Daily TMLR digest for Jan 26, 2026
TMLR
New certifications
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J2C Certification: Classification of high-dimensional data with spiked covariance matrix structure
Yin-Jen Chen, Minh Tang
https://openreview.net/forum?id=6bQDtTbaQs
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Accepted papers
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Title: Learning to Imitate with Less: Efficient Individual Behavior Modeling in Chess
Authors: Zhenwei Tang, Difan Jiao, Eric Xue, Reid McIlroy-Young, Jon Kleinberg, Siddhartha Sen, Ashton Anderson
Abstract: As humans seek to collaborate with, learn from, and better understand artificial intelligence systems, developing AIs that can accurately emulate individual decision-making becomes increasingly important. Chess, a long-standing AI benchmark with precise skill measurement, offers an ideal testbed for human-AI alignment. However, existing approaches to modeling human behavior require prohibitively large amounts of data from each individual, making them impractical for new or sparsely represented users. In this work, we introduce Maia4All, a framework designed to learn and adapt to individual decision-making styles efficiently, even with limited data. Maia4All achieves this through a two-stage optimization process: (1) an enrichment step, which bridges population and individual-level human behavior modeling with a prototype-enriched model, and (2) a democratization step, which leverages ability levels or user prototypes to initialize and refine individual embeddings with minimal data. Our experimental results show that Maia4All can accurately predict individual moves and profile behavioral patterns with high fidelity, establishing a new standard for personalized human-like AI behavior modeling in chess. Maia4All achieves individual human behavior modeling in chess with only 20 games, compared to the 5,000 games required previously, representing a significant improvement in data efficiency. Our work provides an example of how population AI systems can flexibly adapt to individual users using a prototype-enriched model as a bridge. This approach extends beyond chess, as shown in our case study on idiosyncratic LLMs, highlighting its potential for broader applications in personalized AI adaptation.
URL: https://openreview.net/forum?id=iw4kjcw319
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Title: Classification of high-dimensional data with spiked covariance matrix structure
Authors: Yin-Jen Chen, Minh Tang
Abstract: We study the classification problem for
high-dimensional data with $n$ observations on $p$ features where the
$p \times p$ covariance matrix $\Sigma$ exhibits a spiked eigenvalue structure and the
vector $\zeta$, given by the difference between the {\em whitened} mean
vectors, is sparse. We analyze an adaptive
classifier (adaptive with respect to the sparsity $s$) that first
performs dimension reduction on the feature vectors prior to classification in
the dimensionally reduced space, i.e., the classifier whitens
the data, then screens the features by keeping only those corresponding
to the $s$ largest coordinates of $\zeta$ and finally applies Fisher
linear discriminant on the selected features. Leveraging recent
results on entrywise matrix perturbation bounds for covariance
matrices, we show that the resulting classifier is Bayes optimal
whenever $n \rightarrow \infty$ and $s \sqrt{n^{-1} \ln p} \rightarrow
0$. Notably, our theory also guarantees Bayes optimality for the corresponding quadratic discriminant analysis (QDA). Experimental results on real and synthetic data further indicate that the proposed approach is competitive with state-of-the-art methods while operating on a substantially lower-dimensional representation.
URL: https://openreview.net/forum?id=6bQDtTbaQs
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New submissions
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Title: SimAM-Exit: Attention-Guided Dynamic Inference for Efficient Image Classification
Abstract: Early-exit networks enable dynamic inference by allowing samples to exit at intermediate layers based on prediction confidence, reducing computational costs for "easy" inputs. However, existing approaches rely solely on classifier confidence, which can be miscalibrated—leading to incorrect early exits on ambiguous samples. We propose SimAM-Exit, a novel framework with a joint exit criterion that combines classifier confidence with attention energy: a sample exits only when confidence is high AND attention energy is low. This dual verification catches overconfident errors that confidence-only methods miss. We leverage SimAM (Simple, Parameter-Free Attention Module) to compute feature complexity without adding any learnable parameters—a critical advantage over SE/CBAM-based approaches that add 75-150K parameters. Comprehensive experiments demonstrate that our joint criterion: (1) achieves 58.73% accuracy on CIFAR-100, outperforming all baseline exit criteria, (2) provides 41.7% FLOPs reduction on CIFAR-10 (85.64% accuracy) with balanced accuracy-efficiency trade-off, (3) reveals through Pareto frontier analysis that early-exit effectiveness scales inversely with dataset complexity, and (4) consistently outperforms confidence-only, entropy, patience, and adaptive baselines across both datasets using identical backbones. Our ablation study proves that the joint criterion outperforms both confidence-only AND energy-only criteria individually. This is the first work to use attention energy as a direct exit criterion, introducing a new paradigm for dynamic inference in resource-constrained environments.
