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Active Target Discovery under Uninformative Prior: The Power of Permanent and Transient Memory

Anindya Sarkar, Binglin Ji, Yevgeniy Vorobeychik

TL;DR

This work addresses Active Target Discovery under uninformative priors by blending a pretrained diffusion-based permanent memory with a lightweight transient memory realized via Doob’s $h$-transform. Through an EM-style procedure, the prior is progressively improved as observations accrue, guaranteeing monotonic improvement, while a dynamic $h$-transform enables rapid adaptation in data-scarce settings. A joint sampling strategy balances exploration and exploitation, leveraging a memory-informed posterior and a learnable reward model to rank unobserved locations under a fixed budget. Across diverse domains, including species distribution and remote sensing, EM-PTDM outperforms baselines, demonstrating robust target discovery without domain-specific prior samples and highlighting the value of transient memory for fast adaptation and reliable exploration in dynamic environments.

Abstract

In many scientific and engineering fields, where acquiring high-quality data is expensive--such as medical imaging, environmental monitoring, and remote sensing--strategic sampling of unobserved regions based on prior observations is crucial for maximizing discovery rates within a constrained budget. The rise of powerful generative models, such as diffusion models, has enabled active target discovery in partially observable environments by leveraging learned priors--probabilistic representations that capture underlying structure from data. With guidance from sequentially gathered task-specific observations, these models can progressively refine exploration and efficiently direct queries toward promising regions. However, in domains where learning a strong prior is infeasible due to extremely limited data or high sampling cost (such as rare species discovery, diagnostics for emerging diseases, etc.), these methods struggle to generalize. To overcome this limitation, we propose a novel approach that enables effective active target discovery even in settings with uninformative priors, ensuring robust exploration and adaptability in complex real-world scenarios. Our framework is theoretically principled and draws inspiration from neuroscience to guide its design. Unlike black-box policies, our approach is inherently interpretable, providing clear insights into decision-making. Furthermore, it guarantees a strong, monotonic improvement in prior estimates with each new observation, leading to increasingly accurate sampling and reinforcing both reliability and adaptability in dynamic settings. Through comprehensive experiments and ablation studies across various domains, including species distribution modeling and remote sensing, we demonstrate that our method substantially outperforms baseline approaches.

Active Target Discovery under Uninformative Prior: The Power of Permanent and Transient Memory

TL;DR

This work addresses Active Target Discovery under uninformative priors by blending a pretrained diffusion-based permanent memory with a lightweight transient memory realized via Doob’s -transform. Through an EM-style procedure, the prior is progressively improved as observations accrue, guaranteeing monotonic improvement, while a dynamic -transform enables rapid adaptation in data-scarce settings. A joint sampling strategy balances exploration and exploitation, leveraging a memory-informed posterior and a learnable reward model to rank unobserved locations under a fixed budget. Across diverse domains, including species distribution and remote sensing, EM-PTDM outperforms baselines, demonstrating robust target discovery without domain-specific prior samples and highlighting the value of transient memory for fast adaptation and reliable exploration in dynamic environments.

Abstract

In many scientific and engineering fields, where acquiring high-quality data is expensive--such as medical imaging, environmental monitoring, and remote sensing--strategic sampling of unobserved regions based on prior observations is crucial for maximizing discovery rates within a constrained budget. The rise of powerful generative models, such as diffusion models, has enabled active target discovery in partially observable environments by leveraging learned priors--probabilistic representations that capture underlying structure from data. With guidance from sequentially gathered task-specific observations, these models can progressively refine exploration and efficiently direct queries toward promising regions. However, in domains where learning a strong prior is infeasible due to extremely limited data or high sampling cost (such as rare species discovery, diagnostics for emerging diseases, etc.), these methods struggle to generalize. To overcome this limitation, we propose a novel approach that enables effective active target discovery even in settings with uninformative priors, ensuring robust exploration and adaptability in complex real-world scenarios. Our framework is theoretically principled and draws inspiration from neuroscience to guide its design. Unlike black-box policies, our approach is inherently interpretable, providing clear insights into decision-making. Furthermore, it guarantees a strong, monotonic improvement in prior estimates with each new observation, leading to increasingly accurate sampling and reinforcing both reliability and adaptability in dynamic settings. Through comprehensive experiments and ablation studies across various domains, including species distribution modeling and remote sensing, we demonstrate that our method substantially outperforms baseline approaches.
Paper Structure (49 sections, 8 theorems, 70 equations, 20 figures, 13 tables, 2 algorithms)

This paper contains 49 sections, 8 theorems, 70 equations, 20 figures, 13 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Problem Formulation
  3. Methodology
  4. Progressively Improving the Prior model
  5. Swiftly Adaptable Prior model with Limited Observation
  6. Learning Dynamics of $h$-model
  7. Sampling Strategy for ATD Utilizing the Updated Prior
  8. Empirical Analysis
  9. Active Discovery of Unknown Species from Known Species Distribution
  10. Active Discovery of Unknown Overhead Objects from Ground-view Prior
  11. Enhancing Permanent Memory with Domain Cues for Improved In-Domain Target Discovery
  12. The Role of Transient Memory in Rapid Adaptation to Novel ATD Tasks
  13. An Important Observation: ATD is not just About Reconstruction
  14. Related Work
  15. Acknowledgement
  16. ...and 34 more sections

Key Result

Proposition 1

Let $\phi_k$ denote the parameters of the current prior model, then improving this prior by maximizing the expected log-evidence $\mathbb{E}_{p(y)}[\log q_{\phi_k}(y)]$ with respect to $\phi_k$ is equivalent to maximizing the following surrogate maximization: such that $\mathbb{E}_{p(y)}[\log q_{\phi_{k+1}}(y)] \geq \mathbb{E}_{p(y)}[\log q_{\phi_{k}}(y)]$. Where $q_{\phi_k}(x|y)$ represents the

Figures (20)

  • Figure 1: Interplay of Permanent and Transient Memory to Guide Active Target Discovery.
  • Figure 2: $h$-model adaptability dynamics. We use a Diffusion model trained on MNIST as permanent memory (16 samples per Fig).
  • Figure 3: An Overview of EM-PTDM Framework.
  • Figure 4: Active Discovery of CS species.
  • Figure 5: Active Discovery of Overhead objects.
  • ...and 15 more figures

Theorems & Definitions (16)

  • Proposition 1
  • Definition 1: Doob's h-transform
  • Lemma 1
  • Theorem 1
  • Theorem 2
  • Theorem 3
  • Proposition 2
  • Theorem 4
  • proof
  • proof
  • ...and 6 more