id stringlengths 10 10 | paper dict | code dict | idea dict | context stringlengths 2k 2.19k | metadata unknown |
|---|---|---|---|---|---|
arb_000001 | {
"pdf_path": "../data/raw_papers/ICML_2025_Tv2JDGw920.pdf",
"title": "One-Step Generalization Ratio Guided Optimization for Domain Generalization",
"conference": "ICML",
"year": 2025,
"note_id": "Tv2JDGw920"
} | {
"repo_path": "../data/raw_repos/00ssum_GENIE.git",
"repo_name": "00ssum_GENIE.git"
} | {
"motivation": "Domain Generalization aims to train models that generalize to unseen target domains but often overfit to domain-specific features, known as un- desired correlations",
"mechanism": "Gradient-based DG methods typically guide gradients in a dominant direction but often inadvertently reinforce spurious... | One-Step Generalization Ratio Guided Optimization for Domain Generalization
Sumin Cho * 1 Dongwon Kim * 1 Kwangsu Kim 1
Abstract
Domain Generalization (DG) aims to train models
that generalize to unseen target domains but often
overfit to domain-specific features, known as un-
desired correlations. Gradient-based DG me... | {
"legacy_id": "metric_quad_000001",
"mapping_score": 1.1393162393,
"repo_scan": {
"has_train_script": false,
"has_environment_spec": true
},
"classification": {
"level_1": "Method",
"level_2": "A5"
},
"openreview": {
"venue": "ICML 2025 oral",
"venueid": "ICML.cc/2025/Conference",... |
arb_000002 | {
"pdf_path": "../data/raw_papers/ICML_2025_wxU2LuTE74.pdf",
"title": "Sparse Autoencoders, Again?",
"conference": "ICML",
"year": 2025,
"note_id": "wxU2LuTE74"
} | {
"repo_path": "../data/raw_repos/19dx_FedLLM-Attack",
"repo_name": "19dx_FedLLM-Attack"
} | {
"motivation": "Sparse Autoencoders, Again",
"mechanism": "Is there really much more to say about sparse au- toencoders",
"outcome": "Autoencoders in general, and SAEs in particular, represent deep architectures that are capable of modeling low-dimensional la- tent structure in data",
"source": "legacy_idea"
} | Sparse Autoencoders, Again?
Yin Lu 1 Xuening Zhu 1 Tong He 2 David Wipf 2
Abstract
Is there really much more to say about sparse au-
toencoders (SAEs)? Autoencoders in general, and
SAEs in particular, represent deep architectures
that are capable of modeling low-dimensional la-
tent structure in data. Such structure co... | {
"legacy_id": "metric_quad_000002",
"mapping_score": 0.3636363636,
"repo_scan": {
"has_train_script": false,
"has_environment_spec": true
},
"classification": {
"level_1": "Method",
"level_2": "B7"
},
"openreview": {
"venue": "ICML 2025 poster",
"venueid": "ICML.cc/2025/Conference... |
arb_000003 | {
"pdf_path": "../data/raw_papers/ICML_2025_eNKEToqt1E.pdf",
"title": "Function-Space Learning Rates",
"conference": "ICML",
"year": 2025,
"note_id": "eNKEToqt1E"
} | {
"repo_path": "../data/raw_repos/2bys_luno-experiments",
"repo_name": "2bys_luno-experiments"
} | {
"motivation": "We consider layerwise function-space learning rates, which measure the magnitude of the change in a neural network’s output function in response to an update to a parameter tensor",
"mechanism": "This con- trasts with traditional learning rates, which de- scribe the magnitude of changes in paramete... | Function-Space Learning Rates
Edward Milsom 1 Ben Anson 1 Laurence Aitchison 1
Abstract
We consider layerwise function-space learning
rates, which measure the magnitude of the change
in a neural network’s output function in response
to an update to a parameter tensor. This con-
trasts with traditional learning rates, w... | {
"legacy_id": "metric_quad_000003",
"mapping_score": 0.4444444444,
"repo_scan": {
"has_train_script": true,
"has_environment_spec": true
},
"classification": {
"level_1": "Method",
"level_2": "A4"
},
"openreview": {
"venue": "ICML 2025 poster",
"venueid": "ICML.cc/2025/Conference"... |
arb_000004 | {
"pdf_path": "../data/raw_papers/ICML_2025_C7dmhyTDrx.pdf",
"title": "Clients Collaborate: Flexible Differentially Private Federated Learning with Guaranteed Improvement of Utility-Privacy Trade-off",
"conference": "ICML",
"year": 2025,
"note_id": "C7dmhyTDrx"
} | {
"repo_path": "../data/raw_repos/6lyc_FedCEO_Collaborate-with-Each-Other",
"repo_name": "6lyc_FedCEO_Collaborate-with-Each-Other"
} | {
"motivation": "To defend against privacy leakage of user data, differential privacy is widely used in federated learning, but it is not free",
"mechanism": "The addition of noise randomly disrupts the semantic integrity of the model and this disturbance accumulates with increased communication rounds",
"outcome... | Clients Collaborate: Flexible Differentially Private Federated Learning with
Guaranteed Improvement of Utility-Privacy Trade-off
Yuecheng Li 1 Lele Fu 1 Tong Wang 2 Jian Lou 1 Bin Chen 3 Lei Yang 1 Jian Shen 4 Zibin Zheng 1
Chuan Chen 1
Abstract
To defend against privacy leakage of user data,
differential privacy is wi... | {
"legacy_id": "metric_quad_000004",
"mapping_score": 1.1039360394,
"repo_scan": {
"has_train_script": false,
"has_environment_spec": true
},
"classification": {
"level_1": "Method",
"level_2": "C6"
},
"openreview": {
"venue": "ICML 2025 poster",
"venueid": "ICML.cc/2025/Conference... |
arb_000005 | {
"pdf_path": "../data/raw_papers/ICML_2025_irY40jSwCH.pdf",
"title": "ML$^2$-GCL: Manifold Learning Inspired Lightweight Graph Contrastive Learning",
"conference": "ICML",
"year": 2025,
"note_id": "irY40jSwCH"
} | {
"repo_path": "../data/raw_repos/a-hou_ML2-GCL",
"repo_name": "a-hou_ML2-GCL"
} | {
"motivation": "ML2-GCL: Manifold Learning Inspired Lightweight Graph Contrastive Learning Jianqing Liang 1 Zhiqiang Li 1 Xinkai Wei 1 Yuan Liu 1 Zhiqiang Wang 1 Graph contrastive learning has attracted great interest as a dominant and promising self- supervised representation learning approach in recent years",
"... | ML2-GCL: Manifold Learning Inspired Lightweight Graph Contrastive
Learning
Jianqing Liang 1 Zhiqiang Li 1 Xinkai Wei 1 Yuan Liu 1 Zhiqiang Wang 1
Abstract
Graph contrastive learning has attracted great
interest as a dominant and promising self-
supervised representation learning approach in
recent years. While existing... | {
"legacy_id": "metric_quad_000005",
"mapping_score": 1.1640791476,
"repo_scan": {
"has_train_script": true,
"has_environment_spec": false
},
"classification": {
"level_1": "Method",
"level_2": "A5"
},
"openreview": {
"venue": "ICML 2025 poster",
"venueid": "ICML.cc/2025/Conference... |
arb_000006 | {
"pdf_path": "../data/raw_papers/ICML_2025_MjVmVakGdx.pdf",
"title": "Stochastic Encodings for Active Feature Acquisition",
"conference": "ICML",
"year": 2025,
"note_id": "MjVmVakGdx"
} | {
"repo_path": "../data/raw_repos/a-norcliffe_SEFA",
"repo_name": "a-norcliffe_SEFA"
} | {
"motivation": "Alexander Norcliffe 1 Changhee Lee 2 Fergus Imrie 3 Mihaela van der Schaar 4 Pietro Liò 1 Active Feature Acquisition is an instance-wise, sequential decision making problem",
"mechanism": "The aim is to dynamically select which feature to measure based on current observations, independently for eac... | Stochastic Encodings for Active Feature Acquisition
Alexander Norcliffe 1 Changhee Lee 2 Fergus Imrie 3 Mihaela van der Schaar 4 Pietro Liò 1
Abstract
Active Feature Acquisition is an instance-wise,
sequential decision making problem. The aim is
to dynamically select which feature to measure
based on current observatio... | {
"legacy_id": "metric_quad_000006",
"mapping_score": 1.2,
"repo_scan": {
"has_train_script": false,
"has_environment_spec": false
},
"classification": {
"level_1": "Method",
"level_2": "A5"
},
"openreview": {
"venue": "ICML 2025 poster",
"venueid": "ICML.cc/2025/Conference",
"... |
arb_000007 | {
"pdf_path": "../data/raw_papers/NeurIPS_2025_cHi8QxGrZH.pdf",
"title": "Diffusion Adaptive Text Embedding for Text-to-Image Diffusion Models",
"conference": "NeurIPS",
"year": 2025,
"note_id": "cHi8QxGrZH"
} | {
"repo_path": "../data/raw_repos/aailab-kaist_DATE",
"repo_name": "aailab-kaist_DATE"
} | {
"motivation": "Byeonghu Na1 Minsang Park1 Gyuwon Sim1 Donghyeok Shin1 HeeSun Bae1 Mina Kang1 Se Jung Kwon2 Wanmo Kang1 Il-Chul Moon1,3 1KAIST, 2NAVER Cloud, 3summary",
"mechanism": "ai {byeonghu",
"outcome": "na,pagemu,gkwlaks4886,tlsehdgur0,cat2507,kasong13}@kaist",
"source": "legacy_idea"
} | Diffusion Adaptive Text Embedding for
Text-to-Image Diffusion Models
Byeonghu Na1
Minsang Park1
Gyuwon Sim1
Donghyeok Shin1
HeeSun Bae1
Mina Kang1
Se Jung Kwon2
Wanmo Kang1
Il-Chul Moon1,3
1KAIST, 2NAVER Cloud, 3summary.ai
{byeonghu.na,pagemu,gkwlaks4886,tlsehdgur0,cat2507,kasong13}@kaist.ac.kr,
sejung.kwon@navercorp.c... | {
"legacy_id": "metric_quad_000007",
"mapping_score": 1.0285714286,
"repo_scan": {
"has_train_script": false,
"has_environment_spec": true
},
"classification": {
"level_1": "Method",
"level_2": "B4"
},
"openreview": {
"venue": "NeurIPS 2025 poster",
"venueid": "NeurIPS.cc/2025/Conf... |
arb_000008 | {
"pdf_path": "../data/raw_papers/NeurIPS_2025_fbDHv2LQZJ.pdf",
"title": "Training-Free Safe Text Embedding Guidance for Text-to-Image Diffusion Models",
"conference": "NeurIPS",
"year": 2025,
"note_id": "fbDHv2LQZJ"
} | {
"repo_path": "../data/raw_repos/aailab-kaist_STG",
"repo_name": "aailab-kaist_STG"
} | {
"motivation": "Byeonghu Na1 Mina Kang1 Jiseok Kwak1 Minsang Park1 Jiwoo Shin1 SeJoon Jun1 Gayoung Lee2 Jin-Hwa Kim2,3 Il-Chul Moon1,4 1KAIST, 2NAVER AI Lab, 3SNU AIIS, 4summary",
"mechanism": "ai {byeonghu",
"outcome": "na,kasong13,jskwak,pagemu,natu33,sjmathy,icmoon}@kaist",
"source": "legacy_idea"
} | Training-Free Safe Text Embedding Guidance for
Text-to-Image Diffusion Models
Byeonghu Na1
Mina Kang1
Jiseok Kwak1
Minsang Park1
Jiwoo Shin1
SeJoon Jun1
Gayoung Lee2
Jin-Hwa Kim2,3
Il-Chul Moon1,4
1KAIST, 2NAVER AI Lab, 3SNU AIIS, 4summary.ai
{byeonghu.na,kasong13,jskwak,pagemu,natu33,sjmathy,icmoon}@kaist.ac.kr,
{gayo... | {
"legacy_id": "metric_quad_000008",
"mapping_score": 1.0577276525000001,
"repo_scan": {
"has_train_script": false,
"has_environment_spec": true
},
"classification": {
"level_1": "Method",
"level_2": "C6"
},
"openreview": {
"venue": "NeurIPS 2025 poster",
"venueid": "NeurIPS.cc/202... |
arb_000009 | {
"pdf_path": "../data/raw_papers/ICLR_2025_qssVptHTPN.pdf",
"title": "Locality Alignment Improves Vision-Language Models",
"conference": "ICLR",
"year": 2025,
"note_id": "qssVptHTPN"
} | {
"repo_path": "../data/raw_repos/Aalto-QuML_DiffAlign",
"repo_name": "Aalto-QuML_DiffAlign"
} | {
"motivation": "Published as a conference paper at ICLR 2025 LOCALITY ALIGNMENT IMPROVES VISION-LANGUAGE MODELS Ian Covert, Tony Sun, James Zou∗, Tatsunori Hashimoto∗ Stanford University {icovert, suntony, jamesz, thashim}@stanford",
"mechanism": "edu Vision language models have seen growing adoption in recent yea... | Published as a conference paper at ICLR 2025
LOCALITY ALIGNMENT IMPROVES VISION-LANGUAGE
MODELS
Ian Covert, Tony Sun, James Zou∗, Tatsunori Hashimoto∗
Stanford University
{icovert, suntony, jamesz, thashim}@stanford.edu
ABSTRACT
Vision language models (VLMs) have seen growing adoption in recent years,
but many still st... | {
"legacy_id": "metric_quad_000009",
"mapping_score": 0.41758241760000003,
"repo_scan": {
"has_train_script": true,
"has_environment_spec": true
},
"classification": {
"level_1": "Method",
"level_2": "B1"
},
"openreview": {
"venue": "ICLR 2025 Poster",
"venueid": "ICLR.cc/2025/Conf... |
arb_000010 | {
"pdf_path": "../data/raw_papers/ICLR_2025_GsR3zRCRX5.pdf",
