dataset-review/dataset-review huggingface dataset:
HYGMA: Hypergraph Coordination Networks with Dynamic Grouping for Multi-Agent Reinforcement Learning
ICML 2025

HYGMA: Hypergraph Coordination Networks with Dynamic Grouping
for Multi-Agent Reinforcement Learning
Chiqiang Liu 1 Dazi Li 1
Abstract
Cooperative multi-agent reinforcement learning
faces significant challenges in effectively orga-
nizing agent relationships and facilitating infor-
mation exchange, particularly when agents need
to adapt their coordination patterns dynamically.
This paper presents a novel framework that in-
tegrates dynamic spectral clustering with hyper-
graph neural networks to enable adaptive group
formation and efficient information processing
in multi-agent systems.
The proposed frame-
work dynamically constructs and updates hyper-
graph structures through spectral clustering on
agents’ state histories, enabling higher-order rela-
tionships to emerge naturally from agent interac-
tions. The hypergraph structure is enhanced with
attention mechanisms for selective information
processing, providing an expressive and efficient
way to model complex agent relationships. This
architecture can be implemented in both value-
based and policy-based paradigms through a uni-
fied objective combining task performance with
structural regularization. Extensive experiments
on challenging cooperative tasks demonstrate
that our method significantly outperforms state-
of-the-art approaches in both sample efficiency
and final performance. The code is available at:
https://github.com/mysteryelder/HYGMA.
1. Introduction
Cooperative multi-agent reinforcement learning (MARL)
has emerged as a promising paradigm for addressing com-
plex real-world challenges that require coordinated decision-
making among multiple agents (Charbonnier et al., 2025;
Si et al., 2025). A fundamental challenge in MARL is the
1College of Information Science and Technology, Beijing Uni-
versity of Chemical Technology, Beijing, China. Correspondence
to: Dazi Li <lidz@mail.buct.edu.cn>.
Proceedings of the 42 nd International Conference on Machine
Learning, Vancouver, Canada. PMLR 267, 2025. Copyri...
Adaptive Elicitation of Latent Information Using Natural Language
ICML 2025

Adaptive Elicitation of Latent Information Using Natural Language
Jimmy Wang * 1 Thomas Zollo * 1 Richard Zemel 1 Hongseok Namkoong 1
Abstract
Eliciting information to reduce uncertainty about
a latent entity is a critical task in many appli-
cation domains, e.g., assessing individual stu-
dent learning outcomes, diagnosing underlying
diseases, or learning user preferences. Though
natural language is a powerful medium for this
purpose, large language models (LLMs) and ex-
isting fine-tuning algorithms lack mechanisms for
strategically gathering information to refine their
own understanding of the latent entity. To har-
ness the generalization power and world knowl-
edge of LLMs in developing effective informa-
tion gathering strategies, we propose an adaptive
elicitation framework that actively reduces un-
certainty on the latent entity. Since probabilistic
modeling of an abstract latent entity is difficult,
our framework adopts a predictive view of un-
certainty, using a meta-learned language model
to simulate future observations and enable scal-
able uncertainty quantification over complex nat-
ural language. Through autoregressive forward
simulation, our model quantifies how new ques-
tions reduce epistemic uncertainty, enabling the
development of sophisticated information gather-
ing strategies to choose the most informative next
queries. In experiments on the twenty questions
game, dynamic opinion polling, and adaptive stu-
dent assessment, our method consistently outper-
forms baselines in identifying critical unknowns
and improving downstream predictions, illustrat-
ing the promise of strategic information gathering
in natural language settings.
1. Introduction
The performance of many valuable services and systems
depends on the ability to efficiently elicit information and
1Columbia University.
Correspondence to:
Jimmy
Wang
<jw4209@columbia.edu>,
Thomas
Zollo
<tpz2105@columbia.edu>.
Proceedings of the 42 nd International Conference on Machine
Learning, Vanco...
CROW: Eliminating Backdoors from Large Language Models via Internal Consistency Regularization
ICML 2025

