Title: Right for the Right Reasons: Avoiding Reasoning Shortcuts via Prototypical Neurosymbolic AI
Venue: NeurIPS 2025
Accepted: yes
Keywords: neurosymbolic AI

reasoning shortcuts
Abstract: Neurosymbolic AI is growing in popularity thanks to its ability to combine neural perception and symbolic reasoning in end-to-end trainable models. However, recent findings reveal these are prone to shortcut reasoning, i.e., to learning unindented concepts--or neural predicates--which exploit spurious correlations to satisfy the symbolic constraints. In this paper, we address reasoning shortcuts at their root cause and we introduce Prototypical Neurosymbolic architectures. These models are able to satisfy the symbolic constraints (be right) because they have learnt the correct basic concepts (for the right reasons) and not because of spurious correlations, even in extremely low data regimes. Leveraging the theory of prototypical learning, we demonstrate that we can effectively avoid reasoning shortcuts by training the models to satisfy the background knowledge while taking into account the similarity of the input with respect to the handful of labelled datapoints. We extensively validate our approach on the recently proposed rsbench benchmark suite in a variety of settings and tasks with very scarce supervision: we show significant improvements in learning the right concepts both in synthetic tasks (MNIST-EvenOdd and Kand-Logic) and real-world, high-stake ones (BDD-OIA). Our findings pave the way to prototype grounding as an effective, annotation-efficient strategy for safe and reliable neurosymbolic learning.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes using prototype-augmented neural networks to mitigate reasoning shortcuts. In the proposed method, prototype extractors with learnable parameters first compute class centroids from few labeled examples. A neural network is then trained to classify datapoints based on their proximity to the corresponding centroids while simultaneously satisfying background knowledge constraints.\n\nThe proposed method can be used in conjunction with other NeSy models, such as DPL, LTN, and SL. Experiments show that the proposed method can significantly enhance these NeSy approaches, achieving F1-scores of 98% on MNIST-EvenOdd and 94% on Kand-Logic, even when only one labeled datapoint is provided for each concept.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**\n\n1. To my knowledge, the idea of introducing prototypical neural networks into NeSy research is novel and interesting.\n2. The problem setting is solid and reasonable. The assumption of having one or a few labeled examples per class is intuitive and practically feasible.\n3. The experimental results are quite impressive, as reasoning shortcuts are almost completely avoided on the tested benchmarks, especially MNIST-EvenOdd and Kand-Logic.\n4. The proposed method can be easily incorporated into other NeSy models, thus extending its practical usefulness.\n\n**Weaknesses**\n\n1. The methodology of this work seems to heavily overlap with existing NeSy research that utilizes Semi-Supervised Learning (SSL) techniques, but this relationship is not discussed in detail in the manuscript.\n    - The proposed method is primarily based on the assumption that \"datapoints close to each other are more likely to belong to the same concept,\" which is known as the cluster assumption in SSL research.\n    - To my knowledge, some existing NeSy research already utilizes SSL techniques, either explicitly or implicitly (e.g., by using distance-based metrics between datapoints in loss functions). However, most experimental results reported in the manuscript (except Table 2) are based only on the proposed method or its ablations; a proper comparison with more relevant methods would be helpful.\n    - From an SSL perspective, the proposed framework is quite similar to a process of: 1) using self-training to assign labels to unlabeled datapoints by first initializing centroids from labeled data, assigning pseudo-labels based on distance, and iterating until convergence; and then 2) using this predicted label information to enhance NeSy models. Consequently, the novelty of the proposed method appears somewhat limited.\n\n2. The crucial cluster assumption may not always hold in typical NeSy application scenarios, which relates to the authors' discussion in Section 7 regarding the method's sensitivity to ambiguities in concept definition.\n    - IMHO, a major motivation for using NeSy methods is for scenarios where data alone is insufficient—for example, with very noisy data. In such cases, one hopes a NeSy system can perform better by utilizing symbolic knowledge. \n    - This leads to a paradox: if data is clean and can be easily classified, neural networks are sufficient, and there's no need for NeSy; if data is noisy or concept definitions are ambiguous, NeSy is needed, but the proposed method would not work well in this case. This raises the question of when we should expect to use it.\n\nQUESTIONS:\n1. Consider a direct, intuitive strategy: first, use an SSL or one-shot learning method to predict labels for the unlabeled data, and then provide these pseudo-labels to a NeSy algorithm. What are the differences and potential advantages of the proposed prototype-augmented strategy compared to this approach?\n\nI would be willing to raise my score if the authors could provide convincing evidence that their algorithm is substantially different from, and outperforms, more straightforward counterparts. \n\nSuch a comparison would be necessary to fully support the claimed novelty of their work.\n\nLIMITATIONS:\nAs mentioned in the weaknesses section, the proposed approach assumes that concepts form well-separated clusters. This property may not hold for more realistic NeSy tasks.