How TencentDB Agent Memory Cuts Token Usage by 61% and Boosts Task Success

TencentDB Agent Memory, an open‑source hierarchical memory system for long‑running AI agents, offloads tool calls, structures short‑term and four‑layer long‑term memories, and reduces token consumption by 61% while raising task success rate 51% and persona accuracy from 48% to 76%, all running locally with SQLite and no API keys.

AI agentsOpenClawSQLite

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How TencentDB Agent Memory Cuts Token Usage by 61% and Boosts Task Success

PaperAgent

May 18, 2026 · Artificial Intelligence

How MemWeaver Combines Behavioral and Cognitive Memory to Rebuild LLM Personalization

MemWeaver introduces a hierarchical memory that fuses behavior‑level and cognition‑level user signals, enabling large language models to generate more personalized content across multiple tasks, with extensive experiments, ablations, and an efficient incremental update mechanism demonstrating superior performance over strong baselines.

LLM personalizationLaMP benchmarkbehavioral memory

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How MemWeaver Combines Behavioral and Cognitive Memory to Rebuild LLM Personalization

DataFunTalk

Apr 30, 2026 · Artificial Intelligence

How GenericAgent Cuts Token Costs by 10× While Boosting AI Agent Performance

The technical report on GenericAgent, a self‑evolving LLM‑based agent, shows that by maximizing context information density and using a minimal atomic toolset with hierarchical memory, it achieves up to ten‑fold token savings, 100% task accuracy, and progressive efficiency gains across multiple benchmarks.

AI benchmarksGenericAgentLLM

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How GenericAgent Cuts Token Costs by 10× While Boosting AI Agent Performance

Machine Heart

Apr 5, 2026 · Artificial Intelligence

What Gaps Must Spatial AI Agents Fill to Achieve Action in 2026?

The article analyzes spatial intelligence as a core AI frontier, outlines the 2026 bottleneck of agents lacking spatial‑scale capabilities, reviews recent industry and academic advances such as World Labs' Marble model, hierarchical memory, GNN‑LLM integration, and world‑model research directions.

2026 AI ResearchAgentic CapabilityGNN-LLM Integration

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What Gaps Must Spatial AI Agents Fill to Achieve Action in 2026?