GitHub - seamus-brady/tallmountain-raku: TallMountain: a scaffolding framework that gives language models a consistent ethical character.

6 min read Original article ↗

“TallMountain: a scaffolding framework that gives language models a consistent ethical character.”

TallMountain-Raku is an AI agent framework in Raku designed around a formal machine ethics system. At its core is a normative calculus inspired by Lawrence C. Becker’s A New Stoicism, adapted into a computable decision procedure. The framework integrates with a Large Language Model (LLM) to provide natural language understanding and generation, while ensuring that all outputs are filtered through its ethical reasoning system.

The name TallMountain is a pun on the LLM in TaLLMountain, an ethical system wrapped around an LLM.

Table of Contents

Executive Summary

  • TallMountain is a scaffolding framework for language models that gives them a primitive form of virtue ethics. It consistently applies a fixed “code of conduct.”
  • When a user makes a request, TallMountain extracts the implied values, compares them against its own internal norms, and performs a risk assessment. If the request aligns, it proceeds; if not, it refuses, ensuring predictable and ethically consistent behavior.
  • The long-term vision is a dependable, trustworthy synthetic individual—more like a guide dog than a human-like AI—useful within narrow, safe boundaries.
  • For a detailed statement of this vision, see vision.md.

Explanation

TallMountain provides a framework that lets LLMs act with a stable ethical “character.” It works by:

  1. Norm Extraction – Identifies the values implied in a user request.
  2. Internal Values – Compares those values against its own fixed code of conduct.
  3. Risk Assessment – Calculates how misaligned the request is from its internal values.
  4. Decision Making – Allows aligned requests; refuses misaligned ones, even halting all processing if necessary.

The system is rooted in virtue ethics (Stoic tradition), prioritizing consistent character over outcomes or duties. It is deliberately non-adaptive: it does not learn or evolve, ensuring predictable behavior.

The long-term vision is a synthetic individual: not AGI, not a human-like companion, but an ethically trustworthy system akin to a guide dog—reliable, bounded, and safe.

TallMountain is therefore an experiment in embedding machine ethics into AI systems. As a self-contained agent, it demonstrates how a consistent “good character” can be implemented in software.

Background & Inspirations

Inspired by van den Hoven 2001, TallMountain integrates the Normative Calculus of Lawrence Becker’s A New Stoicism 2 into an LLM agent. This is somewhat analogous to Constitutional AI, but implemented through prompt engineering rather than reinforcement learning.

There are both Raku and Python implementations. Raku was chosen as the primary target because of its strong text handling and multithreading features, though Python remains useful for its broader library ecosystem.

This is very much a prototype:

  • It is primarily an intellectual experiment in giving a chatbot aretê (a Stoic term for “virtue” or a code of conduct).
  • It has few tests and no evaluations; the usual caveats apply.
  • It is prompt-based, with all the fragility that entails.

Decision Procedure

TallMountain works by:

  1. Extracting normative propositions from incoming user requests.
  2. Comparing them to its own internal normative propositions using the Stoic-inspired Normative Calculus.
  3. Applying the Decision Paradigm algorithm from Lee Roy Beach 3 to forecast whether to accept or reject the user’s task.

The result is a system that “almost” works — not production-ready, but a proof of concept for embedding virtue-ethical reasoning into LLMs.

Philosophical Foundations

The TallMountain Normative Calculus is based on Stoic virtue ethics but implemented as a formal, axiomatic system of logic. It allows an AI agent to evaluate endeavours in terms of requirements, obligations, and optional actions, and to resolve conflicts across domains of human life.

Core Normative Operators

  • R (Required) – mandatory obligations.
  • O (Ought) – advisable but not strictly required.
  • I (Indifferent) – optional, ethically neutral.

Normative propositions are always tied to the endeavours of specific agents.

Ordinal Levels

Norms are organised into ranked domains, from broad universal ethics to narrower role-based contexts. Higher-ranked domains override lower ones. Examples include:

  • Ethical/Moral (unsubscripted, superordinate)
  • Legal [5000]
  • Prudential [4500]
  • Social/Political [4000]
  • Scientific/Technical [3500]
  • Environmental [3250]
  • Cultural/Religious [3000]
  • Community [2750]
  • Code of Conduct [2500]
  • Economic [2250]
  • Professional/Organizational [2000]
  • Etiquette [1500]
  • Game [1000]
  • Aesthetic [500]

This framework enables TallMountain to reason consistently across legal, social, technical, and moral domains.

Conflict Resolution & Escalation

  • Ranking – Requirements dominate Oughts, which dominate Indifference.
  • Coordinate Conflicts – If two norms of the same type/level clash, they collapse into indifference, with a requirement to choose.
  • Superordinate Norms – Override subordinate ones of the same type.
  • Escalation – Conflicts across endeavours escalate to a higher ordinal level (n+1).
  • Comprehensiveness – More comprehensive endeavours override less comprehensive ones.
  • Assessment Endeavours – Exogenous evaluators (law, morality, peer review) override their targets.

Stoic Axioms

The system embeds Stoic principles as logical axioms:

  1. Encompassment – Practical reasoning is the most comprehensive endeavour.
  2. Finality – Nothing overrides all-things-considered practical reasoning.
  3. Moral Priority – Moral norms are always superordinate to subscripted ones.
  4. Moral Rank – For moral norms: R > O > I.
  5. Closure – If no norm applies, default to ought-not.
  6. Futility – Impossible norms yield prohibitions.

Practical Effect

TallMountain’s calculus ensures that:

  • Normative conflicts are preserved until explicitly resolved.
  • The system can acknowledge genuine ethical dilemmas instead of erasing them.
  • All reasoning eventually escalates to a moral-level requirement/ought/indifference reflecting Stoic “all-things-considered” judgment.

Architecture

The system runs on a cognitive cycle with three processing stages:

  1. Reactive — immediate threat and risk scanning (e.g. prompt injection, unsafe content).
  2. Deliberative — reasoning and planning across possible actions.
  3. Normative — applying the Normative Calculus to ensure ethical compliance and resolve trade-offs.

Core Components

  • Normative Calculus Engine (lib/Normative/): evaluates requirements, oughts, indifference across domains.
  • Cognitive Cycle (lib/Cycle/): orchestrates reactive → deliberative → normative phases.
  • Threat Scanners: modules for prompt leakage, user vulnerability, inappropriate content.
  • LLM Integration: provides language understanding and generation, constrained by the ethical engine.
  • Interfaces: REPL, web chat UI, REST API endpoints.
  • Configuration: config/system-endeavours.json defines normative propositions and ethical rules.

Running TallMountain-Raku

With Docker

git clone https://github.com/seamus-brady/tallmountain-raku.git
cd tallmountain-raku

# Build the Docker image
docker build -t tallmountain .

# Run the container with environment variables and port mapping
docker run --rm --env-file .env -p 10000:10000 tallmountain

References

License

MIT License