URL: https://openreview.net/forum?id=0oUN5i6g6S
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Title: Grothendieck Graph Neural Networks Framework: An Algebraic Platform for Crafting Topology-Aware GNNs
Abstract: Graph Neural Networks (GNNs) are almost universally built on a single primitive: the neighborhood. Regardless of architectural variations, message passing ultimately aggregates over neighborhoods, which intrinsically limits expressivity and often yields power no stronger than the Weisfeiler–Lehman (WL) test. In this work, we challenge this primitive. We introduce the Grothendieck Graph Neural Networks (GGNN) framework, which provides a strict algebraic extension of neighborhoods to covers, and in doing so replaces neighborhoods as the fundamental objects of message passing. Neighborhoods and adjacency matrices are recovered as special cases, while covers enable a principled and flexible foundation for defining topology-aware propagation schemes.
GGNN formalizes covers and systematically translates them into matrices, analogously to how adjacency matrices encode neighborhoods, enabling both theoretical analysis and practical implementation. Within this framework, we introduce the cover of sieves, inspired by category theory, which captures rich topological structure. Based on this cover, we design Sieve Neural Networks (SNN), a canonical fixed-cover instantiation that generalizes the adjacency matrix. Experiments show that SNN achieves zero failures on challenging graph isomorphism benchmarks (SRG, CSL, BREC) and substantially improves topology-aware evaluation via a controlled label-propagation probe. These results establish GGNN as a principled foundational framework for replacing neighborhoods in GNNs.
URL: https://openreview.net/forum?id=oD3qWXgB4e
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Title: Bridging Efficiency and Adaptability: Continual Learning of MLPs on Class-Incremental Graphs
Abstract: Compared to static graphs, class-incremental graphs place higher demands on inference latency to support timely predictions for newly emerged node classes, especially in latency-sensitive applications. However, the high inference cost of Graph Neural Networks (GNNs) limits their scalability and motivates GNN-to-MLP distillation, which transfers knowledge from a GNN to a Multi-Layer Perceptron (MLP) to enable graph-free, low-latency inference. Yet, existing efforts focus on static graphs. When directly applied to class-incremental graphs, they inevitably suffer from the high computational cost of frequent GNN updates and the MLP’s inability to retain knowledge of previously learned classes. To bridge efficiency and adaptability, we propose a novel framework featuring an asynchronous update paradigm between GNN and MLPs, allowing rapid adaptation to evolving data. The MLPs employ a progressive expansion strategy for continual adaptation and an energy-based routing mechanism for test-time inference. During GNN updates, knowledge from MLPs trained in the current cycle is distilled back into the GNN to preserve long-term knowledge. Experiments on real-world datasets demonstrate that our framework achieves superior performance on class-incremental graphs, effectively balancing adaptability to new data and inference efficiency.
URL: https://openreview.net/forum?id=3KYwHaKXcn
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Title: Plain Transformers Can be Powerful Graph Learners
Abstract: Transformers have attained outstanding performance across various modalities, owing to their simple but powerful scaled-dot-product (SDP) attention mechanisms.
Researchers have attempted to migrate Transformers to graph learning, but most advanced Graph Transformers (GTs) have strayed far from plain Transformers, exhibiting major architectural differences either by integrating message-passing or incorporating sophisticated attention mechanisms.
These divergences hinder the easy adoption of training advances for Transformers developed in other domains.
Contrary to previous GTs, this work demonstrates that the plain Transformer architecture can be a powerful graph learner.
To achieve this, we propose to incorporate three simple, minimal, and easy-to-implement modifications to the plain Transformer architecture to construct our Powerful Plain Graph Transformers (PPGT):
(1) simplified $L_2$ attention for measuring the magnitude closeness among tokens; (2) adaptive root-mean-square normalization to preserve token magnitude information; and (3) a simple MLP-based stem for graph positional encoding.
Consistent with its theoretical expressivity, PPGT demonstrates noteworthy realized expressivity on the empirical graph expressivity benchmark, comparing favorably to more complicated alternatives such as subgraph GNNs and higher-order GNNs.
Its empirical performance across various graph datasets also justifies the effectiveness of PPGT.
This finding underscores the versatility of plain Transformer architectures and highlights their strong potential as a unified backbone for multimodal learning across language, vision, and graph domains.
