http://dylanler.github.io/tags/memory/Recent content in Memory on Dylan LerHugo -- 0.133.0en-usFri, 02 Oct 2026 02:30:00 -0700http://dylanler.github.io/posts/proactive-memory-value/Fri, 02 Oct 2026 02:30:00 -0700http://dylanler.github.io/posts/proactive-memory-value/Reactive agents wait for you. Calendar apps fire on fixed clocks. This note proposes a middle path: give every memory an attached value function \(V_i(t)\). A clock tick recomputes values. When a memory’s value crosses a threshold — with hysteresis so it does not chatter — the agent may send you a short proactive message. The distinctive piece is an oscillatory revival term on top of ordinary exponential decay. Dormant but still-important memories periodically become candidates again (“I’ve been meaning to bring this up”), without requiring a user query and without nagging every hour.http://dylanler.github.io/posts/when-memory-becomes-a-place/Sun, 23 Aug 2026 16:04:00 -0700http://dylanler.github.io/posts/when-memory-becomes-a-place/What if a model did not reread its past in words? That question led to the largest completed experiment in this repository: compress long documents into latent vectors, project those vectors into soft tokens, and compare the result with a text summary buffer. The experiment began with an attractive hypothesis: Latent Pager Memory can preserve useful information with less generation cost than a text buffer. The data supported that hypothesis and exposed a dangerous price.http://dylanler.github.io/posts/latent-pager-memory-what-if-llms-remembered-in-vectors/Wed, 25 Feb 2026 14:00:00 -0800http://dylanler.github.io/posts/latent-pager-memory-what-if-llms-remembered-in-vectors/What happens when you give a language model a 60,000 token document and ask it a question about paragraph 47? It forgets. Or worse, it makes something up. This is the long context problem and it is one of the most important open challenges in language modeling today. Context windows keep growing (Gemini has 1M tokens, Claude has 200K) but models still struggle with information buried deep in the middle of long inputs.http://dylanler.github.io/posts/continuous-learning-context-rot-long-horizon-memory-experiment/Wed, 25 Feb 2026 10:00:00 -0800http://dylanler.github.io/posts/continuous-learning-context-rot-long-horizon-memory-experiment/I wanted to answer one practical question. Can a model keep learning over long sessions without slowly losing grip on earlier facts? This post is a learning oriented walkthrough of one real campaign I ran. It focuses on understanding and decision making, not just reporting scores. Code and implementation are here: GitHub repo: rlm-experiment-codex Live report dashboard Overview I compared two memory methods with the same base model and the same datasets.