mlsanityData to 7 Oct 2026

CaBRNet, an open-source library for developing and evaluating Case-Based Reasoning Models

arXiv 2409.16693 ↗First posted 25 Sep 2024cs.AIcited by 3

On the map: LLM reasoning → Reasoning benchmarks and evaluation

Abstract (arXiv)

In the field of explainable AI, a vibrant effort is dedicated to the design of self-explainable models, as a more principled alternative to post-hoc methods that attempt to explain the decisions after a model opaquely makes them. However, this productive line of research suffers from common downsides: lack of reproducibility, unfeasible comparison, diverging standards. In this paper, we propose CaBRNet, an open-source, modular, backward-compatible framework for Case-Based Reasoning Networks: https://github.com/aiser-team/cabrnet.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning. Builds on 12 of its 18 references that are in the map. First posted on arXiv 25 Sep 2024.

Builds on

12 of 18 references.

References · 12

  1. 2013Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Mapscited by 2,348
  2. 2017SmoothGrad: removing noise by adding noisecited by 1,097
  3. 2018This Looks Like That: Deep Learning for Interpretable Image Recognitioncited by 731
  4. 2019Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networkscited by 575
  5. 2020Neural Prototype Trees for Interpretable Fine-grained Image Recognitioncited by 213
  6. 2021Interpretable Image Classification with Differentiable Prototypes Assignmentcited by 97
  7. 2020ProtoPShare: Prototype Sharing for Interpretable Image Classification and Similarity Discoverycited by 89
  8. 2021This Looks Like That... Does it? Shortcomings of Latent Space Prototype Interpretability in Deep Networkscited by 65
  9. 2020This Looks Like That, Because ... Explaining Prototypes for Interpretable Image Recognitioncited by 50
  10. 2021This looks more like that: Enhancing Self-Explaining Models by Prototypical Relevance Propagationcited by 43
  11. 2023Interpretability Benchmark for Evaluating Spatial Misalignment of Prototypical Parts Explanationscited by 27
  12. 2023Sanity checks for patch visualisation in prototype-based image classificationcited by 8

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 1.

3 papers.

  1. 2025Formal Abductive Latent Explanations for Prototype-Based Networkscited by 5
  2. 2025Comprehensive Evaluation of Prototype Neural Networkscited by 1
  3. 2026Beyond L2: Generalizing Abductive Latent Explanations to Diverse Prototype-Based Architecturescited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026ART: Adaptive Reasoning Trees for Explainable Claim Verification
  2. 2025Argumentative Reasoning with Language Models on Non-factorized Case Bases
  3. 2022MetaLogic: Logical Reasoning Explanations with Fine-Grained Structure
  4. 2026Beyond Post-hoc Explanation: Toward Glassbox AI via Probabilistic Mediation
  5. 2025From Passive to Active Reasoning: Can Large Language Models Ask the Right Questions under Incomplete Information?
  6. 2025BayesL: a Logical Framework for the Verification of Bayesian Networks
  7. 2025ReFIne: A Framework for Trustworthy Large Reasoning Models with Reliability, Faithfulness, and Interpretability
  8. 2025Dialogue-based Explanations for Logical Reasoning using Structured Argumentation