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期刊：Transactions on Machine Learning Research · 摘要：The emergence and evolution of feature reliance in deep reinforcement learning agents remain poorly understood. Here, we introduce a methodological framework for analyzing the learning process through quantitative analysis of saliency maps. This approach aggregates saliency information at the object and modality level into hierarchical attention profiles, quantifying how agents allocate attention over time, thereby forming attention trajectories throughout training. These profiles are then compared across controlled conditions, connected to behavioral measurements and reproduced with differen… · 篇幅：Regular submission (no more than 12 pages of main content) · OpenReview ID：0aa9zthk7k

期刊：Transactions on Machine Learning Research · 摘要：Change point detection (CPD) identifies abrupt and significant changes in sequential data, with applications in human activity recognition, financial markets, cybersecurity, manufacturing, and autonomous systems. Traditional CPD methods often face computational challenges in high-dimensional settings and typically provide limited explanations for detected changes, which can restrict their practical usability. This paper introduces a CPD framework that improves scalability and interpretability by leveraging the Sliced Wasserstein (SW) distance. Our contributions are fourfold: (1) we transform… · 篇幅：Regular submission (no more than 12 pages of main content) · 代码：https://github.com/jsve96/SWCPD_Code · OpenReview ID：4ewaiYXoiv

期刊：Transactions on Machine Learning Research · 摘要：Most existing approaches to referring segmentation achieve strong performance only through fine-tuning or by composing multiple pre-trained models, often at the cost of additional training and architectural modifications. Meanwhile, large-scale generative diffusion models encode rich semantic information, making them attractive as general-purpose feature extractors. In this work, we introduce a new method that directly exploits features—attention scores—from diffusion transformers for downstream tasks, requiring neither architectural modifications nor additional training. To systematically ev… · 篇幅：Regular submission (no more than 12 pages of main content) · 代码：https://github.com/Annusha/refam · OpenReview ID：5DTaxhAP4h

期刊：Transactions on Machine Learning Research · 摘要：LLMs that appear safety-aligned in English can fail under code-mixed prompts, where two languages are blended within a single utterance. Across ten languages and twelve systems, semantically equivalent code-mixed prompts consistently elicit more harmful responses than their English counterparts, with attack success rates rising by up to +58 percentage points in smaller open-weight models and by +8 to +12 points even in frontier API models; the effect replicates on naturally occurring code-mixed social-media data across four language pairs. To explain these failures, we introduce Saliency Drif… · 篇幅：Regular submission (no more than 12 pages of main content) · OpenReview ID：5q6S8E7Brx

期刊：Transactions on Machine Learning Research · 摘要：Decentralized training of large language models offers the opportunity to leverage computational resources across geographically distributed participants, but is often bottlenecked by network communication, particularly in pipeline-parallel settings. While pipeline parallelism partitions model layers across devices to handle large-scale models, it requires frequent communication of intermediate activations, which can be challenging when network bandwidth is limited. To address these issues, we propose TAH-Quant (Tile-wise Adaptive Hadamard Quantization), a novel activation quantization framew… · 篇幅：Regular submission (no more than 12 pages of main content) · OpenReview ID：6ysPGq2RVD

期刊：Transactions on Machine Learning Research · 摘要：Knowledge tracing (KT) aims to estimate a student's evolving knowledge state and predict their performance on new exercises based on performance history. Many realistic classroom settings for KT are typically low-resource in data and require online updates as students' exercise history grows, which creates significant challenges for existing KT approaches. To restore strong performance under low-resource conditions, we revisit the hierarchical knowledge concept (KC) information, which is typically available in many classroom settings and can provide strong prior when data are sparse. We there… · 篇幅：Regular submission (no more than 12 pages of main content) · 代码：https://github.com/UCSB-NLP-Chang/KT2 · OpenReview ID：9kfruXm7e9

