AIBullisharXiv – CS AI · Jun 107/10
🧠Researchers introduce ActiveMem, a distributed memory framework that decouples storage from reasoning in large language models, enabling agents to handle longer tasks without context overload. The system separates executive planning from memory management—inspired by human brain architecture—and demonstrates state-of-the-art performance on complex reasoning benchmarks while reducing computational overhead.
AIBullisharXiv – CS AI · Jun 107/10
🧠Researchers introduce Engram, an open-source memory engine for LLM agents that achieves 83.6% accuracy on long-context tasks using only 9.6k tokens versus 79k for full-history baselines, demonstrating that selective retrieval outperforms exhaustive context replay while reducing computational costs by 8x.
AIBullisharXiv – CS AI · Jun 97/10
🧠Researchers propose DCPM, a dual-process cognitive memory system for LLM agents that organizes memory hierarchically from raw inputs to cross-domain patterns. The system uses a synchronous writer to record belief revisions and an asynchronous engine to induce schemas and detect cross-domain patterns, achieving significant improvements on personalization benchmarks requiring implicit reasoning about user evolution.
AIBullisharXiv – CS AI · Jun 96/10
🧠Researchers introduce Rosetta Memory, an adaptive memory system designed to work seamlessly across different large language models. The system uses profile-conditioned operators to optimize how memory is stored and retrieved, enabling users to switch between models like Claude and GPT without degrading performance.
🧠 Claude
AINeutralarXiv – CS AI · Jun 56/10
🧠TOKI is a formal framework that types contradiction resolution in LLM-agent persistent memory systems as a write-time concurrency control problem. The research proves that four common heuristics used in production systems admit unspecified isolation levels and anomalies, and proposes a bitemporal operator algebra with audit-row provenance that excludes three critical write-time anomalies while maintaining language-model oversight.
AIBullisharXiv – CS AI · Jun 16/10
🧠Researchers introduce SAGE, a memory management system for agentic LLMs that uses novelty detection to efficiently control when new facts are added, merged, or ignored. The approach reduces API costs and latency by 3.4× and 2.5× respectively while maintaining quality, addressing a critical gap in write-side memory control for long-context AI agents.
🧠 GPT-4
AIBullisharXiv – CS AI · May 286/10
🧠Researchers introduce MemTrace, a framework for debugging Large Language Model memory systems by tracing information flow through memory evolution graphs. The system identifies root causes of memory failures and uses attribution signals to automatically optimize prompts, achieving up to 7.62% performance improvements across multiple memory architectures.
AINeutralarXiv – CS AI · May 276/10
🧠Researchers introduce MemFail, a diagnostic benchmark for testing failure modes in LLM memory systems by isolating three core operations: summarization, storage, and retrieval. The benchmark evaluates state-of-the-art memory systems across five adversarially-designed datasets to empirically understand architectural tradeoffs, moving beyond aggregate accuracy metrics.
AINeutralarXiv – CS AI · May 76/10
🧠Researchers introduce Memini, a system that applies biological multi-timescale memory dynamics to external memory in large language models. By organizing knowledge as a directed graph where edges follow coupled fast and slow variables inspired by synaptic consolidation, the system enables LLMs to continuously update their knowledge without explicit management, allowing new information to be immediately useful while less relevant associations gradually fade.
AINeutralarXiv – CS AI · Apr 156/10
🧠A new research paper proposes a governance framework for personal AI memory systems designed to function as 'companion' knowledge wikis that mirror user knowledge while compensating for epistemic failures like entrenchment and evidence suppression. The work addresses an emerging 2026 landscape of memory architectures for large language models through five operational mechanisms (TRIAGE, DECAY, CONTEXTUALIZE, CONSOLIDATE, AUDIT) aimed at preventing user-coupled drift in single-user knowledge systems.