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-06-08"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper proposes combining neuro-symbolic predictors with prototype-augmented neural networks to mitigate reasoning shortcuts. The central idea is to begin training with a minimal set of annotated prototypes. The approach is validated through experiments that demonstrate its effectiveness and analyze concept collapse as the number of supervised prototypes varies.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**: The paper addresses an important problem in the literature with a clear and well-motivated idea. It includes a wide range of experiments and research questions, and it explicitly discusses the limitations of the proposed approach.\n\n**Weaknesses**: Effectively mitigating RSs with a prototype-based network requires a well-initialized prototype for each concept configuration, and assumes that concepts form clear clusters. The zero-annotation setting is explored with less focus (there are interesting experiments in the appendix), and there is no theoretical analysis of how prototypes influence the number of reasoning shortcuts.\n\nQUESTIONS:\n* The method appears to rely heavily on initialization, particularly on having predefined prototypes. In standard disentanglement settings, wouldn’t this require one prototype per concept configuration? For example, in MNIST-Even-Odd with 10 digits per position, this would imply annotating 100 configurations. Have you explored scenarios where disentanglement does not hold in terms of required annotations?\n\n* If prototypes are learned from data, there’s a risk they may collapse to similar representations—i.e., the model may predict different concepts, but their prototypes end up qualitatively indistinguishable. Have you observed such behavior during training?\n\n* A key assumption seems to be that concepts are clusterable in the representation space. Could you elaborate on this requirement and its implications for generalization and applicability?\n\n* Relatedly, have you considered a theoretical analysis of how the use of prototypes impacts the number of reasoning shortcuts that can affect the model?\n\n* How does the method perform in fully unsupervised settings where no prototypes are available, i.e., the standard NeSy setup? You report some interesting results in the appendix (i.e., hidden class count and the BDD-OIA analysis), which seem impactful enough to highlight in the main text.\n\n* You mention that the solution proposed in [1] requires dense annotations across many datapoints, which may be costly. Could you clarify why this is the case? It would also be interesting to compare its annotation efficiency with that of prototype-based networks as well as for other mitigation strategies.\n\n* The code is currently unavailable. Could you clarify the reason for this? \n\n-----\n\n[1] E. Marconato, BEARS Make Neuro-Symbolic Models Aware of their Reasoning Shortcuts, in UAI (2024)\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-06-23"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper addresses the problem of \"reasoning shortcuts\" in Neurosymbolic (NeSy) models, where a model might satisfy symbolic constraints syntactically but fail to learn the correct underlying semantics of concepts. The authors propose a Prototype-Augmented NeSy model, which integrates prototypical networks into standard NeSy frameworks. The core idea is to anchor the latent representations of concepts to prototypes learned from a very small number of labeled examples. The authors conduct experiments on the rs-bench benchmark, demonstrating improvements in concept learning accuracy and a significant reduction in reasoning shortcuts.\n\nSTRENGTHS AND WEAKNESSES:\nStrength:\n1.This paper addresses a real and underexplored problem - reasoning shortcut is NeSY, which contributes to the growing field of trustworthy and interpretable AI.\n2.There are clear empirical improvements across multiple tasks, and three different NeSy frameworks (DPL, SL, LTN).\n3.This paper provides a discussion regarding the limitations, and where the method fails (e.g., in BDD-OIA, a dataset requiring temporal/causal reasoning).\n\nWeakness:\nIntroduction:\n1.The “two-fold intuition” presented as the conceptual motivation lacks clarity and rigor. The first \"intuition\" is essentially a weak assumption (that a user who writes rules knows at least one example), and the second is a methodological design choice rather than a compelling motivation.\n2.The introduction leans heavily on citations to establish context but does not clearly clarify the conceptual novelty of the proposed method. Terms such as semantic alignment and background knowledge satisfaction are introduced early but remain undefined and unquantified.\n\nMethod:\n1.The integration of prototypes appears empirically motivated but conceptually superficial. While prototypical networks are effective in metric-based few-shot learning, symbolic reasoning often involves non-metric, compositional, and hierarchical structures. The paper does not address how these characteristics are preserved by the use of prototypes.\n2.The initialization of prototypes for unspecified classes (Eq. 3) using Gaussian noise added to known centroids is ad hoc and lacks semantic grounding. There is no assurance that these generated prototypes meaningfully represent logically coherent or distinguishable concepts.\n3. A closely relevant work is missing: Visual Recognition with Deep Nearest Centroids, ICLR. \n4.The dependence on augmentation also undermines generality. Despite claiming that “just one example per class is sufficient,” the method relies heavily on aggressive data augmentation (e.g. 200+ augmented samples per concept). This contradicts the narrative of label efficiency.