Yancy567/AIS-Bench huggingface dataset:
Conference: ICML 2025

ADIOS: Antibody Development via Opponent Shaping
Sebastian Towers * 1 2 Aleksandra Kalisz * 1 2 Philippe A. Robert 3 Alicia Higueruelo 4 Francesca Vianello 5
Ming-Han Chloe Tsai 6 Harrison Steel 2 Jakob Foerster 1 2
Abstract
Anti-viral therapies are typically designed to tar-
get only the current strains of a virus, a my-
opic response. However, therapy-induced selec-
tive pressures drive the emergence of new viral
strains, against which the original myopic ther-
apies are no longer effective.
This evolution-
ary response presents an opportunity: our ther-
apies could both defend against and actively influ-
ence viral evolution. This motivates our method
ADIOS: Antibody Development vIa Opponent
Shaping. ADIOS is a meta-learning framework
where the process of antibody therapy design, the
outer loop, accounts for the virus’s adaptive re-
sponse, the inner loop. With ADIOS, antibodies
are not only robust against potential future vari-
ants, they also influence, i.e., shape, which fu-
ture variants emerge. In line with the opponent
shaping literature, we refer to our optimised an-
tibodies as shapers. To demonstrate the value of
ADIOS, we build a viral evolution simulator us-
ing the Absolut! framework, in which shapers
successfully target both current and future viral
variants, outperforming myopic antibodies. Fur-
thermore, we show that shapers modify the dis-
tribution over viral evolutionary trajectories to
result in weaker variants. We believe that our
ADIOS paradigm will facilitate the discovery of
long-lived vaccines and antibody therapies while
also generalising to other domains. Specifically,
domains such as antimicrobial resistance, cancer
treatment, and others with evolutionarily adap-
tive opponents. Our code is available at https:
//github.com/olakalisz/adios.
*Equal contribution
1FLAIR, Foerster Lab for AI Research
2Department of Engineering, University of Oxford, Oxford, UK
3Department of Biomedicine, University of Basel, Basel, Switzer-
land 4Isomorphic ...
Conference: ICML 2025

The Diffusion Duality
Subham Sekhar Sahoo 1 Justin Deschenaux 2 Aaron Gokaslan 1 Guanghan Wang 1 Justin Chiu 3
Volodymyr Kuleshov 1
Abstract
Uniform-state discrete diffusion models hold
the promise of fast text generation due to their
inherent ability to self-correct. However, they are
typically outperformed by autoregressive models
and masked diffusion models. In this work, we
narrow this performance gap by leveraging a
key insight: Uniform-state diffusion processes
naturally emerge from an underlying Gaussian
diffusion. Our method, Duo, transfers powerful
techniques from Gaussian diffusion to improve
both training and sampling. First, we introduce
a curriculum learning strategy guided by the
Gaussian process, doubling training speed by
reducing variance. Models trained with curricu-
lum learning surpass autoregressive models in
zero-shot perplexity on 3 of 7 benchmarks. Sec-
ond, we present Discrete Consistency Distillation,
which adapts consistency distillation from the
continuous to the discrete setting. This algorithm
unlocks few-step generation in diffusion
language models by accelerating sampling by
two orders of magnitude. We provide the code
and model checkpoints on the project page:
https://s-sahoo.com/duo
1. Introduction
An eternal theme in mathematics is that discreteness
emerges from underlying continuity. From quantum me-
chanics, where the quantized energy states of electrons arise
as solutions to continuous wave equations, to the binary
logic of digital circuits, fundamentally driven by smooth
analog currents, discreteness has repeatedly and naturally
emerged from an underlying continuum. Our work contin-
1Computer and Information Science, Cornell Tech, NY, USA.
2School of Computer and Communication Sciences, EPFL Lau-
sanne, Switzerland 3Cohere, NY, USA. Correspondence to: Sub-
ham Sekhar Sahoo <ssahoo@cs.cornell.edu>.
Proceedings of the 42 nd International Conference on Machine
Learning, Vancouver, Canada. PMLR 267, 2025. Copyright 2025
by the auth...
Conference: ICML 2025

