Orionnnn/hub-stats huggingface dataset:
1000+ FPS 4D Gaussian Splatting for Dynamic Scene Rendering
Authors: name: Yuheng Yuan, name: Qiuhong Shen, name: Xingyi Yang, name: Xinchao Wang
Published: 2025-03-20 17:59:44

4D Gaussian Splatting (4DGS) has recently gained considerable attention as a
method for reconstructing dynamic scenes. Despite achieving superior quality,
4DGS typically requires substantial storage and suffers from slow rendering
speed. In this work, we delve into these issues and identify two key sources of
temporal redundancy. (Q1) Short-Lifespan Gaussians: 4DGS uses a large
portion of Gaussians with short temporal span to represent scene dynamics,
leading to an excessive number of Gaussians. (Q2) Inactive Gaussians:
When rendering, only a small subset of Gaussians contributes to each frame.
Despite this, all Gaussians are processed during rasterization, resulting in
redundant computation overhead. To address these redundancies, we present
4DGS-1K, which runs at over 1000 FPS on modern GPUs. For Q1, we
introduce the Spatial-Temporal Variation Score, a new pruning criterion that
effectively removes short-lifespan Gaussians while encouraging 4DGS to capture
scene dynamics using Gaussians with longer temporal spans. For Q2, we store a
mask for active Gaussians across consecutive frames, significantly reducing
redundant computations in rendering. Compared to vanilla 4DGS, our method
achieves a 41times reduction in storage and 9times faster rasterization
speed on complex dynamic scenes, while maintaining comparable visual quality.
Please see our project page at https://4DGS-1K.github.io.
MagicMotion: Controllable Video Generation with Dense-to-Sparse
  Trajectory Guidance
Authors: name: Quanhao Li, name: Zhen Xing, name: Rui Wang, name: Hui Zhang, name: Qi Dai, name: Zuxuan Wu
Published: 2025-03-20 17:59:42

Recent advances in video generation have led to remarkable improvements in
visual quality and temporal coherence. Upon this, trajectory-controllable video
generation has emerged to enable precise object motion control through
explicitly defined spatial paths. However, existing methods struggle with
complex object movements and multi-object motion control, resulting in
imprecise trajectory adherence, poor object consistency, and compromised visual
quality. Furthermore, these methods only support trajectory control in a single
format, limiting their applicability in diverse scenarios. Additionally, there
is no publicly available dataset or benchmark specifically tailored for
trajectory-controllable video generation, hindering robust training and
systematic evaluation. To address these challenges, we introduce MagicMotion, a
novel image-to-video generation framework that enables trajectory control
through three levels of conditions from dense to sparse: masks, bounding boxes,
and sparse boxes. Given an input image and trajectories, MagicMotion seamlessly
animates objects along defined trajectories while maintaining object
consistency and visual quality. Furthermore, we present MagicData, a
large-scale trajectory-controlled video dataset, along with an automated
pipeline for annotation and filtering. We also introduce MagicBench, a
comprehensive benchmark that assesses both video quality and trajectory control
accuracy across different numbers of objects. Extensive experiments demonstrate
that MagicMotion outperforms previous methods across various metrics. Our
project page are publicly available at
https://quanhaol.github.io/magicmotion-site.
MathFusion: Enhancing Mathematic Problem-solving of LLM through
  Instruction Fusion
Authors: name: Qizhi Pei, name: Lijun Wu, name: Zhuoshi Pan, name: Yu Li, name: Honglin Lin, name: Chenlin Ming, name: Xin Gao, name: Conghui He, name: Rui Yan
Published: 2025-03-20 15:00:41

