GenAI4ELab/papercli-papers huggingface dataset:
Improving LLMs for Recommendation with Out-Of-Vocabulary Tokens
Authors: Ting-Ji Huang; Jia-Qi Yang; Chunxu Shen; Kai-Qi Liu; De-Chuan Zhan; Han-Jia Ye
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Characterizing users and items through vector representations is crucial for various tasks in recommender systems. Recent approaches attempt to apply Large Language Models (LLMs) in recommendation through a question\&answer format, where real items (eg, Item No.2024) are represented with compound words formed from in-vocabulary tokens (eg, ``item``, ``20``, ``24``). However, these tokens are not suitable for representing items, as their meanings are shaped by pre-training on natural language tasks, limiting the model's ability to capture user-item relationships effectively. In this paper, we explore how to effectively characterize users and items in LLM-based recommender systems from the token construction view. We demonstrate the necessity of using out-of-vocabulary (OOV) tokens for the characterization of items and users, and propose a well-constructed way of these OOV tokens. By clustering the learned representations from historical user-item interactions, we make the representations of user/item combinations share the same OOV tokens if they have similar properties. This construction allows us to capture user/item relationships well (memorization) and preserve the diversity of descriptions of users and items (diversity). Furthermore, integrating these OOV tokens into the LLM’s vocabulary allows for better distinction between users and items and enhanced capture of user-item relationships during fine-tuning on downstream tasks. Our proposed framework outperforms existing state-of-the-art methods across various downstream recommendation tasks.
Sable: a Performant, Efficient and Scalable Sequence Model for MARL
Authors: Omayma Mahjoub; Sasha Abramowitz; Ruan John de Kock; Wiem Khlifi; Simon Verster Du Toit; Jemma Daniel; Louay Ben Nessir; Louise Beyers; Juan Claude Formanek; Liam Clark; Arnu Pretorius
Venue: ICML 2025 (ICML 2025 poster)

Abstract: As multi-agent reinforcement learning (MARL) progresses towards solving larger and more complex problems, it becomes increasingly important that algorithms exhibit the key properties of (1) strong performance, (2) memory efficiency, and (3) scalability. In this work, we introduce Sable, a performant, memory-efficient, and scalable sequence modelling approach to MARL. Sable works by adapting the retention mechanism in Retentive Networks (Sun et al., 2023) to achieve computationally efficient processing of multi-agent observations with long context memory for temporal reasoning. Through extensive evaluations across six diverse environments, we demonstrate how **Sable is able to significantly outperform existing state-of-the-art methods in a large number of diverse tasks (34 out of 45 tested)**. Furthermore, Sable maintains performance as we scale the number of agents, handling environments with more than a thousand agents while exhibiting a linear increase in memory usage. Finally, we conduct ablation studies to isolate the source of Sable's performance gains and confirm its efficient computational memory usage. **All experimental data, hyperparameters, and code for a frozen version of Sable used in this paper are available on our website:** https://sites.google.com/view/sable-marl. **An improved and maintained version of Sable is available in Mava:** https://github.com/instadeepai/Mava.
Subspace Optimization for Large Language Models with Convergence Guarantees
Authors: Yutong He; Pengrui Li; Yipeng Hu; Chuyan Chen; Kun Yuan
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Subspace optimization algorithms, such as GaLore (Zhao et al., 2024), have gained attention for pre-training and fine-tuning large language models (LLMs) due to their memory efficiency. However, their convergence guarantees remain unclear, particularly in stochastic settings. In this paper, we reveal that GaLore does not always converge to the optimal solution and provide an explicit counterexample to support this finding. We further explore the conditions under which GaLore achieves convergence, showing that it does so when either (i) a sufficiently large mini-batch size is used or (ii) the gradient noise is isotropic. More significantly, we introduce **GoLore** (**G**radient rand**o**m **Lo**w-**r**ank proj**e**ction), a novel variant of GaLore that provably converges in typical stochastic settings, even with standard batch sizes. Our convergence analysis extends naturally to other subspace optimization algorithms. Finally, we empirically validate our theoretical results and thoroughly test the proposed mechanisms. Codes are available at https://github.com/pkumelon/Golore.
