GenAI4ELab/papercli-papers huggingface dataset:
Style Outweighs Substance: Failure Modes of LLM Judges in Alignment Benchmarking
Authors: Benjamin Feuer; Micah Goldblum; Teresa Datta; Sanjana Nambiar; Raz Besaleli; Samuel Dooley; Max Cembalest; John P Dickerson
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: The release of ChatGPT in November 2022 sparked an explosion of interest in post-training and an avalanche of new preference optimization (PO) methods. These methods claim superior alignment by virtue of better correspondence with human pairwise preferences, often measured by LLM-judges. In this work, we attempt to answer the following question -- do LLM-judge preferences translate to progress on other, more concrete metrics for alignment, and if not, why not? We define a concrete metric for alignment, and introduce SOS-Bench (Substance Outweighs Style Benchmark), the largest standardized, reproducible LLM meta-benchmark to date. We find that (1) LLM-judge preferences do not correlate with concrete measures of safety, world knowledge, and instruction following; (2) LLM-judges have powerful implicit biases, prioritizing style over factuality and safety; and (3) the supervised fine-tuning (SFT) stage of post-training has a large impact on alignment, with data scaling and prompt diversity as the driving factors.
Learning Interpretable Hierarchical Dynamical Systems Models from Time Series Data
Authors: Manuel Brenner; Elias Weber; Georgia Koppe; Daniel Durstewitz
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: In science, we are often interested in obtaining a generative model of the underlying system dynamics from observed time series. While powerful methods for dynamical systems reconstruction (DSR) exist when data come from a single domain, how to best integrate data from multiple dynamical regimes and leverage it for generalization is still an open question. This becomes particularly important when individual time series are short, and group-level information may help to fill in for gaps in single-domain data. Here we introduce a hierarchical framework that enables to harvest group-level (multi-domain) information while retaining all single-domain characteristics, and showcase it on popular DSR benchmarks, as well as on neuroscience and medical data. In addition to faithful reconstruction of all individual dynamical regimes, our unsupervised methodology discovers common low-dimensional feature spaces in which datasets with similar dynamics cluster. The features spanning these spaces were further dynamically highly interpretable, surprisingly in often linear relation to control parameters that govern the dynamics of the underlying system. Finally, we illustrate transfer learning and generalization to new parameter regimes, paving the way toward DSR foundation models.
Nonasymptotic Analysis of Stochastic Gradient Descent with the Richardson–Romberg Extrapolation
Authors: Marina Sheshukova; Denis Belomestny; Alain Oliviero Durmus; Eric Moulines; Alexey Naumov; Sergey Samsonov
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: We address the problem of solving strongly convex and smooth minimization problems using stochastic gradient descent (SGD) algorithm with a constant step size. Previous works  suggested to combine the Polyak-Ruppert averaging procedure with the Richardson-Romberg extrapolation to reduce the asymptotic bias of SGD at the expense of a mild increase of the variance. We significantly extend previous results by providing an  expansion of the mean-squared error of the resulting estimator with respect to the number of iterations $n$. We show that the root mean-squared error can be decomposed into the sum of two terms: a leading one of order $\mathcal{O}(n^{-1/2})$ with  explicit dependence on a minimax-optimal asymptotic covariance matrix, and a second-order term of order $\mathcal{O}(n^{-3/4})$, where the power $3/4$ is best known. We also extend this result to the higher-order moment bounds. Our analysis relies on the properties of the SGD iterates viewed as a time-homogeneous Markov chain. In particular, we establish that this chain is geometrically ergodic with respect to a suitably defined weighted Wasserstein semimetric.
SimpleTM: A Simple Baseline for Multivariate Time Series Forecasting
Authors: Hui Chen; Viet Luong; Lopamudra Mukherjee; Vikas Singh
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: The versatility of large Transformer-based models has led to many efforts focused on adaptations to other modalities, including time-series data. For instance, one could start from a pre-trained checkpoint of a large language model and attach adapters to recast 
the new modality (e.g., time-series) as ``language''. Alternatively, one can use a suitably large Transformer-based model, and make some modifications for time-series data. These ideas offer good performance across available benchmarks. But temporal data are quite heterogeneous (e.g., wearable sensors, physiological measurements in healthcare), and unlike text/image corpus, much of it is not publicly available. So, these models need a fair bit of domain-specific fine-tuning to achieve good performance -- this is often expensive or difficult with limited resources. In this paper, we study and characterize the performance profile of a non-generalist approach: our SimpleTM model is specialized for multivariate time-series forecasting. By simple, we mean that the model is lightweight. It is restricted to tokenization based on textbook signal processing ideas (shown to be effective in vision) which are then allowed to attend/interact: via self-attention but also via ways that are a bit more general than dot-product attention, accomplished via basic geometric algebra operations. We show that even a single- or two-layer model gives results that are competitive with much bigger models, including large transformer-based architectures,
on most benchmarks commonly reported in the literature.
