Hongyin Luo, name: Nathaniel Morgan, name: Tina Li, name: Derek Zhao, name: Ai Vy Ngo, name: Philip Schroeder, name: Lijie Yang, name: Assaf Ben-Kish, name: Jack O'Brien, name: James Glass
Published: 2025-07-22 17:30:04

To break the context limits of large language models (LLMs) that bottleneck
reasoning accuracy and efficiency, we propose the Thread Inference Model (TIM),
a family of LLMs trained for recursive and decompositional problem solving, and
TIMRUN, an inference runtime enabling long-horizon structured reasoning beyond
context limits. Together, TIM hosted on TIMRUN supports virtually unlimited
working memory and multi-hop tool calls within a single language model
inference, overcoming output limits, positional-embedding constraints, and
GPU-memory bottlenecks. Performance is achieved by modeling natural language as
reasoning trees measured by both length and depth instead of linear sequences.
The reasoning trees consist of tasks with thoughts, recursive subtasks, and
conclusions based on the concept we proposed in Schroeder et al, 2025. During
generation, we maintain a working memory that retains only the key-value states
of the most relevant context tokens, selected by a rule-based subtask-pruning
mechanism, enabling reuse of positional embeddings and GPU memory pages
throughout reasoning. Experimental results show that our system sustains high
inference throughput, even when manipulating up to 90% of the KV cache in GPU
memory. It also delivers accurate reasoning on mathematical tasks and handles
information retrieval challenges that require long-horizon reasoning and
multi-hop tool use.
Does More Inference-Time Compute Really Help Robustness?
Authors: name: Tong Wu, name: Chong Xiang, name: Jiachen T. Wang, name: Weichen Yu, name: Chawin Sitawarin, name: Vikash Sehwag, name: Prateek Mittal
Published: 2025-07-21 18:08:38

Recently, Zaremba et al. demonstrated that increasing inference-time
computation improves robustness in large proprietary reasoning LLMs. In this
paper, we first show that smaller-scale, open-source models (e.g., DeepSeek R1,
Qwen3, Phi-reasoning) can also benefit from inference-time scaling using a
simple budget forcing strategy. More importantly, we reveal and critically
examine an implicit assumption in prior work: intermediate reasoning steps are
hidden from adversaries. By relaxing this assumption, we identify an important
security risk, intuitively motivated and empirically verified as an inverse
scaling law: if intermediate reasoning steps become explicitly accessible,
increased inference-time computation consistently reduces model robustness.
Finally, we discuss practical scenarios where models with hidden reasoning
chains are still vulnerable to attacks, such as models with tool-integrated
reasoning and advanced reasoning extraction attacks. Our findings collectively
demonstrate that the robustness benefits of inference-time scaling depend
heavily on the adversarial setting and deployment context. We urge
practitioners to carefully weigh these subtle trade-offs before applying
inference-time scaling in security-sensitive, real-world applications.
Semi-off-Policy Reinforcement Learning for Vision-Language Slow-thinking
  Reasoning
Authors: name: Junhao Shen, name: Haiteng Zhao, name: Yuzhe Gu, name: Songyang Gao, name: Kuikun Liu, name: Haian Huang, name: Jianfei Gao, name: Dahua Lin, name: Wenwei Zhang, name: Kai Chen
Published: 2025-07-22 17:59:34

Enhancing large vision-language models (LVLMs) with visual slow-thinking
reasoning is crucial for solving complex multimodal tasks. However, since LVLMs
are mainly trained with vision-language alignment, it is difficult to adopt
on-policy reinforcement learning (RL) to develop the slow thinking ability
because the rollout space is restricted by its initial abilities. Off-policy RL
offers a way to go beyond the current policy, but directly distilling
trajectories from external models may cause visual hallucinations due to
mismatched visual perception abilities across models. To address these issues,
this paper proposes SOPHIA, a simple and scalable Semi-Off-Policy RL for
vision-language slow-tHInking reAsoning. SOPHIA builds a semi-off-policy
behavior model by combining on-policy visual understanding from a trainable
LVLM with off-policy slow-thinking reasoning from a language model, assigns
outcome-based rewards to reasoning, and propagates visual rewards backward.
Then LVLM learns slow-thinking reasoning ability from the obtained reasoning
trajectories using propagated rewards via off-policy RL algorithms. Extensive
experiments with InternVL2.5 and InternVL3.0 with 8B and 38B sizes show the
effectiveness of SOPHIA. Notably, SOPHIA improves InternVL3.0-38B by 8.50% in
average, reaching state-of-the-art performance among open-source LVLMs on
multiple multimodal reasoning benchmarks, and even outperforms some
closed-source models (e.g., GPT-4.1) on the challenging MathVision and
OlympiadBench, achieving 49.08% and 49.95% pass@1 accuracy, respectively.
Analysis shows SOPHIA outperforms supervised fine-tuning and direct on-policy
RL methods, offering a better policy initialization for further on-policy
training.
ObjectGS: Object-aware Scene Reconstruction and Scene Understanding via
  Gaussian Splatting
Authors: name: Ruijie Zhu, name: Mulin Yu, name: Linning Xu, name: Lihan Jiang, name: Yixuan Li, name: Tianzhu Zhang, name: Jiangmiao Pang, name: Bo Dai
Published: 2025-07-21 10:06:23

