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cs.LG2026
TensorLens: End-to-End Transformer Analysis via High-Order Attention Tensors
Ido Andrew Atad, Itamar Zimerman, Shahar Katz +1
Attention matrices are fundamental to transformer research, supporting a broad range of applications including interpretability, visualization, manipulation, and distillation. Yet,…
cs.LG2025
AlignTree: Efficient Defense Against LLM Jailbreak Attacks
Gil Goren, Shahar Katz, Lior Wolf
Large Language Models (LLMs) are vulnerable to adversarial attacks that bypass safety guidelines and generate harmful content. Mitigating these vulnerabilities requires defense mec…
cs.LG2025
Execution Guided Line-by-Line Code Generation
Boaz Lavon, Shahar Katz, Lior Wolf
We present a novel approach to neural code generation that incorporates real-time execution signals into the language model generation process. While large language models (LLMs) h…