期刊：Transactions on Machine Learning Research · 摘要：Open reasoning language models are often compared using mixed sample sizes, partially standardized prompts, and accuracy-centered summaries, making practical model selection difficult. We present a unified evaluation of seven open reasoning language model configurations across ARC-Challenge, GSM8K, MATH levels 1--3, and TruthfulQA MC1 under zero-shot, chain-of-thought (CoT), and few-shot CoT prompting. The study uses a complete matched-core matrix with 238 examples per model--dataset--strategy condition and a larger-sample robustness matrix with 500 examples for ARC-Challenge, GSM8K, and Trut… · 篇幅：Long submission (more than 12 pages of main content) · 代码：https://github.com/mkboch/UDAE · OpenReview ID：FBmgI8UMt8

期刊：Transactions on Machine Learning Research · 摘要：Early Classification of Time Series (ECTS) is vital in fields like industrial monitoring and medical triage, where quick and accurate predictions are essential. One of the core challenges lies in the trigger function, which decides when to make a prediction, independently of the classifier itself. Most existing methods rely on handcrafted rules, but can data-driven approaches outperform them? This paper introduces \textsc{Alert}, a Deep-RL framework that learns trigger functions from any state representation. Systematic comparisons on 30 datasets show that the design of the state space signif… · 篇幅：Regular submission (no more than 12 pages of main content) · OpenReview ID：J7EIFs4sjx

期刊：Transactions on Machine Learning Research · 摘要：Inverse design problems are common in engineering and materials science. The forward direction, i.e., computing output quantities from design parameters, typically requires running a numerical simulation, such as a FEM, as an intermediate step, which is an optimization problem by itself. In many scenarios, several design parameters can lead to the same or similar output values. For such cases, multi-modal probabilistic approaches are advantageous to obtain diverse solutions. A major difficulty in inverse design stems from the structure of the design space, since discrete parameters or further… · 篇幅：Regular submission (no more than 12 pages of main content) · 代码：https://github.com/EmbodiedVision/diffoptloss-invmatdesign · OpenReview ID：JVUk7fT0Ll

期刊：Transactions on Machine Learning Research · 摘要：Edge-attributed multigraphs, in which multiple edges with distinct attributes connect the same pair of nodes, arise naturally in many real-world systems. In these graphs, effective learning requires preserving information from repeated interactions while distinguishing contributions from different neighbors. Existing neural network solutions for edge-attributed multigraphs remain limited: some lose information from repeated interactions, while others break permutation equivariance. To address this, we introduce \emph{neighbor-aware aggregation}, an operator that first combines multi-edge feat… · 篇幅：Regular submission (no more than 12 pages of main content) · 代码：https://github.com/hcagri/MEGA-GNN-TMLR · OpenReview ID：Jo4KBvnMIP

期刊：Transactions on Machine Learning Research · 摘要：Over-parameterized models are often vulnerable to membership inference attacks, which aim to determine whether a specific sample is included in the training set of a target. Previous weight regularization approaches typically impose uniform penalties on all parameters, leading to a suboptimal trade-off between model utility and privacy. In this work, we first show that only a small fraction of the parameters substantially impact privacy risk. Motivated by this analysis, we propose Privacy-aware Sparsity Tuning ($\textbf{PAST}$)—a novel privacy-preserving training method—by employing adaptive… · 篇幅：Regular submission (no more than 12 pages of main content) · OpenReview ID：KlqlcnwqHw

期刊：Transactions on Machine Learning Research · 摘要：Modern large language model (LLM) systems increasingly rely on multi-turn pipelines that are composed of multiple task-specific adapters, yet existing serving frameworks remain inefficient, incurring substantial recomputation overhead when switching between adapters. We present the first LLM serving engine that supports cross-model prefix cache reuse between base and adapted models via Activated LoRA (aLoRA), enabling efficient and fine-grained adapter switching during inference. Our design extends the vLLM framework by introducing base-aligned block hashing and activation-aware masking withi… · 篇幅：Long submission (more than 12 pages of main content) · 代码：https://github.com/tdoublep/vllm/tree/alora · OpenReview ID：Q8nCBmOkyn

期刊：Transactions on Machine Learning Research · 摘要：Video Anomaly Detection (VAD) aims to temporally localize abnormal events in videos. Most existing approaches rely on dataset-specific training and curated annotations, limiting generalization in open-set scenarios. Recent zero-shot methods based on Large Vision-Language Models (LVLMs) alleviate this dependency but often lack temporal continuity and structured reasoning. We propose \textbf{Cog-VADU}, a fully training-free framework that reformulates VAD as a sequential cognitive reasoning task. Cog-VADU introduces \emph{Chain-of-Anomaly Detection Thought Prompting} (CoADTP), which unrolls an… · 篇幅：Long submission (more than 12 pages of main content) · 代码：https://github.com/MohdUbaidwani/Cog-VADU · OpenReview ID：QcuSMNG7J8

