Daily TMLR digest for Jan 19, 2026
TMLR
Accepted papers
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Title: ThinkEval: Practical Evaluation of Knowledge Leakage in LLM Editing using Thought-based Knowledge Graphs
Authors: Manit Baser, Dinil Mon Divakaran, Mohan Gurusamy
Abstract: Robust model-editing techniques are essential for deploying large language models (LLMs) in practical applications, as they enable cost-effective ways to deal with challenges such as privacy breaches, bias mitigation and misinformation spread. For example, an LLM-based healthcare assistance may need to update out-dated or incorrect knowledge to prevent harmful recommendations. However, many editing techniques focus on isolated facts, which critically fail to prevent indirect knowledge leakage---the unintended reconstruction of edited-out information through persistent causal links and contextual relationships. To assist users in selecting the right editing technique, we develop and present ThinkEval, a framework to systematically quantify indirect knowledge leakage and ripple effects in model-editing. ThinkEval builds and employs specialized knowledge graphs to analyze the causal structure of facts before and after editing. To support this approach, we present KnowGIC, a benchmark dataset comprising multi-step reasoning paths that precisely measure these complex knowledge transformation effects. We evaluate five editing techniques: AlphaEdit, RECT, ROME, MEMIT, and PRUNE across multiple LLMs. Our results show that these techniques struggle to balance indirect fact suppression with the preservation of related knowledge, compromising the contextual integrity of a model's knowledge. Our dataset is available at: https://github.com/manitbaser/KnowGIC.
URL: https://openreview.net/forum?id=IR2GAw90BB
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Title: Cost-Free Personalization via Information-Geometric Projection in Bayesian Federated Learning
Authors: Nour Jamoussi, Giuseppe Serra, Photios A. Stavrou, Marios Kountouris
Abstract: Bayesian Federated Learning (BFL) combines uncertainty modeling with decentralized training, enabling the development of personalized and reliable models in the presence of data heterogeneity and privacy constraints. Existing approaches typically rely on Markov Chain Monte Carlo (MCMC) sampling or variational inference, often incorporating personalization mechanisms to better adapt to the local data distributions. In this work, we propose an information-geometric projection framework for personalization in parametric BFL. By projecting the global model onto a neighborhood of the user's local model, our method enables a tunable trade-off between global generalization and local specialization. Under mild assumptions, we show that this projection step is equivalent to computing a barycenter in the statistical manifold, allowing us to derive closed-form solutions and achieve cost-free personalization. We apply the proposed approach within a variational learning setup using the Improved Variational Online Newton (IVON) optimizer and extend it to general aggregation schemes in BFL. Empirical evaluations under heterogeneous data distributions confirm that our method effectively balances global and local performance with minimal computational overhead.
URL: https://openreview.net/forum?id=9y0jCrxjDR
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Title: Batch Entanglement Detection in Parameterized Qubit States using Classical Bandit Algorithms
Authors: Bharati K, Vikesh Siddhu, Krishna Jagannathan
Abstract: Entanglement is a key property of quantum states that acts as a resource for a wide range of tasks in quantum computing. Entanglement detection is a key conceptual and practical challenge. Without adaptive or joint measurements, entanglement detection is constrained by no-go theorems~\citep{tomography2016no-go}, necessitating full state tomography. Batch entanglement detection refers to the problem of identifying all entangled states from amongst a set of $K$ unknown states, which finds applications in quantum information processing. We devise a method for performing batch entanglement detection by measuring a single-parameter family of entanglement witnesses, as proposed by \citet{mintomography}, followed by a thresholding bandit algorithm on the measurement data. The proposed method can perform batch entanglement detection conclusively when the unknown states are drawn from a practically well-motivated class of two-qubit states $\mathcal{F}$, which includes Depolarised Bell states, Bell diagonal states, etc. Our key novelty lies in drawing a connection between batch entanglement detection and a Thresholding Bandit problem in classical Multi-Armed Bandits (MAB). The connection to the MAB problem also enables us to derive theoretical guarantees on the measurement/sample complexity of the proposed technique. We demonstrate the performance of the proposed method through numerical simulations and an experimental implementation. More broadly, this paper highlights the potential for employing classical machine learning techniques for quantum entanglement detection.
