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

Published as a conference paper at ICLR 2025
UNVEILING THE MAGIC OF CODE REASONING
THROUGH REFLECTIVE HYPOTHESIS
DECOMPOSITION AND AMENDMENT
Yuze Zhao1, Tianyun Ji1,∗, Wenjun Feng1,∗, Zhenya Huang1,2,†, Qi Liu1,2,
Zhiding Liu1, Yixiao Ma1, Kai Zhang1, Enhong Chen1
1State Key Laboratory of Cognitive Intelligence,
University of Science and Technology of China
2Institute of Artificial Intelligence, Hefei Comprehensive National Science Center
yuzezhao@mail.ustc.edu.cn huangzhy@ustc.edu.cn
ABSTRACT
The reasoning abilities are one of the most enigmatic and captivating aspects of
large language models (LLMs). Numerous studies are dedicated to exploring and
expanding the boundaries of this reasoning capability. However, tasks that em-
body both reasoning and recall characteristics are often overlooked. In this paper,
we introduce such a novel task, code reasoning, to provide a new perspective for
the reasoning abilities of LLMs. We summarize three meta-benchmarks based
on established forms of logical reasoning, and instantiate these into eight specific
benchmark tasks. Our testing on these benchmarks reveals that LLMs continue
to struggle with identifying satisfactory reasoning pathways. Additionally, we
present a new pathway exploration pipeline inspired by human intricate problem-
solving methods. This Reflective Hypothesis Decomposition and Amendment
(RHDA) pipeline consists of the following iterative steps: (1) Proposing potential
hypotheses based on observations and decomposing them; (2) Utilizing tools to
validate hypotheses and reflection outcomes; (3) Revising hypothesis in light of
observations. Our approach effectively mitigates logical chain collapses arising
from forgetting or hallucination issues in multi-step reasoning, resulting in per-
formance gains of up to 3×. Finally, we expand this pipeline by applying it to
simulate complex household tasks in real-world scenarios, specifically in Virtu-
alHome, enhancing the handling of failure cases. We release our co...
Conference: ICML 2025

Heterogeneous Treatment Effect in Time-to-Event Outcomes: Harnessing
Censored Data with Recursively Imputed Trees
Tomer Meir 1 Uri Shalit 1 2 Malka Gorfine 2
Abstract
Tailoring treatments to individual needs is a cen-
tral goal in fields such as medicine. A key step to-
ward this goal is estimating Heterogeneous Treat-
ment Effects (HTE)—the way treatments impact
different subgroups. While crucial, HTE estima-
tion is challenging with survival data, where time
until an event (e.g., death) is key. Existing meth-
ods often assume complete observation, an as-
sumption violated in survival data due to right-
censoring, leading to bias and inefficiency. Cui
et al. (2023) proposed a doubly-robust method
for HTE estimation in survival data under no hid-
den confounders, combining a causal survival for-
est with an augmented inverse-censoring weight-
ing estimator. However, we find it struggles un-
der heavy censoring, which is common in rare-
outcome problems such as Amyotrophic lateral
sclerosis (ALS). Moreover, most current meth-
ods cannot handle instrumental variables, which
are a crucial tool in the causal inference arsenal.
We introduce Multiple Imputation for Survival
Treatment Response (MISTR), a novel, general,
and non-parametric method for estimating HTE
in survival data. MISTR uses recursively imputed
survival trees to handle censoring without directly
modeling the censoring mechanism. Through ex-
tensive simulations and analysis of two real-world
datasets—the AIDS Clinical Trials Group Proto-
col 175 and the Illinois unemployment dataset we
show that MISTR outperforms prior methods un-
der heavy censoring in the no-hidden-confounders
setting, and extends to the instrumental variable
setting. To our knowledge, MISTR is the first non-
parametric approach for HTE estimation with un-
observed confounders via instrumental variables.
1Department of Data and Decisions Sciences, Technion, Haifa,
Israel 2Department of Statistics and Operations Research, Tel Aviv
Univers...
Conference: ICML 2025

