Orionnnn/hub-stats huggingface dataset:
TAG: A Decentralized Framework for Multi-Agent Hierarchical
  Reinforcement Learning
Authors: name: Giuseppe Paolo, name: Abdelhakim Benechehab, name: Hamza Cherkaoui, name: Albert Thomas, name: Balázs Kégl
Published: 2025-02-21 12:52:16

Hierarchical organization is fundamental to biological systems and human
societies, yet artificial intelligence systems often rely on monolithic
architectures that limit adaptability and scalability. Current hierarchical
reinforcement learning (HRL) approaches typically restrict hierarchies to two
levels or require centralized training, which limits their practical
applicability. We introduce TAME Agent Framework (TAG), a framework for
constructing fully decentralized hierarchical multi-agent systems.TAG enables
hierarchies of arbitrary depth through a novel LevelEnv concept, which
abstracts each hierarchy level as the environment for the agents above it. This
approach standardizes information flow between levels while preserving loose
coupling, allowing for seamless integration of diverse agent types. We
demonstrate the effectiveness of TAG by implementing hierarchical architectures
that combine different RL agents across multiple levels, achieving improved
performance over classical multi-agent RL baselines on standard benchmarks. Our
results show that decentralized hierarchical organization enhances both
learning speed and final performance, positioning TAG as a promising direction
for scalable multi-agent systems.
Stable-SPAM: How to Train in 4-Bit More Stably than 16-Bit Adam
Authors: name: Tianjin Huang, name: Haotian Hu, name: Zhenyu Zhang, name: Gaojie Jin, name: Xiang Li, name: Li Shen, name: Tianlong Chen, name: Lu Liu, name: Qingsong Wen, name: Zhangyang Wang, name: Shiwei Liu
Published: 2025-02-24 11:09:15

This paper comprehensively evaluates several recently proposed optimizers for
4-bit training, revealing that low-bit precision amplifies sensitivity to
learning rates and often causes unstable gradient norms, leading to divergence
at higher learning rates. Among these, SPAM, a recent optimizer featuring
momentum reset and spike-aware gradient clipping, achieves the best performance
across various bit levels, but struggles to stabilize gradient norms, requiring
careful learning rate tuning. To address these limitations, we propose
Stable-SPAM, which incorporates enhanced gradient normalization and clipping
techniques. In particular, Stable-SPAM (1) adaptively updates the clipping
threshold for spiked gradients by tracking their historical maxima; (2)
normalizes the entire gradient matrix based on its historical l_2-norm
statistics; and (3) inherits momentum reset from SPAM to periodically reset
the first and second moments of Adam, mitigating the accumulation of spiked
gradients. Extensive experiments show that Stable-SPAM effectively stabilizes
gradient norms in 4-bit LLM training, delivering superior performance compared
to Adam and SPAM. Notably, our 4-bit LLaMA-1B model trained with Stable-SPAM
outperforms the BF16 LLaMA-1B trained with Adam by up to 2 perplexity.
Furthermore, when both models are trained in 4-bit, Stable-SPAM achieves the
same loss as Adam while requiring only about half the training steps. Code is
available at https://github.com/TianjinYellow/StableSPAM.git.
Can Community Notes Replace Professional Fact-Checkers?
Authors: name: Nadav Borenstein, name: Greta Warren, name: Desmond Elliott, name: Isabelle Augenstein
Published: 2025-02-19 22:26:39

Two commonly-employed strategies to combat the rise of misinformation on
social media are (i) fact-checking by professional organisations and (ii)
community moderation by platform users. Policy changes by Twitter/X and, more
recently, Meta, signal a shift away from partnerships with fact-checking
organisations and towards an increased reliance on crowdsourced community
notes. However, the extent and nature of dependencies between fact-checking and
helpful community notes remain unclear. To address these questions, we use
language models to annotate a large corpus of Twitter/X community notes with
attributes such as topic, cited sources, and whether they refute claims tied to
broader misinformation narratives. Our analysis reveals that community notes
cite fact-checking sources up to five times more than previously reported.
Fact-checking is especially crucial for notes on posts linked to broader
narratives, which are twice as likely to reference fact-checking sources
compared to other sources. In conclusion, our results show that successful
community moderation heavily relies on professional fact-checking.
The snake in the Brownian sphere
Authors: name: Omer Angel, name: Emmanuel Jacob, name: Brett Kolesnik, name: Grégory Miermont
Published: 2025-02-18 17:21:44

