From Agent Traces to Trust: A Survey of Evidence Tracing and Execution Provenance in LLM Agents
Large language model (LLM)-based agents are moving beyond text generation to planning, tool use, retrieval, memory access, environmental interaction, and multi-agent collaboration. Because their outputs depend on information and execution steps across an agent run, their execution processes are difficult to verify, debug, and audit. Even a correct final answer does not reveal which evidence supported its claims, whether tool calls were justified, how memory shaped decisions, or where execution failures originated. Relevant research addresses these questions across evidence attribution, tool-use safety, agent memory, and observability, but these areas emphasize different parts of the execu...
| Authors | Yiqi Wang, Jiaqi Zhang, Zhangkai Wu et al. |
|---|---|
| Primary category | cs.CR |
| Published | 3 June 2026 |
| Last updated at source | 15 October 2026 |
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