dataset-review/dataset-review huggingface dataset:
Discrete Copula Diffusion
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...
PANDAS: Improving Many-shot Jailbreaking via Positive Affirmation, Negative Demonstration, and Adaptive Sampling
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...
Curvature Enhanced Data Augmentation for Regression
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...
Cradle: Empowering Foundation Agents towards General Computer Control
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...
Exploring Large Action Sets with Hyperspherical Embeddings using von Mises-Fisher Sampling
ICML 2025

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 large action sets in reinforcement learning
problems where hyperspherical embedding vec-
tors represent these actions. vMF-exp involves
initially sampling a state embedding representa-
tion using a von Mises-Fisher distribution, then
exploring this representation’s nearest neighbors,
which scales to virtually unlimited numbers of
candidate actions. We show that, under theoretical
assumptions, vMF-exp asymptotically maintains
the same probability of exploring each action as
Boltzmann Exploration (B-exp), a popular alter-
native that, nonetheless, suffers from scalability
issues as it requires computing softmax values
for each action. Consequently, vMF-exp serves
as a scalable alternative to B-exp for exploring
large action sets with hyperspherical embeddings.
Experiments on simulated data, real-world public
data, and the successful large-scale deployment of
vMF-exp on the recommender system of a global
music streaming service empirically validate the
key properties of the proposed method.
1. Introduction
Exploration is a fundamental component of the reinforce-
ment learning (RL) paradigm (Amin et al., 2021; McFar-
lane, 2018; Sutton & Barto, 2018). It allows RL agents to
gather valuable information about their environment and
identify optimal actions that maximize rewards (Amin et al.,
2021; Chiappa et al., 2023; Dulac-Arnold et al., 2015; Jin
et al., 2020; Ladosz et al., 2022; McFarlane, 2018; Reynolds,
1Deezer Research, Paris, France.
2LAMSADE, Universit´e
Paris Dauphine, PSL, Paris, France. 3SPEIT, Shanghai Jiao Tong
University, Shanghai, China. Correspondence to: Walid Bendada
<bendadaw@gmail.com>.
Proceedings of the 42 nd International Conference on M...
Moral Alignment for LLM Agents
ICLR 2025

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
Decision-making agents based on pre-trained Large Language Models (LLMs)
are increasingly being deployed across various domains of human activity. While
their applications are currently rather specialized, several research efforts are un-
derway to develop more generalist agents. As LLM-based systems become more
agentic, their influence on human activity will grow and their transparency will de-
crease. Consequently, developing effective methods for aligning them to human
values is vital.
The prevailing practice in alignment often relies on human preference data (e.g., in
RLHF or DPO), in which values are implicit, opaque and are essentially deduced
from relative preferences over different model outputs. In this work, instead of
relying on human feedback, we introduce the design of reward functions that ex-
plicitly and transparently encode core human values for Reinforcement Learning-
based fine-tuning of foundation agent models. Specifically, we use intrinsic re-
wards for the moral alignment of LLM agents.
We evaluate our approach using the traditional philosophical frameworks of Deon-
tological Ethics and Utilitarianism, quantifying moral rewards for agents in terms
of actions and consequences on the Iterated Prisoner’s Dilemma (IPD) environ-
ment. We also show how moral fine-tuning can be deployed to enable an agent to
unlearn a previously developed selfish strategy. Finally, we find that certain moral
strategies learned on the IPD game generalize to several other matrix game en-
vironments. In summary, we demonstrate that fine-tuning with intrinsic rewards
is a promising general solution for aligning LLM agents to human values, and
it might repr...
Stochastically Dominant Peer Prediction
NeurIPS 2025

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
Eliciting reliable human feedback is essential for many machine learning tasks,
such as learning from noisy labels and aligning AI systems with human prefer-
ences. Peer prediction mechanisms incentivize truthful reporting without ground
truth verification by scoring agents based on correlations with peers. Traditional
mechanisms, which ensure that truth-telling maximizes the expected scores in
equilibrium, can elicit honest information while assuming agents’ utilities are
linear functions of their scores. However, in practice, non-linear payment rules
are usually preferred, or agents’ utilities are inherently non-linear.
We propose stochastically dominant truthfulness (SD-truthfulness) as a stronger
guarantee: the score distribution of truth-telling stochastically dominates all other
strategies, incentivizing truthful reporting for a wide range of monotone utility func-
tions. Our first observation is that no existing peer prediction mechanism naturally
satisfies this criterion without strong assumptions. A simple solution—rounding
scores into binary lotteries—can enforce SD-truthfulness, but often degrades sensi-
tivity, a key property related to fairness and statistical efficiency. We demonstrate
how a more careful application of rounding can better preserve sensitivity. Further-
more, we introduce a new enforced agreement (EA) mechanism that is theoretically
guaranteed to be SD-truthful in binary-signal settings and, under mild assump-
tions, empirically achieves the highest sensitivity among all known SD-truthful
mechanisms.
1
Introduction
Information elicitation studies how to design mechanisms that incentivize honest and high-effort
human feedbac...
Structure Is All You Need: Structural Representation Learning on Hyper-Relational Knowledge Graphs
ICML 2025