Conformity Score Averaging for Classification
Authors: Rui Luo; Zhixin Zhou
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Conformal prediction provides a robust framework for generating prediction sets with finite-sample coverage guarantees, independent of the underlying data distribution. However, existing methods typically rely on a single conformity score function, which can limit the efficiency and informativeness of the prediction sets. In this paper, we present a novel approach that enhances conformal prediction for multi-class classification by optimally averaging multiple conformity score functions. Our method involves assigning weights to different score functions and employing various data splitting strategies. Additionally, our approach bridges concepts from conformal prediction and model averaging, offering a more flexible and efficient tool for uncertainty quantification in classification tasks. We provide a comprehensive theoretical analysis grounded in Vapnik–Chervonenkis (VC) theory, establishing finite-sample coverage guarantees and demonstrating the efficiency of our method. Empirical evaluations on benchmark datasets show that our weighted averaging approach consistently outperforms single-score methods by producing smaller prediction sets without sacrificing coverage.
Efficiently Access Diffusion Fisher: Within the Outer Product Span Space
Authors: Fangyikang Wang; Hubery Yin; Shaobin Zhuang; Huminhao Zhu; Yinan Li; Lei Qian; Chao Zhang; Hanbin Zhao; Hui Qian; Chen Li
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Recent Diffusion models (DMs) advancements have explored incorporating the second-order diffusion Fisher information (DF), defined as the negative Hessian of log density, into various downstream tasks and theoretical analysis.
However, current practices typically approximate the diffusion Fisher by applying auto-differentiation to the learned score network. This black-box method, though straightforward, lacks any accuracy guarantee and is time-consuming.
In this paper, we show that the diffusion Fisher actually resides within a space spanned by the outer products of score and initial data.
Based on the outer-product structure, we develop two efficient approximation algorithms to access the trace and matrix-vector multiplication of DF, respectively.
These algorithms bypass the auto-differentiation operations with time-efficient vector-product calculations. 
Furthermore, we establish the approximation error bounds for the proposed algorithms.
Experiments in likelihood evaluation and adjoint optimization demonstrate the superior accuracy and reduced computational cost of our proposed algorithms.
Additionally, based on the novel outer-product formulation of DF, we design the first numerical verification experiment for the optimal transport property of the general PF-ODE deduced map.
Flat-LoRA: Low-Rank Adaptation over a Flat Loss Landscape
Authors: Tao Li; Zhengbao He; Yujun Li; Yasheng Wang; Lifeng Shang; Xiaolin Huang
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Fine-tuning large-scale pre-trained models is prohibitively expensive in terms of computation and memory costs. Low-Rank Adaptation (LoRA), a popular Parameter-Efficient Fine-Tuning (PEFT) method, offers an efficient solution by optimizing only low-rank matrices. Despite recent progress in improving LoRA's performance, the relationship between the LoRA optimization space and the full parameter space is often overlooked. 
A solution that appears flat in the loss landscape of the LoRA space may still exhibit sharp directions in the full parameter space, potentially compromising generalization.
We introduce Flat-LoRA, which aims to identify a low-rank adaptation situated in a flat region of the full parameter space.
Instead of adopting the well-established sharpness-aware minimization approach, which incurs significant computation and memory overheads, we employ a Bayesian expectation loss objective to preserve training efficiency. 
Further, we design a refined strategy for generating random perturbations to enhance performance and carefully manage memory overhead using random seeds.
Experiments across diverse tasks—including mathematical reasoning, coding abilities, dialogue generation, instruction following, and text-to-image generation—demonstrate that Flat-LoRA improves both in-domain and out-of-domain generalization.
Code is available at https://github.com/nblt/Flat-LoRA.
