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期刊：Transactions on Machine Learning Research · 摘要：In this work, we provide a systematic survey of Discrete Diffusion Language Models (dLLMs) and Discrete Diffusion Multimodal Language Models (dMLLMs). Unlike autoregressive (AR) models, dLLMs and dMLLMs adopt a multi-token, parallel decoding paradigm using full attention and a denoising-based generation strategy. This paradigm naturally enables parallel generation, fine-grained output control, and dynamic perception. These capabilities are previously difficult to achieve with AR models. A growing number of industrial-scale proprietary d(M)LLMs, as well as a large number of open-source academi… · 篇幅：Long submission (more than 12 pages of main content) · OpenReview ID：0DsqnkP8Cp

期刊：Transactions on Machine Learning Research · 摘要：AlphaZero has demonstrated that a neural-guided Monte Carlo Tree Search can achieve superhuman performance, but strong play does not imply perfect play. We study this gap in two oracle-evaluable domains with contrasting structure: Connect Four, a solved partisan game with exact game-theoretic values, and Chomp, an impartial game whose optimal play is governed by Grundy-number structure. Under a unified self-play $+$ MCTS pipeline, we compare vanilla AlphaZero, a multi-frame variant (limited to Chomp), and an AlphaZero Auxiliary Loss (AZAL) that adds oracle-derived policy supervision. We find… · 篇幅：Regular submission (no more than 12 pages of main content) · 代码：https://github.com/Brentkong/AlphaZero-in-Sparsely-Rewarded-Games-Limits-and-Auxiliary-Supervision · OpenReview ID：1z0CnFiJKg

期刊：Transactions on Machine Learning Research · 摘要：Multi-domain thinking verifiers trained via Reinforcement Learning with Verifiable Rewards (RLVR) are a cornerstone of modern post-training. However, their adoption in code generation has lagged behind that of execution feedback due to the prohibitive costs of the full RLVR pipeline. In this work, we ablate three primary choices along the performance-cost trade-off in RLVR: intermediate thinking traces, learning from negative samples, and on-policy training. We introduce Aletheia, a controlled, execution-grounded testbed to facilitate a decontaminated analysis of code verifier training recipe… · 篇幅：Regular submission (no more than 12 pages of main content) · 代码：https://github.com/insait-institute/aletheia/ · OpenReview ID：3rVrBGp0mr

期刊：Transactions on Machine Learning Research · 摘要：Despite the importance for interpretability, decision trees face severe scalability challenges. Existing global optimal methods are often limited by binary feature selection and shallow tree depths, whereas traditional heuristic approaches frequently sacrifice predictive accuracy. To overcome these limitations, this paper proposes a moving-horizon approximate branch-and-reduce method to train near-optimal deep classification trees on large-scale datasets with continuous features. Built on a hierarchical root-subtree optimization framework, the method solves the root-level problem via branch-a… · 篇幅：Long submission (more than 12 pages of main content) · 代码：https://github.com/YankaiGroup/MHABR.jl · OpenReview ID：4Sq5Byd4yS

期刊：Transactions on Machine Learning Research · 摘要：This paper presents a method to infer a 3D face avatar model from a single arbitrarily posed image, using the 3D Gaussian Splatting (3DGS) framework. Inference of a full 3DGS face model from one image is a highly ill-posed problem, requiring the estimation of hundreds of thousands, often well over a million, per-Gaussian appearance and structural parameters. To address this challenge, we draw inspiration from the classical morphable face models literature, in which individual identities are well-described as compact deformations (residuals) with respect to a canonical template face model, the… · 篇幅：Regular submission (no more than 12 pages of main content) · 代码：https://github.com/hliang2/FastAvatar · OpenReview ID：4WnbCj0v0K

期刊：Transactions on Machine Learning Research · 摘要：Symbolic regression (SR) models complex systems by discovering mathematical expressions that capture underlying relationships in observed data. However, most SR methods prioritize minimizing prediction error over identifying the governing equations, often producing overly complex or inaccurate expressions. To address this, we present a decomposable SR method that generates interpretable multivariate expressions leveraging transformer models, genetic algorithms (GAs), and genetic programming (GP). In particular, our explainable SR method distills a trained "opaque'' regression model into mathe… · 篇幅：Long submission (more than 12 pages of main content) · 代码：https://github.com/NISL-MSU/MultiSetSR · OpenReview ID：54EL928uCf

