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期刊：Transactions on Machine Learning Research · 摘要：Recent advances have significantly improved the training efficiency of diffusion transformers. However, these techniques have largely been studied in isolation, leaving unexplored the potential synergies from combining multiple approaches. We present SR-DiT (Speedrun Diffusion Transformer), a framework that systematically integrates token routing, architectural improvements, and training modifications on top of representation alignment. Our approach achieves FID 3.14 and KDD 0.290 on ImageNet-256 using only a 140M parameter model at 400K iterations without classifier-free guidance---comparabl… · 篇幅：Regular submission (no more than 12 pages of main content) · 代码：https://github.com/SwayStar123/SpeedrunDiT · OpenReview ID：0mYu3uPM3j

期刊：Transactions on Machine Learning Research · 摘要：We introduce Functional Group-Aware Representations for Small Molecules (FARM), a novel foundation model designed to bridge the gap between SMILES (a linear string representation of molecular structures), natural language, and molecular graphs. The key innovation of FARM lies in its functional group (FG) annotation at the atomic level, which enables both FG-enhanced SMILES and FG graphs: SMILES are enriched with FG information to specify which functional group each atom belongs to, while the FG graph captures the molecular backbone by showing how the functional groups are connected. This toke… · 篇幅：Regular submission (no more than 12 pages of main content) · 代码：https://github.com/thaonguyen217/farm_molecular_representation · OpenReview ID：2All12TFlh

期刊：Transactions on Machine Learning Research · 摘要：Determining which data samples were used to train a model, known as Membership Inference Attack (MIA), is a well-studied and important problem with implications on data privacy. SotA methods (which are black-box attacks) rely on training many auxiliary reference models to imitate the behavior of the attacked model. As such, they rely on assumptions which rarely hold in real-world settings: (i) the attacker knows the training hyperparameters; (ii) all available non-training samples come from the same distribution as the training data; and (iii) the fraction of training data in the evaluation s… · 篇幅：Regular submission (no more than 12 pages of main content) · 代码：https://github.com/yuvalgol123/ImpMIA-code · OpenReview ID：34bnVED6EZ

期刊：Transactions on Machine Learning Research · 摘要：Matrix manifolds play a fundamental role in machine learning, underpinning data representations (\textit{e.g.}, linear subspaces and covariance matrices) and optimization procedures. These manifolds follow Riemannian geometry, where intrinsic geometric structure plays an important role in geometric learning algorithms. However, traditional visualization methods based on Euclidean assumptions often fail to respect such non-Euclidean structure, leading to distortions in the resulting embeddings. To address this limitation, building upon the established Riemannian t-SNE paradigm, we develop thre… · 篇幅：Long submission (more than 12 pages of main content) · 代码：https://github.com/paradox-going/ManiReduce · OpenReview ID：4EZeC0JwqM

期刊：Transactions on Machine Learning Research · 摘要：The rapid growth of scientific publishing has strained peer review, particularly in machine learning, raising concerns about declining review quality and increasing reviewer workload. Large language models (LLMs) have been proposed as automated review assistants, yet their evaluation has focused largely on imitating human-written reviews rather than supporting the core functions of peer review. Here, we introduce a verification-centric perspective on LLM-assisted peer review, emphasizing error detection as a critical and resource-intensive task. We present a scalable benchmark that evaluates… · 篇幅：Long submission (more than 12 pages of main content) · OpenReview ID：7iX2Z2bPFB

期刊：Transactions on Machine Learning Research · 摘要：Reinforcement Learning (RL) training for Large Language Models (LLMs) often suffers from instability due to the discrepancy between training and inference. This training-inference discrepancy stems from two primary factors: an architectural separation between training and inference engines, and the use of low-precision quantization in inference versus higher-precision computation in training. To address training instability issues caused by high training-inference discrepancy, we present the principles and methods for its adaptive control. We propose Adaptive Control Reinforcement Learning (A… · 篇幅：Long submission (more than 12 pages of main content) · OpenReview ID：8sBZSgAkAH

