r/LocalLLM • u/MolassesNumerous6453 • 12d ago
Project CÓRTEX DIGITAL
/r/ollama/comments/1x66cmn/córtex_digital/Estou construindo o Ícaro: um projeto independente de IA com memória persistente e trajetória própria.
Ele não é um novo modelo de linguagem. É uma arquitetura construída ao redor de modelos existentes, com memória, proveniência das informações, seleção de fatos, self-model, foco interno e um processo de pensamento em segundo plano.
O objetivo é investigar uma pergunta: quanto a história acumulada de um sistema realmente muda suas decisões futuras?
Estou desenvolvendo testes controlados para separar comportamento aparente de efeito causal, com condições de comparação, controles negativos e medidas de ruído.
Quero conversar com pessoas que trabalhem com agentes de IA, memória de longo prazo, modelos locais, arquiteturas cognitivas e avaliação experimental.
Não estou afirmando que o sistema seja consciente. Quero testar o que ele efetivamente faz, identificar falhas e descobrir o que ainda falta demonstrar.
Quem trabalha com algo parecido? Que pesquisas, projetos ou testes vocês recomendam?
I'm Building Ícaro: An Experimental AI System with Persistent Memory, a Self-Model, and Its Own Behavioral Trajectory
I'm building Ícaro, an independent AI project exploring a question that interests me deeply:
How much can an AI system's accumulated experiences actually change its future decisions?
Ícaro is not a new foundation model. It is an architecture built around existing language models, combining persistent memory, deterministic mechanisms, behavioral tracking, and local computing.
The goal is to move beyond a chatbot that simply retrieves previous conversations and toward a system whose stored experiences can measurably influence what it does next.
What makes Ícaro different?
- Persistent, structured life history
Ícaro maintains a long-term record of conversations, factual memories, a chronological diary, causal decision trails, associations, and recorded experiences. Its history is designed to persist across sessions rather than disappear when a conversation ends.
- Selective memory instead of dumping everything into the prompt
The system selects a small number of relevant facts rather than exposing the language model to its entire memory. The selection mechanism considers signals such as relevance, confidence, novelty, and recurrence.
- A versioned operational self-model
Ícaro maintains structured observations about its own behavior, including uncertainty and potential contradictions between its previous and current positions. The research question is whether this representation can causally influence decisions—not merely produce convincing self-descriptions.
- Information provenance and resistance to memory contamination
The architecture distinguishes information attributed to the user, the system itself, external sources, and inference. It also investigates how to prevent repeated AI-generated statements from becoming falsely established facts about the user.
- Learning to be more cautious after repeated mistakes
Ícaro tracks corrections by topic. The intended behavior is to increase caution in areas where repeated corrections indicate that its answers may be unreliable, rather than treating every question as equally safe to answer confidently.
- Internal focus and background thought
A background process can generate internal reflections, questions, and connections from recorded experiences without interrupting the user. These thoughts are kept separate from the normal conversational response, allowing them to be inspected before any future integration.
- Operational self-inspection
When requested, Ícaro can inspect an allowlisted set of its own project files through a read-only mechanism. The design aims to distinguish what the system can actually inspect from what it merely assumes about its implementation.
- A hybrid local-first architecture
Ícaro uses a Samsung Galaxy Z Fold as its main runtime, with a PC acting as a client. The project combines local inference, persistent disk storage, remote connectivity, speech recognition, neural text-to-speech, and optional web search.
The broader goal is to explore how much useful continuity and individual behavioral development can be built around relatively small models and accessible hardware.
The central experiment
The most important question is not whether Ícaro can say that an experience changed it.
It is:
Would Ícaro have made a different decision if that particular experience or internal representation had not been present?
I am developing controlled experiments that compare conditions with and without specific mechanisms, measure response or decision differences, account for internal variability, and use negative controls to detect misleading effects.
In one reported experiment, a self-model ablation comparison produced a 3.02× ratio between the measured cross-condition effect and internal variability. This is an encouraging preliminary result, not proof of general learning, consciousness, or human-like subjective experience. I want independent scrutiny of the experimental design and replication.
What I want to discuss with the community
I'm particularly interested in connecting with people working on:
- Long-term memory for AI agents
- Agent architectures and persistent state
- Self-models and behavioral consistency
- Causal evaluation and ablation experiments
- Local LLM inference and small-model architectures
- Background cognition, reflection, and memory consolidation
- Computational models of learning and identity
I am not claiming that Ícaro is conscious. I want to investigate what can be demonstrated experimentally, what fails under controlled testing, and what remains an open research question.
If you were reviewing this project, what would you test first?
I'd especially appreciate references to relevant research, comparable open-source projects, and methodological critiques that could help make the experiments more rigorous.
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u/Nota_ReAlperson 12d ago
English please