Title: Active Target Discovery under Uninformative Priors: The Power of Permanent and Transient Memory
Venue: NeurIPS 2025
Accepted: yes
Keywords: Diffusion Model

Active Target Discovery
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.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces EM-PTDM (Expectation Maximized Permanent Temporary Diffusion Memory), a novel framework for Active Target Discovery (ATD) designed specifically to operate effectively in environments with uninformative or unavailable priors. The core problem addressed is that existing methods, such as DiffATD, perform poorly when domain-specific data for learning a strong prior is scarce.\nInspired by dual-memory systems in the human brain, the proposed method combines a large, pre-trained diffusion model as a \"permanent memory\" for generalizable knowledge with a lightweight, adaptive module based on Doob's h-transform that serves as a \"transient memory\" for rapid, task-specific adaptation. The framework uses an Expectation-Maximization (EM) style algorithm to guarantee monotonic improvement of the prior with new observations. The sampling strategy intelligently balances exploration and exploitation, and the entire approach is demonstrated to significantly outperform baseline methods across challenging domains, including species discovery and cross-view remote sensing.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n- The conceptual framework is highly original. The inspiration from neuroscience's dual-memory systems provides a compelling narrative and a principled design. The technical implementation is equally novel, combining a pretrained diffusion model with a lightweight Doob's h-transform module to separately model permanent and transient memory. This decomposition of the posterior score is an elegant and powerful idea.\n\n- The paper provides theoretical guarantees for the monotonic improvement of the prior with each new observation (Propositions 1 and Theorems 1, 2), which in turn leads to more accurate sampling scores (Theorem 4). This theoretical rigor adds significant weight to the contribution.\n\n- The experiments are thorough and well-designed:\n\ni) Challenging Tasks: The evaluation on both species distribution modeling (uninformative prior from a related domain) and overhead object discovery (cross-view prior from a disjoint domain) convincingly demonstrates the method's robustness and adaptability.\n\nii)  Strong Baselines: The comparison against relevant baselines, including the prior state-of-the-art (DiffATD), clearly shows the superiority of EM-PTDM.\n\niii) Insightful Ablations: The ablation studies effectively validate the core components of the design, such as the importance of the transient memory h-model (Figure 5) and the scheduler for updating it (Figure 2).\n\nWeaknesses:\n- The paper proposes to tackle a critical and practical limitation of current active discovery methods: the reliance on strong, domain-specific priors. By enabling effective ATD in data-scarce scenarios, this work proposes to significantly broaden the applicability of these techniques to a wide range of real-world problems, such as discovering emerging diseases or rare environmental hazards. To the best of my understanding, this is only partially true. While it avoids needing a prior for the specific task (e.g., you don't need data on the rare \"CS\" species to find it), the entire framework critically depends on a very powerful, highly informative general-purpose prior in the form of the pretrained diffusion model (\"permanent memory\"). The experiments confirm this: the permanent memory was trained on MNIST, a distribution of known species, or ImageNet. The success of the method fundamentally hinges on this permanent memory providing a strong starting point. The paper does not explore the true \"uninformative prior\" scenario: what happens if the permanent memory is also weak, biased, or trained on a completely irrelevant domain? The claim of operating \"without any prior domain samples\" could be read as more general than the method supports. \n\n       i)  Furthermore, the paper does not discuss the constraints this imposes. In many real-world, data-scarce domains where this method is most needed, a powerful, relevant pre-trained diffusion model might not exist. For example, in discovering novel protein structures or unique geological formations, what serves as the \"ImageNet equivalent\"? The paper's solution works by shifting the data dependency from the specific task to a general domain, but it doesn't eliminate the dependency. This is a practical limitation that is not addressed or described as a limitation of the work.\n\n       ii) Is the rigid separation between the memory systems always optimal? Especially if the task provides many observations that strongly contradict the initial prior. The paper's design is efficient, but it assumes the permanent memory is \"correct\" and only needs to be guided. It doesn't consider scenarios where the permanent memory itself might need some correction during the discovery process.