Efficient Core-set Selection for Deep Learning Through Squared Loss Minimization
Authors: Jianting Chen
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Core-set selection (CS) for deep learning has become crucial for enhancing training efficiency and understanding datasets by identifying the most informative subsets. However, most existing methods rely on heuristics or complex optimization, struggling to balance efficiency and effectiveness. To address this, we propose a novel CS objective that adaptively balances losses between core-set and non-core-set samples by minimizing the sum of squared losses across all samples. Building on this objective, we introduce the
Maximum Reduction as Maximum Contribution criterion (MRMC), which identifies samples with the maximal reduction in loss as those making the maximal contribution to overall convergence. Additionally, a balance constraint is incorporated to ensure an even distribution of contributions from the core-set. Experimental results demonstrate that MRMC improves training efficiency significantly while preserving model performance with minimal cost.
Nemotron-CORTEXA: Enhancing LLM Agents for Software Engineering Tasks via Improved Localization and Solution Diversity
Authors: Atefeh Sohrabizadeh; Jialin Song; Mingjie Liu; Rajarshi Roy; Chankyu Lee; Jonathan Raiman; Bryan Catanzaro
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Large Language Models (LLMs) have demonstrated significant potential in code generation by following natural language instructions.  Unfortunately, crucial real-world software engineering tasks, such as debugging or repository-level feature implementation, involve processing extensive contexts beyond current LLM context sizes and performing complex reasoning that is brittle using standard autoregressive decoding. Enhancing LLMs' performance in these scenarios requires careful consideration of the contextual information provided to the model, optimizing how the model leverages that, and identifying tools that enable more effective navigation of the development environment.
To address these challenges, we introduce Nemotron-CORTEXA, an agentic system built on a predefined scaffold that enhances LLMs' ability to navigate and reason efficiently in complex software engineering contexts. Specifically, we develop a novel code embedding model that retrieves the most relevant files with greater precision, along with a localization agent that refines the granularity of the retrieval process. Additionally, we demonstrate that providing diverse contextual information and utilizing different prompt formats enable the model to identify and resolve issues more efficiently. We evaluate Nemotron-CORTEXA using SWE-bench, a benchmark derived from real-world GitHub issues. Compared to the widely used Agentless framework, Nemotron-CORTEXA achieves a higher issue resolution rate at a lower cost, highlighting its practical impact in addressing real-world software engineering challenges.
Optimization Proxies using Limited Labeled Data and Training Time -- A Semi-Supervised Bayesian Neural Network Approach
Authors: Parikshit Pareek; Abhijith Jayakumar; Kaarthik Sundar; Sidhant Misra; Deepjyoti Deka
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Constrained optimization problems arise in various engineering systems such as inventory management and power grids. Standard deep neural network (DNN) based machine learning proxies are ineffective in practical settings where labeled data is scarce and training times are limited. We propose a semi-supervised Bayesian Neural Networks (BNNs) based optimization proxy for this complex regime, wherein training commences in a sandwiched fashion, alternating between a supervised learning step for minimizing cost, and an unsupervised learning step for enforcing constraint feasibility. We show that the proposed semi-supervised BNN outperforms DNN architectures on important non-convex constrained optimization problems from energy network operations, achieving up to a tenfold reduction in expected maximum equality gap and halving the inequality gaps. Further, the BNN's ability to provide posterior samples is leveraged to construct practically meaningful probabilistic confidence bounds on performance using a limited validation data, unlike prior methods.
CodeIO: Condensing Reasoning Patterns via Code Input-Output Prediction
Authors: Junlong Li; Daya Guo; Dejian Yang; Runxin Xu; Yu Wu; Junxian He
Venue: ICML 2025 (ICML 2025 oral)

Abstract: Reasoning is a fundamental capability of Large Language Models. While prior research predominantly focuses on enhancing narrow skills like math or code generation, improving performance on many other reasoning tasks remains challenging due to sparse and fragmented training data. To address this issue, we propose CodeI/O, a novel approach that systematically condenses diverse reasoning patterns inherently embedded in contextually-grounded codes, through transforming the original code into a code input-output prediction format. By training models to predict inputs/outputs given code and test cases entirely in natural language as Chain-of-Thought (CoT) rationales, we expose them to universal reasoning primitives—like logic flow planning, state-space searching, decision tree traversal, and modular decomposition—while decoupling structured reasoning from code-specific syntax and preserving procedural rigor. Experimental results demonstrate CodeI/O leads to consistent improvements across symbolic, scientific, logic, math & numerical, and commonsense reasoning tasks. By matching the existing ground-truth outputs or re-executing the code with predicted inputs, we can verify each prediction and further enhance the CoTs through multi-turn revision, resulting in CodeI/O++ and achieving higher performance. Our data and models will be publicly available.