The Brownian sphere is a random metric space, homeomorphic to the
two-dimensional sphere, which arises as the universal scaling limit of many
types of random planar maps. The direct construction of the Brownian sphere is
via a continuous analogue of the Cori--Vauquelin--Schaeffer (CVS) bijection.
The CVS bijection maps labeled trees to planar maps, and the continuous version
maps Aldous' continuum random tree with Brownian labels (the Brownian snake) to
the Brownian sphere. In this work, we describe the inverse of the continuous
CVS bijection, by constructing the Brownian snake as a measurable function of
the Brownian sphere. Special care is needed to work with the orientation of the
Brownian sphere.
M3-AGIQA: Multimodal, Multi-Round, Multi-Aspect AI-Generated Image
  Quality Assessment
Authors: name: Chuan Cui, name: Kejiang Chen, name: Zhihua Wei, name: Wen Shen, name: Weiming Zhang, name: Nenghai Yu
Published: 2025-02-21 03:05:45

The rapid advancement of AI-generated image (AGI) models has introduced
significant challenges in evaluating their quality, which requires considering
multiple dimensions such as perceptual quality, prompt correspondence, and
authenticity. To address these challenges, we propose M3-AGIQA, a comprehensive
framework for AGI quality assessment that is Multimodal, Multi-Round, and
Multi-Aspect. Our approach leverages the capabilities of Multimodal Large
Language Models (MLLMs) as joint text and image encoders and distills advanced
captioning capabilities from online MLLMs into a local model via Low-Rank
Adaptation (LoRA) fine-tuning. The framework includes a structured multi-round
evaluation mechanism, where intermediate image descriptions are generated to
provide deeper insights into the quality, correspondence, and authenticity
aspects. To align predictions with human perceptual judgments, a predictor
constructed by an xLSTM and a regression head is incorporated to process
sequential logits and predict Mean Opinion Scores (MOSs). Extensive experiments
conducted on multiple benchmark datasets demonstrate that M3-AGIQA achieves
state-of-the-art performance, effectively capturing nuanced aspects of AGI
quality. Furthermore, cross-dataset validation confirms its strong
generalizability. The code is available at
https://github.com/strawhatboy/M3-AGIQA.
GCC: Generative Color Constancy via Diffusing a Color Checker
Authors: name: Chen-Wei Chang, name: Cheng-De Fan, name: Chia-Che Chang, name: Yi-Chen Lo, name: Yu-Chee Tseng, name: Jiun-Long Huang, name: Yu-Lun Liu
Published: 2025-02-24 18:59:54

Color constancy methods often struggle to generalize across different camera
sensors due to varying spectral sensitivities. We present GCC, which leverages
diffusion models to inpaint color checkers into images for illumination
estimation. Our key innovations include (1) a single-step deterministic
inference approach that inpaints color checkers reflecting scene illumination,
(2) a Laplacian decomposition technique that preserves checker structure while
allowing illumination-dependent color adaptation, and (3) a mask-based data
augmentation strategy for handling imprecise color checker annotations. GCC
demonstrates superior robustness in cross-camera scenarios, achieving
state-of-the-art worst-25% error rates of 5.15{\deg} and 4.32{\deg} in
bi-directional evaluations. These results highlight our method's stability and
generalization capability across different camera characteristics without
requiring sensor-specific training, making it a versatile solution for
real-world applications.
Reflective Planning: Vision-Language Models for Multi-Stage Long-Horizon
  Robotic Manipulation
Authors: name: Yunhai Feng, name: Jiaming Han, name: Zhuoran Yang, name: Xiangyu Yue, name: Sergey Levine, name: Jianlan Luo
Published: 2025-02-23 20:42:15