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 triplet. While
many HKG representation learning methods have
been proposed, they often fail to effectively utilize
the HKG’s structure. This paper demonstrates that
thoroughly leveraging the structure of an HKG
is crucial for reasoning on HKGs, and a purely
structure-based representation learning method
can achieve state-of-the-art performance on var-
ious link prediction tasks. We propose MAYPL,
which learns to initialize representation vectors
based on the structure of an HKG and employs
an attentive neural message passing consisting of
fact-level message computation and entity-centric
and relation-centric aggregations, thereby comput-
ing the representations based solely on the struc-
ture. Due to its structure-driven learning, MAYPL
can conduct inductive inferences on new entities
and relations. MAYPL outperforms 40 knowledge
graph completion methods in 10 datasets, com-
pared with different baseline methods on different
datasets to be tested from diverse perspectives.
1. Introduction
Knowledge graphs (KGs) provide a structured representa-
tion of human knowledge, which eases organizing the re-
lationships between concepts or objects (Ji et al., 2022).
In KGs, information is represented in the form of a
triplet (Toutanova & Chen, 2015; Bordes et al., 2013), e.g.,
(Finding Nemo, set in, Sydney). Since the sim-
ple triplet form oversimplifies information, hyper-relational
knowledge graphs (HKGs) have been recently proposed to
add auxiliary details by adding qualifiers to a triplet (Rosso
1School of Computing, KAIST, Daejeon, South Korea. Corre-
spondence to: Joyce Jiyoung Whang <jjwhang@kaist.ac.kr>.
Proceedings of the 42 nd International Conference o...
Lightweight Dataset Pruning without Full Training via Example Difficulty and Prediction Uncertainty
ICML 2025

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
alleviate this demand by discarding redundant ex-
amples. However, many existing methods require
training a model with a full dataset over a large
number of epochs before being able to prune the
dataset, which ironically makes the pruning pro-
cess more expensive than just training the model
on the entire dataset. To overcome this limitation,
we introduce the Difficulty and Uncertainty-
Aware Lightweight (DUAL) score, which aims
to identify important samples from the early train-
ing stage by considering both example difficulty
and prediction uncertainty. To address a catas-
trophic accuracy drop at an extreme pruning ra-
tio, we further propose a pruning ratio-adaptive
sampling using Beta distribution. Experiments
on various datasets and learning scenarios such
as image classification with label noise and im-
age corruption, and model architecture general-
ization demonstrate the superiority of our method
over previous state-of-the-art (SOTA) approaches.
Specifically, on ImageNet-1k, our method reduces
the time cost for pruning to 66% compared to
previous methods while achieving a SOTA 60%
test accuracy at a 90% pruning ratio. On CIFAR
datasets, the time cost is reduced to just 15% while
maintaining SOTA performance. Implementation
is available at github/dual-pruning.
1. Introduction
Advancements in deep learning have been significantly
driven by large-scale datasets. However, recent studies have
*Equal contribution
1Kim Jaechul Graduate School of AI,
KAIST, Seoul, South Korea. Correspondence to: Chulhee Yun
<chulhee.yun@kaist.ac.kr>.
Proceedings of the 42 nd International Conference on Machine
Learning, Vancouver, Canada. PMLR 267, 2025. Copyright 2025
by...
(How) Do Language Models Track State?
ICML 2025

(How) Do Language Models Track State?
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 or fine-tuned
to compose permutations (i.e., to compute the or-
der of a set of objects after a sequence of swaps).
Despite the simple algebraic structure of this prob-
lem, many other tasks (e.g., simulation of finite
automata and evaluation of boolean expressions)
can be reduced to permutation composition, mak-
ing it a natural model for state tracking in gen-
eral. We show that LMs consistently learn one of
two state tracking mechanisms for this task. The
first closely resembles the “associative scan” con-
struction used in recent theoretical work by Liu
et al. (2023) and Merrill et al. (2024). The second
uses an easy-to-compute feature (permutation par-
ity) to partially prune the space of outputs, and
then refines this with an associative scan. LMs
that learn the former algorithm tend to general-
ize better and converge faster, and we show how
to steer LMs toward one or the other with inter-
mediate training tasks that encourage or suppress
the heuristics. Our results demonstrate that trans-
former LMs, whether pre-trained or fine-tuned,
can learn to implement efficient and interpretable
state-tracking mechanisms, and the emergence
of these mechanisms can be predicted and con-
trolled. Code and data are available at https:
//github.com/belindal/state-tracking.
1. Introduction
Language models (LMs) are trained to model the surface
form of text. A growing body of work suggests that model
internals contain a latent, decodable state of the world—e.g.,
situations described by language and results of program
1MIT EECS and CSAIL. Correspondence to: Belinda Z. Li
<bzl@mit.edu>.
Proceedings of the 42 nd International Conference on Machine
Learning, Vancouve...
Physics-Informed Generative Modeling of Wireless Channels
ICML 2025

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
machine learning (ML) for wireless communica-
tions and radar applications. Generative model-
ing offers a promising framework to address this
problem. However, existing approaches pose un-
resolved challenges, including the need for high-
quality training data, limited generalizability, and
a lack of physical interpretability. To address
these issues, we combine the physics-related com-
pressibility of wireless channels with generative
modeling, in particular, sparse Bayesian genera-
tive modeling (SBGM), to learn the distribution of
the underlying physical channel parameters. By
leveraging the sparsity-inducing characteristics of
SBGM, our methods can learn from compressed
observations received by an access point (AP) dur-
ing default online operation. Moreover, they are
physically interpretable and generalize over sys-
tem configurations without requiring retraining.
1. Introduction
The accurate modeling of wireless channels is critical for
system design, network optimization, and performance eval-
uation in wireless communications and radar (Wang et al.,
2018; 2020; Yin & Cheng, 2016). There are two approaches
to wireless channel modeling: physically and analytically
oriented (Almers et al., 2007). The former integrates the
underlying physics in the description of wireless channels
and focuses on their accuracy and realism. In contrast, the
latter captures channel statistics, ignoring the underlying
physics, and provides an easy-to-use framework for evalua-
tion and system design schemes (Almers et al., 2007; Imoize
et al., 2021). Standardized channel models such as the
3GPP (3GPP, 2024a), COST (Liu et al., 2012) or WINNER
1Technical University of Munich...