{"slug": "probing-stability-plasticity-tradeoffs-in-agent-memory-through-cognitive", "title": "Probing Stability-Plasticity Tradeoffs in Agent Memory through Cognitive Experimental Paradigms", "summary": "Researchers introduced MemProbe, a cognitive-science-inspired framework for diagnosing stability-plasticity tradeoffs in agent memory, detailed in arXiv paper 2609.30558v1. MemProbe provides four reusable experimental paradigms — interference, misinformation, consolidation strength, and reconsolidation window — instantiated in a 56-episode diagnostic suite that was used to evaluate six incremental memory systems under a unified protocol. The evaluation found that systems with similar aggregate scores exhibit distinct behavioral profiles, and the code is available at https://github.com/jq-ding/MemProbe.", "body_md": "arXiv:2609.30558v1 Announce Type: new \nAbstract: 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.", "url": "https://wpnews.pro/news/probing-stability-plasticity-tradeoffs-in-agent-memory-through-cognitive", "canonical_source": "https://arxiv.org/abs/2609.30558", "published_at": "2026-09-28 04:00:00+00:00", "updated_at": "2026-09-28 04:18:55.486981+00:00", "lang": "en", "topics": ["ai-agents", "ai-research", "artificial-intelligence", "machine-learning"], "entities": ["MemProbe", "arXiv", "jq-ding/MemProbe"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/probing-stability-plasticity-tradeoffs-in-agent-memory-through-cognitive", "markdown": "https://wpnews.pro/news/probing-stability-plasticity-tradeoffs-in-agent-memory-through-cognitive.md", "text": "https://wpnews.pro/news/probing-stability-plasticity-tradeoffs-in-agent-memory-through-cognitive.txt", "jsonld": "https://wpnews.pro/news/probing-stability-plasticity-tradeoffs-in-agent-memory-through-cognitive.jsonld"}}