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

Inverse Flow and Consistency Models
Yuchen Zhang 1 Jian Zhou 1
Abstract
Inverse generation problems, such as denoising
without ground truth observations, is a critical
challenge in many scientific inquiries and real-
world applications. While recent advances in gen-
erative models like diffusion models, conditional
flow matching, and consistency models achieved
impressive results by casting generation as de-
noising problems, they cannot be directly used for
inverse generation without access to clean data.
Here we introduce Inverse Flow (IF), a novel
framework that enables using these generative
models for inverse generation problems includ-
ing denoising without ground truth. Inverse Flow
can be flexibly applied to nearly any continuous
noise distribution and allows complex dependen-
cies. We propose two algorithms for learning
Inverse Flows, Inverse Flow Matching (IFM) and
Inverse Consistency Model (ICM). Notably, to de-
rive the computationally efficient, simulation-free
inverse consistency model objective, we general-
ized consistency training to any forward diffusion
processes or conditional flows, which have appli-
cations beyond denoising. We demonstrate the
effectiveness of IF on synthetic and real datasets,
outperforming prior approaches while enabling
noise distributions that previous methods cannot
support. Finally, we showcase applications of
our techniques to fluorescence microscopy and
single-cell genomics data, highlighting IF’s utility
in scientific problems. Overall, this work expands
the applications of powerful generative models to
inversion generation problems.
1. Introduction
Recent advances in generative modeling such as diffusion
models (Sohl-Dickstein et al., 2015; Ho et al., 2020; Song
1Lyda Hill Department of Bioinformatics, University of Texas
Southwestern Medical Center, USA. Correspondence to: Jian Zhou
<jian.zhou@utsouthwestern.edu>.
Proceedings of the 42 nd International Conference on Machine
Learning, Vancouver, Canada. PMLR 267, 2025...
Conference: ICML 2025

A Unified Approach to Routing and Cascading for LLMs
Jasper Dekoninck 1 Maximilian Baader 1 Martin Vechev 1
Abstract
The availability of a wide range of large language
models (LLMs) embedded in various agentic
systems has significantly increased the potential
of model selection strategies to improve the cost-
performance tradeoff. Existing strategies involve
either routing, where a single model is chosen per
query, or cascading, which sequentially runs in-
creasingly larger models until a satisfactory an-
swer is found. However, current approaches face
three key limitations: they (1) lack formal proofs
of optimality, (2) fail to identify the conditions
under which these strategies are most effective to
improve the cost-performance tradeoff, and (3)
are unable to combine both paradigms for fur-
ther improvements. To address these issues, we
first derive a novel optimal strategy for cascading
and prove the optimality of an existing routing
strategy. Further, we propose cascade routing,
a unified framework that integrates routing and
cascading into a theoretically optimal strategy.
Through our analysis, we identify good quality
estimators as the critical factor for the success of
model selection paradigms. Finally, in our ex-
periments, we show that cascade routing consis-
tently outperforms the individual approaches by
a large margin and we analyze quality estimators
to determine when routing and/or cascading are
useful paradigms for model selection.1
1. Introduction
Large language models (LLMs) have found applications in
a wide range of tasks, some of which are easily handled by
small models, while others require the full capacity of state-
of-the-art LLMs. This has led to the development of many
fine-tuned models of various sizes that target specific tasks.
1Department
of
Computer
Science,
ETH
Zurich,
Switzerland.
Correspondence
to:
Jasper
Dekoninck
<jasper.dekoninck@inf.ethz.ch>.
Proceedings of the 42 nd International Conference on Machine
Learning, Vancouver, Cana...
Conference: ICLR 2025

Published as a conference paper at ICLR 2025
COMPUTE-CONSTRAINED DATA SELECTION
Junjie Oscar Yin
Johns Hopkins University
jyin27@jhu.edu
Alexander M. Rush
Cornell University
arush@cornell.edu
ABSTRACT
Data selection can reduce the amount of training data needed to finetune LLMs;
however, the efficacy of data selection scales directly with its compute. Motivated
by the practical challenge of compute-constrained finetuning, we consider the set-
ting in which both the cost of selecting data and training are budgeted for. We first
formalize the problem of data selection with a cost-aware utility function, and
model the data selection problem as trading off initial-selection cost for training
gain. We run a comprehensive sweep of experiments across multiple tasks, vary-
ing compute budget by scaling finetuning tokens, model sizes, and data selection
compute. Interestingly we find that many powerful data selection methods are al-
most never compute-optimal, and that cheaper data selection alternatives dominate
both from a theoretical and empirical perspective. For compute-optimal training,
we find that perplexity and gradient data selection require training-to-selection
model size ratios of 5x and 10x, respectively.
1
INTRODUCTION
The growth of large language models (LLMs) has motivated research into their resource profiles.
The compute cost of training LLMs is substantial, and, in many cases, the total compute budget is
predetermined: the number of accelerators and their usage hours are allocated in advanced. Thus, it
is critical to determine the optimal allocation of resources under a budget constraint. Past work on
compute-optimal LLMs (Hoffmann et al., 2022) studies if one could attain a better perplexity for a
given pretraining compute budget by balancing architecture and training decisions.
Similar resource questions exist during post-training finetuning of LLMs. We assume a common
setting where a single-base LLM needs to be finetuned for a downstream task. Numerous...
Conference: ICML 2025