"title": "Robust Simulation-Based Inference under Missing Data via Neural Processes",
"conference": "ICLR",
"year": 2025,
"note_id": "GsR3zRCRX5"
} | {
"repo_path": "../data/raw_repos/Aalto-QuML_RISE",
"repo_name": "Aalto-QuML_RISE"
} | {
"motivation": "Published as a conference paper at ICLR 2025 ROBUST SIMULATION-BASED INFERENCE UNDER MISSING DATA VIA NEURAL PROCESSES Yogesh Verma, Ayush Bharti Department of Computer Science, Aalto University {yogesh",
"mechanism": "verma, ayush",
"outcome": "bharti}@aalto",
"source": "legacy_idea"
} | Published as a conference paper at ICLR 2025
ROBUST SIMULATION-BASED INFERENCE
UNDER MISSING DATA VIA NEURAL PROCESSES
Yogesh Verma, Ayush Bharti
Department of Computer Science, Aalto University
{yogesh.verma, ayush.bharti}@aalto.fi
Vikas Garg
YaiYai Ltd and Aalto University
vgarg@csail.mit.edu
ABSTRACT
Simulation-base... | {
"legacy_id": "metric_quad_000010",
"mapping_score": 1.148705599,
"repo_scan": {
"has_train_script": false,
"has_environment_spec": false
},
"classification": {
"level_1": "Method",
"level_2": "A4"
},
"openreview": {
"venue": "ICLR 2025 Poster",
"venueid": "ICLR.cc/2025/Conference... |
arb_000011 | {
"pdf_path": "../data/raw_papers/NeurIPS_2025_VCORb5Fw8e.pdf",
"title": "On topological descriptors for graph products",
"conference": "NeurIPS",
"year": 2025,
"note_id": "VCORb5Fw8e"
} | {
"repo_path": "../data/raw_repos/Aalto-QuML_tda_graph_product",
"repo_name": "Aalto-QuML_tda_graph_product"
} | {
"motivation": "However, they are bounded in expressivity by the WL hierarchy [33–35, 50] and cannot compute fundamental graph properties such as cycles or connected components [10, 18]",
"mechanism": "Topological descriptors such as those based on persistent homology and the Euler characteristics can provide such... | On topological descriptors for graph products
Mattie Ji
University of Pennsylvania
mji13@sas.upenn.edu
Amauri H. Souza
Federal Institute of Ceará
amauriholanda@ifce.edu.br
Vikas Garg
Aalto University
YaiYai Ltd
vgarg@csail.mit.edu
Abstract
Topological descriptors have been increasingly utilized for capturing multiscale... | {
"legacy_id": "metric_quad_000011",
"mapping_score": 1.2,
"repo_scan": {
"has_train_script": true,
"has_environment_spec": false
},
"classification": {
"level_1": "Method",
"level_2": "A4"
},
"openreview": {
"venue": "NeurIPS 2025 poster",
"venueid": "NeurIPS.cc/2025/Conference",
... |
arb_000012 | {
"pdf_path": "../data/raw_papers/NeurIPS_2025_zOCENGh1Jg.pdf",
"title": "Private Evolution Converges",
"conference": "NeurIPS",
"year": 2025,
"note_id": "zOCENGh1Jg"
} | {
"repo_path": "../data/raw_repos/aaronmueller_MIB",
"repo_name": "aaronmueller_MIB"
} | {
"motivation": "Tomas Gonzalez Carnegie Mellon University tcgonzal@andrew",
"mechanism": "cmu",
"outcome": "edu Giulia Fanti Carnegie Mellon University gfanti@andrew",
"source": "legacy_idea"
} | Private Evolution Converges
Tomas Gonzalez
Carnegie Mellon University
tcgonzal@andrew.cmu.edu
Giulia Fanti
Carnegie Mellon University
gfanti@andrew.cmu.edu
Aaditya Ramdas
Carnegie Mellon University
aramdas@cs.cmu.edu
Abstract
Private Evolution (PE) is a promising training-free method for differentially private
(DP) syn... | {
"legacy_id": "metric_quad_000012",
"mapping_score": 0.48780487800000005,
"repo_scan": {
"has_train_script": false,
"has_environment_spec": true
},
"classification": {
"level_1": "Method",
"level_2": "A4"
},
"openreview": {
"venue": "NeurIPS 2025 poster",
"venueid": "NeurIPS.cc/20... |
arb_000013 | {
"pdf_path": "../data/raw_papers/NeurIPS_2025_zOCENGh1Jg.pdf",
"title": "Private Evolution Converges",
"conference": "NeurIPS",
"year": 2025,
"note_id": "zOCENGh1Jg"
} | {
"repo_path": "../data/raw_repos/aaronwtr_PertEval",
"repo_name": "aaronwtr_PertEval"
} | {
"motivation": "Tomas Gonzalez Carnegie Mellon University tcgonzal@andrew",
"mechanism": "cmu",
"outcome": "edu Giulia Fanti Carnegie Mellon University gfanti@andrew",
"source": "legacy_idea"
} | Private Evolution Converges
Tomas Gonzalez
Carnegie Mellon University
tcgonzal@andrew.cmu.edu
Giulia Fanti
Carnegie Mellon University
gfanti@andrew.cmu.edu
Aaditya Ramdas
Carnegie Mellon University
aramdas@cs.cmu.edu
Abstract
Private Evolution (PE) is a promising training-free method for differentially private
(DP) syn... | {
"legacy_id": "metric_quad_000013",
"mapping_score": 0.511627907,
"repo_scan": {
"has_train_script": true,
"has_environment_spec": true
},
"classification": {
"level_1": "Method",
"level_2": "A4"
},
"openreview": {
"venue": "NeurIPS 2025 poster",
"venueid": "NeurIPS.cc/2025/Confer... |
arb_000014 | {
"pdf_path": "../data/raw_papers/ICML_2025_JCKkum1Qye.pdf",
"title": "Graph Neural Network Generalization With Gaussian Mixture Model Based Augmentation",
"conference": "ICML",
"year": 2025,
"note_id": "JCKkum1Qye"
} | {
"repo_path": "../data/raw_repos/abbahaddou_GRATIN",
"repo_name": "abbahaddou_GRATIN"
} | {
"motivation": "Yassine Abbahaddou 1 * Fragkiskos D",
"mechanism": "Malliaros 2 Johannes F",
"outcome": "Lutzeyer 1 Amine M",
"source": "legacy_idea"
} | Graph Neural Network Generalization
with Gaussian Mixture Model Based Augmentation
Yassine Abbahaddou 1 * Fragkiskos D. Malliaros 2 Johannes F. Lutzeyer 1
Amine M. Aboussalah 3 Michalis Vazirgiannis 1 4
Abstract
Graph Neural Networks (GNNs) have shown
great promise in tasks like node and graph classi-
fication, but the... | {
"legacy_id": "metric_quad_000014",
"mapping_score": 1.1408825093,
"repo_scan": {
"has_train_script": false,
"has_environment_spec": false
},
"classification": {
"level_1": "Method",
"level_2": "A5"
},
"openreview": {
"venue": "ICML 2025 poster",
"venueid": "ICML.cc/2025/Conferenc... |
arb_000015 | {
"pdf_path": "../data/raw_papers/ICML_2025_V1YfPJDliw.pdf",
"title": "Loss Functions and Operators Generated by f-Divergences",
"conference": "ICML",
"year": 2025,
"note_id": "V1YfPJDliw"
} | {
"repo_path": "../data/raw_repos/abenechehab_AdaPTS",
"repo_name": "abenechehab_AdaPTS"
} | {
"motivation": "cross-entropy loss) is one of the most popular loss functions used for mul- ticlass classification",
"mechanism": "It is also the loss function of choice for next-token prediction in language modeling",
"outcome": "It is associated with the Kullback– Leibler divergence and the softargmax oper- at... | Loss Functions and Operators Generated by f-Divergences
Vincent Roulet 1 Tianlin Liu 1 Nino Vieillard 1 Micha¨el E. Sander 1 Mathieu Blondel 1
Abstract
The logistic loss (a.k.a. cross-entropy loss) is one
of the most popular loss functions used for mul-
ticlass classification. It is also the loss function
of choice for... | {
"legacy_id": "metric_quad_000015",
"mapping_score": 0.4210526316,
"repo_scan": {
"has_train_script": true,
"has_environment_spec": true
},
"classification": {
"level_1": "Method",
"level_2": "A4"
},
"openreview": {
"venue": "ICML 2025 poster",
"venueid": "ICML.cc/2025/Conference"... |
arb_000016 | {
"pdf_path": "../data/raw_papers/ICML_2025_DRvtabzN0n.pdf",
"title": "Zero-Inflated Bandits",
"conference": "ICML",
"year": 2025,
"note_id": "DRvtabzN0n"
} | {
"repo_path": "../data/raw_repos/abukharin3_HERON",
"repo_name": "abukharin3_HERON"
} | {
"motivation": "Many real-world bandit applications are charac- terized by sparse rewards, which can significantly hinder learning efficiency",
"mechanism": "Leveraging problem- specific structures for careful distribution model- ing is recognized as essential for improving esti- mation efficiency in statistics",
... | Zero-Inflated Bandits
Haoyu Wei * 1 Runzhe Wan * 2 Lei Shi 2 Rui Song 2
Abstract
Many real-world bandit applications are charac-
terized by sparse rewards, which can significantly
hinder learning efficiency. Leveraging problem-
specific structures for careful distribution model-
ing is recognized as essential for impro... | {
"legacy_id": "metric_quad_000016",
"mapping_score": 0.3076923077,
"repo_scan": {
"has_train_script": true,
"has_environment_spec": true
},
"classification": {
"level_1": "Theoretical_Analysis",
"level_2": "A3"
},
"openreview": {
"venue": "ICML 2025 poster",
"venueid": "ICML.cc/20... |
arb_000017 | {
"pdf_path": "../data/raw_papers/NeurIPS_2025_T4qJuQCFAK.pdf",
"title": "Activation-Informed Merging of Large Language Models",
"conference": "NeurIPS",
"year": 2025,
"note_id": "T4qJuQCFAK"
} | {
"repo_path": "../data/raw_repos/ahnobari_ActivationInformedMerging",
"repo_name": "ahnobari_ActivationInformedMerging"
} | {
"motivation": "Amin Heyrani Nobari∗1, Kaveh Alim∗1, Ali ArjomandBigdeli2 Akash Srivastava3, Faez Ahmed1, Navid Azizan1 1Massachusetts Institute of Technology 2Stony Brook University 3MIT-IBM Watson AI Lab & Red Hat AI Innovation Model merging, a method that combines the parameters and embeddings of mul- tiple fine-... | Activation-Informed Merging of
Large Language Models
Amin Heyrani Nobari∗1, Kaveh Alim∗1, Ali ArjomandBigdeli2
Akash Srivastava3, Faez Ahmed1, Navid Azizan1
1Massachusetts Institute of Technology
2Stony Brook University
3MIT-IBM Watson AI Lab & Red Hat AI Innovation
Abstract
Model merging, a method that combines the pa... | {
"legacy_id": "metric_quad_000017",
"mapping_score": 0.6493506494,
"repo_scan": {
"has_train_script": false,
"has_environment_spec": false
},
"classification": {
"level_1": "Method",
"level_2": "C6"
},
"openreview": {
"venue": "NeurIPS 2025 poster",
"venueid": "NeurIPS.cc/2025/Con... |
arb_000018 | {
"pdf_path": "../data/raw_papers/ICLR_2025_3IFRygQKGL.pdf",
"title": "OptionZero: Planning with Learned Options",
"conference": "ICLR",
"year": 2025,
"note_id": "3IFRygQKGL"
} | {
"repo_path": "../data/raw_repos/ahnobari_OptimizeAnyTopology",
"repo_name": "ahnobari_OptimizeAnyTopology"
} | {
"motivation": "Published as a conference paper at ICLR 2025 OPTIONZERO: PLANNING WITH LEARNED OPTIONS Po-Wei Huang1,2, Pei-Chiun Peng1,2, Hung Guei1, Ti-Rong Wu1† 1Institute of Information Science, Academia Sinica, Taiwan",
"mechanism": "Previous studies have focused on planning with predefined options or learned... | Published as a conference paper at ICLR 2025
OPTIONZERO: PLANNING WITH LEARNED OPTIONS
Po-Wei Huang1,2, Pei-Chiun Peng1,2, Hung Guei1, Ti-Rong Wu1†
1Institute of Information Science, Academia Sinica, Taiwan
2Department of Computer Science, National Yang Ming Chiao Tung University, Taiwan
ABSTRACT
Planning with options ... | {
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"venueid": "ICLR.cc/2025/Conference",... |
arb_000019 | {
"pdf_path": "../data/raw_papers/NeurIPS_2025_KBJSV1XApq.pdf",
"title": "Ascent Fails to Forget",
"conference": "NeurIPS",
"year": 2025,
"note_id": "KBJSV1XApq"
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"motivation": "Ioannis Mavrothalassitis∗ Pol Puigdemont∗ Noam Itzhak Levi∗ Volkan Cevher LIONS, École Polytechnique Fédérale de Lausanne , Lausanne, Switzerland {ioannis",
"mechanism": "mavrothalassitis, pol",
"outcome": "puigdemontplana, noam",
"source": "legacy_idea"
} | Ascent Fails to Forget
Ioannis Mavrothalassitis∗
Pol Puigdemont∗
Noam Itzhak Levi∗
Volkan Cevher
LIONS, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
{ioannis.mavrothalassitis, pol.puigdemontplana, noam.levi}@epfl.ch
Abstract
Contrary to common belief, we show that gradient ascent-based unconst... | {
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"venueid": "NeurIPS.cc/2025/Conf... |
arb_000020 | {
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"title": "Private Evolution Converges",
