GenAI4ELab/papercli-papers huggingface dataset:
Invariance Makes LLM Unlearning Resilient Even to Unanticipated Downstream Fine-Tuning
Authors: Changsheng Wang; Yihua Zhang; Jinghan Jia; Parikshit Ram; Dennis Wei; Yuguang Yao; Soumyadeep Pal; Nathalie Baracaldo; Sijia Liu
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Machine unlearning presents a promising approach to mitigating privacy and safety concerns in large language models (LLMs) by enabling the selective removal of targeted data or knowledge while preserving model utility. However, existing unlearning methods remain over-sensitive to downstream fine-tuning, which can rapidly recover what is supposed to be unlearned information even when the fine-tuning task is entirely unrelated to the unlearning objective.
To enhance robustness, we introduce the concept of `invariance' into unlearning for the first time from the perspective of invariant risk minimization (IRM), a principle for environment-agnostic training. By leveraging IRM, we develop a new invariance-regularized LLM unlearning framework, termed invariant LLM unlearning (ILU). 
We show that the proposed invariance regularization, even using only a single fine-tuning dataset during ILU training, can enable unlearning robustness to generalize effectively across diverse and new fine-tuning tasks at test time.
A task vector analysis is also provided to further elucidate the rationale behind ILU's effectiveness. Extensive experiments on the WMDP benchmark, which focuses on removing an LLM's hazardous knowledge generation capabilities, reveal that ILU significantly outperforms state-of-the-art unlearning methods, including negative preference optimization (NPO) and representation misdirection for unlearning (RMU). Notably, ILU achieves superior unlearning robustness across diverse downstream fine-tuning scenarios (e.g., math, paraphrase detection, and sentiment analysis) while preserving the fine-tuning performance.
Towards LLM Unlearning Resilient to Relearning Attacks: A Sharpness-Aware Minimization Perspective and Beyond
Authors: Chongyu Fan; Jinghan Jia; Yihua Zhang; Anil Ramakrishna; Mingyi Hong; Sijia Liu
Venue: ICML 2025 (ICML 2025 poster)

Abstract: The LLM unlearning technique has recently been introduced to comply with data regulations and address the safety and ethical concerns of LLMs by removing the undesired data-model influence. 
However, state-of-the-art unlearning methods face a critical vulnerability: they are susceptible to ``relearning'' the removed information from a small number of forget data points, known as relearning attacks. In this paper, we systematically investigate how to make unlearned models robust against such attacks. For the first time, we establish a connection between robust unlearning and sharpness-aware minimization (SAM) through a unified robust optimization framework, in an analogy to adversarial training designed to defend against adversarial attacks. Our analysis for SAM reveals that smoothness optimization plays a pivotal role in mitigating relearning attacks. Thus, we further explore diverse smoothing strategies to enhance unlearning robustness. Extensive experiments on benchmark datasets, including WMDP and MUSE, demonstrate that SAM and other smoothness optimization approaches consistently improve the resistance of LLM unlearning to relearning attacks. Notably, smoothness-enhanced unlearning also helps defend against (input-level) jailbreaking attacks, broadening our proposal's impact in robustifying LLM unlearning. Codes are available at https://github.com/OPTML-Group/Unlearn-Smooth.
Towards a Mechanistic Explanation of Diffusion Model Generalization
Authors: Matthew Niedoba; Berend Zwartsenberg; Kevin Patrick Murphy; Frank Wood
Venue: ICML 2025 (ICML 2025 spotlightposter)

Abstract: We propose a simple, training-free mechanism which explains the generalization behaviour of diffusion models. By comparing pre-trained diffusion models to their theoretically optimal empirical counterparts, we identify a shared local inductive bias across a variety of network architectures. From this observation, we hypothesize that network denoisers generalize through localized denoising operations, as these operations approximate the training objective well over much of the training distribution. To validate our hypothesis, we introduce novel denoising algorithms which aggregate local empirical denoisers to replicate network behaviour. Comparing these algorithms to network denoisers across forward and reverse diffusion processes, our approach exhibits consistent visual similarity to neural network outputs, with lower mean squared error than previously proposed methods.
