Overview
The research group is interested in how we engineer multi-agent systems for reliability and transparancy. We have developed the DOAgent framework for delivering such multi-agent systems. This list of Part III / MPhil projects apply information-topographic ideas to engineered multi-agent and organisational systems using DOAgent. The projects listed serve as ideas, we will run a maximum of two or three projects in this space.
Prerequisite
L172 Information, Energy and Intelligence (IEI), or equivalent preparation in information theory, maximum entropy, and information geometry.
Projects in this theme
Action-Sufficiency Audits for LLM Belief Bottlenecks
Supervisors: Christian Cabrera Jojoa, Neil D. Lawrence
Prerequisite: L172 Information, Energy and Intelligence (IEI), or equivalent preparation in information theory, maximum entropy, and information geometry.
Long-horizon LLM agents cannot keep full histories in context. Recent systems compress interaction into belief states or summaries: ABBEL maintains natural-language belief bottlenecks; CoACT optimises observation compression for next-action preservation (NAP); other work uses mutual-information rate between raw context and compression as a proxy for compressor quality (ABBEL, arXiv:2512.20111; CoACT, arXiv:2607.02911; information-theoretic agentic design, arXiv:2512.21720). Passing NAP or improving accuracy is not the same as preserving an action-sufficient representation: a summary $R = f(O)$ such that $p(y\mid o, a) = p(y\mid R, a)$ for consequences $y$ of available actions. Summaries can preserve the next click while discarding distinctions needed for later escalation — a microscopic form of agentic debt. This project builds audits that separate those failure modes.
Causal Information-Flow Instrumentation for Multi-Agent LLM Systems
Supervisors: Christian Cabrera Jojoa, Neil D. Lawrence
Prerequisite: L172 Information, Energy and Intelligence (IEI), or equivalent preparation in information theory, maximum entropy, and information geometry.
Multi-agent LLM systems are fully instrumentable: every message, tool call, and memory write can be logged. Transfer entropy and directed information are the natural language for directed flow, with recent estimators (TREET; AGM-TE) and early applications to LLM-MAS cascade monitoring. Separately, causal audits of latent channels show that end-task performance does not identify whether receivers actually use transmitted content (message permute / drop / other-example interventions). An information topography needs topographic quantities — conductance proxies, saturation, judgement-junction load — that survive causal checks. This project builds that instrumentation layer on DOAgent-quality traces and refuses to treat a TE heatmap as a result.
Escalation as Conductance Intervention — Measuring Agentic Debt
Supervisors: Christian Cabrera Jojoa, Neil D. Lawrence
Prerequisite: L172 Information, Energy and Intelligence (IEI), or equivalent preparation in information theory, maximum entropy, and information geometry.
Knowledge hierarchies escalate exceptions upward so scarce expertise handles hard cases (Garicano, 2000). Generative AI changes that calculus: automation versus augmentation shifts who faces routine work and who absorbs exceptions (recent organisation-theory models of GenAI in knowledge economies). In engineering, multi-agent systems add relays that act as information bottlenecks — helpful when they remove noise, harmful when they drop task-critical context — while governance layers such as the Organizational Control Layer (OCL) separate proposal generation from environment-facing execution. What is missing is a topographic experiment: encode escalation as a conductance constraint, measure whether the judgement junction is actually exercised, and quantify agentic debt when institutional escalation paths exist on paper but are bypassed in the realised communication graph (parallel channels, aggressive summarisation, prompt injection).
Illegible Protocols under Bandwidth — The Monitorability Frontier
Supervisors: Neil D. Lawrence, Christian Cabrera Jojoa
Prerequisite: L172 Information, Energy and Intelligence (IEI), or equivalent preparation in information theory, maximum entropy, and information geometry.