CROW: Eliminating Backdoors from Large Language Models via Internal
Consistency Regularization
Nay Myat Min 1 Long H. Pham 1 Yige Li 1 Jun Sun 1
Abstract
Large Language Models (LLMs) are vulnera-
ble to backdoor attacks that manipulate out-
puts via hidden triggers.
Existing defense
methods—designed for vision/text classification
tasks—fail for text generation. We propose In-
ternal Consistency Regularization (CROW), a de-
fense leveraging the observation that backdoored
models exhibit unstable layer-wise hidden rep-
resentations when triggered, while clean models
show smooth transitions. CROW enforces consis-
tency across layers via adversarial perturbations
and regularization during finetuning, neutraliz-
ing backdoors without requiring clean reference
models or trigger knowledge—only a small clean
dataset. Experiments across Llama-2 (7B, 13B),
CodeLlama (7B, 13B), and Mistral-7B demon-
strate CROW’s effectiveness: it achieves signifi-
cant reductions in attack success rates across di-
verse backdoor strategies (sentiment steering, tar-
geted refusal, code injection) while preserving
generative performance. CROW’s architecture-
agnostic design enables practical deployment.
1. Introduction
Large Language Models (LLMs) have revolutionized natu-
ral language processing but face critical security risks from
backdoor attacks—hidden triggers that manipulate model
outputs during inference. While extensively studied in vi-
sion/text classification (Gu et al., 2019; Chen et al., 2021b),
these attacks pose unique challenges for generative LLMs
due to their complex output spaces and context sensitivity.
Recent work shows that attackers can implant backdoors via
data poisoning (Li et al., 2025) or prompt engineering (Yan
et al., 2024), enabling dangerous behaviors like toxic con-
1School of Computing and Information Systems, Singapore
Management University, Singapore. Correspondence to: Yige Li
<yigeli@smu.edu.sg>.
Proceedings of the 42 nd International Conference on Machine
...
Active Learning for Efficient Discovery of Optimal Combinatorial Perturbations
ICML 2025

Active Learning for Efficient Discovery of Optimal Combinatorial Perturbations
Jason Qin 1 Hans-Hermann Wessels 1 Carlos Fernandez-Granda 2 3 Yuhan Hao 1
Abstract
Combinatorial CRISPR screening enables large-
scale identification of synergistic gene pairs for
combination therapies, but exhaustive experimen-
tation is infeasible. We introduce NAIAD1, an ac-
tive learning framework that efficiently discovers
optimal gene pairs by leveraging single-gene per-
turbation effects and adaptive gene embeddings
that scale with the training data size, mitigating
overfitting in small-sample learning while cap-
turing complex gene interactions as more data is
collected. Evaluated on four CRISPR datasets
with over 350,000 interactions, NAIAD trained
on small datasets outperforms existing models by
up to 40%. Its recommendation system priori-
tizes gene pairs with maximum predicted effects,
accelerating discovery with fewer experiments.
We also extend NAIAD to optimal drug com-
bination identification among 2,000 candidates.
Overall, NAIAD enhances combinatorial pertur-
bation design and drives advances in genomics
research and therapeutic development in combi-
nation therapy. Our code is publicly available at:
https://github.com/NeptuneBio/NAIAD
1. Introduction
Targeting multiple genes through drug combinations or
polypharmacology offers a transformative therapeutic ap-
proach for developing effective treatments across diverse
medical fields, including oncology (Al-Lazikani et al., 2012;
Mokhtari et al., 2017), infectious diseases (Hammond et al.,
2022; Shyr et al., 2021), and metabolic disorders (Samms
et al., 2020; Jastreboff et al., 2023). Combinatorial gene per-
turbations can yield additive or synergistic effects, enhanc-
ing therapeutic outcomes beyond what is achievable with
1Neptune Bio, New York, NY, USA 2Center for Data Science,
New York University, New York, NY, USA 3Courant Institute
of Mathematical Sciences, New York University, New York, NY,
USA . Correspondence to:...
Reading Recognition in the Wild
NeurIPS 2025

