GenAI4ELab/papercli-papers huggingface dataset:
Score Matching with Missing Data
Authors: Josh Givens; Song Liu; Henry Reeve
Venue: ICML 2025 (ICML 2025 oral)

Abstract: Score matching is a vital tool for learning the distribution of data with applications across many areas including diffusion processes, energy based modelling, and graphical model estimation. Despite all these applications, little work explores its use when data is incomplete. We address this by adapting score matching (and its major extensions) to work with missing data in a flexible setting where data can be partially missing over any subset of the coordinates. We provide two separate score matching variations for general use, an importance weighting (IW) approach, and a variational approach. We provide finite sample bounds for our IW approach in finite domain settings and show it to have especially strong performance in small sample lower dimensional cases. Complementing this, we show our variational approach to be strongest in more complex high-dimensional settings which we demonstrate on graphical model estimation tasks on both real and simulated data.
Roll the dice & look before you leap: Going beyond the creative limits of next-token prediction
Authors: Vaishnavh Nagarajan; Chen Henry Wu; Charles Ding; Aditi Raghunathan
Venue: ICML 2025 (ICML 2025 oral)

Abstract: We design a suite of minimal algorithmic tasks that are a loose abstraction of _open-ended_ real-world tasks. This allows us to cleanly and controllably quantify the creative limits of the present-day language model. 
Much like real-world tasks that require a creative, far-sighted leap of thought, our tasks require an implicit, open-ended _stochastic_ planning step that either (a) discovers new connections in an abstract knowledge graph (like in wordplay, drawing analogies, or research) or (b) constructs new patterns  (like in designing math problems or new proteins). In these tasks, we empirically and conceptually argue how next-token learning is myopic;  multi-token approaches, namely teacherless training and diffusion models, comparatively excel in producing diverse and original output. 
 Secondly, to elicit randomness without hurting coherence, we find that injecting noise at the input layer (dubbed _seed-conditioning_) works surprisingly as well as (and in some conditions, better than) temperature sampling from the output layer. Thus, our work offers a principled, minimal test-bed for analyzing open-ended creative skills, and offers new arguments for going beyond next-token learning and temperature sampling. We make part of the code available under https://github.com/chenwu98/algorithmic-creativity
Satori: Reinforcement Learning with Chain-of-Action-Thought Enhances LLM Reasoning via Autoregressive Search
Authors: Maohao Shen; Guangtao Zeng; Zhenting Qi; Zhang-Wei Hong; Zhenfang Chen; Wei Lu; Gregory W. Wornell; Subhro Das; David Daniel Cox; Chuang Gan
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Large language models (LLMs) have demonstrated remarkable reasoning capabilities across diverse domains. Recent studies have shown that increasing test-time computation enhances LLMs' reasoning capabilities. This typically involves extensive sampling at inference time guided by an external LLM verifier, resulting in a two-player system. Despite external guidance, the effectiveness of this system demonstrates the potential of a single LLM to tackle complex tasks. Thus, we pose a new research problem: *Can we internalize the searching capabilities to fundamentally enhance the reasoning abilities of a single LLM?* This work explores an orthogonal direction focusing on post-training LLMs for autoregressive searching (*i.e.,* an extended reasoning process with self-reflection and self-exploration of new strategies). To achieve this, we propose the Chain-of-Action-Thought (COAT) reasoning and a two-stage training paradigm: 1) a small-scale format tuning stage to internalize the COAT reasoning format and 2) a large-scale self-improvement stage leveraging reinforcement learning. Our approach results in Satori, a 7B LLM trained on open-source models and data. Extensive empirical evaluations demonstrate that Satori achieves state-of-the-art performance on mathematical reasoning benchmarks while exhibits strong generalization to out-of-domain tasks. Code, data, and models are fully open-sourced.
