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

arXiv: https://arxiv.org/abs/2510.16676 · HTML: https://mlsanity.com/paper/2510.16676/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 23 references that are in the map.
- First posted on arXiv 19 Oct 2025.

## Builds on (most cited first, 11 of 11)

- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [DOTA: A Large-scale Dataset for Object Detection in Aerial Images](https://mlsanity.com/paper/1711.10398/) (2017)
- [Train faster, generalize better: Stability of stochastic gradient descent](https://mlsanity.com/paper/1509.01240/) (2015)
- [Simulating Diffusion Bridges with Score Matching](https://mlsanity.com/paper/2111.07243/) (2021)
- [Learning When and Where to Zoom with Deep Reinforcement Learning](https://mlsanity.com/paper/2003.00425/) (2020)
- [Consistency driven Sequential Transformers Attention Model for Partially Observable Scenes](https://mlsanity.com/paper/2204.00656/) (2022)
- [GOMAA-Geo: GOal Modality Agnostic Active Geo-localization](https://mlsanity.com/paper/2406.01917/) (2024)
- [A Visual Active Search Framework for Geospatial Exploration](https://mlsanity.com/paper/2211.15788/) (2022)
- [A Partially Supervised Reinforcement Learning Framework for Visual Active Search](https://mlsanity.com/paper/2310.09689/) (2023)
- [Amortized nonmyopic active search via deep imitation learning](https://mlsanity.com/paper/2405.15031/) (2024)
- [Online Feedback Efficient Active Target Discovery in Partially Observable Environments](https://mlsanity.com/paper/2505.06535/) (2025)

## Abstract (arXiv)

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.