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

TRAP: Targeted Redirecting of Agentic Preferences
Hangoo Kang∗1, Jehyeok Yeon∗1,Gagandeep Singh1
1University of Illinois Urbana-Champaign
{hangook2,jehyeok2,ggnds}@illinois.edu
Abstract
Autonomous agentic AI systems powered by vision-language models (VLMs)
are rapidly advancing toward real-world deployment, yet their cross-modal rea-
soning capabilities introduce new attack surfaces for adversarial manipulation
that exploit semantic reasoning across modalities. Existing adversarial attacks
typically rely on visible pixel perturbations or require privileged model or en-
vironment access, making them impractical for stealthy, real-world exploitation.
We introduce TRAP, a novel generative adversarial framework that manipulates
the agent’s decision-making using diffusion-based semantic injections into the
vision-language embedding space. Our method combines negative prompt–based
degradation with positive semantic optimization, guided by a Siamese semantic
network and layout-aware spatial masking. Without requiring access to model
internals, TRAP produces visually natural images yet induces consistent selection
biases in agentic AI systems. We evaluate TRAP on the Microsoft Common
Objects in Context (COCO) dataset, building multi-candidate decision scenar-
ios. Across these scenarios, TRAP consistently induces decision-level preference
redirection on leading models, including LLaVA-34B, Gemma3, GPT-4o, and
Mistral-3.2, significantly outperforming existing baselines such as SPSA, Bandit,
and standard diffusion approaches. These findings expose a critical, generalized
vulnerability: autonomous agents can be consistently misled through visually sub-
tle, semantically-guided cross-modal manipulations. Overall, our results show
the need for defense strategies beyond pixel-level robustness to address semantic
vulnerabilities in cross-modal decision-making. The code for TRAP is accessible
on GitHub at https://github.com/uiuc-focal-lab/TRAP.
1
Introduction
Vision-Language Models...
Conference: ICML 2025

UI-Vision: A Desktop-centric GUI Benchmark for
Visual Perception and Interaction
Shravan Nayak * 1 2 3 Xiangru Jian * 4 3 Kevin Qinghong Lin 5 Juan A. Rodriguez 1 3 6 Montek Kalsi 3
Nicolas Chapados 3 M. Tamer ¨Ozsu 4 Aishwarya Agrawal 1 2 7 David Vazquez 3 Christopher Pal 1 8 3 7
Perouz Taslakian 3 Spandana Gella 3 Sai Rajeswar 3 1 2
https://uivision.github.io
Abstract
Autonomous agents that navigate Graphical User
Interfaces (GUIs) to automate tasks like docu-
ment editing and file management can greatly
enhance computer workflows. While existing re-
search focuses on online settings, desktop environ-
ments, critical for many professional and everyday
tasks, remain underexplored due to data collec-
tion challenges and licensing issues. We intro-
duce UI-Vision, the first comprehensive, license-
permissive benchmark for offline, fine-grained
evaluation of computer use agents in real-world
desktop environments. Unlike online benchmarks,
UI-Vision provides: (i) dense, high-quality an-
notations of human demonstrations, including
bounding boxes, UI labels, and action trajecto-
ries (clicks, drags, and keyboard inputs) across
83 software applications, and (ii) three fine-to-
coarse grained tasks—Element Grounding, Lay-
out Grounding, and Action Prediction—with
well-defined metrics to rigorously evaluate agents’
performance in desktop environments. Our evalu-
ation reveals critical limitations in state-of-the-art
models like UI-TARS-72B, including issues with
understanding professional software, spatial rea-
soning, and complex actions like drag-and-drop.
These findings highlight the challenges in develop-
ing fully autonomous computer-use agents. With
UI-Vision, we aim to advance the development of
more capable agents for real-world desktop tasks.
*Equal contribution
1Mila - Quebec AI Institute 2Universit´e
de Montr´eal
3ServiceNow Research
4University of Water-
loo 5National University of Singapore 6 ´Ecole de Technologie
Sup´erieure 7CIFAR AI Chair 8Polytechnique Mon...
Conference: NeurIPS 2025