\n\n- The paper rightly emphasizes that the transient memory module is lightweight, enabling rapid adaptation. However, the overall framework still relies on forward evaluations of a large, pretrained diffusion model. A more detailed discussion of the total computational cost per sampling step (in terms of time or resources) would be beneficial for understanding its practical deployment feasibility.\n\n- A rather simple approach to the exploration-exploitation phase, which could potentially be improved and more fine-tuned for this specific application.\n\nQUESTIONS:\nI thank the authors for their work and clear paper. I am open to changing my ratings depending on the discussion of the following points:\n1. What happens when a weak pre-trained diffusion model is used for the permanent memory? I believe this could be addressed by adding some flexibility in altering this part of memory, in a conservative way. \n2. I believe the statement that this is performing under an Uninformative Prior is misleading and not truly precise since the work highly leverages the existence of a pretrained diffusion model. I would appreciate this being addressed. Since this is essentially the main contribution, I think it’s relevant to justify this better. Indeed, this is not task-specific prior domain data, as described in the contributions, but it would be relevant to study how this truly affects the performance. \n3. Equation (10) has text over it.\n\nLIMITATIONS:\nThe authors did not address the limitations of this work, which I believe exist. They are described both under Weaknesses and Questions.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 4, "IMPACT": 4, "ORIGINALITY": 3, "DATE": "2025-06-17"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe authors study the problem of active target discovery—identifying regions of interest within a discrete domain $\\mathcal{X}$ from a limited number of labelled observations $(x_i, y_i)_{i=1}^N$, where $y$ is a binary label and $N \\ll \\lvert \\mathcal{X} \\rvert$. This setting is closely related to EM algorithm and Bayesian experimental design, but here the underlying model is a generative model $p(x)$, specifically a diffusion model. The goal is to align the generative model to produce samples predominantly from the target region, by strategically selecting which data points to observe.\nTo achieve this, the authors decompose the generative model into two components: a permanent memory represented by the pretrained model, and a transient memory represented by a Doob's $h$-transformed model. During alignment, only the parameters $\\phi$ of the $h$-transform are updated, enhancing sample efficiency. The objective for updating $\\phi$ is designed to guarantee improvement in the model evidence at each step. Similarly, the acquisition function—parameterised by $\\eta$ and used to guide data selection—is also guaranteed to improve with every update.\nEmpirically, the proposed exploration algorithm demonstrates superior sample efficiency compared to existing diffusion-based approaches, such as DiffATD.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n- The writing is very clear. The authors do an excellent job of presenting complex algorithms in a digestible and accessible manner.\n- The connection between neuroscience and statistical modelling is novel and insightful. I hadn’t previously considered a statistical decomposition of prior and update terms as analogous to brain functions.\n- Theoretical contributions are well-motivated. Each major component—inference, model decomposition, and acquisition scoring—is supported by a corresponding theorem ensuring step-wise improvement. While overall convergence is not yet guaranteed, these results already represent substantial contributions.\n\nWeaknesses\n- The acquisition component is relatively unclear. I found it difficult to follow the details of the reward model and its necessity in the overall framework.\n- While the EM algorithm is known to be unstable, the authors propose heuristics—such as selectively updating the model rather than after every observation—that help mitigate this issue.\n- Variational Search Distribution [1] has researched a very similar task and methodology, which should be compared and discussed in my opinion.\n\nReference\n[1] ICLR 2025, https://arxiv.org/abs/2409.06142\n\nQUESTIONS:\n1. How stable is the training scheme? I could not find any error bars reported in the results. Given that the EM algorithm is known to be potentially unstable, some quantitative measure of variance would help assess robustness.\n2. What is the role of the reward model? From my understanding, the Gaussian mixture fitted to the posterior represents the (approximated) likelihood of $x$ conditioned on $y$, which could be seen as an approximate reward model for the ATD task. Could you clarify why an explicit reward model is introduced?\n3. What is the difference between this work and Variational Search Distribution [1]? I found this work is quite similar.