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"year": 2025,
"note_id": "zOCENGh1Jg"
} | {
"repo_path": "../data/raw_repos/AI-secure_SafeAuto",
"repo_name": "AI-secure_SafeAuto"
} | {
"motivation": "Tomas Gonzalez Carnegie Mellon University tcgonzal@andrew",
"mechanism": "cmu",
"outcome": "edu Giulia Fanti Carnegie Mellon University gfanti@andrew",
"source": "legacy_idea"
} | Private Evolution Converges
Tomas Gonzalez
Carnegie Mellon University
tcgonzal@andrew.cmu.edu
Giulia Fanti
Carnegie Mellon University
gfanti@andrew.cmu.edu
Aaditya Ramdas
Carnegie Mellon University
aramdas@cs.cmu.edu
Abstract
Private Evolution (PE) is a promising training-free method for differentially private
(DP) syn... | {
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"venueid": "NeurIPS.cc/2025/Confer... |
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"title": "Low Precision Streaming PCA",
"conference": "NeurIPS",
"year": 2025,
"note_id": "GYLMYTYqZg"
} | {
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} | {
"motivation": "Low-Precision Streaming PCA Sanjoy Dasgupta University of California San Diego sadasgupta@ucsd",
"mechanism": "edu Syamantak Kumar University of Texas at Austin syamantak@utexas",
"outcome": "edu Shourya Pandey University of Texas at Austin shouryap@utexas",
"source": "legacy_idea"
} | Low-Precision Streaming PCA
Sanjoy Dasgupta
University of California San Diego
sadasgupta@ucsd.edu
Syamantak Kumar
University of Texas at Austin
syamantak@utexas.edu
Shourya Pandey
University of Texas at Austin
shouryap@utexas.edu
Purnamrita Sarkar
University of Texas at Austin
purna.sarkar@utexas.edu
Abstract
Low-prec... | {
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"venueid": "NeurIPS.cc/2025/Conf... |
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"year": 2025,
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"motivation": "Ibrahim Fayad 1 2 Max Zimmer 3 Martin Schwartz 1 Fabian Gieseke 4 Philippe Ciais 1 Gabriel Belouze 1 Sarah Brood 5 Aurelien De Truchis 2 Alexandre d’Aspremont 5 2 Significant efforts have been directed towards adapting self-supervised multimodal learning for Earth observation applications",
"mechan... | DUNIA: Pixel-Sized Embeddings via Cross-Modal Alignment for Earth
Observation Applications
Ibrahim Fayad 1 2 Max Zimmer 3 Martin Schwartz 1 Fabian Gieseke 4 Philippe Ciais 1 Gabriel Belouze 1
Sarah Brood 5 Aurelien De Truchis 2 Alexandre d’Aspremont 5 2
Abstract
Significant efforts have been directed towards
adapting s... | {
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"... |
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"year": 2025,
"note_id": "1qgZXeMTTU"
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"motivation": "Published as a conference paper at ICLR 2025 CORESET SPECTRAL CLUSTERING Ben Jourdan1, Gregory Schwartzman2, Peter Macgregor3, and He Sun1 1University of Edinburgh, UK 2Japan Advanced Institute of Science and Technology , Japan 3University of St Andrews, UK ben",
"mechanism": "jourdan@ed",
"outco... | Published as a conference paper at ICLR 2025
CORESET SPECTRAL CLUSTERING
Ben Jourdan1, Gregory Schwartzman2, Peter Macgregor3, and He Sun1
1University of Edinburgh, UK
2Japan Advanced Institute of Science and Technology (JAIST), Japan
3University of St Andrews, UK
ben.jourdan@ed.ac.uk, greg@jaist.ac.jp, prm4@st-andrews... | {
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"venue": "ICLR 2025 Poster",
"venueid": "ICLR.cc/2025/Conference"... |
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"title": "Private Evolution Converges",
"conference": "NeurIPS",
"year": 2025,
"note_id": "zOCENGh1Jg"
} | {
"repo_path": "../data/raw_repos/aidos-lab_rings",
"repo_name": "aidos-lab_rings"
} | {
"motivation": "Tomas Gonzalez Carnegie Mellon University tcgonzal@andrew",
"mechanism": "cmu",
"outcome": "edu Giulia Fanti Carnegie Mellon University gfanti@andrew",
"source": "legacy_idea"
} | Private Evolution Converges
Tomas Gonzalez
Carnegie Mellon University
tcgonzal@andrew.cmu.edu
Giulia Fanti
Carnegie Mellon University
gfanti@andrew.cmu.edu
Aaditya Ramdas
Carnegie Mellon University
aramdas@cs.cmu.edu
Abstract
Private Evolution (PE) is a promising training-free method for differentially private
(DP) syn... | {
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"venue": "NeurIPS 2025 poster",
"venueid": "NeurIPS.cc/2025/Conference... |
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"conference": "NeurIPS",
"year": 2025,
"note_id": "zOCENGh1Jg"
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"repo_name": "AIFrameResearch_SPO"
} | {
"motivation": "Tomas Gonzalez Carnegie Mellon University tcgonzal@andrew",
"mechanism": "cmu",
"outcome": "edu Giulia Fanti Carnegie Mellon University gfanti@andrew",
"source": "legacy_idea"
} | Private Evolution Converges
Tomas Gonzalez
Carnegie Mellon University
tcgonzal@andrew.cmu.edu
Giulia Fanti
Carnegie Mellon University
gfanti@andrew.cmu.edu
Aaditya Ramdas
Carnegie Mellon University
aramdas@cs.cmu.edu
Abstract
Private Evolution (PE) is a promising training-free method for differentially private
(DP) syn... | {
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"venue": "NeurIPS 2025 poster",
"venueid": "NeurIPS.cc/2025/Confer... |
arb_000026 | {
"pdf_path": "../data/raw_papers/NeurIPS_2025_0MXUkBmm09.pdf",
"title": "Embodied Cognition Augmented End2End Autonomous Driving",
"conference": "NeurIPS",
"year": 2025,
"note_id": "0MXUkBmm09"
} | {
"repo_path": "../data/raw_repos/AIR-DISCOVER_E-cubed-AD",
"repo_name": "AIR-DISCOVER_E-cubed-AD"
} | {
"motivation": "Ling Niu Tsinghua University nl23@mails",
"mechanism": "tsinghua",
"outcome": "edu",
"source": "legacy_idea"
} | Embodied Cognition Augmented End2End
Autonomous Driving
Ling Niu
Tsinghua University
nl23@mails.tsinghua.edu.cn
Xiaoji Zheng
Tsinghua University
zhengxj24@mails.tsinghua.edu.cn
Han Wang
Tsinghua University
kelsulhgod@gmail.com
Chen Zheng
Tsinghua University
zhengchen@air.tsinghua.edu.cn
Ziyuan Yang
Tsinghua University
... | {
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"venue": "NeurIPS 2025 poster",
"venueid": "NeurIPS.cc/2025/Conference",... |
arb_000027 | {
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"year": 2025,
"note_id": "i0m3hToUwf"
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"repo_path": "../data/raw_repos/akekic_intervention-generalization.git",
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"motivation": "Armin Keki´c 1 Sergio Hernan Garrido Mejia 1 2 Bernhard Sch¨olkopf 1 3 4 Estimating causal effects of joint interventions on multiple variables is crucial in many domains, but obtaining data from such simultaneous interven- tions can be challenging",
"mechanism": "Our study explores how to learn jo... | Learning Joint Interventional Effects from Single-Variable Interventions
in Additive Models
Armin Keki´c 1 Sergio Hernan Garrido Mejia 1 2 Bernhard Sch¨olkopf 1 3 4
Abstract
Estimating causal effects of joint interventions on
multiple variables is crucial in many domains, but
obtaining data from such simultaneous inter... | {
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"venueid": "ICML.cc/2025/Conference",
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arb_000028 | {
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"year": 2025,
"note_id": "VCjPjexvpM"
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"motivation": "Amer Krivoˇsija 1 Alexander Munteanu 1 2 Andr´e Nusser 3 Chris Schwiegelshohn 4 This paper introduces k-Dynamic Time Warping , a novel dissimilarity measure for polyg- onal curves",
"mechanism": "k-DTW has stronger metric proper- ties than Dynamic Time Warping and is more robust to outliers than th... | Improved Learning via k-DTW: A Novel Dissimilarity Measure for Curves
Amer Krivoˇsija 1 Alexander Munteanu 1 2 Andr´e Nusser 3 Chris Schwiegelshohn 4
Abstract
This paper introduces k-Dynamic Time Warping
(k-DTW), a novel dissimilarity measure for polyg-
onal curves. k-DTW has stronger metric proper-
ties than Dynamic T... | {
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"venueid": "ICML.cc/2025/Conference",
"... |
arb_000029 | {
"pdf_path": "../data/raw_papers/ICLR_2025_Fs9EabmQrJ.pdf",
"title": "EmbedLLM: Learning Compact Representations of Large Language Models",
"conference": "ICLR",
"year": 2025,
"note_id": "Fs9EabmQrJ"
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"repo_path": "../data/raw_repos/Ali-E_AMUN",
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"motivation": "2023) have led to the creation of a vast array of models, each tailored for different use cases",
"mechanism": "These models, ranging from small, specialized models to large, general-purpose systems (Hao et al",
"outcome": "2022), differ significantly in their architecture, size, training data, a... | Published as a conference paper at ICLR 2025
EMBEDLLM:
LEARNING
COMPACT
REPRESENTA-
TIONS OF LARGE LANGUAGE MODELS
Richard Zhuang∗Tianhao Wu∗
Zhaojin Wen
Andrew Li
Jiantao Jiao
Kannan Ramchandran
University of California, Berkeley
ABSTRACT
With hundreds of thousands of language models available on Huggingface to-
day, ... | {
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"venue": "ICLR 2025 Spotlight",
"venueid": "ICLR.cc/2025/... |
arb_000030 | {
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"year": 2025,
"note_id": "1i4wNFgHDd"
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"repo_path": "../data/raw_repos/alignrm_Generalizable-MM-RM",
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"motivation": "Avery Hee-Woon Ryoo1,2,∗,B Nanda H Krishna1,2,∗,B Ximeng Mao1,2,∗,B Mehdi Azabou3 Eva L Dyer4 Matthew G Perich1,2,† Guillaume Lajoie1,2,5,†,B 1Mila – Quebec AI Institute 2Université de Montréal 3Columbia University 4University of Pennsylvania 5Canada CIFAR AI Chair ∗Co-first authors †Co-senior author... | Generalizable, real-time neural decoding with hybrid
state-space models
Avery Hee-Woon Ryoo1,2,∗,B
Nanda H Krishna1,2,∗,B
Ximeng Mao1,2,∗,B
Mehdi Azabou3
Eva L Dyer4
Matthew G Perich1,2,†
Guillaume Lajoie1,2,5,†,B
1Mila – Quebec AI Institute
2Université de Montréal
3Columbia University
4University of Pennsylvania
5Cana... | {
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"openreview": {
"venue": "NeurIPS 2025 poster",
"venueid": "NeurIPS.cc/202... |
arb_000031 | {
"pdf_path": "../data/raw_papers/ICML_2025_TMRh3ScSCb.pdf",
"title": "Learning Latent Graph Structures and their Uncertainty",
"conference": "ICML",
"year": 2025,
"note_id": "TMRh3ScSCb"
} | {
"repo_path": "../data/raw_repos/allemanenti_Learning-Calibrated-Structures",
"repo_name": "allemanenti_Learning-Calibrated-Structures"
} | {
"motivation": "Graph neural networks use relational informa- tion as an inductive bias to enhance prediction performance",
"mechanism": "Not rarely, task-relevant relations are unknown and graph structure learning ap- proaches have been proposed to learn them from data",
"outcome": "Given their latent nature, n... | Learning Latent Graph Structures and their Uncertainty
Alessandro Manenti 1 Daniele Zambon 1 Cesare Alippi 1 2
Abstract
Graph neural networks use relational informa-
tion as an inductive bias to enhance prediction
performance. Not rarely, task-relevant relations
are unknown and graph structure learning ap-
proaches hav... | {
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"venue": "ICML 2025 poster",