Variational Phylogenetic Inference with Products over Bipartitions
Evan Sidrow 1 Alexandre Bouchard-Cˆot´e 2 Lloyd T. Elliott 1
Abstract
Bayesian phylogenetics is vital for understanding
evolutionary dynamics, and requires accurate and
efficient approximation of posterior distributions
over trees. In this work, we develop a variational
Bayesian approach for ultrametric phylogenetic
trees. We present a novel variational family based
on coalescent times of a single-linkage clustering
and derive a closed-form density for the resulting
distribution over trees. Unlike existing methods
for ultrametric trees, our method performs infer-
ence over all of tree space, it does not require
any Markov chain Monte Carlo subroutines, and
our variational family is differentiable. Through
experiments on benchmark genomic datasets and
an application to the viral RNA of SARS-CoV-2,
we demonstrate that our method achieves compet-
itive accuracy while requiring significantly fewer
gradient evaluations than existing state-of-the-art
techniques.
1. Introduction
The goal of Bayesian phylogenetics is to infer the genealogy
of a collection of taxa given a genetic model and aligned
sequence data. Phylogenetics is used in fields such as epi-
demiology (Li et al., 2020), linguistics (List et al., 2014), and
ecology (Godoy et al., 2018). Bayesian phylogenetic infer-
ence quantifies uncertainty and integrates over phylogenetic
tree structures within a phylogenetic model (Zhang and Mat-
sen IV, 2019). Most Bayesian phylogenetic inference is per-
formed using Markov chain Monte Carlo (MCMC) methods
with candidate trees iteratively proposed and either accepted
or rejected based on their consistency with the observed data.
However, MCMC methods can struggle because the number
of possible trees grows super-exponentially in the number
of taxa and posteriors on trees are highly multi-modal.
1Department of Statistics and Actuarial Science, Simon Fraser
University, Burnaby, Canada 2Department of Statistics...
Conference: ICML 2025

Merge-Friendly Post-Training Quantization for
Multi-Target Domain Adaptation
Juncheol Shin 1 Minsang Seok 2 Seonggon Kim 2 Eunhyeok Park 1 2
Abstract
Model merging has emerged as a powerful tech-
nique for combining task-specific weights, achiev-
ing superior performance in multi-target domain
adaptation. However, when applied to practical
scenarios, such as quantized models, new chal-
lenges arise. In practical scenarios, quantization
is often applied to target-specific data, but this
process restricts the domain of interest and intro-
duces discretization effects, making model merg-
ing highly non-trivial. In this study, we analyze
the impact of quantization on model merging
through the lens of error barriers. Leveraging
these insights, we propose a novel post-training
quantization, HDRQ - Hessian and distant regu-
larizing quantization - that is designed to consider
model merging for multi-target domain adapta-
tion. Our approach ensures that the quantization
process incurs minimal deviation from the source
pre-trained model while flattening the loss surface
to facilitate smooth model merging. To our knowl-
edge, this is the first study on this challenge, and
extensive experiments confirm its effectiveness.
1. Introduction
Large-scale models have driven breakthroughs across vari-
ous domains, particularly in generative AI, enabling efficient
adaptation to multiple tasks and user-specific data. However,
deploying such models on resource-constrained devices re-
mains a significant challenge due to their computational de-
mands. In this perspective, model merging has emerged as a
promising technique that enables multi-target domain adap-
tation without additional training. A recent study on multi-
1Graduate School of Artificial Intelligence, Pohang Univer-
sity of Science and Technology (POSTECH), Pohang, Repub-
lic of Korea 2Department of Computer Science and Engineer-
ing, Pohang University of Science and Technology (POSTECH),
Pohang, Republic of Korea. Correspond...
Conference: ICML 2025

