dataset-review/dataset-review huggingface dataset:
On Measuring Long-Range Interactions in Graph Neural Networks
ICML 2025

On Measuring Long-Range Interactions in Graph Neural Networks
Jacob Bamberger * 1 Benjamin Gutteridge * 1 Scott le Roux * 1 Michael Bronstein 1 2 Xiaowen Dong 1
Abstract
Long-range graph tasks — those dependent on
interactions between ‘distant’ nodes — are an
open problem in graph neural network research.
Real-world benchmark tasks, especially the Long
Range Graph Benchmark, have become popular
for validating the long-range capability of pro-
posed architectures. However, this is an empirical
approach that lacks both robustness and theoret-
ical underpinning; a more principled characteri-
zation of the long-range problem is required. To
bridge this gap, we formalize long-range interac-
tions in graph tasks, introduce a range measure
for operators on graphs, and validate it with syn-
thetic experiments. We then leverage our measure
to examine commonly used tasks and architec-
tures, and discuss to what extent they are, in fact,
long-range. We believe our work advances efforts
to define and address the long-range problem on
graphs, and that our range measure will aid evalu-
ation of new datasets and architectures.
1. Introduction
Graphs have emerged as a rich data modality, capable of
modeling pairwise relationships between entities in diverse
applications. Graph neural networks (GNNs) have become
the dominant paradigm for learning from such data, with
message passing neural networks (MPNNs) (Gilmer et al.,
2020) standing out as the most widely used framework.
MPNNs operate by iteratively updating a node’s representa-
tion based on information aggregated from its local neigh-
borhood. This local message-passing mechanism has been
highly effective in a wide range of tasks, such as social net-
works (Monti et al., 2019), computational chemistry (Gilmer
et al., 2017) and recommendation systems (Fan et al., 2019).
*Equal contribution 1University of Oxford 2AITHYRA. Corre-
spondence to: Jacob Bamberger <jacob.bamberger@cs.ox.ac.uk>,
Benjamin Gutteridge <beng@robots.ox.ac.u...
Uni-MuMER: Unified Multi-Task Fine-Tuning of Vision-Language Model for Handwritten Mathematical Expression Recognition
NeurIPS 2025

Uni-MuMER: Unified Multi-Task Fine-Tuning of
Vision-Language Model for Handwritten
Mathematical Expression Recognition
Yu Li∗
Jin Jiang∗
Jianhua Zhu
Shuai Peng
Baole Wei
Yuxuan Zhou
Liangcai GaoB
Wangxuan Institute of Computer Technology, Peking University, Beijing, China
liyu@stu.pku.edu.cn
jiangjin@stu.pku.edu.cn
zhujianhuapku@pku.edu.cn
glc@pku.edu.cn
Abstract
Handwritten Mathematical Expression Recognition (HMER) remains a persistent
challenge in Optical Character Recognition (OCR) due to the inherent freedom
of symbol layouts and variability in handwriting styles. Prior methods have faced
performance bottlenecks by proposing isolated architectural modifications, making
them difficult to integrate coherently into a unified framework. Meanwhile, recent
advances in pretrained vision-language models (VLMs) have demonstrated strong
cross-task generalization, offering a promising foundation for developing unified
solutions. In this paper, we introduce Uni-MuMER, which fully fine-tunes a VLM
for the HMER task without modifying its architecture, effectively injecting domain-
specific knowledge into a generalist framework. Our method integrates three data-
driven tasks: Tree-Aware Chain-of-Thought (Tree-CoT) for structured spatial rea-
soning, Error-Driven Learning (EDL) for reducing confusion among visually simi-
lar characters, and Symbol Counting (SC) for improving recognition consistency
in long expressions. Experiments on the CROHME and HME100K datasets show
that Uni-MuMER achieves super state-of-the-art performance, outperforming the
best lightweight specialized model SSAN by 16.31% and the top-performing VLM
Gemini2.5-flash by 24.42% under zero-shot setting. Our datasets, models, and
code are open-sourced at: https://github.com/BFlameSwift/Uni-MuMER
1
Introduction
38.64
37.75
37.45
62.58
62.51
65.30 
72.62
64.26
69.14 
82.05
77.94
79.23
30
40
50
60
70
80
90
100
CROHME 2014
CROHME 2016
CROHME 2019
ExpRate(%)
SSAN(2025)
Qwen2.5VL Zero-Shot
Qwen2.5VL SFT(Vanilla)
Un...
Improving the Effective Receptive Field of Message-Passing Neural Networks
ICML 2025

