Orionnnn/hub-stats huggingface dataset:
EntroPE: Entropy-Guided Dynamic Patch Encoder for Time Series
  Forecasting
Authors: name: Sachith Abeywickrama, name: Emadeldeen Eldele, name: Min Wu, name: Xiaoli Li, name: Chau Yuen
Published: 2025-09-30 12:09:56

Transformer-based models have significantly advanced time series forecasting,
with patch-based input strategies offering efficiency and improved long-horizon
modeling. Yet, existing approaches rely on temporally-agnostic patch
construction, where arbitrary starting positions and fixed lengths fracture
temporal coherence by splitting natural transitions across boundaries. This
naive segmentation often disrupts short-term dependencies and weakens
representation learning. In response, we propose EntroPE (Entropy-Guided
Dynamic Patch Encoder), a novel, temporally informed framework that dynamically
detects transition points via conditional entropy and dynamically places patch
boundaries. This preserves temporal structure while retaining the computational
benefits of patching. EntroPE consists of two key modules, namely an
Entropy-based Dynamic Patcher (EDP) that applies information-theoretic criteria
to locate natural temporal shifts and determine patch boundaries, and an
Adaptive Patch Encoder (APE) that employs pooling and cross-attention to
capture intra-patch dependencies and produce fixed-size latent representations.
These embeddings are then processed by a global transformer to model
inter-patch dynamics. Experiments across long-term forecasting benchmarks
demonstrate that EntroPE improves both accuracy and efficiency, establishing
entropy-guided dynamic patching as a promising new paradigm for time series
modeling. Code is available at: https://github.com/Sachithx/EntroPE.
Estimating Time Series Foundation Model Transferability via In-Context
  Learning
Authors: name: Qingren Yao, name: Ming Jin, name: Chengqi Zhang, name: Chao-Han Huck Yang, name: Jun Qi, name: Shirui Pan
Published: 2025-09-28 07:07:13

Time series foundation models (TSFMs) offer strong zero-shot forecasting via
large-scale pre-training, yet fine-tuning remains critical for boosting
performance in domains with limited public data. With the growing number of
TSFMs, efficiently identifying the best model for downstream fine-tuning
becomes increasingly challenging. In this work, we introduce TimeTic, a
transferability estimation framework that recasts model selection as an
in-context-learning problem: given observations on known (source) datasets, it
predicts how a TSFM will perform after fine-tuning on a downstream (target)
dataset. TimeTic flexibly organizes the observed model-data relationships as
contextual information, allowing it to adapt seamlessly to various test-time
scenarios. Leveraging the natural tabular structure formed by dataset
meta-features, model characteristics, and fine-tuned performance, we employ
tabular foundation models to serve as in-context learners. We further introduce
a novel model characterization based on entropy evolution across model layers,
capturing embedding-space distinctions and enabling TimeTic to generalize
across arbitrary model sets. We establish a comprehensive benchmark for
transferability estimation including 10 datasets, 10 foundation models, and 3
forecasting tasks. On this benchmark, TimeTic's estimation demonstrates strong
alignment with actual fine-tuned performance for previously unseen datasets,
achieving a mean rank correlation of approximately 0.6 and a 30% improvement
compared to using zero-shot performance as the transferability score.
Winning the Pruning Gamble: A Unified Approach to Joint Sample and Token
  Pruning for Efficient Supervised Fine-Tuning
Authors: name: Shaobo Wang, name: Jiaming Wang, name: Jiajun Zhang, name: Cong Wang, name: Yue Min, name: Zichen Wen, name: Fei Huang, name: Huiqiang Jiang, name: Junyang Lin, name: Dayiheng Liu, name: Linfeng Zhang
Published: 2025-09-28 13:27:38

