ESCROW: Guarded and Dual-Objective Continual Maintenance for Agents in Policy-Governed Enterprise Workflows
cs.AI
Submitted: 2026-08-03
Updated: 2026-10-01
Comments: Accepted at the CLEA Workshop at NeurIPS 2026
Code: https://github.com/langchain-ai/deepagents
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- GEPA: Reflective Prompt Evolution Can Outperform Reinforcement Learning
- EvoSkill: Automated Skill Discovery for Multi-Agent Systems
- Evaluating Large Language Models Trained on Code
- SkillsBench: Benchmarking How Well Agent Skills Work Across Diverse Tasks
- SKILL0: In-Context Agentic Reinforcement Learning for Skill Internalization
- Memento-Skills: Let Agents Design Agents
- Trace2Skill: Distill Trajectory-Local Lessons into Transferable Agent Skills
- OfficeQA Pro: An Enterprise Benchmark for End-to-End Grounded Reasoning
- SkillX: Automatically Constructing Skill Knowledge Bases for Agents
- Reinforcement Learning for Self-Improving Agent with Skill Library
- SkillRL: Evolving Agents via Recursive Skill-Augmented Reinforcement Learning
- MetaClaw: Just Talk -- An Agent That Meta-Learns and Evolves in the Wild
- Agent Skills for Large Language Models: Architecture, Acquisition, Security, and the Path Forward
- AutoSkill: Experience-Driven Lifelong Learning via Skill Self-Evolution
- ASDA: Automated Skill Distillation and Adaptation for Financial Reasoning
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