"venueid": "ICML.cc/2025/Conference",
"... |
arb_000032 | {
"pdf_path": "../data/raw_papers/ICML_2025_OWIPDWhUcO.pdf",
"title": "AdaSplash: Adaptive Sparse Flash Attention",
"conference": "ICML",
"year": 2025,
"note_id": "OWIPDWhUcO"
} | {
"repo_path": "../data/raw_repos/amazon-science_adaptive-abtester",
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} | {
"motivation": "2017) lies the attention mechanism, where each token in a se- quence attends directly to every other token",
"mechanism": "Attention prob- abilities are computed through the softmax transformation, which always assigns a nonzero probability to every token",
"outcome": "However, for long context i... | ADASPLASH: Adaptive Sparse Flash Attention
Nuno Gonc¸alves 1 Marcos Treviso 2 Andr´e F. T. Martins 1 2 3
Abstract
The computational cost of softmax-based atten-
tion in transformers limits their applicability to
long-context tasks. Adaptive sparsity, of which
α-entmax attention is an example, offers a flexi-
ble data-d... | {
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"openreview": {
"venue": "ICML 2025 oral",
"venueid": "ICML.cc/2025/Conference",... |
arb_000033 | {
"pdf_path": "../data/raw_papers/NeurIPS_2025_yeyaKpaufr.pdf",
"title": "Enforcing convex constraints in Graph Neural Networks",
"conference": "NeurIPS",
"year": 2025,
"note_id": "yeyaKpaufr"
} | {
"repo_path": "../data/raw_repos/AMCO-UniPD_monotonic",
"repo_name": "AMCO-UniPD_monotonic"
} | {
"motivation": "Ahmed Rashwan ˚ University of Bath Keith Briggs BT Research Chris Budd University of Bath Lisa Kreusser University of Bath and Monumo Many machine learning applications require outputs that satisfy complex, dy- namic constraints",
"mechanism": "This task is particularly challenging in Graph Neural ... | Enforcing convex constraints in Graph Neural
Networks
Ahmed Rashwan ˚
University of Bath
Keith Briggs
BT Research
Chris Budd
University of Bath
Lisa Kreusser
University of Bath and Monumo
Abstract
Many machine learning applications require outputs that satisfy complex, dy-
namic constraints. This task is particularly c... | {
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"openreview": {
"venue": "NeurIPS 2025 poster",
"venueid": "NeurIPS.cc/202... |
arb_000034 | {
"pdf_path": "../data/raw_papers/ICML_2025_wwYDQ1vXcZ.pdf",
"title": "Relational Conformal Prediction for Correlated Time Series",
"conference": "ICML",
"year": 2025,
"note_id": "wwYDQ1vXcZ"
} | {
"repo_path": "../data/raw_repos/andreacini_corel",
"repo_name": "andreacini_corel"
} | {
"motivation": "We address the problem of uncertainty quantifi- cation in time series forecasting by exploiting observations at correlated sequences",
"mechanism": "Relational deep learning methods leveraging graph repre- sentations are among the most effective tools for obtaining point estimates from spatiotempor... | Relational Conformal Prediction for Correlated Time Series
Andrea Cini 1 2 Alexander Jenkins 1 3 Danilo Mandic 3 Cesare Alippi 1 4 Filippo Maria Bianchi 5 6
Abstract
We address the problem of uncertainty quantifi-
cation in time series forecasting by exploiting
observations at correlated sequences. Relational
deep lear... | {
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"venueid": "ICML.cc/2025/Conference",
"pa... |
arb_000035 | {
"pdf_path": "../data/raw_papers/ICML_2025_P0RkH1RT5z.pdf",
"title": "Subgroups Matter for Robust Bias Mitigation",
"conference": "ICML",
"year": 2025,
"note_id": "P0RkH1RT5z"
} | {
"repo_path": "../data/raw_repos/anissa218_subgroups_bias_mit",
"repo_name": "anissa218_subgroups_bias_mit"
} | {
"motivation": "Anissa Alloula 1 Charles Jones 2 Ben Glocker 2 Bartłomiej W",
"mechanism": "Papie˙z 1 Despite the constant development of new bias mit- igation methods for machine learning, no method consistently succeeds, and a fundamental question remains unanswered: when and why do bias miti- gation techniques ... | Subgroups Matter for Robust Bias Mitigation
Anissa Alloula 1 Charles Jones 2 Ben Glocker 2 Bartłomiej W. Papie˙z 1
Abstract
Despite the constant development of new bias mit-
igation methods for machine learning, no method
consistently succeeds, and a fundamental question
remains unanswered: when and why do bias miti-
g... | {
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Santhosh Karnik * 1 Anna Veselovska * 2 3 Mark Iwen 4 5 Felix Krahmer 2 3
Abstract
We provide a rigorous analysis of implicit regu-
larization in an overparametrized tensor factoriza-
tion problem beyond the lazy training regime. For
matrix fa... | {
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CAKE: CASCADING
AND ADAPTIVE KV CACHE
EVICTION WITH LAYER PREFERENCES
Ziran Qin1,2∗, Yuchen Cao2, Mingbao Lin3†, Wen Hu2, Shixuan Fan2, Ke Cheng2,
Weiyao Lin1†, Jianguo Li2†
1Shanghai Jiao Tong University, 2Ant Group, 3Independent Researcher
ABSTRACT
Large language models (L... | {
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Max Ruiz Luyten * 1 Antonin Berthon * 1 Mihaela van der Schaar 1
Abstract
Real-world human decision making often relies
on strategic planning, where high-level goals
guide the formulation of sub-goals and subsequent
actions, as evidenced by domains such as he... | {
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Antonio Almud´evar 1 Jos´e Miguel Hern´andez-Lobato 2 Sameer Khurana 3 Ricard Marxer 4 Alfonso Ortega 1
Abstract
Contrastive losses have been extensively used
as a tool for multimodal representation learning.
However, it has been empirically observed... | {
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"outcome": "ox",
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} | Published as a conference paper at ICLR 2025
NEURAL INTERACTIVE PROOFS
Lewis Hammond∗
lewis.hammond@cs.ox.ac.uk
Sam Adam-Day∗
sam.adam-day@cs.ox.ac.uk
Department of Computer Science, University of Oxford, Oxford, United Kingdom
ABSTRACT
We consider the problem of how a trusted, but computationally bounded agent
(a ‘ver... | {
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"motivation": "Yang Chen1, Yirun Zhou1, Weizhong Zhang2,3, Cheng Jin1,3∗ 1College of Computer Science and Artificial Intelligence 2School of Data Science, Fudan University 3Shanghai Key Laboratory of Intelligent Information Processing {chen_yang23,yrzhou22}@m",
"mechanism": "fudan",
"outcome": "edu",
"source"... | Complete Structure Guided Point Cloud Completion
via Cluster- and Instance-Level Contrastive Learning
Yang Chen1, Yirun Zhou1, Weizhong Zhang2,3, Cheng Jin1,3∗
1College of Computer Science and Artificial Intelligence
2School of Data Science, Fudan University
3Shanghai Key Laboratory of Intelligent Information Processin... | {
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"year": 2025,
"note_id": "xjTrTlBbrc"
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"repo_path": "../data/raw_repos/apple_ml-predict",
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"motivation": "World models demonstrate strong capa- bilities in prediction, generation, and planning tasks",
"mechanism": "Existing WMs primarily focus on unstruc- tured data while cannot leverage the ubiquitous structured data, often represented as graphs, in the digital world",
"outcome": "While multiple gra... | Graph World Model
Tao Feng 1 Yexin Wu 1 Guanyu Lin 1 Jiaxuan You 1
Abstract
World models (WMs) demonstrate strong capa-
bilities in prediction, generation, and planning
tasks. Existing WMs primarily focus on unstruc-
tured data while cannot leverage the ubiquitous
structured data, often represented as graphs, in
the di... | {
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arb_000043 | {
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"title": "Is Complex Query Answering Really Complex?",
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"year": 2025,
"note_id": "F8NTPAz5HH"
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"repo_path": "../data/raw_repos/april-tools_is-cqa-complex",
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"motivation": "solving tasks that involve several intermediate steps and sub-goals to be completed",
"mechanism": "Complex query answering (CQA; Hamilton et al",
"outcome": "2021; Arakelyan et al",
"source": "legacy_idea"
} | Is Complex Query Answering Really Complex?
Cosimo Gregucci 1 Bo Xiong 2 Daniel Hern´andez 1 Lorenzo Loconte 3
Pasquale Minervini 3 4
Steffen Staab 1 5
Antonio Vergari 3
Abstract
Complex query answering (CQA) on knowledge
graphs (KGs) is gaining momentum as a challeng-
ing reasoning task. In this paper, we show that
the... | {
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"year": 2025,
"note_id": "s4zitEu2R8"
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"motivation": "Arjun Karuvally Salk Institute for Biological Studies akaruvally@salk",
"mechanism": "edu Franz Nowak ETH Zürich franz",
"outcome": "nowak@inf",
"source": "legacy_idea"
} | Bridging Expressivity and Scalability
with Adaptive Unitary SSMs
Arjun Karuvally
Salk Institute for Biological Studies
akaruvally@salk.edu
Franz Nowak
ETH Zürich
franz.nowak@inf.ethz.ch
T. Anderson Keller
The Kempner Institute for the Study of Natural
and Artificial Intelligence at Harvard University
t.anderson.keller@... | {
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... |
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"note_id": "R2834dhBlo"
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"outcome": "ox",
"source": "legacy_idea"
} | Published as a conference paper at ICLR 2025
NEURAL INTERACTIVE PROOFS
Lewis Hammond∗
lewis.hammond@cs.ox.ac.uk
Sam Adam-Day∗
sam.adam-day@cs.ox.ac.uk
Department of Computer Science, University of Oxford, Oxford, United Kingdom
ABSTRACT
We consider the problem of how a trusted, but computationally bounded agent
(a ‘ver... | {
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"venueid": "ICLR.cc/2025/Conferenc... |
arb_000046 | {
"pdf_path": "../data/raw_papers/ICML_2025_lzzPAQ1TxA.pdf",
"title": "A Bregman Proximal Viewpoint on Neural Operators",
"conference": "ICML",
"year": 2025,
"note_id": "lzzPAQ1TxA"
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"motivation": "2021; 2023), a recent extension of neural networks, have emerged as a versatile framework for learning mappings between function spaces",
"mechanism": "These operators have shown great potential in solving par- tial differential equations and simulating complex dynamical systems",
"outcome": "The... | A Bregman Proximal Viewpoint on Neural Operators
Abdel-Rahim Mezidi 1 * Jordan Patracone 1 * Saverio Salzo 2 * Amaury Habrard 1 3 Massimiliano Pontil 4 5
Remi Emonet 1 3 Marc Sebban 1
Abstract
We present several advances on neural operators
by viewing the action of operator layers as the
minimizers of Bregman regulariz... | {
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"venueid": "ICML.cc/2025/Conference",
"pa... |
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"year": 2025,
"note_id": "e8R0ytPhLv"
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"repo_path": "../data/raw_repos/ArthurLeoM_DRESS-LLM",
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"motivation": "Eluder dimension: localise it",
"mechanism": "Alireza Bakhtiari University of Alberta sbakhtia@ualberta",
"outcome": "ca Alex Ayoub University of Alberta aayoub@ualberta",
"source": "legacy_idea"
} | Eluder dimension: localise it!
