GenAI4ELab/papercli-papers huggingface dataset:
G-Sim: Generative Simulations with Large Language Models and Gradient-Free Calibration
Authors: Samuel Holt; Max Ruiz Luyten; Antonin Berthon; Mihaela van der Schaar
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Constructing robust simulators is essential for asking "what if?" questions and guiding policy in critical domains like healthcare and logistics. However, existing methods often struggle, either failing to generalize beyond historical data or, when using Large Language Models (LLMs), suffering from inaccuracies and poor empirical alignment. We introduce **G-Sim**, a hybrid framework that automates simulator construction by synergizing LLM-driven structural design with rigorous empirical calibration. G-Sim employs an LLM in an iterative loop to propose and refine a simulator's core components and causal relationships, guided by domain knowledge. This structure is then grounded in reality by estimating its parameters using flexible calibration techniques. Specifically, G-Sim can leverage methods that are both **likelihood-free** and **gradient-free** with respect to the simulator, such as **gradient-free optimization** for direct parameter estimation or **simulation-based inference** for obtaining a posterior distribution over parameters. This allows it to handle non-differentiable and stochastic simulators. By integrating domain priors with empirical evidence, G-Sim produces reliable, causally-informed simulators, mitigating data-inefficiency and enabling robust system-level interventions for complex decision-making.
DOLPHIN: A Programmable Framework for Scalable Neurosymbolic Learning
Authors: Aaditya Naik; Jason Liu; Claire Wang; Amish Sethi; Saikat Dutta; Mayur Naik; Eric Wong
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Neurosymbolic learning enables the integration of symbolic reasoning with deep learning but faces significant challenges in scaling to complex symbolic programs, large datasets, or both. We introduce DOLPHIN, a framework that tackles these challenges by supporting neurosymbolic programs in Python, executing complex symbolic reasoning on the CPU while vectorizing probabilistic computations and gradient propagation on the GPU. Across 13 benchmarks spanning tasks over text, image, and video data, with symbolic reasoning features like recursion and blackbox functions, DOLPHIN converges to state-of-the-art accuracies on the more complex benchmarks while existing frameworks such as Scallop, ISED, and IndeCateR+ fail to converge within the time limit. On simpler benchmarks, DOLPHIN matches their performance, while achieving these results 1.71x to 62x faster than the baselines. Overall, DOLPHIN advances the scalability of neurosymbolic frameworks, achieving state-of-the-art efficiency and convergence on difficult benchmarks where existing frameworks struggle. The code is published at https://github.com/Dolphin-NeSy/Dolphin.
GEFA: A General Feature Attribution Framework Using Proxy Gradient Estimation
Authors: Yi Cai; Thibaud Ardoin; Gerhard Wunder
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Feature attribution explains machine decisions by quantifying each feature's contribution.
While numerous approaches rely on exact gradient measurements, recent work has adopted gradient estimation to derive explanatory information under query-level access, a restrictive yet more practical accessibility assumption known as the black-box setting.
Following this direction, this paper introduces GEFA (Gradient-estimation-based Explanation For All), a general feature attribution framework leveraging proxy gradient estimation.
Unlike the previous attempt that focused on explaining image classifiers, the proposed explainer derives feature attributions in a proxy space, making it generally applicable to arbitrary black-box models, regardless of input type.
In addition to its close relationship with Integrated Gradients, our approach, a path method built upon estimated gradients, surprisingly produces unbiased estimates of Shapley Values.
Compared to traditional sampling-based Shapley Value estimators, GEFA avoids potential information waste sourced from computing marginal contributions, thereby improving explanation quality, as demonstrated in quantitative evaluations across various settings.