Large Language Models (LLMs) have shown impressive progress in mathematical
reasoning. While data augmentation is promising to enhance mathematical
problem-solving ability, current approaches are predominantly limited to
instance-level modifications-such as rephrasing or generating syntactic
variations-which fail to capture and leverage the intrinsic relational
structures inherent in mathematical knowledge. Inspired by human learning
processes, where mathematical proficiency develops through systematic exposure
to interconnected concepts, we introduce MathFusion, a novel framework that
enhances mathematical reasoning through cross-problem instruction synthesis.
MathFusion implements this through three fusion strategies: (1) sequential
fusion, which chains related problems to model solution dependencies; (2)
parallel fusion, which combines analogous problems to reinforce conceptual
understanding; and (3) conditional fusion, which creates context-aware
selective problems to enhance reasoning flexibility. By applying these
strategies, we generate a new dataset, MathFusionQA, followed by
fine-tuning models (DeepSeekMath-7B, Mistral-7B, Llama3-8B) on it. Experimental
results demonstrate that MathFusion achieves substantial improvements in
mathematical reasoning while maintaining high data efficiency, boosting
performance by 18.0 points in accuracy across diverse benchmarks while
requiring only 45K additional synthetic instructions, representing a
substantial improvement over traditional single-instruction approaches. Our
datasets, models, and code are publicly available at
https://github.com/QizhiPei/mathfusion.
CaKE: Circuit-aware Editing Enables Generalizable Knowledge Learners
Authors: name: Yunzhi Yao, name: Jizhan Fang, name: Jia-Chen Gu, name: Ningyu Zhang, name: Shumin Deng, name: Huajun Chen, name: Nanyun Peng
Published: 2025-03-20 17:14:34

Knowledge Editing (KE) enables the modification of outdated or incorrect
information in large language models (LLMs). While existing KE methods can
update isolated facts, they struggle to generalize these updates to multi-hop
reasoning tasks that depend on the modified knowledge. Through an analysis of
reasoning circuits -- the neural pathways LLMs use for knowledge-based
inference, we observe that current layer-localized KE approaches, such as MEMIT
and WISE, which edit only single or a few model layers, struggle to effectively
incorporate updated information into these reasoning pathways. To address this
limitation, we propose CaKE (Circuit-aware Knowledge Editing), a novel method
that enables more effective integration of updated knowledge in LLMs. CaKE
leverages strategically curated data, guided by our circuits-based analysis,
that enforces the model to utilize the modified knowledge, stimulating the
model to develop appropriate reasoning circuits for newly integrated knowledge.
Experimental results show that CaKE enables more accurate and consistent use of
updated knowledge across related reasoning tasks, leading to an average of 20%
improvement in multi-hop reasoning accuracy on MQuAKE dataset compared to
existing KE methods. We release the code and data in
https://github.com/zjunlp/CaKE.
Cosmos-Reason1: From Physical Common Sense To Embodied Reasoning
Authors: name: NVIDIA, name: Alisson Azzolini, name: Hannah Brandon, name: Prithvijit Chattopadhyay, name: Huayu Chen, name: Jinju Chu, name: Yin Cui, name: Jenna Diamond, name: Yifan Ding, name: Francesco Ferroni, name: Rama Govindaraju, name: Jinwei Gu, name: Siddharth Gururani, name: Imad El Hanafi, name: Zekun Hao, name: Jacob Huffman, name: Jingyi Jin, name: Brendan Johnson, name: Rizwan Khan, name: George Kurian, name: Elena Lantz, name: Nayeon Lee, name: Zhaoshuo Li, name: Xuan Li, name: Tsung-Yi Lin, name: Yen-Chen Lin, name: Ming-Yu Liu, name: Andrew Mathau, name: Yun Ni, name: Lindsey Pavao, name: Wei Ping, name: David W. Romero, name: Misha Smelyanskiy, name: Shuran Song, name: Lyne Tchapmi, name: Andrew Z. Wang, name: Boxin Wang, name: Haoxiang Wang, name: Fangyin Wei, name: Jiashu Xu, name: Yao Xu, name: Xiaodong Yang, name: Zhuolin Yang, name: Xiaohui Zeng, name: Zhe Zhang
Published: 2025-03-18 22:06:58