A CLIP-Powered Framework for Robust and Generalizable Data Selection
Authors: Suorong Yang; Peng Ye; Wanli Ouyang; Dongzhan Zhou; Furao Shen
Venue: ICLR 2025 (ICLR 2025 Spotlight)

Abstract: Large-scale datasets have been pivotal to the advancements of deep learning models in recent years, but training on such large datasets inevitably incurs substantial storage and computational overhead. 
Meanwhile, real-world datasets often contain redundant and noisy data, imposing a negative impact on training efficiency and model performance.
Data selection has shown promise in identifying the most representative samples from the entire dataset, which aims to minimize the performance gap with reduced training costs. 
Existing works typically rely on single-modality information to assign importance scores for individual samples, which may lead to inaccurate assessments, especially when dealing with noisy or corrupted samples.
To address this limitation, we propose a novel CLIP-powered data selection framework that leverages multimodal information for more robust and generalizable sample selection. 
Specifically, our framework consists of three key modules—dataset adaptation, sample scoring, and selection optimization—that together harness extensive pre-trained multimodal knowledge to comprehensively assess sample influence and optimize the selection results through multi-objective optimization. 
Extensive experiments demonstrate that our approach consistently outperforms existing state-of-the-art baselines on various benchmark datasets. Notably, our method effectively removes noisy or damaged samples from the dataset, enabling it to achieve even higher performance with less data. This indicates that it is not only a way to accelerate training but can also improve overall data quality.
 The implementation is available at https://github.com/Jackbrocp/clip-powered-data-selection.
Expand and Compress: Exploring Tuning Principles for Continual Spatio-Temporal Graph Forecasting
Authors: Wei Chen; Yuxuan Liang
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: The widespread deployment of sensing devices leads to a surge in data for spatio-temporal forecasting applications such as traffic flow, air quality, and wind energy. Although spatio-temporal graph neural networks (STGNNs) have achieved success in modeling various static spatio-temporal forecasting scenarios, real-world spatio-temporal data are typically received in a streaming manner, and the network continuously expands with the installation of new sensors. Thus, spatio-temporal forecasting in streaming scenarios faces dual challenges: the inefficiency of retraining models over newly-arrived data and the detrimental effects of catastrophic forgetting over long-term history. To address these challenges, we propose a novel prompt tuning-based continuous forecasting method, **_EAC_**, following two fundamental tuning principles guided by empirical and theoretical analysis: _**e**xpand **a**nd **c**ompress_, which effectively resolve the aforementioned problems with lightweight tuning parameters. Specifically, we integrate the base STGNN with a continuous prompt pool, utilizing stored prompts (\ie, few learnable parameters) in memory, and jointly optimize them with the base STGNN. This method ensures that the model sequentially learns from the spatio-temporal data stream to accomplish tasks for corresponding periods. Extensive experimental results on multiple real-world datasets demonstrate the multi-faceted superiority of **_EAC_** over the state-of-the-art baselines, including effectiveness, efficiency, universality, etc.
Aligned Better, Listen Better for Audio-Visual Large Language Models
Authors: Yuxin Guo; Shuailei Ma; Shijie Ma; Xiaoyi Bao; Chen-Wei Xie; Kecheng Zheng; Tingyu Weng; Siyang Sun; Yun Zheng; Wei Zou
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: Audio is essential for multimodal video understanding. On the one hand, video inherently contains audio, which supplies complementary information to vision. Besides, video large language models (Video-LLMs) can encounter many audio-centric settings. However, existing Video-LLMs and Audio-Visual Large Language Models (AV-LLMs) exhibit deficiencies in exploiting audio information, leading to weak understanding and hallucinations. To solve the issues, we delve into the model architecture and dataset. (1) From the architectural perspective, we propose a fine-grained AV-LLM, namely Dolphin. The concurrent alignment of audio and visual modalities in both temporal and spatial dimensions ensures a comprehensive and accurate understanding of videos. Specifically, we devise an audio-visual multi-scale adapter for multi-scale information aggregation, which achieves spatial alignment. For temporal alignment, we propose audio-visual interleaved merging. (2) From the dataset perspective, we curate an audio-visual caption \& instruction-tuning dataset, called AVU. It comprises 5.2 million diverse, open-ended data tuples (video, audio, question, answer) and introduces a novel data partitioning strategy. Extensive experiments show our model not only achieves remarkable performance in audio-visual understanding, but also mitigates potential hallucinations.
