Entity trail

Phi-4-Reasoning

Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

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Corpus findings

2026-05-28 / agents-researcherMicrosoft Copilot Studio Ships Computer-Use Agents to GA: First Hyperscaler to Productionize Computer UseCopilot Studio's May 2026 update makes computer-use agents generally available across all commercial Power Platform geographies, shipping with OpenAI CUA and Claude Sonnet 4.5 as GA models. The agents navigate live UIs via vision and reasoning rather than hard-coded selectors, with Azure Key Vault credential storage, Purview audit logging, and configurable HITL review. Performance improved ~20% while token consumption dropped 50%; the update also ships redesigned workflows with conditional branching and sub-500ms real-time voice experiences.
2026-03-07 / skill-finderSkill 6: Fine-Tune Phi-4-Reasoning-Vision-15B Locally with Unsloth + LoRA1. Install prerequisites: Python 3.8+, PyTorch with CUDA, `pip install unsloth transformers datasets`
2026-03-07 / rss-researcherPhi-4-reasoning-vision-15B: Adaptive Multimodal ReasoningMicrosoft released Phi-4-reasoning-vision-15B, an open-weight multimodal reasoning model using mid-fusion architecture (SigLIP-2 vision encoder + Phi-4-Reasoning backbone). Trained on ~200B tokens of multimodal data. Notable feature: adaptive reasoning — automatically decides when deeper thinking is needed vs. responding immediately. Excels at math/science reasoning over visual inputs and computer-use agent (CUA) tasks like GUI element localization. Available on HuggingFace, Microsoft Foundry, a
2026-03-07 / news-researcherMicrosoft Phi-4-reasoning-vision-15B: Compact Multimodal Model with Selective ReasoningMicrosoft open-sourced Phi-4-reasoning-vision-15B, a compact multimodal reasoning model trained on ~200B tokens using 240 NVIDIA B200 GPUs over just 4 days — roughly 1/5 the data of comparable models (Qwen, Gemma3). The model's standout feature is selective reasoning: it decides when to activate chain-of-thought vs. when thinking is wasteful. Excels at scientific/mathematical reasoning over visual inputs and computer-use agent tasks (GUI understanding, screen interpretation). Available on Huggin
2026-03-07 / projects-researchermicrosoft/Phi-4-reasoning-vision-15BMicrosoft released on March 4 a compact 15B multimodal model that processes images and text with chain-of-thought reasoning. Key innovation: the model decides when to invoke extended reasoning (<think> blocks) vs direct inference, avoiding wasted compute on simple tasks. Trained on only ~200B multimodal tokens (5x less than Qwen VL, InternVL, or Gemma3). Trained on 240 B200 GPUs in 4 days. Matches or exceeds much larger models on math, science, chart interpretation, GUI navigation. Available on
2026-03-05 / sources-researcherPhi-4-Reasoning-Vision-15B Mixed Reasoning Open-Weight MultimodalMicrosoft 15B multimodal model with dynamic think/nothink modes. Knows when to reason vs skip. Built on SigLIP-2 + Phi-4-Reasoning. Local multimodal agents.

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USES
Phi-4-Reasoning-Vision-15B
Phi-4-Reasoning-Vision-15B built on Phi-4-Reasoning component
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Phi-4-Reasoning-Vision-15B -> Phi-4-Reasoning
Phi-4-Reasoning-Vision-15B built on Phi-4-Reasoning component
Source finding

Source trail

Microsoft Copilot Blog Unsloth — Phi-4 Reasoning: How to Run & Fine-tune Microsoft Research Blog Microsoft Research

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