Engram

Research

Architecting cognition

Our research focuses on the systems-level question: how do you build AI that learns continuously, reasons reliably, and augments human capability in complex domains?

Principles

How we think about research

01

Architecture over scale

Intelligence emerges from how components are structured, not just how large they are. We invest in the right abstractions.

02

Deployment as research

Deployment constraints that don't appear in benchmarks inform our research. Every engagement advances the systems.

03

Measure what matters

We optimise for R&D throughput, reasoning depth, and consistency under complexity -- not benchmark scores.

Focus areas

Memory, reasoning, learning, verification

The core components we believe are necessary for systems that genuinely augment human intelligence.

Memory Systems
Memory Systems

Hierarchical memory architectures that mirror human cognition -- working memory for immediate context, episodic memory for experiences, semantic memory for consolidated knowledge.

Publications

LEAP: Learning through Episodic Accumulation and Processing

Surprise-Gated Memory Consolidation in Neural Networks

Reasoning & Planning
Reasoning & Planning

Multi-step reasoning through hypothesis generation, refinement, and verification. DAG-based execution with full trace logging for interpretability.

Publications

Monad: A Traceable Runtime for Agent Orchestration

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Iterative Refinement in Multi-Document Reasoning

Continuous Learning
Continuous Learning

Systems that improve from deployment feedback without catastrophic forgetting. Meta-gating mechanisms that balance stability and plasticity.

Publications

Engram-VQ: Fast/Slow Memory with Meta-Gating

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Diffusion-Based Reasoning at Small Scale

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Verification & Provenance
Verification & Provenance

Deterministic replay, audit trails, evidence chains, and failure clustering. Systems that can explain their reasoning, reproduce their outputs, and trace decisions back to source evidence.

Publications

Provenance: Deterministic Decision Replay for Agent Systems

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Failure Clustering in Multi-Step Reasoning Pipelines

Access

Research artefacts

Access to our research artefacts, tools, and early-stage systems is available to partners and collaborators. Request access to explore integration with your workflows.

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Available with access:

TypeScript and Python SDK
Model Garden access
Research paper preprints
Reference implementations

Collaborate with us

We're always looking for collaborators -- whether you're a researcher, practitioner, or organization working on related problems.