CaVe-VLM-CoT: An Interpretable Vision-Language Model Framework
CaVe-VLM-CoT is a modular reflection-based agentic-RAG framework that reduces hallucinations in Vision-Language Models via a five-stage closed-loop pipeline. It introduces a comprehensive 23-metric evaluation suite, anchored by CaVeScore, measuring accuracy, citation precision/recall, attribution, and evidence grounding. Without architectural changes, it achieves 87.1% accuracy on ScienceQA and 55.2% on MMMU.
[2606.18385] CaVe-VLM-CoT: An Interpretable Vision-Language Model Framework
[Submitted on 16 Jun 2026]
Title:CaVe-VLM-CoT: An Interpretable Vision-Language Model Framework
View a PDF of the paper titled CaVe-VLM-CoT: An Interpretable Vision-Language Model Framework, by Sneha Rao and 2 other authors
View PDF HTML (experimental)
Abstract:Vision-Language Models (VLMs) remain prone to hallucinations, producing fluent but visually unfaithful outputs. Existing chain-of-thought and retrieval-augmented methods only partially address this, as they neither enforce step-level citation grounding nor route verification failures back to retrieval for correction. We present CaVe-VLM-CoT, a modular reflection-based agentic-RAG framework that enforces evidence-grounded reasoning through a five-stage closed-loop pipeline: Extractor, Retriever, Solver, Citation Injector, and Verifier, in which detected ungrounded claims trigger structured feedback to the Extractor for targeted re-retrieval. Since no existing framework jointly measures retrieval quality, step-wise citation faithfulness, and cross-modal grounding, we propose a suite of 23 component-wise metrics across all stages, anchored by CaVeScore, a composite metric weighting accuracy, citation precision and recall, attribution, and evidence grounding. Without any architectural or prompt modifications, CaVe-VLM-CoT achieves 87.1\% accuracy and 56.6\% CaVeScore on ScienceQA , and 55.2\% accuracy and 35.7\% CaVeScore on MMMU (30 subjects).
Subjects:
Artificial Intelligence (cs.AI)
Cite as: arXiv:2606.18385 [cs.AI]
(or arXiv:2606.18385v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2606.18385
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Sneha Rao [view email] [v1] Tue, 16 Jun 2026 18:28:47 UTC (1,039 KB)
Full-text links:
Access Paper:
View a PDF of the paper titled CaVe-VLM-CoT: An Interpretable Vision-Language Model Framework, by Sneha Rao and 2 other authors
View PDF
HTML (experimental)
TeX Source
view license
Current browse context:
cs.AI
new | recent | 2026-06
Change to browse by:
cs
References & Citations
NASA ADS
Google Scholar
Semantic Scholar
Loading...
Data provided by:
Bibliographic Tools
Bibliographic and Citation Tools
Bibliographic Explorer Toggle
Bibliographic Explorer (What is the Explorer?)
Connected Papers Toggle
Connected Papers (What is Connected Papers?)
Litmaps Toggle
Litmaps (What is Litmaps?)
scite.ai Toggle
scite Smart Citations (What are Smart Citations?)
Code, Data, Media
Code, Data and Media Associated with this Article
alphaXiv Toggle
alphaXiv (What is alphaXiv?)
Links to Code Toggle
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub Toggle
DagsHub (What is DagsHub?)
GotitPub Toggle
Gotit.pub (What is GotitPub?)
Huggingface Toggle
Hugging Face (What is Huggingface?)
ScienceCast Toggle
ScienceCast (What is ScienceCast?)
Demos
Demos
Replicate Toggle
Replicate (What is Replicate?)
Spaces Toggle
Hugging Face Spaces (What is Spaces?)
Spaces Toggle
TXYZ.AI (What is TXYZ.AI?)
Related Papers
Recommenders and Search Tools
Link to Influence Flower
Influence Flower (What are Influence Flowers?)
Core recommender toggle
CORE Recommender (What is CORE?)
Author
Venue
Institution
Topic
About arXivLabs
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)