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- 01Patch-based Memory Gate Model in Time Series Foundation Model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：Fm2ddpR0awSamuel Yoon, Jongwon Kim, Juyoung Ha et al.
- 02Online Learning and Unlearning: Efficient Algorithms with Near-Optimal Regret Guarantees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：lyvCVfTdiYYaxi Hu, Bernhard Schölkopf, Amartya Sanyal
- 03Symbolic Graphics Programming with Large Language Models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：JfK8EHJAQnYamei Chen, Haoquan Zhang, Yangyi Huang et al.
- 04Learning What to Fail On: Failure-Mode Contextual Bandits for Adversarial Data Curation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：DSpKdN6whZRoie Kazoom, Ofir Cohen, Rami Puzis et al.
- 05FairT2I: Latent Variable Guidance for Training-Free Bias Mitigation with LLM-Assisted Bias Detection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：WHYE1WjMYfJinya Sakurai, Yuki Koyama, Issei Sato
- 06Deep Models, Shallow Alignment: Uncovering the Granularity Mismatch in Neural Decoding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：SzDHkICZpmYang Du, Siyuan Dai, Yonghao Song et al.
- 07World Model Anomaly Detection with a Latent Linear Prior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：VLIzLK3CfRZarif Ikram, Harry Zhao, Ling Pan et al.
- 08Partial Observation Inversion and Batched Belief-State Planning for Information Gathering POMDPs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：K7MPwAV1ATRobert J. Moss, David Zhen Yin, Mahdi Al-Husseini et al.
- 09Invariant Causal Set Covering Machine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：slquR2A8rAThibaud Godon, Baptiste Bauvin, Pascal Germain et al.
- 10Sampling Boltzmann distributions via normalizing flow approximation of transport maps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：4HLZD6LMuJZia Ur Rehman, Gero Friesecke
- 11A Survey of Hybrid Inference Systems for Large Language Models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：OIrJI53MvNArkul Prathamesh Shenoy, Amogh Kulkarni, Atharv Rawal et al.
- 12Chance-Constrained Inference for Hallucination Risk Control in Large Language Models期刊：Transactions on Machine Learning Research · 摘要：Large language models generate outputs stochastically and may produce fluent but invalid responses, including factual hallucinations. Existing mitigation strategies reduce average error rates but do not provide explicit control over the \emph{frequency} of such failures under repeated use. We formulate inference as a deployment-time risk control problem and introduce \emph{chance-constrained inference} (CCI), which directly bounds the probability of hallucinations among accepted generations. Hallucinations are modeled as stochastic constraint violations, and we show that confidence-based sele… · 篇幅：Regular submission (no more than 12 pages of main content) · OpenReview ID：cJDhDC69m9Sreenivasan M
- 13Learning with Local Search MCMC Layers期刊：Transactions on Machine Learning Research · 摘要：Integrating combinatorial optimization layers into neural networks has recently attracted significant research interest. However, many existing approaches lack theoretical guarantees or fail to perform adequately when relying on inexact solvers. This is a critical limitation, as many operations research problems are NP-hard, often necessitating the use of neighborhood-based local search heuristics. In this paper, we introduce a principled approach for learning with such inexact solvers. Inspired by the connection between simulated annealing and Metropolis-Hastings, we transform the problem sp… · 篇幅：Long submission (more than 12 pages of main content) · OpenReview ID：M2a0mbACNfGermain Vivier-Ardisson, Mathieu Blondel, Axel Parmentier
- 14Behavioral Inference at Scale: The Fundamental Asymmetry Between Motivations and Belief Systems期刊：Transactions on Machine Learning Research · 摘要：How much information about an agent's underlying values can be recovered from its observable behavior? This question matters for any approach that infers agent properties from action sequences, yet remains empirically open for LLM-based agents at scale. We address it through controlled experiments: LLM-based agents (Llama~3.1-8B) assigned one of 36 behavioral profiles (9 belief systems x 4 motivations) generate over 1.5 million behavioral sequences across 36 behavioral profiles in grid-world environments, providing ground truth unavailable in human behavioral studies. After filtering, both cl… · 篇幅：Long submission (more than 12 pages of main content) · OpenReview ID：aDMDqtw63HJason Starace, Terry Soule