期刊：Transactions on Machine Learning Research · 摘要：Genomic studies face a vast hypothesis space, while interventions such as gene perturbations remain costly and time-consuming. To accelerate such experiments, gene perturbation models predict the transcriptional outcome of interventions. Since constructing the training set is challenging, active learning is often employed in a “lab-in-the-loop” process. While this strategy makes training more targeted, it is substantially slower, as it fails to exploit the inherent parallelizability of Perturb-seq experiments. Here, we focus on graph neural network–based gene perturbation models and propose a… · 篇幅：Regular submission (no more than 12 pages of main content) · 代码：https://github.com/geopanag/graph-based-subset-selection · OpenReview ID：S5YDiO3Oox

期刊：Transactions on Machine Learning Research · 摘要：Membership Inference Attacks (MIAs) pose a critical privacy threat by enabling adversaries to determine whether a specific sample was included in a model's training dataset. Despite extensive research on MIAs, systematic comparisons between generative and discriminative classifiers remain limited. This work addresses this gap by first providing theoretical motivation for why generative classifiers exhibit heightened susceptibility to MIAs, then validating these insights through comprehensive empirical evaluation. Our study encompasses discriminative, generative, and pseudo-generative text cla… · 篇幅：Regular submission (no more than 12 pages of main content) · 代码：https://github.com/karan25gupta/privacy-attacks-gendisc-classifiers · OpenReview ID：SHMC01wdVM

期刊：Transactions on Machine Learning Research · 摘要：The growing application of Large Language Models (LLMs) to social contexts has led to an increase in unjustifiable social group attributions through their own stereotype-based responses; especially when responding to questions where there is little evidence to support a response or ambiguity to context. The lack of sufficient evidence often leads models to hallucinate socially grounded inferences, undermining fairness and trust. In this work, we attempt to mitigate social bias under ambiguity via epistemic uncertainty. We further introduce BHARATBBQ-R, a rationale-augmented extension of BHARA… · 篇幅：Regular submission (no more than 12 pages of main content) · OpenReview ID：UT5E31pYob

期刊：Transactions on Machine Learning Research · 摘要：Circuit discovery methods identify subgraphs that explain specific model behaviors, and structural differences between discovered circuits are commonly interpreted as evidence of distinct mechanisms. We test this assumption by drawing input tokens from bands defined by their frequency in the pretraining data while holding the task fixed. The discovered circuits appear specialized by frequency when compared structurally, but functional and representational analyses show no reliable evidence of corresponding differences in their computations. We term this mismatch phantom specialization. Using… · 篇幅：Long submission (more than 12 pages of main content) · OpenReview ID：Ur42Xx1QPv

期刊：Transactions on Machine Learning Research · 摘要：Tree-structured data in natural language syntax, program analysis, and other symbolic domains are typically discrete, rooted, and ordered combinatorial objects. Despite their ubiquity, reusable and learnable representations for comparing such discrete structural trees remain limited. Classical methods such as tree edit distance (TED) and tree kernels provide principled structural measures but require pairwise computation, while previous neural encoders often produce latent representations without defining a consistent or interpretable space. We introduce a framework for learning embeddings fo… · 篇幅：Long submission (more than 12 pages of main content) · OpenReview ID：W1bzDTSwzA

期刊：Transactions on Machine Learning Research · 摘要：Approximate Machine Unlearning (MU) methods typically forget specific data by modifying model parameters or learning data-dependent augmentations. However, each unlearning request requires a dedicated optimization process, resulting in high computational overhead and cumulative degradation of model performance over time. Recent approaches have proposed manipulating a small subset of neural activations as a more targeted alternative, yet these methods still rely on on-demand searches for relevant parameters and remain computationally expensive. We propose Minimally Invasive Machine Unlearning… · 篇幅：Regular submission (no more than 12 pages of main content) · 代码：https://github.com/layer6ai-labs/MIMU · OpenReview ID：bVc04v8dV7