Alireza Bakhtiari
University of Alberta
sbakhtia@ualberta.ca
Alex Ayoub
University of Alberta
aayoub@ualberta.ca
Samuel Robertson
University of Alberta
smrobert@ualberta.ca
David Janz
University of Oxford
david.janz@stats.ox.ac.uk
Csaba Szepesvári
University of Alberta
szepesva@ualberta.c... | {
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arb_000048 | {
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"title": "FACTER: Fairness-Aware Conformal Thresholding and Prompt Engineering for Enabling Fair LLM-Based Recommender Systems",
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"year": 2025,
"note_id": "edN2rEemj6"
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"motivation": "We propose FACTER, a fairness-aware frame- work for LLM-based recommendation systems that integrates conformal prediction with dynamic prompt engineering",
"mechanism": "By introducing an adap- tive semantic variance threshold and a violation- triggered mechanism, FACTER automatically tightens fair... | FACTER: Fairness-Aware Conformal Thresholding and Prompt Engineering for
Enabling Fair LLM-Based Recommender Systems
Arya Fayyazi 1 Mehdi Kamal 1 Massoud Pedram 1
Abstract
We propose FACTER, a fairness-aware frame-
work for LLM-based recommendation systems
that integrates conformal prediction with dynamic
prompt engine... | {
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"year": 2025,
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"motivation": "Ho Yin Au Hong Kong Baptist University cshyau@comp",
"mechanism": "hkbu",
"outcome": "edu",
"source": "legacy_idea"
} | Deep Compositional Phase Diffusion for Long Motion
Sequence Generation
Ho Yin Au
Hong Kong Baptist University
cshyau@comp.hkbu.edu.hk
Jie Chen∗
Hong Kong Baptist University
chenjie@comp.hkbu.edu.hk
Junkun Jiang
Hong Kong Baptist University
csjkjiang@comp.hkbu.edu.hk
Jingyu Xiang
Hong Kong Baptist University
csjyxiang@c... | {
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"year": 2025,
"note_id": "FXw0okNcOb"
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"motivation": "Published as a conference paper at ICLR 2025 DISCRETE COPULA DIFFUSION Anji Liu1,2, Oliver Broadrick1, Mathias Niepert2, Guy Van den Broeck1",
"mechanism": "ucla",
"outcome": "edu mathias",
"source": "legacy_idea"
} | Published as a conference paper at ICLR 2025
DISCRETE COPULA DIFFUSION
Anji Liu1,2, Oliver Broadrick1, Mathias Niepert2, Guy Van den Broeck1
1Department of Computer Science, University of California, Los Angeles, USA
2Institute for Artificial Intelligence, University of Stuttgart, Germany
{liuanji,obroadrick,guyvdb}@cs... | {
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arb_000051 | {
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"title": "PANDAS: Improving Many-shot Jailbreaking via Positive Affirmation, Negative Demonstration, and Adaptive Sampling",
"conference": "ICML",
"year": 2025,
"note_id": "sEBfiF8JBu"
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"repo_path": "../data/raw_repos/averyma_pandas",
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"motivation": "Many-shot jailbreaking circumvents the safety alignment of LLMs by exploiting their ability to process long input sequences",
"mechanism": "To achieve this, the malicious target prompt is prefixed with hundreds of fabricated conversational exchanges between the user and the model",
"outcome": "Th... | PANDAS: Improving Many-shot Jailbreaking via
Positive Affirmation, Negative Demonstration, and Adaptive Sampling
Avery Ma 1 Yangchen Pan 2 Amir-massoud Farahmand 3
Abstract
Many-shot jailbreaking circumvents the safety
alignment of LLMs by exploiting their ability
to process long input sequences.
To achieve
this, the m... | {
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"venue": "ICML 2025 spotlightposter",
"venueid": "ICML.cc/2025/Conference... |
arb_000052 | {
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"title": "Curvature Enhanced Data Augmentation for Regression",
"conference": "ICML",
"year": 2025,
"note_id": "l1sx5KiM7Z"
} | {
"repo_path": "../data/raw_repos/azencot-group_CEMS",
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"motivation": "Deep learning models with a large number of pa- rameters, often referred to as over-parameterized models, have achieved exceptional performance across various tasks",
"mechanism": "Despite concerns about overfitting, these models frequently generalize well to unseen data, thanks to effective regula... | Curvature Enhanced Data Augmentation for Regression
Ilya Kaufman 1 Omri Azencot 1
Abstract
Deep learning models with a large number of pa-
rameters, often referred to as over-parameterized
models, have achieved exceptional performance
across various tasks.
Despite concerns about
overfitting, these models frequently gen... | {
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"venueid": "ICML.cc/2025/Conference... |
arb_000053 | {
"pdf_path": "../data/raw_papers/NeurIPS_2025_zOCENGh1Jg.pdf",
"title": "Private Evolution Converges",
"conference": "NeurIPS",
"year": 2025,
"note_id": "zOCENGh1Jg"
} | {
"repo_path": "../data/raw_repos/b04901014_vae-gslm",
"repo_name": "b04901014_vae-gslm"
} | {
"motivation": "Tomas Gonzalez Carnegie Mellon University tcgonzal@andrew",
"mechanism": "cmu",
"outcome": "edu Giulia Fanti Carnegie Mellon University gfanti@andrew",
"source": "legacy_idea"
} | Private Evolution Converges
Tomas Gonzalez
Carnegie Mellon University
tcgonzal@andrew.cmu.edu
Giulia Fanti
Carnegie Mellon University
gfanti@andrew.cmu.edu
Aaditya Ramdas
Carnegie Mellon University
aramdas@cs.cmu.edu
Abstract
Private Evolution (PE) is a promising training-free method for differentially private
(DP) syn... | {
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"motivation": "Weihao Tan 1 * Wentao Zhang 1 * Xinrun Xu 2 * Haochong Xia 1 † Ziluo Ding 3 † Boyu Li 3 † Bohan Zhou 4 † Junpeng Yue 4 † Jiechuan Jiang 4 † Yewen Li 1 † Ruyi An 1 † Molei Qin 1 † Chuqiao Zong 1 † Longtao Zheng 1 † Yujie Wu 5 † Xiaoqiang Chai 5 † Yifei Bi 3 Tianbao Xie 6 Pengjie Gu 1 Xiyun Li 3 Ceyao ... | CRADLE: Empowering Foundation Agents
Towards General Computer Control
Weihao Tan 1 * Wentao Zhang 1 * Xinrun Xu 2 * Haochong Xia 1 † Ziluo Ding 3 † Boyu Li 3 † Bohan Zhou 4 †
Junpeng Yue 4 † Jiechuan Jiang 4 † Yewen Li 1 † Ruyi An 1 † Molei Qin 1 † Chuqiao Zong 1 † Longtao Zheng 1 †
Yujie Wu 5 † Xiaoqiang Chai 5 † Yife... | {
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"motivation": "Walid Bendada 1 2 Guillaume Salha-Galvan 3 Romain Hennequin 1 Th´eo Bontempelli 1 Thomas Bouabc¸a 1 Tristan Cazenave 2 This paper introduces von Mises-Fisher explo- ration , a scalable method for explor- ing large action sets in reinforcement learning problems where hyperspherical embedding vec- tors... | Exploring Large Action Sets with Hyperspherical Embeddings
using von Mises-Fisher Sampling
Walid Bendada 1 2 Guillaume Salha-Galvan 3 Romain Hennequin 1
Th´eo Bontempelli 1 Thomas Bouabc¸a 1 Tristan Cazenave 2
Abstract
This paper introduces von Mises-Fisher explo-
ration (vMF-exp), a scalable method for explor-
ing lar... | {
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"year": 2025,
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"motivation": "Published as a conference paper at ICLR 2025 MORAL ALIGNMENT FOR LLM AGENTS Elizaveta Tennant University College London University of Bologna l",
"mechanism": "karmannaya",
"outcome": "16@ucl",
"source": "legacy_idea"
} | Published as a conference paper at ICLR 2025
MORAL ALIGNMENT FOR LLM AGENTS
Elizaveta Tennant
University College London
University of Bologna
l.karmannaya.16@ucl.ac.uk
Stephen Hailes
University College London
s.hailes@ucl.ac.uk
Mirco Musolesi
University College London
University of Bologna
m.musolesi@ucl.ac.uk
ABSTRACT... | {
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"title": "Stochastically Dominant Peer Prediction",
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"year": 2025,
"note_id": "kmooIAb3Pd"
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"repo_path": "../data/raw_repos/Baran-phys_Tropical-Attention",
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} | {
"motivation": "Yichi Zhang DIMACS, Rutgers University yz1636@dimacs",
"mechanism": "rutgers",
"outcome": "edu Shengwei Xu University of Michigan, Ann Arbor shengwei@umich",
"source": "legacy_idea"
} | Stochastically Dominant Peer Prediction
Yichi Zhang
DIMACS, Rutgers University
yz1636@dimacs.rutgers.edu
Shengwei Xu
University of Michigan, Ann Arbor
shengwei@umich.edu
David Pennock
DIMACS, Rutgers University
dpennock@dimacs.rutgers.edu
Grant Schoenebeck
University of Michigan, Ann Arbor
schoeneb@umich.edu
Abstract
E... | {
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arb_000058 | {
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"title": "Private Evolution Converges",
"conference": "NeurIPS",
"year": 2025,
"note_id": "zOCENGh1Jg"
} | {
"repo_path": "../data/raw_repos/basics-lab_spectral-explain",
"repo_name": "basics-lab_spectral-explain"
} | {
"motivation": "Tomas Gonzalez Carnegie Mellon University tcgonzal@andrew",
"mechanism": "cmu",
"outcome": "edu Giulia Fanti Carnegie Mellon University gfanti@andrew",
"source": "legacy_idea"
} | Private Evolution Converges
Tomas Gonzalez
Carnegie Mellon University
tcgonzal@andrew.cmu.edu
Giulia Fanti
Carnegie Mellon University
gfanti@andrew.cmu.edu
Aaditya Ramdas
Carnegie Mellon University
aramdas@cs.cmu.edu
Abstract
Private Evolution (PE) is a promising training-free method for differentially private
(DP) syn... | {
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arb_000059 | {
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"title": "Structure Is All You Need: Structural Representation Learning on Hyper-Relational Knowledge Graphs",
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"year": 2025,
"note_id": "2tH2vexW1Z"
} | {
"repo_path": "../data/raw_repos/bdi-lab_MAYPL",
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"motivation": "Hyper-relational knowledge graphs en- rich knowledge graphs by extending a triplet to a hyper-relational fact, where a set of qualifiers adds auxiliary information to a triplet",
"mechanism": "While many HKG representation learning methods have been proposed, they often fail to effectively utilize ... | Structure Is All You Need: Structural Representation Learning on
Hyper-Relational Knowledge Graphs
Jaejun Lee 1 Joyce Jiyoung Whang 1
Abstract
Hyper-relational knowledge graphs (HKGs) en-
rich knowledge graphs by extending a triplet to
a hyper-relational fact, where a set of qualifiers
adds auxiliary information to a t... | {
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"mechanism": "To overcome this limitation, we introduce the Difficulty and UncertaintyAware Lightweight (DUAL) score, which aims to identify important samples from the early train... | Lightweight Dataset Pruning without Full Training
via Example Difficulty and Prediction Uncertainty
Yeseul Cho * 1 Baekrok Shin * 1 Changmin Kang 1 Chulhee Yun 1
Abstract
Recent advances in deep learning rely heavily
on massive datasets, leading to substantial stor-
age and training costs. Dataset pruning aims to
allev... | {
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Belinda Z. Li 1 Zifan Carl Guo 1 Jacob Andreas 1
Abstract
Transformer language models (LMs) exhibit
behaviors—from storytelling to code generation—
that seem to require tracking the unobserved state
of an evolving world. How do they do this? We
study state tracking in LMs trained o... | {
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arb_000062 | {
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"title": "Physics-Informed Generative Modeling of Wireless Channels",
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"year": 2025,
"note_id": "FFJFT93oa7"
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"motivation": "Benedikt B¨ock 1 Andreas Oeldemann 2 Timo Mayer 2 Francesco Rossetto 2 Wolfgang Utschick 1 Learning the site-specific distribution of the wire- less channel within a particular environment of interest is essential to exploit the full potential of machine learning for wireless communica- tions and rad... | Physics-Informed Generative Modeling of Wireless Channels
Benedikt B¨ock 1 Andreas Oeldemann 2 Timo Mayer 2 Francesco Rossetto 2 Wolfgang Utschick 1
Abstract
Learning the site-specific distribution of the wire-