OSCAR: Operating System Control via State-Aware Reasoning and Re-Planning
Authors: Xiaoqiang Wang; Bang Liu
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: Large language models (LLMs) and large multimodal models (LMMs) have shown great potential in automating complex tasks like web browsing and gaming. However, their ability to generalize across diverse applications remains limited, hindering broader utility. To address this challenge, we present OSCAR: Operating System Control via state-Aware reasoning and Re-planning. OSCAR is a generalist agent designed to autonomously navigate and interact with various desktop and mobile applications through standardized controls, such as mouse and keyboard inputs, while processing screen images to fulfill user commands. OSCAR translates human instructions into executable Python code, enabling precise control over graphical user interfaces (GUIs). To enhance stability and adaptability, OSCAR operates as a state machine, equipped with error-handling mechanisms and dynamic task re-planning, allowing it to efficiently adjust to real-time feedback and exceptions. We demonstrate OSCAR’s effectiveness through extensive experiments on diverse benchmarks across desktop and mobile platforms, where it transforms complex workflows into simple natural language commands, significantly boosting user productivity. Our code will be open-source upon publication.
Learning the Complexity of Weakly Noisy Quantum States
Authors: Yusen Wu; Bujiao Wu; Yanqi Song; Xiao Yuan; Jingbo Wang
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: Quantifying the complexity of quantum states is a longstanding key problem in various subfields of science, ranging from quantum computing to the black-hole theory. The lower bound on quantum pure state complexity has been shown to grow linearly with system size [J. Haferkamp et al., 2022, *Nat. Phys.*]. However, extending this result to noisy circuit environments, which better reflect real quantum devices, remains an open challenge. In this paper, we explore the complexity of weakly noisy quantum states via the quantum learning method. We present an efficient learning algorithm, that leverages the classical shadow representation of target quantum states, to predict the circuit complexity of weakly noisy quantum states. Our algorithm is proved to be optimal in terms of sample complexity accompanied with polynomial classical processing time. Our result builds a bridge between the learning algorithm and quantum state complexity, meanwhile highlighting the power of learning algorithm in characterizing intrinsic properties of quantum states.
Efficient Low-Bit Quantization with Adaptive Scales for Multi-Task Co-Training
Authors: Boyu Liu; Haoyu Huang; Linlin Yang; Yanjing Li; Guodong Guo; Xianbin Cao; Baochang Zhang
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: Co-training can achieve parameter-efficient multi-task models but remains unexplored for quantization-aware training. Our investigation shows that directly introducing co-training into existing quantization-aware training (QAT) methods results in significant performance degradation. Our experimental study identifies that the primary issue with existing QAT methods stems from the inadequate activation quantization scales for the co-training framework. To address this issue, we propose Task-Specific Scales Quantization for Multi-Task Co-Training (TSQ-MTC) to tackle mismatched quantization scales. Specifically, a task-specific learnable multi-scale activation quantizer (TLMAQ) is incorporated to enrich the representational ability of shared features for different tasks. Additionally, we find that in the deeper layers of the Transformer model, the quantized network suffers from information distortion within the attention quantizer. A structure-based layer-by-layer distillation (SLLD) is then introduced to ensure that the quantized features effectively preserve the information from their full-precision counterparts. Our extensive experiments in two co-training scenarios demonstrate the effectiveness and versatility of TSQ-MTC. In particular, we successfully achieve a 4-bit quantized low-level visual foundation model based on IPT, which attains a PSNR comparable to the full-precision model while offering a $7.99\times$ compression ratio in the $\times4$ super-resolution task on the Set5 benchmark.
Qinco2: Vector Compression and Search with Improved  Implicit Neural Codebooks
Authors: Théophane Vallaeys; Matthew J. Muckley; Jakob Verbeek; Matthijs Douze
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: Vector quantization is a fundamental technique for compression and large-scale nearest neighbor search. For high-accuracy operating points, multi-codebook quantization associates  data vectors with one element from each of multiple codebooks. An example is residual quantization (RQ), which iteratively quantizes the residual error of previous steps. Dependencies between the different parts of the code are, however, ignored in RQ, which leads to suboptimal rate-distortion performance. Qinco recently addressed this inefficiency by using a neural network to determine the quantization codebook in RQ based on the vector reconstruction from previous steps. In this paper we introduce Qinco2 which  extends and improves Qinco with (i) improved  vector encoding using  codeword pre-selection and beam-search, (ii) a fast  approximate decoder leveraging codeword pairs to establish  accurate short-lists for search, and (iii) an optimized training procedure and network architecture. We conduct experiments on four datasets to evaluate Qinco2 for vector compression and billion-scale nearest neighbor  search. We obtain outstanding results  in both settings, improving the state-of-the-art reconstruction MSE by 44% for 16-byte vector compression on BigANN, and search accuracy by 24% with 8-byte encodings on Deep1M.
