Papers & Ideas Alicia Draws From

My Alicia stands on the shoulders of researchers, designers, and frameworks that saw something the rest of us hadn't yet. This page lists the papers, concepts, and intellectual foundations that shaped the project — every one with a link to the source.

The fuller version with extended "how myalicia uses it" descriptions is in the repo at docs/PAPERS.md.

Foundational

Humorphism

Source: humorphism.com · Geordie Kaytes' write-up on Lab Leaks

The design philosophy that technology should take the shape of the human, not the other way around. Stop building chatbots everyone learns to talk to; start designing relationships that learn the person they're with. The entire three-loop architecture is humorphism made buildable.

Thin Harness, Fat Skills

Source: Garry Tan on X · GitHub

Architectural pattern for agent design: a small, opinionated orchestration core surrounded by many composable skill modules. Visible in the folder structure: myalicia/core/ stays tiny while myalicia/skills/ holds ~80 modules. The core stays out of the way so the skills can be where the personality lives.

Autoresearch

Source: Andrej Karpathy on X · GitHub

The pattern of autonomous research loops where an agent runs reflection and synthesis on its own, without human prompting. The Notice and Know loops are myalicia's local, personal, single-user version of autoresearch — synthesis runs while you sleep, novelty detection on conversations, weekly deep passes over the full vault.

Module-Level Frameworks

Papers that name or directly shape specific skill modules.

Reflexion: Language Agents with Verbal Reinforcement Learning

Authors: Shinn et al., 2023 · ArXiv: 2303.11366

Agents critique themselves after tasks and store linguistic feedback for future episodes. Implemented in reflexion.py and meta_reflexion.py.

Constitutional AI: Harmlessness from AI Feedback

Authors: Bai et al., 2022 (Anthropic) · ArXiv: 2212.08073

Self-criticism and revision against a set of principles. Implemented in constitution.py — outputs are scored against an evolving constitution before being acted on.

Generative Agents: Interactive Simulacra of Human Behavior

Authors: Park et al., 2023 (Stanford) · ArXiv: 2304.03442

Multi-timescale reflection loops in agent architectures. The three-cadence design (Listen / Notice / Know) draws on this work's insight that agents need different reflection horizons to behave consistently over time.

ReAct: Synergizing Reasoning and Acting in Language Models

Authors: Yao et al., 2023 · ArXiv: 2210.03629

Interleaving reasoning traces and acting. The handle_message pipeline mirrors this pattern — every message routes through reasoning steps before any tool is called. The tool_router is the action-selection layer.

Voyager: An Open-Ended Embodied Agent with LLMs

Authors: Wang et al., 2023 (NVIDIA) · ArXiv: 2305.16291

Self-extending agents that author new skills based on observed gaps. Inspires skill_author.py — when My Alicia notices a gap in her own behavior, she can draft a new skill module to handle it.

Toolformer: Language Models Can Teach Themselves to Use Tools

Authors: Schick et al., 2023 (Meta) · ArXiv: 2302.04761

Self-supervised tool-use selection. Background influence on tool_router.py — the function-calling dispatcher that chooses which skill to invoke given a message and context.

Tree of Thoughts: Deliberate Problem Solving with LLMs

Authors: Yao et al., 2023 · ArXiv: 2305.10601

Tree-search over reasoning paths. Background influence on the metacognition module's confidence-driven escalation: when the Listen-loop response confidence is low, the request escalates to a deeper Sonnet/Opus pass that explores more thoroughly.

Background Influence

Ideas that shaped the project's worldview. Less direct than the module-level citations, but the influence is visible in the code.

Anthropic's Tool Use & Model Card

Source: Anthropic Tool Use docs

Function-calling primitives that tool_router.py wraps. The whole tool-execution loop in handle_message uses Anthropic's tool-use schema.

Intrinsic Motivation in Agents

Source: Schmidhuber's curiosity work

Curiosity-driven exploration research. The curiosity_engine.py uses three novelty signals derived from this tradition.

Knowledge Graphs / Personal Knowledge Management

Source: Obsidian · Andy Matuschak's notes

Vault structure assumptions and the link-as-first-class-citizen pattern. graph_intelligence.py builds on these by surfacing missing or weak links between notes the user has accumulated.

The Anthropic Claude API

Source: docs.claude.com

The runtime that makes all of this possible. Three model tiers (Haiku / Sonnet / Opus) map directly to the three loops; the metacognition layer escalates between them.

Caveat

The lineage above is honest but partial. As the project grows and contributors add awareness primitives drawing on different traditions, this page will grow. If you see a connection we should add (or a citation we got wrong), open an issue.

For more on the specific implementation of each, the source code's docstrings are often the clearest reference — most skill modules cite the work that inspired them at the top.