1 citations · 2 across the 10 of their papers we have counts for
11 papers
SIPTraj: Map-Free End-to-End Trajectory Prediction via Physics-Guided Scene Interaction
Feifei Liu, Zejun Wei, Haozhe Wang +6
Trajectory prediction of surrounding agents is a prerequisite for safe planning and decision making in autonomous driving. Without high-definition (HD) maps, sensor-derived bird's-…
Asynchronous Verified Semantic Caching for Tiered LLM Architectures
Asmit Kumar Singh, Haozhe Wang, Laxmi Naga Santosh Attaluri +2
Large language models (LLMs) now sit in the critical path of search, assistance, and agentic workflows, making semantic caching essential for reducing inference cost and latency. P…
EvoCUA: Evolving Computer Use Agents via Learning from Scalable Synthetic Experience
Taofeng Xue, Chong Peng, Mianqiu Huang +13
The development of native computer-use agents (CUA) represents a significant leap in multimodal AI. However, their potential is currently bottlenecked by the constraints of static…
CogDoc: Towards Unified thinking in Documents
Qixin Xu, Haozhe Wang, Che Liu +2
Current document reasoning paradigms are constrained by a fundamental trade-off between scalability (processing long-context documents) and fidelity (capturing fine-grained, multim…
From Illusion to Intention: Visual Rationale Learning for Vision-Language Reasoning
Changpeng Wang, Haozhe Wang, Xi Chen +6
Recent advances in vision-language reasoning underscore the importance of thinking with images, where models actively ground their reasoning in visual evidence. Yet, prevailing fra…
Beyond ADE and FDE: A Comprehensive Evaluation Framework for Safety-Critical Prediction in Multi-Agent Autonomous Driving Scenarios
Feifei Liu, Haozhe Wang, Zejun Wei +5
Current evaluation methods for autonomous driving prediction models rely heavily on simplistic metrics such as Average Displacement Error (ADE) and Final Displacement Error (FDE).…
Emergent Hierarchical Reasoning in LLMs through Reinforcement Learning
Haozhe Wang, Qixin Xu, Che Liu +3
Reinforcement Learning (RL) has proven highly effective at enhancing the complex reasoning abilities of Large Language Models (LLMs), yet underlying mechanisms driving this success…
Reverse-Engineered Reasoning for Open-Ended Generation
Haozhe Wang, Haoran Que, Qixin Xu +9
While the ``deep reasoning'' paradigm has spurred significant advances in verifiable domains like mathematics, its application to open-ended, creative generation remains a critical…
Language-Driven Object-Oriented Two-Stage Method for Scene Graph Anticipation
Xiaomeng Zhu, Changwei Wang, Haozhe Wang +2
A scene graph is a structured representation of objects and their spatio-temporal relationships in dynamic scenes. Scene Graph Anticipation (SGA) involves predicting future scene g…
VL-Rethinker: Incentivizing Self-Reflection of Vision-Language Models with Reinforcement Learning
Haozhe Wang, Chao Qu, Zuming Huang +3
Recently, slow-thinking systems like GPT-o1 and DeepSeek-R1 have demonstrated great potential in solving challenging problems through explicit reflection. They significantly outper…
Pixel Reasoner: Incentivizing Pixel-Space Reasoning with Curiosity-Driven Reinforcement Learning
Haozhe Wang, Alex Su, Weiming Ren +2
Chain-of-thought reasoning has significantly improved the performance of Large Language Models (LLMs) across various domains. However, this reasoning process has been confined excl…