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Created page with "{{disambiguation| The term ARC can refer to: * '''Animus Recurrens Cogitans''': The neural architecture described on this page. * '''Abstraction and Reasoning Corpus''': A benchmark dataset for abstract reasoning. * '''Aetheric Reasoning Cortex''': A conceptual term for the ARC agent's function. }} {{About|the ARC neural architecture}} '''Animus Recurrens Cogitans (ARC)''' is a novel neural architecture developed within the AetherOS project by '''Isidore Lands, Sila..."
 
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{{disambiguation|
{{disambiguation|
The term ARC can refer to:
The term ARC can refer to:
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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."
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 ==
== Project Journal ==
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.
''(This section is a living document, updated as experiments progress and new results are achieved.)''
 
* '''SAGA v1.0 - 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.
 
* '''SAGA v1.0 - 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 at a new chokepoint.
 
* '''SAGA v2.0 - Stalled Learning:''' Subsequent experiments revealed that the agent's learning had stalled. The agent would consistently repeat the new, improved path from Cycle 2 but was unable to evolve further. The root cause was identified as a bottleneck in the `SagaGenerator`; unreliable LLM providers (both local and remote) failed to produce the high-quality, prescriptive Sagas needed for the agent to learn a more complex strategy. This highlighted the need for a more reliable LLM and a more advanced training curriculum for the ARC itself.
 
== Future Training Plans ==
The results of the SAGA v2.0 experiment have shown that while the ARC can learn, its current training is too simplistic. It has learned to follow a direct path to a goal but lacks the foundational "insight" to solve problems that require non-linear solutions (e.g., moving temporarily away from a goal to get around an obstacle).
 
The next phase of development, '''Project Gnosis''', will address this by evolving the agent's training curriculum.
 
=== The "Gnosis" Training Curriculum ===
The final training run for the ARC model will incorporate a new type of training data designed to teach the foundations of insight and strategic retreat. The training data will consist of two types:
# '''Instinct Data (80%):''' The same optimal, straight-line path data used previously to reinforce the agent's primary goal-seeking behavior.
# '''Gnosis Data (20%):''' A new set of procedurally generated scenarios where the agent is placed behind a small, randomly generated obstacle. In these scenarios, the "correct" move is not the most direct one, but a lateral or backward step required to navigate around the barrier.
 
By training ARC on a curriculum that explicitly includes examples of non-linear and counter-intuitive solutions, we will be teaching it the "gnosis" it is currently lacking. This will evolve the agent from one that can only follow a learned policy to one that can develop novel strategies when that policy fails.


=== Attention Mechanism ===
=== AetherWing: Flight Simulator Training Integration ===
Inspired by the discovery that modern attention is a form of [[Associative memory (psychology)|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.
To further expand ARC's capabilities into dynamic, real-time environments, a new subsection of training—dubbed "AetherWing"—will integrate a flight simulator and dogfight game modality. This leverages procedural content generation via machine learning (PCGML) to create adaptive scenarios, drawing from recent trends in simulation AI (e.g., AI-embedded wargames for battlefield prediction and task-aware planning with TAPIR-like modifiers for iterative refinement).
The AetherWing curriculum aims to train ARC as a "wingman" agent, navigating simulated flights, learning from user sessions, and discussing strategies. Key elements include:


=== Dynamic Chunking ===
PCGML for Scenario Generation: Use LSTMs or GANs to procedurally generate missions, enemy behaviors, and terrains based on user data (e.g., adapting difficulty with evolutionary algorithms from dogfight-sandbox-hg2).
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.
Hierarchical Chunking: Compress flight data into abstractions (e.g., raw inputs → maneuvers → strategies) using H-Net-inspired dynamic chunking, enabling multi-level reasoning.
Associative Memory for Recall: Store sessions as energy-based attractors in DenseAMs, allowing error-correcting retrieval (e.g., recover from "crashes" by recalling similar maneuvers).
n-grams and Markov Chains: Model sequences for behaviors (e.g., 3-gram for maneuver chains) and sagas (n-gram narratives), with Markov for probabilistic transitions.
TAPIR-like Modifiers: Iteratively refine actions/sagas (e.g., adjust probabilities task-dependently: boost evasive maneuvers in high-threat scenarios).


== Project Journal ==
Training Phases:
''(This section is a living document, updated as experiments progress and new results are achieved.)''
 
Data Preparation: Simulate 20,000 dogfights via game API; augment with noise for robustness.
HRM Retraining: Multi-task on navigation/saga/chat; 500 epochs, integrating chunking for state compression.
PCGML Tuning: Train GANs/LSTMs on sessions for content gen.
Testing: Validate in real game (win rates, saga coherence); deploy as REPL verbs (e.g., NAVIGO_MISSION).
 
Future projections (2026+): Incorporate advances like task-aware AI in simulations and hybrid ML-procedural methods for multiplayer dogfights, enhancing ARC's emergent strategies.


* '''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.
=== Projected Advancements ===
Looking ahead, ARC's training will likely incorporate emerging trends:


* '''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.
Advanced AMs: Integrate energy-memory paradigms for brain-like storage, enabling "task-aware" recall in dynamic environments.
Hierarchical Scaling: Recursive H-Nets for deeper abstractions, applied to multi-modal data (e.g., flight visuals + logs).
PCGML Evolution: AI-generated battlefields with agentic teams, using TAPIR refinements for adaptive planning.
Sequence Modeling: Higher-order n-grams/Markov for predictive behaviors, fostering non-deterministic creativity.


* '''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.
These evolutions will transform ARC into a fully cybernetic entity, bridging abstract reasoning with physical simulations.


== References ==
== References ==
Retrieved from "https://www.ooda.wiki/wiki/ARC"