6 citations · 20 across the 27 of their papers we have counts for
12 papers · 1 filter
Watch and Learn: Teaching Visual In-Context Learning by Removing Textual Shortcuts
Nimrod Shabtay, Asaf Yehudai, Avihu Dekel +2
Vision-language models (VLMs) struggle with in context learning compared to their textual counterparts. Our preliminary study shows that VLMs rely on textual shortcuts and underuti…
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…
Agentic CLEAR: Automating Multi-Level Evaluation of LLM Agents
Asaf Yehudai, Lilach Eden, Michal Shmueli-Scheuer
Agentic systems are becoming more capable: agents define strategies, take actions, and interact with different environments. This autonomy poses serious challenges for overseeing a…
Time to REFLECT: Can We Trust LLM Judges for Evidence-based Research Agents?
Leyao Wang, Yanan He, Peng Chen +5
Deep research agents increasingly automate complex information-seeking tasks, producing evidence-grounded reports via multi-step reasoning, tool use, and synthesis. Their growing r…
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…
Mediocrity is the key for LLM as a Judge Anchor Selection
Shachar Don-Yehiya, Asaf Yehudai, Leshem Choshen +1
The ``LLM-as-a-judge'' paradigm has become a standard method for evaluating open-ended generation. To address the quadratic scalability costs of pairwise comparisons, popular bench…
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…
Will it Merge? On The Causes of Model Mergeability
Adir Rahamim, Asaf Yehudai, Boaz Carmeli +3
Model merging has emerged as a promising technique for combining multiple fine-tuned models into a single multitask model without retraining. However, the factors that determine wh…