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Mixture-of-Experts models, now popular for scaling capacity at fixed inference speed, switch experts at nearly every token. Once a model outgrows available GPU memory, this churn can render optimizations like offloading and pre-fetching ineffective. We make the case that the options framework in reinforcement learning is a perfect match to tackle this problem, and argue for temporally extended mixture-of-experts layers. Building on the option-critic framework with deliberation costs, we add a controller to each layer that learns when to switch expert sets and which to load. By applying this to gpt-oss-20b with low-rank adapters and a self-distillation reward, our method reduces switch rates from over 50% to below 5% while retaining up to 90% of base-model accuracy on MATH, MMLU, and MMMLU. This shows that even existing pre-trained models can be converted to temporally extended MoEs with lightweight training, with the deliberation cost allowing model trainers to trade off switching rates against capability. We hope this opens a principled path, grounded in the options framework, for memory-efficient serving and continual learning in ever-growing MoE models.
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come to the most ai pilled country per capita @anthropicai we want u https://t.co/u2as58vuWy
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Unitree unveiled a wheeled version of its G1 robot, demonstrating enhanced mobility and versatility. This innovation could expand the robot's applications in logistics and last-mile delivery.
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An interactive editorial on Peter Pang's argument that AI-first is not AI-assisted tooling, but a full redesign of architecture, QA, deployment, and engineering roles around agent execution.
Open briefingAn interactive editorial on Anthropic's harness-design essay: why long-running app agents drift, when planner-generator-evaluator loops help, and how to keep only the scaffolding that still matters.
Open briefingAn interactive audit walkthrough of arXiv:2603.01919 — from utility divergence to fingerprint failures in shadow LLM APIs.
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Open briefingNotion CEO Ivan Zhao explores how AI reshapes knowledge work across three scales: individuals (bicycles to cars), organizations (wood to steel), and economies (Florence to Tokyo).
Open briefingAn interactive breakdown of Boris Cherny's interview: why IDEs are dead, code shelf-life is 6 months, and how Anthropic achieved a 150% productivity leap.
Open briefingExplore verified OpenClaw implementations across productivity, creative workflows, research, and infrastructure, with direct links to each project.
Open briefingA structured bilingual briefing of Dotey’s long-form interview notes: scaling laws, power concentration, labor displacement, and startup strategy.
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Open briefingA provocative framework shift: stop building for human clicks and start designing for agent-native execution.
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Open briefingA synthesis of summit signals on AI adoption, org health, mid-level engineer risk, refactoring, and agile’s next phase.
Open briefingA bullish counter-thesis: AI may trigger painful local destruction, but macro outcomes are more likely rotation than collapse.
Open briefingAn interactive bilingual briefing of the 2026 China economy debate: macro recovery vs micro sentiment, growth-engine transition, and confidence flywheel.
Open briefingThe shift from writing code to designing self-correcting loops. Engineering agents aren't just faster programmers—they're PID controllers optimizing over machine-readable constraints.
Open briefing