Banyan: Improved Representation Learning with Explicit Structure
Authors: Mattia Opper; Siddharth N
Venue: ICML 2025 (ICML 2025 poster)

Abstract: We present Banyan, a model that efficiently learns semantic representations by leveraging explicit hierarchical structure. While transformers excel at scale, they struggle in low-resource settings. Conversely recent structured models have shown promise as efficient learners, but lack performance. Banyan bridges this gap with two key innovations: an entangled hierarchical tree structure and diagonalized message passing, enabling it to outperform larger transformer models with just 14 non-embedding parameters. It excels in low-resource settings, offering a viable alternative for under-represented languages and highlighting its potential for efficient, interpretable NLP in resource-constrained environments.
LongVU: Spatiotemporal Adaptive Compression for Long Video-Language Understanding
Authors: Xiaoqian Shen; Yunyang Xiong; Changsheng Zhao; Lemeng Wu; Jun Chen; Chenchen Zhu; Zechun Liu; Fanyi Xiao; Balakrishnan Varadarajan; Florian Bordes; Zhuang Liu; Hu Xu; Hyunwoo J. Kim; Bilge Soran; Raghuraman Krishnamoorthi; Mohamed Elhoseiny; Vikas Chandra
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Multimodal Large Language Models (MLLMs) have shown promising progress in understanding and analyzing video content. However, processing long videos remains a significant challenge constrained by LLM's context size. To address this limitation, we propose \textbf{LongVU}, a spatiotemporal adaptive compression mechanism that reduces the number of video tokens while preserving visual details of long videos. Our idea is based on leveraging cross-modal query and inter-frame dependencies to adaptively reduce temporal and spatial redundancy in videos. Specifically, we leverage DINOv2 features to remove redundant frames that exhibit high similarity. Then we utilize text-guided cross-modal query for selective frame feature reduction. Further, we perform spatial token reduction across frames based on their temporal dependencies. Our adaptive compression strategy effectively processes a large number of frames with little visual information loss within given context length. Our LongVU consistently surpass existing methods across a variety of video understanding benchmarks, especially on hour-long video understanding tasks such as VideoMME and MLVU. Given a light-weight LLM, our LongVU also scales effectively into a smaller size with state-of-the-art video understanding performance.
Reasoning-as-Logic-Units: Scaling Test-Time Reasoning in Large Language Models Through Logic Unit Alignment
Authors: Cheryl Li; Tianyuan Xu; Steven Y. Guo
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Chain-of-Thought (CoT) prompting has shown promise in enhancing the reasoning capabilities of large language models (LLMs) by generating natural language (NL) rationales that lead to the final answer. However, it struggles with numerical computation, which has somehow led to the development of program-aided techniques.
Despite their potential, a persistent challenge remains: inconsistencies between LLM-reported reasoning steps and the logic in generated programs, which we term ``reasoning hallucinations." This stems from the inherent ambiguities of NL and the statistical nature of LLMs, which often lack rigorous logical coherence.
To address this challenge, we propose a novel test-time scaling framework, Reasoning-as-Logic-Units (RaLU), which constructs a more reliable reasoning path by aligning logical units between the generated program and their corresponding NL descriptions.
By decomposing the initially generated program into discrete units using static analysis, RaLU engages in an iterative dialogue with the LLM to judge, refine, and explain each unit.
A rewind-and-correct mechanism ensures alignment between code statements and task requirements in each unit, ultimately forming a cohesive reasoning path under the program's logic, from which the model reaches a final solution.
Our experiments demonstrate that RaLU significantly outperforms existing baselines in mathematical reasoning (GSM8K, MATH) and algorithmic reasoning (HumanEval+, MBPP+), underscoring its potential to advance LLM reasoning and programming by offering enhanced accuracy and interpretability.