\n\nThere remains a significant conceptual gap between the metric-based mechanism (prototype proximity) and the symbolic reasoning objectives. The assumption that proximity to a prototype leads to logical consistency is not theoretically grounded. The paper would benefit from a clearer, step-by-step formal justification of how and why prototype alignment improves symbolic constraint satisfaction. The paper in its current form feels more like a practical engineering solution effective in constrained synthetic settings, rather than a fundamental advance in neuro-symbolic reasoning.\n\nEvaluation:\n1.The performance gains reported (e.g. MNIST-EvenOdd F1 jump from 5% to 98%) are implausibly large. This raises concerns about the simplicity or synthetic nature of the benchmark tasks. Such gains suggest that these tasks may not meaningfully test for generalizable reasoning.\n2.The impressive results are confined to synthetic, small-scale benchmarks. The far more modest gains on the more realistic BDD-OIA dataset (F1 from 0.04 to 0.13) suggest that the method's effectiveness severely diminishes when faced with real-world ambiguity, visual complexity, and the need for richer contextual reasoning. \n3.The use of the term reasoning is overly broad. The benchmarks primarily involve basic logical structures such as addition or first-order logic. It's unclear how it would help with more complex, multi-step, or abstract reasoning tasks where the concepts themselves are not easily represented by a single visual prototype. \n4.The baselines are dated: DeepProbLog (2018), Semantic Loss (2018), and Logic Tensor Networks (2016). The absence of comparisons with more recent NeSy or hybrid neuro-causal models weakens the empirical case for the method’s state-of-the-art relevance.\n\nQUESTIONS:\n1.How does prototype proximity translate into logical consistency? Can the authors formally explain why minimizing the distance between an embedding and a prototype should lead to better satisfaction of logical constraints?\n2.What is the precise relationship between semantic alignment and symbolic satisfaction? The authors use the term “semantic alignment” extensively, but it is never formally defined. How is it measured, and how does it relate to the logical correctness of concept assignments?\n\nLIMITATIONS:\nThe limitations of this work primarily concern the scope and depth of the evaluation; see details in the weaknesses section.", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces a method called PNet for utilizing limited labeled data in Neuro-Symbolic tasks under the guiding principle of constructing class prototypes. Specifically, for classes with labeled data, the mean of the (augmented) embeddings is used as the class prototype; for classes without labeled data, a prototype is randomly initialized based on the distances between existing prototypes. By doing so, this paper introduces an additional loss term based on the distance between embeddings and prototypes, alongside the traditional NeSy loss. This approach balances logical constraints and label information, mitigating the issue of reasoning shortcuts. Authors conducted multiple experiments to validate the effectiveness of the proposed method.\n\nSTRENGTHS AND WEAKNESSES:\nStrength:\n\n1.Authors considered multiple factors that could affect the effectiveness of the method and demonstrated its performance under different conditions through experiments.\n\n2.Authors demonstrated the limitations of the method through experiments.\n\nWeakness:\n\n1.The main experiment considered in this paper is overly simplistic—a (minor) variant of MNIST add with a small amount of labeled data. It is well-known that simple CNN networks can achieve over 95% accuracy on the MNIST dataset even when each class contains only one labeled image. Therefore, to validate the effectiveness of the proposed method, a baseline that first performs supervised learning using labeled data and then conducts neuro-symbolic learning should be added.\n\n2.The comparison between this method and others is not entirely fair. Other methods target more challenging scenarios without labeled data, whereas in cases where some labeled data is available for supervised pretraining, I believe the comparison methods could achieve similar performance to the proposed method.\n\n3.The paper addresses a classic neuro-symbolic task but introduces a new and relatively complex set of symbols in Section 2, particularly merging the Concept space and Final label space, which makes it difficult to read.\n\n4.The paper contains several typos and imprecisions, such as: two instances of \"(iii)\" in Section 2; the definition of mm on line 87 is not a mapping to the power set, which contradicts Example 2; and Equation (3) includes two $C_c$ symbols with entirely different meanings, further complicating readability.\n\nQUESTIONS:\n1. Why does the prototype selection for classes without labeled data require introducing randomness (Equation (3))?\n2. Since this paper considers a classic neuro-symbolic task, it would be beneficial to adopt a more concise and traditional set of symbols to enhance the readability.\n3. If the proposed method can demonstrate a significant performance advantage in the fair comparisons mentioned in the weaknesses, I would consider increasing the score.\n4. If the method can showcase its superiority in more challenging scenarios, I would consider increasing the score.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 4, "CLARITY": 1, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-05"}