- 15Lost in Aggregation: On a Fundamental Expressivity Limit of Message-Passing Graph Neural Networks期刊：Transactions on Machine Learning Research · 摘要：We define an information-complexity property for aggregation functions, capturing a vast range of practical aggregations, and prove that any Message-Passing Graph Neural Network (MP-GNN) model with such aggregations induces only a polynomial number of equivalence classes on all graphs - while the number of non-isomorphic graphs is super-exponential (in number of vertices). Adding a familiar perspective, we observe that merely 2 iterations of Color Refinement (CR) induce at least an exponential number of equivalence classes, making the aforementioned MP-GNNs relatively infinitely weaker. Previ… · 篇幅：Regular submission (no more than 12 pages of main content) · OpenReview ID：F4OlpAJ0o2Eran Rosenbluth
- 16A Closed-Form Persistence-Landmark Pipeline for Certified Point-Cloud and Graph Classification期刊：Transactions on Machine Learning Research · 摘要：We introduce PLACE (Persistence-Landmark Analytic Classification Engine), a closed-form pipeline for classifying point clouds and graphs through their persistent-homology signatures. Three quantitative guarantees—a margin-based excess-risk rate, a closed-form descriptor-selection rule, and a per-prediction certificate—are derived from training labels alone, with no learned weights or held-out calibration. The embedding sums Mitra–Virk single-point coordinate functions over a sparse landmark grid; the closed-form weight rule $w_k^2 \propto (d_{k+1}^2 - d_k^2)/R_k^2$ maximizes the distortion sl… · 篇幅：Long submission (more than 12 pages of main content) · 代码：https://github.com/akritihq/place-palace · OpenReview ID：4kZxNlE5VeSushovan Majhi, Atish Mitra, Ziga Virk et al.
- 17Physics-Aware Variational Autoencoder for Urban Travel Demand Calibration期刊：Transactions on Machine Learning Research · 摘要：Urban mobility digital twins are revolutionizing how cities manage increasingly complex transportation systems, enabling real-time optimization across multiple stakeholders, services, and dynamic operations. Central to these digital twins is the origin-destination (OD) calibration problem—estimating travel demand patterns that produce realistic traffic simulations matching observed conditions. However, existing calibration methods face critical limitations: they require a prohibitively large number of expensive simulation runs and struggle with high-dimensional city-scale networks. To mitigat… · 篇幅：Regular submission (no more than 12 pages of main content) · OpenReview ID：r5oS1XXbT3Defu Cao, Sam Griesemer, Zijun Cui et al.
- 18On the Statistical Limits of Self-Improving Agents期刊：Transactions on Machine Learning Research · 摘要：We develop a learning-theoretic framework for analyzing self-improving agents by decomposing self-modification into five axes. Within this framework, we prove a sharp boundary: under standard i.i.d. assumptions, distribution-free PAC learnability is preserved if and only if the policy-reachable family remains uniformly capacity-bounded. If reachable capacity can grow without bound, utility-rational self-changes can make learnable tasks unlearnable. We further introduce a simple Two-Gate guardrail—a validation-improvement requirement plus a capacity cap—that preserves this boundary and yields… · 篇幅：Regular submission (no more than 12 pages of main content) · OpenReview ID：q4vuDMtYgFCharles L. Wang, Keir Dorchen, Peter Jin
- 19Clarity: The Flexibility-Interpretability Trade-Off in Sparsity-aware Concept Bottleneck Models期刊：Transactions on Machine Learning Research · 摘要：The widespread adoption of deep learning models in computer vision has intensified concerns about interpretability. Despite strong performance, these models are often treated as black boxes, with limited systematic investigation of their decision-making processes. While many interpretability methods exist, objective evaluation of learned representations remains limited, particularly for approaches that rely on sparsity to ``induce'' interpretability. In this work, we investigate how modeling choices in Concept Bottleneck Models (CBMs) affect the semantic alignment of concept representations.… · 篇幅：Long submission (more than 12 pages of main content) · 代码：https://github.com/konpanousis/cbm-clarity · OpenReview ID：IyQEQBRR4MKonstantinos P. Panousis, Diego Marcos