S4S: Solving for a Fast Diffusion Model Solver
Eric Frankel 1 Sitan Chen 2 Jerry Li 1 Pang Wei Koh 1 3 Lillian J. Ratliff 1 Sewoong Oh 1
Abstract
Diffusion models (DMs) create samples from a
data distribution by starting from random noise
and iteratively solving a reverse-time ordinary dif-
ferential equation (ODE). Because each step in
the iterative solution requires an expensive neural
function evaluation (NFE), there has been sig-
nificant interest in approximately solving these
diffusion ODEs with only a few NFEs without
modifying the underlying model. However, in the
few NFE regime, we observe that tracking the
true ODE evolution is fundamentally impossible
using traditional ODE solvers. In this work, we
propose a new method that learns a good solver
for the DM, which we call Solving for the Solver
(S4S). S4S directly optimizes a solver to obtain
good generation quality by learning to match the
output of a strong teacher solver. We evaluate S4S
on six different pre-trained DMs, including pixel-
space and latent-space DMs for both conditional
and unconditional sampling. In all settings, S4S
uniformly improves the sample quality relative to
traditional ODE solvers. Moreover, our method
is lightweight, data-free, and can be plugged in
black-box on top of any discretization schedule
or architecture to improve performance. Building
on top of this, we also propose S4S-Alt, which
optimizes both the solver and the discretization
schedule. By exploiting the full design space of
DM solvers, with 5 NFEs, we achieve an FID
of 3.73 on CIFAR10 and 13.26 on MS-COCO,
representing a 1.5× improvement over previous
training-free ODE methods.
1University of Washington
2Harvard University
3Allen
Institute for AI. Correspondence to:
Eric Frankel <er-
icsf@cs.washington.edu>.
Proceedings of the 42 nd International Conference on Machine
Learning, Vancouver, Canada. PMLR 267, 2025. Copyright 2025
by the author(s).
Figure 1. Abstraction of the S4S approach. In low-NFE environ-
ments, co...
Conference: ICLR 2025

Published as a conference paper at ICLR 2025
U-SHAPED AND INVERTED-U SCALING BEHIND
EMERGENT ABILITIES OF LARGE LANGUAGE MODELS
Tung-Yu Wu, Melody Lo
National Taiwan University
{b08901133, peiyulo}@ntu.edu.tw
ABSTRACT
Large language models (LLMs) have been shown to exhibit emergent abilities
in some downstream tasks, where model performance stagnates at first and then
improves sharply and unpredictably with scale beyond a threshold. In this work,
we investigate the phenomenon by grouping questions based on difficulty level
and provide a possible explanation for emergent abilities. Specifically, we observe
U-shaped scaling for hard questions and inverted-U scaling followed by steady
improvement for easy questions. The two scaling patterns initially offset each
other, causing stagnant overall performance. The performance starts to soar when
the scaling pattern of easy questions reverts from inverse to standard scaling,
leading to emergent abilities. Based on this finding, we propose a simple yet
effective pipeline, called Slice-and-Sandwich, to predict the emergence threshold
and model performance beyond the threshold. Our code is publicly available at
https://github.com/tony10101105/ExpEmergence.
1
INTRODUCTION
Large language models (LLMs) (Team et al., 2023; Achiam et al., 2023; Brown, 2020; Touvron
et al., 2023a;b; Workshop et al., 2022; Li et al., 2023; Jiang et al., 2024) have shown strong potential
in various downstream applications (Jumper et al., 2021; Fawzi et al., 2022; Naveed et al., 2023;
Kaddour et al., 2023). Though the training-loss scaling law has been well established (Kaplan
et al., 2020; Hoffmann et al., 2022), the literature is inconclusive regarding how performance on
downstream tasks scales. In particular, for certain downstream tasks (Srivastava et al., 2023; Lin
et al., 2022a; Pilehvar & Camacho-Collados, 2019), LLMs display emergent abilities: performance
stagnates even when model training compute scales up hundredfold, and then improves sharp...
Conference: ICML 2025