Improving the Effective Receptive Field of Message-Passing Neural Networks
Shahaf E. Finder 1 2 Ron Shapira Weber 1 2 Moshe Eliasof 3 Oren Freifeld 1 2 4 Eran Treister 1 2
Abstract
Message-Passing Neural Networks (MPNNs)
have become a cornerstone for processing and an-
alyzing graph-structured data. However, their ef-
fectiveness is often hindered by phenomena such
as over-squashing, where long-range dependen-
cies or interactions are inadequately captured and
expressed in the MPNN output. This limitation
mirrors the challenges of the Effective Receptive
Field (ERF) in Convolutional Neural Networks
(CNNs), where the theoretical receptive field is
underutilized in practice. In this work, we show
and theoretically explain the limited ERF prob-
lem in MPNNs.
Furthermore, inspired by re-
cent advances in ERF augmentation for CNNs,
we propose an Interleaved Multiscale Message-
Passing Neural Networks (IM-MPNN) architec-
ture to address these problems in MPNNs. Our
method incorporates a hierarchical coarsening
of the graph, enabling message-passing across
multiscale representations and facilitating long-
range interactions without excessive depth or pa-
rameterization. Through extensive evaluations
on benchmarks such as the Long-Range Graph
Benchmark (LRGB), we demonstrate substantial
improvements over baseline MPNNs in captur-
ing long-range dependencies while maintaining
computational efficiency.
1. Introduction
Graph Neural Networks (GNNs) have emerged as power-
ful tools for solving a wide range of problems modeled
as graphs. Applications span diverse domains, including
combinatorial optimization (Cappart et al., 2023), particle
1Department of Computer Science, Ben-Gurion University, Is-
rael 2Data Science Research Center, Ben-Gurion University, Israel
3Department of Applied Mathematics and Theoretical Physics,
University of Cambridge, United Kingdom 4School of Brain Sci-
ences and Cognition, Ben-Gurion University, Israel. Correspon-
dence to: Shahaf E. Finder <finder...
Imagined Autocurricula
NeurIPS 2025

Imagined Autocurricula
Ahmet H. Güzel∗
University College London AI Centre
Matthew T. Jackson
University of Oxford
Jarek L. Liesen
University of Oxford
Tim Rocktäschel
University College London AI Centre
Jakob N. Foerster
University of Oxford
Ilija Bogunovic
University College London AI Centre
Jack Parker-Holder
University College London AI Centre
Abstract
Training agents to act in embodied environments typically requires vast training
data or access to accurate simulation, neither of which exists for many cases in
the real world. Instead, world models are emerging as an alternative–leveraging
offline, passively collected data, they make it possible to generate diverse worlds
for training agents in simulation. In this work, we harness world models to gen-
erate “imagined” environments to train robust agents capable of generalizing to
novel task variations. One of the challenges in doing this is ensuring the agent
trains on useful generated data. We thus propose a novel approach IMAC (Imag-
ined Autocurricula) leveraging Unsupervised Environment Design (UED), which
induces an automatic curriculum over generated worlds. In a series of challeng-
ing, procedurally generated environments, we show it is possible to achieve strong
transfer performance on held-out environments having trained only inside a world
model learned from a narrower dataset. We believe this opens the path to utilizing
larger-scale, foundation world models for generally capable agents.
1
Introduction
Despite significant advancements in AI, we remain far from generally capable agents [1]. In the
2010s, progress towards this goal was driven by breakthroughs in deep reinforcement learning (RL,
[2]) where agents such as AlphaGo [3] showed it was possible to discover new knowledge beyond
human capabilities, like the famous move37. However, scaling RL in simulation to produce a more
general intelligence remains bottlenecked by the lack of sufficiently diverse and real-world simula-
tors [4, 5]. By contrast...
CO-MOT: Boosting End-to-end Transformer-based Multi-Object Tracking via Coopetition Label Assignment and Shadow Sets
ICLR 2025

