arXiv Computer Vision
CBX-Bench: A Human-Aligned MLLM Council for Benchmarking Concept Bottleneck Model Explanations
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arXiv AI
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Explaining is Harder Than Predicting Alone: Evaluating Concept-based Explanations of MLLMs as ICL Visual Classifiers
arXiv:2605. 28215v2 Announce Type: replace Abstract: In-context learning (ICL) enables multimodal large language models (MLLMs) to classify images from a few labelled examples.
arXiv Computation and Language
Oct 16
M$^3$R-Bench: A Unified Benchmark for Evidence-Grounded Multimodal Metaphor Understanding
M$^3$R-Bench is a new benchmark comprising 1,000 human‑annotated image‑text pairs that jointly label metaphor occurrence, Target‑Source mapping, and sentiment, and provide step‑by‑step explanations for evidence identification, mapping, and sentiment inference. Existing models often rely on textual shortcuts and produce mappings that conflict with visual evidence, as shown by evaluations on this dataset. To address this, the authors propose M$^3$R‑Reasoner, a model that uses curriculum‑based reasoning supervision and task‑aware reinforcement learning, achieving a 37.90% Joint Label Accuracy and outperforming both proprietary baselines and GPT‑5.5 on evidence and sentiment justification.
arXiv AI
Jun 24
Rule2Text: A Framework for Generating and Evaluating Natural Language Explanations of Knowledge Graph Rules
arXiv:2508. 10971v2 Announce Type: replace-cross Abstract: Knowledge graphs (KGs) can be enhanced through rule mining; however, the resulting logical rules are often difficult for humans to interpret due to their inherent complexity and the idiosyncratic labeling conventions of individual KGs.
arXiv AI
Aug 18
Defake-o3: From Speculative Rationales to Verifiable Evidence for Explainable AIGI Detection
arXiv:2608. 16259v1 Announce Type: cross Abstract: The rapid progress of image generation models calls for AI-generated image (AIGI) detectors that are not only accurate but also explainable and reliable.
arXiv AI
Sep 28
Beyond Bag-of-Words: Diagnosing Compositional Binding Failures in Vision-Language Models
The paper introduces Auto-Comp, a fully automated, concept-driven pipeline that generates photorealistic compositional benchmarks for vision‑language models. Auto‑Comp creates paired Minimal and Contextual samples for each concept, enabling isolation of core binding abilities from visio‑linguistic complexity. Evaluations across 25 models reveal consistent failures in attribute and relational binding, with context helping relational tasks but hindering attribute tasks due to visual clutter.
arXiv AI
Oct 15
VisExMEM: Visual Evidence-Based Explainable Multimodal Evaluation Metric
arXiv:2610.16441v1 Announce Type: cross Abstract: Current image-text evaluation often relies on a global score, which gives limited insight into which semantic details are supported by the image. Exp...