dataset-review/dataset-review huggingface dataset:
Strategic Planning: A Top-Down Approach to Option Generation
ICML 2025

Strategic Planning: A Top-Down Approach to Option Generation
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 health-
care, business, and urban policy. Despite notable
successes in controlled settings, conventional re-
inforcement learning (RL) follows a bottom-up
framework, which can struggle to adapt to real-
world complexities such as sparse rewards and
limited exploration budgets. While methods like
hierarchical RL and environment shaping provide
partial solutions, they frequently rely on either
ad hoc designs (e.g. choosing the set of high-
level actions) or purely data-driven discovery of
high-level actions that still requires significant ex-
ploration. In this paper, we introduce a top-down
RL framework that explicitly leverages human-
inspired strategy to reduce sample complexity,
guide exploration, and enable high-level decision
making. We first formalize the Strategy Problem,
which frames policy generation as finding distri-
butions over policies that balance specificity and
value. Building on this definition, we propose
the Strategist agent—an iterative framework that
leverages large language models to synthesize do-
main knowledge into a structured representation
of actionable strategies and sub-goals. We fur-
ther develop a reward shaping methodology that
translates these strategies expressed in natural lan-
guage into quantitative feedback for RL methods.
Empirically, we demonstrate that our framework
significantly enhances the performance of differ-
ent underlying RL algorithms, leading to faster
convergence and the discovery of more complex
behaviors. Taken together, our findings highlight
that top-down strategic exploration opens new
avenues to improve RL in real-world decision
problems.
*Equal contribution 1University of Cambridge. Corresponden...
Aligning Multimodal Representations through an Information Bottleneck
ICML 2025

Aligning Multimodal Representations through an Information Bottleneck
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 that
their use is not effective to learn an aligned rep-
resentation space. In this paper, we argue that
this phenomenon is caused by the presence of
modality-specific information in the representa-
tion space. Although some of the most widely
used contrastive losses maximize the mutual in-
formation between representations of both modali-
ties, they are not designed to remove the modality-
specific information. We give a theoretical de-
scription of this problem through the lens of the
Information Bottleneck Principle. We also em-
pirically analyze how different hyperparameters
affect the emergence of this phenomenon in a con-
trolled experimental setup. Finally, we propose a
regularization term in the loss function that is de-
rived by means of a variational approximation and
aims to increase the representational alignment.
We analyze in a set of controlled experiments and
real-world applications the advantages of includ-
ing this regularization term.
1. Introduction
Multimodal Learning is an area of AI that is focused on pro-
cessing and integrating information from multiple modali-
ties (e.g., text, image or audio). It is becoming a pivotal topic
in the community because of multiple reasons, including,
but not limited to, (i) it permits to mimic human cognition
processes (Fei et al., 2022; Lee et al., 2023); (ii) it allows to
use a greater amount of training data from different modali-
ties, which tends to improve the performance of the models
1ViVoLab, Arag´on Institute for Engineering Research (I3A),
University of Zaragoza, Zaragoza, Spain 2University of Cam-
bridge, Cambridge, UK 3Mitsubishi Electric Research Labora-
tories (MERL), Cambridge, U...
Neural Interactive Proofs
ICLR 2025

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 ‘verifier’) can learn to interact with one or more powerful but untrusted agents
(‘provers’) in order to solve a given task. More specifically, we study the case in
which agents are represented using neural networks and refer to solutions of this
problem as neural interactive proofs. First we introduce a unifying framework based
on prover-verifier games (Anil et al., 2021), which generalises previously proposed
interaction protocols. We then describe several new protocols for generating neural
interactive proofs, and provide a theoretical comparison of both new and existing
approaches. Finally, we support this theory with experiments in two domains: a
toy graph isomorphism problem that illustrates the key ideas, and a code validation
task using large language models. In so doing, we aim to create a foundation for
future work on neural interactive proofs and their application in building safer AI
systems.
1
INTRODUCTION
Recent years have witnessed the proliferation of large machine learning (ML) systems (Villalobos
et al., 2022), useful for solving an increasingly wide range of tasks. Often, however, it can be difficult
to trust the output of these systems, raising concerns about their safety and limiting their applicability
in high-stakes situations (Amodei et al., 2016; Bengio et al., 2023; Hendrycks et al., 2023). At
the same time, traditional approaches in verification do not scale to today’s most powerful systems
(Seshia et al., 2022). There is thus a pressing need to identify new angles via which to gain such
assurances.
qƧǩƽΞƧǩ
UǩǙͭƧǩ
Figure 1: On receiving input x from distribution P the
agents exchange messages before the verifier decides on
an out...
Complete Structure Guided Point Cloud Completion via Cluster- and Instance-Level Contrastive Learning
NeurIPS 2025