Isolated Causal Effects of Natural Language
Victoria Lin 1 Louis-Philippe Morency 1 Eli Ben-Michael 1
Abstract
As language technologies become widespread, it
is important to understand how changes in lan-
guage affect reader perceptions and behaviors.
These relationships may be formalized as the
isolated causal effect of some focal language-
encoded intervention (e.g., factual inaccuracies)
on an external outcome (e.g., readers’ beliefs).
In this paper, we introduce a formal estimation
framework for isolated causal effects of language.
We show that a core challenge of estimating iso-
lated effects is the need to approximate all non-
focal language outside of the intervention. Draw-
ing on the principle of omitted variable bias, we
provide measures for evaluating the quality of
both non-focal language approximations and iso-
lated effect estimates themselves. We find that
poor approximation of non-focal language can
lead to bias in the corresponding isolated effect
estimates due to omission of relevant variables,
and we show how to assess the sensitivity of ef-
fect estimates to such bias along the two key axes
of fidelity and overlap. In experiments on semi-
synthetic and real-world data, we validate the abil-
ity of our framework to correctly recover isolated
effects and demonstrate the utility of our proposed
measures.
1. Introduction
The widespread use of language technologies has given
rise to an ever-expanding amount of human- and machine-
generated text data. From this vast body of data emerges
the opportunity to understand how information contained in
language relates to real-world outcomes. Elucidating these
relationships can help answer scientifically interesting ques-
tions and provide interpretability to texts and the models
that generate them. For instance, what language attributes
1Carnegie Mellon University, Pittsburgh, PA, USA. Correspon-
dence to: Victoria Lin <vlin2@andrew.cmu.edu>.
Proceedings of the 42 nd International Conference on Machine
Learni...
Conference: NeurIPS 2025

Alignment of Large Language Models with
Constrained Learning
Botong Zhang
University of Pennsylvania
bzhang16@seas.upenn.edu
Shuo Li
Amazon
lishuo1@seas.upenn.edu
Ignacio Hounie
University of Pennsylvania
ihounie@seas.upenn.edu
Osbert Bastani
University of Pennsylvania
obastani@seas.upenn.edu
Dongsheng Ding∗
University of Tennessee, Knoxville
dongshed@utk.edu
Alejandro Ribeiro
University of Pennsylvania
aribeiro@seas.upenn.edu
Abstract
We study the problem of computing an optimal large language model (LLM)
policy for the constrained alignment problem, where the goal is to maximize
a primary reward objective while satisfying constraints on secondary utilities.
Despite the popularity of Lagrangian-based LLM policy search in constrained
alignment, iterative primal-dual methods often fail to converge, and non-iterative
dual-based methods do not achieve optimality in the LLM parameter space. To
address these challenges, we employ Lagrangian duality to develop an iterative
dual-based alignment method that alternates between updating the LLM policy via
Lagrangian maximization and updating the dual variable via dual descent. In theory,
we characterize the primal-dual gap between the primal value in the distribution
space and the dual value in the LLM parameter space. We further quantify the
optimality gap of the learned LLM policies at near-optimal dual variables with
respect to both the objective and the constraint functions. These results prove that
dual-based alignment methods can find an optimal constrained LLM policy, up to
an LLM parametrization gap. We demonstrate the effectiveness and merits of our
approach through extensive experiments conducted on the PKU-SafeRLHF and
Anthropic HH-RLHF datasets.
1
Introduction
Large language models (LLMs), built upon the transformer architecture [43], have become a core
tool for a wide range of natural language processing tasks (e.g., code generation [17], translation [50],
robotics [37]). Central to these remarkable capabilities ...
Conference: NeurIPS 2025