期刊：Transactions on Machine Learning Research · 摘要：Fragment-level representations provide a natural way to capture recurring molecular sub- structures and reuse their learned representations across molecules. However, a shared fragment identity alone may not fully describe how a fragment is instantiated in a particu- lar molecule, since the same fragment can exhibit different chemical behavior depending on its surrounding atomic environment. Effective fragment-based molecular learning therefore requires representations that are both reusable across molecules and sensitive to local atomic context. We introduce BiScale-GTR, a self-supervised mo… · 篇幅：Long submission (more than 12 pages of main content) · 代码：https://github.com/AI4Science2025/biscale-gtr-2026-tmlr. · OpenReview ID：97L9IRPWu7

期刊：Transactions on Machine Learning Research · 摘要：For robots to operate reliably in real-world environments, they need to perceive their surroundings, act, and reason about the consequences of those actions. Rapid progress in the domains of representation learning, vision-language-action (VLA) models, and world models has significantly enhanced the capabilities of robot learning systems, enabling robots to work in increasingly complex environments. However, these paradigms are typically developed in isolation, resulting in fragmented systems that struggle with generalization, long-horizon temporal reasoning and planning, and deployment in un… · 篇幅：Long submission (more than 12 pages of main content) · OpenReview ID：AXv7OjfuiT

期刊：Transactions on Machine Learning Research · 摘要：In mission critical domains such as sensor networks, operators often face the critical decision of whether to act on incomplete information or whether collecting missing values is likely to change the prediction. Existing methods typically focus on imputing missing values or quantifying model uncertainty, but they do not directly assess the stability of a prediction if missing values were to be revealed. To address this gap, we first introduce a framework for Missing Value Uncertainty (MVU), which is the distribution of predictions induced by incomplete inputs at inference time. We formalize… · 篇幅：Regular submission (no more than 12 pages of main content) · 代码：https://github.com/inouye-lab/MissingValueUncertainty/releases/tag/tmlr · OpenReview ID：BRWTS5e03Z

期刊：Transactions on Machine Learning Research · 摘要：Graph Transformers (GTs) enable all-to-all interactions, but the optimal connectivity is task-dependent: some problems favor sparse, topology-aligned message passing, while others need global attention. We propose MaskGT, a GT-agnostic module that learns a discrete sparse gate over attention edges. By learning which node pairs may communicate within self-attention, MaskGT injects a task-adaptive relational inductive bias without fully committing to the input adjacency. Across synthetic and real-world benchmarks, MaskGT improves performance and robustness by suppressing spurious interactions u… · 篇幅：Regular submission (no more than 12 pages of main content) · 代码：https://github.com/anitasyang/mask-gt · OpenReview ID：CS4BJcbCGF

期刊：Transactions on Machine Learning Research · 摘要：Membership inference attacks (MIAs) against Diffusion Models (DMs) raise pressing privacy concerns by revealing whether a sample was part of the training set. While existing methods typically rely on measuring reconstruction error across multiple denoising steps as a test statistic, they often incur significant computational overhead. In this work, we present a simple yet successful attack statistic using only the predicted noise vectors from the DM's denoiser, or equivalently, the score. Specifically, we show that the expected denoiser output points toward a kernel-weighted local mean of nea… · 篇幅：Regular submission (no more than 12 pages of main content) · 代码：https://github.com/mx-ethan-rao/SimA · OpenReview ID：Ckvsu5xRmf

期刊：Transactions on Machine Learning Research · 摘要：Visual grounding aims to localize image regions corresponding to natural language expressions. While recent Large Vision-Language Models (LVLMs) have shown impressive multi-modal understanding capabilities, their application to visual grounding typically requires fine-tuning and architectural modifications. This requirement, however, can be ignored, considering that text and images tend to have similar feature representations that appear to be approximately linearly disentangled, enabling cleaner extraction of spatial information from LVLMs without any task-specific training. From this viewpo… · 篇幅：Long submission (more than 12 pages of main content) · OpenReview ID：CnrAiXbw8a