Published as a conference paper at ICLR 2025
CO-MOT: BOOSTING END-TO-END TRANSFORMER-
BASED MULTI-OBJECT TRACKING VIA COOPETITION
LABEL ASSIGNMENT AND SHADOW SETS
Feng Yan⇤, Weixin Luo⇤, Yujie Zhong, Yiyang Gan, Lin Ma†
Meituan Inc., China.
{yanfeng05, luoweixin, zhongyujie, ganyiyang}@meituan.com
forest.linma@gmail.com
ABSTRACT
Existing end-to-end Multi-Object Tracking (e2e-MOT) methods have not sur-
passed non-end-to-end tracking-by-detection methods. One possible reason lies
in the training label assignment strategy that consistently binds the tracked objects
with tracking queries and assigns few newborns to detection queries. Such an as-
signment, with one-to-one bipartite matching, yields an unbalanced training, i.e.,
scarce positive samples for detection queries, especially for an enclosed scenew
with the majority of the newborns at the beginning of videos. As such, e2e-MOT
will incline to generate a tracking terminal without renewal or re-initialization,
compared to other tracking-by-detection methods. To alleviate this problem, we
propose Co-MOT, a simple yet effective method to facilitate e2e-MOT by a novel
coopetition label assignment with a shadow concept. Speciﬁcally, we add tracked
objects to the matching targets for detection queries when performing the label
assignment for training the intermediate decoders. For query initialization, we ex-
pand each query by a set of shadow counterparts with limited disturbance to itself.
With extensive ablation studies, Co-MOT achieves superior performances without
extra costs, e.g., 69.4% HOTA on DanceTrack and 52.8% TETA on BDD100K.
Impressively, Co-MOT only requires 38% FLOPs of MOTRv2 with comparable
performances, resulting in the 1.4⇥faster inference speed. Source code is publicly
available at https://github.com/BingfengYan/CO-MOT.
1
INTRODUCTION
Multi-Object Tracking (MOT) is traditionally tackled by a series of tasks, e.g., object detection (Zhao
et al., 2024; Zou et al., 2023), appearance Re-ID (Zheng et al....
Brain network science modelling of sparse neural networks enables Transformers and LLMs to perform as fully connected
NeurIPS 2025

Brain network science modelling of sparse neural
networks enables Transformers and LLMs to perform
as fully connected
Yingtao Zhang1,2, Diego Cerretti1,2, Jialin Zhao1,2, Ziheng Liao1,2, Wenjing Wu1,2
Umberto Michieli4,5 & Carlo Vittorio Cannistraci1,2,3∗
1Center for Complex Network Intelligence (CCNI)†
2Dept. of Computer Science & Technology, 3School of Biomedical Engineering, Tsinghua University
4University of Padova, 5Canva Research
Abstract
This study aims to enlarge our current knowledge on the application of brain-
inspired network science principles for training artificial neural networks (ANNs)
with sparse connectivity. Dynamic sparse training (DST) emulates the synaptic
turnover of real brain networks, reducing the computational demands of train-
ing and inference in ANNs. However, existing DST methods face difficulties in
maintaining peak performance at high connectivity sparsity levels. The Cannistraci-
Hebb training (CHT) is a brain-inspired method that is used in DST for growing
synaptic connectivity in sparse neural networks. CHT leverages a gradient-free,
topology-driven link regrowth mechanism, which has been shown to achieve ultra-
sparse (1% connectivity or lower) advantage across various tasks compared to
fully connected networks. Yet, CHT suffers two main drawbacks: (i) its time
complexity is O(N · d3)- N node network size, d node degree - hence it can be
efficiently applied only to ultra-sparse networks. (ii) it rigidly selects top link
prediction scores, which is inappropriate for the early training epochs, when the
network topology presents many unreliable connections. Here, we design the first
brain-inspired network model - termed bipartite receptive field (BRF) - to initialize
the connectivity of sparse artificial neural networks. Then, we propose a matrix
multiplication GPU-friendly approximation of the CH link predictor, which reduces
the computational complexity to O(N 3), enabling a fast implementation of link
prediction in large-scale m...
Adaptive Cannistraci-Hebb Network Automata Modelling of Complex Networks for Path-based Link Prediction
NeurIPS 2025

