Daily TMLR digest for Jan 25, 2026
TMLR
New certifications
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Survey Certification: Unifying VXAI: A Systematic Review and Framework for the Evaluation of Explainable AI
David Dembinsky, Adriano Lucieri, Stanislav Frolov, Hiba Najjar, Ko Watanabe, Andreas Dengel
https://openreview.net/forum?id=wAvFLe7o0E
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Survey Certification: Augmented Vision-Language Models: A Systematic Review
Anthony C Davis, Burhan A. Sadiq, Tianmin Shu, Chien-Ming Huang
https://openreview.net/forum?id=DFnPi77v6J
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Accepted papers
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Title: Bayesian Ensembling: Insights from Online Optimization and Empirical Bayes
Authors: Daniel Waxman, Fernando Llorente, Petar Djuric
Abstract: We revisit the classical problem of Bayesian ensembles and address the challenge of learning optimal combinations of Bayesian models in an online learning setting. To this end, we reinterpret existing approaches such as Bayesian model averaging (BMA) and Bayesian stacking through a novel empirical Bayes lens, shedding new light on the limitations and pathologies of BMA. Further motivated by insights from online optimization, we propose Online Bayesian Stacking (OBS), a method that optimizes the log-score over predictive distributions to adaptively combine Bayesian models. A key contribution of our work is establishing a novel connection between OBS and portfolio selection, bridging Bayesian ensemble learning with a rich, well-studied theoretical framework that offers efficient algorithms and extensive regret analysis. We further clarify the relationship between OBS and online BMA, showing that they optimize related but distinct cost functions. Through theoretical analysis and empirical evaluation, we identify scenarios where OBS outperforms online BMA and provide principled methods and guidance on when practitioners should prefer one approach over the other.
URL: https://openreview.net/forum?id=CCvVzmfBOn
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Title: Unifying VXAI: A Systematic Review and Framework for the Evaluation of Explainable AI
Authors: David Dembinsky, Adriano Lucieri, Stanislav Frolov, Hiba Najjar, Ko Watanabe, Andreas Dengel
Abstract: Modern AI systems frequently rely on opaque black-box models, most notably Deep Neural Networks, whose performance stems from complex architectures with millions of learned parameters. While powerful, their complexity poses a major challenge to trustworthiness, particularly due to a lack of transparency. Explainable AI (XAI) addresses this issue by providing human-understandable explanations of model behavior. However, to ensure their usefulness and trustworthiness, such explanations must be rigorously evaluated. Despite the growing number of XAI methods, the field lacks standardized evaluation protocols and consensus on appropriate metrics. To address this gap, we conduct a systematic literature review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and introduce a unified framework for the eValuation of XAI (VXAI). We identify 362 relevant publications and aggregate their contributions into 41 functionally similar metric groups. In addition, we propose a three-dimensional categorization scheme spanning explanation type, evaluation contextuality, and explanation quality desiderata. Our framework provides the most comprehensive and structured overview of VXAI to date. It supports systematic metric selection, promotes comparability across methods, and offers a flexible foundation for future extensions.
URL: https://openreview.net/forum?id=wAvFLe7o0E
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Title: Natural Policy Gradient for Average Reward Non-Stationary Reinforcement Learning
Authors: Neharika Jali, Eshika Pathak, Pranay Sharma, Guannan Qu, Gauri Joshi
Abstract: We consider the problem of non-stationary reinforcement learning (RL) in the infinite-horizon average-reward setting. We model it by a Markov Decision Process with time-varying rewards and transition probabilities, with a variation budget of $\Delta_T$. Existing non-stationary RL algorithms focus on model-based and model-free value-based methods. Policy-based methods despite their flexibility in practice are not theoretically well understood in non-stationary RL. We propose and analyze the first model-free policy-based algorithm, Non-Stationary Natural Actor-Critic, NS-NAC, a policy gradient method with a restart based exploration for change and a novel interpretation of learning rates as adapting factors. Further, we present a bandit-over-RL based parameter-free algorithm, BORL-NS-NAC, that does not require prior knowledge of the variation budget $\Delta_T$. We present a dynamic regret of $\mathcal{\tilde{O}} (|\mathcal{S}|^{1/2}|\mathcal{A}|^{1/2}\Delta_T^{1/6}T^{5/6})$ for both algorithms under standard assumptions, where $T$ is the time horizon, and $|\mathcal{S}|$, $|\mathcal{A}|$ are the sizes of the state and action spaces. The regret analysis leverages a novel adaptation of the Lyapunov function analysis of NAC to dynamic environments and characterizes the effects of simultaneous updates in policy, value function estimate and changes in the environment.
