Interfaze: The Future of AI is built on Task-Specific Small Models

TL;DR

Interfaze introduces a modular system that combines small models, external tools, and a controller to enhance AI application flexibility, efficiency, and performance across diverse tasks.

Contribution

The paper presents Interfaze, a novel architecture that integrates heterogeneous small models, external data sources, and a control layer to improve AI task handling and efficiency.

Findings

Achieves high accuracy on multiple benchmarks (e.g., 83.6% on MMLU-Pro).

Handles most queries with small models and tools, reducing reliance on large LLMs.

Demonstrates strong multimodal performance across various datasets.

Abstract

We present Interfaze, a system that treats modern LLM applications as a problem of building and acting over context, not just picking the right monolithic model. Instead of a single transformer, we combine (i) a stack of heterogeneous DNNs paired with small language models as perception modules for OCR involving complex PDFs, charts and diagrams, and multilingual ASR with (ii) a context-construction layer that crawls, indexes, and parses external sources (web pages, code, PDFs) into compact structured state, and (iii) an action layer that can browse, retrieve, execute code in a sandbox, and drive a headless browser for dynamic web pages. A thin controller sits on top of this stack and exposes a single, OpenAI-style endpoint: it decides which small models and actions to run and always forwards the distilled context to a user-selected LLM that produces the final response. On this architecture, Interfaze-Beta achieves 83.6% on MMLU-Pro, 91.4% on MMLU, 81.3% on GPQA-Diamond, 57.8% on LiveCodeBench v5, and 90.0% on AIME-2025, along with strong multimodal scores on MMMU (val) (77.3%), AI2D (91.5%), ChartQA (90.9%), and Common Voice v16 (90.8%). We show that most queries are handled primarily by the small-model and tool stack, with the large LLM operating only on distilled context, yielding competitive accuracy while shifting the bulk of computation away from the most expensive and monolithic models.