- 20Analyzing the Effect of Noise in LLM Fine-Tuning期刊：Transactions on Machine Learning Research · 摘要：Fine-Tuning is the dominant paradigm for adapting pretrained large language models (LLMs) to downstream NLP tasks. In practice, fine-tuning datasets may contain various forms of noise that arise from annotation errors or automated data collection. Although prior work has concentrated on designing robust learning algorithms to mitigate performance degradation under noisy conditions, comparatively little is known about how different types of noise affect the internal learning dynamics of LLMs during fine-tuning. In this work, we systematically study the impact of noise on model behaviour across… · 篇幅：Regular submission (no more than 12 pages of main content) · 代码：https://github.com/LingfangLi/analyzing-noise-llm-finetuning · OpenReview ID：NlSBeHZEz5Lingfang Li, Procheta Sen
- 21Same-Expert Iteration Improves a Translation MoE Where Expert Communication Does Not期刊：Transactions on Machine Learning Research · 摘要：Mixture-of-Experts (MoE) models achieve scalability through sparse expert routing, but experts process tokens independently. A natural hypothesis is that enabling expert communication—through learned topologies, message passing, or sequential chains—should improve performance. We test this hypothesis on WMT14 En-De translation with a small decoder-only Transformer, evaluating ten communication approaches across seven exper-imental axes. We find no clear evidence that any variant improves over standard MoE, though modest sample sizes limit power for detecting small effects; several variants de… · 篇幅：Regular submission (no more than 12 pages of main content) · OpenReview ID：hPD4MjMfoNLibo Sun, Peixiong He, Po-wei Harn et al.
- 22From Centerlines to Hemodynamics: Anisotropic RBF Decoders for Coronary Arteries期刊：Transactions on Machine Learning Research · 摘要：Accurate and rapid estimation of hemodynamic metrics, such as pressure and wall shear stress (WSS), is important for assessing the severity of Coronary Artery Disease (CAD). Existing approaches, including invasive Fractional Flow Reserve (FFR) measurements and computationally expensive Computational Fluid Dynamics (CFD) simulations, face challenges in invasiveness, cost, and speed. We present a learned surrogate for fast prediction of CFD-simulated coronary hemodynamics from vessel centerline geometry. The model encodes 1D vessel centerlines together with inlet flow rate using a transformer-b… · 篇幅：Regular submission (no more than 12 pages of main content) · OpenReview ID：AoJUrVjufPReza Akbarian Bafghi, Sukirt Thakur, Maziar Raissi
- 23EHR2Path: Comprehensive Pathway-Level Modeling of Longitudinal Patient Trajectories from Multimodal Electronic Health Records期刊：Transactions on Machine Learning Research · 摘要：Forecasting how a patient’s condition is likely to evolve, including possible deterioration, recovery, treatment needs, and care transitions, could support more proactive and personalized care, but requires modeling heterogeneous and longitudinal electronic health record (EHR) data. Yet, existing approaches typically focus on isolated prediction tasks, narrow feature spaces, or short context windows, limiting their ability to model full patient pathways. To address this gap, we introduce EHR2Path, a multimodal framework for forecasting and simulating full in-hospital patient pathways from rou… · 篇幅：Regular submission (no more than 12 pages of main content) · 代码：https://github.com/ChantalMP/EHR2Path · OpenReview ID：ywa71iOykgChantal Pellegrini, Ege Özsoy, David Bani-Harouni et al.
- 24Toward Greater Autonomy in Materials Discovery Agents: Unifying Planning, Physics, and Scientists期刊：Transactions on Machine Learning Research · 摘要：We aim at designing language agents with greater autonomy for crystal materials discovery. While most of existing studies restrict the agents to perform specific tasks within predefined workflows, we aim to automate workflow planning given high-level goals and scientist intuition. To this end, we propose Materials Agent unifying Planning, Physics, and Scientists, known as MAPPS. MAPPS consists of a Workflow Planner, a Tool Code Generator, and a Scientific Mediator. The Workflow Planner uses large language models (LLMs) to generate structured and multi-step workflows. The Tool Code Generator s… · 篇幅：Regular submission (no more than 12 pages of main content) · OpenReview ID：Cwq1U8tbWWLianhao Zhou, Hongyi Ling, Keqiang Yan et al.