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 Processing
{chen_yang23,yrzhou22}@m.fudan.edu.cn, {weizhongzhang,jc}@fudan.edu.cn
Abstract
Point cloud completion, aiming to reconstruct missing part from incomplete point
clouds, is a pivotal task in 3D computer vision. Traditional supervised approaches
often necessitate complete point clouds for training supervision, which are not
readily accessible in real-world applications. Recent studies have attempted to
mitigate this dependency by employing self-supervise mechanisms. However, these
approaches frequently yield suboptimal results due to the absence of complete
structure in the point cloud data during training. To address these issues, in this
paper, we propose an effective framework to complete the point cloud under the
guidance of self learned complete structure. A key contribution of our work
is the development of a novel self-supervised complete structure reconstruction
module, which can learn the complete structure explicitly from incomplete point
clouds and thus eliminate the reliance on training data from complete point clouds.
Additionally, we introduce a contrastive learning approach at both the cluster-
and instance-level to extract shape features guided by the complete structure and
to capture style features, respectively. This dual-level learning design ensures
that the generated point clouds are both shape-completed and detail-preserving.
Extensive experiments on both synthetic and real-world datasets demonstrate that
our approach significantly outperforms state-of-the-art self-supervised methods.
1
Introduction
The advancement and widespread adoption of 3D sensors, particularly LiDAR, have led to the
emergence of point clouds as a dominant re...
Graph World Model
ICML 2025

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 digital world. While multiple graph foun-
dation models have been proposed, they focus
on graph learning tasks and cannot extend to
diverse multi-modal data and interdisciplinary
tasks. To address these challenges, we propose the
Graph World Model (GWM), a world model that
supports both unstructured and graph-structured
states with multi-modal information and repre-
sents diverse tasks as actions.
The core of a
GWM is a generic message-passing algorithm
to aggregate structured information, either over
a unified multi-modal token space by converting
multi-modal data into text (GWM-T) or a uni-
fied multi-modal embedding space by modality-
specific encoders (GWM-E). Notably, GWM in-
troduces action nodes to support diverse tasks,
where action nodes are linked to other nodes via
direct reference or similarity computation. Exten-
sive experiments on 6 tasks from diverse domains,
including multi-modal generation and matching,
recommendation, graph prediction, multi-agent,
retrieval-augmented generation, and planning and
optimization, show that the same GWM out-
performs or matches domain-specific baselines’
performance, benefits from multi-hop structures,
and demonstrate strong zero-shot/few-shot ca-
pabilities on unseen new tasks. Our codes for
GWM is released at https://github.com/
ulab-uiuc/GWM.
1Department of Computer Science, University of Illinois Ur-
bana Champaign Urbana, IL, USA. Correspondence to: Tao Feng
<taofeng2@illinois.edu>, Jiaxuan You <jiaxuan@illinois.edu>.
Proceedings of the 42 nd International Conference on Machine
Learning, Vancouver, Canada. PMLR 267, 2025. Copyright 2025
by the author(s).
1. Introduction
A world model (WM) (Ha & Schmid...
Is Complex Query Answering Really Complex?
ICML 2025

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 current benchmarks for CQA might not be as
complex as we think, as the way they are built dis-
torts our perception of progress in this field. For
example, we find that in these benchmarks, most
queries (up to 98% for some query types) can be
reduced to simpler problems, e.g. link prediction,
where only one link needs to be predicted.The
performance of state-of-the-art CQA models de-
creases significantly when such models are eval-
uated on queries that cannot be reduced to easier
types. Thus, we propose a set of more challeng-
ing benchmarks composed of queries that require
models to reason over multiple hops and better
reflect the construction of real-world KGs. In a
systematic empirical investigation, the new bench-
marks show that current methods leave much to
be desired from current CQA methods.
1. Introduction
A crucial challenge in AI and ML is learning to perform
complex reasoning, i.e., solving tasks that involve several
intermediate steps and sub-goals to be completed. Complex
query answering (CQA; Hamilton et al., 2018; Zhang et al.,
2021; Arakelyan et al., 2021; Zhu et al., 2022) emerged as a
way to measure complex reasoning over external knowledge
bases, encoded as knowledge graphs (KGs; Hogan et al.,
2021). For instance, to answer the query:
“Which actor performed in a movie filmed in
New York City and distributed on Blue Ray?”
(q1)
Shared supervision 1Institute for Artificial Intelligence, Uni-
versity of Stuttgart, Germany 2Stanford University 3School of
Informatics University of Edinburgh Edinburgh, UK 4Miniml.AI
5University of Southampton, UK. Correspondence to: Cosimo
Gregucci <cosimo.gregucci@ki.uni-stuttgart.de>.
Proceedings of th...
Bridging Expressivity and Scalability with Adaptive Unitary SSMs
NeurIPS 2025