Optimistic Algorithms for
Adaptive Estimation of the Average Treatment Effect
Ojash Neopane 1 Aaditya Ramdas 1 2 Aarti Singh 1
Abstract
Estimation and inference for the Average Treat-
ment Effect (ATE) is a cornerstone of causal infer-
ence and often serves as the foundation for devel-
oping procedures for more complicated settings.
Although traditionally analyzed in a batch setting,
recent advances in martingale theory have paved
the way for adaptive methods that can enhance
the power of downstream inference. Despite these
advances, progress in understanding and develop-
ing adaptive algorithms remains in its early stages.
Existing work either focus on asymptotic analy-
ses that overlook exploration-exploitation trade-
offs relevant in finite-sample regimes or rely on
simpler but suboptimal estimators. In this work,
we address these limitations by studying adaptive
sampling procedures that take advantage of the
asymptotically optimal Augmented Inverse Prob-
ability Weighting (AIPW) estimator. Our analy-
sis uncovers challenges obscured by asymptotic
approaches and introduces a novel algorithmic
design principle reminiscent of optimism in multi-
armed bandits. This principled approach enables
our algorithm to achieve significant theoretical
and empirical gains compared to previous meth-
ods. Our findings mark a step forward in the ad-
vancement of adaptive causal inference methods
in theory and practice.
1. Introduction
The problem of estimating the average treatment effect
(ATE) is central to causal inference and has been extensively
studied. We have a precise understanding of the difficulty of
this problem in both asymptotic and nonasymptotic regimes.
1Machine Learning Department, Carnegie Mellon Uni-
versity
2Department of Statistics & Data Science, Carnegie
Mellon University.
Correspondence to:
Ojash Neopane
<oneopane@andrew.cmu.edu>.
Proceedings of the 42 nd International Conference on Machine
Learning, Vancouver, Canada. PMLR 267, 2025. Copyright 2025
by the a...
Conference: ICML 2025

Partially Observable Reinforcement Learning with Memory Traces
Onno Eberhard 1 2 Michael Muehlebach 1 Claire Vernade 2
Abstract
Partially observable environments present a con-
siderable computational challenge in reinforce-
ment learning due to the need to consider long his-
tories. Learning with a finite window of observa-
tions quickly becomes intractable as the window
length grows. In this work, we introduce memory
traces. Inspired by eligibility traces, these are
compact representations of the history of observa-
tions in the form of exponential moving averages.
We prove sample complexity bounds for the prob-
lem of offline on-policy evaluation that quantify
the return errors achieved with memory traces
for the class of Lipschitz continuous value esti-
mates. We establish a close connection to the
window approach, and demonstrate that, in cer-
tain environments, learning with memory traces
is significantly more sample efficient. Finally,
we underline the effectiveness of memory traces
empirically in online reinforcement learning ex-
periments for both value prediction and control.
1. Introduction
Learning and acting in partially observable environments
requires memory. It is generally not enough to act simply
according to the most recent observation, and an agent must
therefore keep track of the past, as every piece of informa-
tion is likely to be relevant for future actions. In the absence
of additional assumptions, optimal behavior requires the
agent to reason over its entire history of observations. This
space quickly becomes intractable, and just like with contin-
uous state spaces in fully observed systems, it is therefore
necessary to resort to approximation. This article inves-
tigates the fundamental limitations and trade-offs arising
from these approximations. We focus on extracting features
from the history of observations and analyze the complexity
of learning functions of these features. In contrast to meth-
1Max Planck Institute for Intelligent Sy...
Conference: ICML 2025

Hardware and Software Platform Inference
Cheng Zhang * 1 Hanna Foerster * 2 Robert D. Mullins 2 Yiren Zhao 1 Ilia Shumailov 3
Abstract
It is now a common business practice to buy ac-
cess to large language model (LLM) inference
rather than self-host, because of significant up-
front hardware infrastructure and energy costs.
However, as a buyer, there is no mechanism
to verify the authenticity of the advertised ser-
vice including the serving hardware platform, e.g.
that it is actually being served using an NVIDIA
H100. Furthermore, there are reports suggest-
ing that model providers may deliver models that
differ slightly from the advertised ones, often
to make them run on less expensive hardware.
That way, a client pays premium for a capable
model access on more expensive hardware, yet
ends up being served by a (potentially less capa-
ble) cheaper model on cheaper hardware. In this
paper we introduce hardware and software plat-
form inference (HSPI) – a method for identify-
ing the underlying GPU architecture and software
stack of a (black-box) machine learning model
solely based on its input-output behavior. Our
method leverages the inherent differences of var-
ious GPU architectures and compilers to distin-
guish between different GPU types and software
stacks. We evaluate HSPI against models served
on different real hardware and find that in a white-
box setting we can distinguish between differ-
ent GPUs with between 83.9% and 100% accu-
racy. Even in a black-box setting we achieve
results that are up to 3× higher than random
guess accuracy. Our code is available at https:
//github.com/ChengZhang-98/HSPI.
*Equal contribution
1Department of Computing, Impe-
rial College London, London, United Kingdom 2Department
of Computer Science and Technology, University of Cam-
bridge, Cambridge, United Kingdom 3Google DeepMind, Lon-
don, United Kingdom.
Correspondence to:
Cheng Zhang
<cheng.zhang122@imperial.ac.uk>.
Proceedings of the 42 nd International Conference on Mach...
Conference: ICML 2025