- 25Predicting Chain-of-Thought Correctness from Trajectory Geometry期刊：Transactions on Machine Learning Research · 摘要：We ask whether the geometry of a reasoning trajectory, that is, how a chain-of-thought (CoT) trace moves through semantic space beyond its raw length, predicts whether the final answer is correct, and whether that prediction is useful in practice. Across 2,800 CoT traces spanning three reasoning benchmarks (FOLIO, GSM8K, and PrOntoQA) and five language models, we extract interpretable trajectory-level features (adjacent-step transition energy, path entropy, semantic drift, loopiness, discourse-graph spectra, and direction-sensitive drift) and predict per-trace correctness. Under problem-group… · 篇幅：Regular submission (no more than 12 pages of main content) · OpenReview ID：H9cBkEqVeYArjun Balaji
- 26Gen-MURE: Generalized Multiplicative Unbiased Risk Estimate期刊：Transactions on Machine Learning Research · 摘要：Coherent imaging modalities such as ultrasound and synthetic aperture radar (SAR) images are degraded by signal-dependent multiplicative noise, where the noise distributions vary widely across acquisition scenarios. Existing self-supervised image denoising methods either assume zero-mean additive noise, independence across pixels or require the noise distribution to be known, which often limit their applicability in real-world image denoising systems. We propose a Generalized Multiplicative Unbiased Risk Estimate (Gen-MURE), a model-agnostic self-supervised image denoising framework for enhan… · 篇幅：Long submission (more than 12 pages of main content) · OpenReview ID：Hie13qRm1xSoumee Guha, Scott Acton
- 27A Cross-Model Study of Over-Compliance in Large Lan- guage Models期刊：Transactions on Machine Learning Research · 摘要：Large language models increasingly mediate decisions in healthcare, legal advisory, and financial analysis, settings in which a model’s willingness to answer an inadequate prompt can matter as much as the accuracy of its answer. Yet systematic cross-model evidence on this behavior remains scarce. The present study examined over-compliance, understood as the generation of substantive content when the input warrants clarification, refusal, or deferral. Four frontier models from Ope- nAI, Google, Meta, and Anthropic were evaluated on a benchmark of 400 prompts spanning under- specification, ambi… · 篇幅：Long submission (more than 12 pages of main content) · 代码：https://github.com/hgus107/LLM-Over-Complaince · OpenReview ID：LnUP74YNzeHarish Gupta
- 28Navigating the Labyrinth: Evaluating LLMs’ Ability to Reason About Search Problems期刊：Transactions on Machine Learning Research · 摘要：Large Language Models (LLMs) have recently achieved impressive performance in math and reasoning benchmarks. However, they often struggle with logic problems and puzzles that are relatively easy for humans. To further investigate this, we introduce a new benchmark, SearchBench, which contains 11 unique search problems inspired by intuitive puzzles. Each SearchBench problem type is equipped with automated pipelines to generate an arbitrary number of instances and analyze the feasibility, correctness, and optimality of LLM-generated solutions. We show that using step-by-step, language-only reas… · 篇幅：Regular submission (no more than 12 pages of main content) · OpenReview ID：oub2I1ioL5Nasim Borazjanizadeh, Roei Herzig, Trevor Darrell et al.
- 29Hyperedge Anomaly Detection with Hypergraph Neural Network期刊：Transactions on Machine Learning Research · 摘要：Hypergraph is a data structure that enables us to model higher-order associations among data entities. Conventional graph-structured data can represent pairwise relationships only, whereas hypergraph enables us to associate any number of entities, which is essential in many real-life applications. Hypergraph learning algorithms have been well-studied for numerous problem settings, such as node classification, link prediction, etc. However, much less research has been conducted on anomaly detection from hypergraphs. Anomaly detection identifies events that deviate from the usual pattern and ca… · 篇幅：Regular submission (no more than 12 pages of main content) · OpenReview ID：etPYIk1BqOMd. Tanvir Alam, Md Mahmudur Rahman, Md. Fahim Arefin et al.
