Papers by Huawei Ji
VisPCO: Visual Token Pruning Configuration Optimization via Budget-Aware Pareto-Frontier Learning for Vision-Language Models (2026.acl-long)
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| Challenge: | Existing methods for visual token pruning rely on predefined configurations without determining whether they achieve optimal performance. |
| Approach: | They propose a framework that formulates visual token pruning as a Pareto configuration optimization problem to automatically identify optimal configurations. |
| Outcome: | The proposed framework approximates the empirical Pareto frontier obtained through grid search and generalizes well across pruning methods and VLM architectures. |
StealthGraph: Exposing Domain-Specific Risks in LLMs through Knowledge-Graph-Guided Harmful Prompt Generation (2026.acl-long)
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| Challenge: | Domain-specific datasets of harmful prompts are scarce and often rely on manual construction. Existing efforts to improve domain knowledge and reduce harmful prompt generation are lacking. |
| Approach: | They propose a framework that transforms domain knowledge into actionable constraints and increases the implicitness of generated harmful prompts. |
| Outcome: | The proposed framework yields high-quality datasets combining strong domain relevance with implicitness, enabling more realistic red-teaming and advancing LLM safety research. |