Daily TMLR digest for Dec 11, 2025
TMLR
New certifications
==================
J2C Certification: Training Dynamics of Learning 3D-Rotational Equivariance
Max W Shen, Ewa Nowara, Michael Maser, Kyunghyun Cho
https://openreview.net/forum?id=DLOIAW18W3
---
Accepted papers
===============
Title: Training Dynamics of Learning 3D-Rotational Equivariance
Authors: Max W Shen, Ewa Nowara, Michael Maser, Kyunghyun Cho
Abstract: While data augmentation is widely used to train symmetry-agnostic models, it remains unclear how quickly and effectively they learn to respect symmetries. We investigate this by deriving a principled measure of equivariance error that, for convex losses, calculates the percent of total loss attributable to imperfections in learned symmetry. We focus our empirical investigation to 3D-rotation equivariance on high-dimensional molecular tasks (flow matching, force field prediction, denoising voxels) and find that models reduce equivariance error quickly to $\leq$2\% held-out loss within 1k-10k training steps, a result robust to model and dataset size. This happens because learning 3D-rotational equivariance is an easier learning task, with a smoother and better-conditioned loss landscape, than the main prediction task. For 3D rotations, the loss penalty for non-equivariant models is small throughout training, so they may achieve lower test loss than equivariant models per GPU-hour unless the equivariant ``efficiency gap'' is narrowed. We also experimentally and theoretically investigate the relationships between relative equivariance error, learning gradients, and model parameters.
URL: https://openreview.net/forum?id=DLOIAW18W3
---
Title: Mind the Confidence Gap: Overconfidence, Calibration, and Distractor Effects in Large Language Models
Authors: Prateek Chhikara
Abstract: Large Language Models (LLMs) show remarkable proficiency in natural language tasks, yet their frequent overconfidence—misalignment between predicted confidence and true correctness—poses significant risks in critical decision-making applications. We present a comprehensive analysis on calibration in LLMs across nine LLMs and three factual Question-Answering (QA) datasets, systematically comparing standard free-generation settings against structured distractor-augmented prompts. Our evaluation reveals that explicitly incorporating distractors can substantially mitigate miscalibration, achieving relative accuracy improvements up to 460% and ECE reductions up to 90%. Despite general trends, we uncover nuanced findings: large RLHF-tuned models display inherent calibration strengths but can paradoxically suffer increased miscalibration on easier queries, whereas smaller models benefit disproportionately from distractor prompts but remain significantly miscalibrated. Through detailed analyses across question types, we identify persistent calibration failures, particularly in person-based queries. We conclude with concrete recommendations—targeted fine-tuning, structured prompting, and strategic model choice—to ensure reliable, trustworthy LLM deployments.
URL: https://openreview.net/forum?id=lyaHnHDdZl
---
Title: A Review of Bayesian Uncertainty Quantification in Deep Probabilistic Image Segmentation
Authors: Amaan Valiuddin, Ruud Van Sloun, Christiaan Viviers, Peter H.N. de With, Fons van der Sommen
Abstract: Advances in architectural design, data availability, and compute have driven remarkable progress in semantic segmentation. Yet, these models often rely on relaxed Bayesian assumptions, omitting critical uncertainty information needed for robust decision-making. Despite growing interest in probabilistic segmentation to address point-estimate limitations, the research landscape remains fragmented. In response, this review synthesizes foundational concepts in uncertainty modeling, analyzing how feature- and parameter-distribution modeling impact four key segmentation tasks: Observer Variability, Active Learning, Model Introspection, and Model Generalization. Our work establishes a common framework by standardizing theory, notation, and terminology, thereby bridging the gap between method developers, task specialists, and applied researchers. We then discuss critical challenges, including the nuanced distinction between uncertainty types, strong assumptions in spatial aggregation, the lack of standardized benchmarks, and pitfalls in current quantification methods. We identify promising avenues for future research, such as uncertainty-aware active learning, data-driven benchmarks, transformer-based models, and novel techniques to move from simple segmentation problems to uncertainty in holistic scene understanding. Based on our analysis, we offer practical guidelines for researchers on method selection, evaluation, reproducibility, and meaningful uncertainty estimation. Ultimately, our goal is to facilitate the development of more reliable, efficient, and interpretable segmentation models that can be confidently deployed in real-world applications.
