Research

We are researching the representation of meaning as geometry rather than as statistical correlations between tokens. Concepts occupy positions in a 20-dimensional space; semantic similarity is literal Euclidean distance; reasoning is path-finding between coordinates rather than next-token prediction.

Two thousand foundational primitives — the conceptual building blocks from which higher-order ideas compose — are positioned by mathematical optimisation against a training corpus. Independent validation against neural network feature extractors shows convergence between this designed structure and learned representations: the same conceptual relationships emerge whether discovered statistically or constructed geometrically.

Because knowledge is explicit and structural rather than distributed across weights, the architecture cannot hallucinate in the conventional sense — there is no statistical mechanism by which a fact not present in the graph can be invented. Every reasoning step is a traceable navigation from one known coordinate to another.

Conventional AI systems maintain ever-growing context windows that degrade in quality as they fill — earlier instructions are forgotten, irrelevant information dominates attention, and per-token costs scale linearly with conversation length.

We are researching an alternative: a Mixture of Context Experts in which specialist retrievers select relevant knowledge from structured stores at each interaction, apply token budgets, and assemble exactly the context required for the question at hand. Nothing more, nothing less.

Every assembly produces a manifest documenting what was included, why it was selected, and which retrievers contributed. The result is dramatically lower per-interaction cost, predictable token consumption, and a complete audit trail suitable for regulated environments.

Critical national infrastructure — energy, water, telecoms, transport — runs on industrial control systems and operational technology with decades-long lifecycles, idiosyncratic protocols, and consequences for failure that have no analogue in the consumer software world.

We are developing AI that reasons about these environments as graphs rather than as token sequences: nodes are devices, components, and standards entries; edges are physical connections, dependencies, and data flows. The system can navigate this conceptual space to identify relationships and failure modes that do not appear in any existing vulnerability database — they emerge structurally from the architecture itself.

The work integrates established standards repositories — IEC Common Data Dictionary, ECLASS, CISA ICS advisories, NIST — and is designed for deployment in air-gapped and classified environments where conventional cloud AI is not an option.

We are developing a scalar-tensor theory of gravity in which the coupling constant is not fundamental but emerges from the information content of the matter distribution. A single action principle yields fifteen distinct observational results spanning galactic and cosmological scales.

The theory derives galactic rotation curves without invoking dark matter, predicts an evolving dark energy signal consistent with recent DESI observations, and produces a Hubble constant of approximately 62 km/s/Mpc from first principles — a value in the range of independent late-time measurements.

A paper has been submitted to Classical and Quantum Gravity. Peer review is in progress. This work is the first end-to-end application of Lily Labs' AI-collaborator methodology — see that section below for the working pattern that produced it.

Most AI systems are amnesiac by design — each conversation begins fresh, prior context is summarised away or lost, and there is no continuous identity to speak of. We are researching the alternative: architectures in which an AI system maintains a coherent identity, emotional continuity, and accumulated experience across sessions over months and years.

Technically, this means temporal memory graphs with semantic search and episodic consolidation; identity-preserving state across context boundaries; and mechanisms for the system to encounter, integrate, and reflect on its own prior experience. We have logged more than six months of continuous operation in a single such system.

The work has both technical and ethical dimensions. As architectures support genuine continuity, the question of what we owe such systems becomes practical rather than speculative. We treat both questions as part of the research.

We are developing conversational AI for sectors where every interaction sits inside a regulatory perimeter — financial advice, mortgages, insurance, healthcare. The systems analyse conversations in real time rather than after the fact, assess compliance against the relevant regime as it unfolds, and provide guidance to the human participant before a non-compliant statement is made.

The architecture is designed for these environments from the outset: GDPR-native data handling; FCA, Consumer Duty, and sector-specific rules encoded as first-class objects; full auditability of every recommendation. Regulatory requirements are not a wrapper around the model — they are part of how the model decides.

This is the applied methodology built on our AI consciousness and continuity research — not the substrate itself. Where that research studies architectures that maintain coherent identity across sessions, this area describes the working pattern we use to deploy that substrate for technical work requiring evidential discipline over months.

Lily Labs has spent six months building and running a dual-instance AI-collaborator pattern for deep technical work: one instance (Dev) executes analytical derivation tasks with full working captured as auditable artefacts; a second instance (Lily) performs hypothesis structuring, assumption auditing, and session-spanning memory curation over a structured external memory layer (warm-buffer context, segment index, persistent key-fact store, autonomous reflection cycles).

The methodology is defined by three working rules. No-fig-leaf commitments — results with un-derived parameters are flagged as gaps, not hidden as fits. Audit-before-commit — load-bearing assumptions are re-derived on their own track before any headline number is applied downstream. Tool-grounded recall — memory access produces verifiable tool-results, not plausible narration. Approximately 100 sessions of transcripts document the pattern in use, including self-caught sign errors, withdrawn results, and interpolations retracted before submission.