Papers by Xingrui Yu

2 papers
Safety Sidecar: Reflection-Driven Runtime Control for Safer Agents (2026.findings-acl)

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Challenge: Existing safety controls fail to provide runtime intervention or cross-architecture portability for autonomous LLM agents.
Approach: They propose a model-agnostic, plug-and-play module to provide arbitrary agent safety control and auditability.
Outcome: The proposed module improves the secure-solution rate by 2.9–11.2 percentage points . it adds only 3.2s to end-to-end latency and a negligible average cost of 5.37 10-4 per scenario .
From Language to Driving: A Dual-Loop SLM-Enhanced Framework for Multi-Planner Scheduling via a Domain-Specific Language (2026.acl-long)

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Challenge: Recent large language model-based AD research offers new avenues to address this challenge.
Approach: They propose a small language model (SLM) for high-level semantic reasoning and schedule generation, while an inner loop performs low-level, high-frequency schedule execution and vehicle control.
Outcome: The proposed framework improves instruction completion rates while maintaining high safety and compliance relative to multiple baselines.

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