Solving complex long-horizon robotic manipulation problems requires
sophisticated high-level planning capabilities, the ability to reason about the
physical world, and reactively choose appropriate motor skills. Vision-language
models (VLMs) pretrained on Internet data could in principle offer a framework
for tackling such problems. However, in their current form, VLMs lack both the
nuanced understanding of intricate physics required for robotic manipulation
and the ability to reason over long horizons to address error compounding
issues. In this paper, we introduce a novel test-time computation framework
that enhances VLMs' physical reasoning capabilities for multi-stage
manipulation tasks. At its core, our approach iteratively improves a pretrained
VLM with a "reflection" mechanism - it uses a generative model to imagine
future world states, leverages these predictions to guide action selection, and
critically reflects on potential suboptimalities to refine its reasoning.
Experimental results demonstrate that our method significantly outperforms
several state-of-the-art commercial VLMs as well as other post-training
approaches such as Monte Carlo Tree Search (MCTS). Videos are available at
https://reflect-vlm.github.io.
MONSTER: Monash Scalable Time Series Evaluation Repository
Authors: name: Angus Dempster, name: Navid Mohammadi Foumani, name: Chang Wei Tan, name: Lynn Miller, name: Amish Mishra, name: Mahsa Salehi, name: Charlotte Pelletier, name: Daniel F. Schmidt, name: Geoffrey I. Webb
Published: 2025-02-21 00:54:40

We introduce MONSTER-the MONash Scalable Time Series Evaluation Repository-a
collection of large datasets for time series classification. The field of time
series classification has benefitted from common benchmarks set by the UCR and
UEA time series classification repositories. However, the datasets in these
benchmarks are small, with median sizes of 217 and 255 examples, respectively.
In consequence they favour a narrow subspace of models that are optimised to
achieve low classification error on a wide variety of smaller datasets, that
is, models that minimise variance, and give little weight to computational
issues such as scalability. Our hope is to diversify the field by introducing
benchmarks using larger datasets. We believe that there is enormous potential
for new progress in the field by engaging with the theoretical and practical
challenges of learning effectively from larger quantities of data.
X-Dancer: Expressive Music to Human Dance Video Generation
Authors: name: Zeyuan Chen, name: Hongyi Xu, name: Guoxian Song, name: You Xie, name: Chenxu Zhang, name: Xin Chen, name: Chao Wang, name: Di Chang, name: Linjie Luo
Published: 2025-02-24 18:47:54

We present X-Dancer, a novel zero-shot music-driven image animation pipeline
that creates diverse and long-range lifelike human dance videos from a single
static image. As its core, we introduce a unified transformer-diffusion
framework, featuring an autoregressive transformer model that synthesize
extended and music-synchronized token sequences for 2D body, head and hands
poses, which then guide a diffusion model to produce coherent and realistic
dance video frames. Unlike traditional methods that primarily generate human
motion in 3D, X-Dancer addresses data limitations and enhances scalability by
modeling a wide spectrum of 2D dance motions, capturing their nuanced alignment
with musical beats through readily available monocular videos. To achieve this,
we first build a spatially compositional token representation from 2D human
pose labels associated with keypoint confidences, encoding both large
articulated body movements (e.g., upper and lower body) and fine-grained
motions (e.g., head and hands). We then design a music-to-motion transformer
model that autoregressively generates music-aligned dance pose token sequences,
incorporating global attention to both musical style and prior motion context.
Finally we leverage a diffusion backbone to animate the reference image with
these synthesized pose tokens through AdaIN, forming a fully differentiable
end-to-end framework. Experimental results demonstrate that X-Dancer is able to
produce both diverse and characterized dance videos, substantially
outperforming state-of-the-art methods in term of diversity, expressiveness and
realism. Code and model will be available for research purposes.
VideoGrain: Modulating Space-Time Attention for Multi-grained Video
  Editing
Authors: name: Xiangpeng Yang, name: Linchao Zhu, name: Hehe Fan, name: Yi Yang
Published: 2025-02-24 15:39:14

Recent advancements in diffusion models have significantly improved video
generation and editing capabilities. However, multi-grained video editing,
which encompasses class-level, instance-level, and part-level modifications,
remains a formidable challenge. The major difficulties in multi-grained editing
include semantic misalignment of text-to-region control and feature coupling
within the diffusion model. To address these difficulties, we present
VideoGrain, a zero-shot approach that modulates space-time (cross- and self-)
attention mechanisms to achieve fine-grained control over video content. We
enhance text-to-region control by amplifying each local prompt's attention to
its corresponding spatial-disentangled region while minimizing interactions
with irrelevant areas in cross-attention. Additionally, we improve feature
separation by increasing intra-region awareness and reducing inter-region
interference in self-attention. Extensive experiments demonstrate our method
achieves state-of-the-art performance in real-world scenarios. Our code, data,
and demos are available at https://knightyxp.github.io/VideoGrain_project_page/
RIFLEx: A Free Lunch for Length Extrapolation in Video Diffusion
  Transformers
Authors: name: Min Zhao, name: Guande He, name: Yixiao Chen, name: Hongzhou Zhu, name: Chongxuan Li, name: Jun Zhu
Published: 2025-02-21 19:28:05