As supervised fine-tuning (SFT) evolves from a lightweight post-training step
into a compute-intensive phase rivaling mid-training in scale, data efficiency
has become critical for aligning large language models (LLMs) under tight
budgets. Existing data pruning methods suffer from a fragmented design: they
operate either at the sample level or the token level in isolation, failing to
jointly optimize both dimensions. This disconnect leads to significant
inefficiencies--high-value samples may still contain redundant tokens, while
token-level pruning often discards crucial instructional or corrective signals
embedded in individual examples. To address this bottleneck, we introduce the
Error-Uncertainty (EU) Plane, a diagnostic framework that jointly characterizes
the heterogeneous utility of training data across samples and tokens. Guided by
this insight, we propose Quadrant-based Tuning (Q-Tuning), a unified framework
that strategically coordinates sample pruning and token pruning. Q-Tuning
employs a two-stage strategy: first, it performs sample-level triage to retain
examples rich in informative misconceptions or calibration signals; second, it
applies an asymmetric token-pruning policy, using a context-aware scoring
mechanism to trim less salient tokens exclusively from misconception samples
while preserving calibration samples in their entirety. Our method sets a new
state of the art across five diverse benchmarks. Remarkably, on SmolLM2-1.7B,
Q-Tuning achieves a +38\% average improvement over the full-data SFT baseline
using only 12.5\% of the original training data. As the first dynamic pruning
approach to consistently outperform full-data training, Q-Tuning provides a
practical and scalable blueprint for maximizing data utilization in
budget-constrained LLM SFT.
jina-reranker-v3: Last but Not Late Interaction for Document Reranking
Authors: name: Feng Wang, name: Yuqing Li, name: Han Xiao
Published: 2025-09-29 17:23:54

jina-reranker-v3 is a 0.6B parameter multilingual document reranker that
introduces a novel last but not late interaction. Unlike late interaction
models such as ColBERT that perform separate encoding followed by multi-vector
matching, our approach conducts causal self-attention between query and
documents within the same context window, enabling rich cross-document
interactions before extracting contextual embeddings from the last token of
each document. This compact architecture achieves state-of-the-art BEIR
performance with 61.94 nDCG@10 while being ten times smaller than generative
listwise rerankers.
BuildBench: Benchmarking LLM Agents on Compiling Real-World Open-Source
  Software
Authors: name: Zehua Zhang, name: Ati Priya Bajaj, name: Divij Handa, name: Siyu Liu, name: Arvind S Raj, name: Hongkai Chen, name: Hulin Wang, name: Yibo Liu, name: Zion Leonahenahe Basque, name: Souradip Nath, name: Vishal Juneja, name: Nikhil Chapre, name: Yan Shoshitaishvili, name: Adam Doupé, name: Chitta Baral, name: Ruoyu Wang
Published: 2025-09-27 03:02:46

Automatically compiling open-source software (OSS) projects is a vital,
labor-intensive, and complex task, which makes it a good challenge for LLM
Agents. Existing methods rely on manually curated rules and workflows, which
cannot adapt to OSS that requires customized configuration or environment
setup. Recent attempts using Large Language Models (LLMs) used selective
evaluation on a subset of highly rated OSS, a practice that underestimates the
realistic challenges of OSS compilation. In practice, compilation instructions
are often absent, dependencies are undocumented, and successful builds may even
require patching source files or modifying build scripts. We propose a more
challenging and realistic benchmark, BUILD-BENCH, comprising OSS that are more
diverse in quality, scale, and characteristics. Furthermore, we propose a
strong baseline LLM-based agent, OSS-BUILD-AGENT, an effective system with
enhanced build instruction retrieval module that achieves state-of-the-art
performance on BUILD-BENCH and is adaptable to heterogeneous OSS
characteristics. We also provide detailed analysis regarding different
compilation method design choices and their influence to the whole task,
offering insights to guide future advances. We believe performance on
BUILD-BENCH can faithfully reflect an agent's ability to tackle compilation as
a complex software engineering tasks, and, as such, our benchmark will spur
innovation with a significant impact on downstream applications in the fields
of software development and software security.
ProfVLM: A Lightweight Video-Language Model for Multi-View Proficiency
  Estimation
Authors: name: Edoardo Bianchi, name: Jacopo Staiano, name: Antonio Liotta
Published: 2025-09-30 14:00:41