LotteryCodec: Searching the Implicit Representation in a Random Network for Low-Complexity Image Compression
Authors: Haotian Wu; Gongpu Chen; Pier Luigi Dragotti; Deniz Gunduz
Venue: ICML 2025 (ICML 2025 spotlightposter)

Abstract: We introduce and validate the lottery codec hypothesis, which states that untrained subnetworks within randomly initialized networks can serve as synthesis networks for overfitted image compression, achieving rate-distortion (RD) performance comparable to trained networks. This hypothesis leads to a new paradigm for image compression by encoding image statistics into the network substructure. Building on this hypothesis, we propose LotteryCodec, which overfits a binary mask to an individual image, leveraging an over-parameterized and randomly initialized network shared by the encoder and the decoder. To address over-parameterization challenges and streamline subnetwork search, we develop a rewind modulation mechanism that improves the RD performance. LotteryCodec outperforms VTM and sets a new state-of-the-art in single-image compression. LotteryCodec also enables adaptive decoding complexity through adjustable mask ratios, offering flexible compression solutions for diverse device constraints and application requirements.
Deep Neural Cellular Potts Models
Authors: Koen Minartz; Tim D'Hondt; Leon Hillmann; Jörn Starruß; Lutz Brusch; Vlado Menkovski
Venue: ICML 2025 (ICML 2025 poster)

Abstract: The cellular Potts model (CPM) is a powerful computational method for simulating collective spatiotemporal dynamics of biological cells.
To drive the dynamics, CPMs rely on physics-inspired Hamiltonians. However, as first principles remain elusive in biology, these Hamiltonians only approximate the full complexity of real multicellular systems.
To address this limitation, we propose NeuralCPM, a more expressive cellular Potts model that can be trained directly on observational data.
At the core of NeuralCPM lies the Neural Hamiltonian, a neural network architecture that respects universal symmetries in collective cellular dynamics.
Moreover, this approach enables seamless integration of domain knowledge by combining known biological mechanisms and the expressive Neural Hamiltonian into a hybrid model.
Our evaluation with synthetic and real-world multicellular systems demonstrates that NeuralCPM is able to model cellular dynamics that cannot be accounted for by traditional analytical Hamiltonians.
Flow-of-Options: Diversified and Improved LLM Reasoning by Thinking Through Options
Authors: Lakshmi Nair; Ian Trase; J. Mark Kim
Venue: ICML 2025 (ICML 2025 poster)

Abstract: We present a novel reasoning approach called Flow-of-Options (FoO), designed to address intrinsic biases in Large Language Models (LLMs). Flow-of-Options enables LLMs to systematically explore a diverse range of possibilities in their reasoning, as demonstrated by an FoO-based agentic framework developed for autonomously solving Machine Learning (ML) tasks. FoO enforces diversity in LLM solutions through compressed and interpretable task representations, resulting in improvements of 38.2% - 69.2% on standard data science tasks, and 37.4% - 47.9% on therapeutic chemistry tasks, as compared to state-of-the-art baselines. With an overall operation cost under $1 per task, our framework is well-suited for cost-sensitive applications. Going beyond tabular classification and regression, we show the broader applicability of our FoO-based agentic system to tasks such as reinforcement learning and image generation. Our code is open-sourced at: https://github.com/flagshippioneering/Flow-of-Options.
Guided Search Strategies in Non-Serializable Environments with Applications to Software Engineering Agents
Authors: Karina Zainullina; Alexander Golubev; Maria Trofimova; Sergei Polezhaev; Ibragim Badertdinov; Daria Litvintseva; Simon Karasik; Filipp Fisin; Sergei Skvortsov; Maksim Nekrashevich; Anton Shevtsov; Boris Yangel
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Large language models (LLMs) have recently achieved remarkable results in complex multi-step tasks, such as mathematical reasoning and agentic software engineering. However, they often struggle to maintain consistent performance across multiple solution attempts. One effective approach to narrow the gap between average-case and best-case performance is guided test-time search, which explores multiple solution paths to identify the most promising one. Unfortunately, effective search techniques (e.g. MCTS) are often unsuitable for *non-serializable* RL environments, such as Docker containers, where intermediate environment states cannot be easily saved and restored. We investigate two complementary search strategies applicable to such environments: 1-step lookahead and trajectory selection, both guided by a learned action-value function estimator. On the SWE-bench Verified benchmark, a key testbed for agentic software engineering, we find these methods to double the average success rate of a fine-tuned Qwen-72B model, achieving $40.8$\%, the new state-of-the-art for open-weights models. Additionally, we show that these techniques are transferable to more advanced closed models, yielding similar improvements with GPT-4o.