期刊：Transactions on Machine Learning Research · 摘要：Model-based evaluation systems increasingly use panels of inexpensive LLM judges to make accept-or-escalate decisions. In factuality settings, accepting a claim because several reference-free judges agree can create a hidden risk: agreement may arise from shared false-negative blind spots rather than independent evidence. We introduce JuryProbe, an empirical consensus-risk diagnostic for reference-free factuality judge panels, paired with a calibration-based routing policy. JuryProbe estimates panel-level consensus risk from a labeled calibration probe using false-negative-only (FN-only) judg… · 篇幅：Regular submission (no more than 12 pages of main content) · OpenReview ID：dgBczhxcZY

期刊：Transactions on Machine Learning Research · 摘要：Quantum process tomography reconstructs an unknown quantum channel from finite measurement counts. For sample-based uncertainty reporting, the sampled candidate channels should also remain physically valid, meaning completely positive and trace preserving (CPTP). We introduce a matrix-free, CPTP-by-construction reconstruction-and-sampling framework evaluated in a controlled full-basis simulator setting. Its primary contribution combines indexed residual evaluation, which avoids materializing the dense Choi design matrix, with normalized Kraus factors that construct physical candidate channels… · 篇幅：Regular submission (no more than 12 pages of main content) · 代码：https://github.com/DDDIGHE/matrix-free-cptp-qpt · OpenReview ID：f2LcKcJL2m

期刊：Transactions on Machine Learning Research · 摘要：This paper presents a comprehensive review of industrial anomaly synthesis (IAS). Existing surveys on industrial anomalies mainly focus on anomaly detection, while IAS is typically treated as an auxiliary component rather than as an independent topic. However, owing to its increasing importance in data augmentation, downstream model training, and controllable industrial inspection, IAS has become a research direction of growing interest. To address the lack of a dedicated review, we survey a broad range of representative methods and organize them into four paradigms: hand-crafted synthesis, d… · 篇幅：Long submission (more than 12 pages of main content) · OpenReview ID：f9qjl5xCVW

期刊：Transactions on Machine Learning Research · 摘要：Real-world decision-making often requires optimizing multiple competing objectives simultaneously. In reinforcement learning (RL), this is typically addressed by combining reward signals into a single scalar objective via a scalarization function, which can be fragile: small changes in the weights can induce drastically different policies. Multi-objective reinforcement learning (MORL) instead produces sets of policies that explicitly represent trade-offs between objectives. However, these policies are typically presented to the decision maker only through their value vectors, which can obscur… · 篇幅：Regular submission (no more than 12 pages of main content) · 代码：https://github.com/ffelten/Behavior-vs-Objective-Space · OpenReview ID：hfnMLNCCYz

期刊：Transactions on Machine Learning Research · 摘要：Human organs are composed of interconnected substructures whose geometry and spatial relationships constrain one another. Yet, most deep-learning approaches treat these parts independently, producing anatomically implausible reconstructions. We introduce PrIntMesh, a template-based, topology-preserving framework that reconstructs organs as unified systems. Starting from a connected template, PrIntMesh jointly deforms all substructures to match patient-specific anatomy, while explicitly preserving internal boundaries and enforcing smooth, artifact-free surfaces. We demonstrate its effectivenes… · 篇幅：Regular submission (no more than 12 pages of main content) · 代码：https://github.com/cvlab-epfl/printmesh · OpenReview ID：httuGMEO9Q

期刊：Transactions on Machine Learning Research · 摘要：Differential privacy (DP) is the gold standard for privacy-preserving analysis but introduces a fundamental trade-off between privacy guarantees and model performance. Selecting the optimal balance is a critical challenge, framed as a multi-objective optimization (MOO) problem of discovering the Pareto front and eliciting a decision-maker's preference. While interactive MOO offers a solution, standard approaches---which model objectives separately and rely on simple pairwise feedback---are suboptimal for DP because they do not utilize the problem structure. In this work, we propose a method,… · 篇幅：Long submission (more than 12 pages of main content) · 代码：https://github.com/YangYaohong1/PACE · OpenReview ID：ire2TQNxfv

