Papers by Hailong Sun
HIPO: A Hierarchical Prompt Optimization Framework with Task Awareness and Fine-Grained Debugging (2026.findings-acl)
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| Challenge: | Existing methods for prompt optimization apply the same prompt across all samples . existing methods ignore variation in sample difficulty . |
| Approach: | They propose a framework that shifts the paradigm from dataset-level to sample-level optimization. |
| Outcome: | The proposed framework outperforms baselines on 27 tasks and reduces API calls, token consumption and overall cost by 1.2 to 80. |
UICOMPASS: UI Map Guided Mobile Task Automation via Adaptive Action Generation (2025.emnlp-main)
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| Challenge: | Mobile task automation is an emerging technology that leverages AI to automatically execute routine tasks by users’ commands on mobile devices like Android. |
| Approach: | They propose a UI Map-guided LLM-based approach to automate mobile tasks using static analysis and LLMs. |
| Outcome: | The proposed approach achieves a 15.87% higher task execution success rate than SOTA approaches even when only APK is available. |
Beyond Superficial Tests: Adversarial Refinement for Reliable Property-Based Testing (2026.findings-acl)
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| Challenge: | Large Language Models (LLMs) have demonstrated remarkable proficiency in code generation, yet their application to Property-Based Testing (PBT) remains fraught with a superficiality gap. |
| Approach: | They propose an agentic framework that hardens software properties through Adversarial Refinement. |
| Outcome: | a new framework hardens software properties through Adversarial Refinement that detects and fixes bugs in top-tier libraries. |