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版本更新 · v3arXiv:2610.23002

Robotic Spatiotemporal Editing: A Unified Interface for Controllable Action Generation via Masked Action Reconstruction

Yanbang Li, Bu He, Yi Tian, Yu-Chiao Huang, Chenyu Zhang, Ruoqu Chen, Yuanzhe Liu, Yuxiang Gao 等 9 位作者

原文摘要

Generative policies can synthesize robot behaviors, yet controlling and editing them through diverse spatiotemporal references remains challenging. In the real world, human intent requires robot behaviors that satisfy constraints on where, when, and how actions are executed. Our key insight is that spatiotemporal control references, such as keypoints, flows, orientations, and keyposes, can be viewed as partial specifications of the same temporal Cartesian action space. We introduce the MAP (Masked Action Space) interface to unify these action references and formulate controllable manipulation as an action inpainting problem. Our Inpainting Policy (IP) reconstructs missing action dimensions of the end-effector, combining progress-guided alignment with multi-mask training supporting unseen action-constraint combinations. We further adopt a training-free RePaint variant that iteratively inpaints the action during denoising to reduce control error. On ManiSkill and LIBERO-10, our policy achieves average task success rates of 92.67% and 64.67%, respectively.Real-world experiments demonstrate spatiotemporal control with average positional and rotational errors of 44.3 mm and 7°, respectively, showing that our policy can use diverse references to guide executable robot behaviors.

cs.RO

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.30844

Fine-Tuned Language Models Exhibit Improved Information Conveyance

Yuwei Cheng, Weiyi Tian, Haifeng Xu

原文摘要

Fine-tuning is often believed to reduce uncertainty and diversity in large language models, but existing analyses overlook output length, a key confounder, and therefore fail to capture how uncertainty is distributed across an entire generation rollout. To address this, we propose Canopy Entropy ($\mathrm{CE}^\star$), a measure that views language generation from a tree perspective, where "canopy" represents the space of all possible rollouts, making $\mathrm{CE}^\star$ naturally quantify the effective size of the generation space. $\mathrm{CE}^\star$ jointly captures uncertainty in both the output length $N$ and the generated sequence $Y_{1:N}$ -- indeed, we show that it equals total Shannon entropy $H(N, Y_{1:N}\mid X)$, where $X$ denotes the prompt. This formulation yields interpretable metrics, including a length-entropy correlation term $ρ(N, r_N)$, where $r_N$ is the entropy rate, quantifying information conveyance efficiency by indicating whether longer outputs are more or less informative per token. Empirically, across tasks and model families, we find that fine-tuned models predominantly exhibit more positive correlation $ρ(N, r_N)$, even when total entropy decreases. Furthermore, after controlling for model family, task, prompt, and output-length effects, we find that fine-tuning nearly triples the correlation strength between entropy rate and semantic diversity, suggesting that aligned models convert token uncertainty into semantic diversity more efficiently. Overall, these results demonstrate that fine-tuning does not simply reduce uncertainty, but fundamentally reorganizes it into more informative and semantically meaningful generations. Our code is available at https://github.com/WeiyiTian/canopy-entropy.

cs.CL · cs.AI · stat.ML

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2604.23334

A Lagrangian Approach to Interdiction of Linear Minimization Problems

Yu Cong, Kangyi Tian

原文摘要

We study budgeted interdiction of linear minimization problems. Given a family of feasible sets, element weights, and deletion costs, the objective is to delete elements within a prescribed budget and minimize the remaining weight of a feasible set. We give an alternative, simpler Lagrangian approach that generalizes the connectivity interdiction framework of Huang, Obscura Acosta, and Yingchareonthawornchai (IPCO' 24). When the interdiction optimum is positive, we show that the optimal Lagrange multiplier gives truncated element weights, in which every feasible set supporting an optimal interdiction solution has weight less than twice the minimum under the truncated weights. We apply the reduction to graph cuts, $k$ cuts, and cocircuits of perturbed graphic matroids. We obtain deterministic fully polynomial-time approximation schemes for graph connectivity interdiction and for cogirth interdiction on perturbed graphic matroids of fixed perturbation rank. For graph connectivity, the running time is $\tilde O(n^3m^2/\varepsilon)$. The $k$-cut application gives a fully polynomial-time randomized approximation scheme.

cs.DS

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2511.17971

Beyond MACs: Deployment-Constrained Design Space Exploration for Tensorized Neural Networks

Jinsong Zhang, Minghe Li, Jiayi Tian, Jinming Lu, Zheng Zhang

原文摘要

Tensor-train (TT) compression reduces neural-network parameters, but the latency of a tensorized layer is set by its contraction order and its mapping onto hardware: a contraction path with fewer multiply-accumulate operations (MACs) is not necessarily faster on a systolic array. We present a deployment-constrained, latency-guided design space exploration (DSE) framework for fixed-rank tensorized neural networks (TNNs) under a fixed processing-element (PE) budget. Using MAC-guided path pruning, it evaluates feasible combinations of contraction paths, three PE-array partitions, and input-, output-, and weight-stationary dataflows. By minimizing the sum of inference and training latency estimates, it selects a single physical PE partition shared across layers and both modes, while allowing layer- and mode-specific paths and dataflows. On ResNet-18 and ViT-Ti/4, 25%-50% of the layers select a path other than the minimum-MAC one, the best dataflow varies across layers and models, and on a representative ViT layer the latency-selected candidate reduces simulated latency by 23.5% relative to the minimum-MAC candidate. Deployed on a Xilinx VU9P FPGA accelerator that supports both inference and training, the complete TT+INT8 system achieves 3.28x-4.00x inference and 3.42x-3.85x training speedups over dense implementations with the same PE budget.

cs.AR · cs.AI

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arXiv ↗PDF ↗