Papers
Published papers with direct downloads and linked public records where available.
Library
Abstracts are reproduced from the released source papers.
Use each paper detail page for DOI links, repository links, release records, and the direct download file.
AGIF Fabric v1 presents a bounded, governed multi-cell software architecture for local adaptive workflows. This work positions AGIF v1 as an empirical software proof that specialized cells, tissues, shared workspace coordination, descriptor exchange, bounded adaptation, and governed memory can improve structured workflow performance under resource-sovereign constraints. The paper documents the architecture,...
AGIF Tasklet Cells are a software-artifact model for embedding bounded, offline AI capabilities directly inside native applications. A Tasklet Cell is a single-task, contract-driven artifact packaged with explicit input and output schemas, integrity metadata, and a Verifier Pack, and it may execute only under a local Runner that validates the artifact and enforces offline-first execution, bounded authority,...
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Embedded Neural Firmware (ENF) reframes device software as a sealed Neural BIOS-offline-only, immutable, and bound to silicon-and as a framework that specifies, builds, and proves it. ENF compiles a task-specific, quantized model into ROM/Flash and executes bare-metal via a single FSM with static memory (no heap/GC or recursion) and per-state WCET, yielding bit-for-bit reproducibility and tractable verification....
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Domestic water leaks are a rising threat to the financial stability of homeowners and insurers in cold-climate Europe. A press release dated 7 November 2025 reports that burst pipes and leaky fittings generated EUR4.9 billion in insured losses in Germany during 2024 and that more than half of all building-insurance claims now stem from water damage (Finanztip, 2025). Complementary data from the German Insurance...
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Embedded AI has moved from simple, static firmware into cloud-dependent, update-driven systems that expand attack surface, weaken determinism, and create lifecycle fragility when vendors, networks, or update pipelines fail. This technical note frames that failure mode-energy, security, and privacy limits of the mainstream IoT/LLM/OTA paradigm-and proposes Embedded Neural Firmware (ENF) as an architectural exit....
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This technical note surveys the most relevant prior work and standards that shape the design space for Embedded Neural Firmware (ENF). It reviews the TinyML ecosystem and its prevailing assumptions (runtime interpreters, flexible toolchains, and operational updateability), then contrasts these with ENF's sealed, deterministic compile-and-flash model. The note summarizes secure-boot and device-identity approaches...
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This technical note specifies a reference architecture for Embedded Neural Firmware (ENF)-a sealed, deterministic embedded intelligence stack designed for MCU-class devices operating without an operating system, cloud dependency, telemetry, or over-the-air updates. The architecture is structured across six layers: (1) a constrained hardware substrate (MCU-scale Flash/SRAM) with hierarchical power domains; (2) an...
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This technical note formalizes Embedded Neural Firmware (ENF) as a framework-level method for generating deterministic, sealed embedded intelligences from a compact design tuple. It defines ENF as a compile-time system: ENF(T, P, S, F, C) -> L_sealed, where Task Complexity (T), Power Model (P), Security Level (S), Fallback Architecture (F), and Communication Scope (C) fully specify an agent whose behavior is...