6 citations · 20 across the 27 of their papers we have counts for
8 papers · 1 filter
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…
Teaching Values to Machines: Simulating Human-Like Behavior in LLMs
Asaf Yehudai, Naama Rozen, Ariel Gera
Large Language Models (LLMs) demonstrate a remarkable capacity to adopt different personas and roles; however, it remains unclear whether they can manifest behavior that adheres to…
Every Eval Ever: A Unifying Schema and Community Repository for AI Evaluation Results
Jan Batzner, Sree Harsha Nelaturu, Damian Stachura +45
AI evaluations are widely used for testing and understanding progress. However, the diverse evaluators bring with them inconsistencies that challenge analysis and comparison. First…
Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting
Avijit Ghosh, Anka Reuel, Jenny Chim +45
AI evaluation results are produced at scale but reported inconsistently across leaderboards, model cards, benchmark papers, and company blogs. The cost is interpretive: readers can…
A Matter of TASTE: Improving Coverage and Difficulty of Agent Benchmarks
Tomer Keren, Nitay Calderon, Asaf Yehudai +3
As agent capabilities advance, existing benchmarks, such as -Bench, are becoming increasingly saturated. Yet constructing new benchmark tasks remains complex, costly, and labo…
CUBE: A Standard for Unifying Agent Benchmarks
Alexandre Lacoste, Nicolas Gontier, Oleh Shliazhko +23
The proliferation of agent benchmarks has created critical fragmentation that threatens research productivity. Each new benchmark requires substantial custom integration, creating…
General Agent Evaluation
Elron Bandel, Asaf Yehudai, Lilach Eden +12
General-purpose agents perform tasks in unfamiliar environments without domain-specific manual customization. Yet no study has systematically measured how agent architecture shapes…
Survey on Evaluation of LLM-based Agents
Asaf Yehudai, Lilach Eden, Alan Li +5
LLM-based agents represent a paradigm shift in AI, enabling autonomous systems to plan, reason, and use tools while interacting with dynamic environments. This paper provides the f…