[Automatic PR] Automatically add papers from authors

_posts/papers/2025-06-03-2506.05393.md

@@ -5,10 +5,10 @@ openAccessPdf:
   url: ''
   status:
   license:
-  disclaimer: 'Notice: This content is from the open access paper or abstract available
-    at https://arxiv.org/abs/2506.05393, which is subject to the license by the author
-    or copyright owner provided with this content. Please go to the source to verify
-    the license and copyright information for your use.'
+  disclaimer: 'Notice: Paper or abstract available at https://arxiv.org/abs/2506.05393,
+    which is subject to the license by the author or copyright owner provided with
+    this content. Please go to the source to verify the license and copyright information
+    for your use.'
 names: Shenyang Huang, Alipanah Parviz, Emma Kondrup, Zachary Yang, Zifeng Ding, Michael
   Bronstein, Reihaneh Rabbany, Guillaume Rabusseau
 tags:

_posts/papers/2025-06-10-10.22318-icls2025.100255.md

@@ -0,0 +1,30 @@
+---
+title: A Systematic Literature Review of Large Language Model Applications in the
+  Algebra Domain
+venue: Proceedings (International Conference of the Learning Sciences)
+openAccessPdf:
+  url: ''
+  status:
+  license:
+  disclaimer: 'Notice: Paper or abstract available at https://api.unpaywall.org/v2/10.22318/icls2025.100255?email=<INSERT_YOUR_EMAIL>
+    or https://doi.org/10.22318/icls2025.100255, which is subject to the license by
+    the author or copyright owner provided with this content. Please go to the source
+    to verify the license and copyright information for your use.'
+names: Yajie Song, Yimei Zhang, D. Precup, Reihaneh Rabbany, Maria Cutumisu
+tags:
+- Proceedings (International Conference of the Learning Sciences)
+link: https://doi.org/10.22318/icls2025.100255
+author: Reihaneh Rabbany
+categories: Publications
+
+---
+
+*{{ page.names }}*
+
+**{{ page.venue }}**
+
+{% include display-publication-links.html pub=page %}
+
+## Abstract
+
+None

_posts/papers/2025-06-18-2506.15794.md

@@ -0,0 +1,30 @@
+---
+title: 'Veracity: An Open-Source AI Fact-Checking System'
+venue: ''
+openAccessPdf:
+  url: ''
+  status:
+  license:
+  disclaimer: 'Notice: Paper or abstract available at https://arxiv.org/abs/2506.15794,
+    which is subject to the license by the author or copyright owner provided with
+    this content. Please go to the source to verify the license and copyright information
+    for your use.'
+names: Taylor Lynn Curtis, M. P. Touzel, William Garneau, Manon Gruaz, Mike Pinder,
+  Li Wei Wang, Sukanya Krishna, Luda Cohen, J. Godbout, Reihaneh Rabbany, Kellin Pelrine
+tags:
+- ''
+link: https://arxiv.org/abs/2506.15794
+author: Taylor Lynn Curtis
+categories: Publications
+
+---
+
+*{{ page.names }}*
+
+**{{ page.venue }}**
+
+{% include display-publication-links.html pub=page %}
+
+## Abstract
+
+The proliferation of misinformation poses a significant threat to society, exacerbated by the capabilities of generative AI. This demo paper introduces Veracity, an open-source AI system designed to empower individuals to combat misinformation through transparent and accessible fact-checking. Veracity leverages the synergy between Large Language Models (LLMs) and web retrieval agents to analyze user-submitted claims and provide grounded veracity assessments with intuitive explanations. Key features include multilingual support, numerical scoring of claim veracity, and an interactive interface inspired by familiar messaging applications. This paper will showcase Veracity's ability to not only detect misinformation but also explain its reasoning, fostering media literacy and promoting a more informed society.

records/semantic_paper_ids_ignored.json

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@@ -234,6 +236,7 @@
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