Federated Oriented Learning: A Practical One-Shot Personalized Federated Learning Framework
Authors: Guan Huang; Tao Shu
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Personalized Federated Learning (PFL) has become a promising learning paradigm, enabling the training of high-quality personalized models through multiple communication rounds between clients and a central server. However, directly applying traditional PFL in real-world environments where communication is expensive, limited, or infeasible is challenging, as seen in Low Earth Orbit (LEO) satellite constellations, which face severe communication constraints due to their high mobility, limited contact windows. To address these issues, we introduce Federated Oriented Learning (FOL), a novel four-stage one-shot PFL algorithm designed to enhance local model performance by leveraging neighboring models within stringent communication constraints. FOL comprises model pretraining, model collection, model alignment (via fine-tuning, pruning, post fine-tuning, and ensemble refinement), and knowledge distillation stages. We establish two theoretical guarantees on empirical risk discrepancy between student and teacher models and the convergence of the distillation process.  Extensive experiments on datasets Wildfire, Hurricane, CIFAR-10, CIFAR-100, and SVHN demonstrate that FOL consistently outperforms state-of-the-art one-shot Federated Learning (OFL) methods; for example, it achieves accuracy improvements of up to 39.24\% over the baselines on the Wildfire dataset.
Feedforward Few-shot Species Range Estimation
Authors: Christian Lange; Max Hamilton; Elijah Cole; Alexander Shepard; Samuel Heinrich; Angela Zhu; Subhransu Maji; Grant Van Horn; Oisin Mac Aodha
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Knowing where a particular species can or cannot be found on Earth is crucial for ecological research and conservation efforts. 
By mapping the spatial ranges of all species, we would obtain deeper insights into how global biodiversity is affected by climate change and habitat loss. However, accurate range estimates are only available for a relatively small proportion of all known species. For the majority of the remaining species, we typically only have a small number of records denoting the spatial locations where they have previously been observed. We outline a new approach for few-shot species range estimation to address the challenge of accurately estimating the range of a species from limited data. During inference, our model takes a set of spatial locations as input, along with optional metadata such as text or an image, and outputs a species encoding that can be used to predict the range of a previously unseen species in a feedforward manner. We evaluate our approach on two challenging benchmarks, where we obtain state-of-the-art range estimation performance, in a fraction of the compute time, compared to recent alternative approaches.
Improving Multi-Class Calibration through Normalization-Aware Isotonic Techniques
Authors: Alon Arad; Saharon Rosset
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Accurate and reliable probability predictions are essential for multi-class supervised learning tasks, where well-calibrated models enable rational decision-making. While isotonic regression has proven effective for binary calibration, its extension to multi-class problems via one-vs-rest calibration often produces suboptimal results, limiting its practical adoption. In this work, we propose novel isotonic normalization-aware techniques for multi-class calibration, grounded in natural and intuitive assumptions expected by practitioners. Unlike prior approaches, our methods inherently account for probability normalization by either incorporating normalization directly into the optimization process (**NA-FIR**) or modeling the problem as a cumulative bivariate isotonic regression (**SCIR**). Empirical evaluations on a variety of text and image classification datasets across different model architectures reveal that our approach consistently improves log loss and expected calibration error (ECE) metrics. These findings underscore the potential of our approach to enhance a-parametric multi-class calibration practices, offering an adaptable solution for real-world applications.