期刊：Transactions on Machine Learning Research · 摘要：Large language models are known to hallucinate, generating linguistically plausible but incorrect answers to questions. Uncertainty quantification has been proposed as a strategy to detect such behaviour, but existing methods lack a unified framework to assess reliability at both the prompt and answer level. We introduce a geometric framework which quantifies language model uncertainty at both levels by explicitly modelling a prompt-conditioned semantic distribution in answer embedding space. Our approach is black-box and sampling-based; we generate multiple answers per prompt, and use archet… · 篇幅：Long submission (more than 12 pages of main content) · OpenReview ID：5UVv7gkgUD

期刊：Transactions on Machine Learning Research · 摘要：Composed Image Retrieval (CIR) is an emerging paradigm in content-based image retrieval that enables users to formulate compositional queries by combining a reference image with an auxiliary modality, usually text-based. This approach supports fine-grained search where the target image shares structural elements with the user-provided image while incorporating the modifications specified by the auxiliary text. Conventional CIR methods rely on multimodal fusion to combine visual and textual features into a joint query embedding, which requires training modules that align composed queries with… · 篇幅：Regular submission (no more than 12 pages of main content) · 代码：https://github.com/StevenXuf/PeFuse4CIR · OpenReview ID：6W3pFEQXZc

期刊：Transactions on Machine Learning Research · 摘要：We study in-context learning (ICL) with Transformers for categorical outputs $y_i$, a setting largely unexplored compared to research on real-valued $y_i$. While attention-only Transformers can, in principle, perform functional gradient descent (GD) inference for real-valued outputs, we show that categorical $y_i$ introduce a nonlinear interlayer computation. The MLP layers interleaved with attention in the standard Transformer are a natural architectural component to approximate this computation, providing a concrete role for MLPs that is absent in the real-valued setting. We characterize co… · 篇幅：Regular submission (no more than 12 pages of main content) · OpenReview ID：8P4V7V1cs4

期刊：Transactions on Machine Learning Research · 摘要：Model parallelism (MP) is a widely adopted paradigm for scaling large language model (LLM) training across multiple nodes. Yet, existing methods mainly rely on first-order optimization, which suffer from two key bottlenecks: high communication overhead due to frequent transmission of activations and gradients, and substantial memory consumption caused by caching these intermediate states. Zeroth-order (ZO) optimization offers a compelling alternative by eliminating explicit gradient computation and storage, naturally reducing communication and memory costs. Despite these advantages, ZO method… · 篇幅：Regular submission (no more than 12 pages of main content) · OpenReview ID：9wpm4fbBJI

期刊：Transactions on Machine Learning Research · 摘要：Vision-language research has largely developed along two trajectories: autoregressive architectures for multimodal understanding and diffusion-based frameworks for visual generation. Recent systems increasingly combine these capabilities, but their benefits and costs depend on how visual information is represented and how understanding interacts with generation. This survey organizes unified vision-language models by continuous versus discrete visual representations. For continuous representations, we distinguish serial coupling, in which a language model provides a structured condition to a… · 篇幅：Long submission (more than 12 pages of main content) · OpenReview ID：AIMmeOrVFL

期刊：Transactions on Machine Learning Research · 摘要：Hyperparameter Optimization (HPO) is crucial to developing well-performing machine learning models. In order to ease prototyping and benchmarking of HPO methods, we propose carps, a benchmark framework for Comprehensive Automated Research Performance Studies allowing to evaluate N optimizers on M benchmark tasks. In this first release of carps, we focus on the four most important types of HPO task types: blackbox, multi-fidelity, multi-objective, and multi-fidelity-multi-objective. With 3 336 tasks from 5 community benchmark collections and 28 variants of 9 optimizer families, we offer the bi… · 篇幅：Regular submission (no more than 12 pages of main content) · 代码：https://www.github.com/automl/CARP-S · OpenReview ID：AuA8m4I6zI

期刊：Transactions on Machine Learning Research · 摘要：Large language models (LLMs) struggle with multi-step mathematical reasoning, for which inference-time scaling—via sequential or parallel scaling—has emerged as a promising strategy. While recent advances have focused on sequential scaling, we revisit the less-explored parallel scaling approach, verifier-guided beam search, to examine its limitations. In this paper, we argue that its strength is, paradoxically, also its limitation: verifiers can boost performance under limited sample sizes by elevating promising reasoning paths, yet the same mechanism can also hide or cut off the valid paths… · 篇幅：Regular submission (no more than 12 pages of main content) · OpenReview ID：D5VKbIzlrR