Reading Recognition in the Wild
Charig Yang1,2, Samiul Alam3, Shakhrul Iman Siam3, Michael J. Proulx1, Lambert Mathias1,
Kiran Somasundaram1, Luis Pesqueira1, James Fort1, Sheroze Sheriffdeen1, Omkar Parkhi1,
Carl Ren1, Mi Zhang3, Yuning Chai1, Richard Newcombe1, Hyo Jin Kim1
1Meta Reality Labs Research
2VGG, University of Oxford
3The Ohio State University
charig@robots.ox.ac.uk, kimhyojin@meta.com
https://www.projectaria.com/datasets/reading-in-the-wild/
Abstract
To enable egocentric contextual AI in always-on smart glasses, it is crucial to be able
to keep a record of the user’s interactions with the world, including during reading.
In this paper, we introduce a new task of reading recognition to determine when
the user is reading. We first introduce the first-of-its-kind large-scale multimodal
Reading in the Wild dataset, containing 100 hours of reading and non-reading videos
in diverse and realistic scenarios. We then identify three modalities (egocentric
RGB, eye gaze, head pose) that can be used to solve the task, and present a flexible
transformer model that performs the task using these modalities, either individually
or combined. We show that these modalities are relevant and complementary to
the task, and investigate how to efficiently and effectively encode each modality.
Additionally, we show the usefulness of this dataset towards classifying types of
reading, extending current reading understanding studies conducted in constrained
settings to larger scale, diversity and realism. Code, model, and data will be public.
Figure 1: Am I reading? The left figure shows a timeline as the user navigates the world. We aim to solve the
task of reading recognition to enable AI assistants in always-on wearables. We identify three modalities: eye
gaze (in colored dot patterns), RGB crop around gaze (in red box), and inertial sensors performs the task to high
accuracy (with Prediction and GT shown). Images from our Reading in the Wild dataset, which features 100
hours ...
The Brain's Bitter Lesson: Scaling Speech Decoding With Self-Supervised Learning
ICML 2025

The Brain’s Bitter Lesson:
Scaling Speech Decoding With Self-Supervised Learning
Dulhan Jayalath 1 Gilad Landau 1 Brendan Shillingford 2 Mark Woolrich3 Oiwi Parker Jones 1
Abstract
The past few years have seen remarkable progress
in the decoding of speech from brain activity,
primarily driven by large single-subject datasets.
However, due to individual variation, such as
anatomy, and differences in task design and scan-
ning hardware, leveraging data across subjects
and datasets remains challenging. In turn, the
field has not benefited from the growing number
of open neural data repositories to exploit large-
scale deep learning. To address this, we develop
neuroscience-informed self-supervised objectives,
together with an architecture, for learning from
heterogeneous brain recordings. Scaling to nearly
400 hours of MEG data and 900 subjects, our ap-
proach shows generalisation across participants,
datasets, tasks, and even to novel subjects. It
achieves improvements of 15-27% over state-of-
the-art models and matches surgical decoding
performance with non-invasive data. These ad-
vances unlock the potential for scaling speech
decoding models beyond the current frontier.
1. Introduction
In his Bitter Lesson, Richard Sutton argues that a major
conclusion of 70 years of AI research is that general methods
exploiting large-scale computation will outperform model-
based approaches as the availability of compute increases
(Sutton, 2019). The ability of deep learning to learn from
ever-larger datasets has enabled seemingly arbitrary scaling
with computation, leading to astounding advances across
a diverse set of domains (Jumper et al., 2021; Caron et al.,
2021; OpenAI, 2023; Radford et al., 2023).
In the domain of brain data, and of tasks like speech de-
coding, the bitter lesson has not yet been fully assimilated.
1PNPL
/ 3OHBA, University of Oxford 2Google DeepMind.
Correspondence to: <{dulhan, oiwi}@robots.ox.ac.uk>.
Proceedings of the 42 nd International Conference on...
Inverse Bridge Matching Distillation
ICML 2025

Inverse Bridge Matching Distillation
Nikita Gushchin 1 2 David Li * 1 3 Daniil Selikhanovych * 1 4 5 Evgeny Burnaev 1 2 Dmitry Baranchuk 4
Alexander Korotin 1 2
Abstract
Learning diffusion bridge models is easy; mak-
ing them fast and practical is an art. Diffusion
bridge models (DBMs) are a promising extension
of diffusion models for applications in image-to-
image translation. However, like many modern
diffusion and flow models, DBMs suffer from
the problem of slow inference. To address it,
we propose a novel distillation technique based
on the inverse bridge matching formulation and
derive the tractable objective to solve it in prac-
tice. Unlike previously developed DBM distil-
lation techniques, the proposed method can dis-
till both conditional and unconditional types of
DBMs, distill models in a one-step generator, and
use only the corrupted images for training. We
evaluate our approach for both conditional and
unconditional types of bridge matching on a wide
set of setups, including super-resolution, JPEG
restoration, sketch-to-image, and other tasks, and
show that our distillation technique allows us to
accelerate the inference of DBMs from 4x to
100x and even provide better generation qual-
ity than used teacher model depending on par-
ticular setup. We provide the code at https:
//github.com/ngushchin/IBMD.
1. Introduction
Diffusion Bridge Models (DBMs) represent a specialized
class of diffusion models designed for data-to-data tasks,
such as image-to-image translation. Unlike standard diffu-
sion models, which operate by mapping noise to data (Ho
et al., 2020; Sohl-Dickstein et al., 2015), DBMs construct
diffusion processes directly between two data distributions
(Peluchetti, 2023a; Liu et al., 2022b; Somnath et al., 2023;
*Equal contribution 1Skolkovo Institute of Science and Technol-
ogy 2Artificial Intelligence Research Institute 3Moscow Institute
of Physics and Technology 4Yandex Research 5HSE University.
Correspondence to: Nikita Gushchin <n.gushchi...
Reasoning Planning for Language Models
NeurIPS 2025

