Macaron-Tall

📖 Blog: Introducing Macaron-V1 🚀 Hosted API: Macaron API Platform 🧩 Artifacts: Macaron Artifacts 🛠️ Self-hosted serving: Mixture of LoRA (MoL) serving harness 📄 Technical report: Macaron-V1: Towards Open Continual Learning with Self-Improvement and Mixture-of-LoRA (arXiv:2608.09819) ✉️ Correspondence: contact@mindlab.ltd

Macaron-V1-Tall is a Mixture of LoRA (MoL) model in the Macaron-V1 family, built on Qwen3.6-35B-A3B for personal intelligence, tool use, coding workflows, and code-native Generative UI.

The model combines a Qwen3.6 MoE base with the Macaron-V1 specialist system: four LoRA specialists for chat, personal-agent tasks, coding, and GenUI, with an L0 router selecting the most suitable specialist for each new user request.

Macaron-V1-Tall is released alongside Macaron-V1-Venti as a sibling model in the Macaron-V1 family. Both models share the same product-facing specialist layout and routed serving interface while targeting different deployment and experimentation profiles.

Highlights

• A Qwen3.6-35B-A3B-based Macaron-V1 model with the full Macaron specialist system.
• Mixture of LoRA (MoL) architecture with four specialists across Chat, Agent, Coding, and GenUI.
• Built for personal-agent workflows, tool use, repository-level coding, and UI4A Generative UI.
• BF16 checkpoint with four LoRA adapters stored under loras/L0 through loras/L3.
• Supports a 262K context length according to the released checkpoint configuration.

Model Overview

On parameter counts. Tall's adapter configurations use a different rank and expert target set from Macaron-V1-Venti, so its aggregate is reported as a rounded release footprint rather than inferred from base size alone. The two adapter budgets are not directly comparable because the bases expose different target-module and expert structures. The sidebar figure on this page is an automatic safetensors tensor count and may differ from this architectural figure.

Mixture of LoRA (MoL) Architecture

At runtime, L0 routes each new user request to the most suitable specialist. Ongoing reasoning and tool interactions remain within the selected LoRA, while completed work can be shared across specialists through concise summaries.

Routing Behavior and Cost

Measured over 48 multi-turn mixed-domain requests at temperature 0. The routing overhead share (~30%) is stable across base sizes, while absolute loop latency is roughly 2.7× lower than Macaron-V1-Venti's 4.68 s.

On the same 6,448-sample trace used for Venti — verified identical by matching every sample ID and input hash — Tall reaches 99.04% routing accuracy with 100% canonical-label compliance, so routing quality is stable across base sizes. L3 GenUI reaches 100% and L1 Agent is lowest at 95.1%. The trace is drawn from LoRA training data, so this is an implementation diagnostic rather than a generalization estimate.

Evaluation

Macaron-V1-Tall compared with its Qwen3.6-35B-A3B base on the seven benchmarks evaluated for both systems. Both use the same protocol within each row; higher is better, normalized to 0–100.

Tall has a larger point estimate than its base on all seven rows, from +1.3 on Macaron LivingBench to +25.4 on UI4A-Bench. This is an end-to-end system comparison, not a parameter-matched component ablation: the two systems differ in parameter footprint, adapters, routing, and potentially harness behavior. The table does not identify which component produces each difference, and it does not establish scaling behavior from Macaron-V1-Tall to Macaron-V1-Venti.

The headline Macaron-V1 comparison against frontier baselines is reported for Macaron-V1-Venti; see the Venti model card and Table 8 of the technical report. Per-benchmark judges, case counts, and metrics are documented in Appendix B of the report.

Multimodal Behavior

The Macaron-V1-Tall specialists are trained on text-only data, while the Qwen3.6-35B-A3B base is multimodal. The following full-dataset point estimates (no-thinking mode) co