RoSTE: An Efficient Quantization-Aware Supervised Fine-Tuning Approach for
Large Language Models
Quan Wei * 1 Chung-Yiu Yau * 2 Hoi-To Wai 2 Yang (Katie) Zhao 1 Dongyeop Kang 3 Youngsuk Park 4
Mingyi Hong 1
Abstract
Supervised fine-tuning is a standard method
for adapting pre-trained large language models
(LLMs) to downstream tasks. Quantization has
been recently studied as a post-training technique
for efficient LLM deployment. To obtain quan-
tized fine-tuned LLMs, conventional pipelines
would first fine-tune the pre-trained models, fol-
lowed by post-training quantization. This often
yields suboptimal performance as it fails to lever-
age the synergy between fine-tuning and quanti-
zation. To effectively realize low-bit quantization
of weights, activations and KV caches in LLMs,
we propose an algorithm named Rotated Straight-
Through-Estimator (RoSTE), which combines
quantization-aware supervised fine-tuning (QA-
SFT) with an adaptive rotation strategy that iden-
tifies an effective rotation configuration to reduce
activation outliers.
We provide theoretical in-
sights on RoSTE by analyzing its prediction error
when applied to an overparameterized least square
quantized training problem. Our findings reveal
that the prediction error is directly proportional to
the quantization error of the converged weights,
which can be effectively managed through an op-
timized rotation configuration. Experiments on
Pythia, Qwen and Llama models of different sizes
demonstrate the effectiveness of RoSTE. Com-
pared to existing post-SFT quantization baselines,
our method consistently achieves superior perfor-
mances across various tasks and different LLM
architectures. Our code is available at https:
//github.com/OptimAI-Lab/RoSTE.
*Equal contribution 1Department of Electrical and Computer
Engineering, University of Minnesota, USA. 2Department of Sys-
tems Engineering and Engineering Management, The Chinese Uni-
versity of Hong Kong, Hong Kong SAR of China. 3Department
of Compute...
Conference: NeurIPS 2025

FlashMoE: Fast Distributed MoE in a Single Kernel
Osayamen Jonathan Aimuyo
Cornell University
oja7@cornell.edu
Byungsoo Oh
Cornell University
bo239@cornell.edu
Rachee Singh
Cornell University
rs2293@cornell.edu
Abstract
The computational sparsity of Mixture-of-Experts (MoE) models enables sub-
linear growth in compute cost as model size increases, thus offering a scalable
path to training massive neural networks. However, existing implementations
suffer from low GPU utilization, significant latency overhead, and a fundamen-
tal inability to leverage task locality, primarily due to CPU-managed scheduling,
host-initiated communication, and frequent kernel launches. To overcome these
limitations, we develop FlashMoE, a fully GPU-resident MoE operator that fuses
expert computation and inter-GPU communication into a single persistent GPU ker-
nel. FlashMoE enables fine-grained pipelining of dispatch, compute, and combine
phases, eliminating launch overheads and reducing idle gaps. Unlike existing work,
FlashMoE obviates bulk-synchronous collectives for one-sided, device-initiated,
inter-GPU (R)DMA transfers, thus unlocking payload efficiency, where we elim-
inate bloated or redundant network payloads in sparsely activated layers. When
evaluated on an 8-H100 GPU node with MoE models having up to 128 experts
and 16K token sequences, FlashMoE achieves up to 9× higher GPU utilization,
6× lower latency, 5.7× higher throughput, and 4× better overlap efficiency com-
pared to state-of-the-art baselines—despite using FP32 while baselines use FP16.
FlashMoE shows that principled GPU kernel-hardware co-design is key to unlock-
ing the performance ceiling of large-scale distributed ML. We provide code at
https://github.com/osayamenja/FlashMoE.
FlashMoE
Comet
FasterMoE
Megatron
DeepEP
0
20
40
60
80
100
Average SM Util (%)
93.17%
42.31%
9.67%
59.11%
13.55%
Forward Pass | E = 64 | k = 2 | 2 A100s | 
 is better
(a) GPU SM Utilization
4K
8K
16K
Number of Tokens
0
10
20
30
40
Runtime (ms)...
Conference: ICML 2025