期刊：Transactions on Machine Learning Research · 摘要：LLM-as-a-judge is a large language model (LLM) that evaluates the output of another LLM. While LLMs excel at qualitative evaluation, they often struggle to predict human preferences and numeric scores. We propose quantitative LLM judges, which align LLM judges in a given domain using post-hoc calibration. Specifically, we train regression models to improve the score of the original judge using its rationale and score. We present four quantitative judges for different types of absolute and relative feedback, which shows the generality and versatility of our framework. Our framework does not re… · 篇幅：Regular submission (no more than 12 pages of main content) · OpenReview ID：isUKzVxmOB

期刊：Transactions on Machine Learning Research · 摘要：Multi-task learning (MTL) concurrently trains a model on diverse task datasets to exploit common features, thereby improving overall performance across the tasks. Recent studies have dedicated efforts to merging multiple independent model parameters into a unified model for MTL, thus circumventing the need for training data and expanding the scope of applicable scenarios of MTL. However, current approaches to model merging predominantly concentrate on enhancing performance within in-domain (ID) datasets, often overlooking their efficacy on out-of-domain (OOD) datasets. In this work, we propos… · 篇幅：Regular submission (no more than 12 pages of main content) · OpenReview ID：msiXRyQgxO

期刊：Transactions on Machine Learning Research · 摘要：Predictive systems may combine a predictor, a sequential monitor, a prediction set, and an online strategy, each governed by a different optimality criterion. We study when a guarantee for one component can be transferred to another. Four admissibility geometries shape sequential and distribution-free inference: Blackwell risk dominance over convex risk sets; anytime-valid admissibility within the $e$-process class; fixed-level marginal coverage with expected-length efficiency within a declared rank-indexed family under exchangeability; and choice-based approachability (CApp) boundary-feasibi… · 篇幅：Long submission (more than 12 pages of main content) · OpenReview ID：nZVEXyfvyM

期刊：Transactions on Machine Learning Research · 摘要：Feature importance measures are widely studied and are essential for understanding model behavior, guiding feature selection, and enhancing interpretability. However, many machine learning fitted models involve complex interactions between features. Existing feature importance metrics fail to capture these pairwise or higher-order effects, while existing interaction metrics often suffer from limited applicability or excessive computation; no methods exist to conduct statistical inference for feature interactions. To bridge this gap, we first propose a new model-agnostic metric, interaction Le… · 篇幅：Regular submission (no more than 12 pages of main content) · OpenReview ID：pUGr1uA99f

期刊：Transactions on Machine Learning Research · 摘要：Dense video captioning presents the dual challenge of temporally localizing events and generating descriptive captions within long videos. However, existing methods often struggle to handle evolving contexts in streaming settings or depend on static, global retrieval mechanisms. To address these limitations, we introduce a novel framework that embeds a dynamic, factorized retrieval mechanism directly into a causally-aware video processing backbone. Unlike approaches utilizing static global retrieval, our method dynamically retrieves concise action and object phrases at each timestep as the vi… · 篇幅：Regular submission (no more than 12 pages of main content) · OpenReview ID：rL7ns0ngAQ

期刊：Transactions on Machine Learning Research · 摘要：Pretraining large language models (LLMs) is computationally intensive, typically requiring massive datasets and training iterations. Although recent advances in data selection have shown improvement in training efficiency, their gains often diminish under scaling laws. In this work, we dive into the impact of sequence length on language model pretraining and propose a length-based online data scheduling method for acceleration. Specifically, we design a dense-balanced sequence scheduling framework for LLM pretraining: 1) at the first stage, the model is exposed to uniform-length dense token b… · 篇幅：Regular submission (no more than 12 pages of main content) · 代码：https://github.com/warriors-30/DeBLAS · OpenReview ID：s0MTAeYvww

期刊：Transactions on Machine Learning Research · 摘要：The integration of Large Language Model (LLMs) blocks with Vision Transformers (ViTs) holds significant promise for vision-only tasks by leveraging the rich semantic knowledge and reasoning capabilities of LLMs. However, a fundamental challenge lies in the inherent modality mismatch between the text-centric pre-training of LLMs and the vision-centric training of ViTs. Direct fusion often fails to fully exploit the LLM's potential and suffers from unstable finetuning. Consequently, prior works typically keep LLM blocks frozen while learning only the vision components. To address these challeng… · 篇幅：Regular submission (no more than 12 pages of main content) · 代码：https://www.github.com/selimkuzucu/LAVIE · OpenReview ID：s2T8Kgj6Rd