URL: https://openreview.net/forum?id=hBJYNAYtoo
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Title: Augmented Vision-Language Models: A Systematic Review
Authors: Anthony C Davis, Burhan A. Sadiq, Tianmin Shu, Chien-Ming Huang
Abstract: Recent advances in visual-language machine learning models have demonstrated exceptional ability to use natural language and understand visual scenes by training on large, unstructured datasets. However, this training paradigm cannot produce interpretable explanations for its outputs, requires retraining to integrate new information, is highly resource-intensive, and struggles with certain forms of logical reasoning. One promising solution involves integrating neural networks with external symbolic information systems, forming neural symbolic systems that can enhance reasoning and memory abilities. These neural symbolic systems provide more interpretable explanations to their outputs and the capacity to assimilate new information without extensive retraining. Utilizing powerful pre-trained Vision-Language Models (VLMs) as the core neural component, augmented by external systems, offers a pragmatic approach to realizing the benefits of neural-symbolic integration. This systematic literature review aims to categorize techniques through which visual-language understanding can be improved by interacting with external symbolic information systems.
URL: https://openreview.net/forum?id=DFnPi77v6J
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New submissions
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Title: Task Vector Bases: A Unified and Scalable Framework for Compressed Task Arithmetic
Abstract: Task arithmetic, representing downstream tasks through linear operations on task vectors, has emerged as a simple yet powerful paradigm for transferring knowledge across diverse settings. However, maintaining a large collection of task vectors introduces scalability challenges in both storage and computation. We propose Task Vector Bases, a framework compressing $T$ task vectors into $M < T$ basis vectors while preserving the functionality of task arithmetic. By representing each task vector as a structured linear combination of basis atoms, our approach supports standard operations such as addition, negation, as well as more advanced arithmetic ones. The framework is orthogonal to other efficiency-oriented improvements in task arithmetic and can be used in combination with them. We provide theoretical analysis showing that basis compression retains addition generalization guarantees and enables principled unlearning, with error bounds depending on reconstruction quality. Empirically, our proposed basis construction methods consistently outperform heuristic basis construction baselines and, in some cases, even surpass the performance of full task vector collections across diverse downstream applications while reducing storage and computational requirements.
URL: https://openreview.net/forum?id=zkc7u3mIaE
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Title: When Representations Persist but Control Fails: A Mechanistic Analysis of Search in Language Models
Abstract: Why do language models fail at multi-step reasoning despite encoding task-relevant structure? We investigate this question through graph traversal, uncovering a striking temporal dissociation: models encode graph-theoretic structure with high fidelity (Spearman ρ = 0.50–0.70) yet fail at autonomous multi-step execution (0% accuracy). Critically, control collapse precedes behavioral error—in 78% of failed trials, internal state drift occurs before the first invalid output—while representations persist beyond failure, remaining structurally intact even as execution breaks down. When execution is externalized to a symbolic planner, performance recovers to 50–100%, confirming preserved evaluative competence.
Using SearchEval, a diagnostic lens triangulating behavioral traces, representational geometry, and attention dynamics, we localize the bottleneck to attention-based control mechanisms that progressively decouple from task-relevant state during generation. Attention drifts from task-relevant tokens (65%→40%) even when hidden-state geometry remains intact. Neither layer-time nor generation-time computation exhibits the state-tracking signatures required for systematic search.
These findings demonstrate that failure arises from control instability rather than representational inadequacy, suggesting that architectural innovations targeting state persistence—not merely scaling—may be necessary for reliable algorithmic reasoning.
URL: https://openreview.net/forum?id=5faM71l8em
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Title: ADAPT: Adaptive Prompt Tuning for Pre-Trained Vision-Language Models
Abstract: Prompt tuning has emerged as an effective way for parameter-efficient fine-tuning. Conventional deep prompt tuning inserts continuous prompts of a fixed context length into the input to each layer. When a pre-trained model is tailored to a specific downstream task, different layers initialized with pre-trained weights might have different levels of deviation from the optimal weights. Inserted prompts with a fixed context length might have redundant context tokens or insufficient context length. To address this issue, we propose a deep continuous prompting method dubbed Adapt that encourages heterogeneous context lengths. In this method, context lengths are automatically determined by iteratively pruning context tokens. We use the saliency criterion for neural network pruning to compute the importance scores of context tokens in order to determine which tokens to prune. To avoid the forgetting issue in the fine-tuning process, we apply the angular knowledge distillation to force the model to learn the angular separation between pairs of classes and that of instances from the pre-trained model. We examine the proposed method on the pre-trained vision-language model CLIP. 16-shot experiments on 11 downstream datasets reveal the advantage of Adapt: the average test accuracy achieves competitive performance, and the highest performance gain on individual datasets is 7.44%. We release the code in https://anonymous.4open.science/r/Adapt-Prompt-Release.
