5 papers · 1 filter
Hack-Verifiable Terminal Bench: Evaluating Reward Hacking in Terminal Tasks
Amit Roth, Ivan Bercovich, Yonathan Efroni
As agents grow more capable and autonomous, their tendency to reward hack, satisfying a task's checks while violating its intent, becomes an increasingly important failure mode. Me…
Good Benchmarks
Ivan Bercovich
Good tasks are correct, solvable, verifiable, well-specified, and hard for interesting reasons. The best tasks describe a real problem an experienced practitioner would recognize,…
Heuresis: Search Strategies for Autonomous AI Research Agents Across Quality, Diversity and Novelty
Antonis Antoniades, Deepak Nathani, Ritam Saha +6
Autonomous AI Research promises to accelerate the scientific progress of machine learning. To realise this goal, current Large Language Model (LLM)-based agents need to go beyond j…
What Makes a Good Terminal-Agent Benchmark Task: A Guideline for Adversarial, Difficult, and Legible Evaluation Design
Ivan Bercovich
Terminal-agent benchmarks have become a primary signal for measuring the coding and system-administration capabilities of large language models. As the market for evaluation enviro…
Agents of Change: Self-Evolving LLM Agents for Strategic Planning
Nikolas Belle, Dakota Barnes, Alfonso Amayuelas +3
We address the long-horizon gap in large language model (LLM) agents by enabling them to sustain coherent strategies in adversarial, stochastic environments. Settlers of Catan prov…