Adaptive Sensing beyond Non-Adaptive Information Limits: End-to-End Co-Design of Geometry, Policy, and Inference

• URL: http://arxiv.org/abs/2604.25193v1
• Date: Tue, 28 Apr 2026 04:02:17 GMT
• Title: Adaptive Sensing beyond Non-Adaptive Information Limits: End-to-End Co-Design of Geometry, Policy, and Inference
• Abstract summary: We propose a single optimization over the continuous hardware geometry and a Bellman-optimal adaptive measurement policy.<n>Across three case studies, joint-DP beats the natural baseline of its community by a large factor.<n>For any sensor whose hardware is designed once but whose policy runs for the device's lifetime, joint optimization of hardware and policy is the minimum principled procedure.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Inverse design has made vast physical parameter spaces a substrate for emergent behavior. In sensing, the stakes of this principle are sharpest at the analog-to-digital boundary, where any information the hardware fails to capture is information no downstream algorithm can recover; hardware optimization alone is therefore not enough, and the geometry must be co-designed with a rule for what to measure next. We formulate this co-design as \emph{joint dynamic programming} (joint-DP): a single optimization over the continuous hardware geometry and a Bellman-optimal adaptive measurement policy. The outer hardware gradient is computed by differentiable dynamic programming with a sharp Bellman maximum, which the envelope theorem makes exact and bias-free, and a relaxation hierarchy carries the common framework from small discrete POMDPs to $10^5$-pixel photonic topologies. Across three case studies, joint-DP beats the natural baseline of its community by a large factor: on a radar beam-search POMDP, classical information-bound-guided geometry selection loses $2.8\times$ in attainable adaptive value; on a superconducting-qubit flux sensor, joint-DP reduces deployed mean-squared error by $11.3\times$ over the joint Bayesian Cramér--Rao baseline, with both geometries numerically co-optimized under their respective objectives; and on a $90{,}000$-pixel photonic metasensor, joint-DP via the Bayesian Fisher-information-matrix surrogate reduces deployed mean-squared error by $123\times$ relative to a randomized baseline. For any sensor whose hardware is designed once but whose policy runs for the device's lifetime, joint optimization of hardware and policy is the minimum principled procedure.