- 30Unbiased Stochastic Optimization for Gaussian Processes on Finite Dimensional RKHS期刊：Transactions on Machine Learning Research · 摘要：Current methods for stochastic hyperparameter learning in Gaussian Processes (GPs) rely onapproximations, suchascomputingbiasedstochasticgradientsorusinginducingpointsin stochastic variational inference. However, when using such methods, we are not guaranteed to converge to a stationary point of the true marginal likelihood. In this work, we propose algorithms for exact stochastic inference of GPs with kernels that induce a Reproducing Kernel Hilbert Space (RKHS) of moderate finite dimension. Our approach can also be extendedtoinfinitedimensionalRKHSsatthecostofforgoingexactness. Bothforfinit… · 篇幅：Long submission (more than 12 pages of main content) · OpenReview ID：wDCulUZla4Neta Shoham, Haim Avron
- 31XFeat Revisited: Reproducibility and Evaluation of a Lightweight Image Matcher期刊：Transactions on Machine Learning Research · 摘要：We present a reproducibility study of XFeat, a lightweight local feature extractor and matcher designed to identify corresponding points across images efficiently on resource-constrained hardware. We re-implement the architecture based on the paper and supplementary material, re-evaluate the authors' released checkpoint alongside our re-implementation, and conduct additional architectural ablations to examine design choices that were not fully justified in the original work. This distinction between re-evaluation and reproduction is important, as the paper, supplement, and public code differ… · 篇幅：Long submission (more than 12 pages of main content) · 代码：https://github.com/GalaxyGHz/xfeat-revisited · OpenReview ID：2WI889UlinLazar Đoković, Aimee Lin
- 32eDQA: Efficient Deep Quantization of DNN Activations on Edge Devices期刊：Transactions on Machine Learning Research · 摘要：Quantization of Deep Neural Network (DNN) activations is a commonly used technique to reduce compute and memory demands during DNN inference, which can be particularly beneficial on resource-constrained edge devices. To achieve high accuracy, existing methods for quantizing activations rely on complex mathematical computations or perform extensive online searches for the best hyperparameters. However, these expensive operations are impractical on edge devices with limited computational capabilities, memory capacities, and energy budgets. Furthermore, many existing methods either do not focus… · 篇幅：Regular submission (no more than 12 pages of main content) · 代码：https://github.com/gicLAB/eDQA · OpenReview ID：SEIBCdgE5WWenhao Hu, Jude Haris, Paul Henderson et al.
- 33VQEL: Enabling Self-Play in Emergent Language Games via Agent Internal Vector Quantization期刊：Transactions on Machine Learning Research · 摘要：Emergent Language (EL) focuses on the emergence of communication among artificial agents. Although symbolic communication channels more closely mirror the discrete nature of human language, learning such protocols remains fundamentally difficult due to the non-differentiability of symbol sampling. Existing approaches typically rely on high-variance gradient estimators such as REINFORCE or on continuous relaxations such as Gumbel–Softmax, both of which suffer from limitations in training stability and scalability when learning a language from scratch. Motivated by cognitive theories that empha… · 篇幅：Long submission (more than 12 pages of main content) · OpenReview ID：5nqQlGWlsWMahdi Samiei, Mehdi Jamalkhah, Mahdieh Soleymani Baghshah
- 34QMoE+: Hybrid Quantum Mixture of Experts期刊：Transactions on Machine Learning Research · 摘要：Quantum mixture of experts (QMoE) extends conditional computation to the NISQ setting by distributing learning across parameterized quantum circuit (PQC) experts selected via a routing mechanism. Existing approaches are limited by single-block experts, lack of load balancing, and aggregation schemes that ignore routing amplitudes. We propose QMoE+, which uses two-block data re-uploading experts with learnable offsets, a coherent aggregation circuit over the joint routing-data Hilbert space, and a Switch-style load-balancing loss. Under top-k=1 sparse routing, QMoE+ activates only ∼28% of its… · 篇幅：Regular submission (no more than 12 pages of main content) · 代码：https://github.com/HbjNiser/qmoe-plus · OpenReview ID：l1JaPqZ6K5Haribandhu Jena, Subhankar Mishra
- 35Enhance the Safety in Reinforcement Learning by ADRC Lagrangian Methods期刊：Transactions on Machine Learning Research · 摘要：Safe reinforcement learning (Safe RL) seeks to maximize rewards while satisfying safety constraints, typically addressed through Lagrangian-based methods. However, existing approaches, including PID and classical Lagrangian methods, suffer from oscillations and frequent safety violations due to parameter sensitivity and inherent phase lag. To address these limitations, we propose ADRC-Lagrangian methods that leverage Active Disturbance Rejection Control (ADRC) for enhanced robustness and reduced oscillations. Our unified framework subsumes a broad class of PID Lagrangian updates as frozen-par… · 篇幅：Regular submission (no more than 12 pages of main content) · OpenReview ID：3IbuT8uzYSMingxu Zhang, Huicheng Zhang, Jiaming Ji et al.
