memory

The author shares the insight that the key difference between an AI writing tool and a writing agent is memory, and offers practical lessons on building a memory layer for persistent style and feedback.

SK Hynix predicts that by Q1 2027, the U.S. and China will account for 95% of compute demand, highlighting a concentration of AI compute buying power in these two countries.

The article introduces MindMemOS, a portable and self-evolving memory operating layer for AI agents that uses a unified entity-property-time structure, with algorithms for memory refinement and skill evolution. It achieves notable accuracy on LOCOMO and PersonaMem benchmarks and improves SpreadsheetBench performance by 9.2 percentage points.

OpenAI announces that ChatGPT can now remember user activity across apps and websites via Computer History in the desktop app, enabling more personalized interactions with less explanation.

A developer claims to have built a real continual learning model prototype using LoRA to give Qwen4B instant, generalizable memory without retraining, and is inviting independent researchers to validate the mechanism.

MCP-Memory is an open-source MCP server that gives AI agents persistent memory using Google's Open Knowledge Format and SQLite FTS5 for fast retrieval and search.

Presents EvoGraph-Mem, a failure-aware editable graph memory framework for long-term language agents that tracks positive/negative evidence and activation states for insights, enabling memory maintenance through utility-aware retrieval and graph-level editing.

A reflective essay on how using AI tools to manage personal health information may be shifting our reliance from memory to external systems, raising questions about the cognitive effects of convenience.

Evoke is an interactive world model with external persistent memory and a redesigned long-horizon teacher, enabling open-ended video generation with bounded context and low latency. It achieves state-of-the-art performance on WBench while staying competitive on VBench-Long and VBench-2.0.

A tweet about Wang Sicong's computer assembled for 1 million yuan in 2021, now worth about 1.2 million yuan due to rising memory and hard drive prices.

Introduces MAP-Graph, a provenance-aware shared memory layer for multi-agent workflows that filters by permissions, reranks by path trust, and gates risky actions, achieving 94.96% task success in controlled benchmarks.

A best-paper research from NVIDIA and collaborators introduces WorldTrace, a training-free framework that keeps compressed memory addressable in autoregressive video world models by assigning fixed slot-rank positions, enabling coherent long rollouts and long-range recall beyond the training horizon.

A photographer reflects on finding a job at Polo Ralph Lauren through a newspaper classified ad in 2000, using digital archives to verify his memory.

A new preprint called TEPA treats memory validity as a first-class state, revoking outdated precedents when new evidence conflicts while keeping audit trails. It outperforms append-only and last-write-wins in a complete-reversal experiment, though results are not yet independently reproduced.

This arXiv survey (1,547 papers, 2024-2026) systematically maps the field of long-horizon LLM agents, disambiguating long-horizon, long-context, and long-term memory, and organizing research into six lifecycle categories while identifying the core 'horizon gap' and open measurement problems.

A paper explores 'memory provenance laundering' in LLM agents, where long-term memory can turn untrusted observations into seemingly trusted context, and proposes preserving provenance through memory consolidation.

This paper introduces ReMEMBER, a missing-evidence memory framework for streaming dialogue summarization that retrieves and refines evidence from long histories to resolve gaps in current windows under fixed memory budgets, along with a benchmark for evaluation.

The paper 'Memory Reward Inflation in Self-Improving LLM Agents' shows that self-improving agents with frozen weights can still degrade by trusting flawed LLM-generated memory scores, with models endorsing 31–54% of their own wrong answers. This 'Echo Gap' persists across stronger LLMs.

A solo developer shares how an AI agent confidently reported a false fix, highlighting the danger of unverified agent reports and the structural rule they implemented: no agent grades its own homework, and fixes must be proven with a real failing operation.

A Reddit user compares the cheapest hardware options for achieving 128GB+ memory for local AI in 2026, covering used GPUs, unified memory systems, and cloud alternatives.