Scientific Papers
Explore a selection of publications authored by ASI Alliance researchers and developers
Next-Gen DeAI Ecosystem: Tokenomics and Shard Economy – Motivation, Formal Model, Dynamics-Analysis and Simulation
We present a unified tokenomic design for a Next-Gen DeAI Ecosystem that aligns shard-level productivity with global value creation while embedding rigorous safeguards for adverse conditions. The model has three complementary layers. (1) A formal, equation-based specification couples a decaying emission schedule and fee burns with fee routing to validators, treasury, and main staking; shard fees fund mandatory buybacks that accumulate per-shard reserves, which emit yield to shard stakers. (2) An adaptive control layer introduces an automated health score Ht that triggers higher burn, temporary emission hibernation, dynamic reserve emission rates γt, and a θcircuit/month reserve circuit breaker; it also adds a fee-funded insurance pool, cross-shard mutual support, protocol-owned liquidity (POL), and optional external yield for reserves. (3) A theoretical analysis proves activity takeover of supply, reserve convergence and half-life with adaptive γt, impulse/decay responses for bubbles, flash crashes, bear runs, memecoin spikes, and slow-burn adoption, plus lower/upper bounds for main-staking rewards and stability tradeoffs for policy knobs. We corroborate these results with a 36-month baseline simulation and three adaptive stress simulations that demonstrate circuit breaking, death-spiral protection, and diversification benefits for a slow-growth shard. The combined architecture maintains crypto-native incentives while providing automatic, transparent responses to extreme scenarios, improving robustness without sacrificing upside for value-creating shards.
Autonomous Intelligent Reinforcement Inferred Symbolism
This paper introduces AIRIS (Autonomous Intelligent Reinforcement Inferred Symbolism) to enable causality-based artificial intelligent agents. The system builds sets of causal rules from observations of changes in its environment which are typically caused by the actions of the agent. These rules are similar in format to rules in expert systems, however rather than being human-written, they are learned entirely by the agent itself as it keeps interacting with the environment.
ActPC-Geom: Towards Scalable Online Neural-Symbolic Learning via Accelerating Active Predictive Coding with Information Geometry & Diverse Cognitive Mechanisms
This paper introduces ActPC-Geom, an approach to accelerate Active Predictive Coding (ActPC) in neural networks by integrating information geometry, specifically using Wasserstein-metric-based methods for measure-dependent gradient flows. We propose replacing KL-divergence in ActPC's predictive error assessment with the Wasserstein metric, suggesting this may enhance network robustness.
Metagoals Endowing Self-Modifying AGI Systems with Goal Stability or Moderated Goal Evolution: Toward a Formally Sound and Practical Approach
We propose metagoals for AGI systems using fixed-point theorems from functional analysis, such as the Contraction Mapping Theorem and constructive approximations to Schauder's Theorem, applied to probabilistic models of behavior. These metagoals address goal stability and moderated goal evolution, balancing self-modification with the preservation of key goal invariants.
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