URL: https://openreview.net/forum?id=0v27eMBVZ0
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New submissions
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Title: Rethinking Dataset Quantization: Efficient Coreset Selection via Semantically-Aware Data Augmentation
Abstract: Coreset selection aims to reduce the computational burden of training large-scale deep learning models by identifying representative subsets from massive datasets. However, existing state-of-the-art methods face a fundamental accessibility dilemma: they either require extensive training on the target dataset to compute selection metrics, or depend heavily on large pre-trained models, undermining the core purpose of coreset selection in resource-constrained scenarios. Dataset Quantization (DQ) avoids full dataset training but relies on expensive pre-trained models, introducing computational overhead and domain-specific biases that limit generalization. In this work, we comprehensively redesign the DQ framework to establish a truly accessible, theoretically sound, and domain-agnostic paradigm for coreset selection. Through rigorous analysis, we identify that: (1) MAE functions primarily as biased data augmentation leveraging memorized ImageNet patterns; (2) MAE benefits ImageNet-related datasets but harms out-of-distribution performance; (3) the original pipeline suffers from feature inconsistency between selection and training phases. We propose DQ_v2, which: (1) eliminates pre-trained model dependencies via Semantically-Aware Data Augmentation (SDA) using randomly initialized CNNs; (2) restructures the pipeline by performing augmentation before selection, ensuring feature consistency. Extensive experiments demonstrate that DQ_v2 achieves superior performance across diverse domains (such as ImageNet-1k, CUB-200, Food-101, and medical imaging) while reducing computational costs by 75% in the augmentation phase, establishing a robust and practical solution for resource-constrained scenarios.
URL: https://openreview.net/forum?id=Mb2nn1yx66
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Title: FastLane: Efficient Routed Systems for Late-Interaction Retrieval
Abstract: Late-interaction retrieval models like ColBERT achieve superior accuracy by enabling token-level interactions, but their computational cost hinders scalability and integration with Approximate Nearest Neighbor Search (ANNS). We introduce FastLane, a novel retrieval framework that dynamically routes queries to their most informative representations, eliminating redundant token comparisons. FastLane employs a learnable routing mechanism optimized alongside the embedding model, leveraging self-attention and differentiable selection to maximize efficiency. Our approach reduces computational complexity by up to 30x while maintaining competitive retrieval performance. By bridging late-interaction models with ANNS, FastLane enables scalable, low-latency retrieval, making it feasible for large-scale applications such as search engines, recommendation systems, and question-answering platforms. This work opens pathways for multi-lingual, multi-modal, and long-context retrieval, pushing the frontier of efficient and adaptive information retrieval.
URL: https://openreview.net/forum?id=IYEC6VJCFg
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Title: Hybrid Combinatorial Multi-armed Bandits with Probabilistically Triggered Arms
Abstract: The problem of combinatorial multi-armed bandits with probabilistically triggered arms (CMAB-T) has been extensively studied. Prior work primarily focuses on either the online setting where an agent learns about the unknown environment through iterative interactions, or the offline setting where a policy is learned solely from logged data. However, each of these paradigms has inherent limitations: online algorithms suffer from high interaction costs and slow adaptation, while offline methods are constrained by dataset quality and lack of exploration capabilities. To address these complementary weaknesses, we propose hybrid CMAB-T, a new framework that integrates offline data with online interaction in a principled manner. Our proposed hybrid CUCB algorithm leverages offline data to guide exploration and accelerate convergence, while strategically incorporating online interactions to mitigate the insufficient coverage or distributional bias of the offline dataset. We provide theoretical guarantees on the algorithm’s regret, demonstrating that hybrid CUCB significantly outperforms purely online approaches when high-quality offline data is available, and effectively corrects the bias inherent in offline-only methods when the data is limited or misaligned. Empirical results further demonstrate the consistent advantage of our algorithm.
URL: https://openreview.net/forum?id=ZRzB7EVlui
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Title: Efficient Block Bi-clustering by Alternating Semidefinite Programming Relaxation
Abstract: The bi-clustering problem is one of the most common problems in data mining. In this paper, we solve the block bi-clustering problem by using the semidefinite programming (SDP) relaxation alternately for clustering rows and columns of the data matrix. Theoretically, in common noisy cases, our algorithm can accurately identify the checkerboard pattern; if there is no noise in the data matrix, we establish an exact recovery for the checkerboard pattern. In both simulated and real data experiments, we show that our algorithm performs comparably or better than other bi-clustering methods in terms of both accuracy and efficiency.
