Cognitive Physics vs Nara Mobile AI Lab

Cognitive Physics began as a single, hard-edged question: How do mind and matter belong to the same set of laws? That question led to years of work in Cognitive Physics—and eventually to something tangible. Something you can carry. Something you can use every day. This app runs on Systemic Narrative Integration (SNI), a research technology inspired by how the great minds of physics search for structure inside uncertainty. SNI treats learning as a disciplined process. It measures coherence, tracks uncertainty, and when something is incomplete, it refuses cleanly instead of pretending. The goal is not to sound intelligent—it’s to behave honestly. Cognitive Physics turns your device into a truth-seeking instrument, not a chat toy. Inside the app lives a real AI system designed to grow with you. You don’t just use an assistant—you train your own AI from scratch to research alongside you. It learns from the material you import, adapts based on what you approve or reject, and improves through structured feedback. Rather than dumping answers, it behaves like a teacher—guiding you to ask better questions, build valid requests, verify claims, and develop the habits that lead to real understanding. This is a personal research lab where intelligence isn’t a costume—it’s a process you can observe, shape, and refine. A place where mind and matter meet as measurable structure. A place where your AI doesn’t just talk—it learns the way a serious system should: carefully, transparently, and with you in the loop. What You Can Do with Cognitive Physics Train Your Own AI • Build a personal AI from scratch using your own PDFs, notes, links, and research • Learn through approval/rejection—not blind consumption • Structured feedback improves behavior over time • No forced answers, no hallucinated confidence Research Like a Scientist • Live web, RSS, and paper discovery • Duplicate prevention and source tracking • Date, time, year, and origin preserved for every finding • Automatic evaluation using coherence and entropy signals Ask Questions with Citations • Ask questions directly against your own library • Answers are grounded in retrieved sources • Clickable citations open the exact passage used • If evidence is missing, the app tells you—honestly Build a Permanent Knowledge Base • Import and manage PDFs • Automatic text extraction and semantic search • Quality-based organization (high, medium, low, trash) • Everything stored locally and permanently Multi-Agent Intelligence • Specialized agents for planning, coding, reviewing, testing, and optimizing • Agents coordinate tasks and self-correct • Offline learning from success and failure patterns • Designed for serious workflows, not roleplay Automatic Paper Generation • LaTeX papers generated from accumulated research • Offline diagram and figure creation • Reproducible outputs tied to your source library • Built for real academic and technical use Measure Learning, Not Just Output • Coherence and entropy tracked for every interaction • Structural vs semantic uncertainty separated • Refusal is treated as signal, not failure • Stability and dispersion trends visualized over time Offline-First, Instrument-Grade Design • On-device embeddings and local inference • Works without internet once your library exists • No cloud dependency required • Built with safety, performance, and transparency in mind Cognitive Physics is not about replacing your thinking. It’s about giving you a system that teaches you how to think better, alongside an AI that learns the same way you do.

Nara Mobile AI Lab is a developer-focused test bench for exploring Apple built-in AI capabilities directly on iPhone. The app helps developers, builders, and technical teams understand how on-device AI performs in real app workflows before investing in a larger implementation. For the most accurate test, turn on Airplane Mode before running experiments. This helps verify whether results are coming from the device’s built-in AI capability instead of a cloud service. With Nara Mobile AI Lab, you can test practical AI workflows such as: Speech-to-text with structured JSON extraction Vision and OCR extraction from photos Prompt response testing Mock tool and callback execution User-defined tool function testing LocalKnowledge search testing Object detection using preset Core ML models Image classification using preset Core ML models Prompt to Image using Image Playground Latency, validation, manual scoring, and notes Exporting results for review or project planning The app includes built-in templates and one-time page hints so you can quickly test common developer scenarios without building a full prototype first. You can inspect raw extraction, AI output, validation status, benchmark timing, manual score, and notes. Nara Mobile AI Lab is especially useful for evaluating whether on-device AI is suitable for privacy-focused apps, offline-first workflows, mobile automation, field tools, form extraction, image analysis, local knowledge search, and AI-assisted business software. Core ML Vision Tests Nara Mobile AI Lab includes preset Core ML model tests for object detection and image classification. The current preset models include YOLOv3TinyFP16 for object detection and FastViTMA36F16 for image classification. These tests provide practical developer examples for reviewing on-device model behavior, latency, and output format. Tool Calling Tests The app includes mock tool-calling tests so developers can evaluate whether the Apple Foundation Model chooses the correct function, passes reasonable arguments, and returns a useful result. Write-style actions are mocked for safety, allowing the app to demonstrate tool-calling behavior without performing real external changes. LocalKnowledge Tests The app includes local knowledge search testing so developers can explore how user-defined local information can be used in offline-first AI workflows. Test data stays on the device unless the user manually exports it. Nara Mobile AI Lab is a testing and learning tool. AI output may be incomplete or inaccurate and should always be reviewed by the user.