Progressively Label Enhancement for Large Language Model Alignment
Biao Liu 1 2 Ning Xu 1 2 Xin Geng 1 2
Abstract
Large Language Models (LLM) alignment aims to
prevent models from producing content that mis-
aligns with human expectations, which can lead
to ethical and legal concerns. In the last few years,
Reinforcement Learning from Human Feedback
(RLHF) has been the most prominent method for
achieving alignment. Due to challenges in sta-
bility and scalability with RLHF stages, which
arise from the complex interactions between mul-
tiple models, researchers are exploring alternative
methods to achieve effects comparable to those
of RLHF. However, these methods often rely on
large high-quality datasets. Despite some meth-
ods considering the generation of additional data
to expand datasets, they often treat model training
and data generation as separate and static pro-
cesses, overlooking the fact that these processes
are highly interdependent, leading to inefficient
utilization of the generated data. To deal with
this problem, we propose PLE, i.e., Progressively
Label Enhancement for LLM Alignment, a frame-
work that dynamically adjusts the model’s train-
ing process based on the evolving quality of the
generated data. Specifically, we prompt the model
to generate responses for both the original query
and a set of carefully designed principle-guided
query, and then utilize a dynamic threshold to
determine the appropriate training approach for
both responses based on their corresponding re-
ward scores. Experimental results demonstrate the
effectiveness of PLE compared to existing LLM
alignment methods.
1School of Computer Science and Engineering, South-
east University, Nanjing, China.
E-mail: {liubiao01, xning,
xgeng}@seu.edu.cn. 2Key Laboratory of New Generation Artifi-
cial Intelligence Technology and Its Interdisciplinary Applications
(Southeast University), Ministry of Education, China. Correspon-
dence to: Ning Xu <xning@seu.edu.cn>.
Proceedings of the 42 n...
Conference: ICML 2025

Robust Offline Reinforcement Learning with Linearly Structured
f-Divergence Regularization
Cheng Tang * 1 Zhishuai Liu * 2 Pan Xu 2
Abstract
The Robust Regularized Markov Decision Pro-
cess (RRMDP) is proposed to learn policies ro-
bust to dynamics shifts by adding regularization
to the transition dynamics in the value function.
Existing methods mostly use unstructured reg-
ularization, potentially leading to conservative
policies under unrealistic transitions. To address
this limitation, we propose a novel framework,
the d-rectangular linear RRMDP (d-RRMDP),
which introduces latent structures into both tran-
sition kernels and regularization. We focus on
offline reinforcement learning, where an agent
learns policies from a precollected dataset in the
nominal environment. We develop the Robust
Regularized Pessimistic Value Iteration (R2PVI)
algorithm that employs linear function approxi-
mation for robust policy learning in d-RRMDPs
with f-divergence based regularization terms on
transition kernels. We provide instance-dependent
upper bounds on the suboptimality gap of R2PVI
policies, demonstrating that these bounds are in-
fluenced by how well the dataset covers state-
action spaces visited by the optimal robust policy
under robustly admissible transitions. We estab-
lish information-theoretic lower bounds to verify
that our algorithm is near-optimal. Finally, numer-
ical experiments validate that R2PVI learns robust
policies and exhibits superior computational effi-
ciency compared to baseline methods.
1. Introduction
Offline reinforcement learning (RL) (Levine et al., 2020)
facilitates policy learning from fixed datasets, eliminating
the need for direct interaction with the environment. When
*Equal contribution 1 University of Illinois Urbana-Champaign,
work was done when Cheng was in Tsinghua University 2Duke
University. Correspondence to: Pan Xu <pan.xu@duke.edu>.
Proceedings of the 42 st International Conference on Machine
Learning, Vancouver, Canada. PMLR 267...
Conference: ICML 2025

Tensor Product Neural Networks for Functional ANOVA Model
Seokhun Park 1 Insung Kong 2 Yongchan Choi 3 Chanmoo Park 1 4 Yongdai Kim 1
Abstract
Interpretability for machine learning models is
becoming more and more important as machine
learning models become more complex. The func-
tional ANOVA model, which decomposes a high-
dimensional function into a sum of lower dimen-
sional functions (commonly referred to as compo-
nents), is one of the most popular tools for inter-
pretable AI, and recently, various neural networks
have been developed for estimating each compo-
nent in the functional ANOVA model. However,
such neural networks are highly unstable when
estimating each component since the components
themselves are not uniquely defined. That is, there
are multiple functional ANOVA decompositions
for a given function. In this paper, we propose a
novel neural network which guarantees a unique
functional ANOVA decomposition and thus is
able to estimate each component stably. We call
our proposed neural network ANOVA Tensor Prod-
uct Neural Network (ANOVA-TPNN) since it is
motivated by the tensor product basis expansion.
Theoretically, we prove that ANOVA-TPNN can
approximate any smooth function well. Empiri-
cally, we show that ANOVA-TPNN provide much
more stable estimation of each component and
thus much more stable interpretation when train-
ing data and initial values of the model parameters
vary than existing neural networks do. Our source
code is released at https://github.com/
ParkSeokhun/ANOVA-TPNN
1. Introduction
Interpretability has become more important as artificial in-
telligence (AI) models have become more sophisticated and
complicated in recent years. Various methods such as LIME
1Department of Statistics, Seoul National University, Seoul, Re-
public of Korea 2Department of Applied Mathematics, University
of Twente, Enschede, Netherlands 3Toss Bank, Seoul, Republic of
Korea 4Samsung Electronics Co., Ltd, Republic of Korea. Corre-
spondence to: Yongdai ...
Conference: ICML 2025

