Memento 3: Model-Based Recursive Self-Improvement through Reflective Rulebooks
cs.AI, cs.CL, cs.CV, cs.LG
Submitted: 2026-10-08
Updated: 2026-10-08
Terminology
Sources
- ARC-AGI-3: A New Challenge for Frontier Agentic Intelligence
- Belief-Calibrated Optimization: An Explicit World Model for Agentic Optimization
- OPINE-World: Programmatic World Modeling with Ontology-error-Prioritized Interactive Exploration for ARC-AGI-3
- A Review for Deep Reinforcement Learning in Atari:Benchmarks, Challenges, and Solutions
- GDI: Rethinking What Makes Reinforcement Learning Different From Supervised Learning
- NVIDIA-labs OO Agents: Native Python Object-Oriented Agents
- World Models
- Continual Harness: Online Adaptation for Self-Improving Foundation Agents
- Tycho: Active Abstraction with Programmatic World Models for ARC-AGI-3
- Code World Models for General Game Playing
- Do Coding Agents Need Executable World Models, Simplification, and Verification to Solve ARC-AGI-3?
- Executable World Models for ARC-AGI-3 in the Era of Coding Agents
- Graph-Based Exploration for ARC-AGI-3 Interactive Reasoning Tasks
- Workspace Optimization: How to Train Your Agent
- Memento 2: Learning by Stateful Reflective Memory
- Mastering Atari Games with Limited Data
- Memento: Fine-tuning LLM Agents without Fine-tuning LLMs
- Memento-Skills: Let Agents Design Agents
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