WikiBigEdit: Understanding the Limits of Lifelong Knowledge Editing in LLMs
Lukas Thede 1 2 3 Karsten Roth 1 2 3 Matthias Bethge 1 Zeynep Akata 2 3 4 Thomas Hartvigsen 5
Abstract
Keeping large language models factually up-to-
date is crucial for deployment, yet costly retrain-
ing remains a challenge. Knowledge editing of-
fers a promising alternative, but methods are only
tested on small-scale or synthetic edit benchmarks.
In this work, we aim to bridge research into life-
long knowledge editing to real-world edits at
a practically relevant scale. We first introduce
WikiBigEdit; a large-scale benchmark of real-
world Wikidata edits, built to automatically ex-
tend lifelong for future-proof benchmarking. In
its first instance, it includes over 500K question-
answer pairs for knowledge editing alongside a
comprehensive evaluation pipeline. Finally, we
use WikiBigEdit to study existing knowledge
editing techniques’ ability to incorporate large
volumes of real-world facts and contrast their ca-
pabilities to generic modification techniques such
as retrieval augmentation and continual finetun-
ing to acquire a complete picture of the practical
extent of current lifelong knowledge editing.1
1. Introduction
Keeping large language models factually up-to-date is a
critical challenge in their practical deployment (Hartvigsen
et al., 2023; Meng et al., 2023; 2022; Wang et al., 2024a;
Zhang et al., 2024) due to given knowledge cutoffs and
limited tail-end concept coverage (Cheng et al., 2024; Zheng
et al., 2024; Zhang et al., 2024; Udandarao et al., 2024)
incurring inaccuracies. Finding ways to inject or update
new and revised facts is crucial; Particularly for factuality-
critical applications like medicine (Bao et al., 2023; Jeong
et al., 2024), law (Colombo et al., 2024; Lai et al., 2024) or
education (Team et al., 2024c; Wang et al., 2024b).
1T¨ubingen AI Center, University of T¨ubingen 2Helmholtz Mu-
nich 3Munich Center for Machine Learning (MCML) 4Technical
University, M...
Conference: ICML 2025

A Sub-Problem Quantum Alternating Operator Ansatz for Correlation
Clustering
Lucas Fabian Naumann 1 Jannik Irmai 1 Bjoern Andres 1 2
Abstract
The Quantum Alternating Operator Ansatz
(QAOA) is a hybrid quantum-classical variational
algorithm for approximately solving combinato-
rial optimization problems on Noisy Intermediate-
Scale Quantum (NISQ) devices. Although it has
been successfully applied to a variety of problems,
there is only limited work on correlation cluster-
ing due to the difficulty of modelling the problem
constraints with the ansatz. Motivated by this, we
present a generalization of QAOA that is more
suitable for this problem. In particular, we mod-
ify QAOA in two ways: Firstly, we use nucleus
sampling for the computation of the expected cost.
Secondly, we split the problem into sub-problems,
solving each individually with QAOA. We call
this generalization the Sub-Problem Quantum Al-
ternating Operator Ansatz (SQAOA) and show
theoretically that optimal solutions to correlation
clustering instances can be obtained with certainty
when the depth of the ansatz tends to infinity.
Further, we show experimentally that SQAOA
achieves better approximation ratios than QAOA
for correlation clustering, while using only one
qubit per node of the respective problem instance
and reducing the runtime (of simulations).
1. Introduction
The term “quantum supremacy” (Preskill, 2012) refers to
the ability of quantum computers to perform tasks efficiently
that classical computers cannot. From a theoretical point
of view, algorithms achieving this supremacy for problems
of practical interest have long been established. However,
applying these algorithms to problem instances of classi-
cally intractable sizes is not possible on current quantum
1Faculty of Computer Science, TU Dresden 2Center for Scal-
able Data Analytics and AI, Dresden/Leipzig. Correspondence to:
Bjoern Andres <bjoern.andres@tu-dresden.de>.
Proceedings of the 42 nd International Conference on Machine
Lea...
Conference: NeurIPS 2025