less channel within a particular environment of
interest is essential to exploit the full potential of
machin... | {
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arb_000063 | {
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"motivation": "Long-range graph tasks — those dependent on interactions between ‘distant’ nodes — are an open problem in graph neural network research",
"mechanism": "Real-world benchmark tasks, especially the Long Range Graph Benchmark, have become popular for validating the long-range capability of pro- posed a... | 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 be... | {
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arb_000064 | {
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"title": "Uni-MuMER: Unified Multi-Task Fine-Tuning of Vision-Language Model for Handwritten Mathematical Expression Recognition",
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"year": 2025,
"note_id": "oHbVboLXz6"
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"motivation": "This work addresses an important challenge in the field by identifying limitations in existing approaches",
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"outcome": "Experimental results demonstrate the effectiveness of our approach com... | 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
zhu... | {
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arb_000065 | {
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"year": 2025,
"note_id": "QJLGj57MfZ"
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"motivation": "Shahaf E",
"mechanism": "Message-Passing Neural Networks have become a cornerstone for processing and an- alyzing graph-structured data",
"outcome": "However, their ef- fectiveness is often hindered by phenomena such as over-squashing, where long-range dependen- cies or interactions are inadequat... | 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-
... | {
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arb_000066 | {
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"year": 2025,
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} | {
"motivation": "Ahmet H",
"mechanism": "Güzel∗ University College London AI Centre Matthew T",
"outcome": "Jackson University of Oxford Jarek L",
"source": "legacy_idea"
} | 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
Univer... | {
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arb_000067 | {
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"title": "CO-MOT: Boosting End-to-end Transformer-based Multi-Object Tracking via Coopetition Label Assignment and Shadow Sets",
"conference": "ICLR",
"year": 2025,
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"motivation": "This work addresses an important challenge in the field by identifying limitations in existing approaches",
"mechanism": "We propose a novel method that leverages advanced techniques to overcome these limitations",
"outcome": "Experimental results demonstrate the effectiveness of our approach com... | 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.co... | {
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"conference": "NeurIPS",
"year": 2025,
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"repo_name": "biomedical-cybernetics_Cannistraci-Hebb-Training-Soft-Rule-"
} | {
"motivation": "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 † 2Dept",
"mechanism": "of Computer Science & Technology, 3School of Biomedical Engineering, Tsinghua University 4Univer... | 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 Compu... | {
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"venueid": "NeurIPS.cc/2025/Conf... |
arb_000069 | {
"pdf_path": "../data/raw_papers/NeurIPS_2025_fxxMReBRhi.pdf",
"title": "Adaptive Cannistraci-Hebb Network Automata Modelling of Complex Networks for Path-based Link Prediction",
"conference": "NeurIPS",
"year": 2025,
"note_id": "fxxMReBRhi"
} | {
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"repo_name": "biomedical-cybernetics_Cannistraci_Hebb_network_automata"
} | {
"motivation": "Jialin Zhao1,2 Alessandro Muscoloni1,3,4 Umberto Michieli5,6 Yingtao Zhang1,2 Carlo Vittorio Cannistraci1,2,3,4 ∗ 1Center for Complex Network Intelligence † 2Dept",
"mechanism": "of Computer Science & Technology, 3School of Biomedical Engineering, Tsinghua University 4Biomedical Cybernetics Group, ... | 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 ... | {
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"venueid": "NeurIPS.cc/2025/Confe... |
arb_000070 | {
"pdf_path": "../data/raw_papers/ICML_2025_P0wSGDoip1.pdf",
"title": "Gradient-based Explanations for Deep Learning Survival Models",
"conference": "ICML",
"year": 2025,
"note_id": "P0wSGDoip1"
} | {
"repo_path": "../data/raw_repos/bips-hb_Survival-XAI-ICML",
"repo_name": "bips-hb_Survival-XAI-ICML"
} | {
"motivation": "Sophie Hanna Langbein * 1 2 Niklas Koenen * 1 2 Marvin N",
"mechanism": "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",
"outcome": "We propose a framework... | 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... | {
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"venue": "ICML 2025 poster",
"venueid": "ICML.cc/2025/Conference",
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arb_000071 | {
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"conference": "ICML",
"year": 2025,
"note_id": "Oty1LQrnFc"
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"motivation": "Jie Bao 1 2 Chuangyin Dang 3 Rui Luo† 2 3 Hanwei Zhang 4 Zhixin Zhou 5 As deep learning models are increasingly de- ployed in high-risk applications, robust defenses against adversarial attacks and reliable perfor- mance guarantees become paramount",
"mechanism": "Moreover, accuracy alone does not ... | 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... | {
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"pdf_path": "../data/raw_papers/ICML_2025_jnPHZqcUdn.pdf",
"title": "scSSL-Bench: Benchmarking Self-Supervised Learning for Single-Cell Data",
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"year": 2025,
"note_id": "jnPHZqcUdn"
} | {
"repo_path": "../data/raw_repos/BoevaLab_scSSL-Bench",
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"motivation": "Olga Ovcharenko * 1 Florian Barkmann * 2 Philip Toma * 2 Imant Daunhawer 2 Julia E",
"mechanism": "Vogt 2 Sebastian Schelter † 1 Valentina Boeva † 2 3 4 Self-supervised learning has proven to be a powerful approach for extracting biologically meaningful representations from single-cell data",
"ou... | 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
meani... | {
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} | {
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"mechanism": "By training a neural network to approximate bridge dynamics, our approach eliminates the need for computationally intensive Markov Chain Monte Carlo methods or score modeling",
"outcome"... | 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 computational... | {
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"year": 2025,
"note_id": "rQK6IWHdzA"
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"motivation": "A Sample-Efficient Conditional Independence Test in the Presence of Discretization Boyang Sun 1 Yu Yao 2 Xinshuai Dong 3 Zongfang Liu 1 Tongliang Liu 2 1 Yumou Qiu 4 Kun Zhang 3 1 In many real-world scenarios, interested variables are often represented as discretized values due to measurement limitat... | A Sample-Efficient Conditional Independence Test
in the Presence of Discretization
Boyang Sun 1 Yu Yao 2 Xinshuai Dong 3 Zongfang Liu 1 Tongliang Liu 2 1 Yumou Qiu 4 Kun Zhang 3 1
Abstract
In many real-world scenarios, interested variables
are often represented as discretized values due
to measurement limitations.
Appl... | {
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"year": 2025,
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"mechanism": "Existing WMs primarily focus on unstruc- tured data while cannot leverage the ubiquitous structured data, often represented as graphs, in the digital world",
"outcome": "While multiple gra... | Graph World Model
Tao Feng 1 Yexin Wu 1 Guanyu Lin 1 Jiaxuan You 1
Abstract
World models (WMs) demonstrate strong capa-
bilities in prediction, generation, and planning
tasks. Existing WMs primarily focus on unstruc-
tured data while cannot leverage the ubiquitous
structured data, often represented as graphs, in
the di... | {
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"venue": "ICML 2025 poster",
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arb_000076 | {
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"year": 2025,
"note_id": "IVUjRWnU6c"
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"repo_path": "../data/raw_repos/brendel-group_llm-line",
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"motivation": "Prasanna Mayilvahanan * 1 2 3 4 Thadd¨aus Wiedemer * 1 2 3 4 Sayak Mallick 1 4 Matthias Bethge 2 3 4 Wieland Brendel 1 2 3 Scaling laws guide the development of large lan- guage models by offering estimates for the optimal balance of model size, tokens, and compute",
"mechanism": "More recently, lo... | LLMs on the Line: Data Determines Loss-to-Loss Scaling Laws
Prasanna Mayilvahanan * 1 2 3 4 Thadd¨aus Wiedemer * 1 2 3 4 Sayak Mallick 1 4 Matthias Bethge 2 3 4
Wieland Brendel 1 2 3
Abstract
Scaling laws guide the development of large lan-
guage models (LLMs) by offering estimates for
the optimal balance of model size... | {
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arb_000077 | {
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"year": 2025,
"note_id": "wxU2LuTE74"
} | {
"repo_path": "../data/raw_repos/BriansIDP_CASEBench",
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"motivation": "Sparse Autoencoders, Again",
"mechanism": "Is there really much more to say about sparse au- toencoders",
"outcome": "Autoencoders in general, and SAEs in particular, represent deep architectures that are capable of modeling low-dimensional la- tent structure in data",
"source": "legacy_idea"
} | Sparse Autoencoders, Again?
Yin Lu 1 Xuening Zhu 1 Tong He 2 David Wipf 2
Abstract
Is there really much more to say about sparse au-
toencoders (SAEs)? Autoencoders in general, and
SAEs in particular, represent deep architectures
that are capable of modeling low-dimensional la-
tent structure in data. Such structure co... | {
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"openreview": {
"venue": "ICML 2025 poster",
"venueid": "ICML.cc/2025/Conference",
"... |
arb_000078 | {
"pdf_path": "../data/raw_papers/NeurIPS_2025_4jgsUhWWaF.pdf",
"title": "Brain-tuning Improves Generalizability and Efficiency of Brain Alignment in Speech Models",
"conference": "NeurIPS",
"year": 2025,
"note_id": "4jgsUhWWaF"
} | {
"repo_path": "../data/raw_repos/bridge-ai-neuro_multi-brain-tuning",
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} | {
"motivation": "Omer Moussa Max Planck Institute for Software Systems Saarbrücken, Germany omoussa@mpi-sws",
"mechanism": "org Mariya Toneva Max Planck Institute for Software Systems Saarbrücken, Germany mtoneva@mpi-sws",
"outcome": "org Pretrained language models are remarkably effective in aligning with human ... | Brain-tuning Improves Generalizability and Efficiency
of Brain Alignment in Speech Models
Omer Moussa
Max Planck Institute for Software Systems
Saarbrücken, Germany
omoussa@mpi-sws.org
Mariya Toneva
Max Planck Institute for Software Systems
Saarbrücken, Germany
mtoneva@mpi-sws.org
Abstract
Pretrained language models ar... | {
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"openreview": {
"venue": "NeurIPS 2025 poster",
"venueid": "NeurIPS.cc/2025/Conference",... |
arb_000079 | {
"pdf_path": "../data/raw_papers/ICLR_2025_MeGDmZjUXy.pdf",
"title": "Moral Alignment for LLM Agents",
"conference": "ICLR",
"year": 2025,
"note_id": "MeGDmZjUXy"
} | {
"repo_path": "../data/raw_repos/bryanbocao_fca",
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"motivation": "Published as a conference paper at ICLR 2025 MORAL ALIGNMENT FOR LLM AGENTS Elizaveta Tennant University College London University of Bologna l",
"mechanism": "karmannaya",
"outcome": "16@ucl",
"source": "legacy_idea"
} | Published as a conference paper at ICLR 2025
MORAL ALIGNMENT FOR LLM AGENTS
Elizaveta Tennant
University College London
