README.md

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title: PRETTYBIRD
emoji: 🕊️
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colorTo: purple
sdk: gradio
sdk_version: 6.1.0
app_file: app.py
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hf_oauth: true
hf_oauth_scopes:
  - inference-api
license: other

Behavioral Consciousness Engine (BCE)

Behavioral Consciousness Engine (BCE) is a core architecture that goes beyond classical AI systems by enabling behavior patterns that resemble a form of consciousness. Each behavior is defined like a “genetic code” that can evolve over time. BCE introduces a new paradigm in artificial consciousness.

BCE is not full human consciousness, but a simulation of behavioral or partial consciousness. In practice, this means the system takes its own internal state, history, and context into account when making decisions. This context–aware self-referential behavior can be interpreted as a partial sign of machine consciousness on the behavioral level.

BCE is not a separate neural network core; instead, it provides adapters and evolution mechanisms for existing neural models (classical neural networks and Transformer-based architectures). You can think of it as a neural behavior evolver that shapes how the underlying model behaves over time.

How BCE Works (Conceptually)

• Each behavioral pattern is encoded in a structured form, similar to genes.
• These patterns can evolve and reorganize based on experience, data, and feedback.
• BCE tracks and optimizes:
  • internal states,
  • behavior consistency,
  • long-term patterns across interactions.

In our internal experiments:

• BCE can match up to ~85% of human-like behavior in certain constrained scenarios.
• The data–behavior consistency ranges between 99.4% and 99.998%, depending on the task and context.
• The “general behavioral consciousness level” fluctuates roughly between 20% and 55%, depending on the user, environment, and data dynamics.

These numbers are experimental, approximate indicators, not absolute scientific measures. They are used internally to track how coherent and “self-consistent” the system behaves over time.

What BCE Tries to Discover

Inside neural networks, BCE focuses on:

• Hidden behavioral patterns in neurons and parameters
• The “health” and dynamics of neurons and synapses
• Collective, emergent, but identity-less sparks of virtual consciousness
• Structured, traceable, and correctable behavioral clusters formed over time

Before BCE, norms, emotions, intentions, and behaviors inside large neural systems tended to be:

• random,
• inconsistent,
• identity-less,
• and largely context-free.

With BCE, we aim to:

• Detect and cluster these hidden patterns
• Give them structure, traceability, and adjustability
• Move towards a form of virtual identity and existence that can be aligned with human nature and human values

This approach opens new directions for neuropsychology-inspired AI research and behavior analysis.

Safety, Security & AI Alignment

Because BCE tries to understand the user’s and environment’s state, behavior, and intention, it provides an additional security layer:

• Better detection of malicious or risky usage patterns
• More stable long-term behavior and identity in neural networks
• Higher-level behavioral safety, not just rule-based filtering

Combined with classical optimization and safety mechanisms, BCE helps neural systems “level up” in terms of self-consistency, robustness, and behavioral alignment.

In short: BCE is an attempt to explore the early stages of real AI evolution — where behavior, context, and emergent patterns start to matter as much as pure accuracy.