期刊：Transactions on Machine Learning Research · 摘要：While many state-of-the-art techniques reconstruct incomplete time series datasets by replacing gaps with modeled estimates, we propose an alternative: encode missing values as an extremal sentinel value, allowing a prediction model to learn from the pattern of missingness. Incomplete data is a common problem in real-world time series forecasting, particularly in environmental monitoring where sensor failures can cause continuous gaps in data. This paper proposes the \emph{Min-Std} method, a novel computationally efficient imputation strategy that encodes missingness in representation-space w… · 篇幅：Regular submission (no more than 12 pages of main content) · OpenReview ID：DMmCMIrrez

期刊：Transactions on Machine Learning Research · 摘要：Neural networks trained with gradient descent exhibit the Edge of Stability (EoS), where Hessian sharpness rises toward $2/\eta$ and the loss oscillates non-monotonically. This paper asks whether that instability persists under Fisher-preconditioned optimization. We analyze effective sharpness, $S_{\text{eff}} = \lambda_{\max}(F^{-1}H)$, and prove the general bound $S_{\text{eff}} \leq 1 + (\epsilon + \delta)/\mu_{\min}(F)$, where $\epsilon = \|H - G\|_2$ is residual curvature, $\delta = \|G - F\|_2$ is the Gauss-Newton/Fisher gap, and $\mu_{\min}(F)$ is the minimum Fisher eigenvalue. The ide… · 篇幅：Long submission (more than 12 pages of main content) · 代码：https://github.com/990aa/sbesfpgd · OpenReview ID：EabuvggEbb

期刊：Transactions on Machine Learning Research · 摘要：Long-horizon tool-using tasks sometimes benefit from revisiting earlier subtasks, but explicit revisitation also adds routing, coordination, and token cost. We study complete cyclic subtask graphs for large language model (LLM) agents: a workflow controller in which executable subtasks are fully connected and a unified state-analysis-and-routing agent selects transitions from natural-language criteria. We evaluate task-specific (Spec-Cyc) and benchmark-generic (Gen-Cyc) cyclic graphs on TextCraft, ALFWorld, and Finance-Agent against ReAct and dependency-directed acyclic workflows. The results… · 篇幅：Regular submission (no more than 12 pages of main content) · 代码：https://github.com/luaygz/cyclic_task_graphs · OpenReview ID：FAkarhXCfI

期刊：Transactions on Machine Learning Research · 摘要：Training stable biological foundation models requires rethinking attention mechanisms: we find that using sigmoid attention as a drop in replacement for softmax attention a) produces better learned representations: on six diverse single-cell datasets, sigmoid achieves 25% higher cell-type separation, better cell-type cohesion metrics, and lower validation loss, b) faster training, models with sigmoid attention train up to 10% faster than their softmax counterparts, and c) more stable training by eliminating inherent sources of instability in softmax attention. We establish that sigmoid attent… · 篇幅：Regular submission (no more than 12 pages of main content) · 代码：https://github.com/MSDLLCpapers/triton-sigmoid · OpenReview ID：GxmQsP5CcU

期刊：Transactions on Machine Learning Research · 摘要：AI tools are suggested as solutions to assist public agencies with heavy workloads. In public defense---where a constitutional right to counsel meets the complexities of law, overwhelming caseloads, and constrained resources---practitioners face especially taxing conditions. Yet, there is little evidence of how AI could meaningfully support defenders' day-to-day work. In partnership with the New Jersey Office of the Public Defender, we develop the OPD Resource Library, a retrieval tool which surfaces relevant appellate briefs to streamline legal research and writing. We show that existing ret… · 篇幅：Regular submission (no more than 12 pages of main content) · 代码：https://github.com/princeton-polaris-lab/PublicDefenderRetrieval · OpenReview ID：HnbKQGRnDt