Physical AI systems need to perceive, understand, and perform complex actions
in the physical world. In this paper, we present the Cosmos-Reason1 models that
can understand the physical world and generate appropriate embodied decisions
(e.g., next step action) in natural language through long chain-of-thought
reasoning processes. We begin by defining key capabilities for Physical AI
reasoning, with a focus on physical common sense and embodied reasoning. To
represent physical common sense, we use a hierarchical ontology that captures
fundamental knowledge about space, time, and physics. For embodied reasoning,
we rely on a two-dimensional ontology that generalizes across different
physical embodiments. Building on these capabilities, we develop two multimodal
large language models, Cosmos-Reason1-8B and Cosmos-Reason1-56B. We curate data
and train our models in four stages: vision pre-training, general supervised
fine-tuning (SFT), Physical AI SFT, and Physical AI reinforcement learning (RL)
as the post-training. To evaluate our models, we build comprehensive benchmarks
for physical common sense and embodied reasoning according to our ontologies.
Evaluation results show that Physical AI SFT and reinforcement learning bring
significant improvements. To facilitate the development of Physical AI, we will
make our code and pre-trained models available under the NVIDIA Open Model
License at https://github.com/nvidia-cosmos/cosmos-reason1.
Fin-R1: A Large Language Model for Financial Reasoning through
  Reinforcement Learning
Authors: name: Zhaowei Liu, name: Xin Guo, name: Fangqi Lou, name: Lingfeng Zeng, name: Jinyi Niu, name: Zixuan Wang, name: Jiajie Xu, name: Weige Cai, name: Ziwei Yang, name: Xueqian Zhao, name: Chao Li, name: Sheng Xu, name: Dezhi Chen, name: Yun Chen, name: Zuo Bai, name: Liwen Zhang
Published: 2025-03-20 15:46:18

Reasoning large language models are rapidly evolving across various domains.
However, their capabilities in handling complex financial tasks still require
in-depth exploration. In this paper, we introduce Fin-R1, a reasoning large
language model specifically designed for the financial sector. Fin-R1 is built
using a two-stage architecture, leveraging a financial reasoning dataset
distilled and processed based on DeepSeek-R1. Through supervised fine-tuning
(SFT) and reinforcement learning (RL) training, it demonstrates performance
close to DeepSeek-R1 with a parameter size of 7 billion across a range of
financial reasoning tasks. It achieves the state-of-the-art (SOTA) in the FinQA
and ConvFinQA tasks between those LLMs in our evaluation, surpassing larger
models in other tasks as well. Fin-R1 showcases strong reasoning and
decision-making capabilities, providing solutions to various problems
encountered in the financial domain. Our code is available at
https://github.com/SUFE-AIFLM-Lab/Fin-R1.
M3: 3D-Spatial MultiModal Memory
Authors: name: Xueyan Zou, name: Yuchen Song, name: Ri-Zhao Qiu, name: Xuanbin Peng, name: Jianglong Ye, name: Sifei Liu, name: Xiaolong Wang
Published: 2025-03-20 17:59:12

We present 3D Spatial MultiModal Memory (M3), a multimodal memory system
designed to retain information about medium-sized static scenes through video
sources for visual perception. By integrating 3D Gaussian Splatting techniques
with foundation models, M3 builds a multimodal memory capable of rendering
feature representations across granularities, encompassing a wide range of
knowledge. In our exploration, we identify two key challenges in previous works
on feature splatting: (1) computational constraints in storing high-dimensional
features for each Gaussian primitive, and (2) misalignment or information loss
between distilled features and foundation model features. To address these
challenges, we propose M3 with key components of principal scene components and
Gaussian memory attention, enabling efficient training and inference. To
validate M3, we conduct comprehensive quantitative evaluations of feature
similarity and downstream tasks, as well as qualitative visualizations to
highlight the pixel trace of Gaussian memory attention. Our approach
encompasses a diverse range of foundation models, including vision-language
models (VLMs), perception models, and large multimodal and language models
(LMMs/LLMs). Furthermore, to demonstrate real-world applicability, we deploy
M3's feature field in indoor scenes on a quadruped robot. Notably, we claim
that M3 is the first work to address the core compression challenges in 3D
feature distillation.
Uni-3DAR: Unified 3D Generation and Understanding via Autoregression on
  Compressed Spatial Tokens
Authors: name: Shuqi Lu, name: Haowei Lin, name: Lin Yao, name: Zhifeng Gao, name: Xiaohong Ji, name: Weinan E, name: Linfeng Zhang, name: Guolin Ke
Published: 2025-03-20 16:07:04