Differentiable Quadratic Optimization For the Maximum Independent Set Problem
Authors: Ismail Alkhouri; Cedric Le Denmat; Yingjie Li; CUNXI YU; Jia Liu; Rongrong Wang; Alvaro Velasquez
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Combinatorial Optimization (CO) addresses many important problems, including the challenging Maximum Independent Set (MIS) problem. Alongside exact and heuristic solvers, differentiable approaches have emerged, often using continuous relaxations of quadratic objectives. Noting that an MIS in a graph is a Maximum Clique (MC) in its complement, we propose a new quadratic formulation for MIS by incorporating an MC term, improving convergence and exploration. We show that every maximal independent set corresponds to a local minimizer, derive conditions with respect to the MIS size, and characterize stationary points. To tackle the non-convexity of the objective, we propose optimizing several initializations in parallel using momentum-based gradient descent, complemented by an efficient MIS checking criterion derived from our theory. We dub our method as **p**arallelized **C**lique-Informed **Q**uadratic **O**ptimization for MIS (pCQO-MIS). Our experimental results demonstrate the effectiveness of the proposed method compared to exact, heuristic, sampling, and data-centric approaches. Notably, our method avoids the out-of-distribution tuning and reliance on (un)labeled data required by data-centric methods, while achieving superior MIS sizes and competitive run-time relative to their inference time. Additionally, a key advantage of pCQO-MIS is that, unlike exact and heuristic solvers, the run-time scales only with the number of nodes in the graph, not the number of edges. Our code is available at the GitHub repository: https://github.com/ledenmat/pCQO-mis-benchmark/tree/refactor.
Shielded Diffusion: Generating Novel and Diverse Images using Sparse Repellency
Authors: Michael Kirchhof; James Thornton; Louis Béthune; Pierre Ablin; Eugene Ndiaye; marco cuturi
Venue: ICML 2025 (ICML 2025 poster)

Abstract: The adoption of text-to-image diffusion models raises concerns over reliability, drawing scrutiny under the lens of various metrics like calibration, fairness, or compute efficiency. We focus in this work on two issues that arise when deploying these models: a lack of diversity when prompting images, and a tendency to recreate images from the training set. To solve both problems, we propose a method that coaxes the sampled trajectories of pretrained diffusion models to land on images that fall outside of a reference set. We achieve this by adding repellency terms to the diffusion SDE throughout the generation trajectory, which are triggered whenever the path is expected to land too closely to an image in the shielded reference set. Our method is sparse in the sense that these repellency terms are zero and inactive most of the time, and even more so towards the end of the generation trajectory. Our method, named SPELL for sparse repellency, can be used either with a static reference set that contains protected images, or dynamically, by updating the set at each timestep with the expected images concurrently generated within a batch, and with the images of previously generated batches. We show that adding SPELL to popular diffusion models improves their diversity while impacting their FID only marginally, and performs comparatively better than other recent training-free diversity methods. We also demonstrate how SPELL can ensure a shielded generation away from a very large set of protected images by considering all 1.2M images from ImageNet as the protected set.
Raising the Bar: Investigating the Values of Large Language Models via Generative Evolving Testing
Authors: Han Jiang; Xiaoyuan Yi; Zhihua Wei; Ziang Xiao; Shu Wang; Xing Xie
Venue: ICML 2025 (ICML 2025 poster)

Abstract: *Warning: Contains harmful model outputs.*

Despite significant advancements, the propensity of Large Language Models (LLMs) to generate harmful and unethical content poses critical challenges.
Measuring value alignment of LLMs becomes crucial for their regulation and responsible deployment. Although numerous benchmarks have been constructed to assess social bias, toxicity, and ethical issues in LLMs, those static benchmarks suffer from *evaluation chronoeffect*, in which, as models rapidly evolve, existing benchmarks may leak into training data or become saturated, *overestimating* ever-developing LLMs. To tackle this problem, we propose GETA, a novel *generative evolving testing* approach based on adaptive testing methods in measurement theory. Unlike traditional adaptive testing methods that rely on a static test item pool, GETA probes the underlying moral boundaries of LLMs by dynamically generating test items tailored to model capability. GETA co-evolves with LLMs by learning a joint distribution of item difficulty and model value conformity, thus effectively addressing evaluation chronoeffect. 