Reasoning Planning for Language Models
Bao Nguyen1
Hieu Trung Nguyen1
Ruifeng She2
Xiaojin Fu2
Viet Anh Nguyen1
1 The Chinese University of Hong Kong
2 Huawei Noah’s Ark Lab
nbnguyen@se.cuhk.edu.hk, thnguyen@se.cuhk.edu.hk
she.ruifeng@huawei.com, fuxiaojin32@hotmail.com, nguyen@se.cuhk.edu.hk
Abstract
Selecting an appropriate reasoning method for a given query remains a key chal-
lenge in language model generation. Existing approaches typically generate multi-
ple candidate responses and use an aggregation strategy to select the output answer,
often assuming that more candidate answers yield higher accuracy. We revisit this
assumption through a rigorous theoretical analysis, deriving accuracy bounds for
standard aggregation methods under fixed generation distributions and candidate
sizes. Building on these insights, we introduce EPIC, an Ensemble PlannIng with
Contrastive learning framework to learn a shared representation space that captures
both model reasoning abilities and query-method compatibility. EPIC incorpo-
rates our probability bounds as a regularizer in a utility-driven optimization that
balances accuracy and computational cost. Experiments on diverse mathematical
reasoning tasks show that EPIC consistently selects optimal reasoning methods,
improving accuracy while reducing computational overhead. Our code can be
found at https://github.com/nguyenngocbaocmt02/EPIC.
1
Introduction
Large Language Models (LLMs) have demonstrated remarkable abilities to understand and reason
in human natural language. These advancements have transformed applications, including travel
planning [Xie et al., 2024a], AI teaching platforms [Jin et al., 2024], and human population simula-
tions [Park et al., 2023, Bui et al., 2025]. However, even with a pre-trained LLM, the computational
expense of serving LLM-powered systems remains a significant bottleneck due to the massive scale
of the models [Lin et al., 2024], the quadratic complexity of the attention mechanism [Dao et al....
SCENIR: Visual Semantic Clarity through Unsupervised Scene Graph Retrieval
ICML 2025

SCENIR: Visual Semantic Clarity through
Unsupervised Scene Graph Retrieval
Nikolaos Chaidos 1 Angeliki Dimitriou 1 Maria Lymperaiou 1 Giorgos Stamou 1
Abstract
Despite the dominance of convolutional and
transformer-based architectures in image-to-
image retrieval, these models are prone to biases
arising from low-level visual features, such as
color. Recognizing the lack of semantic under-
standing as a key limitation, we propose a novel
scene graph-based retrieval framework that em-
phasizes semantic content over superficial im-
age characteristics. Prior approaches to scene
graph retrieval predominantly rely on supervised
Graph Neural Networks (GNNs), which require
ground truth graph pairs driven from image cap-
tions.
However, the inconsistency of caption-
based supervision stemming from variable text en-
codings undermine retrieval reliability. To address
these, we present SCENIR, a Graph Autoencoder-
based unsupervised retrieval framework, which
eliminates the dependence on labeled training
data. Our model demonstrates superior perfor-
mance across metrics and runtime efficiency, out-
performing existing vision-based, multimodal,
and supervised GNN approaches.
We further
advocate for Graph Edit Distance (GED) as a
deterministic and robust ground truth measure
for scene graph similarity, replacing the inconsis-
tent caption-based alternatives for the first time
in image-to-image retrieval evaluation. Finally,
we validate the generalizability of our method
by applying it to unannotated datasets via auto-
mated scene graph generation, while substantially
contributing in advancing state-of-the-art in coun-
terfactual image retrieval. The source code is
available at https://github.com/nickhaidos/scenir-
icml2025.
1Artificial Intelligence and Learning Systems Laboratory, Na-
tional Technical University of Athens. Correspondence to: Niko-
laos Chaidos <nchaidos@ails.ece.ntua.gr>.
Proceedings of the 42 nd International Conference on Machine
Learning, Vancouver, Canada....
Random Feature Representation Boosting
ICML 2025

