Papers by Junjie Sheng

2 papers
Agentic Episodic Control (2026.findings-acl)

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Challenge: Existing methods for reinforcement learning (RL) are limited by poor data efficiency and weak generalization.
Approach: They propose a novel architecture that integrates large language models into episodic RL.
Outcome: The proposed architecture achieves 2–6 higher data efficiency than baselines and is the only method to solve complex tasks like UnlockLocal with over 90% success.
Towards Scalable Lightweight GUI Agents via Multi-role Orchestration (2026.findings-acl)

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Challenge: Advanced GUI agents suffer from prohibitive deployment costs on resource-constrained devices.
Approach: They propose a lightweight GUI agent with GUI-specific knowledge and task scalability . LAMO-3B supports monolithic execution and MAS-style orchestration .
Outcome: The proposed GUI agent LAMO-3B supports monolithic execution and MAS-style orchestration.

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