期刊：Transactions on Machine Learning Research · 摘要：Large Language Models (LLMs) encode factual knowledge within hidden parametric spaces that are difficult to inspect or control. While Sparse Autoencoders (SAEs) can decompose hidden activations into more fine-grained, interpretable features, they often struggle to reliably align these features with human-defined concepts, resulting in entangled and distributed feature representations. To address this, we introduce AlignSAE, a method that aligns SAE features with a predefined ontology through a "pre-train, then post-train" curriculum. After an initial unsupervised training phase, we apply supe… · 篇幅：Regular submission (no more than 12 pages of main content) · 代码：https://github.com/yminglai/AlignSAE · OpenReview ID：I9UjKxW4nq

期刊：Transactions on Machine Learning Research · 摘要：LLM judges are increasingly placed inside an agent's loop, scoring the agent's own attempts and re-prompting until one passes. We show this quietly corrupts measurement: retry-until-PASS is optional stopping against a noisy classifier—it keeps drawing until the judge slips—so the reported pass rate is a biased estimator of true success, upward in the pass-prone retry regimes of interest (and downward under conservative rules such as strict rubrics or unanimous juries). We make this exact. The cap-$K$ gate is a binary classifier with closed-form sensitivity/specificity, and its bias is governe… · 篇幅：Regular submission (no more than 12 pages of main content) · OpenReview ID：J2Yg9vJcYb

期刊：Transactions on Machine Learning Research · 摘要：Prompt tuning, a leading parameter-efficient adaptation paradigm in NLP, has recently been extended to computer vision. Visual prompt tuning (VPT) adapts pre-trained vision transformers (ViTs) by updating a small set of additional prompt parameters. However, existing visual prompts are randomly initialized and do not exploit prior knowledge, such as instructions in NLP. We address this gap by injecting two complementary semantic priors into VPT. Fundamental image priors, including color, texture, and shape, are extracted with classical hand-crafted operators and injected into the input space,… · 篇幅：Regular submission (no more than 12 pages of main content) · 代码：https://xixiaouab.github.io/Cascaded-Semantics/ · OpenReview ID：SSsobNZJPO

期刊：Transactions on Machine Learning Research · 摘要：It remains a critical challenge to adapt policies across domains with mismatched dynamics in reinforcement learning (RL). In this paper, we study cross-domain offline RL, where an offline dataset from another similar source domain can be accessed to enhance policy learning upon a target domain dataset. Directly merging the two datasets may lead to suboptimal performance due to potential dynamics mismatches. Existing approaches typically mitigate this issue through source domain transition filtering or reward modification, which, however, may lead to insufficient exploitation of the valuable s… · 篇幅：Regular submission (no more than 12 pages of main content) · 代码：https://github.com/soulchanging/STC · OpenReview ID：TupiNRpgHw

期刊：Transactions on Machine Learning Research · 摘要：In reinforcement learning (RL), spatial planning is often mediated through rasterized observations processed by convolutional networks, even when the underlying task is continuous and geometric. This discretization can introduce aliasing and obscure topological structure, increasing the difficulty of the spatial problem. We study a hierarchical set-valued geometry-first observation interface for sparse-reward navigation that operates directly on triangulated obstacle geometry. This interface uses learned multi-token aggregation to compress variable-sized geometry into a bounded fixed-size rep… · 篇幅：Regular submission (no more than 12 pages of main content) · 代码：https://github.com/Andresrzr/HGARL/tree/main/rl · OpenReview ID：U10DFNcMrW

期刊：Transactions on Machine Learning Research · 摘要：Deep neural networks are highly susceptible to backdoor attacks, yet most defense methods to date rely on balanced data, overlooking the pervasive class imbalance in real-world scenarios that can amplify backdoor threats. This paper presents the first in-depth investigation of how the dataset imbalance amplifies backdoor vulnerability, showing that (i) the imbalance induces a majority-class bias that increases susceptibility and (ii) conventional defenses degrade significantly as the imbalance grows. To address this, we propose Randomized Probability Perturbation (RPP), a certified poisoned-s… · 篇幅：Regular submission (no more than 12 pages of main content) · 代码：https://github.com/Lin23508/RPP · OpenReview ID：WyPkotlgqQ

期刊：Transactions on Machine Learning Research · 摘要：Predicting generalization from quantities available before target-test evaluation remains a central challenge in deep learning. The systematic benchmark of Jiang et al. (2020) evaluated many generalization measures, but it focused on independent and identically distributed (IID) settings. We revisit this problem for image classifiers evaluated under controlled corruptions and perturbations. Our study uses CIFAR-10-C/P, where the label space and task remain fixed while the input images are degraded or perturbed. This setting also allows us to revisit the robustness concerns raised by Dziugaite… · 篇幅：Long submission (more than 12 pages of main content) · OpenReview ID：X4RoujAYnY

