Guy Bar-Shalom


Hi! I am an Empire AI Postdoctoral Fellow in the Computer Science Department at Cornell University, hosted by Yoav Artzi. I completed my Ph.D. in the Computer Science Department at the Technion under the joint supervision of Haggai Maron and Ran El-Yaniv. My research aims to make LLMs safer by detecting their misbehaviors, such as hallucinations, using structure-aware deep learning models that learn directly from their internals. I am also interested in weight-space learning, where neural networks process other neural networks. I have also worked on Geometric Deep Learning, leveraging data symmetries to build better models, with applications to Graph Neural Networks.

Previously (Summer 2022), I was a research scientist intern at Google-Verily working with Michael Elad on video analysis. In the summer of 2025, I was a research scientist intern at Meta.

Mail Scholar Github Twitter LinkedIn CV (updated Aug. 2026)

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News

  • 2026 Received the KLA Research Excellence Award, Faculty of Computer Science, Technion.

Selected Publications

* indicates equal contribution

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LEMON: Equivariant Learning over Attention Maps for Zero-Shot Misbehavior Monitoring on New LLMs
Guy Bar-Shalom, Mor Geva, Fabrizio Frasca, Haggai Maron
arXiv preprint, 2026
Project Page
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Learning Cross-Model Activation Alignments with Explicit Many-to-Many Layer Maps
Alina Sudakov*, Guy Bar-Shalom*, Fabrizio Frasca, Haggai Maron
arXiv preprint, 2026
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A Graph Meta-Network for Learning on Kolmogorov-Arnold Networks
Guy Bar-Shalom, Ami Tavory, Itay Evron, Maya Bechler-Speicher, Ido Guy, Haggai Maron
International Conference on Learning Representations (ICLR), 2026
Code
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Neural Message-Passing on Attention Graphs for Hallucination Detection
Fabrizio Frasca*, Guy Bar-Shalom*, Yftah Ziser, Haggai Maron
International Conference on Learning Representations (ICLR), 2026
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Beyond Next Token Probabilities: Learnable, Fast Detection of Hallucinations and Data Contamination on LLM Output Distributions (A shorter version also appeared in the Reliable and Responsible Foundation Models Workshop @ ICML 2025 as an oral presentation)
Guy Bar-Shalom*, Fabrizio Frasca*, Derek Lim, Yoav Gelberg, Yftah Ziser, Ran El-Yaniv, Gal Chechik, Haggai Maron
AAAI Conference on Artificial Intelligence (AAAI), 2026
Code
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Beyond Token Probes: Hallucination Detection via Activation Tensors with ACT-ViT
Guy Bar-Shalom*, Fabrizio Frasca*, Yaniv Galron, Yftah Ziser, Haggai Maron
Advances in Neural Information Processing Systems (NeurIPS), 2025
Code / Errata
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Topological Blindspots: Understanding and Extending Topological Deep Learning Through the Lens of Expressivity (Oral presentation)
Yam Eitan*, Yoav Gelberg*, Guy Bar-Shalom, Fabrizio Frasca, Michael Bronstein, Haggai Maron
International Conference on Learning Representations (ICLR), 2025
Code
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A Flexible, Equivariant Framework for Subgraph GNNs via Graph Products and Graph Coarsening (A shorter version also appeared in the Symmetry and Geometry in Neural Representations Workshop @ NeurIPS 2024 winning the best paper award)
Guy Bar-Shalom*, Yam Eitan*, Fabrizio Frasca, Haggai Maron
Advances in Neural Information Processing Systems (NeurIPS), 2024
Poster / Code / Short talk
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Subgraphormer: Unifying Subgraph GNNs and Graph Transformers via Graph Products
Guy Bar-Shalom, Beatrice Bevilacqua, Haggai Maron
International Conference on Machine Learning, (ICML), 2024
Poster / Code / Short talk / Slides
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Weakly-supervised Representation Learning for Video Alignment and Analysis
Guy Bar-Shalom, George Leifman, Michael Elad
IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2024
Poster / Short talk
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Window-Based Distribution Shift Detection for Deep Neural Networks
Guy Bar-Shalom, Yonatan Geifman, Ran El-Yaniv
Advances in Neural Information Processing Systems (NeurIPS), 2023
Poster / Code / Short talk
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TransBoost: Improving the Best ImageNet Performance using Deep Transduction
Omer Belhasin*, Guy Bar-Shalom*, Ran El-Yaniv
Advances in Neural Information Processing Systems (NeurIPS), 2022
Poster / Code / Short talk

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