Daily TMLR digest for Feb 12, 2026
TMLR
Accepted papers
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Title: Moment Constrained Optimal Transport for Control Applications
Authors: Thomas Le Corre, Ana Busic, Sean P. Meyn
Abstract: This paper concerns the application of techniques from optimal transport (OT) to mean field control, in which the probability measures of interest in OT correspond to empirical distributions associated with a large collection of controlled agents. The control objective of interest motivates a one-sided relaxation of OT, in which the first marginal is fixed and the second marginal is constrained to a “moment class”: a set of probability measures defined by generalized moment constraints. This relaxation is particularly interesting for control problems as it enables the coordination of agents without the need to know the desired distribution beforehand. The inclusion of an entropic regularizer is motivated by both computational considerations, and also to impose hard constraints on agent behavior. A computational approach inspired by the Sinkhorn algorithm is proposed to solve this problem. This new approach to distributed control is illustrated with an application of charging a fleet of electric vehicles while satisfying grid constraints. An online version is proposed and applied in a case study on the ElaadNL dataset containing 10,000 electric vehicle charging sessions in the Netherlands. This empirical validation demonstrates the applicability of the proposed approach to optimizing flexibility while respecting grid constraints.
URL: https://openreview.net/forum?id=2hAtSpnat9
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Title: Concept Flow Models: Anchoring Concept-Based Reasoning with Hierarchical Bottlenecks
Authors: Ya Wang, Adrian Paschke
Abstract: Concept Bottleneck Models (CBMs) enhance interpretability by projecting learned features into a human-understandable concept space. Recent approaches leverage vision-language models to generate concept embeddings, reducing the need for manual concept annotations. However, these models suffer from a critical limitation: as the number of concepts approaches the embedding dimension, information leakage increases, enabling the model to exploit spurious or semantically irrelevant correlations and undermining interpretability. In this work, we propose Concept Flow Models (CFMs), which replace the flat bottleneck with a hierarchical, concept-driven decision tree. Each internal node in the hierarchy focuses on a localized subset of discriminative concepts, progressively narrowing the prediction scope. Our framework automatically constructs decision hierarchies from visual embeddings, distributes semantic concepts at each hierarchy level, and trains differentiable concept weights through probabilistic tree traversal. Extensive experiments on diverse benchmarks demonstrate that CFMs match the predictive performance of flat CBMs, while substantially reducing effective concept usage and information leakage. Furthermore, CFMs yield stepwise decision flows that enable transparent and auditable model reasoning.
URL: https://openreview.net/forum?id=TNYLf65I3I
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Title: Semantic-aware Adversarial Fine-tuning for CLIP
Authors: Jiacheng Zhang, Jinhao Li, Hanxun Huang, Sarah Monazam Erfani, Benjamin I. P. Rubinstein, Feng Liu
Abstract: Recent studies have shown that CLIP model's adversarial robustness in zero-shot classification tasks can be enhanced by adversarially fine-tuning its image encoder with adversarial examples (AEs), which are generated by minimizing the cosine similarity between images and a hand-crafted template (e.g., ''A photo of a {label}''). However, it has been shown that the cosine similarity between a single image and a single hand-crafted template is insufficient to measure the similarity for image-text pairs. Building on this, in this paper, we find that the AEs generated using cosine similarity may fail to fool CLIP when the similarity metric is replaced with semantically enriched alternatives, making the image encoder fine-tuned with these AEs less robust. To overcome this issue, we first propose a semantic-ensemble attack to generate semantic-aware AEs by minimizing the average similarity between the original image and an ensemble of refined textual descriptions. These descriptions are initially generated by a foundation model to capture core semantic features beyond hand-crafted templates and are then refined to reduce hallucinations. To this end, we propose Semantic-aware Adversarial Fine-Tuning (SAFT), which fine-tunes CLIP's image encoder with semantic-aware AEs. Extensive experiments show that SAFT outperforms current methods, achieving substantial improvements in zero-shot adversarial robustness across 16 datasets. Our code is available at: https://github.com/tmlr-group/SAFT.
