How CASCADE Enables LLM Agents to Learn from Experience During Live Deployment

The paper introduces CASCADE, a deployment‑time learning framework that lets LLM agents continuously select and reuse past cases via a contextual‑bandit approach, achieving higher long‑term success rates across diverse online tasks without updating the base model.

CASCADECase-Based ReasoningContextual Bandit

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How CASCADE Enables LLM Agents to Learn from Experience During Live Deployment

AI Architecture Hub

Mar 25, 2026 · Artificial Intelligence

How Memento-Skills Enables Continuous Learning for Frozen LLM Agents

The article analyzes the limitations of frozen LLM agents—fixed parameters, loss of state, and costly fine‑tuning—and introduces the Memento‑Skills framework, which adds an external, evolvable skill memory to achieve deployment‑time learning, detailed architecture, optimization knobs, and strong experimental gains.

AI researchDeployment-Time LearningLLM agents

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How Memento-Skills Enables Continuous Learning for Frozen LLM Agents

Architect

Mar 22, 2026 · Artificial Intelligence

Can Frozen LLMs Keep Learning? Inside Memento‑Skills' Deployment‑Time Learning

The article analyses the Memento‑Skills paper and its open‑source implementation, showing how a frozen large language model can continuously improve by treating skills as external memory, using a five‑step Observe‑Read‑Act‑Feedback‑Write loop, advanced routing, and modular architecture to achieve significant gains on GAIA and HLE benchmarks.

AI ArchitectureAgentDeployment-Time Learning

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Can Frozen LLMs Keep Learning? Inside Memento‑Skills' Deployment‑Time Learning