Adaptive Data Collection for Robust Learning Across Multiple Distributions
Authors: Chengbo Zang; Mehmet Kerem Turkcan; Gil Zussman; Zoran Kostic; Javad Ghaderi
Venue: ICML 2025 (ICML 2025 poster)

Abstract: We propose a framework for adaptive data collection aimed at robust learning in multi-distribution scenarios under a fixed data collection budget. In each round, the algorithm selects a distribution source to sample from for data collection and updates the model parameters accordingly. The objective is to find the model parameters that minimize the expected loss across all the data sources. Our approach integrates upper-confidence-bound (UCB) sampling with online gradient descent (OGD) to dynamically collect and annotate data from multiple sources. By bridging online optimization and multi-armed bandits, we provide theoretical guarantees for our UCB-OGD approach, demonstrating that it achieves a minimax regret of $O(T^{\frac{1}{2}}(K\ln T)^{\frac{1}{2}})$ over $K$ data sources after $T$ rounds. We further provide a lower bound showing that the result is optimal up to a $\ln T$ factor. Extensive evaluations on standard datasets and a real-world testbed for object detection in smart-city intersections validate the consistent performance improvements of our method compared to baselines such as random sampling and various active learning methods.
Can We Predict Performance of Large Models across Vision-Language Tasks?
Authors: Qinyu Zhao; Ming Xu; Kartik Gupta; Akshay Asthana; Liang Zheng; Stephen Gould
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Evaluating large vision-language models (LVLMs) is very expensive, due to high computational cost and the wide variety of tasks. The good news is that if we already have some observed performance scores, we may be able to infer unknown ones. In this study, we propose a new framework for predicting unknown performance scores based on observed ones from other LVLMs or tasks. We first formulate the performance prediction as a matrix completion task. Specifically, we construct a sparse performance matrix $\boldsymbol{R}$, where each entry $R_{mn}$ represents the performance score of the $m$-th model on the $n$-th dataset. By applying probabilistic matrix factorization (PMF) with Markov chain Monte Carlo (MCMC), we can complete the performance matrix, i.e., predict unknown scores. Additionally, we estimate the uncertainty of performance prediction based on MCMC. Practitioners can evaluate their models on untested tasks with higher uncertainty first, which quickly reduces the prediction errors. We further introduce several improvements to enhance PMF for scenarios with sparse observed performance scores. Our experiments demonstrate the accuracy of PMF in predicting unknown scores, the reliability of uncertainty estimates in ordering evaluations, and the effectiveness of our enhancements for handling sparse data. Our code is available at https://github.com/Qinyu-Allen-Zhao/CrossPred-LVLM.
Regress, Don't Guess: A Regression-like Loss on Number Tokens for Language Models
Authors: Jonas Zausinger; Lars Pennig; Anamarija Kozina; Sean Sdahl; Julian Sikora; Adrian Dendorfer; Timofey Kuznetsov; Mohamad Hagog; Nina Wiedemann; Kacper Chlodny; Vincent Limbach; Anna Ketteler; Thorben Prein; Vishwa Mohan Singh; Michael Danziger; Jannis Born
Venue: ICML 2025 (ICML 2025 poster)

Abstract: While language models have exceptional capabilities at text generation, they lack a natural inductive bias for emitting numbers and thus struggle in tasks involving quantitative reasoning, especially arithmetic. One fundamental limitation is the nature of the cross-entropy (CE) loss, which assumes a nominal scale and thus cannot convey proximity between generated number tokens. In response, we here present a regression-like loss that operates purely on token level. Our proposed Number Token Loss (NTL) comes in two flavors and minimizes either the  $\mathcal{L}_p$ norm or the Wasserstein distance between the numerical values of the real and predicted number tokens. NTL can easily be added to any language model and extend the CE objective during training without runtime overhead. We evaluate the proposed scheme on various mathematical datasets and find that it consistently improves performance in math-related tasks. In a direct comparison on a regression task, we find that NTL can match the performance of a regression head, despite operating on token level. Finally, we scale NTL up to 3B parameter models and observe improved performance, demonstrating its potential for seamless integration into LLMs. 