Linearly Constrained Diffusion Implicit Models
Vivek Jayaram1
Ira Kemelmacher-Shlizerman1
Steven M. Seitz1
John Thickstun2
1University of Washington 2Cornell University
Abstract
We introduce Linearly Constrained Diffusion Implicit Models (CDIM), a fast and
accurate approach to solving noisy linear inverse problems using diffusion models.
Traditional diffusion-based inverse methods rely on numerous projection steps to
enforce measurement consistency in addition to unconditional denoising steps.
CDIM achieves a 10–50× reduction in projection steps by dynamically adjusting
the number and size of projection steps to align a residual measurement energy
with its theoretical distribution under the forward diffusion process. This adap-
tive alignment preserves measurement consistency while substantially accelerat-
ing constrained inference. For noise-free linear inverse problems, CDIM exactly
satisfies the measurement constraints with few projection steps, even when exist-
ing methods fail. We demonstrate CDIM’s effectiveness across a range of applica-
tions, including super-resolution, denoising, inpainting, deblurring, and 3D point
cloud reprojection. Code and an interactive demo can be found on our project
website. 1
1
Introduction
Recovering an unknown signal from noisy linear measurements is a fundamental challenge encom-
passing tasks like super-resolution, inpainting, and denoising. These are examples of linear inverse
problems, for which we observe a partial measurements y ∈Rd that was generated through a linear
operator A ∈Rd×n applied to a signal x ∈Rn. In the noisy case, measurements y are corrupted
with noise sampled i.i.d. from a gaussian distribution with variance σ2
y:
y = Ax + σ,
where σ ∼N(0, σ2
yI)
(1)
We aim to use a pretrained diffusion model [1] as a prior to solve these underdetermined inverse
problems. However, adapting diffusion models to solve inverse problems has traditionally presented
two key challenges. First, recovering the original signal from...
Conference: ICML 2025

AdvPrompter: Fast Adaptive Adversarial Prompting for LLMs
Anselm Paulus * 1 2 Arman Zharmagambetov * 3 Chuan Guo 3 Brandon Amos 3 † Yuandong Tian 3 †
Abstract
Large Language Models (LLMs) are vulnerable
to jailbreaking attacks that lead to generation of
inappropriate or harmful content. Manual red-
teaming requires a time-consuming search for ad-
versarial prompts, whereas automatic adversar-
ial prompt generation often leads to semantically
meaningless attacks that do not scale well. In this
paper, we present a novel method that uses an-
other LLM, called AdvPrompter, to generate
human-readable adversarial prompts in seconds.
AdvPrompter, which is trained using an alter-
nating optimization algorithm, generates suffixes
that veil the input instruction without changing its
meaning, such that the TargetLLM is lured to
give a harmful response. Experimental results on
popular open source TargetLLMs show highly
competitive results on the AdvBench and Harm-
Bench datasets, that also transfer to closed-source
black-box LLMs. We also show that training on
adversarial suffixes generated by AdvPrompter
is a promising strategy for improving the ro-
bustness of LLMs to jailbreaking attacks. Our
code is available at http://github.com/
facebookresearch/advprompter.
1. Introduction
Large Language Models (LLMs) are capable of generating
fluent, natural language dialogue and can follow complex in-
structions (Zheng et al., 2023b; Touvron et al., 2023; Dubey
et al., 2024; OpenAI et al., 2024; Jiang et al., 2023; Mistral-
Large, 2024), but also tend to replicate toxic behavior and
generate content that could be considered inappropriate, of-
fensive or harmful (Ganguli et al., 2022; Zou et al., 2023).
To mitigate this issue, most LLMs undergo a process called
safety-alignment, where the model is fine-tuned with hu-
man preferences that reflect positive societal values, leading
*Equal contribution ,†Equal advising 1University of T¨ubingen
2Work done at Meta 3FAIR at Meta. Correspondenc...
Conference: ICML 2025

Aligned Multi Objective Optimization
Yonathan Efroni 1 Ben Kretzu 2 Daniel R. Jiang 1 Jalaj Bhandari 1 Zheqing Zhu 1 Karen Ullrich 1
Abstract
To date, the multi-objective optimization litera-
ture has mainly focused on conflicting objectives,
studying the Pareto front, or requiring users to
balance tradeoffs. Yet, in machine learning prac-
tice, there are many scenarios where such con-
flict does not take place. Recent findings from
multi-task learning, reinforcement learning, and
LLMs training show that diverse related tasks can
enhance performance across objectives simultane-
ously. Despite this evidence, such phenomenon
has not been examined from an optimization per-
spective. This leads to a lack of generic gradient-
based methods that can scale to scenarios with a
large number of related objectives. To address this
gap, we introduce the Aligned Multi-Objective
Optimization framework, propose new algorithms
for this setting, and provide theoretical guarantees
of their superior performance compared to naive
approaches.
1. Introduction
In many real-world optimization problems, we have access
to multi-dimensional feedback rather than a single scalar ob-
jective. The multi-objective optimization (MOO) literature
has largely focused on the setting where these objectives
conflict with each other, which necessitates the Pareto domi-
nance notion of optimality. A closely related area of study is
multi-task learning (Caruana, 1997; Teh et al., 2017; Sener
and Koltun, 2018; Yu et al., 2020; Lin et al., 2021; Liu et al.,
2021; Navon et al., 2022; Zhou et al., 2022; Lin and Zhang,
2023; Liu et al., 2023a; Chen et al., 2023; Achituve et al.,
2024; He et al., 2024; Liu and Vicente, 2024), where mul-
tiple tasks are learned jointly, typically with both shared
and task-specific parameters. The hope is that the model
can perform better on individual tasks by sharing common
information across tasks. Indeed, the phenomenon of im-
*Equal contribution 1Meta AI 2Technion. Corresponde...
Conference: NeurIPS 2025