We evaluated various popular LLMs with GETA and demonstrated that 1) GETA can dynamically create difficulty-tailored test items and 2) GETA's evaluation results are more consistent with models' performance on unseen OOD and i.i.d. items, laying the groundwork for future evaluation paradigms.
Theoretical guarantees on the best-of-n alignment policy
Authors: Ahmad Beirami; Alekh Agarwal; Jonathan Berant; Alexander Nicholas D'Amour; Jacob Eisenstein; Chirag Nagpal; Ananda Theertha Suresh
Venue: ICML 2025 (ICML 2025 poster)

Abstract: A simple and effective method for the inference-time alignment of generative models is the best-of-$n$ policy, where $n$ samples are drawn from a reference policy, ranked based on a reward function, and the highest ranking one is selected. A commonly used analytical expression in the literature  claims that the KL divergence between the best-of-$n$ policy and  the reference policy is equal to $\log (n) - (n-1)/n.$ We disprove the validity of this claim, and show that it is an upper bound on the actual KL divergence. We also explore the tightness of this upper bound in different regimes, and propose a new estimator for the KL divergence and empirically show that it provides a tight approximation. We also show that the win rate of the best-of-$n$ policy against the reference policy is upper bounded by $n/(n+1)$ and derive bounds on the tightness of this characterization. We conclude with analyzing the tradeoffs between win rate and KL divergence of the best-of-$n$ alignment policy, which demonstrate that very good tradeoffs are achievable with $n < 1000$.
LaMAGIC2: Advanced Circuit Formulations for Language Model-Based Analog Topology Generation
Authors: Chen-Chia Chang; Wan-Hsuan Lin; Yikang Shen; Yiran Chen; Xin Zhang
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Automation of analog topology design is crucial due to customized requirements of modern applications with heavily manual engineering efforts. 
The state-of-the-art work applies a sequence-to-sequence approach and supervised finetuning on language models to generate topologies given user specifications.
However, its circuit formulation is inefficient due to $O(|V|^2)$ token length and suffers from low precision sensitivity to numeric inputs.
In this work, we introduce LaMAGIC2, a succinct float-input canonical formulation
with identifier (SFCI) for language model-based analog topology generation.
SFCI addresses these challenges by improving component-type recognition through identifier-based representations, reducing token length complexity to $O(|V|)$, and enhancing numeric precision sensitivity for better performance under tight tolerances.
Our experiments demonstrate that LaMAGIC2 achieves 34\% higher success rates under a tight tolerance 0.01 and 10X lower MSEs compared to a prior method. 
LaMAGIC2 also exhibits better transferability for circuits with more vertices with up to 58.5\% improvement.
These advancements establish LaMAGIC2 as a robust framework for analog topology generation.
Cavia: Camera-controllable Multi-view Video Diffusion with View-Integrated Attention
Authors: Dejia Xu; Yifan Jiang; Chen Huang; Liangchen Song; Thorsten Gernoth; Liangliang Cao; Zhangyang Wang; Hao Tang
Venue: ICML 2025 (ICML 2025 poster)

Abstract: In recent years there have been remarkable breakthroughs in image-to-video generation. However, the 3D consistency and camera controllability of generated frames have remained unsolved. Recent studies have attempted to incorporate camera control into the generation process, but their results are often limited to simple trajectories or lack the ability to generate consistent videos from multiple distinct camera paths for the same scene. To address these limitations, we introduce Cavia, a novel framework for camera-controllable, multi-view video generation, capable of converting an input image into multiple spatiotemporally consistent videos. Our framework extends the spatial and temporal attention modules into view-integrated attention modules, improving both viewpoint and temporal consistency. This flexible design allows for joint training with diverse curated data sources, including scene-level static videos, object-level synthetic multi-view dynamic videos, and real-world monocular dynamic videos. To the best of our knowledge, Cavia is the first framework that enables users to generate multiple videos of the same scene with precise control over camera motion, while simultaneously preserving object motion. Extensive experiments demonstrate that Cavia surpasses state-of-the-art methods in terms of geometric consistency and perceptual quality.