期刊：Transactions on Machine Learning Research · 摘要：Robot foundation models (RFMs), including vision-language-action (VLA) policies, are often discussed through a scaling view: more data, larger models, and broader benchmarks should improve generalization. In robotics, however, a model can generalize while work still remains before it can run on a robot with a particular body. The work required differs across methods and target robots, and those differences affect practical deployment. We call the gap between reusable models, representations, or data and their use in execution on the target robot the embodiment gap. This survey examines what can… · 篇幅：Long submission (more than 12 pages of main content) · OpenReview ID：D0XcH9Cso4

期刊：Transactions on Machine Learning Research · 摘要：We introduce a failure-aware adversarial retrieval-augmented framework for improving robustness in natural language understanding. Rather than selecting synthetic examples with a fixed reward threshold, our method formulates adversarial data curation as a failure-mode contextual bandit problem. Candidate examples are generated with retrieval-augmented prompting, filtered by the current target model, automatically validated by an LLM judge ensemble, and clustered into recurring failure modes. A stochastic policy then selects which failure modes to sample for retraining, and is updated using va… · 篇幅：Regular submission (no more than 12 pages of main content) · OpenReview ID：DSpKdN6whZ

期刊：Transactions on Machine Learning Research · 摘要：Recently reconstruction-based deep models have been widely used for time series anomaly detection, but as their capacity and generalization capability increase, these models tend to over-generalize, often reconstructing unseen anomalies accurately. Prior works have attempted to mitigate this by incorporating a memory architecture that stores prototypes of normal patterns. Nevertheless, these approaches suffer from high training costs and have yet to be effectively integrated with time series foundation models (TSFMs). To address these challenges, we propose MOMEMTO, an improved TSFM variant f… · 篇幅：Regular submission (no more than 12 pages of main content) · OpenReview ID：Fm2ddpR0aw

期刊：Transactions on Machine Learning Research · 摘要：Path-based link prediction methods reconstruct missing links between two vertices of a knowledge graph. They reconstruct a missing link by finding a path through the knowledge graph connecting both vertices. The path is the reasoning of the link prediction method. However, path-based link prediction methods are vulnerable to Clever Hans biases. They learn invalid reasoning patterns if these patterns are dominant and generalize well to the training and validation set. As a result, performance drops when evaluated on the real-world distribution. Whether reasoning is valid, that is, semantically… · 篇幅：Long submission (more than 12 pages of main content) · OpenReview ID：FrMBxE7J0v

期刊：Transactions on Machine Learning Research · 摘要：Despite deep learning models running well-defined mathematical functions, we lack a formal mathematical framework for describing model architectures. Ad-hoc notation, diagrams, and pseudocode poorly handle nonlinear broadcasting and the relationship between individual components and composed models. This paper introduces a categorical framework for deep learning models that formalizes broadcasting through the novel axis-stride and array-broadcasted categories. This allows the mathematical function underlying architectures to be precisely expressed and manipulated in a compositional manner. Th… · 篇幅：Long submission (more than 12 pages of main content) · 代码：https://github.com/mit-zardini-lab/pyncd · OpenReview ID：GiO8eom0jD

期刊：Transactions on Machine Learning Research · 摘要：Deep neural networks often suffer from poor generalization in long-tailed settings. From a loss landscape perspective, this degradation is largely attributed to the tendency of the optimization process to converge into sharp, unstable minima for underrepresented data. We investigate the recently proposed Muon optimizer, providing theoretical evidence that its gradient orthogonalization balances deterministic update strength across positive-curvature modes, thereby reducing the relative dominance of sharp directions. While effective, the Muon optimizer imposes heavy computational overhead in l… · 篇幅：Regular submission (no more than 12 pages of main content) · OpenReview ID：I19SJW09zn

期刊：Transactions on Machine Learning Research · 摘要：WiFi Channel State Information (CSI) has emerged as a privacy-preserving alternative to cameras for human pose estimation. However, existing approaches treat pose inference as an instantaneous regression problem and do not model temporal dynamics, making future motion prediction infeasible. Naively applying vision-based prediction methods compounds the estimation noise already present in CSI-derived poses, as autoregressive rollouts amplify errors at every step. We propose KOALA, the framework for human motion prediction directly from WiFi CSI, by lifting noisy CSI-derived pose sequences into… · 篇幅：Long submission (more than 12 pages of main content) · OpenReview ID：JQA0EfQIfj

