Novel Memory Forgetting Techniques for Autonomous AI Agents: Balancing Relevance and Efficiency
TLDR
This paper introduces an adaptive budgeted forgetting framework to improve long-horizon AI agents by regulating memory and reducing false memories.
Key contributions
- Addresses memory decay and false memory propagation in long-horizon AI agents.
- Proposes an adaptive budgeted forgetting framework for memory regulation.
- Integrates recency, frequency, and semantic alignment for stable memory management.
- Achieves improved F1 scores, higher retention, and reduced false memories without extra context.
Why it matters
Long-horizon AI agents struggle with memory accumulation, leading to performance degradation and false information. This paper offers a novel framework to manage memory efficiently, ensuring stable reasoning and preventing unbounded growth. It's crucial for building more robust and coherent conversational AI.
Original 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.
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