Decoding Rewards in Competitive Games: Inverse Game Theory with Entropy Regularization
Authors: Junyi Liao; Zihan Zhu; Ethan X Fang; Zhuoran Yang; Vahid Tarokh
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Estimating the unknown reward functions driving agents' behavior is a central challenge in inverse games and reinforcement learning. This paper introduces a unified framework for reward function recovery in two-player zero-sum matrix games and Markov games with entropy regularization. Given observed player strategies and actions, we aim to reconstruct the underlying reward functions. This task is challenging due to the inherent ambiguity of inverse problems, the non-uniqueness of feasible rewards, and limited observational data coverage. To address these challenges, we establish reward function identifiability using the quantal response equilibrium (QRE) under linear assumptions. Building on this theoretical foundation, we propose an algorithm to learn reward from observed actions, designed to capture all plausible reward parameters by constructing confidence sets. Our algorithm works in both static and dynamic settings and is adaptable to incorporate other methods, such as Maximum Likelihood Estimation (MLE). We provide strong theoretical guarantees for the reliability and sample-efficiency of our algorithm. Empirical results demonstrate the framework’s effectiveness in accurately recovering reward functions across various scenarios, offering new insights into decision-making in competitive environments.
Testing the Limits of Fine-Tuning for Improving Visual Cognition in Vision Language Models
Authors: Luca M. Schulze Buschoff; Konstantinos Voudouris; Elif Akata; Matthias Bethge; Joshua B. Tenenbaum; Eric Schulz
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Pre-trained vision language models still fall short of human visual cognition. In an effort to improve visual cognition and align models with human behavior, we introduce visual stimuli and human judgments on visual cognition tasks, allowing us to systematically evaluate performance across cognitive domains under a consistent environment. We fine-tune models on ground truth data for intuitive physics and causal reasoning and find that this improves model performance in the respective fine-tuning domain. Furthermore, it can improve model alignment with human behavior. However, we find that task-specific fine-tuning does not contribute to robust human-like generalization to data with other visual characteristics or to tasks in other cognitive domains.
Aligned Multi Objective Optimization
Authors: Yonathan Efroni; Ben Kretzu; Daniel Jiang; Jalaj Bhandari; Zheqing Zhu; Karen Ullrich
Venue: ICML 2025 (ICML 2025 poster)

Abstract: To date, the multi-objective optimization literature has mainly focused on conflicting objectives, studying the Pareto front, or requiring users to balance tradeoffs. Yet, in machine learning practice, there are many scenarios where such conflict does not take place. Recent findings from multi-task learning, reinforcement learning, and LLMs training show that diverse related tasks can enhance performance across objectives simultaneously. Despite this evidence, such phenomenon has not been examined from an optimization perspective. This leads to a lack of generic gradient-based methods that can scale to scenarios with a large number of related objectives.  To address this gap, we introduce the Aligned Multi-Objective Optimization framework, propose new algorithms for this setting, and provide theoretical guarantees of its superior performance compared to naive approaches.
Organize the Web: Constructing Domains Enhances Pre-Training Data Curation
Authors: Alexander Wettig; Kyle Lo; Sewon Min; Hannaneh Hajishirzi; Danqi Chen; Luca Soldaini
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Modern language models are trained on large, unstructured datasets consisting of trillions of tokens and obtained by crawling the web. The unstructured nature makes it difficult to reason about their contents and develop systematic approaches to data curation. In this paper, we unpack monolithic web corpora by developing taxonomies of their contents and organizing them into domains. We introduce WebOrganizer, a framework for organizing web pages in terms of both their topic and format. Using these two complementary notions of domains, we automatically annotate pre-training data by distilling annotations from a large language model into efficient classifiers. This allows us to study how data from different domains should be mixed to improve models on downstream tasks, and we show that we can combine insights about effective topics and formats to further boost performance. We demonstrate that our domain mixing also improves existing methods that select data based on quality. Furthermore, we study and compare how quality-based methods will implicitly change the domain mixture. Overall, our work demonstrates that constructing and mixing domains provides a valuable complement to quality-based data curation methods, opening new avenues for effective and insightful pre-training data curation.