IT$^3$: Idempotent Test-Time Training
Authors: Nikita Durasov; Assaf Shocher; Doruk Oner; Gal Chechik; Alexei A Efros; Pascal Fua
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Deep learning models often struggle when deployed in real-world settings due to distribution shifts between training and test data. While existing approaches like domain adaptation and test-time training (TTT) offer partial solutions, they typically require additional data or domain-specific auxiliary tasks. We present Idempotent Test-Time Training (IT3), a novel approach that enables on-the-fly adaptation to distribution shifts using only the current test instance, without any auxiliary task design. Our key insight is that enforcing idempotence---where repeated applications of a function yield the same result---can effectively replace domain-specific auxiliary tasks used in previous TTT methods. We theoretically connect idempotence to prediction confidence and demonstrate that minimizing the distance between successive applications of our model during inference leads to improved out-of-distribution performance. Extensive experiments across diverse domains (including image classification, aerodynamics prediction, and aerial segmentation) and architectures (MLPs, CNNs, GNNs) show that IT3 consistently outperforms existing approaches while being simpler and more widely applicable. Our results suggest that idempotence provides a universal principle for test-time adaptation that generalizes across domains and architectures.
Improving Soft Unification with Knowledge Graph Embedding Methods
Authors: Xuanming Cui; Chionh Wei Peng; Adriel Kuek; Ser-Nam Lim
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Neural Theorem Provers (NTPs) present a promising framework for neuro-symbolic reasoning, combining end-to-end differentiability with the interpretability of symbolic logic programming. However, optimizing NTPs remains a significant challenge due to their complex objective landscape and gradient sparcity. On the other hand, Knowledge Graph Embedding (KGE) methods offer smooth optimization with well-defined learning objectives but often lack interpretability. In this work, we propose several strategies to integrate the strengths of NTPs and KGEs, and demonstrate substantial improvements in both accuracy and computational efficiency. Specifically,  we show that by leveraging the strength of structural learning in KGEs, we can greatly improve NTPs' poorly structured embedding space, while by substituting NTPs with efficient KGE operations, we can  significantly reduce evaluation time by over 1000$\times$ on large-scale dataset such as WN18RR with a mild accuracy trade-off.
SITCOM: Step-wise Triple-Consistent Diffusion Sampling For Inverse Problems
Authors: Ismail Alkhouri; Shijun Liang; Cheng-Han Huang; Jimmy Dai; Qing Qu; Saiprasad Ravishankar; Rongrong Wang
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Diffusion models (DMs) are a class of generative models that allow sampling from a distribution learned over a training set. When applied to solving inverse problems, the reverse sampling steps are modified to approximately sample from a measurement-conditioned distribution. However, these modifications may be unsuitable for certain settings (e.g., presence of measurement noise) and non-linear tasks, as they often struggle to correct errors from earlier steps and generally require a large number of optimization and/or sampling steps. To address these challenges, we state three conditions for achieving measurement-consistent diffusion trajectories. Building on these conditions, we propose a new optimization-based sampling method that not only enforces standard data manifold measurement consistency and forward diffusion consistency, as seen in previous studies, but also incorporates our proposed step-wise and network-regularized backward diffusion consistency that maintains a diffusion trajectory by optimizing over the input of the pre-trained model at every sampling step. By enforcing these conditions (implicitly or explicitly), our sampler requires significantly fewer reverse steps. Therefore, we refer to our method as **S**tep-w**i**se **T**riple-**Co**nsistent Sa**m**pling (**SITCOM**). Compared to SOTA baselines, our experiments across several linear and non-linear tasks (with natural and medical images) demonstrate that SITCOM achieves competitive or superior results in terms of standard similarity metrics and run-time.
