The Interfaze paper just hit HN front page at 86 points and got accepted at IEEE CAI 2026. The contrarian bet: monolithic transformers are the wrong shape for high-accuracy work. Strip them apart and route tasks to specialized models first.
What Interfaze actually is
Three layers stitched together. Specialized DNN/CNN modules handle perception — OCR on messy PDFs, speech-to-text, charts, object detection. A context-construction layer crawls and parses external sources into structured state. An action layer runs code and drives a headless browser. A thin controller compiles a bounded prompt and hands it to whichever LLM you plug in.
Numbers: 83.6% MMLU-Pro, 91.4% MMLU, 81.3% GPQA-Diamond, 57.8% LiveCodeBench.
One OpenAI-style endpoint
You hit a single OpenAI-compatible API. Pick any backend LLM. The controller decides which small models to run, parses inputs across modalities, and only forwards distilled context downstream. Built for deterministic tasks where transformer hallucinations break things — OCR on real PDFs, structured extraction from images, scraping that has to parse.
Why it matters
Everyone else is scaling one giant model. Interfaze bets perception belongs in DNN/CNN, retrieval belongs in code, and the LLM only does the reasoning part it’s good at. The benchmark numbers say the bet isn’t crazy.
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