URL: https://openreview.net/forum?id=Yzf4anYwao
---
Title: Real-Time Privacy Preservation for Robot Visual Perception
Authors: Minkyu Choi, Yunhao Yang, Neel P. Bhatt, Kushagra Gupta, Sahil Shah, Aditya Rai, David Fridovich-Keil, ufuk topcu, Sandeep P. Chinchali
Abstract: Many robots (e.g., iRobot's Roomba) operate based on visual observations from live video streams, and such observations may inadvertently include privacy-sensitive objects, such as personal identifiers. Existing approaches for preserving privacy rely on deep learning models, differential privacy, or cryptography. They lack guarantees for the complete concealment of all sensitive objects. Guaranteeing concealment requires post-processing techniques and thus is inadequate for real-time video streams. We develop a method for privacy-constrained video streaming, PCVS, that conceals sensitive objects within real-time video streams. PCVS takes a logical specification constraining the existence of privacy-sensitive objects, e.g., never show faces when a person exists. It uses a detection model to evaluate the existence of these objects in each incoming frame. Then, it blurs out a subset of objects such that the existence of the remaining objects satisfies the specification. We then propose a conformal prediction approach to (i) establish a theoretical lower bound on the probability of the existence of these objects in a sequence of frames satisfying the specification and (ii) update the bound with the arrival of each subsequent frame. Quantitative evaluations show that PCVS achieves over 95 percent specification satisfaction rate in multiple datasets, significantly outperforming other methods. The satisfaction rate is consistently above the theoretical bounds across all datasets, indicating that the established bounds hold. Additionally, we deploy PCVS on robots in real-time operation and show that the robots operate normally without being compromised when PCVS conceals objects.
URL: https://openreview.net/forum?id=uMf2vn8396
---
Title: Any-Property-Conditional Molecule Generation with Self-Criticism using Spanning Trees
Authors: Alexia Jolicoeur-Martineau, Aristide Baratin, Kisoo Kwon, Boris Knyazev, Yan Zhang
Abstract: Generating novel molecules is challenging, with most representations of molecules leading to generative models producing many invalid molecules. Spanning Tree-based Graph Generation (STGG) is a promising approach to ensure the generation of valid molecules, outperforming state-of-the-art generative models for unconditional generation. In practice, it is desirable to generate molecules conditional on one or multiple target properties rather than unconditionally. Thus, we extend STGG to multi-property conditional generation. Our approach, STGG+, incorporates a modern Transformer architecture, random masking of properties during training (enabling conditioning on any subset of properties and classifier-free guidance), an auxiliary property-prediction loss (allowing the model to self-criticize molecules and select the best ones), and other improvements. We show that STGG+ achieves state-of-the-art performance on in-distribution and out-of-distribution conditional generation, as well as reward maximization.
URL: https://openreview.net/forum?id=QGZd5Bfb1L
---
Title: PASCAL: Precise and Efficient ANN- SNN Conversion using Spike Accumulation and Adaptive Layerwise Activation
Authors: Pranav Ramesh, Gopalakrishnan Srinivasan
Abstract: Spiking Neural Networks (SNNs) have been put forward as an energy-efficient alternative to Artificial Neural Networks (ANNs) since they perform sparse Accumulate operations instead of the power-hungry Multiply-and-Accumulate operations. ANN-SNN conversion is a widely used method to realize deep SNNs with accuracy comparable to that of ANNs.~\citeauthor{bu2023optimal} recently proposed the Quantization-Clip-Floor-Shift (QCFS) activation as an alternative to ReLU to minimize the accuracy loss during ANN-SNN conversion. Nevertheless, SNN inferencing requires a large number of timesteps to match the accuracy of the source ANN for real-world datasets. In this work, we propose PASCAL, which performs ANN-SNN conversion in such a way that the resulting SNN is mathematically equivalent to an ANN with QCFS-activation, thereby yielding similar accuracy as the source ANN with minimal inference timesteps. In addition, we propose a systematic method to configure the quantization step of QCFS activation in a layerwise manner, which effectively determines the optimal number of timesteps per layer for the converted SNN. Our results show that the ResNet-34 SNN obtained using PASCAL achieves an accuracy of $\approx$74\% on ImageNet with a 56$\times$ reduction in the number of inference timesteps compared to existing approaches.