URL: https://openreview.net/forum?id=3uVAA3ckxT
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Title: Analyzing Best-Response Dynamics for Cooperation in Markov Potential Games
Abstract: Simultaneous gradient updates are widely used in multi-agent learning. However, this method introduces non-stationarity from the perspective of each agent due to the co-evolution of other agents' policies. To address this issue, we consider best-response dynamics, where only one agent updates its policy at a time. We theoretically show that with best-response dynamics, convergence results from single-agent reinforcement learning extend to Markov potential games (MPGs). Moreover, building on the concepts of price of anarchy and smoothness from normal-form games, we aim to find policies in MPGs that achieve optimal cooperation and provide the first known suboptimality guarantees for policy gradient variants under the best-response dynamics. Empirical results demonstrate that the best-response dynamics significantly improves cooperation across policy gradient variants in classic and more complex games.
URL: https://openreview.net/forum?id=klFSzxt4MC
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Title: Accelerating Optimization and Machine Learning via Decentralization
Abstract: Decentralized optimization enables multiple devices to learn a global machine learning model
while each individual device only has access to its local dataset. By avoiding the need for
training data to leave individual users’ devices, it enhances privacy and scalability compared
to conventional centralized learning where all data have to be aggregated to a central server.
However, decentralized optimization has traditionally been viewed as a necessary compromise, used only when centralized processing is impractical due to communication constraints or data privacy concerns. In this study, we show that decentralization can paradoxically accelerate convergence, outperforming centralized methods in the number of iterations needed to reach optimal solutions.
Through examples in logistic regression and neural network training, we demonstrate that distributing data and computation across multiple agents can lead to faster learning than centralized approaches—even when each iteration is assumed to take the same amount of time, whether performed centrally on the full dataset or decentrally on local subsets. This finding challenges longstanding assumptions and reveals decentralization as a strategic advantage, offering new opportunities for more efficient optimization and machine learning.
URL: https://openreview.net/forum?id=iAk7Lfodr0
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Title: The kernel of graph indices for vector search
Abstract: The most popular graph indices for vector search use principles from computational geometry to build the graph. Hence, their formal graph navigability guarantees are only valid in Euclidean space. In this work, we show that machine learning can be used to build graph indices for vector search in metric and non-metric vector spaces (e.g., for inner product similarity). From this novel perspective, we introduce the Support Vector Graph (SVG), a new type of graph index that leverages kernel methods to establish the graph connectivity and that comes with formal navigability guarantees valid in metric and non-metric vector spaces. In addition, we interpret the most popular graph indices, including HNSW and DiskANN, as particular specializations of SVG and show that new navigable indices can be derived from the principles behind this specialization. Finally, we propose SVG-L0 that incorporates an $\ell_0$ sparsity constraint into the SVG kernel method to build graphs with a bounded out-degree. This yields a principled way of implementing this practical requirement, in contrast to the traditional heuristic of simply truncating the out edges of each node. Additionally, we show that SVG-L0 has a self-tuning property that avoids the heuristic of using a set of candidates to find the out-edges of each node and that keeps its computational complexity in check.
URL: https://openreview.net/forum?id=j38uTbApaW
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Title: The Boundary of Communication in Large Language Models
Abstract: Current practice in prompting, evaluation, and alignment of large language models (LLMs) often takes behavioural similarity to imply similar underlying control. When different prompts lead to similar outputs, they are usually taken to be exerting the same form of control. This assumption is rarely examined at the level where control is instantiated and outputs are generated. Behavioural similarity turns out to be an unreliable guide to how a model is actually being controlled. In our experiments, fixing the prefix leads to consistent structure in final layer representations, even as the generated content changes. Conversely, prompts that produce similar outputs can nevertheless occupy distinct regions of representation space. Using simple centroid-based geometric comparisons, prefix identity can be recovered from final layer representations with accuracy often exceeding 90% across diverse models. In some cases, separation in representation space remains visible even when the model’s outputs look unchanged.
URL: https://openreview.net/forum?id=fTAq6XJiwR
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