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memory-systems
5 postsPersistent AI Agent Memory Is a Write-Path Cost Problem
AI agent memory costs are driven by write-path LLM calls, not retrieval. Engineering levers to cut cost and latency without sacrificing quality.
VetoBench Tests Whether AI Agents Forget Rejected Decisions
VetoBench is an open benchmark asking whether AI memory systems re-propose previously rejected engineering decisions, not just whether retrieval works. Results are striking.
Flint: Efficiently Leveraging High Bandwidth Flash for LLM Inference
FLINT is a workload-driven hardware substrate that integrates High Bandwidth Flash as a scalable memory tier for efficient, capacity-constrained LLM inference.
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commitbrief.comMemory Engineering Is a Promotion Pipeline, Not Notes
An AI agent framework runs memory through a promotion pipeline with evidence and approval instead of direct writes; rejection counts as a healthy outcome.
TencentDB-Agent-Memory: A 4-Layer Memory System for AI Agents
TencentCloud's open-source TencentDB-Agent-Memory gives AI agents a 4-layer memory system with Mermaid-based compression.