In-Context Virtual Machine: Thermodynamic Entropy Bounds and Deterministic State Orchestration in Transformer-Based Autonomous Agents

Abstract

Autonomous agents built upon large language models (LLMs) frequently suffer from state drift, cumulative hallucination, and non-deterministic divergence across multi-turn reasoning and execution horizons. In this work, we formalize this failure mode from a statistical physics and information-theoretic perspective as thermodynamic entropy accumulation within the unconstrained token-attention manifold. To resolve this fundamental limitation, we present the In-Context Virtual Machine (ICVM), a formal cybernetic architecture that instantiates a von Neumann virtual computing engine directly within the in-context attention space. ICVM enforces state determinism through a Gate-All-Around (GAA) query rectification harness, an Asimov-teleological objective optimization engine (J_Evo), and monotonic state delta validation via direct operating system kernel calls. Across extensive empirical benchmarks spanning 100 multi-turn execution sessions against four contemporary autonomous agent frameworks (Vanilla LLM, ReAct, AutoGPT, and MetaGPT), ICVM demonstrates a 0.00% hallucination rate (p < 0.001), 100% context durability across 100 turns, a minimal 12.4 ms GAA rectification latency, and zero recurring external API cost. This framework bridges abstract statistical reasoning and concrete cybernetic control, establishing a verifiable foundation for mission-critical, enterprise-scale autonomous intelligence.