期刊：Transactions on Machine Learning Research · 摘要：Generative modeling for mixed-type tabular data has recently been dominated by diffusion-based methods, but their gains often come with schedule design, time dependent score parameterization, and multi-step solvers that increase computational overhead and tuning difficulty. We present \textbf{TabFlowM}, a lightweight framework that asks a more targeted question: once mixed-type records are mapped into a decoder compatible continuous transport space, is diffusion style score learning still necessary? TabFlowM answers this by training a single time conditioned velocity field via flow matching t… · 篇幅：Long submission (more than 12 pages of main content) · OpenReview ID：t5kygrpSIz

期刊：Transactions on Machine Learning Research · 摘要：This tutorial paper argues that model comparison in machine learning can be much improved by using \emph{paired testing}, i.e.\ comparing the predictions of methods A and B on each (common) test example. Due to the limitations of null hypothesis significance testing, a Bayesian approach is recommended, including the use of the region of practical equivalence (ROPE; Kruschke 2015a; Kruschke and Liddell 2018; Benavoli, Corani, Dem\v{s}ar, and Zaffalon 2017). We discuss a Bayesian $t$-test and a Bayesian McNemar test for comparisons on a single task, and Bayesian hierarchical models for comparis… · 篇幅：Long submission (more than 12 pages of main content) · 代码：https://github.com/ckiwgithub/compareMLModels · OpenReview ID：t6xhcnLlEl

期刊：Transactions on Machine Learning Research · 摘要：Despite their success, deep neural networks remain opaque: it is often unclear why a model fails on a particular input, and classical generalization theory offers limited guidance in the overparameterized regime. Gradient-descent training naturally gives rise to path-dependent inner products between data points, but the resulting kernel matrices are asymmetric and can have negative eigenvalues, precluding their use as proper kernels or similarity measures. We show that a simple modification -- replacing output gradients with loss gradients in these inner products -- restores symmetry and posi… · 篇幅：Long submission (more than 12 pages of main content) · 代码：https://github.com/RyanPyle1/PLGK-Kernel · OpenReview ID：tE0RWpe5z2

期刊：Transactions on Machine Learning Research · 摘要：Estimating individual treatment effects (ITEs) in the presence of unobserved confounding remains a central challenge in causal inference. Existing proxy-based methods aim to recover latent confounders from observational proxies, but typically produce only point estimates without uncertainty quantification. This lack of uncertainty modeling provides incomplete and potentially insufficient information for downstream decision-making, especially when uncertainty is inherent in the data. We propose IntervalGP-VAE, a novel framework that combines variational autoencoders with Interval Gaussian Proc… · 篇幅：Long submission (more than 12 pages of main content) · 代码：https://github.com/ZhigaoGuo/IntervalGP-VAE · OpenReview ID：vh7cshQCAE

期刊：Transactions on Machine Learning Research · 摘要：Pixel-based deep reinforcement learning agents are typically trained on heavily downsampled visual observations, a convention inherited from early benchmarks rather than grounded in principled design. In this work, we show that observation resolution is a critical yet overlooked variable for policy learning: higher-resolution inputs can substantially improve both performance and generalization, provided the network architecture can process them effectively. We find that the widely used Impala encoder, which flattens spatial features into a vector, suffers from quadratic parameter growth as re… · 篇幅：Regular submission (no more than 12 pages of main content) · 代码：https://github.com/raphajaner/visual-scaling-drl · OpenReview ID：xNm5W5Widp

期刊：Transactions on Machine Learning Research · 摘要：Graphs are foundational abstractions in data-intensive domains, yet the scale of modern datasets strains computation and memory for downstream learning. From recommender systems to biological networks, graphs have emerged as a fundamental substrate for learning. As graph sizes grow, the cost of training and inference becomes prohibitive, thereby necessitating compact surrogates that retain spectral properties and feature semantics. We propose an entropy-regularized, semi-supervised framework for attributed graph coarsening that jointly leverages the original graph’s Laplacian, node features,… · 篇幅：Regular submission (no more than 12 pages of main content) · OpenReview ID：xZAwzxUP7c