Existing approaches to skill proficiency estimation often rely on black-box
video classifiers, ignoring multi-view context and lacking explainability. We
present ProfVLM, a compact vision-language model that reformulates this task as
generative reasoning: it jointly predicts skill level and generates expert-like
feedback from egocentric and exocentric videos. Central to our method is an
AttentiveGatedProjector that dynamically fuses multi-view features, projected
from a frozen TimeSformer backbone into a language model tuned for feedback
generation. Trained on EgoExo4D with expert commentaries, ProfVLM surpasses
state-of-the-art methods while using up to 20x fewer parameters and reducing
training time by up to 60%. Our approach not only achieves superior accuracy
across diverse activities, but also outputs natural language critiques aligned
with performance, offering transparent reasoning. These results highlight
generative vision-language modeling as a powerful new direction for skill
assessment.
DeepScientist: Advancing Frontier-Pushing Scientific Findings
  Progressively
Authors: name: Yixuan Weng, name: Minjun Zhu, name: Qiujie Xie, name: Qiyao Sun, name: Zhen Lin, name: Sifan Liu, name: Yue Zhang
Published: 2025-09-30 17:49:32

While previous AI Scientist systems can generate novel findings, they often
lack the focus to produce scientifically valuable contributions that address
pressing human-defined challenges. We introduce DeepScientist, a system
designed to overcome this by conducting goal-oriented, fully autonomous
scientific discovery over month-long timelines. It formalizes discovery as a
Bayesian Optimization problem, operationalized through a hierarchical
evaluation process consisting of "hypothesize, verify, and analyze". Leveraging
a cumulative Findings Memory, this loop intelligently balances the exploration
of novel hypotheses with exploitation, selectively promoting the most promising
findings to higher-fidelity levels of validation. Consuming over 20,000 GPU
hours, the system generated about 5,000 unique scientific ideas and
experimentally validated approximately 1100 of them, ultimately surpassing
human-designed state-of-the-art (SOTA) methods on three frontier AI tasks by
183.7\%, 1.9\%, and 7.9\%. This work provides the first large-scale evidence of
an AI achieving discoveries that progressively surpass human SOTA on scientific
tasks, producing valuable findings that genuinely push the frontier of
scientific discovery. To facilitate further research into this process, we will
open-source all experimental logs and system code at
https://github.com/ResearAI/DeepScientist/.
TTT3R: 3D Reconstruction as Test-Time Training
Authors: name: Xingyu Chen, name: Yue Chen, name: Yuliang Xiu, name: Andreas Geiger, name: Anpei Chen
Published: 2025-09-30 17:59:51

Modern Recurrent Neural Networks have become a competitive architecture for
3D reconstruction due to their linear-time complexity. However, their
performance degrades significantly when applied beyond the training context
length, revealing limited length generalization. In this work, we revisit the
3D reconstruction foundation models from a Test-Time Training perspective,
framing their designs as an online learning problem. Building on this
perspective, we leverage the alignment confidence between the memory state and
incoming observations to derive a closed-form learning rate for memory updates,
to balance between retaining historical information and adapting to new
observations. This training-free intervention, termed TTT3R, substantially
improves length generalization, achieving a 2times improvement in global
pose estimation over baselines, while operating at 20 FPS with just 6 GB of GPU
memory to process thousands of images. Code available in
https://rover-xingyu.github.io/TTT3R
A Cartography of Open Collaboration in Open Source AI: Mapping
  Practices, Motivations, and Governance in 14 Open Large Language Model
  Projects
Authors: name: Johan Linåker, name: Cailean Osborne, name: Jennifer Ding, name: Ben Burtenshaw
Published: 2025-09-29 18:55:18

The proliferation of open large language models (LLMs) is fostering a vibrant
ecosystem of research and innovation in artificial intelligence (AI). However,
the methods of collaboration used to develop open LLMs both before and after
their public release have not yet been comprehensively studied, limiting our
understanding of how open LLM projects are initiated, organized, and governed
as well as what opportunities there are to foster this ecosystem even further.
We address this gap through an exploratory analysis of open collaboration
throughout the development and reuse lifecycle of open LLMs, drawing on
semi-structured interviews with the developers of 14 open LLMs from grassroots
projects, research institutes, startups, and Big Tech companies in North
America, Europe, Africa, and Asia. We make three key contributions to research
and practice. First, collaboration in open LLM projects extends far beyond the
LLMs themselves, encompassing datasets, benchmarks, open source frameworks,
leaderboards, knowledge sharing and discussion forums, and compute
partnerships, among others. Second, open LLM developers have a variety of
social, economic, and technological motivations, from democratizing AI access
and promoting open science to building regional ecosystems and expanding
language representation. Third, the sampled open LLM projects exhibit five
distinct organizational models, ranging from single company projects to
non-profit-sponsored grassroots projects, which vary in their centralization of
control and community engagement strategies used throughout the open LLM
lifecycle. We conclude with practical recommendations for stakeholders seeking
to support the global community building a more open future for AI.
Who invented deep residual learning?
Authors: name: Juergen Schmidhuber
Published: 2025-09-29 12:57:35