ReFrame: Layer Caching for Accelerated Inference in Real-Time Rendering
Authors: Lufei Liu; Tor M. Aamodt
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Graphics rendering applications increasingly leverage neural networks in tasks such as denoising, supersampling, and frame extrapolation to improve image quality while maintaining frame rates.
The temporal coherence inherent in these tasks presents an opportunity to reuse intermediate results from previous frames and avoid redundant computations.
Recent work has shown that caching intermediate features to be reused in subsequent inferences is an effective method to reduce latency in diffusion models.
We extend this idea to real-time rendering and present ReFrame, which explores different caching policies to optimize trade-offs between quality and performance in rendering workloads.
ReFrame can be applied to a variety of encoder-decoder style networks commonly found in rendering pipelines.
Experimental results show that we achieve 1.4$\times$ speedup on average with negligible quality loss in three real-time rendering tasks.
Code available: https://ubc-aamodt-group.github.io/reframe-layer-caching/
End-to-End Learning Framework for Solving Non-Markovian Optimal Control
Authors: Xiaole Zhang; Peiyu Zhang; Xiongye Xiao; Shixuan Li; Vasileios Tzoumas; Vijay Gupta; Paul Bogdan
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Integer-order calculus fails to capture the long-range dependence (LRD) and memory effects found in many complex systems. Fractional calculus addresses these gaps through fractional-order integrals and derivatives, but fractional-order dynamical systems pose substantial challenges in system identification and optimal control tasks. In this paper, we theoretically derive the optimal control via linear quadratic regulator (LQR) for fractional-order linear time-invariant (FOLTI) systems and develop an end-to-end deep learning framework based on this theoretical foundation. Our approach establishes a rigorous mathematical model, derives analytical solutions, and incorporates deep learning to achieve data-driven optimal control of FOLTI systems. Our key contributions include: (i) proposing a novel method for system identification and optimal control strategy in FOLTI systems, (ii) developing the first end-to-end data-driven learning framework, Fractional-Order Learning for Optimal Control (FOLOC), that learns control policies from observed trajectories, and (iii) deriving theoretical bounds on the sample complexity for learning accurate control policies under fractional-order dynamics. Experimental results indicate that our method accurately approximates fractional-order system behaviors without relying on Gaussian noise assumptions, pointing to promising avenues for advanced optimal control.
FourierMamba: Fourier Learning Integration with State Space Models for Image Deraining
Authors: Dong Li; Yidi Liu; Xueyang Fu; Jie Huang; Senyan Xu; Qi Zhu; Zheng-Jun Zha
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Image deraining aims to remove rain streaks from rainy images and restore clear backgrounds. Currently, some research that employs the Fourier transform has proved to be effective for image deraining, due to it acting as an effective frequency prior for capturing rain streaks. However, despite there exists dependency of low frequency and high frequency in images, these Fourier-based methods rarely exploit the correlation of different frequencies for conjuncting their learning procedures, limiting the full utilization of frequency information for image deraining. Alternatively, the recently emerged Mamba technique depicts its effectiveness and efficiency for modeling correlation in various domains (e.g., spatial, temporal), and we argue that introducing Mamba into its unexplored Fourier spaces to correlate different frequencies would help improve image deraining. This motivates us to propose a new framework termed FourierMamba, which performs image deraining with Mamba in the Fourier space. Owing to the unique arrangement of frequency orders in Fourier space, the core of FourierMamba lies in the scanning encoding of different frequencies, where the low-high frequency order formats exhibit differently in the spatial dimension  (unarranged in axis) and channel dimension (arranged in axis). Therefore, we design FourierMamba that correlates Fourier space information in the spatial and channel dimensions with distinct designs. Specifically, in the spatial dimension Fourier space, we introduce the zigzag coding to scan the frequencies to rearrange the orders from low to high frequencies,  thereby orderly correlating the connections between frequencies; in the channel dimension  Fourier space with arranged orders of frequencies in axis, we can directly use Mamba to perform frequency correlation and improve the channel information representation. Extensive experiments reveal that our method outperforms state-of-the-art methods both qualitatively and quantitatively.