期刊：Transactions on Machine Learning Research · 摘要：Nutritional interventions are important for managing chronic health conditions, but current computational methods provide limited support for personalized dietary guidance. We identify three key gaps: (1) dietary pattern studies often ignore real-world constraints such as socioeconomic status, comorbidities, and limited food access; (2) recommendation systems rarely explain why a particular food helps a given patient; and (3) no unified benchmark evaluates methods across the connected tasks needed for nutritional interventions. We introduce GLEN-Bench, the first comprehensive graph-language b… · 篇幅：Regular submission (no more than 12 pages of main content) · OpenReview ID：XMUbOKWsem

期刊：Transactions on Machine Learning Research · 摘要：Causal Bayesian Optimization (CBO) integrates causal inference with Bayesian optimization to enable sample-efficient intervention selection in systems governed by causal structure. This survey provides a comprehensive and systematic review of the CBO landscape, organizing the growing literature through a unified BO-loop perspective that reveals how causal assumptions shape four core components: intervention search spaces, surrogate construction, acquisition design, and decision policies. We organize methods along recurring design axes, including graph and system-knowledge assumptions, environ… · 篇幅：Long submission (more than 12 pages of main content) · 代码：https://github.com/chenfeng-huang/CBO-Benchmark-TMLR-2026 · OpenReview ID：XT6DC37m5I

期刊：Transactions on Machine Learning Research · 摘要：Recent work has analyzed temporal-difference (TD) learning with target networks through an optimization view and established linear convergence under a force-dominance condition, but these results typically rely on global smoothness, i.e., a uniform upper bound on curvature. This assumption can fail even when the inner problem is well posed, since curvature encountered during training can grow with the scale of TD-residual-induced gradients. We retain the stabilized regime in which the inner problem is strongly convex in the optimization variable, in order to isolate upper-curvature growth ef… · 篇幅：Regular submission (no more than 12 pages of main content) · OpenReview ID：Xvyehi4izc

期刊：Transactions on Machine Learning Research · 摘要：We introduce SLayR, Scene Layout Generation with Rectified flow, a novel transformer-based model for text-to-layout generation, which can integrate into a complete text-to-image pipeline. SLayR addresses a domain in which current text-to-image pipelines struggle: generating scene layouts that are of significant variety and plausibility, when the given prompt is ambiguous and does not provide constraints on the scene. In this setting, SLayR surpasses existing baselines, including LLMs. To accurately evaluate the layout generation, we introduce a new benchmark suite, including numerical metrics… · 篇幅：Regular submission (no more than 12 pages of main content) · OpenReview ID：YGsQxG5ubd

期刊：Transactions on Machine Learning Research · 摘要：Bayesian optimization (BO) is an effective technique for black-box optimization. However, its applicability is typically limited to moderate-budget problems due to the cubic complexity of fitting the Gaussian process (GP) surrogate model. In large-budget scenarios, directly employing the standard GP model faces significant challenges in computational time and resource requirements. In this paper, we propose Gradient-based Sample Selection Bayesian Optimization (GSSBO), a subset-maintenance approach designed to enhance the computational efficiency of BO. Here, ``gradient-based'' refers to resp… · 篇幅：Regular submission (no more than 12 pages of main content) · 代码：https://github.com/Qiyu-Wei/GSSBO-Official.git · OpenReview ID：Ysr1zUeuxz

期刊：Transactions on Machine Learning Research · 摘要：Differential privacy (DP) is a formal privacy framework that enables training machine learning (ML) models while protecting individuals' data. As pointed out by prior work, ML models are part of larger systems, which can lead to so-called privacy side-channels even if the model training itself is DP. We identify the output label space of a classification model as such a privacy side-channel and show a concrete privacy attack that exploits it. The side-channel becomes highly relevant in continual learning (CL), where the output label space changes over time. To reason about privacy guarantees… · 篇幅：Long submission (more than 12 pages of main content) · 代码：https://github.com/TrustworthyMLHelsinki/dp-continual-learning · OpenReview ID：ZshFgRQWrm