Modern AI is based on deep artificial neural networks (NNs). As of 2025, the
most cited scientific article of the 21st century is an NN paper on deep
residual learning with residual connections. Who invented this? We present a
timeline of the evolution of deep residual learning.
MANI-Pure: Magnitude-Adaptive Noise Injection for Adversarial
  Purification
Authors: name: Xiaoyi Huang, name: Junwei Wu, name: Kejia Zhang, name: Carl Yang, name: Zhiming Luo
Published: 2025-09-29 17:22:40

Adversarial purification with diffusion models has emerged as a promising
defense strategy, but existing methods typically rely on uniform noise
injection, which indiscriminately perturbs all frequencies, corrupting semantic
structures and undermining robustness. Our empirical study reveals that
adversarial perturbations are not uniformly distributed: they are predominantly
concentrated in high-frequency regions, with heterogeneous magnitude intensity
patterns that vary across frequencies and attack types. Motivated by this
observation, we introduce MANI-Pure, a magnitude-adaptive purification
framework that leverages the magnitude spectrum of inputs to guide the
purification process. Instead of injecting homogeneous noise, MANI-Pure
adaptively applies heterogeneous, frequency-targeted noise, effectively
suppressing adversarial perturbations in fragile high-frequency, low-magnitude
bands while preserving semantically critical low-frequency content. Extensive
experiments on CIFAR-10 and ImageNet-1K validate the effectiveness of
MANI-Pure. It narrows the clean accuracy gap to within 0.59 of the original
classifier, while boosting robust accuracy by 2.15, and achieves the top-1
robust accuracy on the RobustBench leaderboard, surpassing the previous
state-of-the-art method.
Mem-α: Learning Memory Construction via Reinforcement Learning
Authors: name: Yu Wang, name: Ryuichi Takanobu, name: Zhiqi Liang, name: Yuzhen Mao, name: Yuanzhe Hu, name: Julian McAuley, name: Xiaojian Wu
Published: 2025-09-30 08:02:34

Large language model (LLM) agents are constrained by limited context windows,
necessitating external memory systems for long-term information understanding.
Current memory-augmented agents typically depend on pre-defined instructions
and tools for memory updates. However, language models may lack the ability to
determine which information to store, how to structure it, and when to update
it, especially as memory systems become more complex. This results in
suboptimal memory construction and information loss. To this end, we propose
Mem-alpha, a reinforcement learning framework that trains agents to effectively
manage complex memory systems through interaction and feedback. We also
construct a specialized training dataset spanning diverse multi-turn
interaction patterns paired with comprehensive evaluation questions designed to
teach effective memory management. During training, agents process sequential
information chunks, learn to extract and store relevant content, then update
the memory system. The reward signal derives from downstream question-answering
accuracy over the full interaction history, directly optimizing for memory
construction. To illustrate the effectiveness of our training framework, we
design a memory architecture comprising core, episodic, and semantic
components, equipped with multiple tools for memory operations. Empirical
evaluation demonstrates that Mem-alpha achieves significant improvements over
existing memory-augmented agent baselines. Despite being trained exclusively on
instances with a maximum length of 30k tokens, our agents exhibit remarkable
generalization to sequences exceeding 400k tokens, over 13x the training
length, highlighting the robustness of Mem-alpha.
DA^2: Depth Anything in Any Direction
Authors: name: Haodong Li, name: Wangguangdong Zheng, name: Jing He, name: Yuhao Liu, name: Xin Lin, name: Xin Yang, name: Ying-Cong Chen, name: Chunchao Guo
Published: 2025-09-30 17:55:37