We hope to inspire LLM developers to improve their pretraining objectives and distribute NTL as a minimalistic and lightweight PyPI package *ntloss*: https://ibm.biz/ntl-pypi-repo. Development code for full paper reproduction is available separately.
Efficient Optimization with Orthogonality Constraint: a Randomized Riemannian Submanifold Method
Authors: Andi Han; Pierre-Louis Poirion; Akiko Takeda
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Optimization with orthogonality constraints frequently arises in various fields such as machine learning. Riemannian optimization offers a powerful framework for solving these problems by equipping the constraint set with a Riemannian manifold structure and performing optimization intrinsically on the manifold. This approach typically involves computing a search direction in the tangent space and updating variables via a retraction operation. However, as the size of the variables increases, the computational cost of the retraction can become prohibitively high, limiting the applicability of Riemannian optimization to large-scale problems.  To address this challenge and enhance scalability, we propose a novel approach that restricts each update on a random submanifold, thereby significantly reducing the per-iteration complexity. We introduce two sampling strategies for selecting the random submanifolds and theoretically analyze the convergence of the proposed methods. We provide convergence results for general nonconvex functions and functions that satisfy Riemannian Polyak–Łojasiewicz condition as well as for stochastic optimization settings. Additionally, we demonstrate how our approach can be generalized to quotient manifolds derived from the orthogonal manifold.  Extensive experiments verify the benefits of the proposed method, across a wide variety of problems.
Towards Cost-Effective Reward Guided Text Generation
Authors: Ahmad Rashid; Ruotian Wu; Rongqi Fan; Hongliang Li; Agustinus Kristiadi; Pascal Poupart
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Reward-guided text generation (RGTG) has emerged as a viable alternative to offline reinforcement learning from human feedback (RLHF). 
RGTG methods can align baseline language models to human preferences without further training as in standard RLHF methods. 
However, they rely on a reward model to score each candidate token generated by the language model at inference, incurring significant test-time overhead. Additionally, the reward model is usually only trained to score full sequences, which can lead to sub-optimal choices for partial sequences. In this work, we present a novel reward model architecture that is trained, using a Bradley-Terry loss, to prefer the optimal expansion of a sequence with just a single call to the reward model at each step of the generation process.  That is, a score for all possible candidate tokens is generated simultaneously, leading to efficient inference. We theoretically analyze various RGTG reward models and demonstrate that prior techniques prefer sub-optimal sequences compared to our method during inference. Empirically, our reward model leads to significantly faster inference than other RGTG methods. It requires fewer calls to the reward model and performs competitively compared to previous RGTG and offline RLHF methods.
Learning State-Based Node Representations from a Class Hierarchy for Fine-Grained Open-Set Detection
Authors: Spandan Pyakurel; Qi Yu
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Fine-Grained Openset Detection (FGOD) poses a fundamental challenge due to the similarity between the openset classes and those closed-set ones. Since real-world objects/entities tend to form a hierarchical structure, the fine-grained relationship among the closed-set classes as captured by the hierarchy could potentially improve the FGOD performance. Intuitively, the hierarchical dependency among different classes allows the model to recognize their subtle differences, which in turn makes it better at differentiating similar open-set classes even they may share the same parent. However, simply performing openset detection in a top-down fashion by building a local detector for each node may result in a poor detection performance. Our theoretical analysis also reveals that maximizing the probability of the path leading to the ground-truth leaf node also results in a sub-optimal training process. To systematically address this issue, we propose to formulate a novel state-based node representation, which constructs a state space based upon the entire hierarchical structure. We prove that the state-based representation guarantees to maximize the probability on the path leading to the ground-truth leaf node. Extensive experiments on multiple real-world hierarchical datasets clearly demonstrate the superior performance of the proposed method.
