Papers by Xianghe Pang

3 papers
VLMGuard-R1: Proactive Safety Alignment for VLMs via Reasoning-Driven Prompt Optimization (2026.findings-acl)

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Challenge: integrating vision and language models with safety standards is essential to mitigate multimodal complexity . integrating visual inputs with vision and text unveils subtle threats beyond the reach of conventional safeguards .
Approach: They propose a framework that combines vision and language to provide a multimodal reasoning-driven prompt rewriting.
Outcome: The proposed framework outperforms baseline models on five benchmarks with six VLMs.
MCP-Flow: Facilitating LLM Agents to Master Real-World, Diverse and Scaling MCP Tools (2026.acl-long)

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Challenge: Existing research on Large Language Models (LLMs) relies on few servers and lacks training support.
Approach: They propose a web-agent-driven pipeline for large-scale server discovery, data synthesis, and model training that collects and filters data from 1166 servers and 11536 tools.
Outcome: Empirical evidence shows that MCP-Flow generates higher quality instruction-function call pairs and higher agentic task performance than previous work.
Synthesizing Post-Training Data for LLMs through Multi-Agent Simulation (2025.acl-long)

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Challenge: Extensive experiments demonstrate that our framework effectively generates both general and domain-specific data.
Approach: They propose a multi-agent simulator that automatically generates diverse text-based scenarios, capturing a wide range of real-world human needs.
Outcome: Experiments show that the proposed model outperforms Meta’s Llama-3-8B-Instruct model on AlpacaEval 2 and Arena-Hard benchmarks with just 20K instruction-response pairs.

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