University of Bologna
l.karmannaya.16@ucl.ac.uk
Stephen Hailes
University College London
s.hailes@ucl.ac.uk
Mirco Musolesi
University College London
University of Bologna
m.musolesi@ucl.ac.uk
ABSTRACT... | {
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arb_000080 | {
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"title": "Sliding Puzzles Gym: A Scalable Benchmark for State Representation in Visual Reinforcement Learning",
"conference": "ICML",
"year": 2025,
"note_id": "vlF9bZHrJg"
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"motivation": "Bryan L",
"mechanism": "M",
"outcome": "de Oliveira 1 2 Luana G",
"source": "legacy_idea"
} | Sliding Puzzles Gym: A Scalable Benchmark for
State Representation in Visual Reinforcement Learning
Bryan L. M. de Oliveira 1 2 Luana G. B. Martins 1 Bruno Brand˜ao 1 2 Murilo L. da Luz 1 2
Telma W. de L. Soares 1 2 Luckeciano C. Melo 1 3
Abstract
Effective visual representation learning is crucial
for reinforcement le... | {
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"openreview": {
"venue": "ICML 2025 poster",
"venueid": "ICML.cc/2025/Confere... |
arb_000081 | {
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"title": "HybridNorm: Towards Stable and Efficient Transformer Training via Hybrid Normalization",
"conference": "NeurIPS",
"year": 2025,
"note_id": "NligLHO7yG"
} | {
"repo_path": "../data/raw_repos/BryceZhuo_HybridNorm",
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} | {
"motivation": "Zhijian Zhuo1, 2 Yutao Zeng2, † Ya Wang2, † Sijun Zhang2 Jian Yang3 Xiaoqing Li4 Xun Zhou2 Jinwen Ma1 1 School of Mathematical Sciences, Peking University 2 ByteDance Seed 3 Beihang University 4 Capital University of Economics and Business Transformers have become the de facto architecture for a wide... | HybridNorm: Towards Stable and Efficient
Transformer Training via Hybrid Normalization
Zhijian Zhuo1, 2
Yutao Zeng2, †
Ya Wang2, †
Sijun Zhang2
Jian Yang3
Xiaoqing Li4
Xun Zhou2
Jinwen Ma1
1 School of Mathematical Sciences, Peking University
2 ByteDance Seed
3 Beihang University
4 Capital University of Economics and Bu... | {
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"level_2": "A4"
},
"openreview": {
"venue": "NeurIPS 2025 poster",
"venueid": "NeurIPS.cc/2025/Conference",
... |
arb_000082 | {
"pdf_path": "../data/raw_papers/NeurIPS_2025_zOCENGh1Jg.pdf",
"title": "Private Evolution Converges",
"conference": "NeurIPS",
"year": 2025,
"note_id": "zOCENGh1Jg"
} | {
"repo_path": "../data/raw_repos/BryceZhuo_PolyCom",
"repo_name": "BryceZhuo_PolyCom"
} | {
"motivation": "Tomas Gonzalez Carnegie Mellon University tcgonzal@andrew",
"mechanism": "cmu",
"outcome": "edu Giulia Fanti Carnegie Mellon University gfanti@andrew",
"source": "legacy_idea"
} | Private Evolution Converges
Tomas Gonzalez
Carnegie Mellon University
tcgonzal@andrew.cmu.edu
Giulia Fanti
Carnegie Mellon University
gfanti@andrew.cmu.edu
Aaditya Ramdas
Carnegie Mellon University
aramdas@cs.cmu.edu
Abstract
Private Evolution (PE) is a promising training-free method for differentially private
(DP) syn... | {
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"openreview": {
"venue": "NeurIPS 2025 poster",
"venueid": "NeurIPS.cc/2025/Confer... |
arb_000083 | {
"pdf_path": "../data/raw_papers/ICML_2025_hErdffTsLu.pdf",
"title": "Modular Duality in Deep Learning",
"conference": "ICML",
"year": 2025,
"note_id": "hErdffTsLu"
} | {
"repo_path": "../data/raw_repos/buptcmm_phnhvvs",
"repo_name": "buptcmm_phnhvvs"
} | {
"motivation": "An old idea in optimization theory says that since the gradient is a dual vector it may not be subtracted from the weights without first being mapped to the primal space where the weights reside",
"mechanism": "We take this idea seriously in this paper and construct such a duality map for general n... | Modular Duality in Deep Learning
Jeremy Bernstein 1 Laker Newhouse 1
Abstract
An old idea in optimization theory says that
since the gradient is a dual vector it may not be
subtracted from the weights without first being
mapped to the primal space where the weights
reside. We take this idea seriously in this paper
and ... | {
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"openreview": {
"venue": "ICML 2025 poster",
"venueid": "ICML.cc/2025/Conferenc... |
arb_000084 | {
"pdf_path": "../data/raw_papers/NeurIPS_2025_zOCENGh1Jg.pdf",
"title": "Private Evolution Converges",
"conference": "NeurIPS",
"year": 2025,
"note_id": "zOCENGh1Jg"
} | {
"repo_path": "../data/raw_repos/buseg_diagram-understanding",
"repo_name": "buseg_diagram-understanding"
} | {
"motivation": "Tomas Gonzalez Carnegie Mellon University tcgonzal@andrew",
"mechanism": "cmu",
"outcome": "edu Giulia Fanti Carnegie Mellon University gfanti@andrew",
"source": "legacy_idea"
} | Private Evolution Converges
Tomas Gonzalez
Carnegie Mellon University
tcgonzal@andrew.cmu.edu
Giulia Fanti
Carnegie Mellon University
gfanti@andrew.cmu.edu
Aaditya Ramdas
Carnegie Mellon University
aramdas@cs.cmu.edu
Abstract
Private Evolution (PE) is a promising training-free method for differentially private
(DP) syn... | {
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},
"openreview": {
"venue": "NeurIPS 2025 poster",
"venueid": "NeurIPS.cc/20... |
arb_000085 | {
"pdf_path": "../data/raw_papers/ICML_2025_GaCo82yC7z.pdf",
"title": "Guarantees of a Preconditioned Subgradient Algorithm for Overparameterized Asymmetric Low-rank Matrix Recovery",
"conference": "ICML",
"year": 2025,
"note_id": "GaCo82yC7z"
} | {
"repo_path": "../data/raw_repos/caesarcai_OPSA",
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} | {
"motivation": "Paris Giampouras 1 HanQin Cai 2 Ren´e Vidal 3 In this paper, we focus on a matrix factorization- based approach to recover low-rank asymmet- ric matrices from corrupted measurements",
"mechanism": "We propose an Overparameterized Preconditioned Subgradient Algorithm and provide, for the first time ... | Guarantees of a Preconditioned Subgradient Algorithm for
Overparameterized Asymmetric Low-rank Matrix Recovery
Paris Giampouras 1 HanQin Cai 2 Ren´e Vidal 3
Abstract
In this paper, we focus on a matrix factorization-
based approach to recover low-rank asymmet-
ric matrices from corrupted measurements. We
propose an Ove... | {
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"venueid": "ICML.cc/2025/Con... |
arb_000086 | {
"pdf_path": "../data/raw_papers/ICLR_2025_9OMvtboTJg.pdf",
"title": "LLMOPT: Learning to Define and Solve General Optimization Problems from Scratch",
"conference": "ICLR",
"year": 2025,
"note_id": "9OMvtboTJg"
} | {
"repo_path": "../data/raw_repos/caigaojiang_LLMOPT",
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} | {
"motivation": "Formulating and then solving optimization problems described by natural language often requires highly specialized human expertise, which could block the widespread applica- tion of optimization-based decision making",
"mechanism": "To automate problem formulation and solving, leveraging large lang... | Published as a conference paper at ICLR 2025
LLMOPT: LEARNING TO DEFINE AND SOLVE GEN-
ERAL OPTIMIZATION PROBLEMS FROM SCRATCH
Caigao Jiang♥∗,
Xiang Shu♣∗,
Hong Qian♣†,
Xingyu Lu♥†,
Jun Zhou♥,
Aimin Zhou♣,
Yang Yu♦
♣East China Normal University, China
♥Ant Group, China
♦Nanjing University, China
shux@stu.ecnu.edu.cn,
{... | {
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"venue": "ICLR 2025 Poster",
"venueid": "ICLR.cc/2025/Conference"... |
arb_000087 | {
"pdf_path": "../data/raw_papers/ICLR_2025_YbURbViE7l.pdf",
"title": "GOttack: Universal Adversarial Attacks on Graph Neural Networks via Graph Orbits Learning",
"conference": "ICLR",
"year": 2025,
"note_id": "YbURbViE7l"
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"repo_path": "../data/raw_repos/cakcora_GOttack",
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"motivation": "Published as a conference paper at ICLR 2025 GOTTACK: UNIVERSAL ADVERSARIAL ATTACKS ON GRAPH NEURAL NETWORKS VIA GRAPH ORBITS LEARNING Zulfikar Alom1, Tran Gia Bao Ngo1, Murat Kantarcioglu2, Cuneyt Gurcan Akcora3",
"mechanism": "alom@umanitoba",
"outcome": "ca, ngot1@myumanitoba",
"source": "le... | Published as a conference paper at ICLR 2025
GOTTACK: UNIVERSAL ADVERSARIAL ATTACKS ON
GRAPH NEURAL NETWORKS VIA GRAPH ORBITS
LEARNING
Zulfikar Alom1,
Tran Gia Bao Ngo1,
Murat Kantarcioglu2,
Cuneyt Gurcan Akcora3
1Department of Computer Science, University of Manitoba, Canada
2Department of Computer Science, Virginia T... | {
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"venue": "ICLR 2025 Poster",
"venueid": "ICLR.cc/2025/Conference"... |
arb_000088 | {
"pdf_path": "../data/raw_papers/ICML_2025_uBMnbCBEtZ.pdf",
"title": "Progressive Tempering Sampler with Diffusion",
"conference": "ICML",
"year": 2025,
"note_id": "uBMnbCBEtZ"
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"repo_name": "cambridge-mlg_Progressive-Tempering-Sampler-with-Diffusion"
} | {
"motivation": "Severi Rissanen * 1 RuiKang OuYang * 2 Jiajun He 2 Wenlin Chen 2 3 Markus Heinonen 1 Arno Solin 1 Jos´e Miguel Hern´andez-Lobato 2 Recent research has focused on designing neu- ral samplers that amortize the process of sam- pling from unnormalized densities",
"mechanism": "However, de- spite signif... | Progressive Tempering Sampler with Diffusion
Severi Rissanen * 1 RuiKang OuYang * 2 Jiajun He 2 Wenlin Chen 2 3 Markus Heinonen 1 Arno Solin 1
Jos´e Miguel Hern´andez-Lobato 2
Abstract
Recent research has focused on designing neu-
ral samplers that amortize the process of sam-
pling from unnormalized densities. However... | {
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"mapping_score": 1.1129032258,
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},
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"level_1": "Method",
"level_2": "A4"
},
"openreview": {
"venue": "ICML 2025 poster",
"venueid": "ICML.cc/2025/Conference"... |
arb_000089 | {
"pdf_path": "../data/raw_papers/ICML_2025_WaJB9V2fIy.pdf",
"title": "Generating Hypotheses of Dynamic Causal Graphs in Neuroscience: Leveraging Generative Factor Models of Observed Time Series",
"conference": "ICML",
"year": 2025,
"note_id": "WaJB9V2fIy"
} | {
"repo_path": "../data/raw_repos/carlson-lab_redcliff-s-hypothesizing-dynamic-causal-graphs",
"repo_name": "carlson-lab_redcliff-s-hypothesizing-dynamic-causal-graphs"
} | {
"motivation": "Zachary C",
"mechanism": "The field of hypothesis generation promises to re- duce costs in neuroscience by narrowing the range of interventional studies needed to study various phenomena",
"outcome": "Existing machine learning methods can generate scientific hypotheses from complex datasets, but ... | Generating Hypotheses of Dynamic Causal Graphs in Neuroscience:
Leveraging Generative Factor Models of Observed Time Series
Zachary C. Brown 1 David Carlson 1 2
Abstract
The field of hypothesis generation promises to re-
duce costs in neuroscience by narrowing the range
of interventional studies needed to study various... | {
"legacy_id": "metric_quad_000089",
"mapping_score": 1.0230769231,
"repo_scan": {
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"has_environment_spec": false
},
"classification": {
"level_1": "Method",
"level_2": "A5"
},
"openreview": {
"venue": "ICML 2025 poster",
"venueid": "ICML.cc/2025/Conferenc... |
arb_000090 | {
"pdf_path": "../data/raw_papers/ICML_2025_tpbtodnI1p.pdf",
"title": "World Model Implanting for Test-time Adaptation of Embodied Agents",
"conference": "ICML",
"year": 2025,
"note_id": "tpbtodnI1p"
} | {
"repo_path": "../data/raw_repos/chaewoonbae_ZEN",
"repo_name": "chaewoonbae_ZEN"
} | {
"motivation": "In embodied AI, a persistent challenge is enabling agents to robustly adapt to novel domains with- out requiring extensive data collection or retrain- ing",
"mechanism": "To address this, we present a world model implanting framework that combines the reasoning capabilities of large language mod- e... | World Model Implanting for Test-time Adaptation of Embodied Agents
Minjong Yoo 1 Jinwoo Jang 1 Sihyung Yoon 1 Honguk Woo 1
Abstract
In embodied AI, a persistent challenge is enabling
agents to robustly adapt to novel domains with-
out requiring extensive data collection or retrain-
ing. To address this, we present a wo... | {
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"mapping_score": 0.5291371994,
