EconSkills: Studying Skill Transfer and Retrieval for Web Agents on Live Economic Data
On the map: LLM agents and tool use → Agent skill learning and evolution
Abstract (arXiv)
Web agents often revisit the same sites, yet most evaluations discard the procedures learned in earlier successful interactions. We introduce EconSkills, a skill library and evaluation framework that distills verified EconWebArena trajectories into parameterized standard operating procedures for retrieving live economic data. Each skill records its scope, navigation procedure, site-specific guidance, verification checks, and recovery steps while replacing source-instance values with placeholders. EconSkills separates two questions: whether a known relevant procedure transfers to a held-out task, and whether an agent can retain that benefit when selecting from a library. In controlled transfer, matched skills improve success over no-skill prompting and require fewer steps on paired successes. Under the evaluated prompt formats, the parameterized skill prompt substantially outperforms the corresponding raw-trajectory prompt. With the full 50-skill library, retrieval is competitive with the no-skill baseline overall and performs best on tasks with a direct family match; approximate matches on other tasks offset these gains. Browser trajectories further identify when procedural guidance shortens portal-specific navigation and when semantic verification remains necessary. These results establish that reusable economic web procedures can transfer across task instances and provide a concrete design target for match-aware selection and context delivery.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in Agent skill learning and evolution, one of 16 directions in LLM agents and tool use. Builds on 23 of its 27 references that are in the map. First posted on arXiv 17 Sep 2026.
Builds on
23 of 27 references.
Closest ancestors · 2
Same direction, newest first
- 2026SkillGen: Verified Inference-Time Agent Skill Synthesis9 May
- 2026SkillsBench: Benchmarking How Well Agent Skills Work Across Diverse Tasks13 Feb
Other references · 21
- 2023Reflexion: Language Agents with Verbal Reinforcement Learningcited by 4,276
- 2023Voyager: An Open-Ended Embodied Agent with Large Language Modelscited by 2,191
- 2023Mind2Web: Towards a Generalist Agent for the Webcited by 1,242
- 2023ExpeL: LLM Agents Are Experiential Learnerscited by 876
- 2024GPT-4V(ision) is a Generalist Web Agent, if Groundedcited by 563
- 2024WebVoyager: Building an End-to-End Web Agent with Large Multimodal Modelscited by 438
- 2024Agent Workflow Memorycited by 383
- 2025SkillWeaver: Web Agents can Self-Improve by Discovering and Honing Skillscited by 192
- 2024WebLINX: Real-World Website Navigation with Multi-Turn Dialoguecited by 180
- 2024The BrowserGym Ecosystem for Web Agent Researchcited by 161
- 2023EconAgent: Large Language Model-Empowered Agents for Simulating Macroeconomic Activitiescited by 154
- 2024WebCanvas: Benchmarking Web Agents in Online Environmentscited by 121
9 more
- 2025Mind2Web 2: Evaluating Agentic Search with Agent-as-a-Judgecited by 76
- 2024VideoWebArena: Evaluating Long Context Multimodal Agents with Video Understanding Web Taskscited by 43
- 2024FinRobot: An Open-Source AI Agent Platform for Financial Applications using Large Language Modelscited by 40
- 2025PolySkill: Learning Generalizable Skills Through Polymorphic Abstractioncited by 27
- 2024EconLogicQA: A Question-Answering Benchmark for Evaluating Large Language Models in Economic Sequential Reasoningcited by 22
- 2024InvAgent: A Large Language Model based Multi-Agent System for Inventory Management in Supply Chainscited by 22
- 2026ContractSkill: Repairable Contract-Based Skills for Multimodal Web Agentscited by 13
- 2025EconWebArena: Benchmarking Autonomous Agents on Economic Tasks in Realistic Web Environmentscited by 9
- 2025CRMAgent: A Multi-Agent LLM System for E-Commerce CRM Message Template Generationcited by 6
Built on by
Citing papers per year: 2026: 1.
1 paper.
Similar, not cited
Close in wording, no citation either way
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