#01Nov 9, 2026
cs.LG
Anchor Behavior, Not Parameters: Rehearsal-Free Continual Learning with a Single Shared Prompt
Duy-Cuong Bui, Trung-Anh Dang, Vincent Nguyen and 2 more
Prompt-based continual learning adapts a frozen pre-trained vision transformer by tuning a small set of prompt tokens. Recently, one-prompt methods which share one single prompt across all tasks make this paradigm maximally parameter efficient. The open question is how a shared prompt can keep the knowledge of old classes while being updated for new ones. Existing methods protect old knowledge in parameter space, where they freeze task-specific prompts, expand new components, or sparsely gate which tokens receive gradients. However, this method sacrifices plasticity, yet the drift it still permits in shared tokens can corrupt the prompt's function on past classes. What must be preserved is the prompt's behavior. We propose TEA-Prompt, which couples (i) a token evaluation module that assigns each prompt token a continuous, input-conditional relevance score injected into the attention logits, softly specializing tokens across tasks without discarding any of them, and (ii) an attention-behavior anchoring loss that constrains each token's attention output around class-specific query regions using only compact Gaussian query statistics without stored old images, weighted by the token's past relevance. Anchoring the attention value allows the parameters to continue updating freely, as long as their interaction remains unchanged. TEA-Prompt achieves state-of-the-art accuracy with markedly lower forgetting on CIFAR-100, ImageNet-R, and CUB-200 with only 0.38M parameters. Code is publicy available at https://github.com/cuonga1cvp/TEA-Prompt.git
#02Nov 9, 2026
cs.CL
PORT: Preference Optimization via Robust Token-Level Reweighting
Ding Zhu, Xiukun Wei, Tian Xie and 3 more
Preference optimization has become a central approach for aligning large language models with human values, but its effectiveness depends heavily on the quality of preference annotations. In practice, preference data is often noisy due to annotation errors and ambiguity. Existing robust preference optimization methods primarily operate at the sequence level, implicitly treating entire responses as uniformly correct or incorrect. However, rejected responses may still contain informative reasoning steps, while preferred responses can include subtle errors. In this work, we propose \textbf{P}reference \textbf{O}ptimization via \textbf{R}obust \textbf{T}oken-level reweighting \textbf{(PORT)}, a fine-grained framework for robust alignment under noisy preferences. PORT performs token-level reweighting using the empirical cumulative distribution function (CDF) of token log-probabilities, yielding an efficient forward-pass-only proxy to gradient-norm penalization that selectively suppresses corrupted tokens without additional backward-pass computation. We provide theoretical analysis showing that PORT reduces the gradient bias from noisy preference labels by minimizing an upper bound on the bias term. Extensive experiments across diverse noise settings demonstrate that PORT consistently improves robustness and outperforms existing sequence-level baselines. The code is available at https://github.com/osu-srml/PORT
#03Nov 9, 2026
cs.LG
Smartphone-Enabled Depth-Aware Transillumination Imaging for Detection of Chicken Breast Fillet Myopathies in Chicken Fillets
Chaitanya Pallerla, Ramesh Bahadur Bist, Yihong Feng and 6 more
Woody Breast (WB) myopathy is a major quality defect in broiler chickens characterized by fibrosis, increased tissue hardness, and altered structural properties that affect product quality. Conventional palpation is subjective and labor-intensive, while many non-destructive methods require specialized equipment or substantial computational resources. This study developed a smartphone-based transillumination and 3D point-cloud framework for non-destructive WB severity classification. A total of 267 chicken breast fillets, including normal breast (NB, n = 87), mild WB (MWB, n = 85), and severe WB (SWB, n = 95), were evaluated using RAW transillumination images and 3D point clouds acquired with an iPhone 16 Pro. A total of 91 features, comprising 14 image-derived, 76 geometric, and one physics-informed Beer-Lambert attenuation feature, were extracted. Partial correlation controlling for thickness revealed a significant association between image characteristics and WB severity (r = 0.413, p < 0.0001), indicating optical information independent of fillet geometry. SWB tissue exhibited approximately 26% greater light attenuation per unit thickness than NB. The 2D-only models achieved approximately 82% test accuracy, while the multimodal ensemble combining Fusion-DGCNN, XGBoost, and physics-guided logistic regression (PGLR) achieved approximately 90% accuracy. These findings demonstrate the complementary value of optical, geometric, and physics-informed features and establish the feasibility of smartphone-based, non-destructive WB severity assessment.
#04Nov 9, 2026
cs.LG
Guiding Data Allocation for Robust Subpopulation Generalization
Zhaoying Pan, Yipei Wang, Shenyu Lu and 1 more
Machine learning models often achieve high average accuracy in the training data while performing poorly on test data due to subpopulation shift. A common data-centric solution is to enforce balanced subgroup proportions, but full balance can be costly, infeasible, and not always necessary. In this work, we study whether balanced training data is the unique optimal configuration for robust subpopulation generalization. We extend a Gaussian feature-model framework to theoretically characterize the performance landscape across general training subgroup distributions. We show that multiple configurations, including substantially imbalanced ones, can perform comparably to the balanced one. We further derive gradient-based directions for adjusting subgroup proportions to improve adjusted accuracy and worst-group accuracy more efficiently. These findings suggest that strategically allocating data offers a more flexible and principled path to robust performance than simple balancing. The code is available at https://github.com/zhaoyingpan/subpop-data-allocation.
#05Nov 9, 2026
cs.LG
Inversion-Free High-Dimensional Counterfactual Inference
Saketh Vishnubhatla, Pingchuan Ma, Shu Wan and 4 more
Counterfactual inference is the task of answering hypothetical "what-if" queries about a unit, given an observational dataset, modeling assumptions, and a causal graph. This enables a modeler to reason about hypothetical scenarios that cannot otherwise be answered without assumptions or a causal graph. The simpler case involves a causal graph with scalar 1-D variables that denote different attributes in the data-generation process. While the literature is clear in the 1-D case, extending this to settings with high-dimensional nodes (e.g., images) remains challenging. Conventionally, in the literature, methods recover the latent state from the factual image (inversion), generate counterfactual attributes from the graph, and render the counterfactual image from the latent state using the updated attributes. In our work, we investigate the necessity of the inversion step in computing high-dimensional counterfactuals. We first discuss moving from the scalar 1-D case to the higher-dimensional case. Secondly, we discuss the relevance of the inversion step and evaluate an inversion-free counterpart repurposed from the image-editing literature. Finally, on image datasets such as CelebA and ADNI, we benchmark our approach using two measures: namely, effectiveness and identity preservation. While effectiveness is a measure of how faithfully the counterfactual attribute is rendered, identity preservation measures whether the same unit (image) is being rendered, both of which are crucial for high-dimensional counterfactual inference. We present the tradeoffs of using inversion-based and inversion-free approaches in our work.