期刊：Transactions on Machine Learning Research · 摘要：Large language models (LLMs) excel at program synthesis, yet their ability to produce symbolic graphics programs (SGPs) that render into precise visual content remains underexplored. We study symbolic graphics programming, where the goal is to generate an SGP from a natural-language description. This task also serves as a lens into how LLMs understand the visual world by prompting them to generate images rendered from SGPs. Among various SGPs, we focus on scalable vector graphics (SVGs), as they are widely used and can be easily rendered into images. We begin by examining the extent to which… · 篇幅：Long submission (more than 12 pages of main content) · 代码：https://github.com/Sphere-AI-Lab/SGP-RL · OpenReview ID：JfK8EHJAQn

期刊：Transactions on Machine Learning Research · 摘要：We present the inversion variational autoencoder ($\mathcal{I}$-VAE), a conditional generative model for efficient belief-state planning in partially observable sequential decision-making problems. The $\mathcal{I}$-VAE maps partial observations to stochastic posterior state samples by learning an observation-conditioned latent prior, enabling consistent belief updates without an explicit likelihood model. We further fine-tune the belief model with a trajectory-based mutual information objective to improve latent space consistency across observation sequences. To support scalable planning wit… · 篇幅：Long submission (more than 12 pages of main content) · OpenReview ID：K7MPwAV1AT

期刊：Transactions on Machine Learning Research · 摘要：Behavioral data, reflecting dynamic and complex interactions among entities, are pivotal for advancing multidisciplinary research and practical applications. Effective modeling and representation of behavioral data facilitate enhanced understanding, predictive analytics, and informed decision-making across diverse domains. This paper presents a comprehensive taxonomy of behavioral data representation learning methods, categorized by data modalities: tabular data, event sequences, dynamic graphs, and natural language. Within each category, we further dissect methods based on distinct modeling… · 篇幅：Long submission (more than 12 pages of main content) · 代码：https://github.com/chenxi1228/Awesome-BehavioralData · OpenReview ID：NSdV2qvglw

期刊：Transactions on Machine Learning Research · 摘要：Publicly available clinical machine learning models pose an underappreciated privacy risk: their parameters or outputs can be exploited to recover information from patients whose data were used during training. Moreover, this risk is exacerbated by models such as logistic regression (LR), which are typically preferred in clinical settings for their transparency. To assess this empirically, we attack LORIS, a publicly available LR model for immunotherapy response prediction hosted on a U.S. government website. From evaluations through its public interface, we recover the model parameters and i… · 篇幅：Long submission (more than 12 pages of main content) · 代码：https://github.com/joserapa98/tns4loris · OpenReview ID：QtG3fC1v5t

期刊：Transactions on Machine Learning Research · 摘要：Neural visual decoding is a central problem in brain–computer interface research, aiming to reconstruct human visual perception and to elucidate the structure of neural representations. Recent contrastive neural visual decoding methods commonly align neural signals with the final embeddings of pretrained vision encoders. However, such representations are optimized for high-level semantic invariance, whereas EEG/MEG signals contain information spanning multiple levels of visual abstraction, potentially creating a representational granularity mismatch. Motivated by prior evidence that brain rep… · 篇幅：Regular submission (no more than 12 pages of main content) · OpenReview ID：SzDHkICZpm

期刊：Transactions on Machine Learning Research · 摘要：We propose Semantic F1 Scores, novel evaluation metrics for subjective or fuzzy multi-label classification that quantify semantic relatedness between predicted and gold labels. Unlike the conventional F1 metrics that treat semantically related predictions as complete failures, Semantic F1 incorporates a label similarity matrix to compute soft precision-like and recall-like scores, from which the Semantic F1 scores are derived. Unlike existing similarity-based metrics, our novel two-step precision-recall formulation enables the comparison of label sets of arbitrary sizes without discarding lab… · 篇幅：Regular submission (no more than 12 pages of main content) · 代码：https://github.com/gchochla/semantic-f1-score · OpenReview ID：U0YJpGuFEc