Generalized additive models via direct optimization of regularized decision stump forests
Authors: Magzhan Gabidolla; Miguel Á. Carreira-Perpiñán
Venue: ICML 2025 (ICML 2025 poster)

Abstract: We explore ensembles of axis-aligned decision stumps, which can be viewed as a generalized additive model (GAM). In this model, stumps utilizing the same feature are grouped to form a shape function for that feature. Instead of relying on boosting or bagging, we employ alternating optimization to learn a fixed-size stump forest. We optimize the parameters of each stump exactly through enumeration, given the other stumps are fixed. For fixed stump splits, the leaf values are optimized jointly by solving a convex problem. To address the overfitting issue inherent in naive optimization of stump forests, we propose effective regularization techniques. Our regularized stump forests achieve accuracy comparable to state-of-the-art GAM methods while using fewer parameters. This work is the first to successfully learn stump forests without employing traditional ensembling techniques like bagging or boosting.
Behavioral Exploration: Learning to Explore via In-Context Adaptation
Authors: Andrew Wagenmaker; Zhiyuan Zhou; Sergey Levine
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Developing autonomous agents that quickly explore an environment and adapt their behavior online is a canonical challenge in robotics and machine learning. While humans are able to achieve such fast online exploration and adaptation, often acquiring new information and skills in only a handful of interactions, existing algorithmic approaches tend to rely on random exploration and slow, gradient-based behavior updates. How can we endow autonomous agents with such capabilities on par with humans? Taking inspiration from recent progress on both in-context learning and large-scale behavioral cloning, in this work we propose behavioral exploration: training agents to internalize what it means to explore and adapt in-context over the space of ''expert'' behaviors. To achieve this, given access to a dataset of expert demonstrations, we train a long-context generative model to predict expert actions conditioned on a context of past observations and a measure of how ''exploratory'' the expert's behaviors are relative to this context. This enables the model to not only mimic the behavior of an expert, but also, by feeding its past history of interactions into its context, to select different expert behaviors than what have been previously selected, thereby allowing for fast online adaptation and targeted, ''expert-like'' exploration. We demonstrate the effectiveness of our method in both simulated locomotion and manipulation settings, as well as on real-world robotic manipulation tasks, illustrating its ability to learn adaptive, exploratory behavior.
Thinking LLMs: General Instruction Following with Thought Generation
Authors: Tianhao Wu; Janice Lan; Weizhe Yuan; Jiantao Jiao; Jason E Weston; Sainbayar Sukhbaatar
Venue: ICML 2025 (ICML 2025 poster)

Abstract: LLMs are typically trained to answer user questions or follow instructions similarly to how human experts respond. However, in the standard alignment framework they lack the basic ability of explicit thinking before answering. Thinking is important for complex questions that require reasoning and planning -- but can be applied to *any* task. We propose a training method for equipping existing LLMs with such thinking abilities for general instruction following without use of additional human data. We achieve this by an iterative search and optimization procedure that explores the space of possible thought generations, allowing the model to learn how to think without direct supervision. For each instruction, the thought candidates are scored using a judge model to evaluate their responses only, and then optimized via preference optimization. We show that this procedure leads to superior performance on AlpacaEval and Arena-Hard, and  shows gains from thinking on  non-reasoning categories such as marketing, health and general knowledge, in addition to more traditional reasoning & problem-solving tasks.
LLMs can see and hear without any training
Authors: Kumar Ashutosh; Yossi Gandelsman; Xinlei Chen; Ishan Misra; Rohit Girdhar
Venue: ICML 2025 (ICML 2025 poster)

Abstract: We present MILS: Multimodal Iterative LLM Solver, a surprisingly simple, training-free approach, to imbue multimodal capabilities into
your favorite LLM. Leveraging their innate ability to perform multi-step reasoning, MILS prompts the LLM to generate candidate outputs, each of which are scored and fed back iteratively, eventually generating a solution to the task. This enables various applications that typically require training specialized models on task-specific data. In particular, we establish a new state-of-the-art on emergent zero-shot image, video and audio captioning. MILS seamlessly applies to media generation as well, discovering prompt rewrites to improve text-to-image generation, and even edit prompts for style transfer! Finally, being a gradient-free optimization approach, MILS can invert multimodal embeddings into text, enabling applications like cross-modal arithmetic.