Recent advancements in large language models and their multi-modal extensions
have demonstrated the effectiveness of unifying generation and understanding
through autoregressive next-token prediction. However, despite the critical
role of 3D structural generation and understanding ({3D GU}) in AI for science,
these tasks have largely evolved independently, with autoregressive methods
remaining underexplored. To bridge this gap, we introduce Uni-3DAR, a unified
framework that seamlessly integrates {3D GU} tasks via autoregressive
prediction. At its core, Uni-3DAR employs a novel hierarchical tokenization
that compresses 3D space using an octree, leveraging the inherent sparsity of
3D structures. It then applies an additional tokenization for fine-grained
structural details, capturing key attributes such as atom types and precise
spatial coordinates in microscopic 3D structures. We further propose two
optimizations to enhance efficiency and effectiveness. The first is a two-level
subtree compression strategy, which reduces the octree token sequence by up to
8x. The second is a masked next-token prediction mechanism tailored for
dynamically varying token positions, significantly boosting model performance.
By combining these strategies, Uni-3DAR successfully unifies diverse {3D GU}
tasks within a single autoregressive framework. Extensive experiments across
multiple microscopic {3D GU} tasks, including molecules, proteins, polymers,
and crystals, validate its effectiveness and versatility. Notably, Uni-3DAR
surpasses previous state-of-the-art diffusion models by a substantial margin,
achieving up to 256\% relative improvement while delivering inference speeds up
to 21.8x faster. The code is publicly available at
https://github.com/dptech-corp/Uni-3DAR.
Towards Unified Latent Space for 3D Molecular Latent Diffusion Modeling
Authors: name: Yanchen Luo, name: Zhiyuan Liu, name: Yi Zhao, name: Sihang Li, name: Kenji Kawaguchi, name: Tat-Seng Chua, name: Xiang Wang
Published: 2025-03-19 08:56:13

3D molecule generation is crucial for drug discovery and material science,
requiring models to process complex multi-modalities, including atom types,
chemical bonds, and 3D coordinates. A key challenge is integrating these
modalities of different shapes while maintaining SE(3) equivariance for 3D
coordinates. To achieve this, existing approaches typically maintain separate
latent spaces for invariant and equivariant modalities, reducing efficiency in
both training and sampling. In this work, we propose Unified
Variational Auto-Encoder for 3D Molecular Latent
Diffusion Modeling (UAE-3D), a multi-modal VAE that compresses 3D
molecules into latent sequences from a unified latent space, while maintaining
near-zero reconstruction error. This unified latent space eliminates the
complexities of handling multi-modality and equivariance when performing latent
diffusion modeling. We demonstrate this by employing the Diffusion
Transformer--a general-purpose diffusion model without any molecular inductive
bias--for latent generation. Extensive experiments on GEOM-Drugs and QM9
datasets demonstrate that our method significantly establishes new benchmarks
in both de novo and conditional 3D molecule generation, achieving
leading efficiency and quality.
Deceptive Humor: A Synthetic Multilingual Benchmark Dataset for Bridging
  Fabricated Claims with Humorous Content
Authors: name: Sai Kartheek Reddy Kasu, name: Shankar Biradar, name: Sunil Saumya
Published: 2025-03-20 10:58:02

This paper presents the Deceptive Humor Dataset (DHD), a novel resource for
studying humor derived from fabricated claims and misinformation. In an era of
rampant misinformation, understanding how humor intertwines with deception is
essential. DHD consists of humor-infused comments generated from false
narratives, incorporating fabricated claims and manipulated information using
the ChatGPT-4o model. Each instance is labeled with a Satire Level, ranging
from 1 for subtle satire to 3 for high-level satire and classified into five
distinct Humor Categories: Dark Humor, Irony, Social Commentary, Wordplay, and
Absurdity. The dataset spans multiple languages including English, Telugu,
Hindi, Kannada, Tamil, and their code-mixed variants (Te-En, Hi-En, Ka-En,
Ta-En), making it a valuable multilingual benchmark. By introducing DHD, we
establish a structured foundation for analyzing humor in deceptive contexts,
paving the way for a new research direction that explores how humor not only
interacts with misinformation but also influences its perception and spread. We
establish strong baselines for the proposed dataset, providing a foundation for
future research to benchmark and advance deceptive humor detection models.
LLM-Mediated Guidance of MARL Systems
Authors: name: Philipp D. Siedler, name: Ian Gemp
Published: 2025-03-16 20:16:13

