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2026 顶会论文

作者位次 #3/4ICLR 2026Poster· Deep Learning / Graph Neural Networks

Efficient Learning on Large Graphs using a Densifying Regularity Lemma

Jonathan Kouchly · Ben Finkelshtein · Michael Bronstein · Ron Levie

效率与系统OpenReview

作者位次 #3/4ICLR 2026Poster· Applications / Time Series

ResCP: Reservoir Conformal Prediction for Time Series Forecasting

Roberto Neglia · Andrea Cini · Michael Bronstein · Filippo Maria Bianchi

OpenReview

作者位次 #4/6ICLR 2026Poster· Deep Learning / Generative Models and Autoencoders

Carré du champ flow matching: better quality-generalisation tradeoff in generative models

Jacob Bamberger · Iolo Jones · Dennis Duncan · Michael Bronstein · Pierre Vandergheynst · Adam Gosztolai

OpenReview

作者位次 #5/7ICLR 2026Poster· Deep Learning / Graph Neural Networks

Flock: A Knowledge Graph Foundation Model via Learning on Random Walks

Jinwoo Kim · Xingyue Huang · Krzysztof Olejniczak · Kyungbin Min · Michael Bronstein · Seunghoon Hong · Ismail I Ceylan

检索与知识OpenReview

作者位次 #4/5ICLR 2026Poster· Deep Learning / Generative Models and Autoencoders

Generalised Flow Maps for Few-Step Generative Modelling on Riemannian Manifolds

Oscar Davis · Michael Albergo · Nicholas Boffi · Michael Bronstein · Joey Bose

OpenReview

作者位次 #5/8ICLR 2026Poster· Applications / Chemistry and Drug Discovery

Efficient Regression-based Training of Normalizing Flows for Boltzmann Generators

Danyal Rehman · Oscar Davis · Jiarui Lu · Jian Tang · Michael Bronstein · Yoshua Bengio · Alexander Tong · Joey Bose

效率与系统OpenReview

作者位次 #3/4ICLR 2026Poster· Applications / Chemistry and Drug Discovery

Learning Escorted Protocols For Multistate Free-Energy Estimation

Lars Holdijk · Nithishwer Mouroug Anand · Michael Bronstein · Max Welling

OpenReview

作者位次 #10/14ICLR 2026Oral

Scaling Atomistic Protein Binder Design with Generative Pretraining and Test-Time Compute

Kieran Didi · Zuobai Zhang · Guoqing Zhou · Danny Reidenbach · Zhonglin Cao · Sooyoung Cha · Tomas Geffner · Christian Dallago · Jian Tang · Michael Bronstein · Martin Steinegger · Emine Kucukbenli · Arash Vahdat · Karsten Kreis

推理数据与预训练OpenReview

作者位次 #3/4ICLR 2026Poster· Deep Learning / Graph Neural Networks

HYPER: A Foundation Model for Inductive Link Prediction with Knowledge Hypergraphs

Xingyue Huang · Mikhail Galkin · Michael Bronstein · Ismail I Ceylan

检索与知识OpenReview

作者位次 #5/8ICLR 2026Oral· Deep Learning / Generative Models and Autoencoders

Planner Aware Path Learning in Diffusion Language Models Training

Zhangzhi Peng · Zachary Bezemek · Jarrid Rector-Brooks · Shuibai Zhang · Michael Bronstein · Anru Zhang · Joey Bose · Alexander Tong

长上下文与架构OpenReview

作者位次 #8/11ICLR 2026Poster· Applications / Chemistry and Drug Discovery

OXtal: An All-Atom Diffusion Model for Organic Crystal Structure Prediction

Emily Jin · Andrei Nica · Mikhail Galkin · Jarrid Rector-Brooks · Kin Long Kelvin Lee · Santiago Miret · Frances Arnold · Michael Bronstein · Joey Bose · Alexander Tong · Chenghao Liu

OpenReview

作者位次 #5/7ICLR 2026Poster· Deep Learning / Attention Mechanisms

Attention Sinks and Compression Valleys in LLMs are Two Sides of the Same Coin

Enrique Queipo-de-Llano · Alvaro Arroyo · Federico Barbero · Xiaowen Dong · Michael Bronstein · Yann LeCun · Ravid Shwartz-Ziv

可解释性效率与系统OpenReview

作者位次 #3/5ICLR 2026Poster· Applications / Chemistry and Drug Discovery

MarS-FM: Generative Modeling of Molecular Dynamics via Markov State Models

Kacper Kapusniak · Cristian Gabellini · Michael Bronstein · Prudencio Tossou · Francesco Di Giovanni

OpenReview

作者位次 #2/2ICLR 2026Poster

LLMs Can Hide Text in Other Text of the Same Length

Antonio Norelli · Michael Bronstein

OpenReview

作者位次 #7/10ICLR 2026Poster· Deep Learning / Graph Neural Networks

TGM: A Modular and Efficient Library for Machine Learning on Temporal Graphs

Jacob Chmura · Shenyang(Andy) Huang · Tran Gia Bao Ngo · Ali Parviz · Farimah Poursafaei · Jure Leskovec · Michael Bronstein · Guillaume Rabusseau · Matthias Fey · Reihaneh Rabbany

效率与系统OpenReview

作者位次 #4/5ICLR 2026Poster· Deep Learning / Graph Neural Networks

Towards Quantifying Long-Range Interactions in Graph Machine Learning: a Large Graph Dataset and a Measurement

Huidong Liang · Haitz Sáez de Ocáriz Borde · Baskaran Sripathmanathan · Michael Bronstein · Xiaowen Dong

评测与基准数据与预训练OpenReview

作者位次 #4/4ICLR 2026Poster· Deep Learning / Graph Neural Networks

gLSTM: Mitigating Over-Squashing by Increasing Storage Capacity

Hugh Blayney · Alvaro Arroyo · Xiaowen Dong · Michael Bronstein

OpenReview

Michael Bronstein

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2026 顶会论文

Efficient Learning on Large Graphs using a Densifying Regularity Lemma

ResCP: Reservoir Conformal Prediction for Time Series Forecasting

Carré du champ flow matching: better quality-generalisation tradeoff in generative models

Flock: A Knowledge Graph Foundation Model via Learning on Random Walks

Generalised Flow Maps for Few-Step Generative Modelling on Riemannian Manifolds

Efficient Regression-based Training of Normalizing Flows for Boltzmann Generators

Learning Escorted Protocols For Multistate Free-Energy Estimation

Scaling Atomistic Protein Binder Design with Generative Pretraining and Test-Time Compute

HYPER: A Foundation Model for Inductive Link Prediction with Knowledge Hypergraphs

Planner Aware Path Learning in Diffusion Language Models Training

OXtal: An All-Atom Diffusion Model for Organic Crystal Structure Prediction

Attention Sinks and Compression Valleys in LLMs are Two Sides of the Same Coin

MarS-FM: Generative Modeling of Molecular Dynamics via Markov State Models

LLMs Can Hide Text in Other Text of the Same Length

TGM: A Modular and Efficient Library for Machine Learning on Temporal Graphs

Towards Quantifying Long-Range Interactions in Graph Machine Learning: a Large Graph Dataset and a Measurement

gLSTM: Mitigating Over-Squashing by Increasing Storage Capacity

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