Anywhere Intelligence Research

Built from first principles.

Before we built Haven, we spent 18 months designing its operational AI layer, AIOS (Anywhere Intelligence Operating System), around two key questions: what makes AI genuinely personal, and what makes it trustworthy. The answers required fundamentally new architecture — not a local wrapper around an existing cloud model, but a different kind of system designed from the ground up for personal intelligence.

These papers are our contribution to that conversation. We share them to advance research on personal, trustworthy AI — and to explain the architecture behind Haven.

Personal

AI that accumulates what things mean to you — your relationships, your context, your language — not statistical averages calibrated across millions of users. Every session, remembered. Every meaning, yours to authorize.

Trust

AI that can show you, at any moment, exactly how it reached an answer — what it consulted, what it ruled out, and why. No hallucination without a trace. No action without authorization. Trust earned through architecture, not marketing.

Published Papers

Paper I · Personal Intelligence

Personal World Models: Toward Human-Centric Personal Intelligence Systems

Justin Holbrook, Swaroop Kallakuri, Joshua Holbrook, Lucas Burgess · Anywhere Intelligence · 2026

The dominant paradigm in AI optimizes for machine-side intelligence: scale, benchmark performance, agentic capability. This framing is correct for many objectives and incorrect for one — systems designed to live with a specific human over years. For such systems, the central missing architecture is not more intelligence, but a different kind of structure: one that encodes not how the world generally behaves, but what the world specifically means to this user, in their specific history, with their specific relationships.

Formally distinguishes the personal world model from user profiling, persistent LLM memory, and graph RAG — structural differences, not cosmetic ones.

Introduces three-layer knowledge separation: ontology primitives, common-sense priors, and personal meaning commitments — user-authorized, provenance-tagged, reversible.

Derives continuous engagement (no session boundaries) as a necessary architectural consequence of coherence constraints, not a product feature.

Introduces constitutional continuity: an alignment approach for systems that accompany a human life across years, during which values genuinely evolve.

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Paper II · Trust Architecture

The Intelligence Processing Unit: Architectural Explainability as a Foundation for Trust in AI Systems

Justin Holbrook, Swaroop Kallakuri, Joshua Holbrook, Lucas Burgess · Anywhere Intelligence · 2026

Trust between humans rests on a simple mechanism: the ability to ask "why did you do that?" and receive an answer that is coherent, inspectable, and open to challenge. Current AI systems built on large language models lack this architecture — they act first, only later permitting post-process scrutability. The IPU addresses the trust deficit at the architectural level.

Introduces the IPU as the atomic unit of AI execution: a bounded, append-only, immutable record from query to response — every subsystem consulted, every routing decision made.

No durable system effect — memory creation, knowledge modification, scheduled action — can occur without a complete, unfailed IPU trace.

Argues that process-based explainability (why did the system take this path?) is more fundamental than prediction-based explainability (why did the model predict this token?).

Presents the IPU as implemented in AIOS, the Anywhere Intelligence Operating System running on Haven.

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Research in Preparation

Forthcoming

Intelligence Squared (I²)

Why compact models with stronger geometry and routing can match or outperform much larger systems in targeted workloads — and why this makes Haven possible without cloud-scale infrastructure.

Forthcoming

Geometric Superposition Intent Classifier (GSIC)

A lightweight, ultra-low latency intent classifier built on a single-pass multi-head MLP employing orthogonal vector layers to simultaneously enable query classification and routing.

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Haven ships Summer 2026. Get research notes and product updates as we build.

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