3D Gaussian Splatting is renowned for its high-fidelity reconstructions and
real-time novel view synthesis, yet its lack of semantic understanding limits
object-level perception. In this work, we propose ObjectGS, an object-aware
framework that unifies 3D scene reconstruction with semantic understanding.
Instead of treating the scene as a unified whole, ObjectGS models individual
objects as local anchors that generate neural Gaussians and share object IDs,
enabling precise object-level reconstruction. During training, we dynamically
grow or prune these anchors and optimize their features, while a one-hot ID
encoding with a classification loss enforces clear semantic constraints. We
show through extensive experiments that ObjectGS not only outperforms
state-of-the-art methods on open-vocabulary and panoptic segmentation tasks,
but also integrates seamlessly with applications like mesh extraction and scene
editing. Project page: https://ruijiezhu94.github.io/ObjectGS_page
RefCritic: Training Long Chain-of-Thought Critic Models with Refinement
  Feedback
Authors: name: Qiaoyu Tang, name: Hao Xiang, name: Le Yu, name: Bowen Yu, name: Hongyu Lin, name: Yaojie Lu, name: Xianpei Han, name: Le Sun, name: Junyang Lin
Published: 2025-07-20 16:19:51

With the rapid advancement of Large Language Models (LLMs), developing
effective critic modules for precise guidance has become crucial yet
challenging. In this paper, we initially demonstrate that supervised
fine-tuning for building critic modules (which is widely adopted in current
solutions) fails to genuinely enhance models' critique abilities, producing
superficial critiques with insufficient reflections and verifications. To
unlock the unprecedented critique capabilities, we propose RefCritic, a
long-chain-of-thought critic module based on reinforcement learning with dual
rule-based rewards: (1) instance-level correctness of solution judgments and
(2) refinement accuracies of the policy model based on critiques, aiming to
generate high-quality evaluations with actionable feedback that effectively
guides model refinement. We evaluate RefCritic on Qwen2.5-14B-Instruct and
DeepSeek-R1-Distill-Qwen-14B across five benchmarks. On critique and refinement
settings, RefCritic demonstrates consistent advantages across all benchmarks,
e.g., 6.8\% and 7.2\% gains on AIME25 for the respective base models. Notably,
under majority voting, policy models filtered by RefCritic show superior
scaling with increased voting numbers. Moreover, despite training on
solution-level supervision, RefCritic outperforms step-level supervised
approaches on ProcessBench, a benchmark to identify erroneous steps in
mathematical reasoning.
ThinkAct: Vision-Language-Action Reasoning via Reinforced Visual Latent
  Planning
Authors: name: Chi-Pin Huang, name: Yueh-Hua Wu, name: Min-Hung Chen, name: Yu-Chiang Frank Wang, name: Fu-En Yang
Published: 2025-07-22 17:59:46

Vision-language-action (VLA) reasoning tasks require agents to interpret
multimodal instructions, perform long-horizon planning, and act adaptively in
dynamic environments. Existing approaches typically train VLA models in an
end-to-end fashion, directly mapping inputs to actions without explicit
reasoning, which hinders their ability to plan over multiple steps or adapt to
complex task variations. In this paper, we propose ThinkAct, a dual-system
framework that bridges high-level reasoning with low-level action execution via
reinforced visual latent planning. ThinkAct trains a multimodal LLM to generate
embodied reasoning plans guided by reinforcing action-aligned visual rewards
based on goal completion and trajectory consistency. These reasoning plans are
compressed into a visual plan latent that conditions a downstream action model
for robust action execution on target environments. Extensive experiments on
embodied reasoning and robot manipulation benchmarks demonstrate that ThinkAct
enables few-shot adaptation, long-horizon planning, and self-correction
behaviors in complex embodied AI tasks.
SPAR: Scholar Paper Retrieval with LLM-based Agents for Enhanced
  Academic Search
Authors: name: Xiaofeng Shi, name: Yuduo Li, name: Qian Kou, name: Longbin Yu, name: Jinxin Xie, name: Hua Zhou
Published: 2025-07-21 05:06:53

Recent advances in large language models (LLMs) have opened new opportunities
for academic literature retrieval. However, existing systems often rely on
rigid pipelines and exhibit limited reasoning capabilities. We introduce SPAR,
a multi-agent framework that incorporates RefChain-based query decomposition
and query evolution to enable more flexible and effective search. To facilitate
systematic evaluation, we also construct SPARBench, a challenging benchmark
with expert-annotated relevance labels. Experimental results demonstrate that
SPAR substantially outperforms strong baselines, achieving up to +56% F1 on
AutoScholar and +23% F1 on SPARBench over the best-performing baseline.
Together, SPAR and SPARBench provide a scalable, interpretable, and
high-performing foundation for advancing research in scholarly retrieval. Code
and data will be available at: https://github.com/xiaofengShi/SPAR