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01 ·Agents·★ MEMBER·PAPER·15 min read Paper Walkthrough: Training Agents to Evolve with Their Harness Why compact models break when you rewrite prompts and tool definitions every week, and what the team behind Taobao Live's AI avatar streamers does about it. A ground-up walkthrough of Harness-Aware Training, from intuition to equations to measured numbers and stated limits. 02 ·Inference & Serving·★ MEMBER·PAPER·14 min read Paper Walkthrough: From Production Traffic to Post-Training — Folding 200 Internal Apps Into One Self-Hosted LLM How a company forced to self-host by data-residency rules collapsed a sprawling model fleet into one checkpoint: classify production failures by hand, train one GRPO expert per weak axis, then merge the weights with SLERP. Includes three real reward-hacking failures. 03 ·Agents·★ MEMBER·PAPER·13 min read Paper Explained: FreeToken — Treating Your Own PC as a Single Elastic Inference Platform FreeToken is an edge-native serving system for frontier-scale MoE models on personal hardware. Its centerpiece is a q* policy that decides how many missed experts to ship over PCIe versus execute in place on the CPU — using nothing but two measured bandwidths. 04 ·Agents·★ MEMBER·PAPER·8 min read Paper Walkthrough: Macaron-V1 — A Frozen Base plus a Mixture of LoRAs, Built to Keep Learning After Launch A guided tour of Mind Lab's open agent-model family Macaron-V1, straight from the paper: the Mixture-of-LoRA architecture that stacks four specialist adapters on a frozen 744B base and picks one per turn, and the recursive self-improvement loop that trains the model and its harness as a versioned pair.