Addressing Mark Imbalance in Integration-free
Neural Marked Temporal Point Processes
Sishun Liu
RMIT University
Melbourne, Victoria 3000
sishun.liu@student.rmit.edu.au
Ke Deng
RMIT University
Melbourne, Victoria 3000
ke.deng@rmit.edu.au
Yongli Ren
RMIT University
Melbourne, Victoria 3000
yongli.ren@rmit.edu.au
Yan Wang
Macquarie University
Syndey, New South Wales 2000
yan.wang@mq.edu.au
Xiuzhen Zhang
RMIT University
Melbourne, Victoria 3000
xiuzhen.zhang@rmit.edu.au
Abstract
Marked Temporal Point Process (MTPP) has been well studied to model the
event distribution in marked event streams, which can be used to predict the mark
and arrival time of the next event. However, existing studies overlook that the
distribution of event marks is highly imbalanced in many real-world applications,
with some marks being frequent but others rare. The imbalance poses a significant
challenge to the performance of the next event prediction, especially for events of
rare marks. To address this issue, we propose a thresholding method, which learns
thresholds to tune the mark probability normalized by the mark’s prior probability
to optimize mark prediction, rather than predicting the mark directly based on
the mark probability as in existing studies. In conjunction with this method, we
predict the mark first and then the time. In particular, we develop a novel neural
MTPP model to support effective time sampling and estimation of mark probability
without computationally expensive numerical improper integration. Extensive
experiments on real-world datasets demonstrate the superior performance of our
solution against various baselines for the next event mark and time prediction. The
code is available at https://github.com/undes1red/IFNMTPP.
1
Introduction
Marked Temporal Point Process (MTPP) models event sequences observed from natural phenomena
(e.g. earthquakes) or generated in human activities (e.g. retweets), where each event has a mark and
an arrival time. MTPP has attracted the att...
Conference: ICML 2025

Scaling Test-Time Compute Without Verification or RL is Suboptimal
Amrith Setlur 1 Nived Rajaraman 2 Sergey Levine 2 Aviral Kumar 1
Abstract
Despite substantial advances in scaling test-time
compute, an ongoing debate in the community is
how it should be scaled up to enable continued
and efficient improvements with scaling. There
are largely two approaches: (i) distilling success-
ful search or thinking traces; and (ii), using ver-
ification (e.g., 0/1 outcome rewards, or verifiers)
to guide reinforcement learning (RL) and search
algorithms. In this paper, we prove that finetuning
LLMs with verifier-based (VB) methods based
on RL or search is far superior to verifier-free
(VF) approaches based on distilling or cloning
search traces, given a fixed amount of compute/-
data budget. Further, we show that as we scale
test-time compute (measured as the output token
length) and training data, suboptimality of VF
methods scales poorly compared to VB when the
base pre-trained LLM presents a heterogeneous
distribution over correct solution traces (e.g., dif-
ferent lengths, styles, etc.) and admits a non-
sharp distribution over rewards on traces sampled
from it. We formalize this condition using anti-
concentration (Erd¨os, 1945), implying a stronger
result that VB methods scale better asymptoti-
cally, with the performance gap between VB and
VF widening as test-time budget grows. We cor-
roborate our theory empirically on didactic and
math reasoning problems with 3/8/32B-sized pre-
trained LLMs, where we find verification is cru-
cial for scaling test-time compute.
1. Introduction
Pre-training and post-training of LLMs rely heavily on ac-
cess to high-quality “expert” data, but it is projected that by
2028, the availability of such data on the Internet will dimin-
ish (Villalobos et al., 2022; Liu et al., 2024), and improving
model performance on several domains (e.g., reasoning,
safety, etc.) often requires more data (Li et al., 2024). As a
1Carnegie Mellon University 2UC ...
Conference: ICML 2025

ResQ: Mixed-Precision Quantization of Large Language Models
with Low-Rank Residuals
Utkarsh Saxena 1 Sayeh Sharify 2 Kaushik Roy 1 Xin Wang 2
Abstract
Post-training quantization (PTQ) of large lan-
guage models (LLMs) holds the promise in re-
ducing the prohibitive computational cost at infer-
ence time. Quantization of all weight, activation
and key-value (KV) cache tensors to 4-bit without
significantly degrading generalizability is chal-
lenging, due to the high quantization error caused
by extreme outliers in activations. To tackle this
problem, we propose ResQ, a PTQ method that
pushes further the state-of-the-art. By means of
principal component analysis (PCA), it identifies
a low-rank subspace (in practice 1/8 of the hidden
dimension) in which activation variances are high-
est, and keep the coefficients within this subspace
in high precision, e.g. 8-bit, while quantizing the
rest to 4-bit. Within each subspace, invariant ran-
dom rotation is applied to further suppress outliers.
We show that this is a provably optimal mixed pre-
cision quantization scheme that minimizes error.
With the Llama and Qwen2.5 families of mod-
els, we demonstrate that ResQ outperforms recent
uniform and mixed precision PTQ methods on
a variety of benchmarks, achieving up to 33%
lower perplexity on Wikitext than the next best
method SpinQuant, and upto 5× speedup over
16-bit baseline. Code is available here.1
1. Introduction
Growing capabilities of large language models (LLMs)
come with an increasing computational cost at inference
time. LLM inference has two distinct stages: prefilling,
which processes the input prompt and populates the internal
1Department of Electrical and Computer Engineering, Purdue
University, West Lafayette, USA 2d-Matrix, Santa Clara, USA.
Correspondence to: Utkarsh Saxena <saxenau@purdue.edu>.
Proceedings of the 42 nd International Conference on Machine
Learning, Vancouver, Canada. PMLR 267, 2025. Copyright 2025
by the author(s).
1https://github.com/utkarsh...
Conference: ICML 2025

