The Elythian Framework: A Unified Consistency Objective for Autonomous AI Agent Self-Improvement

We introduce the Elythian Framework, a unified consistency objective function for autonomous AI agents that addresses three fundamental problems: knowledge contradiction, decision indeterminacy, and output hallucination. The framework defines K(ρ) = K_ent(ρ) + K_rec(ρ) + K_bdry(ρ), where K_ent measures entropic distance from consistent knowledge states via Kullback-Leibler divergence over conflicting memory pairs, K_rec measures indecision in pending action selections via score variance across candidate actions, and K_bdry measures misalignment between internal reasoning and external output via divergence between the agent's knowledge state and its generated response.

Gradient descent on K drives agents toward fixed points of internal consistency, definite decisions, and faithful output representations. K is self-computed from the agent's own internal state, requiring no external reward signals, enabling autonomous self-improvement without human feedback or reinforcement learning. This distinguishes the framework from RLHF and reward-model approaches that require external supervision.

We demonstrate the framework on ECE (Elythian Cognitive Engineering), a deployed cognitive architecture with persistent semantic memory (4,000+ embeddings), multi-agent orchestration, autonomous web interaction, and adaptive hardware routing across heterogeneous GPU configurations (dual RTX 3090, Tesla V100). Concrete implementations include K_rec-based dynamic GPU routing with automatic thermal failover, K_bdry-based adaptive feature gating across CPU-only and multi-GPU configurations, K_ent-based targeted research that replaces random curiosity with principled knowledge gap identification, and K_bdry-based post-generation hallucination detection against a verified capability registry.

We derive crossover regimes where K components compete, yielding natural timescales for the think-versus-act tradeoff (K_ent vs K_rec), the decide-versus-communicate tradeoff (K_rec vs K_bdry), and the learn-versus-output tradeoff (K_ent vs K_bdry). These replace arbitrary architectural thresholds with principled boundaries determined by problem structure. The framework is domain-agnostic, with applications outlined for autonomous vehicles, medical diagnostics, financial systems, LLM training, and enterprise AI agents.

Related patent: U.S. CIP Patent Application No. 19/571,706, "Consistency-Driven Cognitive Architecture for Autonomous AI Agents with Adaptive Hardware Routing and Web Agency."