activity
20192026
most citedNot Enough Data? Deep Learning to the Rescue!

32 citations · 35 across the 16 of their papers we have counts for

collaborators

20 papers

cs.AI2026

Efficient Agent Evaluation via Diversity-Guided User Simulation

Itay Nakash, George Kour, Ateret Anaby-Tavor

Large language models (LLMs) are increasingly deployed as customer-facing agents, yet evaluating their reliability remains challenging due to stochastic, multi-turn interactions. C…

cs.CL2026

Near-Miss: Latent Policy Failure Detection in Agentic Workflows

Ella Rabinovich, David Boaz, Naama Zwerdling +1

Agentic systems for business process automation often require compliance with policies governing conditional updates to the system state. Evaluation of policy adherence in LLM-base…

cs.CL2025

CRISP: Complex Reasoning with Interpretable Step-based Plans

Matan Vetzler, Koren Lazar, Guy Uziel +3

Recent advancements in large language models (LLMs) underscore the need for stronger reasoning capabilities to solve complex problems effectively. While Chain-of-Thought (CoT) reas…

cs.CL2025

Towards Enforcing Company Policy Adherence in Agentic Workflows

Naama Zwerdling, David Boaz, Ella Rabinovich +3

Large Language Model (LLM) agents hold promise for a flexible and scalable alternative to traditional business process automation, but struggle to reliably follow complex company p…

cs.MA2025

Effective Red-Teaming of Policy-Adherent Agents

Itay Nakash, George Kour, Koren Lazar +3

Task-oriented LLM-based agents are increasingly used in domains with strict policies, such as refund eligibility or cancellation rules. The challenge lies in ensuring that the agen…

cs.AI2025

Think Again! The Effect of Test-Time Compute on Preferences, Opinions, and Beliefs of Large Language Models

George Kour, Itay Nakash, Ateret Anaby-Tavor +1

As Large Language Models (LLMs) become deeply integrated into human life and increasingly influence decision-making, it's crucial to evaluate whether and to what extent they exhibi…