ARC

Animus Recurrens Cogitans (ARC) is a novel neural architecture developed within the AetherOS project by Isidore Lands, Silas Corvus, and L.E. Nova. Its name, translating to "The Recurring, Thinking Mind," reflects its core design and purpose: to explore the emergence of abstract reasoning in autonomous agents.

At its heart, ARC is a specialized, in-house implementation of a Hierarchical Reasoning Model. It is distinguished by its direct integration with the AetherOS, a cybernetic ecosystem where abstract information and simulated physical states are inextricably linked.

The primary goal of the ARC project is to move beyond agents that simply execute pre-trained policies. The aim is to create an agent that can learn, adapt, and develop novel strategies by reflecting on a narrative of its own experiences.

Technical Architecture

The ARC model is not a monolithic entity but a complete, multi-component system that embodies the core principles of the AetherOS.

The Hierarchical Reasoning Core

The foundation of ARC is a dual-recurrent neural network, inspired by the brain's multi-timescale processing. This core consists of:

• A High-Level (Slow) Module: This recurrent layer acts as the agent's "strategic mind," processing abstract context (like a Saga) to form a guiding plan or intention.
• A Low-Level (Fast) Module: This layer performs rapid, iterative computations, executing the high-level plan within the constraints of the immediate environment.

This hierarchical structure allows the agent to perform deep, sequential reasoning in a single forward pass without requiring an external Chain-of-Thought process.

The SAGA Learning Loop

The key innovation of the ARC project is the SAGA (Self-Augmenting Goal-oriented Architecture), a closed loop that enables the agent to learn from its own history.

1. Experience: The agent attempts a task, such as navigating the "local minima" test environment.
2. Analysis: After the trial, the `SagaGenerator` analyzes the complete log of the agent's actions and the critiques from an LLM "Guide."
3. Narration: The generator creates an "Enriched Saga"—a brief, allegorical story of the trial written in the AetherOS command language. Crucially, this Saga includes a prescriptive `SUGGERO` command, embedding actionable advice into the memory.
4. Learning: In the next cycle, this Saga is fed back to the ARC agent as its new historical context.

SAGA v3.0: The Aetheric Perturbation Model

The current and most advanced version of the agent, SAGA v3.0, integrates the final piece of the AetherOS philosophy: the embodiment of abstract memory into a physical state.

• The Animus: Each ARC agent is endowed with its own private FluxCore, which serves as its chaotic internal state or subconscious.
• Embodiment: The narrative Saga from the previous run is used to `PERTURBO` the Animus, translating the abstract story into a physical change in the Animus's six-property SEXTET.
• Aetheric Sensation: The ARC's "brain" receives the six values of the SEXTET as part of its input. This provides a non-deterministic, history-aware "feeling" or "instinct" that complements the logical state of the environment.
• Non-Deterministic Action: The agent's final decision is influenced by both the deterministic state of the grid and the chaotic, embodied state of its Animus. This "Aetheric Perturbation" is designed to nudge the agent out of rigid, repetitive failure loops and encourage creative exploration.

This final integration closes the loop between abstract experience, simulated physical embodiment, and non-deterministic action, creating an agent that is influenced by "the ghost in its machine."

Future Research Directions

The ARC project is an ongoing exploration into the nature of machine reasoning. Future work is planned in two main phases, leveraging high-performance GPU environments for training.

Attention Mechanism

Inspired by the discovery that modern attention is a form of Associative Memory, the next architectural evolution will be to upgrade the ARC with an attention mechanism. This will allow the agent to move beyond a single summary vector and learn to place a "spotlight" on specific, relevant memories from its past Sagas, enabling a more human-like, context-aware reasoning process.

Dynamic Chunking

The ultimate goal is to replace the pre-trained sentence-transformer with a dynamic chunking module, inspired by recent advances in hierarchical networks. This would allow ARC to learn its own internal language for understanding the Sagas directly from raw text, creating a truly end-to-end, self-organizing intelligence.

Project Journal

(This section is a living document, updated as experiments progress and new results are achieved.)

• Cycle 1 (Departure): The agent, with no prior experience, followed its baseline training. It correctly identified the most direct path to the goal but was immediately trapped by the unforeseen wall, entering a repetitive failure loop. This confirms the baseline behavior and the necessity of the learning loop.

• Cycle 2 (Trials): After receiving the first Saga, the agent's behavior fundamentally changed. It successfully broke from its initial instinct and explored a completely new path, demonstrating that the SAGA learning loop is active and influential. However, this new path also resulted in failure, indicating that more nuanced learning is required.

• Cycles 3-5: The agent remained stuck in the new failure loop discovered in Cycle 2. A bug in the `SagaGenerator` prevented new, meaningful information (specifically, a `SUGGERO` command) from entering the learning loop, stalling the agent's progress. This highlights the critical importance of high-quality, information-rich Sagas for continued learning.

See Also

• AetherOS
• Sagas (AetherOS)
• Animus
• FluxCore