TrajAgent: An LLM-Agent Framework for Trajectory
Modeling via Large-and-Small Model Collaboration
Yuwei Du∗, Jie Feng∗†, Jie Zhao, Yong Li†
Department of Electronic Engineering, BRNist, Tsinghua University, Beijing, China
{fengjie,liyong07}@tsinghua.edu.cn
Abstract
Trajectory modeling, which includes research on trajectory data pattern mining
and future prediction, has widespread applications in areas such as life services,
urban transportation, and public administration. Numerous methods have been
proposed to address specific problems within trajectory modeling. However, the
heterogeneity of data and the diversity of trajectory tasks make effective and reliable
trajectory modeling an important yet highly challenging endeavor, even for domain
experts. In this paper, we propose TrajAgent, a agent framework powered by
large language models (LLMs), designed to facilitate robust and efficient trajectory
modeling through automation modeling. This framework leverages and optimizes
diverse specialized models to address various trajectory modeling tasks across
different datasets effectively. In TrajAgent, we first develop UniEnv, an execution
environment with a unified data and model interface, to support the execution
and training of various models. Building on UniEnv, we introduce an agentic
workflow designed for automatic trajectory modeling across various trajectory
tasks and data. Furthermore, we introduce collaborative learning schema between
LLM-based agents and small speciallized models, to enhance the performance
of the whole framework effectively. Extensive experiments on four tasks using
four real-world datasets demonstrate the effectiveness of TrajAgent in automated
trajectory modeling, achieving a performance improvement of 2.38%-69.91% over
baseline methods. The codes and data can be accessed via https://github.
com/tsinghua-fib-lab/TrajAgent.
1
Introduction
With the rapid development of web services and mobile devices [68, 6], large-scale trajectory data,
suc...
Conference: ICML 2025

SHIELDAGENT: Shielding Agents via Verifiable Safety Policy Reasoning
Zhaorun Chen 1 Mintong Kang 2 Bo Li 1 2
Abstract
Autonomous agents powered by foundation mod-
els have seen widespread adoption across vari-
ous real-world applications. However, they re-
main highly vulnerable to malicious instructions
and attacks, which can result in severe conse-
quences such as privacy breaches and financial
losses. More critically, existing guardrails for
LLMs are not applicable due to the complex
and dynamic nature of agents. To tackle these
challenges, we propose SHIELDAGENT, the first
guardrail agent designed to enforce explicit safety
policy compliance for the action trajectory of
other protected agents through logical reason-
ing. Specifically, SHIELDAGENT first constructs
a safety policy model by extracting verifiable
rules from policy documents and structuring them
into a set of action-based probabilistic rule cir-
cuits. Given the action trajectory of the protected
agent, SHIELDAGENT retrieves relevant rule cir-
cuits and generates a shielding plan, leveraging its
comprehensive tool library and executable code
for formal verification. In addition, given the
lack of guardrail benchmarks for agents, we in-
troduce SHIELDAGENT-BENCH, a dataset with
3K safety-related pairs of agent instructions and
action trajectories, collected via SOTA attacks
across 6 web environments and 7 risk categories.
Experiments show that SHIELDAGENT achieves
SOTA on SHIELDAGENT-BENCH and three ex-
isting benchmarks, outperforming prior meth-
ods by 11.3% on average with a high recall of
90.1%.
Additionally, SHIELDAGENT reduces
API queries by 64.7% and inference time by
58.2%, demonstrating its high precision and effi-
ciency in safeguarding agents. Our project is avail-
able and continuously maintained here: https:
//shieldagent-aiguard.github.io/
1University of Chicago, Chicago IL, USA 2University of Illinois
at Urbana-Champaign, Champaign IL, USA. Correspondence to:
Zhaorun Chen, Bo Li <{zhaor...
Conference: NeurIPS 2025