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@gmail.com
Carmen Amo Alonso
Computer Science Department
Stanford University
camoalon@stanford.edu
Terrence J. Sejnowski
Salk Institute for Biological Studies
terry@salk.edu
Hava T. Siegelmann
University of Massachusetts Amherst
hava@umass.edu
Abstract
Recent work has revealed that state space models (SSMs), while efficient for long-
sequence processing, are fundamentally limited in their ability to represent formal
languages—particularly due to time-invariant and real-valued recurrence structures.
In this work, we draw inspiration from adaptive and structured dynamics observed
in biological neural systems and introduce the Adaptive Unitary State Space Model
(AUSSM): a novel class of SSMs that leverages skew-symmetric, input-dependent
recurrence to achieve unitary evolution and high expressive power. Using algebraic
automata theory, we prove that AUSSM can perform modulo counting and simulate
solvable group automata at precision logarithmically bounded in the input length,
enabling SSMs to model a broad class of regular languages out of reach for other
SSM architectures. To overcome the practical inefficiencies of adaptive recurrence,
we develop a separable convolution formulation and a CUDA implementation that
enables scalable parallel training. Empirically, we show that AUSSM and its hybrid
variant—interleaved with Mamba—outperform prior SSMs on formal algorithmic
tasks such as parity and modular arithmetic, and achieve competent performance
on real-world long time-series classification benchmarks. Our results demonstrate
that adaptive unitary recurrence provides a powerful and efficient inductive bias
for both symbolic and continuous sequence modelin...
A Bregman Proximal Viewpoint on Neural Operators
ICML 2025

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 regularized optimization
problems over Banach function spaces. The pro-
posed framework allows interpreting the activa-
tion operators as Bregman proximity operators
from dual to primal space. This novel viewpoint
is general enough to recover classical neural op-
erators as well as a new variant, coined Bregman
neural operators, which includes the inverse acti-
vation operator and features the same expressivity
of standard neural operators. Numerical experi-
ments support the added benefits of the Bregman
variant of Fourier neural operators for training
deeper and more accurate models.
1. Introduction
Neural operators (Kovachki et al., 2021; 2023), a recent
extension of neural networks, have emerged as a versatile
framework for learning mappings between function spaces.
These operators have shown great potential in solving par-
tial differential equations (PDEs) and simulating complex
dynamical systems. The exploration of neural architectures
for the approximation and learning of operators has led to
the development of a variety of models.
One influential contribution is the Fourier Neural Opera-
tor (FNO) (Li et al., 2021a), sketched in Figure 1, which
transforms encoded input data into frequency components
in order to learn intricate relationships in the frequency
domain. More recently, the Group-Equivariant FNO (G-
FNO) (Helwig et al., 2023) additionally leverages symme-
*Equal contribution
1Université Jean Monnet Saint-Etienne,
CNRS, Institut d’Optique Graduate School, Inria, Laboratoire
Hubert Curien UMR 5516, F-42023, SAINT-ETIENNE, France
2DIAG, Sapienza University of Rome, 00185 Rome, Italy 3Institut
Universitaire de France (IUF) 4Computational Statisti...
Eluder dimension: localise it!
NeurIPS 2025