Hybrid Quantum-Classical Multi-Agent Pathfinding
Authors: Thore Gerlach; Loong Kuan Lee; Frederic BARBARESCO; Nico Piatkowski
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Multi-Agent Path Finding (MAPF) focuses on determining conflict-free paths for multiple agents navigating through a shared space to reach specified goal locations.  This problem becomes computationally challenging, particularly when handling large numbers of agents, as frequently encountered in practical applications like coordinating autonomous vehicles. Quantum Computing (QC) is a promising candidate in overcoming such limits. However, current quantum hardware is still in its infancy and thus limited in terms of computing power and error robustness. In this work, we present the first optimal hybrid quantum-classical MAPF algorithms which are based on branch-and-cut-and-prize. QC is integrated by iteratively solving QUBO problems, based on conflict graphs. Experiments on actual quantum hardware and results on benchmark data suggest that our approach dominates previous QUBO formulations and state-of-the-art MAPF solvers.
OrcaLoca: An LLM Agent Framework for Software Issue Localization
Authors: Zhongming Yu; Hejia Zhang; Yujie Zhao; Hanxian Huang; Matrix Yao; Ke Ding; Jishen Zhao
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Recent developments in Large Language Model (LLM) agents are revolutionizing Autonomous Software Engineering (ASE), enabling automated coding, problem fixes, and feature improvements. However, localization -- precisely identifying software problems by navigating to relevant code sections -- remains a significant challenge. Current approaches often yield suboptimal results due to a lack of effective integration between LLM agents and precise code search mechanisms. This paper introduces OrcaLoca, an LLM agent framework that improves accuracy for software issue localization by integrating priority-based scheduling for LLM-guided action, action decomposition with relevance scoring, and distance-aware context pruning. Experimental results demonstrate that OrcaLoca becomes the new open-source state-of-the-art (SOTA) in function match rate (65.33%) on SWE-bench Lite. It also improves the final resolved rate of an open-source framework by 6.33 percentage points through its patch generation integration.
Of Mice and Machines: A Comparison of Learning Between Real World Mice and RL Agents
Authors: Shuo Han; German Espinosa; Junda Huang; Daniel Dombeck; Malcolm A. MacIver; Bradly C. Stadie
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Recent advances in reinforcement learning (RL) have demonstrated impressive capabilities in complex decision-making tasks. This progress raises a natural question: how do these artificial systems compare to biological agents, which have been shaped by millions of years of evolution? To help answer this question, we undertake a comparative study of biological mice and RL agents in a predator-avoidance maze environment. Through this analysis, we identify a striking disparity: RL agents consistently demonstrate a lack of self-preservation instinct, readily risking ``death'' for marginal efficiency gains. These risk-taking strategies are in contrast to biological agents, which exhibit sophisticated risk-assessment and avoidance behaviors. Towards bridging this gap between the biological and artificial, we propose two novel mechanisms that encourage more naturalistic risk-avoidance behaviors in RL agents. Our approach leads to the emergence of naturalistic behaviors, including strategic environment assessment, cautious path planning, and predator avoidance patterns that closely mirror those observed in biological systems.
SHARP-Distill: A 68× Faster Recommender System with Hypergraph Neural Networks and Language Models
Authors: Saman Forouzandeh; Parham Moradi; Mahdi Jalili
Venue: ICML 2025 (ICML 2025 poster)

Abstract: This paper proposes SHARP-Distill (\textbf{S}peedy \textbf{H}ypergraph \textbf{A}nd \textbf{R}eview-based \textbf{P}ersonalised \textbf{Distill}ation), a novel knowledge distillation approach based on the teacher-student framework that combines Hypergraph Neural Networks (HGNNs) with language models to enhance recommendation quality while significantly improving inference time. The teacher model leverages HGNNs to generate user and item embeddings from interaction data, capturing high-order and group relationships, and employing a pre-trained language model to extract rich semantic features from textual reviews. We utilize a contrastive learning mechanism to ensure structural consistency between various representations. The student includes a shallow and lightweight GCN called CompactGCN designed to inherit high-order relationships while reducing computational complexity. Extensive experiments on real-world datasets demonstrate that SHARP-Distill achieves up to 68× faster inference time compared to HGNN and 40× faster than LightGCN while maintaining competitive recommendation accuracy.