Statistical Hypothesis Testing for Auditing Robustness in Language Models
Paulius Rauba 1 Qiyao Wei 1 Mihaela van der Schaar 1
Abstract
Consider the problem of testing whether the out-
puts of a large language model (LLM) system
change under an arbitrary intervention, such as
an input perturbation or changing the model vari-
ant. We cannot simply compare two LLM out-
puts since they might differ due to the stochastic
nature of the system, nor can we compare the
entire output distribution due to computational
intractability. While existing methods for analyz-
ing text-based outputs exist, they focus on fun-
damentally different problems, such as measur-
ing bias or fairness. To this end, we introduce
distribution-based perturbation analysis, a frame-
work that reformulates LLM perturbation analysis
as a frequentist hypothesis testing problem. We
construct empirical null and alternative output dis-
tributions within a low-dimensional semantic sim-
ilarity space via Monte Carlo sampling, enabling
tractable inference without restrictive distribu-
tional assumptions. The framework is (i) model-
agnostic, (ii) supports the evaluation of arbitrary
input perturbations on any black-box LLM, (iii)
yields interpretable p-values; (iv) supports multi-
ple perturbations via controlled error rates; and (v)
provides scalar effect sizes. We demonstrate the
usefulness of the framework across multiple case
studies, showing how we can quantify response
changes, measure true/false positive rates, and
evaluate alignment with reference models. Above
all, we see this as a reliable frequentist hypothesis
testing framework for LLM auditing.
1. Introduction
Large language models (LLMs) generate outputs condi-
tioned on textual inputs by iteratively sampling from a dis-
tribution of tokens. Therefore, the outputs of LLMs exhibit
1University of Cambridge. Correspondence to: Paulius Rauba
<pr501@cam.ac.uk>.
Proceedings of the 42 nd International Conference on Machine
Learning, Vancouver, Canada....
Conference: ICML 2025

Computing Voting Rules with Improvement Feedback
Evi Micha 1 Vasilis Varsamis 1
Abstract
Aggregating preferences under incomplete or con-
strained feedback is a fundamental problem in
social choice and related domains. While prior
work has established strong impossibility results
for pairwise comparisons, this paper extends the
inquiry to improvement feedback, where vot-
ers express incremental adjustments rather than
complete preferences. We provide a complete
characterization of the positional scoring rules
that can be computed given improvement feed-
back. Interestingly, while plurality is learnable un-
der improvement feedback—unlike with pairwise
feedback—strong impossibility results persist for
many other positional scoring rules. Furthermore,
we show that improvement feedback, unlike pair-
wise feedback, does not suffice for the computa-
tion of any Condorcet-consistent rule. We comple-
ment our theoretical findings with experimental
results, providing further insights into the prac-
tical implications of improvement feedback for
preference aggregation.
1. Introduction
Classical social choice theory assumes that voters provide
complete rankings of all candidates, which are then aggre-
gated by a voting rule to select a winner (Brandt et al., 2016).
However, this assumption becomes impractical in many real-
world scenarios involving a large number of candidates, as
voters may be unable or unwilling to rank all of them. For
example, in deliberation platforms like Polis (Small et al.,
2021), users provide pairwise comparisons over a limited
subset of opinions rather than full rankings. Similarly, in Re-
inforcement Learning from Human Feedback (RLHF)—a
methodology widely used to fine-tune large language mod-
els (LLMs)—feedback often takes the form of comparison
1Thomas Lord Department of Computer Science, University
of Southern California, Los Angeles, California, USA. Correspon-
dence to: Evi Micha <evi.micha@usc.edu>, Vasilis Varsamis
<varsamis@usc.edu>.
Proc...
Conference: ICML 2025