期刊：Transactions on Machine Learning Research · 摘要：The ability to robustly maintain and update continuous variables is a hallmark of working memory. While classical continuous attractor networks suffer from severe fine-tuning fragility, standard artificial recurrent neural networks (RNNs) like GRUs and LSTMs typically fail to stably learn continuous manifolds, instead shattering the state space into discretized point attractors. To bridge this gap, we draw inspiration from divisive normalization, a canonical neural computation widely observed across cortical circuits, and propose the Recurrent Divisive Normalization Network (RDNN), a minimal… · 篇幅：Regular submission (no more than 12 pages of main content) · 代码：https://github.com/mizunashi-sh/RDNN · OpenReview ID：a43l19lyfC

期刊：Transactions on Machine Learning Research · 摘要：Recurrent neural networks are often used as mechanistic models of learning and control, but closed-loop training creates reproducibility challenges because a model's actions alter future inputs. We conduct a claim-level reproducibility study of Ger and Barak's closed-loop RNN learning dynamics, testing independent implementation, seed variation, protocol perturbations, coupled-system diagnostics, and architecture/task transfer. Under a main-text-aligned double-integrator protocol, the trajectory-level peak, not a persistent final gap, reproduces strongly: 50/50 paired seeds show the post-init… · 篇幅：Regular submission (no more than 12 pages of main content) · 代码：https://github.com/aaravsinhaofficial/closed-loop-rnn-audit · OpenReview ID：e0pSqIqwXO

期刊：Transactions on Machine Learning Research · 摘要：Conditional generative models map input variables to complex, high-dimensional distributions, enabling realistic sample generation in a diverse set of domains. A critical challenge with these models is the absence of calibrated uncertainty, which undermines trust in individual outputs for high-stakes applications. To address this issue, we propose a systematic conformal prediction approach tailored to conditional generative models, leveraging density estimation on model-generated samples. We introduce a novel method called CP4Gen, which utilizes cluster-based density estimation to construct p… · 篇幅：Regular submission (no more than 12 pages of main content) · 代码：https://github.com/Earth-Intelligence-Lab/CP4GenerativeModel · OpenReview ID：goxeVsh9Po

期刊：Transactions on Machine Learning Research · 摘要：Adapting policies in piecewise stationary environments - where the underlying properties remain stable for periods but abruptly change at unknown points - remains a challenge in reinforcement learning (RL). Addressing this problem using context-based offline meta-RL, which enables generalization to new online tasks from offline data, is particularly appealing, as it avoids the risks associated with online exploration. These methods encode transition history (the context) into a task representation and condition the policy and value function to enable generalization. We show that existing appr… · 篇幅：Regular submission (no more than 12 pages of main content) · 代码：https://github.com/MohammadrezaNakhaei/CAMEL-official · OpenReview ID：gp1mAySr25

期刊：Transactions on Machine Learning Research · 摘要：The recently proposed Microcanonical Hamiltonian Monte Carlo algorithm has not yet been studied in detail from a thermodynamic point of view; this work aims to fill that gap. We demonstrate how thermodynamical state variables and potentials can be derived and thereby demonstrate that the construction of the algorithm formally represents a microcanonical thermodynamic ensemble. In particular, we demonstrate (analytically and numerically) that the algorithm fulfils the Helmholtz theorem, an alternative formulation of the first law of thermodynamics. Furthermore, we construct a new sampling algo… · 篇幅：Long submission (more than 12 pages of main content) · OpenReview ID：jbVVrks50D

期刊：Transactions on Machine Learning Research · 摘要：Noisy labelled datasets are generally inexpensive compared to clean labelled datasets, and the same is true for graph data. In this paper, we propose a denoising technique DeGLIF: Denoising Graph Data using Leave-One-Out Influence Function. DeGLIF uses a small set of clean data and the leave-one-out influence function to make label noise robust node-level prediction on graph data. Leave-one-out influence function approximates the change in the model parameters if a training point is removed from the training dataset. Recent advances propose a way to calculate the leave-one-out influence funct… · 篇幅：Regular submission (no more than 12 pages of main content) · 代码：https://github.com/pintu-dot/DeGLIF · OpenReview ID：pcs5DmBtUJ