Automated Red Teaming with GOAT: the Generative Offensive Agent Tester
Authors: Maya Pavlova; Erik Brinkman; Krithika Iyer; Vítor Albiero; Joanna Bitton; Hailey Nguyen; Cristian Canton Ferrer; Ivan Evtimov; Aaron Grattafiori
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Red teaming aims to assess how large language models (LLMs) can produce content that violates norms, policies, and rules set forth during their safety training. However, most existing automated methods in literature are not representative of the way common users exploit the multi-turn conversational nature of AI models. While manual testing addresses this gap, it is an inefficient and often expensive process. To address these limitations, we introduce the Generative Offensive Agent Tester (GOAT), an automated agentic red teaming system that simulates plain language adversarial conversations while leveraging multiple adversarial prompting techniques to identify vuLnerabilities in LLMs. We instantiate GOAT with 7 red teaming attacks by prompting a general purpose model in a way that encourages reasoning through the choices of methods available, the current target model’s response, and the next steps. Our approach is designed to be extensible and efficient, allowing human testers to focus on exploring new areas of risk while automation covers the scaled adversarial stress-testing of known risk territory. We present the design and evaluation of GOAT, demonstrating its effectiveness in identifying vulnerabilities in state-of-the-art LLMs, with an ASR@10 of 96% against smaller models such as Llama 3.1 8B, and 91% against Llama 3.1 70B and 94% for GPT-4o when evaluated against larger models on the JailbreakBench dataset.
SketchDNN: Joint Continuous-Discrete Diffusion for CAD Sketch Generation
Authors: Sathvik Reddy Chereddy; John Femiani
Venue: ICML 2025 (ICML 2025 poster)

Abstract: We present SketchDNN, a generative model for synthesizing CAD sketches that jointly models both continuous parameters and discrete class labels through a unified continuous-discrete diffusion process. Our core innovation is Gaussian-Softmax diffusion, where logits perturbed with Gaussian noise are projected onto the probability simplex via a softmax transformation, facilitating blended class labels for discrete variables. This formulation addresses 2 key challenges, namely, the heterogeneity of primitive parameterizations and the permutation invariance of primitives in CAD sketches. Our approach significantly improves generation quality, reducing Fréchet Inception Distance (FID) from 16.04 to 7.80 and negative log-likelihood (NLL) from 84.8 to 81.33, establishing a new state-of-the-art in CAD sketch generation on the SketchGraphs dataset.
Instance-Optimal Pure Exploration for Linear Bandits on Continuous Arms
Authors: Sho Takemori; Yuhei Umeda; Aditya Gopalan
Venue: ICML 2025 (ICML 2025 poster)

Abstract: This paper studies a pure exploration problem with linear bandit feedback on continuous arm sets, aiming to identify an $\epsilon$-optimal arm with high probability. Previous approaches for continuous arm sets have employed instance-independent methods due to technical challenges such as the infinite dimensionality of the space of probability measures and the non-smoothness of the objective function. This paper proposes a novel, tractable algorithm that addresses these challenges by leveraging a reparametrization of the sampling distribution and projected subgradient descent. However, this approach introduces new challenges related to the projection and reconstruction of the distribution from the reparametrization. We address these by focusing on the connection to the approximate Carath\'eodory  problem. Compared to the original optimization problem on the infinite-dimensional space, our method is tractable, requiring only the solution of quadratic and fractional quadratic problems on the arm set. We establish an instance-dependent optimality for our method, and empirical results on synthetic data demonstrate its superiority over existing instance-independent baselines.