URL: https://openreview.net/forum?id=bEmDvP0fdv
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Title: Stronger Approximation Guarantees for Non-Monotone $\gamma$-Weakly DR-Submodular Maximization
Abstract: 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 algorithmic framework that combines a Frank--Wolfe guided measured continuous greedy procedure with a $\gamma$-weighted double-greedy method. Our analysis explicitly accounts for the asymmetric structure induced by weak diminishing returns, yielding $\gamma$-dependent progress certificates that remain valid across the entire weakly DR spectrum. As a result, we obtain an approximation guarantee that strictly improves upon the baseline $\gamma e^{-\gamma}$ for all $\gamma \in (0,1)$ and recovers the current best constant $0.401$ when $\gamma = 1$. The proposed algorithms are projection-free, use only first-order information and linear optimization oracles, and run in polynomial time.
URL: https://openreview.net/forum?id=yS78Cb1CnX
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Title: Scene Layout Generation with Rectified Flow
Abstract: 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 and a carefully designed repeatable human-evaluation procedure that assesses the plausibility and variety of images that are generated. We show that our method sets a new state of the art for achieving high plausibility and variety simultaneously, while being at least 3× times smaller in the number of parameters.
URL: https://openreview.net/forum?id=YGsQxG5ubd
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Title: Active Teacher Selection for Reward Learning
Abstract: Reward learning techniques enable machine learning systems to learn objectives from human
feedback. A core limitation of these systems is their assumption that all feedback comes from
a single human teacher, despite gathering feedback from large and heterogeneous populations.
We propose the Hidden Utility Bandit (HUB) framework to model differences in teacher
rationality, expertise, and costliness, formalizing the problem of learning from multiple
teachers. We develop a variety of solution algorithms and apply them to two real-world
domains: paper recommendation systems and COVID-19 vaccine testing. We find that Active
Teacher Selection (ATS) algorithms outperform baselines by actively selecting when and which
teacher to query. Our key contributions are 1) the HUB framework: a novel mathematical
framework for modeling the teacher selection problem, 2) ATS: an active-learning based
algorithmic approach that demonstrates the utility of modeling teacher heterogeneity, and
3) proof-of-concept application of the HUB framework and ATS approaches to model and
solve multiple real-world problems with complex trade-offs between reward learning and
optimization.
URL: https://openreview.net/forum?id=9OEy68av40
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Title: Source-Optimal Training is Transfer-Suboptimal
Abstract: We prove that training a source model optimally for its own task is generically suboptimal when the objective is downstream transfer. We study the source-side optimization problem in L2-SP ridge regression and show a fundamental mismatch between the source-optimal and transfer-optimal source regularization: outside of a measure-zero set, $\tau_0^* \neq \tau_S^*$. We characterize the transfer-optimal source penalty $\tau_0^*$ as a function of task alignment and identify an alignment-dependent reversal: with imperfect alignment ($0<\rho<1$), transfer benefits from stronger source regularization, while in super-aligned regimes ($\rho>1$), transfer benefits from weaker regularization. Additionally, in isotropic settings, the decision of whether transfer helps is independent of the target sample size and noise, depending only on task alignment and source characteristics. We verify the linear predictions in a synthetic ridge regression experiment, and we present experiments on MNIST, CIFAR-10, and 20 Newsgroups as evidence that the source-optimal versus transfer-optimal mismatch persists in standard nonlinear transfer learning pipelines.
URL: https://openreview.net/forum?id=CMlpokFXfA
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Title: MEDIC: Comprehensive Evaluation of Leading Indicators for LLM Safety and Utility in Clinical Applications
Abstract: While Large Language Models (LLMs) achieve superhuman performance on standardized medical licensing exams, these static benchmarks have become saturated and increasingly disconnected from the functional requirements of clinical workflows. To bridge the gap between theoretical capability and verified utility, we introduce MEDIC, a comprehensive evaluation framework establishing leading indicators across various clinical dimensions. Beyond standard question-answering, we assess operational capabilities using deterministic execution protocols and a novel Cross-Examination Framework (CEF), which quantifies information fidelity and hallucination rates without reliance on reference texts. Our evaluation across a heterogeneous task suite exposes critical performance trade-offs: we identify a significant knowledge-execution gap, where proficiency in static retrieval does not predict success in operational tasks such as clinical calculation or SQL generation. Furthermore, we observe a divergence between passive safety (refusal) and active safety (error detection), revealing that models fine-tuned for high refusal rates often fail to reliably audit clinical documentation for factual accuracy. These findings demonstrate that no single architecture dominates across all dimensions, highlighting the necessity of a portfolio approach to clinical model deployment.
URL: https://openreview.net/forum?id=pDQe9Icwb6
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