期刊：Transactions on Machine Learning Research · 摘要：Most vision models operate on 8-bit standard RGB (sRGB) images produced by dedicated image sensor processing pipelines designed for human perception rather than machine reasoning. In contrast, RAW images preserve sensor measurements, dynamic range, and fine-grained scene structure that can be critical for downstream understanding. Yet, progress in the RAW-domain vision remains limited by the lack of large-scale, high-quality benchmarks. To close this gap, we introduce RawDet-7, a multi-scenario benchmark for object detection and object description on quantized RAW images, comprising ~25k trai… · 篇幅：Regular submission (no more than 12 pages of main content) · OpenReview ID：UHTJrsYieo

期刊：Transactions on Machine Learning Research · 摘要：Model-based reinforcement learning (MBRL) learns world models—internal simulators of environment dynamics—to plan by imagining future trajectories. However, when these models incorrectly predict state transitions, they generate unrealistic states that mislead agents into learning delusional policies. Inspired by human vision, we propose anomaly detection in world model with \textbf{L}inear \textbf{P}rior (LP), a three‐stage approach that 1) enforces a lightweight linear prior on successive latent states, 2) flags generated states that deviate from this prior, and 3) removes their contribution… · 篇幅：Regular submission (no more than 12 pages of main content) · 代码：https://github.com/zarifikram/lp-dreamer · OpenReview ID：VLIzLK3CfR

期刊：Transactions on Machine Learning Research · 摘要：Text-to-image models have transformed visual content creation, but their reliance on large uncurated web data can encode and amplify societal biases. We present \emph{FairT2I}, a training-free, inference-time framework that leverages large language models to detect implicit bias dimensions in prompts and mitigate them during generation. FairT2I has three components. First, LLM-based bias detection identifies bias-relevant attributes implied by the prompt and makes them explicit for control. Second, attribute resampling generates bias-aware prompts by sampling these attributes from a user-spec… · 篇幅：Regular submission (no more than 12 pages of main content) · 代码：https://github.com/jinya1013/fairt2i · OpenReview ID：WHYE1WjMYf

期刊：Transactions on Machine Learning Research · 摘要：We introduce \texttt{OTIS}, an \textbf{o}pen \textbf{ti}me \textbf{s}eries encoder that yields high-quality time series features for downstream deployment on \emph{any} system, including resource-constrained wearables and industrial sensors. Currently, the development of powerful general-purpose encoders relies on the scaling laws hypothesis, using large encoder sizes to memorise the heterogeneous distributions of multi-domain training data. However, this reliance on scale creates a barrier to real-world utility, rendering deployment on resource-constrained systems infeasible due to strict me… · 篇幅：Regular submission (no more than 12 pages of main content) · 代码：https://github.com/oetu/otis · OpenReview ID：WW206A1Tru

期刊：Transactions on Machine Learning Research · 摘要：Physics-informed neural networks (PINNs) have earned high expectations in solving partial differential equations (PDEs), but their optimization usually faces thorny challenges due to the unique derivative-dependent loss function. By analyzing the loss distribution, previous research observed the propagation failure phenomenon of PINNs, intuitively described as the correct supervision for model outputs cannot ``propagate'' from initial states or boundaries to the interior domain. Going beyond intuitive understanding, this paper provides a formal and in-depth study of propagation failure and it… · 篇幅：Regular submission (no more than 12 pages of main content) · OpenReview ID：Wy54lrFd46

期刊：Transactions on Machine Learning Research · 摘要：Medical image segmentation models based on deep learning require dense voxel-level annotations, which are costly to obtain for 3D medical imaging tasks. To address this limitation, we propose MINDFeed (Mutual Information per Decoder as Feedback), a semi-supervised training pipeline for 3D medical image segmentation. MINDFeed estimates predictive uncertainty via mutual information across stochastic forward passes and uses this signal to adaptively modulate decoder representations as a feedback gate, encouraging consistency in reliable regions while suppressing ambiguous responses. Unlike many… · 篇幅：Long submission (more than 12 pages of main content) · 代码：https://github.com/AdiPadi2703/MINDFeed · OpenReview ID：YI7AX6mrZO

