MemGPT is a great parallel. Paging between fast context and slow storage is essentially the same idea; memory needs an explicit policy, not just a bigger buffer. The schema approach pushes that decision even earlier, to ingestion time rather than retrieval time.
LLMS
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Man spells strawberry while counter tracks number of Rs
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Omni: A man spells out strawberry while a counter keeps track of the number of Rs he says. pic.twitter.com/SxwJcJusMo
— fofr (@fofrAI) 26 mai 2026Omni: A man spells out strawberry while a counter keeps track of the number of Rs he says.
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LLM Multi-Hop Retrieval: Schema vs Sequential Queries
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That works when the graph is small and the hops are predictable. At scale, sequential LLM queries for every multi-hop retrieval add latency and token cost per question. The schema lets you answer it in one structured traversal instead of chaining open-ended lookups. Not
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Researchers Identify Neurons Behind AI Safety Refusals
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Someone just found the exact neurons that make AI say "no." Language models refuse harmful prompts, but nobody knows how that refusal works inside. Most steering methods edit the residual stream and wreck output quality. A new paper proposes a sharper fix: Contrastive Neuron
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From Stack Overflow bug to autonomous features in 5 years
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How did we go from opening Stack Overflow to solve one bug, to agents writing entire features on their own? That didn’t happen overnight, but in slow progress in about 5 years. And today, I want to walk you through the full story. The timeline, the tools, the tweets, the
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Multi-Turn Agent Evaluation and Context Compaction Limits
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Paper does test multi-turn (24 tool calls in OfficeQA, 30 turns in SpreadsheetBench, 50 steps in ALFWorld), but mid-session auto-compaction isn't part of the eval. Skill being under 2K tokens probably helps it survive compaction, but not validated against that failure mode.
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Hundred-Page Language Models Book with PyTorch by Burkov
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The Hundred-Page Language Models Book — Hands-on with PyTorch: http://
amzn.to/4sJl7YC by @burkov -
Troubleshooting AI behavior by removing persistent context
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I do use structured prompts, plan/goal mode, Notion context, and a few local reference docs. Memory is involved. Could be instruction/memory interference. I’ll try a clean run without persistent context and see if it changes the behavior. thanks!
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GPT instruction following worsened, hallucinations and errors compared to 5.5
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both id say. Instruction following has worsened. GPT repeatedly hallucinates during the task, then admits mistakes, tries to correct them, and makes further serious errors. I haven't encountered such serious errors with version 5.5.
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Automated 30-day review of recent AI work using memory and git
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The text version of the prompt: "Look back over my recent work from the last 30 days using all available context. Use available evidence in this order:
– Session Memory summaries and MEMORY. md entries
– Git log and recent commit history across branches
– CLAUDE. md and