URL: https://openreview.net/forum?id=xPA4Xg0IvL
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Title: Visualization of High-Dimensional Matrix Manifolds
Abstract: Matrix manifolds play a fundamental role in machine learning, underpinning data representations (e.g., linear subspaces and covariance matrices) and optimization procedures. These manifolds adhere to Riemannian geometry, where intrinsic curvature significantly impacts the performance of geometric learning algorithms. However, traditional visualization methods based on Euclidean assumptions disregard curvature information, leading to distortions of the underlying non-Euclidean structure. To address this limitation, we generalize the popular t-SNE paradigm to the context of Riemannian manifolds and apply it to three types of matrix manifolds, which are the Grassmann manifolds, Correlation manifolds, and Symmetric Positive Semi-Definite (SPSD) manifolds, respectively. By constructing a probability distribution mapping between the original and target spaces, our method transforms high-dimensional manifold-valued data points into low-dimensional ones, preserving curvature information and avoiding distortion caused by Euclidean flattening. This work provides a foundation for general-purpose dimensionality reduction of high-dimensional matrix manifolds. Extensive experimental comparisons with existing visualization methods across synthetic and benchmarking datasets demonstrate the efficacy of our proposal in preserving geometric properties of the data.
URL: https://openreview.net/forum?id=4EZeC0JwqM
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Title: Efficient Unsupervised Band Selection for Hyperspectral Imagery with Mamba-based Classifier – An In-Depth Comparative Analysis
Abstract: Band selection is a critical step in processing hyperspectral imagery (HSI), reducing input dimensionality to mitigate redundancy, enhance computational efficiency and improve learning accuracy. Efficient unsupervised deep-learning-based band selection methods have recently garnered significant attention due to their strong feature representation capabilities. In existing literature, we observe that there is a broader and more general line of research regarding feature selection, which some recent deep learning-based HSI band selection methods have drawn inspiration from. This work concentrates on efficient unsupervised deep-learning-based band selection methods from the standpoint of unifying two research lines: the more general feature selection and the more specific HSI band selection. Specifically, we conduct an in-depth comparative analysis in terms of downstream classification performance and computation cost, on six state-of-the-art efficient unsupervised HSI band selection methods, of which one does not involve deep learning and the other five do. Classification experiments are carried out using three publicly available remote sensing benchmark datasets, where we incorporate a recent Mamba-based classifier that outperforms the typical SVM substantially in classification accuracy by a ∼10-20% margin. To our best knowledge, this is the first work that puts together and compares the aforementioned efficient unsupervised methods in the context of HSI band selection and employs a Mamba-based classifier in the analysis.
URL: https://openreview.net/forum?id=HUnt637Ldy
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Title: Modality-Inconsistent Continual Learning of Multimodal Large Language Models
Abstract: In this paper, we introduce Modality-Inconsistent Continual Learning (MICL), a new continual learning scenario for Multimodal Large Language Models (MLLMs) that involves tasks with inconsistent modalities (image, audio, or video) and varying task types (captioning or question-answering). Unlike existing vision-only or modality-incremental settings, MICL combines modality and task type shifts, both of which drive catastrophic forgetting. To address these challenges, we propose MoInCL, which employs a Pseudo Targets Generation Module to mitigate forgetting caused by task type shifts in previously seen modalities. It also incorporates Instruction-based Knowledge Distillation to preserve the model's ability to handle previously learned modalities when new ones are introduced. We benchmark MICL using a total of six tasks and conduct experiments to validate the effectiveness of our MoInCL. The experimental results highlight the superiority of MoInCL, showing significant improvements over representative and state-of-the-art continual learning baselines.
URL: https://openreview.net/forum?id=FD8or43fBU
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Title: Glitch: Persona-Consistent Hallucination and Alignment Inversion in Parameter-Efficient Fine-Tuning
Abstract: Current benchmarks for Large Language Models, such as MMLU and TruthfulQA, prioritize factual accuracy and helpfulness, often if not always penalizing a trait required for character-simulating AIs like CharacterAI: Hallucinations. This paper introduces Glitch v1.2, a Llama 3.1 8B model fine-tuned to replicate a neurotic, opinionated, and rather ordinary human persona. Through qualitative and quantitative testing, we identify two critical phenomena: Persona-Consistent Hallucination (PCH), where factual errors may serve as features rather than "bugs" in the sense of character adherence and an Alignment Hierarchy where identity-based bias overrides Llama 3.1 model's safety rails but fails to override the base model's servility. We compare these findings against a control group of the base Llama 3.1 model, demonstrating that fine-tuning is required to prevent breaking of persona in language models, where models break character to admit their artificial nature. We propose the PCH metric as a necessary alternative for evaluating character-based AI. Our results show the fine-tuned model achieving an 88% PCH success rate compared to the base model's 18%, with failures specifically mapping to an Alignment Hierarchy in the Llama 3.1 8B models.
URL: https://openreview.net/forum?id=maH5gBVawc
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