期刊：Transactions on Machine Learning Research · 摘要：In classification with tabular data, one often utilizes tree-based models. Those can be competitive with deep neural networks on tabular data and, under some conditions, explainable. The explainability depends on the tree's depth and the accuracy of each leaf. Decision trees containing leaves with unbalanced accuracy can provide misleading explanations. Low-accuracy leaves provide less useful explanations to the individuals they classify. Here, we train a shallow tree that minimizes the maximum misclassification error across leaf nodes. The shallow tree provides a more useful global explanati… · 篇幅：Regular submission (no more than 12 pages of main content) · 代码：https://github.com/Epanemu/LeafLAT · OpenReview ID：y4FJDMpWq3

期刊：Transactions on Machine Learning Research · 摘要：Gaussian process (GP) regression is widely used for uncertainty quantification, yet the standard formulation assumes noise-free covariates. When inputs are measured with error, this errors-in-variables (EIV) setting can lead to optimistically narrow posterior intervals and biased decisions. We study GP regression under input measurement uncertainty by representing each noisy input as a probability measure and defining covariance through Wasserstein distances between these measures. Building on this perspective, we instantiate a deterministic projected Wasserstein ARD (PWA) kernel whose one-di… · 篇幅：Regular submission (no more than 12 pages of main content) · OpenReview ID：yEZjju9CUw

期刊：Transactions on Machine Learning Research · 摘要：Finding Nash equilibria in two-player zero-sum imperfect-information games remains a central challenge in multi-agent reinforcement learning. Recent multi-round regularization methods offer a promising direction, yet existing approaches either require full enumeration of the game tree or rely on non-policy-gradient inner solvers that underperform in practice, leaving a scalable policy-gradient-based solution open. In this paper, we propose a novel multi-round regularization procedure and show that it guarantees strictly monotonic reduction in Bregman divergence to Nash equilibria and eventual… · 篇幅：Regular submission (no more than 12 pages of main content) · 代码：https://github.com/ntu-agents/nashpg · OpenReview ID：yIA2Fjs1FK

期刊：Transactions on Machine Learning Research · 摘要：Voice agents built on ASR-LLM-TTS pipelines allocate compute statically. It wastes resources on simple queries but does not cater to complex ones. So, we created PAVO (Pipeline-Aware Voice Orchestrator), which routes each turn through a three-stage pipeline. We orchestrate these routes based on demand signals extracted before the transcription even begins. We noticed that ASR errors propagate to downstream LLMs in two distinct regimes. One of them was a sharp factual accuracy cliff and the other one was gradual semantic degradation. This resulted in creating inter stage coupling constraints t… · 篇幅：Regular submission (no more than 12 pages of main content) · OpenReview ID：zrneoIxlFx

期刊：Transactions on Machine Learning Research · 摘要：Diffusion models represent the state-of-the-art for solving inverse problems such as image restoration tasks. Diffusion-based inverse solvers incorporate a likelihood term to guide prior sampling, generating data consistent with the posterior distribution. However, due to the intractability of the likelihood, most methods rely on isotropic Gaussian approximations, which can push estimates off the data manifold and produce inconsistent, poor reconstructions. We propose Equivariance Regularized (EquiReg) diffusion, a general plug-in framework that improves posterior sampling by penalizing traje… · 篇幅：Regular submission (no more than 12 pages of main content) · 代码：https://github.com/Anima-Lab/Equireg · OpenReview ID：3iaYKJLcyG

期刊：Transactions on Machine Learning Research · 摘要：Interactive prompting is an appealing approach to vision–language fusion using frozen uni-modal transformers, yet recent progress often relies on increasingly complex prompting architectures. A natural question arises: instead of refining prompt designs, can fusion be improved more effectively by directly adapting internal representations within attention layers? Our analysis, from a representation-centric perspective, suggests that within each frozen attention layer, prompt tokens have limited direct control over the value representations of original modality tokens and their query–key inter… · 篇幅：Regular submission (no more than 12 pages of main content) · 代码：https://github.com/YYJmay/representation-centric-multimodal-fusion · OpenReview ID：yBVwYxHxUq

