Novel Memory Forgetting Techniques for Autonomous AI Agents: Balancing Relevance and Efficiency
2026-04-02 • Artificial Intelligence
Artificial IntelligenceComputer Vision and Pattern Recognition
AI summaryⓘ
The authors address the problem of chatbots forgetting important information or remembering wrong things when conversations go on for a long time. They propose a method that carefully decides which memories to keep or forget based on how relevant and recent they are. Their approach helps the chatbot remember better without needing more space, reducing mistakes and improving accuracy in long talks. Tests show their method works better than previous ones. Overall, they show that smart forgetting helps maintain good conversation memory over time.
conversational agentspersistent memorytemporal decayfalse memoryadaptive forgettingsemantic alignmentcontext managementlong-horizon reasoningmemory retentionF1 score
Authors
Payal Fofadiya, Sunil Tiwari
Abstract
Long-horizon conversational agents require persistent memory for coherent reasoning, yet uncontrolled accumulation causes temporal decay and false memory propagation. Benchmarks such as LOCOMO and LOCCO report performance degradation from 0.455 to 0.05 across stages, while MultiWOZ shows 78.2% accuracy with 6.8% false memory rate under persistent retention. This work introduces an adaptive budgeted forgetting framework that regulates memory through relevanceguided scoring and bounded optimization. The approach integrates recency, frequency, and semantic alignment to maintain stability under constrained context. Comparative analysis demonstrates improved long-horizon F1 beyond 0.583 baseline levels, higher retention consistency, and reduced false memory behavior without increasing context usage. These findings confirm that structured forgetting preserves reasoning performance while preventing unbounded memory growth in extended conversational settings.