Recursive Experiential-Working Memory Evolution for Long-Horizon Agent Harnesses
cs.AI, cs.CL
Submitted: 2026-08-25
Updated: 2026-08-25
Code: https://github.com/Gen-Verse/Recuris
Terminology
Sources
- GEPA: Reflective Prompt Evolution Can Outperform Reinforcement Learning
- SagaLLM: Context Management, Validation, and Transaction Guarantees for Multi-Agent LLM Planning
- Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolution
- AutoGuide: Automated Generation and Selection of Context-Aware Guidelines for Large Language Model Agents
- Automated Design of Agentic Systems
- DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines
- ReflAct: World-Grounded Decision Making in LLM Agents via Goal-State Reflection
- SkillsInjector: Dynamic Skill Context Construction for LLM Agents
- How Well Do Agentic Skills Work in the Wild: Benchmarking LLM Skill Usage in Realistic Settings
- A Self-Improving Coding Agent
- StateAct: Enhancing LLM Base Agents via Self-prompting and State-tracking
- Goedel Machines: Self-Referential Universal Problem Solvers Making Provably Optimal Self-Improvements
- AgentSquare: Automatic LLM Agent Search in Modular Design Space
- Your Agent May Misevolve: Emergent Risks in Self-evolving LLM Agents
- PACE: Anytime-Valid Acceptance Tests for Self-Evolving Agents
- Reflexion: Language Agents with Verbal Reinforcement Learning
- LADDER: Self-Improving LLMs Through Recursive Problem Decomposition
- Cognitive Architectures for Language Agents
- Voyager: An Open-Ended Embodied Agent with Large Language Models
- SAGE: A Novelty Gate for Efficient Memory Evolution in Agentic LLMs
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