URL: https://openreview.net/forum?id=kIdB7Xp1Iv
---
New submissions
===============
Title: Multimodal Masked Point Distillation for 3D Representation Learning
Abstract: We propose a two-stage pre-training approach using point clouds for a diverse set of 3D understanding tasks. In the first stage, we pre-train the 3D encoder to acquire knowledge from the other modalities such as vision and language. This stage aligns 3D representations with multiple modalities by leveraging several pre-trained foundation models, unlike the current cross-modal paradigm that typically uses only a single pre-trained model. In the second stage, the pre-training approach is improved upon masked point modeling by global-local feature distillation of semantic 3D embeddings and token shuffling approach. These techniques enable the model to focus on the 3D modality while leveraging the multimodal information associated with the point clouds. This pre-training approach is model-agnostic and can be applied to any 3D transformer encoder. We conduct extensive experiments on a wide range of 3D understanding tasks, from synthetic and real-world object recognition to indoor semantic segmentation and object detection, achieving state-of-the-art results. For instance, on the ScanObjectNN variants, our approach achieves $\textbf{96.1\%}$, $\textbf{94.2\%}$ and $\textbf{91.2\%}$ accuracy using multi-scale 3D encoder proposed in Point-M2AE.
URL: https://openreview.net/forum?id=Gxb3z4VlM7
---
Title: Fine-Tuning without Forgetting: Domain Generalizable Adaptation of 3D Vision-Language Models
Abstract: Domain adaptation remains a central challenge in 3D vision, especially for multimodal foundation models that align 3D point clouds with visual and textual data. While these models demonstrate strong general capabilities, adapting them to downstream domains with limited data often leads to overfitting and catastrophic forgetting. To address this, we introduce ReFine3D, a regularized fine-tuning framework designed for domain-generalizable tuning of 3D large multimodal models (LMMs). ReFine3D combines selective layer tuning with two targeted regularization strategies: multi-view consistency across augmented point clouds and text diversity through synonym-based prompts generated by large language models. Additionally, we incorporate point-rendered vision supervision and a test-time scaling strategy to further enhance robustness. Extensive experiments across different 3D domain generalization benchmarks show that ReFine3D improves base-to-novel class generalization by 1.36%, cross-dataset transfer by 2.43%, robustness to corruption by 1.80%, and few-shot accuracy by up to 3.11%-outperforming prior state-of-the-art methods with minimal added computational overhead.
URL: https://openreview.net/forum?id=453uT7O7wc
---
Title: AdaCtrl: Towards Adaptive and Controllable Reasoning via Difficulty-Aware Budgeting
Abstract: Large language models (LLMs) have demonstrated remarkable reasoning abilities through extensive test-time inference. However, such deep and lengthy reasoning frequently results in substantial computational overhead. Current methods either uniformly minimize reasoning tokens, thereby neglecting the necessity for more intricate reasoning on complex tasks, or employ precise token-level control, which often hinges on accurate difficulty estimation and suffers from unreliable model interpretation for nuanced instructions. To address these limitations, we introduce AdaCtrl, a novel framework that can dynamically adjust its reasoning length based on the model’s self-assessed problem difficulty and also allow human-in-the-loop control of the budget to prioritize either efficiency or effectiveness. Specifically, we carefully develop a two-stage training pipeline: 1) Cold-start fine-tuning stage, where we first design explicit difficulty-aware tags (e.g., ``[Easy]'' or ``[Hard]'') to indicate difficulty of problems, and train the model on a curated dataset to align its reasoning behavior with these difficulty levels; and 2) Difficulty-aware reinforcement learning stage, which further refines the model’s adaptive reasoning behavior and calibrates its self-assessment of problem difficulty. In this way, AdaCtrl not only empowers the model to adaptively assess the difficulty of problem and adjust reasoning budget allocation, but also enables the user to explicitly control the desired reasoning mode by injecting the specific difficulty-aware tag. Empirical results across four benchmarks show that, compared to different types of baselines, AdaCtrl effectively balances performance and computational efficiency, leading to performance improvements while dynamically reducing response lengths by up to 90%.
URL: https://openreview.net/forum?id=4J2Ako20V4
---
Title: Regret Is Not Enough: Teaching and Stability in Non-Stationary Reinforcement Learning
Abstract: Standard treatments of non-stationary reinforcement learning cast it as a tracking problem, tacitly accepting any policy that keeps pace with a drifting optimum and relegating instability to a minor algorithmic concern. Yet in safety-critical, value-laden domains, decisions answer to external stakeholders, and the central question becomes not just how fast we track non-stationarity, but whether the learner is teachable under drift without sacrificing performance or stability.
We formalize this question in what we call the \emph{Teaching--Regret--Stability (TRS) Principle} for \emph{Teachable Non-stationary RL (TNRL)}. Under standard variation-budget assumptions and a Lipschitz policy-update condition, we prove a high-level theorem showing that a bounded-budget teacher can simultaneously drive the teaching error to an arbitrarily small target, keep dynamic regret sublinear, and ensure that the policy sequence remains stable on average.
URL: https://openreview.net/forum?id=FHSSHQs7DV
---