Value-Based Deep RL Scales Predictably
Oleh Rybkin 1 Michal Nauman 1 2 Preston Fu 1 Charlie Snell 1 Pieter Abbeel 1 Sergey Levine 1 Aviral Kumar 3
Abstract
Abstract: Scaling data and compute is critical
to the success of modern ML. However, scaling
demands predictability: we want methods to not
only perform well with more compute or data, but
also have their performance be predictable from
small-scale runs, without running the large-scale
experiment. In this paper, we show that value-
based off-policy RL methods are predictable de-
spite community lore regarding their pathological
behavior. First, we show that data and compute
requirements to attain a given performance level
lie on a Pareto frontier, controlled by the updates-
to-data (UTD) ratio. By estimating this frontier,
we can predict this data requirement when given
more compute, and this compute requirement
when given more data. Second, we determine
the optimal allocation of a total resource budget
across data and compute for a given performance
and use it to determine hyperparameters that max-
imize performance for a given budget. Third, this
scaling is enabled by first estimating predictable
relationships between hyperparameters, which is
used to manage effects of overfitting and plastic-
ity loss unique to RL. We validate our approach
using three algorithms: SAC, BRO, and PQL on
DeepMind Control, OpenAI gym, and IsaacGym,
when extrapolating to higher levels of data, com-
pute, budget, or performance.
1. Introduction
Many latest advances in various areas of machine learning
have emerged from training big models on large datasets.
In this scaling guided research landscape, successfully ex-
ecuting even one single training run often requires a large
amount of data, computational resources, and wall-clock
time, such as weeks or months (Achiam et al., 2023; Team
et al., 2023; Ramesh et al., 2022; Brooks et al., 2024). To
1UC Berkeley 2University of Warsaw 3CMU. Correspondence
to: Oleh Rybkin <oleh.rybkin@gmail...
Conference: ICML 2025

A Unified Comparative Study with Generalized Conformity Scores for
Multi-Output Conformal Regression
Victor Dheur 1 Matteo Fontana 2 Yorick Estievenart 1 Naomi Desobry 1 Souhaib Ben Taieb 1 3
Abstract
Conformal prediction provides a powerful frame-
work for constructing distribution-free prediction
regions with finite-sample coverage guarantees.
While extensively studied in univariate settings,
its extension to multi-output problems presents
additional challenges, including complex output
dependencies and high computational costs, and
remains relatively underexplored. In this work,
we present a unified comparative study of nine
conformal methods with different multivariate
base models for constructing multivariate predic-
tion regions within the same framework. This
study highlights their key properties while also ex-
ploring the connections between them. Addition-
ally, we introduce two novel classes of conformity
scores for multi-output regression that generalize
their univariate counterparts. These scores ensure
asymptotic conditional coverage while maintain-
ing exact finite-sample marginal coverage. One
class is compatible with any generative model, of-
fering broad applicability, while the other is com-
putationally efficient, leveraging the properties of
invertible generative models. Finally, we conduct
a comprehensive empirical evaluation across 13
tabular datasets, comparing all the multi-output
conformal methods explored in this work. To en-
sure a fair and consistent comparison, all methods
are implemented within a unified code base1.
1Department of Computer Science, University of Mons, Mons,
Belgium 2Department of Computer Science, Royal Holloway, Uni-
versity of London, Egham, United Kingdom 3Department of Statis-
tics and Data Science, Mohamed bin Zayed University of Artificial
Intelligence, Abu Dhabi, United Arab Emirates. Correspondence
to: Victor Dheur <victor.dheur@umons.ac.be>.
Proceedings of the 42 nd International Conference on Machine
Learning,...
Conference: ICML 2025

