In robotic manipulation, the tight coupling between grasping and motion planning often obscures the true source of failure, leading to inefficient trial-and-error. To enable efficient long-horizon manipulation, we propose GTP-FA (Grasp-Then-Plan with Failure Attribution), a task-oriented two-stage 'grasp-then-plan' fra...
Jiahao Xu, Peiyuan Wang, Hanzhuo Zhang et al.· 0 citations
Reliable probability estimates are critical in many machine learning applications, yet modern classifiers are often poorly calibrated. Post-hoc calibration provides a simple and widely used solution, but the large number of proposed methods, combined with small-scale and inconsistent evaluations, makes it difficult to...
Eug\`ene Berta, David Holzm\"uller, Francis Bach et al.· 0 citations
PlanAhead is introduced, a static planner-executor framework that evaluates the impact of plan representation on agent performance and shows that both, the plan formulation and the underlying LLM generating the plan, significantly influence web-agent robustness and task success.
Alejandra Zambrano, Sara Vera Marjanovic, Imene Kerboua et al.· arXiv.org· 1 citation
Of $1{,}536$ Gaussian release covariances we tested for single-layer hidden-state privacy, zero achieve both moderate utility and moderate privacy against an adaptive retrieval attacker. We prove a complementary Fisher-ball lower bound: every full-rank Gaussian release at $O(1)$ Fisher utility admits a direction whose...
Alexander Okezue Bell· 0 citations
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Large Language Models (LLMs) are increasingly integrated into software systems for diverse purposes, due to their versatility, flexibility, and ability to simulate human reasoning to some extent. However, poor integration of LLM inference in source code can undermine software system quality. Therefore, inadequate LLM i...
Zacharie Chenail-Larcher, Brahim Mahmoudi, Naouel Moha et al.· 0 citations
Optimization of LLM training and inference configurations, such as hyperparameters, data mixtures, and prompts, is critical to performance, but it is often approached heuristically in practice, leading to potentially suboptimal outcomes. By framing them as noisy, expensive, and derivative-free optimization problems, Ba...
Ruth Wan Theng Chew, Zhiliang Chen, Apivich Hemachandra et al.· 0 citations
Pretraining and alignment update the same transformer weights, and we ask in which directions each stage changes them. The residual stream gives every matrix a shared coordinate system, in which a weight change can be compared with the principal subspace of the activations the matrix reads and with the prediction subsp...
Vision-Language-Action (VLA) models remain brittle in long-horizon, contact-rich manipulation because success-only imitation provides little supervision for execution drift, while failed rollouts are often discarded. We introduce RePO-VLA, a recovery-driven policy optimization framework that assigns distinct roles to s...
Weijia Liufu, Xiaoyu Guo, Ruiyi Chen et al.· 0 citations
This paper provides an empirical instantiation of shared/private parameter partitioning for unsupervised generative Time-Series Anomaly Detection (TSAD), mapping the split to analysis and synthesis modules across the generative architectures.
Usevalad Milasheuski, P. Baraldi, Enrico Zio et al.· IEEE Internet of Things Jour...· 1 citation
Data selection methods address a critical challenge in LLM post-training: effectively leveraging scarce, high-fidelity target data alongside abundant but imperfectly aligned general training data. In this work, we move beyond the data-selection framing and introduce Dr. Post-Training (Data-Regularized Post-Training), a...
Pingbang Hu, Xueshen Liu, Z. Morley Mao et al.· 0 citations
Neural PDE simulators often receive only a single observed field at deployment. In this setting, a field-to-future predictor can collapse distinct latent problem states into the same deterministic interface, losing the ambiguity needed for reliable rollout and downstream decisions. We propose posterior-first neural PDE...
A sharp analysis of KL-PCB (Zhao et al., 2026), showing that it achieves a sample complexity of $\tilde{O}(\eta SAC^{\pi^*}/\epsilon)$ under large regularization and a sample complexity of $\tilde{\Omega}(SAC^{\pi^*}/\epsilon^2)$ under small regularization.
Kaixuan Ji, Qi-Wei Di, He-Yang Zhao et al.· arXiv.org· 4 citations· ⚡1