- 36DS-STAR: Data Science Agent for Solving Diverse Tasks across Heterogeneous Formats and Open-Ended Queries期刊：Transactions on Machine Learning Research · 摘要：While large language models (LLMs) have shown promise in automating data science, existing agents often struggle with the complexity of real-world workflows that require exploring multiple sources and synthesizing open-ended insights. In this paper, we introduce DS-STAR, a specialized agent to bridge this gap. Unlike prior approaches, DS-STAR is designed to (1) seamlessly process and integrate data across diverse, heterogeneous formats, and (2) move beyond simple QA to generate comprehensive research reports for open-ended queries. Extensive evaluation shows that DS-STAR achieves state-of-the… · 篇幅：Long submission (more than 12 pages of main content) · 代码：https://github.com/google-research/ds-star · OpenReview ID：Yz3ZPLzYaUJaehyun Nam, Jinsung Yoon, Jiefeng Chen et al.
- 37The 2025 Foundation Model Transparency Index期刊：Transactions on Machine Learning Research · 摘要：Foundation model developers are among the world’s most important companies. As these companies become increasingly consequential, how do their transparency practices evolve? The 2025 Foundation Model Transparency Index is the third edition of an annual effort to characterize and quantify the transparency of foundation model developers. The 2025 FMTI introduces new indicators related to data acquisition, usage data, and monitoring and evaluates companies like Alibaba, DeepSeek, and xAI for the first time. The 2024 FMTI reported that transparency was improving, but the 2025 FMTI finds this prog… · 篇幅：Long submission (more than 12 pages of main content) · OpenReview ID：1jT253XtyfAlexander Wan, Kevin Klyman, Sayash Kapoor et al.
- 38Optimal or Greedy Decision Trees? Revisiting their Objectives, Tuning, and Performance期刊：Transactions on Machine Learning Research · 摘要：Recently there has been a surge of interest in optimal decision tree (ODT) methods that globally optimize accuracy directly, in contrast to traditional approaches that locally optimize an impurity or information metric. However, the literature shows conflicting evidence on the value of ODTs, with some demonstrating superior out-of-sample performance of ODTs over greedy approaches, while others show the opposite. The value and performance of ODTs therefore remains one of several open question regarding ODTs, most of which could not be answered before due to lack of scalability. With our experi… · 篇幅：Long submission (more than 12 pages of main content) · 代码：https://github.com/ConSol-Lab/opt-vs-greedy-dts · OpenReview ID：DvDOAtskXlJacobus G. M. van der Linden, Daniël Vos, Mathijs de Weerdt et al.
- 39SAFT: Structure-Aware Fine-Tuning of Large Language Models for AMR-to-Text Generation期刊：Transactions on Machine Learning Research · 摘要：Large Language Models (LLMs) are increasingly applied to tasks involving structured inputs such as semantic graphs, yet adapting them to such inputs remains non-trivial. Common approaches either linearize graphs, discarding structural information, or rely on specialized architectures that are not directly compatible with standard pretrained LLMs. We present SAFT, a structure-aware fine-tuning method that augments LLMs with graph positional encodings derived from the magnetic Laplacian of the input graph. These encodings are projected into the LLM embedding space, introducing relational induct… · 篇幅：Regular submission (no more than 12 pages of main content) · 代码：https://github.com/guerrantif/saft · OpenReview ID：QZoUMyzYDBRafiq Kamel, Filippo Guerranti, Simon Geisler et al.