期刊：Transactions on Machine Learning Research · 摘要：Can pretrained models generalize to new datasets without any retraining? We deploy pretrained image models on datasets they were not trained for, and investigate whether their embeddings form meaningful clusters. Our suite of benchmarking experiments uses encoders pretrained solely on ImageNet-1k with either supervised or self-supervised training techniques, deployed on image datasets that were not seen during training, and clustered with conventional clustering algorithms. This evaluation provides new insights into the embeddings of self-supervised models, which prioritize different features… · 篇幅：Regular submission (no more than 12 pages of main content) · 代码：https://github.com/scottclowe/zs-ssl-clustering/ · OpenReview ID：gdwg7ntmT5

期刊：Transactions on Machine Learning Research · 摘要：We consider a setting where agents take action by following their role models in a social network, and study strategies for a social planner to help agents by revealing whether the role models are positive or negative. Specifically, agents observe a local neighborhood of possible role models they can emulate, but do not know their true labels. Revealing a positive label encourages emulation, while revealing a negative one redirects agents toward alternative options. The social planner observes all labels, but operates under a limited disclosure budget that it selectively allocates to maximize… · 篇幅：Regular submission (no more than 12 pages of main content) · 代码：https://github.com/knaggita/InformationDisclosure · OpenReview ID：jdcXfoENf0

期刊：Transactions on Machine Learning Research · 摘要：Joint Embedding Predictive Architectures (JEPAs) learn representations by predicting latent target embeddings from contextual views, but their predictors are typically shallow feed‑forward networks with limited control over multi‑step dynamics and stability. We introduce \emph{Learnable Iterated Function Systems} (LIFS), a recursive and contractive latent operator that replaces the standard JEPA predictor with a mixture of affine maps applied over multiple refinement steps. The mixture weights are conditioned on the context embedding, enabling input‑adaptive geometric structure while preservi… · 篇幅：Long submission (more than 12 pages of main content) · OpenReview ID：k2Z2gPOtlq

期刊：Transactions on Machine Learning Research · 摘要：CLIP-style image encoders are trained to be discriminative for every category set a user might supply, since the category set is unknown at training time. This makes the encoder's job harder than the job any single deployment actually requires, and is part of why small image encoders underperform large ones on zero-shot classification. In CLIP, the class prompts available at inference are used only to define the classifier head; we argue they carry more task structure than this role exposes, enough to also modulate the image encoder's feature extraction through a small channel (BatchNorm scal… · 篇幅：Regular submission (no more than 12 pages of main content) · 代码：https://github.com/locuslab/HyperCLIP · OpenReview ID：kX7iwF5s3v

期刊：Transactions on Machine Learning Research · 摘要：We formalize online learning-unlearning (OLU) in the Online Convex Optimization (OCO) setting, where a learner updates a model sequentially on a stream of convex losses while accommodating occasional unlearning requests between updates. We require that after a deletion, the distribution of all future outputs is statistically indistinguishable from that of a learner trained on the same stream without the deleted data. We propose two OLU algorithms based on Online Gradient Descent (OGD). Passive OLU leverages the contractive dynamics of OGD and injects calibrated noise, incurring no additional… · 篇幅：Regular submission (no more than 12 pages of main content) · OpenReview ID：lyvCVfTdiY

期刊：Transactions on Machine Learning Research · 摘要：We present a novel view of diffusion-like generative modeling from the perspective of iterative Gaussian posterior inference. By treating the generated sample as an unknown variable, we formulate the sampling process in the language of Bayesian probability: at each step, a model predicts the unknown sample from our current belief state and we compute a posterior belief from that prediction. Based on this formulation, we propose the generative model Bayesian Sample Inference (BSI). In addition to a rigorous theoretical analysis, we show that our perspective includes Bayesian Flow Networks (BFN… · 篇幅：Regular submission (no more than 12 pages of main content) · 代码：https://github.com/martenlienen/bsi · OpenReview ID：n8qQPnalVW

