Soft Symbol Grounding for Prototypical Concepts

• URL: http://arxiv.org/abs/2609.12247v1
• Date: Thu, 10 Sep 2026 22:10:33 GMT
• Title: Soft Symbol Grounding for Prototypical Concepts
• Abstract summary: textbfSoft-PNet reframes concept grounding as a Metropolis walk over a cache of feasible symbolic solutions.<n>It trains against one KL objective between the prototype-weighted cache and the network's concept predictions.<n>On textttMNIST-EvenOdd, Visual Sudoku, and textttKand-Logic under scarce supervision, Soft-PNet matches loss-engineered networks at the concept and label levels and recovers concepts that soft-grounding baselines miss.
• Score: 1.9584254864124988
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Neuro-symbolic models are usually trained with supervision only on final labels, leaving the intermediate concepts unobserved. Since many concept assignments are consistent with a given label, training can predict labels correctly while recovering the wrong concepts, a failure known as a reasoning shortcut. Prototypical networks reduce shortcuts by anchoring each concept to a few labeled examples, but existing methods still couple perception and reasoning through a hand-crafted, task-specific differentiable loss that must be redesigned for every task. We introduce \textbf{Soft-PNet}, which removes this loss: it reframes concept grounding as a Metropolis walk over a precomputed cache of feasible symbolic solutions, guided by a prototype distribution built from a single labeled anchor per concept, and trains against one KL objective between the prototype-weighted cache and the network's concept predictions. The objective is identical across tasks and remains applicable when the solution space cannot be enumerated. On \texttt{MNIST-EvenOdd}, Visual Sudoku, and \texttt{Kand-Logic} under scarce supervision, Soft-PNet matches loss-engineered prototypical networks at the concept and label levels and recovers concepts that soft-grounding baselines miss, with no loss engineering and lower training time.

Related papers

• Interpreting Object-Dependent Concept Brittleness in Text-to-Image Diffusion Models [105.53190946111586]
  We term this phenomenon object-dependent concept brittleness.<n>We present an interpretability-oriented framework to audit and minimally correct these failures.<n>We evaluate the proposed framework on style and attribute failure cases across multiple diffusion backbones.
  arXiv  Detail & Related papers  (2026-09-09T09:07:40Z)
• Concept Unlearning via Cross-Attention Activation Projection for Diffusion Models [6.701045531467819]
  Concept unlearning aims to erase a target concept from a pretrained text-to-image diffusion model without retraining.<n>Existing closed-form methods represent the target concept through the text encoder's response to a few short anchor prompts that name it.<n>We propose PURE, a closed-form method that builds the forget and retain bases from per-layer cross-attention activations.
  arXiv  Detail & Related papers  (2026-05-25T12:18:47Z)
• Beyond Heuristic Prompting: A Concept-Guided Bayesian Framework for Zero-Shot Image Recognition [81.2779530670268]
  Vision-Language Models (VLMs) have significantly advanced zero-shot image recognition.<n>This paper enhances prompt by incorporating class-specific concepts.<n>Our method consistently outperforms state-of-the-art approaches.
  arXiv  Detail & Related papers  (2026-03-09T03:11:11Z)
• Hierarchical Concept-based Interpretable Models [23.16720677779406]
  Concept Embedding Models (CEMs) map inputs to human-interpretable concept representations from which tasks can be predicted.<n>Yet, CEMs fail to represent inter-concept relationships and require concept annotations at different granularities during training.<n>We introduce Hierarchical Concept Embedding Models (HiCEMs), a new family of CEMs that explicitly model concept relationships through hierarchical structures.
  arXiv  Detail & Related papers  (2026-02-27T11:49:56Z)
• ConceptPrism: Concept Disentanglement in Personalized Diffusion Models via Residual Token Optimization [11.472088067393074]
  ConceptPrism is a novel framework that automatically disentangles the shared visual concept from image-specific residuals.<n>In experiments, ConceptPrism effectively resolves concept entanglement, achieving a significantly improved trade-off between fidelity and alignment.
  arXiv  Detail & Related papers  (2026-02-23T07:46:19Z)
• FaCT: Faithful Concept Traces for Explaining Neural Network Decisions [56.796533084868884]
  Deep networks have shown remarkable performance across a wide range of tasks, yet getting a global concept-level understanding of how they function remains a key challenge.<n>We put emphasis on the faithfulness of concept-based explanations and propose a new model with model-inherent mechanistic concept-explanations.<n>Our concepts are shared across classes and, from any layer, their contribution to the logit and their input-visualization can be faithfully traced.
  arXiv  Detail & Related papers  (2025-10-29T13:35:46Z)
• Preserving Task-Relevant Information Under Linear Concept Removal [19.953631890863498]
  SPLICE eliminates sensitive concepts from representations while preserving their covariance with a target label.<n>It is the unique solution that removes linear concept predictability and maintains target covariance with minimal embedding distortion.<n> Empirically, SPLICE outperforms baselines on benchmarks such as Bias in Bios and Winobias.
  arXiv  Detail & Related papers  (2025-06-12T13:53:29Z)
• Zero-shot Concept Bottleneck Models [17.70684428339905]
  Concept bottleneck models (CBMs) are inherently interpretable and intervenable neural network models.<n>We present textitzero-shot concept bottleneck models (Z-CBMs), which predict concepts and labels in a fully zero-shot manner without training neural networks.
  arXiv  Detail & Related papers  (2025-02-13T07:11:07Z)
• How to Continually Adapt Text-to-Image Diffusion Models for Flexible Customization? [91.49559116493414]
  We propose a novel Concept-Incremental text-to-image Diffusion Model (CIDM) It can resolve catastrophic forgetting and concept neglect to learn new customization tasks in a concept-incremental manner. Experiments validate that our CIDM surpasses existing custom diffusion models.
  arXiv  Detail & Related papers  (2024-10-23T06:47:29Z)
• Discover-then-Name: Task-Agnostic Concept Bottlenecks via Automated Concept Discovery [52.498055901649025]
  Concept Bottleneck Models (CBMs) have been proposed to address the 'black-box' problem of deep neural networks. We propose a novel CBM approach -- called Discover-then-Name-CBM (DN-CBM) -- that inverts the typical paradigm. Our concept extraction strategy is efficient, since it is agnostic to the downstream task, and uses concepts already known to the model.
  arXiv  Detail & Related papers  (2024-07-19T17:50:11Z)
• Concept Bottleneck Model with Additional Unsupervised Concepts [0.5939410304994348]
  We propose a novel interpretable model based on the concept bottleneck model (CBM) CBM uses concept labels to train an intermediate layer as the additional visible layer. By seamlessly training these two types of concepts while reducing the amount of computation, we can obtain both supervised and unsupervised concepts simultaneously.
  arXiv  Detail & Related papers  (2022-02-03T08:30:51Z)
• Kernelized Concept Erasure [108.65038124096907]
  We propose a kernelization of a linear minimax game for concept erasure. It is possible to prevent specific non-linear adversaries from predicting the concept. However, the protection does not transfer to different nonlinear adversaries.
  arXiv  Detail & Related papers  (2022-01-28T15:45:13Z)

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.