Daily TMLR digest for Dec 06, 2025
TMLR
New certifications
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Featured Certification, J2C Certification: Angular Regularization for Positive-Unlabeled Learning on the Hypersphere
Vasileios Sevetlidis, George Pavlidis, Antonios Gasteratos
https://openreview.net/forum?id=XQhO0Ly6el
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Accepted papers
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Title: Angular Regularization for Positive-Unlabeled Learning on the Hypersphere
Authors: Vasileios Sevetlidis, George Pavlidis, Antonios Gasteratos
Abstract: Positive–Unlabeled (PU) learning addresses classification problems where only a subset of positive examples is labeled and the remaining data is unlabeled, making explicit negative supervision unavailable. Existing PU methods often rely on negative-risk estimation or pseudo-labeling, which either require strong distributional assumptions or can collapse in high-dimensional settings. We propose AngularPU, a novel PU framework that operates on the unit hypersphere using cosine similarity and angular margin. In our formulation, the positive class is represented by a learnable prototype vector, and classification reduces to thresholding the cosine similarity between an embedding and this prototype—eliminating the need for explicit negative modeling. To counteract the tendency of unlabeled embeddings to cluster near the positive prototype, we introduce an angular regularizer that encourages dispersion of the unlabeled set over the hypersphere, improving separation. We provide theoretical guarantees on the Bayes-optimality of the angular decision rule, consistency of the learned prototype, and the effect of the regularizer on the unlabeled distribution. Experiments on benchmark datasets demonstrate that AngularPU achieves competitive or superior performance compared to state-of-the-art PU methods, particularly in settings with scarce positives and high-dimensional embeddings, while offering geometric interpretability and scalability.
URL: https://openreview.net/forum?id=XQhO0Ly6el
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Title: Task-agnostic Prompt Compression with Context-aware Sentence Embedding and Reward-guided Task Descriptor
Authors: Barys Liskavets, Shuvendu Roy, Maxim Ushakov, Mark Klibanov, Ali Etemad, Shane K. Luke
Abstract: The rise of Large Language Models (LLMs) has led to significant interest in prompt compression, a technique aimed at reducing the length of input prompts while preserving critical information. However, the prominent approaches in prompt compression often require explicit questions or handcrafted templates for compression, limiting their generalizability. We propose Task-agnostic Prompt Compression (TPC), a novel framework that generalizes compression across tasks and domains without requiring input questions or templates. TPC generates a context-relevant task description using a task descriptor trained on a curated dataset of context and query pairs, and fine-tuned via reinforcement learning with a reward function designed to capture the most relevant information. The task descriptor is then utilized to compute the relevance of each sentence in the prompt to generate the compressed prompt. We introduce 3 model sizes (Base, Large, and Huge), where the largest model outperforms the existing state-of-the-art methods on LongBench and ZeroSCROLLS, and our smallest model performs comparable to the existing solutions while being considerably smaller.
URL: https://openreview.net/forum?id=TpGcX9UTOt
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Title: Unifying Linear-Time Attention via Latent Probabilistic Modelling
Authors: Rares Dolga, Lucas Maystre, Marius Cobzarenco, David Barber
Abstract: Transformers have achieved state-of-the-art results across a range of domains, but their quadratic attention mechanism poses significant challenges for long-sequence modelling. Recent efforts to design linear-time attention mechanisms have yielded more scalable alternatives, yet often at the cost of performance, particularly on discrete data such as language. In this work, we revisit linear attention through the lens of probabilistic graphical models. We first show that standard linear attention can be interpreted as an undirected latent variable model, revealing a key limitation: the absence of directionality. To address this, we propose a novel directed parameterisation of linear attention that introduces an asymmetric structure, enabling an interpretation aligned with the causal and sequential nature of language. Our formulation integrates global latent-variable attention with local standard attention in a fully probabilistic framework. Additionally, we introduce a recurrent parameterisation of queries and keys that avoids reliance on relative positional encodings, often incompatible with linear attention. Experiments on language modelling benchmarks demonstrate that our model achieves competitive performance with standard attention and outperforms existing linear attention variants.