Harnessing Heterogeneous Statistical Strength for Personalized Federated Learning via Hierarchical Bayesian Inference
Authors: Mahendra Singh Thapa; Rui Li
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Personalized federated learning (PFL) based on Bayesian approach tackle the challenges from statistical heterogeneity of client data by computing a personalized posterior distribution over the parameters of each client's local model and constructing a global distribution by aggregating the parameters of these personalized posteriors. However, the heuristic aggregation methods introduce strong biases and result in global models with poor generalization. We thus propose a novel hierarchical Bayesian inference framework for PFL by specifying a conjugate hyper-prior over the parameters of the personalized posteriors. This allows us to jointly compute a global posterior distribution for aggregation and the personalized ones at local level. This hierarchical Bayesian inference framework achieves elegant balance between local personalization and global model robustness. Extensive empirical study shows that by effectively sharing the heterogeneous statistical strength across the local models while retaining their distinctive characteristics, our framework yields state-of-the-art performance. We also show that existing Bayesian PFLs are special cases of our framework.
Field Matching: an Electrostatic Paradigm to Generate and Transfer Data
Authors: Alexander Kolesov; S. I. Manukhov; Vladimir Vladimirovich Palyulin; Alexander Korotin
Venue: ICML 2025 (ICML 2025 poster)

Abstract: We propose Electrostatic Field Matching (EFM), a novel method that is suitable for both generative modelling and distribution transfer tasks. Our approach is inspired by the physics of an electrical capacitor. We place source and target distributions on the capacitor plates and assign them positive and negative charges, respectively. We then learn the capacitor's electrostatic field using a neural network approximator. To map the distributions to each other, we start at one plate of the capacitor and move the samples along the learned electrostatic field lines until they reach the other plate. We theoretically justify that this approach provably yields the distribution transfer. In practice, we demonstrate the performance of our EFM in toy and image data experiments.
Efficiently Vectorized MCMC on Modern Accelerators
Authors: Hugh Dance; Pierre Glaser; Peter Orbanz; Ryan P Adams
Venue: ICML 2025 (ICML 2025 spotlightposter)

Abstract: With the advent of automatic vectorization tools (e.g., JAX's vmap), writing multi-chain MCMC algorithms is often now as simple as invoking those tools on single-chain code. Whilst convenient, for various MCMC algorithms this results in a synchronization problem---loosely speaking, at each iteration all chains running in parallel must wait until the last chain has finished drawing its sample. In this work, we show how to design single-chain MCMC algorithms in a way that avoids synchronization overheads when vectorizing with tools like vmap, by using the framework of finite state machines (FSMs). Using a simplified model, we derive an exact theoretical form of the obtainable speed-ups using our approach, and use it to make principled recommendations for optimal algorithm design. We implement several popular MCMC algorithms as FSMs, including Elliptical Slice Sampling, HMC-NUTS, and Delayed Rejection, demonstrating speed-ups of up to an order of magnitude in experiments.
R*: Efficient Reward Design via Reward Structure Evolution and Parameter Alignment Optimization with Large Language Models
Authors: Pengyi Li; Jianye HAO; Hongyao Tang; Yifu Yuan; Jinbin Qiao; Zibin Dong; YAN ZHENG
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Reward functions are crucial for policy learning. Large Language Models (LLMs), with strong coding capabilities and valuable domain knowledge, provide an automated solution for high-quality reward design. 
However, 
code-based reward functions require precise guiding logic and parameter configurations within a vast design space, leading to low optimization efficiency.
To address the challenges,
we propose an efficient automated reward design framework, called R*,
which decomposes reward design into two parts: reward structure evolution and parameter alignment optimization. To design high-quality reward structures, R* maintains a reward function population and modularizes the functional components. LLMs are employed as the mutation operator, and module-level crossover is proposed to facilitate efficient exploration and exploitation.
To design more efficient reward parameters, R* first leverages LLMs to generate multiple critic functions for trajectory comparison and annotation. Based on these critics, a voting mechanism is employed to collect the trajectory segments with high-confidence labels.
These labeled segments are then used to refine the reward function parameters through preference learning.
Experiments on diverse robotic control tasks demonstrate that R* outperforms strong baselines in both reward design efficiency and quality, surpassing human-designed reward functions.