Localizing Knowledge in Diffusion Transformers
Arman Zarei1∗, Samyadeep Basu3, Keivan Rezaei1, Zihao Lin2, Sayan Nag3, Soheil Feizi1
1University of Maryland
2University of California, Davis
3Adobe
Abstract
Understanding how knowledge is distributed across the layers of generative models
is crucial for improving interpretability, controllability, and adaptation. While prior
work has explored knowledge localization in UNet-based architectures, Diffusion
Transformer (DiT)-based models remain underexplored in this context. In this
paper, we propose a model- and knowledge-agnostic method to localize where
specific types of knowledge are encoded within the DiT blocks. We evaluate our
method on state-of-the-art DiT-based models, including PixArt-α, FLUX, and
SANA, across six diverse knowledge categories. We show that the identified
blocks are both interpretable and causally linked to the expression of knowledge in
generated outputs. Building on these insights, we apply our localization framework
to two key applications: model personalization and knowledge unlearning. In
both settings, our localized fine-tuning approach enables efficient and targeted
updates, reducing computational cost, improving task-specific performance, and
better preserving general model behavior with minimal interference to unrelated
or surrounding content. Overall, our findings offer new insights into the internal
structure of DiTs and introduce a practical pathway for more interpretable, efficient,
and controllable model editing. 1
1
Introduction
Diffusion and flow models [14, 26, 22, 13, 17, 8, 19] have rapidly become the leading paradigm for
a wide range of generative tasks, particularly text-to-image (T2I) synthesis. With access to such
powerful pretrained models, it is crucial to explore their potential for applications beyond mere
generation. A growing body of work [11, 32, 16, 37] has focused on localizing different types of
knowledge and capabilities within these models, enabling more targete...
Conference: ICML 2025

Making Hard Problems Easier with Custom Data Distributions
and Loss Regularization: A Case Study in Modular Arithmetic
Eshika Saxena * 1 Alberto Alfarano * 1 Emily Wenger † 1 2 Kristin Lauter † 1
Abstract
Recent work showed that ML-based attacks on
Learning with Errors (LWE), a hard problem used
in post-quantum cryptography, outperform classi-
cal algebraic attacks in certain settings. Although
promising, ML attacks struggle to scale to more
complex LWE settings. Prior work connected this
issue to the difficulty of training ML models to
do modular arithmetic, a core feature of the LWE
problem. To address this, we develop techniques
that significantly boost the performance of ML
models on modular arithmetic tasks—enabling
the models to sum up to N = 128 elements mod-
ulo q ≤974269. Our core innovation is the use
of custom training data distributions and a care-
fully designed loss function that better represents
the problem structure. We apply an initial proof
of concept of our techniques to LWE specifically
and find that they allow recovery of 2x harder se-
crets than prior work. Our techniques also help
ML models learn other well-studied problems bet-
ter, including copy, associative recall, and parity,
motivating further study.
1. Introduction
Modular arithmetic is a key component of many crypto-
graphic hard problems, including Learning with Errors
(LWE), which is the basis for several newly standardized
post-quantum cryptosystems (Chen et al., 2022). The LWE
problem involves determining a secret vector given two
pieces of information: A ∈Zm×n
q
, a matrix sampled uni-
formly at random mod q, and b = A·s+e ∈Zm
q , the noisy
dot product of A with the secret vector s mod q. When s
is binary, which often happens in practical applications of
LWE, computing b from A requires computing a subset
sum mod q, which is a modular addition problem.
*Equal contribution † Equal contribution 1Meta AI 2Duke Uni-
versity. Correspondence to: Eshika Saxena <eshika@meta.com>.
Proceed...