mlsanityData to 7 Oct 2026

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

arXiv 2602.04101 ↗First posted 4 Feb 2026cs.AIcited by 2

Not on the map. Why?

Abstract (arXiv)

We present Interfaze, a native hybrid model that fuses task-specific deep neural networks (CNNs and DNNs) directly into a transformer decoder through a shared embedding space. Specialized perceptual encoders handle optical character recognition (OCR) over complex multilingual PDFs, open-vocabulary object and graphical user interface (GUI) detection, and multilingual speech recognition with diarization. Each is exposed through a task-specific adapter and can be activated on its own, so a query touches only the parameters it needs. A built-in action foundation supplies a grounded external state: a proxied headless browser and scraper, a code sandbox, a multi-domain web index, and a scalable vector store. The decoder filters and merges these signals, reasons over them when a task requires it, and emits deterministic outputs built on confidence. The raw specialist metadata (bounding boxes, confidence scores, timestamps) is preserved and returned alongside the answer as precontext. On this architecture, Interfaze-Beta leads a suite of deterministic developer-task benchmarks. It reaches 70.7% on OCRBench v2, 85.7% on olmOCR, 82.1% on RefCOCO, a 2.4% word error rate on VoxPopuli, 52.9% on Spider-2.0-Lite, 92.4% on GPQA-Diamond, 90.9% on MMMLU, 71.1% on MMMU-Pro, and 80.5% value accuracy on the Structured Output Benchmark (SOB), ahead of comparably priced generalist models (Gemini- 3-Flash, Gemini-3.5-Flash, Claude-Sonnet-4.6, GPT-5.4-Mini, and Grok-4.3) on every task. Because fused specialist encoders resolve perception in a single pass instead of through repeated tool calls into a large model, Interfaze reaches high accuracy with verifiable metadata on deterministic tasks while running at flash-tier cost.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 54 of its 69 references that are in the map. First posted on arXiv 4 Feb 2026.

Builds on

54 of 69 references.

References · 54

  1. 2023Visual Instruction Tuningcited by 7,792
  2. 2020Measuring Massive Multitask Language Understandingcited by 7,768
  3. 2023BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Modelscited by 6,069
  4. 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,601
  5. 2019DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lightercited by 4,243
  6. 2019Parameter-Efficient Transfer Learning for NLPcited by 4,195
  7. 2024Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolutioncited by 4,151
  8. 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,072
  9. 2022Robust Speech Recognition via Large-Scale Weak Supervisioncited by 3,972
  10. 2020wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representationscited by 3,853
  11. 2017Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layercited by 3,413
  12. 2023Sigmoid Loss for Language Image Pre-Trainingcited by 3,048
38 more
  1. 2021Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsitycited by 3,014
  2. 2024SAM 2: Segment Anything in Images and Videoscited by 2,784
  3. 2023GPQA: A Graduate-Level Google-Proof Q&A Benchmarkcited by 2,748
  4. 2023MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGIcited by 2,044
  5. 2024LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Codecited by 1,906
  6. 2024Mixtral of Expertscited by 1,785
  7. 2024MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmarkcited by 1,758
  8. 2022ChartQA: A Benchmark for Question Answering about Charts with Visual and Logical Reasoningcited by 1,449
  9. 2019Common Voice: A Massively-Multilingual Speech Corpuscited by 1,358
  10. 2023HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Facecited by 1,173
  11. 2023ImageBind: One Embedding Space To Bind Them Allcited by 1,162
  12. 2016Modeling Context in Referring Expressionscited by 1,054
  13. 2024Chameleon: Mixed-Modal Early-Fusion Foundation Modelscited by 904
  14. 2016A Diagram Is Worth A Dozen Imagescited by 793
  15. 2023FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performancecited by 791
  16. 2020It's Not Just Size That Matters: Small Language Models Are Also Few-Shot Learnerscited by 668
  17. 2020ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verificationcited by 606
  18. 2024MMMU-Pro: A More Robust Multi-discipline Multimodal Understanding Benchmarkcited by 494
  19. 2023SALMONN: Towards Generic Hearing Abilities for Large Language Modelscited by 492
  20. 2022LayoutLMv3: Pre-training for Document AI with Unified Text and Image Maskingcited by 481
  21. 2020MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devicescited by 476
  22. 2021VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation Learning, Semi-Supervised Learning and Interpretationcited by 455
  23. 2023Florence-2: Advancing a Unified Representation for a Variety of Vision Taskscited by 403
  24. 2021OCR-free Document Understanding Transformercited by 400
  25. 2024Hybrid LLM: Cost-Efficient and Quality-Aware Query Routingcited by 354
  26. 2023Chameleon: Plug-and-Play Compositional Reasoning with Large Language Modelscited by 352
  27. 2023Hiera: A Hierarchical Vision Transformer without the Bells-and-Whistlescited by 287
  28. 2019Character Region Awareness for Text Detectioncited by 273
  29. 2025Small Language Models are the Future of Agentic AIcited by 254
  30. 2022Unified Scaling Laws for Routed Language Modelscited by 249
  31. 2023Nougat: Neural Optical Understanding for Academic Documentscited by 240
  32. 2024Spider 2.0: Evaluating Language Models on Real-World Enterprise Text-to-SQL Workflowscited by 223
  33. 2024OmniParser for Pure Vision Based GUI Agentcited by 193
  34. 2024OCRBench v2: An Improved Benchmark for Evaluating Large Multimodal Models on Visual Text Localization and Reasoningcited by 165
  35. 2025olmOCR: Unlocking Trillions of Tokens in PDFs with Vision Language Modelscited by 120
  36. 2023Retrieval meets Long Context Large Language Modelscited by 114
  37. 2022PP-OCRv3: More Attempts for the Improvement of Ultra Lightweight OCR Systemcited by 105
  38. 2025Universal Model Routing for Efficient LLM Inferencecited by 80

50 most cited of 54 All 54 (JSON)

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026Compiling Deterministic Structure into SLM Harnessescited by 0
  2. 2026Automatic Ontology Construction Using LLMs as an External Layer of Memory, Verification, and Planning for Hybrid Intelligent Systemscited by 0