Panorama has a full FoV (360^circtimes180^circ), offering a more
complete visual description than perspective images. Thanks to this
characteristic, panoramic depth estimation is gaining increasing traction in 3D
vision. However, due to the scarcity of panoramic data, previous methods are
often restricted to in-domain settings, leading to poor zero-shot
generalization. Furthermore, due to the spherical distortions inherent in
panoramas, many approaches rely on perspective splitting (e.g., cubemaps),
which leads to suboptimal efficiency. To address these challenges, we propose
DA^{2}: Depth Anything in
Any Direction, an accurate, zero-shot generalizable, and
fully end-to-end panoramic depth estimator. Specifically, for scaling up
panoramic data, we introduce a data curation engine for generating high-quality
panoramic depth data from perspective, and create sim543K panoramic
RGB-depth pairs, bringing the total to sim607K. To further mitigate the
spherical distortions, we present SphereViT, which explicitly leverages
spherical coordinates to enforce the spherical geometric consistency in
panoramic image features, yielding improved performance. A comprehensive
benchmark on multiple datasets clearly demonstrates DA^{2}'s SoTA
performance, with an average 38% improvement on AbsRel over the strongest
zero-shot baseline. Surprisingly, DA^{2} even outperforms prior in-domain
methods, highlighting its superior zero-shot generalization. Moreover, as an
end-to-end solution, DA^{2} exhibits much higher efficiency over fusion-based
approaches. Both the code and the curated panoramic data will be released.
Project page: https://depth-any-in-any-dir.github.io/.
Learning Human-Perceived Fakeness in AI-Generated Videos via Multimodal
  LLMs
Authors: name: Xingyu Fu, name: Siyi Liu, name: Yinuo Xu, name: Pan Lu, name: Guangqiuse Hu, name: Tianbo Yang, name: Taran Anantasagar, name: Christopher Shen, name: Yikai Mao, name: Yuanzhe Liu, name: Keyush Shah, name: Chung Un Lee, name: Yejin Choi, name: James Zou, name: Dan Roth, name: Chris Callison-Burch
Published: 2025-09-26 17:59:54

Can humans identify AI-generated (fake) videos and provide grounded reasons?
While video generation models have advanced rapidly, a critical dimension --
whether humans can detect deepfake traces within a generated video, i.e.,
spatiotemporal grounded visual artifacts that reveal a video as machine
generated -- has been largely overlooked. We introduce DeeptraceReward, the
first fine-grained, spatially- and temporally- aware benchmark that annotates
human-perceived fake traces for video generation reward. The dataset comprises
4.3K detailed annotations across 3.3K high-quality generated videos. Each
annotation provides a natural-language explanation, pinpoints a bounding-box
region containing the perceived trace, and marks precise onset and offset
timestamps. We consolidate these annotations into 9 major categories of
deepfake traces that lead humans to identify a video as AI-generated, and train
multimodal language models (LMs) as reward models to mimic human judgments and
localizations. On DeeptraceReward, our 7B reward model outperforms GPT-5 by
34.7% on average across fake clue identification, grounding, and explanation.
Interestingly, we observe a consistent difficulty gradient: binary fake v.s.
real classification is substantially easier than fine-grained deepfake trace
detection; within the latter, performance degrades from natural language
explanations (easiest), to spatial grounding, to temporal labeling (hardest).
By foregrounding human-perceived deepfake traces, DeeptraceReward provides a
rigorous testbed and training signal for socially aware and trustworthy video
generation.
LayerD: Decomposing Raster Graphic Designs into Layers
Authors: name: Tomoyuki Suzuki, name: Kang-Jun Liu, name: Naoto Inoue, name: Kota Yamaguchi
Published: 2025-09-29 17:50:12

Designers craft and edit graphic designs in a layer representation, but
layer-based editing becomes impossible once composited into a raster image. In
this work, we propose LayerD, a method to decompose raster graphic designs into
layers for re-editable creative workflow. LayerD addresses the decomposition
task by iteratively extracting unoccluded foreground layers. We propose a
simple yet effective refinement approach taking advantage of the assumption
that layers often exhibit uniform appearance in graphic designs. As
decomposition is ill-posed and the ground-truth layer structure may not be
reliable, we develop a quality metric that addresses the difficulty. In
experiments, we show that LayerD successfully achieves high-quality
decomposition and outperforms baselines. We also demonstrate the use of LayerD
with state-of-the-art image generators and layer-based editing.