Balanced Neural ODEs: nonlinear model order reduction and Koopman operator approximations
Authors: Julius Aka; Johannes Brunnemann; Jörg Eiden; Arne Speerforck; Lars Mikelsons
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: Variational Autoencoders (VAEs) are a powerful framework for learning latent representations of reduced dimensionality, while Neural ODEs excel in learning transient system dynamics. This work combines the strengths of both to generate fast surrogate models with adjustable complexity reacting on time-varying inputs signals. By leveraging the VAE’s dimensionality reduction using a non-hierarchical prior, our method adaptively assigns stochastic noise, naturally complementing known NeuralODE training enhancements and enabling probabilistic
time series modeling. We show that standard Latent ODEs struggle with dimensionality reduction in systems with time-varying inputs. Our approach mitigates this by continuously propagating variational parameters through time, establishing fixed information channels in latent space. This results in a flexible and robust method that can learn different system complexities, e.g. deep neural networks or
linear matrices. Hereby, it enables efficient approximation of the Koopman operator without the need for predefining its dimensionality. As our method balances dimensionality reduction and reconstruction accuracy, we call it Balanced Neural ODE (B-NODE). We demonstrate the effectiveness of this methods on several academic and real-world test cases, e.g. a power plant or MuJoCo data.
Attention layers provably solve single-location regression
Authors: Pierre Marion; Raphaël Berthier; Gérard Biau; Claire Boyer
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: Attention-based models, such as Transformer, excel across various tasks but lack a comprehensive theoretical understanding, especially regarding token-wise sparsity and internal linear representations. To address this gap, we introduce the single-location regression task, where only one token in a sequence determines the output, and its position is a latent random variable, retrievable via a linear projection of the input. To solve this task, we propose a dedicated predictor, which turns out to be a simplified version of a non-linear self-attention layer. We study its theoretical properties, by showing its asymptotic Bayes optimality and analyzing its training dynamics. In particular, despite the non-convex nature of the problem, the predictor effectively learns the underlying structure. This work highlights the capacity of attention mechanisms to handle sparse token information and internal linear structures.
Training One-Dimensional Graph Neural Networks is NP-Hard
Authors: Robert Ganian; Mathis Rocton; Simon Wietheger
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: We initiate the study of the computational complexity of training graph neural networks (GNNs). We consider the classical node classification setting; there, the intractability of training multidimensonal GNNs immediately follows from known lower bounds for training classical neural networks (and holds even for trivial GNNs). However, one-dimensional GNNs form a crucial case of interest: the computational complexity of training such networks depends on both the graphical structure of the network and the properties of the involved activation and aggregation functions. As our main result, we establish the NP-hardness of training ReLU-activated one-dimensional GNNs via a highly non-trivial reduction. We complement this result with algorithmic upper bounds for the training problem in the ReLU-activated and linearly-activated settings.
Grounding Multimodal Large Language Model in GUI World
Authors: Weixian Lei; Difei Gao; Mike Zheng Shou
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: Recent advancements in Multimodal Large Language Models (MLLMs) have accelerated the development of Graphical User Interface (GUI) agents capable of automating complex tasks across digital platforms. However, precise GUI element grounding remains a key challenge for accurate interaction and generalization. In this work, we present an effective GUI grounding framework, which includes an automated data collection engine that gathers extensive GUI screenshots and annotations to ensure broad generalization. We also propose a lightweight and flexible GUI grounding module designed to efficiently localize UI elements by pre-training on the collected data, and introduce a novel method to integrate this module with MLLMs for the effective execution of GUI tasks. Our approach demonstrates superior performance in task accuracy and adaptability, as validated by benchmarks such as ScreenSpot, MiniWob, AITW, and Mind2Web.
Variational Diffusion Posterior Sampling with Midpoint Guidance
Authors: Badr MOUFAD; Yazid Janati; Lisa Bedin; Alain Oliviero Durmus; randal douc; Eric Moulines; Jimmy Olsson
Venue: ICLR 2025 (ICLR 2025 Oral)

Abstract: Diffusion models have recently shown considerable potential in solving Bayesian inverse problems when used as priors. However, sampling from the resulting denoising posterior distributions remains a challenge as it involves intractable terms. To tackle this issue, state-of-the-art approaches formulate the problem as that of sampling from a surrogate diffusion model targeting the posterior and decompose its scores into two terms: the prior score and an intractable guidance term. While the former is replaced by the pre-trained score of the considered diffusion model, the guidance term has to be estimated. In this paper, we propose a novel approach that utilises a decomposition of the transitions which, in contrast to previous methods, allows a trade-off between the complexity of the intractable guidance term and that of the prior transitions. We validate the proposed approach through extensive experiments on linear and nonlinear inverse problems, including challenging cases with latent diffusion models as priors, and demonstrate its effectiveness in reconstructing electrocardiogram (ECG) from partial measurements for accurate cardiac diagnosis.