URL: https://openreview.net/forum?id=TDFIjR7ynG
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Title: Understanding Class Bias Amplification in Graph Representation Learning
Authors: Shengzhong Zhang, Wenjie Yang, Yimin Zhang, Hongwei Zhang, Zengfeng Huang
Abstract: Recent research reveals that GNN-based graph representation learning may inadvertently introduce various structural biases. In this work, we discover a phenomenon of structural bias in graph representation learning called class bias amplification, which refers to the exacerbation of performance bias between different classes by GNN encoder. We conduct an in-depth theoretical study of this phenomenon from a novel spectral perspective. Our analysis suggests that structural disparities between nodes in different classes result in varying local convergence speeds for node embeddings. This phenomenon leads to bias amplification in the classification results of downstream tasks. Based on the theoretical insights, we propose random graph coarsening, which is proved to be effective in dealing with the above issue. Finally, we propose an unsupervised graph contrastive learning model called Random Graph Coarsening Contrastive Learning (RGCCL), which utilizes random coarsening as data augmentation and mitigates class bias amplification by contrasting the coarsened graph with the original graph. Extensive experiments on various datasets demonstrate the advantage of our method when dealing with class bias amplification.
URL: https://openreview.net/forum?id=SqpgDUdRE9
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New submissions
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Title: Leveraging Reference Documents for Zero-Shot Ranking via Large Language Models
Abstract: Large language models (LLMs) have proven strong zero-shot rerankers, yet the two dominant paradigms expose a sharp accuracy-efficiency trade-off. Existing methods mainly fall into two categories: Individual-scoring (pointwise) issues $O(n)$ parallel calls but suffers from calibration drift across isolated prompts; Comparative-sorting (pairwise/listwise) alleviates drift via explicit inter-document comparison, but incurs higher-than-linear inference or long single-call latency. To address their limitations, we propose **RefRank**, a reference-anchored framework that marries the throughput of Individual-scoring with the calibration benefits of Comparative-sorting. RefRank prompts the LLM to score each candidate relative to a fixed anchor document harvested from the first-stage top-k list; all candidates are thus implicitly compared through the same anchors while parallelism is preserved. The method is training-free, adds no extra model calls, and keeps complexity at O(n). Across six standard benchmarks and multiple backbones, RefRank significantly outperforms Individual-scoring baselines and surpasses Comparative-sorting competitors with only negligible overhead.
URL: https://openreview.net/forum?id=4XBTDxpLYK
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Title: PRISM: Patch Diffusion with Dynamic Retrieval Augmented Guidance and Permutation Invariant Conditioning
Abstract: Diffusion models have achieved state-of-the-art results in image generation but often require extensive computational resources and large-scale datasets, limiting their practicality in resource-constrained settings. To address these challenges, we introduce PRISM, a retrieval-guided, patch-based method that trains solely on image patches instead of full resolution images.
PRISM achieves superior global coherence and outperforms patch-only baselines, even when trained on only a fraction of the data. For each training example, PRISM retrieves semantically related neighbors from a disjoint retrieval set using CLIP embeddings. It aggregates their unordered signals with a Set Transformer, ensuring permutation-invariant conditioning that captures higher-order relationships. A dynamic neighbor-annealing schedule optimizes the contextual guidance over time, leading to more coherent results. Experiments on unconditional image generation tasks using CIFAR-10, CelebA, ImageNet-100, and AFHQv2 datasets, along with ablation studies, validate our approach, demonstrating that retrieval-augmented, set-based conditioning closes the coherence gap in patch-only diffusion.
URL: https://openreview.net/forum?id=ru712j5D2d
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Title: SCUT : Spectral Clustering for Unsupervised classification Trees
Abstract: The lack of annotated data often limits the training of machine learning models. In addition, during the labelling process, some data points may remain unlabelled. While unsupervised methods such as clustering can reveal the underlying structure of the data, they are typically unsuitable to place new samples into existing clusters. Here, we propose Spectral Clustering for Unsupervised decision Tree (SCUT), a novel hierarchical clustering method based on algebraic connectivity that can position new data points appropriately within the clustering structure. By leveraging a feature-splitting approach, SCUT also enables straightforward extraction of {\em ante-hoc} explanations for its clustering decisions. Formally, SCUT works by recursively splitting the data through the solution of the Normalized Cut (NCUT) problem—a graph-partitioning formulation that seeks to split a graph into balanced subsets while minimizing the total connection strength between them—on a bipartite graph. We demonstrate, both visually and quantitatively, that SCUT captures the intrinsic structure of data more effectively than existing methods, while offering competitive performance compared to common hierarchical clustering algorithms.