期刊：Transactions on Machine Learning Research · 摘要：The concept of Markov chain Monte Carlo (MCMC) cycles, an analogy to cyclic processes in heat engines, is presented in order to examine Bayesian inference problems. In this effort, we develop adaptive ensemble schedulers that allow the tuning of external parameters of a Bayesian canonical ensemble during an MCMC run, realising the MCMC cycles in practice. We run these cycles on different statistical models. As a fundamental insight, we find (both theoretically and in practice) that such systems can produce a non-zero net work output if and only if the considered model is non-Gaussian. As such… · 篇幅：Long submission (more than 12 pages of main content) · OpenReview ID：88KWqihymD

期刊：Transactions on Machine Learning Research · 摘要：We study what provable privacy attacks can be shown for trained 2-layer ReLU neural networks, focusing on two types of attacks: membership inference and data reconstruction. We prove that theoretical results on the implicit bias of 2-layer neural networks can be used to provably identify with high probability whether a given point was used in the training set in a high-dimensional setting, and can also be used to construct a set of which at least a constant fraction are training points in a univariate setting. To the best of our knowledge, our work is the first to show provable vulnerabilitie… · 篇幅：Regular submission (no more than 12 pages of main content) · 代码：https://github.com/guy120494/Provable-Privacy-Attacks-on-Trained-Shallow-Neural-Networks · OpenReview ID：6lCkCCw2ds

期刊：Transactions on Machine Learning Research · 摘要：We present a reinforcement-learning agent that solves symbolic equations step by step --- both nonlinear \emph{closed} equations (radicals, exponentials, trigonometric) and, for the first time, a controlled class of \emph{restricted-open} families requiring a change of variables (CoV) such as completing the square. We cast algebra as an MDP with a dynamic action space and a tree-structured policy (TreeMLP). The \emph{main} policy learns the full solution procedure from reward alone, with no supervised solution traces; the CoV substitution itself comes from a supervised generator interchangeab… · 篇幅：Regular submission (no more than 12 pages of main content) · OpenReview ID：JlC5BJiikD

期刊：Transactions on Machine Learning Research · 摘要：Selecting the best data mixture is critical for successful Supervised Fine-Tuning (SFT) of Multimodal Large Language Models. However, determining the optimal mixture weights across multiple domain-specific datasets remains a significant bottleneck due to the combinatorial search space and the high cost associated with even a single training run. This is the so-called Data Mixture Optimization (DMO) problem. On the other hand, model merging unifies domain-specific experts through parameter interpolation. This strategy is efficient, as it requires only a single training run per domain, yet it o… · 篇幅：Regular submission (no more than 12 pages of main content) · 代码：https://github.com/BerasiDavide/mLLMs_merging_4_DMO · OpenReview ID：rxaQbSybeT

期刊：Transactions on Machine Learning Research · 摘要：Modeling long sequences is critical for numerous large-scale models. However, extending existing architectures to handle significantly longer sequences poses substantial technical and computational challenges. One inevitable issue is the overfitting of large models to positional encodings during pretraining, which limits their ability to generalize to unseen positional encoding scales. Additionally, extending sequence lengths requires extensive computational resources and time. Existing positional encoding methods often rely on carefully designed scaling factors but typically yield suboptimal… · 篇幅：Long submission (more than 12 pages of main content) · 代码：https://github.com/fisalt/TMLR_CRG-NTK_and_MS · OpenReview ID：qLNYDuNYKZ

期刊：Transactions on Machine Learning Research · 摘要：Activation steering has emerged as a powerful method for guiding the behavior of generative models towards desired outcomes such as toxicity mitigation. However, most existing methods apply interventions uniformly across all inputs, degrading model performance when steering is unnecessary. We introduce Dynamically Scaled Activation Steering (DSAS), a method-agnostic steering framework that decouples when to steer from how to steer. DSAS adaptively modulates the strength of existing steering transformations across layers and inputs, intervening strongly only when undesired behavior is detected… · 篇幅：Regular submission (no more than 12 pages of main content) · OpenReview ID：EXKv1KMNtR