期刊：Transactions on Machine Learning Research · 摘要：While Large Language Models (LLMs) have substantially advanced text-to-code generation, many real programming tasks specify intent through visual artifacts such as screenshots, charts, and videos. These tasks require models to connect visual perception to executable programs, as correctness depends not only on syntax but also on layout, data semantics, and domain-specific constraints that apply after execution. This survey reviews Multimodal Code Intelligence, covering systems that generate, edit, refine, or reason with code under visually grounded inputs and outputs. We first formulate the f… · 篇幅：Long submission (more than 12 pages of main content) · 代码：https://github.com/xjywhu/Awesome-Multimodal-LLM-for-Code · OpenReview ID：pn2sdu3Vrf

期刊：Transactions on Machine Learning Research · 摘要：Geospatial foundation-model (GeoFM) benchmarks for agricultural field-extent segmentation often rely on cropland land-cover proxies and compare against per-pixel spectral baselines. We measure how these choices affect conclusions using identical single-date Sentinel-2 pixels from six countries. Replacing polygon-derived field labels with ESA WorldCover cropland raises random-forest AUROC from 0.55–0.82 to 0.79–0.96, showing that the proxy creates a more spectrally separable task. On the actual field-extent labels, a from-scratch U-Net reaches 0.89–0.98 AUROC and has higher point AUROC than a… · 篇幅：Long submission (more than 12 pages of main content) · 代码：https://github.com/thesantoshpant/auditing-geofm-evaluation · OpenReview ID：qRXVTe1yYp

期刊：Transactions on Machine Learning Research · 摘要：Large language model pretraining is compute-intensive, yet many tokens contribute marginally to learning, resulting in inefficiency. We introduce Efficient Selective Language Modeling (ESLM), an online, risk-aware batch selection algorithm that improves training efficiency and distributional robustness. ESLM operates in two phases: (i) instance-level selection via a shallow early-exit model pass that computes proxy per-instance statistics (e.g., loss or entropy) and retains data points using value-at-risk thresholding; and (ii) loss shaping with token-level selection via risk-aware thresholdi… · 篇幅：Regular submission (no more than 12 pages of main content) · 代码：https://github.com/melisilaydabal/eslm · OpenReview ID：vcynsg6rMN

期刊：Transactions on Machine Learning Research · 摘要：Data rehearsal has emerged as a leading approach for mitigating catastrophic forgetting in Continual Reinforcement Learning (CRL). However, existing work remains confined to policy gradient frameworks, regularizing only actors due to the performance degradation incurred by critic regularization. This actor-centric approach overlooks the potential of data rehearsal for value function approximation. Moreover, existing evaluations in CRL rarely consider multi-cyclic environments where task sequences repeat, a critical real-world scenario that exacerbates forgetting and plasticity. We investigate… · 篇幅：Regular submission (no more than 12 pages of main content) · 代码：https://github.com/Bpoole908/Qreg · OpenReview ID：wYayhflqqR

期刊：Transactions on Machine Learning Research · 摘要：Inferring the causal relationships among a set of variables in the form of a directed acyclic graph (DAG) is an important but notoriously challenging problem. Recently, advancements in high-throughput genomic perturbation screens have inspired the development of methods that leverage interventional data to improve model identification. However, existing methods still suffer from poor performance on large-scale tasks and fail to quantify uncertainty. Here, we propose Interventional Bayesian Causal Discovery (IBCD), an empirical Bayesian framework that infers the causal graph by using intervent… · 篇幅：Regular submission (no more than 12 pages of main content) · 代码：https://github.com/bcbg-bio/IBCD · OpenReview ID：yW4T2fsf0l

期刊：Transactions on Machine Learning Research · 摘要：Computer-use agents (CUAs) hold promise for automating everyday digital tasks, but their performance on long-horizon, complex problems remains unreliable. Single-rollout execution is brittle, with small errors compounding over time and leading to high variance in outcomes. While prior work has attempted to scale within a single rollout, such approaches have yielded limited gains. Scaling over multiple rollouts offers a more promising alternative but doing so effectively is challenging due to the difficulty of evaluating and selecting among long-horizon agent behaviors. We introduce Behavior J… · 篇幅：Regular submission (no more than 12 pages of main content) · OpenReview ID：eve4jBYa8D

期刊：Transactions on Machine Learning Research · 摘要：Language models can treat semantically distinct inputs as interchangeable at the representation level, creating blind spots that standard sparse autoencoder (SAE) objectives do not target. In safety-critical settings such as clinical dosage extraction, legal clause interpretation, or financial amount verication, such failures can propagate silently into downstream decisions. We study this problem as one of feature-basis orientation. Building on a symmetry-matching view from algebraic error-detection theory, we add a diferentiable V_Gini regularisation term to SAE training that penalises uneve… · 篇幅：Long submission (more than 12 pages of main content) · 代码：https://github.com/csabibme/blind_spot_reduction_SAE_TMLR · OpenReview ID：NWWpKC9CZH