DictPFL: Efficient and Private Federated Learning
on Encrypted Gradients
Jiaqi Xue1, Mayank Kumar1, Yuzhang Shang1, Shangqian Gao2, Rui Ning3
Mengxin Zheng1, Xiaoqian Jiang4, Qian Lou1
1University of Central Florida
2Florida State University
3Old Dominion University
4University of Texas Health Science Center at Houston
{jiaqi.xue,mayank.kumar,yuzhang.shang,mengxin.zheng,qian.lou}@ucf.edu
sgao@cs.fsu.edu, rning@odu.edu, xiaoqian.jiang@uth.tmc.edu
Abstract
Federated Learning (FL) enables collaborative model training across institutions
without sharing raw data. However, gradient sharing still risks privacy leakage,
such as gradient inversion attacks. Homomorphic Encryption (HE) can secure
aggregation but often incurs prohibitive computational and communication over-
head. Existing HE-based FL methods sit at two extremes: encrypting all gradients
for full privacy at high cost, or partially encrypting gradients to save resources
while exposing vulnerabilities. We present DictPFL, a practical framework that
achieves full gradient protection with minimal overhead. DictPFL encrypts every
transmitted gradient while keeping non-transmitted parameters local, preserving
privacy without heavy computation. It introduces two key modules: Decompose-
for-Partial-Encrypt (DePE), which decomposes model weights into a static dic-
tionary and an updatable lookup table—only the latter is encrypted and aggregated,
while the static dictionary remains local and requires neither sharing nor encryp-
tion; and Prune-for-Minimum-Encrypt (PrME), which applies encryption-aware
pruning to minimize encrypted parameters via consistent, history-guided masks.
Experiments show that DictPFL reduces communication cost by 402–748× and
accelerates training by 28–65× compared to fully encrypted FL, while outper-
forming state-of-the-art selective encryption methods by 51–155× in overhead
and 4–19× in speed. Remarkably, DictPFL’s runtime is within 2× of plaintext
FL, demonstrating—for the first time—that HE...
Conference: ICLR 2025

Published as a conference paper at ICLR 2025
FICTITIOUS SYNTHETIC DATA CAN IMPROVE LLM
FACTUALITY VIA PREREQUISITE LEARNING
Yujian Liu
UC Santa Barbara
yujianliu@ucsb.edu
Shiyu Chang
UC Santa Barbara
chang87@ucsb.edu
Tommi Jaakkola
MIT CSAIL
tommi@csail.mit.edu
Yang Zhang
MIT-IBM Watson AI Lab
yang.zhang2@ibm.com
ABSTRACT
Recent studies have identified one aggravating factor of LLM hallucinations as
the knowledge inconsistency between pre-training and fine-tuning, where unfa-
miliar fine-tuning data mislead the LLM to fabricate plausible but wrong outputs.
In this paper, we propose a novel fine-tuning strategy called PREREQ-TUNE to
address this knowledge inconsistency and reduce hallucinations. Fundamentally,
PREREQ-TUNE disentangles the learning of skills and knowledge, so the model
learns only the task skills without being impacted by the knowledge inconsistency.
To achieve this, PREREQ-TUNE introduces an additional prerequisite learning
stage to learn the necessary knowledge for SFT, allowing subsequent SFT to focus
only on task skills. PREREQ-TUNE can also be combined with fictitious synthetic
data to enhance the grounding of LLM outputs to their internal knowledge. Ex-
periments show that PREREQ-TUNE outperforms existing baselines in improving
LLM’s factuality across short QA and long-form generation tasks. It also opens
new possibilities for knowledge-controlled generation in LLMs. Our code is avail-
able at https://github.com/UCSB-NLP-Chang/Prereq_tune.git.
1
INTRODUCTION
Hallucination of large language models (LLMs) refers to the phenomenon where LLMs’ outputs
look plausible but diverge from real-world facts. It has become a major concern of LLMs, seriously
undermining their reliability and trustworthiness (Huang et al., 2023; Ji et al., 2023).
Recent research has unveiled one aggravating factor of LLM hallucination, which is the knowledge
inconsistency between the pre-training and tuning (e.g., instruction- or fine-tuning) stages (Gekhman
et al., 2024; Kang...
Conference: ICML 2025

