Probing Stability-Plasticity Tradeoffs in Agent Memory through Cognitive Experimental Paradigms

2026-09-24

AI Summary

This study addresses the limitation of existing agent memory evaluations that focus solely on final accuracy while overlooking the complex stability-plasticity trade-off. To this end, this work proposes MemProbe, a novel framework that pioneers cognitive experimental paradigms for memory diagnostics. By integrating interference and misinformation paradigms, it constructs a diagnostic suite comprising 56 scenarios to conduct a unified behavioral analysis of six incremental memory systems. The findings reveal that systems with comparable overall scores exhibit significant disparities in memory updating, retention, and attribution mechanisms. Ultimately, this research facilitates a paradigm shift from opaque black-box scoring toward transparent, interpretable behavioral profiling.

Abstract

Agent memory systems are increasingly used to maintain long-term user preferences, task states and evolving facts, but current evaluations often collapse memory behavior into final-answer accuracy. We introduce MemProbe, a cognitive-science-inspired framework for diagnosing stability-plasticity tradeoffs in agent memory. The framework is motivated by a core insight from cognitive memory research: memory is reconstructive and shaped by interference, source reliability, reinforcement, and reactivation. MemProbe turns this insight into four reusable experimental paradigms (interference, misinformation, consolidation strength, and reconsolidation window) that manipulate when a memory should be updated, preserved, or treated as uncertain. It further decomposes correctness into behavioral profiles that reveal how systems update, preserve, attribute, and temporally organize information. We instantiate these paradigms in a 56-episode diagnostic suite and evaluate six incremental memory systems under a unified protocol. Results show that systems with similar aggregate scores exhibit distinct behavioral profiles. MemProbe provides such a diagnostic lens, turning aggregate performance into interpretable profiles of memory maintenance over time. Code is available at https://github.com/jq-ding/MemProbe.

Problem

• Agent Memory
• Stability-Plasticity Tradeoff
• Memory Evaluation
• Cognitive Paradigms
• Behavioral Profiles

Innovation

• Agent Memory
• Stability-Plasticity Tradeoff
• Cognitive Experimental Paradigms
• Behavioral Profiling
• MemProbe