In complex multi-agent environments, achieving efficient learning and
desirable behaviours is a significant challenge for Multi-Agent Reinforcement
Learning (MARL) systems. This work explores the potential of combining MARL
with Large Language Model (LLM)-mediated interventions to guide agents toward
more desirable behaviours. Specifically, we investigate how LLMs can be used to
interpret and facilitate interventions that shape the learning trajectories of
multiple agents. We experimented with two types of interventions, referred to
as controllers: a Natural Language (NL) Controller and a Rule-Based (RB)
Controller. The NL Controller, which uses an LLM to simulate human-like
interventions, showed a stronger impact than the RB Controller. Our findings
indicate that agents particularly benefit from early interventions, leading to
more efficient training and higher performance. Both intervention types
outperform the baseline without interventions, highlighting the potential of
LLM-mediated guidance to accelerate training and enhance MARL performance in
challenging environments.
SWEET-RL: Training Multi-Turn LLM Agents on Collaborative Reasoning
  Tasks
Authors: name: Yifei Zhou, name: Song Jiang, name: Yuandong Tian, name: Jason Weston, name: Sergey Levine, name: Sainbayar Sukhbaatar, name: Xian Li
Published: 2025-03-19 17:55:08

Large language model (LLM) agents need to perform multi-turn interactions in
real-world tasks. However, existing multi-turn RL algorithms for optimizing LLM
agents fail to perform effective credit assignment over multiple turns while
leveraging the generalization capabilities of LLMs and it remains unclear how
to develop such algorithms. To study this, we first introduce a new benchmark,
ColBench, where an LLM agent interacts with a human collaborator over multiple
turns to solve realistic tasks in backend programming and frontend design.
Building on this benchmark, we propose a novel RL algorithm, SWEET-RL (RL with
Step-WisE Evaluation from Training-time information), that uses a carefully
designed optimization objective to train a critic model with access to
additional training-time information. The critic provides step-level rewards
for improving the policy model. Our experiments demonstrate that SWEET-RL
achieves a 6% absolute improvement in success and win rates on ColBench
compared to other state-of-the-art multi-turn RL algorithms, enabling
Llama-3.1-8B to match or exceed the performance of GPT4-o in realistic
collaborative content creation.
LLM-FE: Automated Feature Engineering for Tabular Data with LLMs as
  Evolutionary Optimizers
Authors: name: Nikhil Abhyankar, name: Parshin Shojaee, name: Chandan K. Reddy
Published: 2025-03-18 17:11:24

Automated feature engineering plays a critical role in improving predictive
model performance for tabular learning tasks. Traditional automated feature
engineering methods are limited by their reliance on pre-defined
transformations within fixed, manually designed search spaces, often neglecting
domain knowledge. Recent advances using Large Language Models (LLMs) have
enabled the integration of domain knowledge into the feature engineering
process. However, existing LLM-based approaches use direct prompting or rely
solely on validation scores for feature selection, failing to leverage insights
from prior feature discovery experiments or establish meaningful reasoning
between feature generation and data-driven performance. To address these
challenges, we propose LLM-FE, a novel framework that combines evolutionary
search with the domain knowledge and reasoning capabilities of LLMs to
automatically discover effective features for tabular learning tasks. LLM-FE
formulates feature engineering as a program search problem, where LLMs propose
new feature transformation programs iteratively, and data-driven feedback
guides the search process. Our results demonstrate that LLM-FE consistently
outperforms state-of-the-art baselines, significantly enhancing the performance
of tabular prediction models across diverse classification and regression
benchmarks.
SkyLadder: Better and Faster Pretraining via Context Window Scheduling
Authors: name: Tongyao Zhu, name: Qian Liu, name: Haonan Wang, name: Shiqi Chen, name: Xiangming Gu, name: Tianyu Pang, name: Min-Yen Kan
Published: 2025-03-19 17:31:15

Recent advancements in LLM pretraining have featured ever-expanding context
windows to process longer sequences. However, our pilot study reveals that
models pretrained with shorter context windows consistently outperform their
long-context counterparts under a fixed token budget. This finding motivates us
to explore an optimal context window scheduling strategy to better balance
long-context capability with pretraining efficiency. To this end, we propose
SkyLadder, a simple yet effective approach that implements a short-to-long
context window transition. SkyLadder preserves strong standard benchmark
performance, while matching or exceeding baseline results on long context
tasks. Through extensive experiments, we pre-train 1B-parameter models (up to
32K context) and 3B-parameter models (8K context) on 100B tokens, demonstrating
that SkyLadder yields consistent gains of up to 3.7% on common benchmarks,
while achieving up to 22% faster training speeds compared to baselines. The
code is at https://github.com/sail-sg/SkyLadder.