Adaptive Cannistraci-Hebb Network Automata
Modelling of Complex Networks for Path-based Link
Prediction
Jialin Zhao1,2
Alessandro Muscoloni1,3,4
Umberto Michieli5,6
Yingtao Zhang1,2
Carlo Vittorio Cannistraci1,2,3,4 ∗
1Center for Complex Network Intelligence (CCNI) †
2Dept. of Computer Science & Technology, 3School of Biomedical Engineering, Tsinghua University
4Biomedical Cybernetics Group, Technische Universität Dresden, Germany ‡
5University of Padova, Italy, 6Canva Research
Abstract
Many complex networks have partially observed or evolving connectivity, making
link prediction a fundamental task. Topological link prediction infers missing
links using only network topology, with applications in social, biological, and
technological systems. The Cannistraci-Hebb (CH) theory provides a topological
formulation of Hebbian learning, grounded on two pillars: (1) the minimization
of external links within local communities, and (2) the path-based definition
of local communities that capture homophilic (similarity-driven) interactions via
paths of length 2 and synergetic (diversity-driven) interactions via paths of length
3. Building on this, we introduce the Cannistraci-Hebb Adaptive (CHA) network
automata, an adaptive learning machine that automatically selects the optimal CH
rule and path length to model each network. CHA unifies theoretical interpretability
and data-driven adaptivity, bridging physics-inspired network science and machine
intelligence. Across 1,269 networks from 14 domains, CHA consistently surpasses
state-of-the-art methods—including SPM, SBM, graph embedding methods, and
message-passing graph neural networks—while revealing the mechanistic princi-
ples governing link formation. Our code is available at https://github.com/
biomedical-cybernetics/Cannistraci_Hebb_network_automata.
1
Introduction
Many complex networks have a connectivity that might be only partially detected or that tends to grow
over time, hence the prediction of non-observed links is a...
Gradient-based Explanations for Deep Learning Survival Models
ICML 2025

Gradient-based Explanations for Deep Learning Survival Models
Sophie Hanna Langbein * 1 2 Niklas Koenen * 1 2 Marvin N. Wright 1 2 3
Abstract
Deep learning survival models often outperform
classical methods in time-to-event predictions,
particularly in personalized medicine, but their
“black box” nature hinders broader adoption. We
propose a framework for gradient-based explana-
tion methods tailored to survival neural networks,
extending their use beyond regression and clas-
sification. We analyze the implications of their
theoretical assumptions for time-dependent ex-
planations in the survival setting and propose ef-
fective visualizations incorporating the temporal
dimension. Experiments on synthetic data show
that gradient-based methods capture the magni-
tude and direction of local and global feature
effects, including time dependencies. We intro-
duce GradSHAP(t), a gradient-based counterpart
to SurvSHAP(t), which outperforms SurvSHAP(t)
and SurvLIME in a computational speed vs. accu-
racy trade-off. Finally, we apply these methods to
medical data with multi-modal inputs, revealing
relevant tabular features and visual patterns, as
well as their temporal dynamics.
1. Introduction
As medical databases expand to include patients’ detailed
medical history and genetic information, healthcare is shift-
ing from population-based models and traditional statistical
approaches targeting the “average” patient to more complex
personalized medicine, which tailors diagnoses to individ-
ual patient characteristics. Survival analysis is fundamental
to medical data analysis, modeling time-to-event outcomes
while accounting for censoring, enabling personalized risk
predictions, and assessing treatment effects to advance clini-
*Equal contribution 1Leibniz Institute for Prevention Research
and Epidemiology – BIPS, Bremen, Germany 2Faculty of Math-
ematics and Computer Science, University of Bremen, Germany
3Department of Public Health, University of Copenhagen, Den-
mark. Corre...
Enhancing Adversarial Robustness with Conformal Prediction: A Framework for Guaranteed Model Reliability
ICML 2025

Enhancing Adversarial Robustness with Conformal Prediction: A Framework
for Guaranteed Model Reliability
Jie Bao 1 2 Chuangyin Dang 3 Rui Luo† 2 3 Hanwei Zhang 4 Zhixin Zhou 5
Abstract
As deep learning models are increasingly de-
ployed in high-risk applications, robust defenses
against adversarial attacks and reliable perfor-
mance guarantees become paramount. Moreover,
accuracy alone does not provide sufficient assur-
ance or reliable uncertainty estimates for these
models. This study advances adversarial train-
ing by leveraging principles from Conformal Pre-
diction. Specifically, we develop an adversarial
attack method, termed OPSA (OPtimal Size At-
tack), designed to reduce the efficiency of con-
formal prediction at any significance level by
maximizing model uncertainty without requiring
coverage guarantees. Correspondingly, we intro-
duce OPSA-AT (Adversarial Training), a defense
strategy that integrates OPSA within a novel con-
formal training paradigm. Experimental evalua-
tions demonstrate that our OPSA attack method
induces greater uncertainty compared to baseline
approaches for various defenses. Conversely, our
OPSA-AT defensive model significantly enhances
robustness not only against OPSA but also other
adversarial attacks, and maintains reliable pre-
diction. Our findings highlight the effectiveness
of this integrated approach for developing trust-
worthy and resilient deep learning models for
safety-critical domains. Our code is available
at https://github.com/bjbbbb/Enha
ncing-Adversarial-Robustness-wit
h-Conformal-Prediction.
1Huaiyin Institute of Technology, Huai’an, China 2Chengdu
Research Institute, City University of Hong Kong, Chengdu, China
3City University of Hong Kong, Hong Kong, China 4Saarland Uni-
versity, Saarbr¨ucken, Germany 5Alpha Benito Research, Los Ange-
les, USA. Correspondence to: Rui Luo <ruiluo@cityu.edu.hk>.
Proceedings of the 42 nd International Conference on Machine
Learning, Vancouver, Canada. PMLR 267, 2025. Copyright 202...
scSSL-Bench: Benchmarking Self-Supervised Learning for Single-Cell Data
ICML 2025

