6 papers
IndustrialVLA-Bench: A Traceable Multi-Axis Evaluation of Open Robot Policy Models
Yiqi Wang, Zhifeng Rao, Jiaqi Zhang +7
Open robot policies increasingly follow two paradigms: vision-language-action models (VLAs) directly map observations and instructions to actions, whereas world-action models (WAMs…
GAPL: Grounded Action-effect Policy Learning for LLM-Based Trajectory Planning
Zhihong Cui, Hengyu Liu, Zhangkai Wu +5
Trajectory planning for autonomous driving requires both high-level reasoning and precise low-level control. Large Language Models (LLMs) offer semantic-rich planning capabilities,…
MAP-Graph: Provenance-Aware Shared Memory for Multi-Agent Workflows
Yiqi Wang, Zihao Yan, Jiaqi Zhang +5
Shared memory helps language-model agents reuse information across long workflows, yet relevant evidence may not be admissible for a particular agent or action. Because restriction…
From Faulty Memories to Corrected Actions: Dependency-Guided Rollback Repair for Memory-Augmented Agents
Caili Yu, Yiqi Wang, Jiaqi Zhang +5
Persistent memory lets language model agents reuse information across sessions, but it also makes errors durable: a poisoned, stale, or misattributed record can alter reasoning, to…
From Agent Traces to Trust: A Survey of Evidence Tracing and Execution Provenance in LLM Agents
Yiqi Wang, Jiaqi Zhang, Zhangkai Wu +9
Large language model (LLM)-based agents are moving beyond text generation to planning, tool use, retrieval, memory access, environmental interaction, and multi-agent collaboration.…
Reliable AI Needs to Externalize Implicit Knowledge: A Human-AI Collaboration Perspective
Hengyu Liu, Tianyi Li, Zhihong Cui +5
This position paper argues that reliable AI requires infrastructure for human validation of implicit knowledge. AI learns from both explicit knowledge (papers, documentation, struc…