activity
20232026
most citedCrashAgent: Crash Scenario Generation via Multi-modal Reasoning

1 citations · 1 across the 5 of their papers we have counts for

collaborators

7 papers

cs.RO2026

Controllable and Photorealistic Pedestrian Risky Motion Generation for End-to-End Driving Safety Evaluation

Siyuan Liu, Miao Li, Haibao Yu +4

Evaluating end-to-end autonomous driving under rare, safety-critical vehicle-pedestrian interactions requires photorealistic, sensor-level scenarios. However, trajectory-based scen…

cs.RO2026

Simple Agentic Memory for Generalist Robot Policies

Yuyou Zhang, Yunbei Zhang, Miao Li +4

Visual-memory systems commonly retain or compress past observations. Robot control additionally requires interaction-derived state that no individual frame may explicitly represent…

cs.AI2026

Fishing Out Free Riders: Shapley-Based Reward Attribution for Parallel Reasoning via Reinforcement Learning

Wentao Zhang, Haoyu Zhang, Xinke Jiang +7

Large Language Models (LLMs) excel at multi-step reasoning, yet current parallel reasoning approaches often fail to distinguish the contributions of individual reasoning paths. Man…

cs.RO2025★ 1 cited

CrashAgent: Crash Scenario Generation via Multi-modal Reasoning

Miao Li, Wenhao Ding, Haohong Lin +4

Training and evaluating autonomous driving algorithms requires a diverse range of scenarios. However, most available datasets predominantly consist of normal driving behaviors demo…

cs.CL2025

Safety is Not Only About Refusal: Reasoning-Enhanced Fine-tuning for Interpretable LLM Safety

Yuyou Zhang, Miao Li, William Han +3

Large Language Models (LLMs) are vulnerable to jailbreak attacks that exploit weaknesses in traditional safety alignment, which often relies on rigid refusal heuristics or represen…

cs.CL2025

Your Language Model May Think Too Rigidly: Achieving Reasoning Consistency with Symmetry-Enhanced Training

Yihang Yao, Zhepeng Cen, Miao Li +6

Large Language Models (LLMs) have demonstrated strong reasoning capabilities across various tasks. However, even minor variations in query phrasing, despite preserving the underlyi…

cs.RO2023

Privacy Risks in Reinforcement Learning for Household Robots

Miao Li, Wenhao Ding, Ding Zhao

The prominence of embodied Artificial Intelligence (AI), which empowers robots to navigate, perceive, and engage within virtual environments, has attracted significant attention, o…