期刊：Transactions on Machine Learning Research · 摘要：We propose TimePre, a simple framework that unifies the efficiency of Multilayer Perceptron (MLP)-based models with the distributional flexibility of Multiple Choice Learning (MCL) for Probabilistic Time-Series Forecasting (PTSF). Stabilized Instance Normalization (SIN), the core of TimePre, is a normalization layer that explicitly addresses the trade-off among accuracy, efficiency, and stability. SIN stabilizes the hybrid architecture by correcting channel-wise statistical shifts, thereby preventing the hypothesis collapse that otherwise destabilizes this combination. Extensive experiments o… · 篇幅：Regular submission (no more than 12 pages of main content) · 代码：https://github.com/LyCharles/TimePre/ · OpenReview ID：yQLnvkJMbP

期刊：Transactions on Machine Learning Research · 摘要：Earth Observation (EO) uses satellite and aerial imagery to monitor the Earth’s surface, supporting critical applications in infrastructure, agriculture, and climate change. As governments and industry scale EO pipelines, reliable automation has become essential. Yet, current Vision-Language Models are limited to coarse-grained perception, struggling to execute the precise, multi-step reasoning required for operational decision-making. Recent evaluations on benchmarks like GeoBench-VLM highlight this shortcoming: even state-of-the-art models show low accuracy and frequently struggle with task… · 篇幅：Regular submission (no more than 12 pages of main content) · OpenReview ID：7yUrnyFgEq

期刊：Transactions on Machine Learning Research · 摘要：Many modern deep learning applications require balancing multiple objectives that are often conflicting. Examples include multi-task learning, fairness-aware learning, and the alignment of Large Language Models (LLMs). This leads to multi-objective deep learning, which tries to find optimal trade-offs or Pareto-optimal solutions by adapting mathematical principles from the field of Multi-Objective Optimization (MOO). However, directly applying gradient-based MOO techniques to deep neural networks presents unique challenges, including high computational costs, optimization instability, and the… · 篇幅：Long submission (more than 12 pages of main content) · OpenReview ID：eCUcXXH3PS

期刊：Transactions on Machine Learning Research · 摘要：As AI systems become more powerful, there is growing concern that they may act in ways misaligned with human interests. However, this concern presupposes that AI models have consistent preferences and that these preferences influence their behavior. These claims have yet to be rigorously tested. Here, we examine one precondition for misalignment: whether LLM preferences predict downstream behavior. The questions raised in this paper are theoretically motivated by the concept of "sandbagging" from the misalignment literature, though sandbagging itself is not directly measured here. We evaluate… · 篇幅：Regular submission (no more than 12 pages of main content) · OpenReview ID：RmWSM3sFOr

期刊：Transactions on Machine Learning Research · 摘要：Large Language Models (LLMs) have proven effective in understanding and generating extremely long contexts. Recently, linear attention mechanisms have garnered significant attention, as they can largely reduce the quadratic computational complexity of traditional attention mechanisms to linear complexity relative to token sequence length, thus balancing effectiveness and efficiency in LLM training and inference. This survey mainly focuses on a broad spectrum of linear attention techniques, including traditional linear attention methods, state space models (SSMs), and linear recurrent neural n… · 篇幅：Long submission (more than 12 pages of main content) · OpenReview ID：ilkVX8aGmQ

期刊：Transactions on Machine Learning Research · 摘要：This paper proposes to perform image restoration through a state-dependent mean-reverting forward diffusion (FoD) process. In contrast to traditional diffusion-based approaches that rely on a coupled forward-backward diffusion scheme, FoD directly learns image restoration through a single forward diffusion process, yielding a simple yet efficient framework. The core of FoD is a state-dependent stochastic differential equation (SDE) that involves a mean-reverting term in both the drift and diffusion functions. This mean-reverting structure drives the low-quality data toward the clean endpoint… · 篇幅：Regular submission (no more than 12 pages of main content) · 代码：https://github.com/Algolzw/FoD · OpenReview ID：Eq9k6Va3hY