# Object-Centric Case-Based Reasoning via Argumentation

arXiv: https://arxiv.org/abs/2510.00185 · HTML: https://mlsanity.com/paper/2510.00185/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Multimodal reasoning, one of 17 directions in LLM reasoning.
- Builds on 28 of its 54 references that are in the map.
- First posted on arXiv 30 Sep 2025.

## Placed in

- LLM reasoning / [Multimodal reasoning](https://mlsanity.com/lr/multimodal-reasoning/)

## Builds on (most cited first, 28 of 28)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)](https://mlsanity.com/paper/1711.11279/) (2017)
- [Object-Centric Learning with Slot Attention](https://mlsanity.com/paper/2006.15055/) (2020)
- [Network Dissection: Quantifying Interpretability of Deep Visual Representations](https://mlsanity.com/paper/1704.05796/) (2017)
- [MONet: Unsupervised Scene Decomposition and Representation](https://mlsanity.com/paper/1901.11390/) (2019)
- [Multi-Object Representation Learning with Iterative Variational Inference](https://mlsanity.com/paper/1903.00450/) (2019)
- [Neurosymbolic AI: The 3rd Wave](https://mlsanity.com/paper/2012.05876/) (2020)
- [GENESIS: Generative Scene Inference and Sampling with Object-Centric Latent Representations](https://mlsanity.com/paper/1907.13052/) (2019)
- [Bridging the Gap to Real-World Object-Centric Learning](https://mlsanity.com/paper/2209.14860/) (2022)
- [SPACE: Unsupervised Object-Oriented Scene Representation via Spatial Attention and Decomposition](https://mlsanity.com/paper/2001.02407/) (2020)
- [Illiterate DALL-E Learns to Compose](https://mlsanity.com/paper/2110.11405/) (2021)
- [Counterfactual Generative Networks](https://mlsanity.com/paper/2101.06046/) (2021)
- [GENESIS-V2: Inferring Unordered Object Representations without Iterative Refinement](https://mlsanity.com/paper/2104.09958/) (2021)
- [Iterative Amortized Inference](https://mlsanity.com/paper/1807.09356/) (2018)
- [Right for the Right Concept: Revising Neuro-Symbolic Concepts by Interacting with their Explanations](https://mlsanity.com/paper/2011.12854/) (2020)
- [Slot-VAE: Object-Centric Scene Generation with Slot Attention](https://mlsanity.com/paper/2306.06997/) (2023)
- [Grounded Object Centric Learning](https://mlsanity.com/paper/2307.09437/) (2023)
- [Scientific Discovery by Generating Counterfactuals using Image Translation](https://mlsanity.com/paper/2007.05500/) (2020)
- [Abstracting Deep Neural Networks into Concept Graphs for Concept Level Interpretability](https://mlsanity.com/paper/2008.06457/) (2020)
- [Monotonicity and Noise-Tolerance in Case-Based Reasoning with Abstract Argumentation (with Appendix)](https://mlsanity.com/paper/2107.06413/) (2021)
- [ABA Learning via ASP](https://mlsanity.com/paper/2308.15877/) (2023)
- [Preference-Based Abstract Argumentation for Case-Based Reasoning (with Appendix)](https://mlsanity.com/paper/2408.00108/) (2024)
- [Free Argumentative Exchanges for Explaining Image Classifiers](https://mlsanity.com/paper/2502.12995/) (2025)
- [Neuro-Argumentative Learning with Case-Based Reasoning](https://mlsanity.com/paper/2505.15742/) (2025)
- [Object-Centric Neuro-Argumentative Learning](https://mlsanity.com/paper/2506.14577/) (2025)
- [Supported Abstract Argumentation for Case-Based Reasoning](https://mlsanity.com/paper/2507.04994/) (2025)

## Built on by (most cited first, 1 of 1)

- [Deep Arguing](https://mlsanity.com/paper/2605.10569/) (2026)

## Abstract (arXiv)

We introduce Slot Attention Argumentation for Case-Based Reasoning (SAA-CBR), a novel neuro-symbolic pipeline for image classification that integrates object-centric learning via a neural Slot Attention (SA) component with symbolic reasoning conducted by Abstract Argumentation for Case-Based Reasoning (AA-CBR). We explore novel integrations of AA-CBR with the neural component, including feature combination strategies, casebase reduction via representative samples, novel count-based partial orders, a One-Vs-Rest strategy for extending AA-CBR to multi-class classification, and an application of Supported AA-CBR, a bipolar variant of AA-CBR. We demonstrate that SAA-CBR is an effective classifier on the CLEVR-Hans datasets, showing competitive performance against baseline models.