Sparse Autoencoders for Hypothesis Generation
Rajiv Movva * 1 Kenny Peng * 2 3 Nikhil Garg 3 Jon Kleinberg 2 Emma Pierson 1
Abstract
We describe HYPOTHESAES, a general method
to hypothesize interpretable relationships between
text data (e.g., headlines) and a target variable
(e.g., clicks). HYPOTHESAES has three steps:
(1) train a sparse autoencoder on text embeddings
to produce interpretable features describing the
data distribution, (2) select features that predict
the target variable, and (3) generate a natural lan-
guage interpretation of each feature (e.g., men-
tions being surprised or shocked) using an LLM.
Each interpretation serves as a hypothesis about
what predicts the target variable. Compared to
baselines, our method better identifies reference
hypotheses on synthetic datasets (at least +0.06
in F1) and produces more predictive hypotheses
on real datasets (∼twice as many significant find-
ings), despite requiring 1-2 orders of magnitude
less compute than recent LLM-based methods.
HYPOTHESAES also produces novel discoveries
on two well-studied tasks: explaining partisan dif-
ferences in Congressional speeches and identify-
ing drivers of engagement with online headlines.
1. Introduction
Large language models (LLMs) show promise as a tool for
hypothesis generation. Discovering relationships between
text data and a target variable is an important and funda-
mental task with diverse applications in economics, political
science, sociology, medicine, and business (Grimmer, 2010;
Rathje et al., 2021; Sun et al., 2022; Gentzkow & Shapiro,
2010; Monroe et al., 2009; Nelson, 2020; Ranard et al.,
2016; Ting et al., 2017). What features of a restaurant re-
view predict a low rating? What features of a social media
post predict whether it will go viral? What features of a
patient’s clinical notes predict if they will develop cancer?
*Equal contribution 1UC Berkeley 2Cornell University 3Cornell
Tech. Correspondence to: Rajiv Movva <rmovva@berkeley.edu>,
Kenny Peng <k...
Conference: ICML 2025

A Machine Learning Approach to Duality in Statistical Physics
Prateek Gupta 1 Andrea E. V. Ferrari 2 3 Nabil Iqbal 4 5
Abstract
The notion of duality – that a given physical sys-
tem can have two different mathematical descrip-
tions – is a key idea in modern theoretical physics.
Establishing a duality in lattice statistical mechan-
ics models requires the construction of a dual
Hamiltonian and a map from the original to the
dual observables. By using neural networks to
parameterize these maps and introducing a loss
function that penalises the difference between cor-
relation functions in original and dual models, we
formulate the process of duality discovery as an
optimization problem. We numerically solve this
problem and show that our framework can redis-
cover the celebrated Kramers-Wannier duality for
the 2d Ising model, numerically reconstructing
the known mapping of temperatures. We further
investigate the 2d Ising model deformed by a pla-
quette coupling and find families of “approximate
duals”. We discuss future directions and prospects
for discovering new dualities within this frame-
work. 1.
1. Background
A key concept in physics is duality, i.e. the idea that the
same physical system can have two different mathematical
descriptions. Duality sits at the heart of modern theoretical
physics. In this work we seek to formalize the notion of
duality in statistical physics in a manner that allows modern
machine learning techniques to be used to systematically
search for dualities.
*Equal contribution 1Max Planck Institute for Human Develop-
ment, Berlin, Germany 2Deutsches Elektronen-Synchrotron DESY,
Germany 3School of Mathematics, The University of Edinburgh
4Department of Mathematical Sciences, Durham University, UK
5Amsterdam Machine Learning Laboratory, University of Ams-
terdam, Netherlands. Correspondence to: Prateek Gupta <con-
tact@pgupta.info>.
Proceedings of the 42 nd International Conference on Machine
Learning, Vancouver, Canada. PMLR 267, 2025. Co...