Related papers

• Shape Operator PCA: Curvature-Aware Projections for Geometric Machine Learning [52.452902154360565]
  SHOPCA (Shape Operator-based Principal Component Analysis) is a novel method for unsupervised metric learning and dimensionality reduction.<n>We evaluate SHOPCA on more than 50 real-world benchmark datasets.
  arXiv  Detail & Related papers  (2026-08-15T16:37:47Z)
• Diffeomorphic Cortical Alignment via Direct Warping of Streamline Endpoints [9.506507471747549]
  Cortical surface registration is often driven by local geometric descriptors.<n>We introduce a novel connectivity-based surface registration method that aligns surfaces by operating directly on white-matter fiber-tract endpoints.<n> Experiments on Human Connectome Project data demonstrate improved tract-level correspondence.
  arXiv  Detail & Related papers  (2026-05-16T01:46:32Z)
• Safety-Critical Contextual Control via Online Riemannian Optimization with World Models [7.720387326648637]
  We study safety-critical contextual control, where a Planner must optimize a task objective using only feasibility samples from a black-box Simulator.<n>We develop a sample-based Penalized Predictive Control framework grounded in online Riemannian optimization.<n>Our main result is a contextual safety bound showing that the distance from the true feasibility manifold is controlled by the score estimation error and a ratio that depends on $(_t)$, both of which improve with richer context.
  arXiv  Detail & Related papers  (2026-04-21T16:28:36Z)
• LG-HCC: Local Geometry-Aware Hierarchical Context Compression for 3D Gaussian Splatting [77.81227097905865]
  Anchor-based 3DGS compression schemes reduce gaussian redundancy through some advanced context models.<n>We propose a Local Geometry-aware Hierarchical Context Compression framework for 3DGS that incorporates inter-anchor geometric correlations into anchor pruning and entropy coding for compact representation.<n>Experiments show that LG-HCC effectively alleviates structural preservation issues,achieving superior geometric integrity and rendering fidelity while reducing storage by up to 30.85x compared to the Scaffold-GS baseline on the Mip-NeRF360 dataset.
  arXiv  Detail & Related papers  (2026-03-30T13:39:35Z)
• Intelligent Control of Collisional Architectures for Deterministic Multipartite State Engineering [0.0]
  We introduce an intelligent, constraint-aware control framework for deterministic generation of symmetric Dicke states $|D_n(m)rangle$ in repeated excitation-within-interaction architectures.<n>The protocol employs partialSWAP collisions between two disjoint qubit registers, mediated by $m$ ancillary shuttle'' qubits, and poses Dickestate preparation as a emph-loop design problem.
  arXiv  Detail & Related papers  (2026-02-09T11:15:32Z)
• Revisiting Weighted Strategy for Non-stationary Parametric Bandits and MDPs [56.246783503873225]
  This paper revisits the weighted strategy for non-stationary parametric bandits.<n>We propose a simpler weight-based algorithm that is as efficient as window/restart-based algorithms.<n>Our framework can be used to improve regret bounds of other parametric bandits.
  arXiv  Detail & Related papers  (2026-01-03T04:50:21Z)
• Exploiting Function-Family Structure in Analog Circuit Optimization [22.334314861424943]
  Off-the-shelf Gaussian-process surrogates impose globally smooth, stationary priors that are misaligned with these regime-switching primitives.<n>We demonstrate that pre-trained models encoding these primitives enable reliable optimization without per-circuit engineering.
  arXiv  Detail & Related papers  (2025-11-30T03:21:13Z)
• Parametrized Multi-Agent Routing via Deep Attention Models [1.0377683220196872]
  We propose a scalable deep learning framework for parametrized sequential decision-making (ParaSDM)<n>A key subclass of this setting is Facility-Location and Pathity (FLPO), where multi-agent systems must simultaneously determine optimal routes and locations.<n>To address this, we integrate Maximum Entropy Principle (MEP) with a neural policy model called the Shortest Path Network (SPN)
  arXiv  Detail & Related papers  (2025-07-30T02:46:45Z)
• SPARE: Symmetrized Point-to-Plane Distance for Robust Non-Rigid 3D Registration [77.13381026159111]
  We propose SPARE, a novel formulation that utilizes a symmetrized point-to-plane distance for robust non-rigid registration.<n>The proposed method greatly improves the accuracy of non-rigid registration problems and maintains relatively high solution efficiency.
  arXiv  Detail & Related papers  (2024-05-30T15:55:04Z)
• Improved Communication-Privacy Trade-offs in $L_2$ Mean Estimation under Streaming Differential Privacy [47.997934291881414]
  Existing mean estimation schemes are usually optimized for $L_infty$ geometry and rely on random rotation or Kashin's representation to adapt to $L$ geometry. We introduce a novel privacy accounting method for the sparsified Gaussian mechanism that incorporates the randomness inherent in sparsification into the DP. Unlike previous approaches, our accounting algorithm directly operates in $L$ geometry, yielding MSEs that fast converge to those of the Gaussian mechanism.
  arXiv  Detail & Related papers  (2024-05-02T03:48:47Z)
• An Adaptive Device-Edge Co-Inference Framework Based on Soft Actor-Critic [72.35307086274912]
  High-dimension parameter model and large-scale mathematical calculation restrict execution efficiency, especially for Internet of Things (IoT) devices. We propose a new Deep Reinforcement Learning (DRL)-Soft Actor Critic for discrete (SAC-d), which generates the emphexit point, emphexit point, and emphcompressing bits by soft policy iterations. Based on the latency and accuracy aware reward design, such an computation can well adapt to the complex environment like dynamic wireless channel and arbitrary processing, and is capable of supporting the 5G URL
  arXiv  Detail & Related papers  (2022-01-09T09:31:50Z)
• Adaptive Subcarrier, Parameter, and Power Allocation for Partitioned Edge Learning Over Broadband Channels [69.18343801164741]
  partitioned edge learning (PARTEL) implements parameter-server training, a well known distributed learning method, in wireless network. We consider the case of deep neural network (DNN) models which can be trained using PARTEL by introducing some auxiliary variables.
  arXiv  Detail & Related papers  (2020-10-08T15:27:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.