- 40Minimax learning rates for estimating binary classifiers under margin conditions期刊：Transactions on Machine Learning Research · 摘要：We study classification problems using binary estimators where the decision boundary is described by horizon functions and where the data distribution satisfies a geometric margin condition. A key novelty of our work is the derivation of lower bounds for the worst-case learning rates over broad classes of functions, under a geometric margin condition---a setting that remains theoretically challenging. Moreover, we work in the noiseless setting, where lower bounds are particularly hard to establish. Our general results cover, in particular, classification problems with decision boundaries belo… · 篇幅：Regular submission (no more than 12 pages of main content) · OpenReview ID：ZIshsqojB6Jonathan García, Philipp Christian Petersen
- 41Influencing Humans to Conform to Preference Models for RLHF期刊：Transactions on Machine Learning Research · 摘要：Designing a reinforcement learning from human feedback (RLHF) algorithm to approximate a human's unobservable reward function requires assuming, implicitly or explicitly, a model of human preferences. In sequential decision making tasks, a preference model that poorly describes how humans generate preferences risks learning a poor approximation of the human’s reward function. In this paper, we conduct human studies to assess whether one can influence the expression of real human preferences to more closely conform to a desired preference model. Importantly, our approach does not seek to alter… · 篇幅：Long submission (more than 12 pages of main content) · OpenReview ID：7YPlw1nUmWStephane Hatgis-Kessell, W. Bradley Knox, Serena Booth et al.
- 42Stronger Approximation Guarantees for Non-Monotone $\gamma$-Weakly DR-Submodular Maximization期刊：Transactions on Machine Learning Research · 摘要：We study the maximization of nonnegative, non-monotone $\gamma$-weakly diminishing-returns (DR) submodular functions over down-closed convex bodies. The weakly DR model relaxes classical diminishing returns by allowing marginal gains to decay up to a multiplicative factor $\gamma \in (0,1]$, capturing a broad class of objectives that interpolate between monotone and fully non-monotone DR submodularity. Existing methods in this regime achieve guarantees that deteriorate rapidly as $\gamma$ decreases and fail to recover the best known bounds in the fully DR case. We develop a $\gamma$-aware alg… · 篇幅：Long submission (more than 12 pages of main content) · OpenReview ID：yS78Cb1CnXHareshkumar Jadav, Ranveer Singh, Vaneet Aggarwal
- 43Analysis of Natural Actor-Critic with Randomized Low- Discrepancy Sampling期刊：Transactions on Machine Learning Research · 摘要：Natural gradient methods are appealing in policy optimization due to their invariance to smooth reparameterization and their ability to account for the local geometry of the policy manifold. These properties often lead to improved conditioning of the optimization problem compared to Euclidean policy gradients. However, their reliance on Monte Carlo estimation introduces high variance and sensitivity to hyperparameters. In this paper, we address these limitations by integrating Randomized Quasi-Monte Carlo (RQMC) sampling into the natural actor-critic (NAC) framework. We revisit the NAC linear… · 篇幅：Long submission (more than 12 pages of main content) · OpenReview ID：kOSx9v6dfbAjin George Joseph, shikher chhawchharia
- 44Transformer–SSM Hybrid Language Models: Systematic Analysis and Design Insights期刊：Transactions on Machine Learning Research · 摘要：Recent progress in large language models demonstrates that hybrid architectures--combining self-attention mechanisms with state-space layers--can achieve a compelling balance between modeling quality and computational efficiency, particularly for long-context tasks. While these Transformer–Mamba-2 hybrid models show promising performance, systematic comparisons of hybridization strategies and analyses on the key factors behind their effectiveness have not been clearly shared with the community. In this work, we present a holistic evaluation of hybrid architectures based on inter-layer (sequen… · 篇幅：Regular submission (no more than 12 pages of main content) · OpenReview ID：x7qyXl8ecTSangmin Bae, Bilge Acun, Chien-Yu Lin et al.
- 45The Impact of Enforcing Representational Consistency of Identical Transformations for Disentangled Representation期刊：Transactions on Machine Learning Research · 摘要：Recent symmetry-based approaches in Variational Autoencoders (VAEs) have advanced disentanglement learning and compositional generalization. However, existing methods can encode identical semantic transformations differently depending on the specific sample pairs, which reduce the representational consistency of identical transformations. In this paper, we analyze how three commonly used symmetry parameterization families in prior work, namely (1) matrix-exponential parameterizations over the general linear group GL(n), (2) vector-additive actions in latent space, and (3) surjective mappings… · 篇幅：Long submission (more than 12 pages of main content) · 代码：https://github.com/GIST-IRR/RCIT · OpenReview ID：VjbBxj4aWbHee-Jun Jung, Hoyong Kim, Ilmin Kang et al.