Recent advancements in video generation have enabled models to synthesize
high-quality, minute-long videos. However, generating even longer videos with
temporal coherence remains a major challenge, and existing length extrapolation
methods lead to temporal repetition or motion deceleration. In this work, we
systematically analyze the role of frequency components in positional
embeddings and identify an intrinsic frequency that primarily governs
extrapolation behavior. Based on this insight, we propose RIFLEx, a minimal yet
effective approach that reduces the intrinsic frequency to suppress repetition
while preserving motion consistency, without requiring any additional
modifications. RIFLEx offers a true free lunch--achieving high-quality
2times extrapolation on state-of-the-art video diffusion transformers in a
completely training-free manner. Moreover, it enhances quality and enables
3times extrapolation by minimal fine-tuning without long videos. Project
page and codes:
https://riflex-video.github.io/{https://riflex-video.github.io/.}
Linguistic Generalizability of Test-Time Scaling in Mathematical
  Reasoning
Authors: name: Guijin Son, name: Jiwoo Hong, name: Hyunwoo Ko, name: James Thorne
Published: 2025-02-24 18:36:15

Scaling pre-training compute has proven effective for achieving
mulitlinguality, but does the same hold for test-time scaling? In this work, we
introduce MCLM, a multilingual math benchmark featuring competition-level
problems in 55 languages. We test three test-time scaling methods-Outcome
Reward Modeling (ORM), Process Reward Modeling (ORM), and Budget Forcing
(BF)-on both Qwen2.5-1.5B Math and MR1-1.5B, a multilingual LLM we trained for
extended reasoning. Our experiments show that using Qwen2.5-1.5B Math with ORM
achieves a score of 35.8 on MCLM, while BF on MR1-1.5B attains 35.2. Although
"thinking LLMs" have recently garnered significant attention, we find that
their performance is comparable to traditional scaling methods like best-of-N
once constrained to similar levels of inference FLOPs. Moreover, while BF
yields a 20-point improvement on English AIME, it provides only a 1.94-point
average gain across other languages-a pattern consistent across the other
test-time scaling methods we studied-higlighting that test-time scaling may not
generalize as effectively to multilingual tasks. To foster further research, we
release MCLM, MR1-1.5B, and evaluation results.
Forecasting Open-Weight AI Model Growth on Hugging Face
Authors: name: Kushal Raj Bhandari, name: Pin-Yu Chen, name: Jianxi Gao
Published: 2025-02-21 22:52:19

As the open-weight AI landscape continues to proliferate-with model
development, significant investment, and user interest-it becomes increasingly
important to predict which models will ultimately drive innovation and shape AI
ecosystems. Building on parallels with citation dynamics in scientific
literature, we propose a framework to quantify how an open-weight model's
influence evolves. Specifically, we adapt the model introduced by Wang et al.
for scientific citations, using three key parameters-immediacy, longevity, and
relative fitness-to track the cumulative number of fine-tuned models of an
open-weight model. Our findings reveal that this citation-style approach can
effectively capture the diverse trajectories of open-weight model adoption,
with most models fitting well and outliers indicating unique patterns or abrupt
jumps in usage.
Audio-FLAN: A Preliminary Release
Authors: name: Liumeng Xue, name: Ziya Zhou, name: Jiahao Pan, name: Zixuan Li, name: Shuai Fan, name: Yinghao Ma, name: Sitong Cheng, name: Dongchao Yang, name: Haohan Guo, name: Yujia Xiao, name: Xinsheng Wang, name: Zixuan Shen, name: Chuanbo Zhu, name: Xinshen Zhang, name: Tianchi Liu, name: Ruibin Yuan, name: Zeyue Tian, name: Haohe Liu, name: Emmanouil Benetos, name: Ge Zhang, name: Yike Guo, name: Wei Xue
Published: 2025-02-23 14:24:15

