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20242026
most citedFrom Grounding to Planning: Benchmarking Bottlenecks in Web Agents

3 citations · 5 across the 10 of their papers we have counts for

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cs.AI2026

Asking for What Was Never Requested: Horizontal and Vertical Proactivity in Agents

Ido Levy, Asaf Yehudai, Segev Shlomov +2

An agent that uses tools typically responds to what the user explicitly asks, yet completing the task may require information the user never requested. Work on proactive agents mai…

cs.AI2026

Governance by Construction for Generalist Agents

Segev Shlomov, Iftach Shoham, Alon Oved +7

Enterprise agents are increasingly expected to operate autonomously across tools and interfaces, yet production deployments require governance by construction. Systems must specify…

cs.AI2026

AgentFixer: From Failure Detection to Fix Recommendations in LLM Agentic Systems

Hadar Mulian, Sergey Zeltyn, Ido Levy +3

We introduce a comprehensive validation framework for LLM-based agentic systems that provides systematic diagnosis and improvement of reliability failures. The framework includes f…

cs.CL2026

TabAgent: Distilling LLM Agent Components into Cheap Classifiers

Ido Levy, Eilam Shapira, Yinon Goldshtein +5

Agentic systems, AI architectures that autonomously execute multi-step workflows to achieve complex goals, are often built using repeated large language model (LLM) calls for close…

cs.CL2026

Textual Planning with Explicit Latent Transitions

Eliezer Shlomi, Ido Levy, Eilam Shapira +6

Planning requires a transition model that predicts how each action changes the current state. When a large language model (LLM) plays this role, every next state is generated token…