# Knowledge Tree

Micius-Flash is a 27-billion-parameter engineering model with native multimodal input and a 256K-token context window, specialized and evaluated primarily on semiconductor engineering, and released as an open-source model. A knowledge-tree-driven data flywheel organizes semiconductor knowledge, synthesizes data and validates quality, and multi-teacher on-policy distillation merges four expert capabilities, knowledge and reasoning, hardware coding, multimodal understanding and agentic tool use, into one student model. It averages 64.2% on 12 hardware coding benchmarks, 15.0 points above a same-size general-purpose baseline, and scores 62.4% on a semiconductor-specific agent benchmark, 17.2 points above that baseline.

Micius-Flash is a 27-billion-parameter engineering model with native multimodal input and a 256K-token context window, specialized and evaluated primarily on semiconductor engineering, and released as an open-source model. A knowledge-tree-driven data flywheel organizes semiconductor knowledge, synthesizes data and validates quality, and multi-teacher on-policy distillation merges four expert capabilities, knowledge and reasoning, hardware coding, multimodal understanding and agentic tool use, into one student model. It averages 64.2% on 12 hardware coding benchmarks, 15.0 points above a same-size general-purpose baseline, and scores 62.4% on a semiconductor-specific agent benchmark, 17.2 points above that baseline.

Sarvam built a knowledge fabric for enterprise agents that compiles documents once into a navigable knowledge tree and a queryable domain ontology, so agents locate material by directory and filter by entity instead of re-retrieving passages for every question. On Harvey's LAB law-firm benchmark with GLM 5.3 as the agent model, answers met 84.2% of expert rubric criteria versus 55.1% for agentic RAG, while average task cost fell from $0.64 to $0.28; on Sierra's tau-knowledge banking benchmark with GLM 5.2, the fabric matched baseline accuracy using 64% fewer tokens.

Sarvam built a knowledge fabric for enterprise agents that compiles documents once into a navigable knowledge tree and a queryable domain ontology, so agents locate material by directory and filter by entity instead of re-retrieving passages for every question. On Harvey's LAB law-firm benchmark with GLM 5.3 as the agent model, answers met 84.2% of expert rubric criteria versus 55.1% for agentic RAG, while average task cost fell from $0.64 to $0.28; on Sierra's tau-knowledge banking benchmark with GLM 5.2, the fabric matched baseline accuracy using 64% fewer tokens.