Recent advancements in audio tokenization have significantly enhanced the
integration of audio capabilities into large language models (LLMs). However,
audio understanding and generation are often treated as distinct tasks,
hindering the development of truly unified audio-language models. While
instruction tuning has demonstrated remarkable success in improving
generalization and zero-shot learning across text and vision, its application
to audio remains largely unexplored. A major obstacle is the lack of
comprehensive datasets that unify audio understanding and generation. To
address this, we introduce Audio-FLAN, a large-scale instruction-tuning dataset
covering 80 diverse tasks across speech, music, and sound domains, with over
100 million instances. Audio-FLAN lays the foundation for unified
audio-language models that can seamlessly handle both understanding (e.g.,
transcription, comprehension) and generation (e.g., speech, music, sound) tasks
across a wide range of audio domains in a zero-shot manner. The Audio-FLAN
dataset is available on HuggingFace and GitHub and will be continuously
updated.
Slamming: Training a Speech Language Model on One GPU in a Day
Authors: name: Gallil Maimon, name: Avishai Elmakies, name: Yossi Adi
Published: 2025-02-19 17:21:15

We introduce Slam, a recipe for training high-quality Speech Language Models
(SLMs) on a single academic GPU in 24 hours. We do so through empirical
analysis of model initialisation and architecture, synthetic training data,
preference optimisation with synthetic data and tweaking all other components.
We empirically demonstrate that this training recipe also scales well with more
compute getting results on par with leading SLMs in a fraction of the compute
cost. We hope these insights will make SLM training and research more
accessible. In the context of SLM scaling laws, our results far outperform
predicted compute optimal performance, giving an optimistic view to SLM
feasibility. See code, data, models, samples at -
https://pages.cs.huji.ac.il/adiyoss-lab/slamming .
Benchmarking Temporal Reasoning and Alignment Across Chinese Dynasties
Authors: name: Zhenglin Wang, name: Jialong Wu, name: Pengfei LI, name: Yong Jiang, name: Deyu Zhou
Published: 2025-02-24 07:27:54

Temporal reasoning is fundamental to human cognition and is crucial for
various real-world applications. While recent advances in Large Language Models
have demonstrated promising capabilities in temporal reasoning, existing
benchmarks primarily rely on rule-based construction, lack contextual depth,
and involve a limited range of temporal entities. To address these limitations,
we introduce Chinese Time Reasoning (CTM), a benchmark designed to evaluate
LLMs on temporal reasoning within the extensive scope of Chinese dynastic
chronology. CTM emphasizes cross-entity relationships, pairwise temporal
alignment, and contextualized and culturally-grounded reasoning, providing a
comprehensive evaluation. Extensive experimental results reveal the challenges
posed by CTM and highlight potential avenues for improvement.
DICEPTION: A Generalist Diffusion Model for Visual Perceptual Tasks
Authors: name: Canyu Zhao, name: Mingyu Liu, name: Huanyi Zheng, name: Muzhi Zhu, name: Zhiyue Zhao, name: Hao Chen, name: Tong He, name: Chunhua Shen
Published: 2025-02-24 13:51:06

Our primary goal here is to create a good, generalist perception model that
can tackle multiple tasks, within limits on computational resources and
training data. To achieve this, we resort to text-to-image diffusion models
pre-trained on billions of images. Our exhaustive evaluation metrics
demonstrate that DICEPTION effectively tackles multiple perception tasks,
achieving performance on par with state-of-the-art models. We achieve results
on par with SAM-vit-h using only 0.06% of their data (e.g., 600K vs. 1B
pixel-level annotated images). Inspired by Wang et al., DICEPTION formulates
the outputs of various perception tasks using color encoding; and we show that
the strategy of assigning random colors to different instances is highly
effective in both entity segmentation and semantic segmentation. Unifying
various perception tasks as conditional image generation enables us to fully
leverage pre-trained text-to-image models. Thus, DICEPTION can be efficiently
trained at a cost of orders of magnitude lower, compared to conventional models
that were trained from scratch. When adapting our model to other tasks, it only
requires fine-tuning on as few as 50 images and 1% of its parameters. DICEPTION
provides valuable insights and a more promising solution for visual generalist
models.