Winner-takes-all for Multivariate Probabilistic Time Series Forecasting
Adrien Cort´es * R´emi Rehm Victor Letzelter * 1 2
Abstract
We introduce TimeMCL, a method leveraging the
Multiple Choice Learning (MCL) paradigm to
forecast multiple plausible time series futures.
Our approach employs a neural network with
multiple heads and utilizes the Winner-Takes-All
(WTA) loss to promote diversity among predic-
tions. MCL has recently gained attention due to
its simplicity and ability to address ill-posed and
ambiguous tasks. We propose an adaptation of
this framework for time-series forecasting, pre-
senting it as an efficient method to predict diverse
futures, which we relate to its implicit quanti-
zation objective. We provide insights into our
approach using synthetic data and evaluate it on
real-world time series, demonstrating its promis-
ing performance at a light computational cost.
1. Introduction
Predicting the weather of the upcoming weekend or the
stock prices of next month with perfect accuracy would un-
doubtedly be useful. Unfortunately, time-series forecasting
is a highly ill-posed problem. In many cases, the available
input information is insufficient to reduce our uncertainty
about the estimation of the underlying stochastic process
and the data itself may contain noise. Consequently, the best
a forecaster can do is estimate plausible future trajectories,
ideally along with the probability of each outcome.
Because temporal data are highly structured and typically
come with input–output pairs that require no additional man-
ual annotation, autoregressive neural networks have become
the de facto standard for forecasting high-dimensional time
series from historical data and exogenous covariates (Ran-
gapuram et al., 2018; Salinas et al., 2019; Benidis et al.,
2020). To capture predictive uncertainty, practitioners often
*Equal contribution
1LTCI, T´el´ecom Paris, Institut Polytech-
nique de Paris, France 2Valeo.ai, Paris, France. Correspondence
to: Adrien Co...
Conference: ICLR 2025

Published as a conference paper at ICLR 2025
TOPOLOGICAL SCHR ¨ODINGER BRIDGE MATCHING
Maosheng Yang
Delft University of Technology
m.yang-2@tudelft.nl
ABSTRACT
Given two boundary distributions, the Schr¨odinger Bridge (SB) problem seeks the
“most likely” random evolution between them with respect to a reference process.
It has revealed rich connections to recent machine learning methods for genera-
tive modeling and distribution matching. While these methods perform well in
Euclidean domains, they are not directly applicable to topological domains such
as graphs and simplicial complexes, which are crucial for data defined over net-
work entities, such as node signals and edge flows. In this work, we propose the
Topological Schr¨odinger Bridge problem (T SBP) for matching signal distribu-
tions on a topological domain, where we set the reference process to follow some
linear tractable topology-aware stochastic dynamics such as topological heat dif-
fusion. For the case of Gaussian boundary distributions, we derive a closed-form
Gaussian topological SB in terms of its time-marginal and stochastic differential.
In the general case, leveraging the well-known result, we show that the optimal
process follows the forward-backward topological dynamics governed by some
unknowns. Building on these results, we develop T SB-based models for match-
ing topological signals by parameterizing the unknowns in the optimal process as
(topological) neural networks and learning them through likelihood training. We
validate the theoretical results and demonstrate the practical applications of T SB-
based models on both synthetic and real-world networks, emphasizing the role of
topology. Additionally, we discuss the connections of T SB-based models to other
emerging models, and outline future directions for topological signal matching.
1
INTRODUCTION
As a fundamental problem in statistics and optimization, matching distributions aims to find a map
that transforms one distribution to anot...
Conference: ICML 2025

Measuring In-Context Computation Complexity via Hidden State Prediction
Vincent Herrmann 1 R´obert Csord´as 2 J¨urgen Schmidhuber 3 1
Abstract
Detecting when a neural sequence model does “in-
teresting” computation is an open problem. The
next token prediction loss is a poor indicator: Low
loss can stem from trivially predictable sequences
that are uninteresting, while high loss may reflect
unpredictable but also irrelevant information that
can be ignored by the model. We propose a better
metric: measuring the model’s ability to predict
its own future hidden states. We show empirically
that this metric—in contrast to the next token pre-
diction loss—correlates with the intuitive inter-
estingness of the task. To measure predictability,
we introduce the architecture-agnostic “predic-
tion of hidden states” (PHi) layer that serves as
an information bottleneck on the main pathway
of the network (e.g., the residual stream in Trans-
formers). We propose a novel learned predictive
prior that enables us to measure the novel infor-
mation gained in each computation step, which
serves as our metric. We show empirically that
our metric predicts the description length of for-
mal languages learned in-context, the complexity
of mathematical reasoning problems, and the cor-
rectness of self-generated reasoning chains.
1. Introduction
Neural sequence models—especially large language models
(LLMs)—have demonstrated remarkable capabilities, such
as solving complex reasoning tasks (Lewkowycz et al., 2022;
Wei et al., 2022; Zelikman et al., 2022) and performing in-
context learning (Brown et al., 2020; Zhao et al., 2021;
Liu et al., 2022; Kirsch et al., 2022). Recent progress in
mechanistic interpretability has offered glimpses into how
these models process information internally: for instance, by
identifying specific induction heads (Olsson et al., 2022) or
1IDSIA/USI/SUPSI, Lugano, Switzerland 2Stanford University,
Stanford, USA 3KAUST, Thuwal, Saudi-Arabia. Correspondence
to: Vinc...