Yancy567/AIS-Bench huggingface dataset:
Conference: 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...
Conference: 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...
Conference: 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...
Conference: 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...
Conference: 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...
Conference: 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...
Conference: 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...
Conference: 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) ...
Conference: ICLR 2025

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.ucla.edu
mathias.niepert@ki.uni-stuttgart.de
ABSTRACT
Discrete diffusion models have recently shown significant progress in modeling
complex data, such as natural languages and DNA sequences. However, unlike
diffusion models for continuous data, which can generate high-quality samples in
just a few denoising steps, modern discrete diffusion models still require hundreds
or even thousands of denoising steps to perform well. In this paper, we identify
a fundamental limitation that prevents discrete diffusion models from achieving
strong performance with fewer steps – they fail to capture dependencies between
output variables at each denoising step. To address this issue, we provide a for-
mal explanation and introduce a general approach to supplement the missing de-
pendency information by incorporating another deep generative model, termed
the copula model. Our method does not require fine-tuning either the diffusion
model or the copula model, yet it enables high-quality sample generation with
significantly fewer denoising steps. When we apply this approach to autoregres-
sive copula models, the combined model outperforms both models individually
in unconditional and conditional text generation. Specifically, the hybrid model
achieves better (un)conditional text generation using 8 to 32 times fewer denois-
ing steps than the diffusion model alone. In addition to presenting an effective
discrete diffusion generation algorithm, this paper emphasizes the importance of
modeling inter-variable dependencies in discrete diffusion.1
1
INTRODUCTION
Discrete diffusion models have recently achieved significant progress in modeling complex data such
as natural lan...
Conference: ICML 2025

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 malicious target prompt is prefixed with
hundreds of fabricated conversational exchanges
between the user and the model. These exchanges
are randomly sampled from a pool of unsafe
question-answer pairs, making it appear as
though the model has already complied with
harmful instructions. In this paper, we present
PANDAS: a hybrid technique that improves
many-shot jailbreaking by modifying these
fabricated dialogues with Positive Affirmations,
Negative Demonstrations, and an optimized
Adaptive Sampling method tailored to the target
prompt’s topic. We also introduce ManyHarm,
a dataset of harmful question–answer pairs, and
demonstrate through extensive experiments that
PANDAS significantly outperforms baseline
methods in long-context scenarios.
Through
attention analysis, we provide insights into how
long-context vulnerabilities are exploited and
show how PANDAS further improves upon many-
shot jailbreaking. Our source code is available at
https://github.com/averyma/pandas.
Warning: This paper contains model behavior
that can be offensive or harmful in nature.
1. Introduction
The growing length of context windows in large lan-
guage models (LLMs) unlocks applications, such as agentic
LLMs (Park et al., 2023), that were previously impractical
or severely limited (Team et al., 2024; Ding et al., 2024; Jin
et al., 2024; Wu et al., 2024; Dong et al., 2024).
1University of Toronto, Vector Institute 2University of Oxford
3Polytechnique Montr´eal, Mila - Quebec AI Institute, University of
Toronto. Correspondence to: Avery Ma <ama@cs.toronto.edu>.
Proceedings of the 42 nd International Conference on Machine
Learning, Vancouver, Canada. PMLR 267, 2025. Copyright 20...
Conference: ICML 2025

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 generalize
well to unseen data, thanks to effective regular-
ization techniques, with data augmentation be-
ing among the most widely used. While data
augmentation has shown great success in clas-
sification tasks using label-preserving transfor-
mations, its application in regression problems
has received less attention. Recently, a novel
manifold learning approach for generating syn-
thetic data was proposed, utilizing a first-order
approximation of the data manifold. Building on
this foundation, we present a theoretical frame-
work and practical tools for approximating and
sampling general data manifolds. Furthermore,
we introduce the Curvature-Enhanced Manifold
Sampling (CEMS) method for regression tasks.
CEMS leverages a second-order representation
of the data manifold to enable efficient sampling
and reconstruction of new data points. Exten-
sive evaluations across multiple datasets and com-
parisons with state-of-the-art methods demon-
strate that CEMS delivers superior performance
in both in-distribution and out-of-distribution sce-
narios, while introducing only minimal compu-
tational overhead. Code is available at https:
//github.com/azencot-group/CEMS.
1. Introduction
Deep neural networks have demonstrated remarkable per-
formance across a wide range of applications in various
fields (Krizhevsky et al., 2012; Long et al., 2015; Mnih
et al., 2015; Noh et al., 2015; Vinyals et al., 2015; He
1Department of Computer Science, Ben-Gurion University of
the Negev, Beer-Sheva, Israel. Correspondence to: Ilya Kaufman
<ilyakau@post.bgu.ac.il>.
Proceedings of the 42 nd International Conference on Machine
Learning, Vancouver, Canada. PMLR 267, 2025. Copyri...
Conference: ICML 2025

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 † Yifei Bi 3 Tianbao Xie 6 Pengjie Gu 1 Xiyun Li 3 Ceyao Zhang 7 Long Tian 5
Chaojie Wang 5 Xinrun Wang 1 ‡ Börje F. Karlsson 3 ‡ † Bo An 1 5 Shuicheng Yan 8 5 Zongqing Lu 4 3
Everyday
Software
Professional
Software
Digital
Games
Memory
Information 
Gathering
Self-Reflection
Task Inference
Skill Curation
Action
Planning
Figure 1: The CRADLE framework empowers nascent foundation models to perform complex computer tasks via the same uniﬁed
interface humans use, i.e., screenshots as input and keyboard & mouse operations as output.
Abstract
Despite the success in speciﬁc scenarios, exist-
ing foundation agents still struggle to generalize
across various virtual scenarios, mainly due to the
dramatically different encapsulations of environ-
ments with manually designed observation and
action spaces. To handle this issue, we propose
the General Computer Control (GCC) setting to
restrict foundation agents to interact with software
through the most uniﬁed and standardized inter-
face, i.e., using screenshots as input and keyboard
and mouse actions as output. Then we introduce
CRADLE, a modular and ﬂexible LMM-powered
framework, as a preliminary attempt towards
GCC. Enhanced by six key modules: Information
Gathering, Self-Reﬂection, Task Inference, Skill
*Equal contribution 1Nanyang Technological University, Sin-
gapore 2Institute of Software, Chinese Academy of Sciences
3Beijing Academy of Artiﬁcial Intelligence 4Peking University
5Skywork AI 6The University of Hong Kong 7The Chinese Univer-
sity of Hong Kong, Shenzhen 8National University of Singapore.
Correspondence to: Weihao Tan <weihao001@ntu.edu.sg>, Bo
An <boan@ntu.edu.sg>, Shuicheng Yan <yansc@nus.edu.sg>,
Zo...