URL: https://openreview.net/forum?id=SzZOBzueK0
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Title: Byzantine-Robust Gossip: Insights from a Dual Approach
Authors: Renaud Gaucher, Hadrien Hendrikx, Aymeric Dieuleveut
Abstract: Distributed learning has many computational benefits but is vulnerable to attacks from a subset of devices transmitting incorrect information. This paper investigates Byzantine-resilient algorithms in a decentralized setting, where devices communicate directly in a peer-to-peer manner within a communication network. We leverage the so-called dual approach for decentralized optimization and propose a Byzantine-robust algorithm. We provide convergence guarantees in the average consensus subcase, discuss the potential of the dual approach beyond this subcase, and re-interpret existing algorithms using the dual framework. Lastly, we experimentally show the soundness of our method.
URL: https://openreview.net/forum?id=wrLiUpfk4s
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Title: Learning to Defer with an Uncertain Rejector via Conformal Prediction
Authors: Yizirui Fang, Eric Nalisnick
Abstract: Learning to defer (L2D) aims to optimize human-AI collaboration by allocating prediction tasks to either a machine learning model or a human expert, depending on which is most likely to be correct. This allocation decision is governed by a rejector: a meta-model that routes inputs based on estimated success probabilities. In practice, a poorly fit or otherwise misspecified rejector can jeopardize the entire L2D workflow due to its crucial role in allocating prediction tasks. In this work, we perform uncertainty quantification for the rejector. We use conformal prediction to allow the rejector to output prediction sets or intervals instead of just the binary outcome of ‘defer’ or not. On tasks ranging from image to hate speech classification, we demonstrate that the uncertainty in the rejector translates to safer decisions via two forms of selective prediction.
URL: https://openreview.net/forum?id=SZQJ8K2DUe
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Title: CEPAE: Conditional Entropy-Penalized Autoencoders for Time Series Counterfactuals
Authors: Tomas Garriga, Gerard Sanz, Eduard Serrahima de Cambra, Axel Brando
Abstract: The ability to accurately perform counterfactual inference on time series is crucial for decision-making in fields like finance, healthcare, and marketing, as it allows us to understand the impact of events or treatments on outcomes over time. In this paper, we introduce a new counterfactual inference approach tailored to time series data impacted by market events, which arises from an industrial context. Utilizing the abduction-action-prediction procedure and the Structural Causal Model framework, we begin employing methods based on variational autoencoders and adversarial autoencoders, both previously used in counterfactual works although not in time series settings. Then, we present the Conditional Entropy-Penalized Autoencoder (CEPAE), a novel autoencoder-based approach for counterfactual inference, which employs an entropy penalization loss over the latent space to achieve disentangled data representations. We validate our approach both theoretically and experimentally on synthetic, semi-synthetic, and real-world datasets, showing that CEPAE outperforms the other approaches in the evaluated metrics.
URL: https://openreview.net/forum?id=X6lrzqOtQo
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Title: Benchmarking Missing Data Imputation Methods in Socioeconomic Surveys
Authors: Siyi Sun, David Antony Selby, Yunchuan Huang, Ayush Patnaik, Sebastian Josef Vollmer, Seth Flaxman, Anisoara Calinescu
Abstract: Missing data imputation is a core challenge in socioeconomic surveys, where data is often longitudinal, hierarchical, high-dimensional, not independent and identically distributed, and missing under complex mechanisms. Socioeconomic datasets like the Consumer Pyramids Household Survey (CPHS)-the largest continuous household survey in India since 2014, covering 174,000 households-highlight the importance of robust imputation, which can reduce survey costs, preserve statistical power, and enable timely policy analysis. This paper systematically evaluates these methods under three missingness mechanisms: missing completely at random (MCAR), missing at random (MAR), and missing not at random (MNAR), across five missingness ratios ranging from 10% to 50%. We evaluate imputation performance on both continuous and categorical variables, assess the impact on downstream tasks, and compare the computational efficiency of each method. Our results indicate that classical machine learning methods such as MissForest and HyperImpute remain strong baselines with favorable trade-offs between accuracy and efficiency, while deep learning methods perform better under complex missingness patterns and higher missingness ratios, but face scalability challenges. We ran experiments on CPHS and multiple synthetic survey datasets, and found consistent patterns across them. Our framework aims to provide a reliable benchmark for structured socioeconomic surveys, and addresses the critical gap in reproducible, domain-specific evaluation of imputation methods. The open-source code is provided.