期刊：Transactions on Machine Learning Research · 摘要：We study the geometry of token representations at the prompt level in large language models through the lens of intrinsic dimension. Viewing transformers as mean-field particle systems, we estimate the intrinsic dimension of the empirical measure at each layer and demonstrate that it correlates with next-token uncertainty. Across models and intrinsic dimension estimators, we find that intrinsic dimension peaks in early to middle layers and increases under syntactic and semantic disruption (by shuffling tokens), and that it is strongly correlated with average surprisal, with a simple analysis… · 篇幅：Regular submission (no more than 12 pages of main content) · OpenReview ID：rBEgNAslpY

期刊：Transactions on Machine Learning Research · 摘要：We introduce a probabilistic neural operator framework for learning conditional distributions over functions from sample observations. The proposed model, the Variational Set Operator Network (VSON), extends set-based operator learners by incorporating an amortised latent representation of the branch outputs that induces predictive distributions over function values conditioned on arbitrary sets of input–output pairs. Uncertainty is represented through a learned variational latent structure implemented with expressive normalising flows, allowing the model to capture non-Gaussian behaviour. Th… · 篇幅：Regular submission (no more than 12 pages of main content) · OpenReview ID：rLgrHnZtzh

期刊：Transactions on Machine Learning Research · 摘要：This paper presents \textbf{FairNVT}, a lightweight debiasing framework for pretrained transformer-based encoders that improves prediction fairness while preserving task performance. FairNVT is motivated by the intuition that reducing sensitive-attribute information in the representation used by the downstream classifier can facilitate fairer predictions. Our approach learns task-relevant and sensitive embeddings via lightweight adapters, applies calibrated Gaussian noise to the sensitive embedding, and fuses it with the task representation. Together with orthogonality constraints and fairnes… · 篇幅：Regular submission (no more than 12 pages of main content) · OpenReview ID：rzm6gZrYgl

期刊：Transactions on Machine Learning Research · 摘要：The dominant paradigm in machine intelligence typically defines learning as the minimization of an empirical loss function. While highly successful, this approach can result in systems prone to catastrophic forgetting and reward hacking, contrasting with the homeostatic stability observed in biological organisms. We propose Constraint-First Machine Learning (CFML), a framework that reformulates learning as the maintenance of feasibility under an expanding set of structural constraints rather than the pursuit of a global optimum. Utilizing Viability Projection Updates (VPU) based on reflected… · 篇幅：Long submission (more than 12 pages of main content) · OpenReview ID：sgvPGWZ1uv

期刊：Transactions on Machine Learning Research · 摘要：Rule-based models, such as decision trees, appeal to practitioners due to their interpretable nature. However, the learning algorithms that produce such models are often vulnerable to spurious associations, and thus, they are not guaranteed to extract causally relevant insights. This limitation reduces their utility in gaining mechanistic insights into a phenomenon of interest. In this work, we build on ideas from the invariant causal prediction literature to propose Invariant Causal Set Covering Machines, an extension of the classical Set Covering Machine (SCM) algorithm for conjunctions/dis… · 篇幅：Regular submission (no more than 12 pages of main content) · 代码：https://github.com/thibgo/icscm-expe-real-data · OpenReview ID：slquR2A8rA

期刊：Transactions on Machine Learning Research · 摘要：We present a comparative study of label-free metrics for assessing the quality of representations in deep neural networks to understand their reliability under a wide variety of configurations. We group existing label-free metrics into three families based on their construction and analytically establish connections between metrics within the same family. We then characterise the sensitivity of spectral metrics through controlled synthetic experiments. Finally, all label-free metrics are evaluated against downstream task accuracy across a diverse set of 260 vision models on six datasets spann… · 篇幅：Long submission (more than 12 pages of main content) · 代码：https://github.com/dannyrichy/representation-quality-metrics · OpenReview ID：yknkAksqr1

期刊：Transactions on Machine Learning Research · 摘要：Matching for causal inference can be computationally expensive at scale and can silently change the target population when overlap is limited. We propose Centroid-Referenced Mahalanobis Matching (CRM), which replaces global pairwise search with stratified sampling in two reference coordinates: each unit's Mahalanobis distance from the treated centroid and its Fisher coordinate along the treated-control mean shift. All covariates enter through the treated covariance geometry; CRM is therefore not principal-component preprocessing followed by nearest-neighbor matching. For $n$ units and $p$ pre… · 篇幅：Long submission (more than 12 pages of main content) · 代码：https://github.com/KemingHu-D/crm-matching · OpenReview ID：z74epfCe3A