What If We Recaption Billions of Web Images
with LLaMA-3?
Xianhang Li * 1 Haoqin Tu * 1 Mude Hui * 1 Zeyu Wang * 1 Bingchen Zhao * 2 Junfei Xiao 3 Sucheng Ren 3
Jieru Mei 3 Qing Liu 4 Huangjie Zheng 5 Yuyin Zhou 1 Cihang Xie 1
Abstract
Web-crawled image-text pairs are inherently noisy.
Prior studies demonstrate that semantically align-
ing and enriching textual descriptions of these
pairs can significantly enhance model training
across various vision-language tasks, particularly
text-to-image generation. However, large-scale
investigations in this area remain predominantly
closed-source.
Our paper aims to bridge this
community effort, leveraging the powerful and
open-sourced LLaMA-3, a GPT-4 level LLM.
Our recaptioning pipeline is simple: first, we
fine-tune a LLaMA-3-8B powered LLaVA-1.5
and then employ it to recaption ∼1.3 billion im-
ages from the DataComp-1B dataset. Our em-
pirical results confirm that this enhanced dataset,
Recap-DataComp-1B, offers substantial benefits
in training advanced vision-language models. For
discriminative models like CLIP, we observe an
average of 3.1% enhanced zero-shot performance
cross four cross-modal retrieval tasks using a
mixed set of the original and our captions. For
generative models like text-to-image Diffusion
Transformers, the generated images exhibit a sig-
nificant improvement in alignment with users’
text instructions, especially in following complex
queries.
Our project page is https://www.
haqtu.me/Recap-Datacomp-1B/.
1. Introduction
The exponential growth in data availability is one of the most
paramount factors in driving the monumental successes of
deep learning over the past decade (Deng et al., 2009; Lin
et al., 2014; Changpinyo et al., 2021; Schuhmann et al.,
*Equal contribution
1University of California, Santa Cruz
2University of Edinburgh 3Johns Hopkins University 4Adobe Inc.
5University of Texas at Austin. Correspondence to: Cihang Xie
<cixie@ucsc.edu>.
Proceedings of the 42 nd International Conference on ...
Conference: NeurIPS 2025

DINGO: Constrained Inference for Diffusion LLMs
Tarun Suresh∗, Debangshu Banerjee∗, Shubham Ugare, Sasa Misailovic, Gagandeep Singh
Department of Computer Science,
University of Illinois Urbana-Champaign
Abstract
Diffusion LLMs have emerged as a promising alternative to conventional autore-
gressive LLMs, offering substantial potential for improving runtime efficiency.
However, existing diffusion models fail to provably enforce user-specified formal
constraints, such as regular expressions, which makes them unreliable for tasks
that require structured outputs, such as fixed-schema JSON generation. Unlike
autoregressive models, which generate tokens sequentially, diffusion LLMs predict
a block of tokens in parallel. This parallelism makes traditional constrained decod-
ing algorithms, designed to enforce constraints with sequential token prediction,
ineffective at preserving the true output distribution. To address this limitation, we
propose DINGO, a dynamic programming-based constrained decoding strategy
that is both efficient and provably distribution-preserving. DINGO enables sam-
pling of output strings with the highest probability under the model’s predicted
distribution while strictly adhering to any user-specified regular expression. On
standard symbolic math and JSON generation benchmarks, DINGO achieves up to
a 68% points of improvement over unconstrained inference. The code is available
at DINGO.
1
Introduction
Autoregressive LLMs demonstrate impressive performance across a wide range of tasks, including
logical reasoning [Pan et al., 2023], theorem proving [Yang et al., 2023], and code generation [et.
al., 2021]. However, because they generate one token at a time, they can be slow when producing
long responses. Recent work has explored using diffusion models to accelerate token generation by
predicting blocks of tokens in parallel. For tasks such as logical reasoning, where the LLM output
is fed into symbolic solvers like Z3 [Fedoseev et al., 2024], synta...