URL: https://openreview.net/forum?id=HLhi9xhRw6
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Title: Implicit Probabilistic Reasoning Does Not Reflect Explicit Answers in Large Language Models
Authors: Manuel Mondal, Ljiljana Dolamic, Gérôme Bovet, Philippe Cudre-Mauroux, Julien Audiffren
Abstract: The handling of probabilities in the form of uncertainty or partial information is an essential task for LLMs in many settings and applications. A common approach to evaluate an LLM's probabilistic reasoning capabilities is to assess its ability to answer questions pertaining to probability through the use of multiple-choice questions (MCQs). However, this paradigm, which we refer to as explicit probabilistic reasoning, has been shown in the literature to yield significant limitations (e.g., sensitivity to answer ordering). In this work, we introduce an alternative approach, named implicit probabilistic reasoning, which evaluates the models' ability to integrate probabilistic reasoning into their text generation process. To achieve this, we rephrase MCQs as text-completion scenarios with a determined set of outcomes and compare the model's next-token probability assignments to the true likelihood of the outcomes. In line with previous work, we find that models exhibit solid performance in their explicit probabilistic reasoning (i.e., answers to MCQs). However, during text completion (i.e., implicit probabilistic reasoning), where the same information must be taken into account to generate text, the models' predictions often significantly diverge from the known ground truth. For instance, our evaluation method reveals that implicit probabilistic reasoning is improperly influenced by many factors, such as independent prior events, partial observations about a result, or statistical background information. All of these issues can cause erroneous results to be produced in text generation, which are not detected by conventional MCQ-based evaluation.
URL: https://openreview.net/forum?id=HaaAY4ZXPa
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New submissions
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Title: Expected Free Energy-based Planning as Variational Inference
Abstract: Planning under uncertainty requires agents to balance goal achievement with information gathering. Active inference addresses this through the Expected Free Energy (EFE), a cost function that unifies instrumental and epistemic objectives. However, existing EFE-based methods typically employ specialized optimization procedures that are difficult to extend or analyze. In this paper, we show that EFE-based planning can be formulated as standard variational inference on a generative model augmented with epistemic priors. Our main result demonstrates that minimizing a Variational Free Energy functional with appropriately chosen priors yields a decomposition into expected plan costs (the EFE) plus a complexity term. This formulation reinforces theoretical consistency with the Free Energy Principle by casting planning as the same inferential process that governs perception and learning. We validate our approach on a T-maze task, demonstrating that the epistemic priors are sufficient for inducing information-seeking behavior.
URL: https://openreview.net/forum?id=Kzm8I1oS1s
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Title: Rethinking Memory Mechanisms of Foundation Agents in the Second Half: A Survey
Abstract: The research of artificial intelligence is undergoing a paradigm shift from prioritizing model innovations over 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 environments, where agents face context explosion and must continuously accumulate, manage, and selectively reuse large volumes of information across extended interactions. Memory, with hundreds of papers released this year, therefore emerges as the critical solution to fill the utility gap. In this survey, we provide a unified view of foundation agent memory along three dimensions: memory substrate (internal and external), cognitive mechanism (episodic, semantic, sensory, working, and procedural), and memory subject (agent- and user-centric). We then analyze how memory is instantiated and operated under different agent topologies and highlight learning policies over memory operations. Finally, we review evaluation benchmarks and metrics for assessing memory utility, and outline various open challenges and future directions.
URL: https://openreview.net/forum?id=XycbogUAeJ
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Title: ViscoReg: Neural Signed Distance Functions via Viscosity Solutions
Abstract: Implicit Neural Representations (INRs) that learn Signed Distance Functions (SDFs) from point cloud data represent the state-of-the-art for geometrically accurate 3D scene reconstruction. However, training these Neural SDFs often involves enforcing the Eikonal equation, an ill-posed equation that also leads to unstable gradient flows. Numerical Eikonal solvers have relied on viscosity approaches for regularization and stability. Motivated by this well-established theory, we introduce ViscoReg, a novel regularizer for Neural SDF methods that provably stabilizes training. Empirically, ViscoReg outperforms state-of-the-art approaches such as SIREN, DiGS, StEik, and HotSpot across most metrics on ShapeNet, Surface Reconstruction Benchmark, 3D scene reconstruction and reconstruction from real scans. We also establish novel generalization error estimates for Neural SDFs in terms of the training error, using the theory of viscosity solutions. Our empirical and theoretical results provide confidence in the general applicability of our method.
