arXiv cs.CL
· Papers
Supersede: Diagnosing and Training the Memory-Update Gap in LLM Agents
arXiv:2606.27472v1 Announce Type: new Abstract: Large language model (LLM) agents operate over long, multi-session interactions in which facts change: a user moves, a price updates, a plan is revised. Acting correctly requires using the current value of a fact and discarding values that have been superseded. We isolate