scSSL-Bench: Benchmarking Self-Supervised Learning for Single-Cell Data
Olga Ovcharenko * 1 Florian Barkmann * 2 Philip Toma * 2 Imant Daunhawer 2 Julia E. Vogt 2
Sebastian Schelter † 1 Valentina Boeva † 2 3 4
Abstract
Self-supervised learning (SSL) has proven to be
a powerful approach for extracting biologically
meaningful representations from single-cell data.
To advance our understanding of SSL methods
applied to single-cell data, we present scSSL-
Bench, a comprehensive benchmark that evalu-
ates nineteen SSL methods. Our evaluation spans
nine datasets and focuses on three common down-
stream tasks: batch correction, cell type anno-
tation, and missing modality prediction.
Fur-
thermore, we systematically assess various data
augmentation strategies.
Our analysis reveals
task-specific trade-offs: the specialized single-cell
frameworks, scVI, CLAIRE, and the finetuned
scGPT excel at uni-modal batch correction, while
generic SSL methods, such as VICReg and Sim-
CLR, demonstrate superior performance in cell
typing and multi-modal data integration. Random
masking emerges as the most effective augmenta-
tion technique across all tasks, surpassing domain-
specific augmentations. Notably, our results indi-
cate the need for a specialized single-cell multi-
modal data integration framework. scSSL-Bench
provides a standardized evaluation platform and
concrete recommendations for applying SSL to
single-cell analysis, advancing the convergence
of deep learning and single-cell genomics.
1. Introduction
Recent progress in single-cell RNA sequencing (scRNA-
seq) and multi-omics sequencing technologies has trans-
formed our understanding of cellular heterogeneity by en-
*† Equal contribution 1BIFOLD & TU Berlin, Berlin, Ger-
many 2Department of Computer Science, ETH Zurich, Zurich,
Switzerland
3Swiss Institute of Bioinformatics,
Lausanne,
Switzerland 4Paris Cit´e University, Cochin Institute, INSERM
U1016, Paris, France. Correspondence to: Olga Ovcharenko
<ovcharenko@tu-berlin....
Neural Guided Diffusion Bridges
ICML 2025

Neural Guided Diffusion Bridges
Gefan Yang 1 Frank van der Meulen 2 Stefan Sommer 1
Abstract
We propose a novel method for simulating condi-
tioned diffusion processes (diffusion bridges) in
Euclidean spaces. By training a neural network
to approximate bridge dynamics, our approach
eliminates the need for computationally intensive
Markov Chain Monte Carlo (MCMC) methods
or score modeling. Compared to existing meth-
ods, it offers greater robustness across various
diffusion specifications and conditioning scenar-
ios. This applies in particular to rare events and
multimodal distributions, which pose challenges
for score-learning- and MCMC-based approaches.
We introduce a flexible variational family, par-
tially specified by a neural network, for approxi-
mating the diffusion bridge path measure. Once
trained, it enables efficient sampling of indepen-
dent bridges at a cost comparable to sampling the
unconditioned (forward) process.
1. Introduction
Diffusion processes play a fundamental role in various fields
such as mathematics, physics, evolutionary biology, and, re-
cently, generative models. In particular, diffusion processes
conditioned to hit a specific point at a fixed future time,
which are often referred to as diffusion bridges, are of great
interest in situations where observations constrain the dy-
namics of a stochastic process. For example, in generative
modeling, stochastic imputation between two given images,
also known as the image translation task, uses diffusion
bridges to model dynamics (Zhou et al., 2024; Zheng et al.,
2025). In the area of stochastic shape analysis and computa-
tional anatomy, random evolutions of biological shapes of
organisms are modeled as non-linear diffusion bridges, and
simulating such bridges is critical to solving inference and
1Department of Computer Science, University of Copenhagen,
Universitetsparken 1, 2100 København, Denmark 2Department of
Mathematics, Vrije Universiteit Amsterdam, De Boelelaan 1111,
1081HV Amsterdam...