collaborators

13 papers

cs.LG2026

Stop Guessing When to Stop Testing: Efficient Model Evaluation with Just Enough Data

Ofir Arviv, Kristjan Greenewald, Yotam Perlitz +3

The inherent rigidity of fixed-size benchmarks makes them an inefficient tool for model evaluation. Diverse evaluation objectives, including model ranking, model selection and test…

cs.AI2026

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 lab…

cs.CL2026

Instructions Shape Production of Language, not Processing

Andreas Waldis, Leshem Choshen, Yufang Hou +1

Instructions trigger a production-centered mechanism in language models. Through a cognitively inspired lens that separates language processing and production, we reveal this mecha…

cs.AI2026

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…

cs.CL2026

Holmes: A Benchmark to Assess the Linguistic Competence of Language Models

Andreas Waldis, Yotam Perlitz, Leshem Choshen +2

We introduce Holmes, a new benchmark designed to assess language models (LMs) linguistic competence - their unconscious understanding of linguistic phenomena. Specifically, we use…

cs.CL2026

PolySQL: Scaling Text-to-SQL Evaluation Across SQL Dialects via Automated Backend Isomorphism

Yotam Perlitz, Elad Venezian, Corentin Royer +2

SQL dialects vary in syntax, types, and functions across database engines. Text-to-SQL benchmarks, however, predominantly support only SQLite. This creates a critical evaluation ga…