期刊：Transactions on Machine Learning Research · 摘要：In a celebrated paper, Noé, Olsson, K\"ohler and Wu (\cite{noe2019boltzmann}) introduced an efficient method for sampling high-dimensional Boltzmann distributions arising in molecular dynamics via normalizing flow approximation of transport maps. Here, we place this approach on a firm mathematical foundation. We prove the existence of a normalizing flow between the reference measure and the true Boltzmann distribution up to an arbitrarily small error in the Wasserstein distance. This result covers general Boltzmann distributions from molecular dynamics, which have low regularity due to the pr… · 篇幅：Regular submission (no more than 12 pages of main content) · 代码：https://github.com/zia7735/boltzmann-sampling-tmlr2026.git · OpenReview ID：4HLZD6LMuJ

期刊：Transactions on Machine Learning Research · 摘要：We present a new meta-learning method to determine the optimal learning rate schedule for gradient descent. It leverages training runs from a hyperparameter search to learn a latent representation of the training process, which is modeled as a dynamical systems. Given current training metrics, it predicts the future learning rate schedule with the best long-term validation performance. Our scheduler generalizes beyond previously observed training dynamics and creates specialized schedules that deviate noticeably from even the best-performing parametric functions. It outperforms all baselines… · 篇幅：Regular submission (no more than 12 pages of main content) · OpenReview ID：SwmzJgB9TA

期刊：Transactions on Machine Learning Research · 摘要：Efficient deployment of large language models (LLMs) requires balancing inference speed with output quality. Speculative decoding accelerates inference by using a smaller draft model to propose future tokens, whereas reasoning-heavy approaches—such as chain-of-thought prompting, ensembles, and dynamic routing—improve output quality through deep search and verification. Although historically treated as isolated research trajectories, the demands of complex, high-difficulty tasks have forced these domains to converge. This paper presents a structured taxonomy and analysis focused specifically o… · 篇幅：Long submission (more than 12 pages of main content) · OpenReview ID：OIrJI53MvN

期刊：Transactions on Machine Learning Research · 摘要：Recent data protection laws have accelerated the adoption of Federated Learning (FL) for privacy-preserving decentralized training. Nevertheless, increasing model sizes impose substantial computational demands on client devices, limiting FL applicability in resource-constrained settings. We introduce a novel multi-domain federated learning framework in which lightweight client-side proxy models collaborate with a server-side Foundation Model (FM) to learn new concepts without sharing private data. Our approach, EFFEKT, enables efficient server-side training of domain-specific LoRA adapters wh… · 篇幅：Regular submission (no more than 12 pages of main content) · 代码：https://github.com/LTTM/EFFEKT · OpenReview ID：jpUDUJfE1K

期刊：Transactions on Machine Learning Research · 摘要：In this paper, In this paper, we introduce causal responder detection (CARD), a method for distributionalwe introduce causal responder detection (CARD), a method for distributional responder analysis that identifies treated subjects whose outcomes significantly depart from the control response distribution while controlling the false discovery rate (FDR) marginally over the tested treated population. CARD builds on the AdaDetect framework and, in randomized settings, inherits finite-sample FDR control under the exchangeability conditions required by AdaDetect when coupled with the Benjamini–H… · 篇幅：Regular submission (no more than 12 pages of main content) · OpenReview ID：zPt0o32mYn

期刊：Transactions on Machine Learning Research · 摘要：Foundation models have achieved remarkable success across many domains, relying on pretraining over vast amounts of data. Graph-structured data often lacks the same scale as unstructured data, making the development of graph foundation models challenging. In this work, we propose Foundation-Informed Message Passing (FIMP), a message-passing framework that repurposes existing pretrained non-textual foundation models for graph-based tasks in multiple capacities, including tokenization, representation extraction, and weight initialization. We show that the self-attention layers of foundation mod… · 篇幅：Regular submission (no more than 12 pages of main content) · OpenReview ID：fj7sjOwtXc