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"level_2": "B3"
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"venue": "ICML 2025 poster",
"venueid": "ICML.cc/2025/Conference... |
arb_000091 | {
"pdf_path": "../data/raw_papers/ICML_2025_lQWTRVrArk.pdf",
"title": "Branches: Efficiently Seeking Optimal Sparse Decision Trees via AO*",
"conference": "ICML",
"year": 2025,
"note_id": "lQWTRVrArk"
} | {
"repo_path": "../data/raw_repos/Chaoukia_branches",
"repo_name": "Chaoukia_branches"
} | {
"motivation": "Decision Tree Learning is a fundamental problem in Interpretable Machine Learning, yet it poses a formidable optimisation challenge",
"mechanism": "Prac- tical algorithms have recently emerged, primarily leveraging Dynamic Programming and Branch & Bound",
"outcome": "However, most of these approa... | Branches: Efficiently Seeking Optimal Sparse Decision Trees via AO*
Ayman Chaouki 1 Jesse Read 1 Albert Bifet 2 3
Abstract
Decision Tree (DT) Learning is a fundamental
problem in Interpretable Machine Learning, yet it
poses a formidable optimisation challenge. Prac-
tical algorithms have recently emerged, primarily
lev... | {
"legacy_id": "metric_quad_000091",
"mapping_score": 1.1218320611,
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"venue": "ICML 2025 poster",
"venueid": "ICML.cc/2025/Conference... |
arb_000092 | {
"pdf_path": "../data/raw_papers/ICML_2025_1nEBAkpfb9.pdf",
"title": "Distillation Scaling Laws",
"conference": "ICML",
"year": 2025,
"note_id": "1nEBAkpfb9"
} | {
"repo_path": "../data/raw_repos/chaoyitud_TabWak",
"repo_name": "chaoyitud_TabWak"
} | {
"motivation": "2017; Rosen- feld et al",
"mechanism": "2020; Hoffmann et al",
"outcome": "2022) revealed that previously trained Language Models could have been more capable if they had followed a compute optimal training paradigm, which determines the model size and the number of training tokens that give the ... | Distillation Scaling Laws
Dan Busbridge 1 Amitis Shidani 2 Floris Weers 1 Jason Ramapuram 1 Etai Littwin 1 Russ Webb 1
Abstract
We propose a distillation scaling law that esti-
mates distilled model performance based on a
compute budget and its allocation between the
student and teacher. Our findings mitigate the
risks... | {
"legacy_id": "metric_quad_000092",
"mapping_score": 0.3921568627,
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"level_2": "A4"
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"openreview": {
"venue": "ICML 2025 poster",
"venueid": "ICML.cc/2025/Conference... |
arb_000093 | {
"pdf_path": "../data/raw_papers/ICML_2025_f4CPc211U1.pdf",
"title": "Provable Policy Gradient for Robust Average-Reward MDPs Beyond Rectangularity",
"conference": "ICML",
"year": 2025,
"note_id": "f4CPc211U1"
} | {
"repo_path": "../data/raw_repos/Charliez7_robust-AMDP",
"repo_name": "Charliez7_robust-AMDP"
} | {
"motivation": "Robust Markov Decision Processes of- fer a promising framework for computing reliable policies under model uncertainty",
"mechanism": "While policy gradient methods have gained increasing popular- ity in robust discounted MDPs, their application to the average-reward criterion remains largely unexp... | Provable Policy Gradient for Robust Average-Reward MDPs
Beyond Rectangularity
Qiuhao Wang * 1 Yuqi Zha * 2 Chin Pang Ho 2 Marek Petrik 3
Abstract
Robust Markov Decision Processes (MDPs) of-
fer a promising framework for computing reliable
policies under model uncertainty. While policy
gradient methods have gained incre... | {
"legacy_id": "metric_quad_000093",
"mapping_score": 1.0181818182,
"repo_scan": {
"has_train_script": false,
"has_environment_spec": false
},
"classification": {
"level_1": "Method",
"level_2": "B3"
},
"openreview": {
"venue": "ICML 2025 poster",
"venueid": "ICML.cc/2025/Conferenc... |
arb_000094 | {
"pdf_path": "../data/raw_papers/ICML_2025_j88QAtutwW.pdf",
"title": "Causal Discovery from Conditionally Stationary Time Series",
"conference": "ICML",
"year": 2025,
"note_id": "j88QAtutwW"
} | {
"repo_path": "../data/raw_repos/charlio23_SDCI",
"repo_name": "charlio23_SDCI"
} | {
"motivation": "Carles Balsells-Rodas 1 Xavier Sumba 1 Tanmayee Narendra 2 Ruibo Tu 3 Gabriele Schweikert 2 Hedvig Kjellstr¨om 3 Yingzhen Li 1 Causal discovery, i",
"mechanism": "e",
"outcome": "inferring underlying causal relationships from observational data, is highly challenging for AI systems",
"source": ... | Causal Discovery from Conditionally Stationary Time Series
Carles Balsells-Rodas 1 Xavier Sumba 1 Tanmayee Narendra 2 Ruibo Tu 3
Gabriele Schweikert 2 Hedvig Kjellstr¨om 3 Yingzhen Li 1
Abstract
Causal discovery, i.e., inferring underlying causal
relationships from observational data, is highly
challenging for AI syste... | {
"legacy_id": "metric_quad_000094",
"mapping_score": 1.133677686,
"repo_scan": {
"has_train_script": false,
"has_environment_spec": true
},
"classification": {
"level_1": "Method",
"level_2": "A5"
},
"openreview": {
"venue": "ICML 2025 poster",
"venueid": "ICML.cc/2025/Conference"... |
arb_000095 | {
"pdf_path": "../data/raw_papers/ICML_2025_gUj2fxQcLZ.pdf",
"title": "Auditing Prompt Caching in Language Model APIs",
"conference": "ICML",
"year": 2025,
"note_id": "gUj2fxQcLZ"
} | {
"repo_path": "../data/raw_repos/chenchenygu_auditing-prompt-caching",
"repo_name": "chenchenygu_auditing-prompt-caching"
} | {
"motivation": "Prompt caching in large language models results in data-dependent timing variations: cached prompts are processed faster than non- cached prompts",
"mechanism": "These timing differences intro- duce the risk of side-channel timing attacks",
"outcome": "For example, if the cache is shared across u... | Auditing Prompt Caching in Language Model APIs
Chenchen Gu 1 Xiang Lisa Li 1 Rohith Kuditipudi 1 Percy Liang 1 Tatsunori Hashimoto 1
Abstract
Prompt caching in large language models (LLMs)
results in data-dependent timing variations:
cached prompts are processed faster than non-
cached prompts. These timing differences... | {
"legacy_id": "metric_quad_000095",
"mapping_score": 1.2,
"repo_scan": {
"has_train_script": false,
"has_environment_spec": true
},
"classification": {
"level_1": "Method",
"level_2": "C6"
},
"openreview": {
"venue": "ICML 2025 poster",
"venueid": "ICML.cc/2025/Conference",
"p... |
arb_000096 | {
"pdf_path": "../data/raw_papers/NeurIPS_2025_zOCENGh1Jg.pdf",
"title": "Private Evolution Converges",
"conference": "NeurIPS",
"year": 2025,
"note_id": "zOCENGh1Jg"
} | {
"repo_path": "../data/raw_repos/ChenDelong1999_subobjects",
"repo_name": "ChenDelong1999_subobjects"
} | {
"motivation": "Tomas Gonzalez Carnegie Mellon University tcgonzal@andrew",
"mechanism": "cmu",
"outcome": "edu Giulia Fanti Carnegie Mellon University gfanti@andrew",
"source": "legacy_idea"
} | Private Evolution Converges
Tomas Gonzalez
Carnegie Mellon University
tcgonzal@andrew.cmu.edu
Giulia Fanti
Carnegie Mellon University
gfanti@andrew.cmu.edu
Aaditya Ramdas
Carnegie Mellon University
aramdas@cs.cmu.edu
Abstract
Private Evolution (PE) is a promising training-free method for differentially private
(DP) syn... | {
"legacy_id": "metric_quad_000096",
"mapping_score": 0.511627907,
"repo_scan": {
"has_train_script": false,
"has_environment_spec": true
},
"classification": {
"level_1": "Method",
"level_2": "A4"
},
"openreview": {
"venue": "NeurIPS 2025 poster",
"venueid": "NeurIPS.cc/2025/Confe... |
arb_000097 | {
"pdf_path": "../data/raw_papers/NeurIPS_2025_kRZVz1qEqa.pdf",
"title": "Teaching Language Models to Reason with Tools",
"conference": "NeurIPS",
"year": 2025,
"note_id": "kRZVz1qEqa"
} | {
"repo_path": "../data/raw_repos/ChengpengLi1003_CoRT",
"repo_name": "ChengpengLi1003_CoRT"
} | {
"motivation": "Chengpeng Li∗1,2, Zhengyang Tang∗2,3, Ziniu Li∗3,4, Mingfeng Xue2, Keqin Bao1,2, Tian Ding4, Ruoyu Sun3,4, Benyou Wang3, Xiang Wang1, Junyang Lin2, and Dayiheng Liu†2 1University of Science and Technology of China 2Qwen Team, Alibaba Inc",
"mechanism": "3The Chinese University of Hong Kong, Shenzhe... | Teaching Language Models to Reason with Tools
Chengpeng Li∗1,2, Zhengyang Tang∗2,3, Ziniu Li∗3,4, Mingfeng Xue2, Keqin Bao1,2, Tian Ding4,
Ruoyu Sun3,4, Benyou Wang3, Xiang Wang1, Junyang Lin2, and Dayiheng Liu†2
1University of Science and Technology of China
2Qwen Team, Alibaba Inc.
3The Chinese University of Hong Kon... | {
"legacy_id": "metric_quad_000097",
"mapping_score": 1.2,
"repo_scan": {
"has_train_script": false,
"has_environment_spec": true
},
"classification": {
"level_1": "Method",
"level_2": "B7"
},
"openreview": {
"venue": "NeurIPS 2025 poster",
"venueid": "NeurIPS.cc/2025/Conference",
... |
arb_000098 | {
"pdf_path": "../data/raw_papers/ICML_2025_wxU2LuTE74.pdf",
"title": "Sparse Autoencoders, Again?",
"conference": "ICML",
"year": 2025,
"note_id": "wxU2LuTE74"
} | {
"repo_path": "../data/raw_repos/ChengZhang-98_HSPI",
"repo_name": "ChengZhang-98_HSPI"
} | {
"motivation": "Sparse Autoencoders, Again",
"mechanism": "Is there really much more to say about sparse au- toencoders",
"outcome": "Autoencoders in general, and SAEs in particular, represent deep architectures that are capable of modeling low-dimensional la- tent structure in data",
"source": "legacy_idea"
} | Sparse Autoencoders, Again?
Yin Lu 1 Xuening Zhu 1 Tong He 2 David Wipf 2
Abstract
Is there really much more to say about sparse au-
toencoders (SAEs)? Autoencoders in general, and
SAEs in particular, represent deep architectures
that are capable of modeling low-dimensional la-
tent structure in data. Such structure co... | {
"legacy_id": "metric_quad_000098",
"mapping_score": 0.3225806452,
"repo_scan": {
"has_train_script": false,
"has_environment_spec": true
},
"classification": {
"level_1": "Method",
"level_2": "B7"
},
"openreview": {
"venue": "ICML 2025 poster",
"venueid": "ICML.cc/2025/Conference... |
arb_000099 | {
"pdf_path": "../data/raw_papers/NeurIPS_2025_B2iPEX5A9c.pdf",
"title": "Information-Theoretic Discrete Diffusion",
"conference": "NeurIPS",
"year": 2025,
"note_id": "B2iPEX5A9c"
} | {
"repo_path": "../data/raw_repos/chenwu98_algorithmic-creativity",
"repo_name": "chenwu98_algorithmic-creativity"
} | {
"motivation": "Moongyu Jeon1 Sangwoo Shin1 Dongjae Jeon2 Albert No1∗",
"mechanism": "Inspired by the I-MMSE identity for the Gaussian setup, we derive analogous results for the discrete setting",
"outcome": "Specifically, we introduce the Information–Minimum Denoising Score Entropy relation, which links mutual ... | Information-Theoretic Discrete Diffusion
Moongyu Jeon1
Sangwoo Shin1
Dongjae Jeon2
Albert No1∗
1Department of Artificial Intelligence, Yonsei University
2Department of Computer Science, Yonsei University
Abstract
We present an information-theoretic framework for discrete diffusion models that
yields principled estimato... | {
"legacy_id": "metric_quad_000099",
"mapping_score": 0.4516129032,
"repo_scan": {
"has_train_script": true,
"has_environment_spec": true
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"classification": {
"level_1": "Method",
"level_2": "B4"
},
"openreview": {
"venue": "NeurIPS 2025 poster",
"venueid": "NeurIPS.cc/2025/Confe... |
arb_000100 | {
"pdf_path": "../data/raw_papers/ICML_2025_WGejWCgrpD.pdf",
"title": "Learning the Electronic Hamiltonian of Large Atomic Structures",
"conference": "ICML",
"year": 2025,
"note_id": "WGejWCgrpD"
} | {
"repo_path": "../data/raw_repos/chexia8_large_atomic_structures.git",
"repo_name": "chexia8_large_atomic_structures.git"
} | {
"motivation": "Chen Hao Xia * 1 Manasa Kaniselvan * 1 Alexandros Nikolaos Ziogas 1 Marko Mladenovi´c 1 Rayen Mahjoub 1 Alexander Maeder 1 Mathieu Luisier 1 Graph neural networks have shown promise in learning the ground-state electronic properties of materials, subverting ab initio den- sity functional theory calcu... | Learning the Electronic Hamiltonian of Large Atomic Structures
Chen Hao Xia * 1 Manasa Kaniselvan * 1 Alexandros Nikolaos Ziogas 1 Marko Mladenovi´c 1 Rayen Mahjoub 1
Alexander Maeder 1 Mathieu Luisier 1
Abstract
Graph neural networks (GNNs) have shown
promise in learning the ground-state electronic
properties of mater... | {
"legacy_id": "metric_quad_000100",
"mapping_score": 1.2,
"repo_scan": {
"has_train_script": true,
"has_environment_spec": true
},
"classification": {
"level_1": "Method",
"level_2": "A5"
},
"openreview": {
"venue": "ICML 2025 poster",
"venueid": "ICML.cc/2025/Conference",
"pa... |
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