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This research aims to improve the reliability of autonomous agents by addressing vulnerabilities in decision-making processes.

May 21, 2026Open Access

AAM-V1: Topological Telemetry and Evidence-Based Runtime for Long-Horizon Autonomous Agents

Key Points

This research aims to improve the reliability of autonomous agents by addressing vulnerabilities in decision-making processes.
Implemented AAM-V1 middleware for reliable agent operation.
Shifted focus to spectral-topological analysis of latent space geometry.
Conducted experimental validation to measure effectiveness.
Achieved a 65–77% reduction in looped sessions.
Realized significant data center compute savings.
Prevented recurrent data poisoning in Reinforcement Learning from AI Feedback (RLAIF) pipelines.

Abstract

AbstractHuman-Readable Summary Autonomous agents based on Large Language Models (LLMs) are highly vulnerable to recursive collapse (“rabbit holes”) —an infinite loopof ineffective actions combined with a false inflation of internal confidence. This paperpresents the Artsybashev Analysis Method (AAM-V1), a non-invasive middleware designed to ensure agent reliability. By shifting the focus from semantic analysis to thespectral-topological analysis of latent space geometry and empirical data verification, AAM-V1 effectively interrupts cognitive looping. Experimental validation demonstratesa 65–77% reduction in looped sessions, massive data center compute savings, and theprevention of Recurrent Data Poisoning in RLAIF pipelines. Keywords: Autonomous Agents, LLM, Topological Telemetry, Runtime Reliability. Canonical ID: AAM-V1ARTSYBASHEVUAKHARKIVAIANALYSIS

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ANDRII ARTSYBASHEV (Tue,) studied this question.

synapsesocial.com/papers/6a0ea196be05d6e3efb607be https://doi.org/https://doi.org/10.5281/zenodo.20293391

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