Latest Paper: Philosophical Hyperparameters, Grounding LLMs via Configurable Ethical and Philosophical Frameworks
Today’s AI systems behave—but they don’t believe.
Their alignment is reactive, shaped by post-hoc constraints and surface-level safety tuning rather than stable, principled grounding.
Philosophical Hyperparameters introduces a new approach to AI alignment, treating ethical and philosophical frameworks not as abstract guidance, but as configurable control parameters for behaviour. Instead of training models toward a single, risk-averse norm, the paper demonstrates how large language models can be grounded in explicit ethical worldviews that act as a moral operating system.
The paper presents a proof-of-concept system in which LLMs are configured with 55 distinct philosophical frameworks, spanning classical ethics, religious traditions, and modern meta-ethical positions. Each configuration is evaluated across 20 complex dilemmas and quantitatively measured along 11 behavioural axes, including harm avoidance, risk appetite, autonomy, rule adherence, and collective good orientation. This produces a multidimensional behavioural signature for each framework, allowing alignment to be measured, compared, and audited rather than assumed.
The results show that LLMs can exhibit deep, coherent, and repeatable behavioural alignment when grounded in explicit philosophical convictions. These behaviours cluster into a small number of emergent moral regimes—such as harm-minimisation absolutism, risk-on activation, and rule-bound legalism—revealing a new design space for controllable, interpretable AI behaviour. The work reframes alignment as configuration rather than constraint, offering a path toward AI systems whose decisions are transparent, intentional, and anchored in known ethical postures rather than opaque optimisation pressures.
Cognitive Geometry for Agentic Memory Systems: Bridging Neural Correlation and Symbolic Reasoning
Today’s AI systems remember—but they don’t think.
Their memories function as static repositories, not dynamic structures of understanding.
Cognitive Geometry in Memory Systems introduces a new way to design memory for AI—one that transforms information into reasoning. The paper presents a four-layer cognitive memory model that maps different types of knowledge to unique geometries:
Facts → Knowledge Webs: semantic graphs that enable deep associative recall.
Experiences → Semantic Temporal Knowledge Webs: time-indexed graphs that preserve sequence, context, and meaning.
Procedures → Causal Trees: hierarchical chains of cause, effect, and subgoal.
Personalisation → Forests of Knowledge Trees: adaptive, individualised reasoning spaces.
This geometry creates a bridge between neural memory (pattern-based learning) and symbolic memory (logic-based reasoning)—allowing systems to store, transform, and reason over knowledge dynamically.
The result is a neuro-symbolic substrate that tackles long-standing LLM challenges: catastrophic forgetting, weak interpretability, and the inability to learn continuously. It reframes memory as the cognitive engine of agentic AI.
My Approach
What began as a passion project has evolved into something more. We’re proud of where we’ve been and even more excited for what’s ahead. What sets us apart isn’t just our process—it’s the intention behind it. We take time to understand, explore, and create with purpose at every turn.
Simple ideas
Through every step, we've focused on staying true to our values and making space for thoughtful, lasting work.
Lasting impact
We build with clarity, act with integrity, and always stay curious.