URL: https://openreview.net/forum?id=DWnMkBU4sF
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Title: ImageNot: A contrast with ImageNet preserves model rankings
Abstract: We introduce ImageNot, a dataset constructed explicitly to be drastically different than ImageNet while matching its scale. ImageNot is designed to test the external validity of deep learning progress on ImageNet. We show that key model architectures developed for ImageNet over the years rank identically to how they rank on ImageNet when trained from scratch and evaluated on ImageNot. Moreover, the relative improvements of each model over earlier models strongly correlate in both datasets. Our work demonstrates a surprising degree of external validity in the relative performance of image classification models when trained and evaluated on an entirely different dataset. This stands in contrast with absolute accuracy numbers that typically drop sharply even under small changes to a dataset.
URL: https://openreview.net/forum?id=YVbhMerXv9
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Title: Zero-Shot Model Search via Text-to-Logit Matching
Abstract: With the increasing number of publicly available models, there are pre-trained, online models for many tasks that users require. In practice, users cannot find the relevant models as current search methods are text-based using the documentation which most models lack of. This paper presents ProbeLog, a method for retrieving classification models that can recognize a target concept, such as "Dog", without access to model metadata or training data. Specifically, ProbeLog computes a descriptor for each output dimension (logit) of each model, by observing its responses to a fixed set of inputs (probes). Similarly, we compute how the target concept is related to each probe. By measuring the distance between the probe responses of logits and concepts, we can identify logits that recognize the target concept. This enables zero-shot, text-based model retrieval ("find all logits corresponding to dogs"). To prevent hubbing, we calibrate the distances of each logit, according to other closely related concepts. We demonstrate that ProbeLog achieves high retrieval accuracy, both in ImageNet and real-world fine-grained search tasks, while being scalable to full-size repositories. Importantly, further analysis reveals that the retrieval order is highly correlated with model and logit accuracies, thus allowing ProbeLog to find suitable and accurate models for users tasks in a zero-shot manner.
URL: https://openreview.net/forum?id=m4Qmst7iH7
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Title: Twin: Tuning Learning Rate and Weight Decay of Deep Homogeneous Classifiers without Validation
Abstract: We introduce \textbf{T}une \textbf{w}ithout Validat\textbf{i}o\textbf{n} (Twin), a simple and effective pipeline for tuning learning rate and weight decay of homogeneous classifiers without validation sets, eliminating the need to hold out data and avoiding the two-step process.
Twin leverages the margin-maximization dynamics of homogeneous networks and an empirical bias–variance scaling law that links training and test losses across hyper-parameter configurations.
This mathematical modeling yields a regime-dependent, validation-free selection rule: in the \emph{non-separable} regime, training loss is monotonic in test loss and therefore predictive of generalization, whereas in the \emph{separable} regime, the parameter norm becomes a reliable indicator of generalization due to margin maximization.
Across 37 dataset-architecture configurations for image classification, we demonstrate that Twin achieves a mean absolute error of 1.28\% compared to an \textit{Oracle} baseline that selects HPs using test accuracy.
We demonstrate Twin’s benefits in scenarios where validation data may be scarce, such as small-data regimes, or difficult and costly to collect, as in medical imaging tasks.
We plan to release our code.
URL: https://openreview.net/forum?id=1SIP2M2HJa
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Title: One Rank at a Time: Cascading Error Dynamics in Sequential Learning
Abstract: Sequential learning --where complex tasks are broken down into simpler, hierarchical components-- has emerged as a paradigm in AI. This paper views sequential learning through the lens of low-rank linear regression, focusing specifically on how errors propagate when learning rank-1 subspaces sequentially. We present an analysis framework that decomposes the learning process into a series of rank-1 estimation problems, where each subsequent estimation depends on the accuracy of previous steps. Our aim is explanatory rather than comparative: we analyze error propagation and derive compute allocation guidance without claiming superiority over joint or one-shot training. Our contribution is a characterization of the error propagation in this sequential process, establishing bounds on how errors --e.g., due to limited computational budgets and finite precision-- affect the overall model accuracy. We prove that these errors compound in predictable ways, with implications for both algorithmic design and stability guarantees.
URL: https://openreview.net/forum?id=EG7XJANxhX
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