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.ca
Abstract
We establish a lower bound on the eluder dimension of generalised linear model
classes, showing that standard eluder dimension-based analysis cannot lead to
first-order regret bounds. To address this, we introduce a localisation method for
the eluder dimension; our analysis immediately recovers and improves on classic
results for Bernoulli bandits, and allows for the first genuine first-order bounds for
finite-horizon reinforcement learning tasks with bounded cumulative returns.
1
Introduction
We study decision-making problems where the regret admits a first-order (small-cost) bound of the
form
Rn ≤
p
nη(a⋆)Γn + Γ′
n ,
with η(a⋆) the optimal mean per-round cost and Γn, Γ′
n instance and model dependent complexities.
The challenge in obtaining first-order bounds is in how we measure the complexity of the task. The
(global) eluder dimension (Russo and Van Roy, 2013) is a standard complexity measure used to
provide worst-case guarantees, but, as we shall argue, it ignores the local structure of the problem:
analyses based on the global eluder dimension often introduce a factor in Γn that scales like a per-
step worst-case information/curvature parameter (denoted κ), which cancels out η(a⋆) and destroys
first-order gains. This subtle issue is present in much of the previous work on first-order bounds.
We show that by localising the eluder dimension—restricting it to a small neighbourhood of the
optimal model—these κ terms can be moved into the lower-order Γ′
n term. This tightens the link
between exploration and the actual difficulty of the instance and yields genuinely first-order bounds.
Our contributions may be summarised as follows:
1. Local...
FACTER: Fairness-Aware Conformal Thresholding and Prompt Engineering for Enabling Fair LLM-Based Recommender Systems
ICML 2025

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 engineering.
By introducing an adap-
tive semantic variance threshold and a violation-
triggered mechanism, FACTER automatically
tightens fairness constraints whenever biased pat-
terns emerge. We further develop an adversar-
ial prompt generator that leverages historical vi-
olations to reduce repeated demographic biases
without retraining the LLM. Empirical results
on MovieLens and Amazon show that FACTER
substantially reduces fairness violations (up to
95.5%) while maintaining strong recommenda-
tion accuracy, revealing semantic variance as a
potent proxy of bias.
1. Introduction
Large Language Models (LLMs) have significantly ad-
vanced natural language processing (NLP), demonstrating
robust generative capabilities across tasks including summa-
rization, dialogue, code completion, and creative composi-
tion. Representative models such as GPT-3 (Brown et al.,
2020), BERT (Devlin et al., 2019), Llama-2 (Touvron et al.,
2023), Llama-3 (Dubey et al., 2024), and Mistral-7B (Jiang
et al., 2023) leverage massive corpora and complex architec-
tures to produce remarkably fluent text, often approaching
or matching human performance in various linguistic bench-
marks. Yet a growing body of work reveals that these models
can inadvertently perpetuate or even amplify biases related
to sensitive attributes such as race, gender, or age (Sheng
et al., 2019; Blodgett et al., 2020; Bary et al., 2021). Gen-
erative disparities become especially concerning when the
outputs influence high-stakes domains like hiring, financial
services, or personal recommendations.
1University of Southern California, Los Angeles, California,
USA. Correspondence to: Arya Fayyazi <afayyazi@usc.e...
Deep Compositional Phase Diffusion for Long Motion Sequence Generation
NeurIPS 2025

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@comp.hkbu.edu.hk
(c) Long-term Motion Generation
[Walk forward (2s)]
[Turn right (2s)]
[Walk with hands 
above head (4s)]
[Lean down (3s)]
[Leap forward (3s)]
[Leap forward (2s)]
(a) Compositional Motion Generation
(b) Compositional Motion Inbetweening
[Turn right (3s)]
User input motion
User input motion
[Walk forward (4s)]
[Walk clockwise (4s)]
[Jump forward (4s)]
[Walk forward (3s)]
[Crawling forward (5s)]
Figure 1: Our Compositional Phase Diffusion framework produces high-quality composite motion
sequences with smooth transitions and semantic alignment. (a) Compositional generation involves
synthesizing multiple motion segments of varying lengths simultaneously, ensuring smooth transitions
between segments. (b) Motion inbetweening allows users to select segments (in blue and yellow) and
create conditional or unconditional bridging motions. (c) Long-term motion generation is achieved
by scaling the framework with additional modules, enabling the parallel denoising of a larger number
of motion segments. The rainbow color indicates time progression.
Abstract
Recent research on motion generation has shown significant progress in generating
semantically aligned motion with singular semantics. However, when employ-
ing these models to create composite sequences containing multiple semantically
generated motion clips, they often struggle to preserve the continuity of motion
dynamics at the transition boundaries between clips, resulting in awkward transi-
tions and abrupt artifacts. To address these challenges, we present Compositional
Phase Diffusion, which leverages the Semantic Phase Diffusion Module (SPDM)
and Transitional Phase Diffusion Module (TPDM) ...