ExplorationBench: Measuring AI Systems' Exploration in Verifiable Alien Worlds

arXiv:2609.30199 · cs.AI, cs.CL · Submitted 2026-09-24 · Read on arXiv

cs.AI, cs.CL

Submitted: 2026-09-24

Updated: 2026-10-08

Terminology

• Accuracy — 31× in this paper · explained in 1 episode
• Benchmark — 22× in this paper · explained in 2 episodes
• Verifier — 7× in this paper · explained in 2 episodes
• In-Context Learning — 4× in this paper · explained in 4 episodes
• Adaptation — 3× in this paper · explained in 1 episode
• In International Conference on Learning Representations — 3× in this paper
• Bayesian Inference — 2× in this paper · explained in 5 episodes
• Benchmarking — 2× in this paper · explained in 3 episodes
• LLM judge — 2× in this paper · explained in 2 episodes
• Self-Evolving Agents — 2× in this paper · explained in 2 episodes
• Transactions on Machine Learning Research — 2× in this paper
• Transformer — 2× in this paper · explained in 2 episodes
• World Model — 2× in this paper · explained in 7 episodes
• AI Agents — 1× in this paper · explained in 2 episodes
• Agent Architectures — 1× in this paper · explained in 1 episode
• Agentic System — 1× in this paper · explained in 2 episodes
• Chain-of-Thought Prompting — 1× in this paper · explained in 2 episodes
• Empirical Methods in Natural Language Processing — 1× in this paper
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Iterative Refinement — 1× in this paper · explained in 3 episodes
• Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Scaling Laws — 1× in this paper · explained in 4 episodes
• System Identification — 1× in this paper · explained in 3 episodes
• Verification — 1× in this paper · explained in 2 episodes

Sources

• Continual Learning Bench: Evaluating Frontier AI Systems in Real-World Stateful Environments
• On the Measure of Intelligence
• CL-bench: A Benchmark for Context Learning
• A Comprehensive Survey of Self-Evolving AI Agents: A New Paradigm Bridging Foundation Models and Lifelong Agentic Systems
• A Survey of Self-Evolving Agents: What, When, How, and Where to Evolve on the Path to Artificial Super Intelligence
• A Survey of Context Engineering for Large Language Models
• In-context Learning and Induction Heads
• Voyager: An Open-Ended Embodied Agent with Large Language Models
• SE-Bench: Benchmarking Self-Evolution with Knowledge Internalization
• NewtonBench: Benchmarking Generalizable Scientific Law Discovery in LLM Agents
• EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments
• Qwen-AgentWorld: Language World Models for General Agents

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