Classical emergent-communication work showed agents invent efficient but opaque codes under bandwidth limits. LLM multi-agent systems inherit the risk in a new form: under token budgets they can drift from English into shorter protocols even in fully cooperative settings (e.g. GlossoGen); vision-language referential games produce covert signalling; steganography literature studies adversarial opacity. Efficiency work (AgentPrune, Agora-style protocols) reduces redundancy but rarely measures loss of human monitorability — or whether illegibility is merely displaced into tools and memory. The question here is can a communication topography predict loss of monitorable language, and can harnesses preserve judgement without killing performance?
Interface-Junction Diagnostics on Organisational Event Logs
Supervisors: Christian Cabrera Jojoa, Neil D. Lawrence
Prerequisite: L172 Information, Energy and Intelligence (IEI), or equivalent preparation in information theory, maximum entropy, and information geometry.
Process mining recovers process models, bottlenecks, and organisational handoff networks from event logs; recent work adds LLM assistance and BPMN extensions for human–agent collaboration. Hand-off design literature stresses confidence thresholds, accountability, and escalation rules when AI enters hybrid workflows. This project hypothesises that AI adoption outcomes follow from what deployment does to interface junctions — points where format, authority, or fidelity changes between human and machine regimes — and proposes a three-axis diagnostic (sensing, modelling, amplification capacity) grounded in the Viable System Model and the Good Regulator condition. This project makes that diagnostic computational on public event logs, with falsifiable predictions about interventions.
Testing Good Regulator Notions in Agentic Loops
Supervisors: Neil D. Lawrence, Christian Cabrera Jojoa
Prerequisite: L172 Information, Energy and Intelligence (IEI), or equivalent preparation in information theory, maximum entropy, and information geometry.
Conant and Ashby’s Good Regulator Theorem is often quoted as “every good regulator must be a model of the system,” but the bare result mainly yields determinism $H(A\mid S)=0$ among minimal entropy-minimising policies, this is a weak sense of “model.” Recent work strengthens or reframes the claim: Wentworth’s Gooder Regulator (information bottleneck forcing an internal posterior), Virgo et al. (2025) on observer-attributed belief updating for embodied agents, and algorithmic / internal-model principles from control theory. Parallel work on action-sufficient representations argues that a regulator need only preserve distinctions that matter for action consequences. Agentic AI systems have generated excitement, alongside them the term “world models,” is used usually without saying what definition is meant. This project builds a controllable agentic loop and tests which operational definition of “has a model” predicts out-of-distribution failure — and when a checkpoint looks like judgement but is only a frozen attenuator (agentic debt).
Three Geometries of Agency — Crooks, Wasserstein, and Schrödinger Bridges
Supervisors: Neil D. Lawrence, Christian Cabrera Jojoa
Prerequisite: L172 Information, Energy and Intelligence (IEI), or equivalent preparation in information theory, maximum entropy, and information geometry.
The IEI module treats intelligent agency as transport of probability mass and distinguishes three geometries that must not be collapsed: (1) Fisher–Rao / Crooks thermodynamic length (near-equilibrium, dissipation bounded by $\mathcal{L}^2/\tau$); (2) Wasserstein (minimum ground-cost mass transport); (3) Schrödinger bridge (maximum-entropy interpolation; discrete MaxEnt coupling via Sinkhorn). Machine learning has made Schrödinger bridges practical generative tools (Sinkhorn bridges with statistical rates; LightSB-M; SB flow for unpaired translation), usually without asking which geometry explains an agent’s belief updates under metabolic or information cost. This project treats the three geometries as competing scientific explanations of agency, not as interchangeable samplers.
Related Group Projects
Interfaces
The Interfaces research programme develops interpretable, self-sustaining multi-agent AI systems, treating software as the interface between socio-technical needs and AI capabilities, with validation in healthcare and other high-stakes settings.
AI Adoption
The AI Adoption programme studies how institutions take up AI in ways that deliver public value — through public dialogue, practitioner partnership, and emerging theory on judgement, model minimisation, and the information topography of organisational change.