- 46Probing Visual Concepts in Lightweight Vision-Language Models for Automated Driving期刊：Transactions on Machine Learning Research · 摘要：The use of Vision-Language Models (VLMs) in automated driving applications is becoming increasingly common, with the aim of leveraging their reasoning and generalisation capabilities to handle long-tail scenarios. However, these models often fail on simple visual questions that are highly relevant to automated driving, and the reasons behind these failures remain poorly understood. In this work, we examine the intermediate activations of VLMs and assess the extent to which specific visual concepts are linearly encoded, with the goal of identifying bottlenecks in the flow of visual information… · 篇幅：Long submission (more than 12 pages of main content) · OpenReview ID：HlBBy19ojCNikos Theodoridis, Reenu Mohandas, Ganesh Sistu et al.
- 47Instance-Level Generation for Representation Learning期刊：Transactions on Machine Learning Research · 摘要：Instance-level recognition (ILR) focuses on identifying individual objects rather than broad categories, offering the highest granularity in image classification. However, this fine-grained nature makes creating large-scale annotated datasets challenging, limiting ILR’s real-world applicability across domains. To overcome this, we introduce a novel approach that synthetically generates diverse object instances from multiple domains under varied conditions and backgrounds, forming a large-scale training set. Unlike prior work on automatic data synthesis, our method is the first to address ILR-… · 篇幅：Regular submission (no more than 12 pages of main content) · 代码：https://github.com/yankungou/ILGen · OpenReview ID：T3JgJXH3ZKYankun Wu, Zakaria Laskar, Giorgos Kordopatis-Zilos et al.
- 48Learning Structured Set Utility Functions with Contrastive Element Representations期刊：Transactions on Machine Learning Research · 摘要：Learning utility functions over sets of elements is central to many machine learning and decision-making tasks such as feature selection, sensor placement, and content recommendation, where the goal is to evaluate and select an optimal subset of elements that provide the largest utility. These utility functions often exhibit desirable properties like monotonicity and submodularity over sets, but are typically expensive to evaluate and may lack an explicit analytical form. Moreover, the utility of a set can vary depending on certain contextual variables, further complicating the learning task.… · 篇幅：Regular submission (no more than 12 pages of main content) · OpenReview ID：SZ8mOziJBxJayanth Bhargav, Younggil Chang, Mahsa Ghasemi et al.
- 49A Survey of Agent Memory in the Second Half: Towards Self-Evolving and Long-Horizon Agents期刊：Transactions on Machine Learning Research · 摘要：Research in artificial intelligence is undergoing a paradigm shift from prioritizing model innovations and benchmark scores towards emphasizing problem definition and rigorous real-world evaluation. As the field enters the "second half," the central challenge becomes real utility in long-horizon, dynamic, and user-dependent settings such as agentic coding, deep research, and computer use, where LLM-based agents face context explosion beyond fixed context windows and must continuously accumulate, manage, and selectively reuse large volumes of information across extended interactions. Memory, w… · 篇幅：Long submission (more than 12 pages of main content) · 代码：https://github.com/AgentMemoryWorld/Awesome-Agent-Memory · OpenReview ID：XycbogUAeJWei-Chieh Huang, Weizhi Zhang, Yueqing Liang et al.
- 50How Much Information Fits in a Vector?期刊：Transactions on Machine Learning Research · 摘要：Recent work in neural network interpretability has suggested that hidden activations of some deep models can be viewed as linear projections of much higher-dimensional vectors of sparse latent ``features.'' In general, this kind of representation is known as a superposition code. This work presents an information-theoretic account of superposition codes in a setting applicable to interpretability. We show that when the number $k$ of active features is very small compared to the number $N$ of total features, simple inference methods currently used by sparse autoencoders can reliably decode a $… · 篇幅：Long submission (more than 12 pages of main content) · OpenReview ID：Nby4pCPIZIChristopher Neil Gadzinski, Decebal Constantin Mocanu
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