Random Feature Representation Boosting
Nikita Zozoulenko 1 Thomas Cass * 1 Lukas Gonon * 1 2
Abstract
We introduce Random Feature Representation
Boosting (RFRBoost), a novel method for con-
structing deep residual random feature neural net-
works (RFNNs) using boosting theory. RFRBoost
uses random features at each layer to learn the
functional gradient of the network representation,
enhancing performance while preserving the con-
vex optimization benefits of RFNNs. In the case
of MSE loss, we obtain closed-form solutions to
greedy layer-wise boosting with random features.
For general loss functions, we show that fitting
random feature residual blocks reduces to solving
a quadratically constrained least squares problem.
Through extensive numerical experiments on tab-
ular datasets for both regression and classification,
we show that RFRBoost significantly outperforms
RFNNs and end-to-end trained MLP ResNets in
the small- to medium-scale regime where RFNNs
are typically applied. Moreover, RFRBoost offers
substantial computational benefits, and theoretical
guarantees stemming from boosting theory.
1. Introduction
Random feature neural networks (RFNNs) are single-
hidden-layer neural networks where all model parameters
are randomly initialized or sampled, with only the linear
output layer being trained. This approach presents a compu-
tationally efficient alternative to neural networks trained via
stochastic gradient descent (SGD), avoiding the challenges
associated with non-convex optimization and vanishing/-
exploding gradients. Despite their simplicity, RFNNs and
related random feature models have strong provable gen-
eralization guarantees (Rahimi & Recht, 2008b; Rudi &
Rosasco, 2017; Lanthaler & Nelsen, 2023; Cheng et al.,
2023), and have demonstrated state-of-the-art performance
∗Equal last authors.
1Department of Mathematics, Imperial
College London, UK 2School of Computer Science, University of
St. Gallen, Switzerland. Correspondence to: Nikita Zozoulenko
<n.zoz...
GRAM: A Generative Foundation Reward Model for Reward Generalization
ICML 2025

GRAM: A Generative Foundation Reward Model
for Reward Generalization
Chenglong Wang 1 Yang Gan 1 Yifu Huo 1 Yongyu Mu 1 Qiaozhi He 1 Murun Yang 1 Bei Li 2 Tong Xiao 1 3
Chunliang Zhang 1 Tongran Liu 4 Jingbo Zhu 1 3
Code
Datasets&Models
Abstract
In aligning large language models (LLMs), re-
ward models have played an important role, but
are standardly trained as discriminative models
and rely only on labeled human preference data.
In this paper, we explore methods that train re-
ward models using both unlabeled and labeled
data. Building on the generative models in LLMs,
we develop a generative reward model that is first
trained via large-scale unsupervised learning and
then fine-tuned via supervised learning. We also
show that by using label smoothing, we are in fact
optimizing a regularized pairwise ranking loss.
This result, in turn, provides a new view of train-
ing reward models, which links generative models
and discriminative models under the same class
of training objectives. The outcome of these tech-
niques is a foundation reward model, which can
be applied to a wide range of tasks with little or no
further fine-tuning effort. Extensive experiments
show that this model generalizes well across sev-
eral tasks, including response ranking, reinforce-
ment learning from human feedback, and task
adaptation with fine-tuning, achieving significant
performance improvements over several strong
baseline models.
1. Introduction
Reward models are a fundamental concept in reinforcement
learning and define what an agent optimizes for. For large
language models (LLMs), fine-tuning with reward models
is a common post-training step to align the model outputs
with desired behaviors and objectives. A widely adopted
1School of Computer Science and Engineering, Northeastern
University, Shenyang, China 2Meituan Inc. 3NiuTrans Research,
Shenyang, China 4CAS Key Laboratory of Behavioral Science,
Institute of Psychology, CAS, Beijing, China. Correspondence
to: Chenglong Wang <clwa...