\n\nLIMITATIONS:\nI could not find a dedicated limitations section in the paper, which is typically included in the conclusion. While the authors may have discussed some limitations elsewhere in the text, it would be helpful to clearly summarise them in the conclusion for the reader.\n\nOne notable limitation is the lack of a global convergence guarantee, while variational search distribution [1] offers. Since the method relies on the EM algorithm, it inherits well-known issues related to stability, slow convergence, and sensitivity to initialization. These concerns have been highlighted in prior work (e.g., [2]) and should be acknowledged.\n\nReference\n[2] \"Singularity, Misspecification, and the Convergence Rate of EM.\" The Annals of Statistics. https://arxiv.org/abs/1810.00828", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-21"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper presents a method for diffusion-based active target discovery (ATD), where no prior samples from the target domain are available to learn the diffusion dynamics a priori. Instead, the proposed method uses a combination of a pre-trained diffusion model and a lightweight update mechanism based on Doob's h-transform to modify the prior. Experiments on two problem settings are presented, showing improvements over the main baseline of DiffATD.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths:**\n- The problem setting is motivated well, and the writing/figures are clear.\n- The results on the two problem settings show a clear improvement over a reasonable choice of baselines.\n- The method is rigorously described.\n\n**Weaknesses:**\n- The paper currently only briefly describes the underlying DiffATD method. It would be helpful if the authors add a brief section describing that method to better ground the reader.\n- It's unclear to me whether the two problem settings shown are representative (not a big weakness; but my confidence would be higher with more evaluation settings shown)\n\nQUESTIONS:\n1. I'm still struggling a bit to understand the terminology of updating the \"prior\". Does this not violate Bayesian principles of encoding beliefs *before* seeing the data? Also, given that we're updating the prior based on very few data points, do you think it is possible that this approach risks overfitting to the presented observations, something that starting with an uninformed prior may not succumb to?\n2. What is the standard deviation one should expect with the experiments shown for each method? Are the current experiments reporting single runs or repeats?\n\nLIMITATIONS:\nNo. To provide a better understanding of capability, it would have been useful if the authors showed scenarios in which their method does not, in fact, perform very well.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-30"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a principled framework EM-PTDM to tackle Active Target Discovery (ATD) in scenarios where no informative prior exists—a situation often faced in domains like rare species detection or novel object discovery. The method consists of two components:\n(1) Permanent Memory: a pretrained diffusion model capturing general knowledge; (2) Transient Memory: a lightweight, adaptive h-transform module that quickly adjusts to task-specific signals. The method uses an EM-style iterative refinement and principled sampling strategies balancing exploration and exploitation. Experimental results on species distribution and remote sensing show notable improvements over baselines, even under severely uninformative priors.\n\nSTRENGTHS AND WEAKNESSES:\nPros:\n* The paper clearly identifies a relevant gap: active search without any informative prior, which is highly practical in many scientific fields. \n* Drawing parallels to permanent and transient memory systems in the brain is creative and helps frame the proposed method intuitively. \n* The EM update of the prior and the integration of the h-transform for conditional sampling are well-motivated and mathematically rigorous. \n* The method shows clear empirical gains over baselines, especially in low-data regimes.\n* The paper provides code and uses public datasets, which facilitates reproducibility.\n\nCons:\n* The paper is quite dense and could be hard to follow for readers without a strong ML background. Adding simpler diagrams or pseudocode could help.\n* The authors mention instability if the h-model is updated too often early on. It’d be helpful to see how sensitive the results are to different update frequencies.\n* While the method beats strong baselines, many of those still rely on prior knowledge. It would be good to test simpler baselines too—for example, training on random samples and then re-ranking spots based on how similar they are to places where targets were found, without retraining the diffusion model.\n\nQUESTIONS:\nSee Cons.\n\nLIMITATIONS:\nN/A", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 2, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-03"}