URL: https://openreview.net/forum?id=wjTysDxSg5
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Title: Synapse: Adaptive Arbitration of Complementary Expertise in Time Series Foundational Models
Abstract: Pre-trained Time Series Foundational Models (TSFMs) represent a significant advance, capable of forecasting diverse time series with complex characteristics, including varied seasonalities, trends, and long-range dependencies. Despite their primary goal of universal time series forecasting, their efficacy is far from uniform; divergent training protocols and data sources cause individual TSFMs to exhibit highly variable performance across different forecasting tasks, domains, and horizons. Leveraging this complementary expertise by arbitrating existing TSFM outputs presents a compelling strategy, yet this remains a largely unexplored area of research. In this paper, we conduct a thorough examination of how different TSFMs exhibit specialized performance profiles across various forecasting settings, and how we can effectively leverage this behavior in arbitration between different time series models. We specifically analyze how factors such as model selection and forecast horizon distribution can influence the efficacy of arbitration strategies. Based on this analysis, we propose Synapse, a novel arbitration framework for TSFMs. Synapse is designed to dynamically leverage a pool of TSFMs, assign and adjust predictive weights based on their relative, context-dependent performance, and construct a robust forecast distribution by adaptively sampling from the output quantiles of constituent models. Experimental results demonstrate that Synapse consistently outperforms other popular ensembling techniques as well as individual TSFMs, demonstrating Synapse's efficacy in time series forecasting.
URL: https://openreview.net/forum?id=j3HqbsCwt1
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Title: Learning Energy-Based Models by Self-Normalising the Likelihood
Abstract: Training an energy-based model (EBM) with maximum likelihood is challenging due to the intractable normalisation constant. Traditional methods rely on expensive Markov chain Monte Carlo (MCMC) sampling to estimate the gradient of logartihm of the normalisation constant. We propose a novel objective called self-normalised log-likelihood (SNL) that introduces a single additional learnable parameter representing the normalisation constant compared to the regular log-likelihood. SNL is a lower bound of the log-likelihood, and its optimum corresponds to both the maximum likelihood estimate of the model parameters and the normalisation constant. We show that the SNL objective is concave in the model parameters for exponential family distributions. Unlike the regular log-likelihood, the SNL can be directly optimised using stochastic gradient techniques by sampling from a crude proposal distribution. We validate the effectiveness of our proposed method on various density estimation and parameter estimation tasks. Our results show that the proposed method, while simpler to implement and tune, outperforms existing techniques on small to moderate dimensions but its performance starts to degrade for very high-dimensional problems. We extend this framework to handle EBM for regression and show the usefulness of our method in this setting as we outperform existing techniques.
URL: https://openreview.net/forum?id=GVaPBqI6ny
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Title: UFO2: The Desktop AgentOS
Abstract: Recent Computer-Using Agents (CUAs), powered by multimodal large language models (LLMs), offer a promising direction for automating complex desktop workflows through natural language. However, most existing CUAs remain conceptual prototypes, hindered by shallow OS integration, fragile screenshot-based interaction, and disruptive execution.
We present UFO2, a multiagent AgentOS for Windows desktops that elevates CUAs into practical, system-level automation. UFO2 features a centralized HostAgent for task decomposition and coordination, alongside a collection of application-specialized AppAgents equipped with native APIs, domain-specific knowledge, and a unified GUI--API action layer. This architecture enables robust task execution while preserving modularity and extensibility. A hybrid control detection pipeline fuses Windows UI Automation (UIA) with vision-based parsing to support diverse interface styles. Runtime efficiency is further enhanced through speculative multi-action planning, reducing per-step LLM overhead. Finally, a Picture-in-Picture (PiP) interface enables automation within an isolated virtual desktop, allowing agents and users to operate concurrently without interference.
We evaluate UFO2 across over 20 real-world Windows applications, demonstrating substantial improvements in robustness and execution accuracy over prior CUAs. Our results show that deep OS integration unlocks a scalable path toward reliable, user-aligned desktop automation.
URL: https://openreview.net/forum?id=iAuZVWCduc
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