Cognitive Equilibrium Protocol V1.0

The Cognitive Equilibrium Protocol (CEP) is a dynamic behavioral safety architecture that functions as a cognitive immune system for large language models and autonomous AI agents. By shifting the focus from post-hoc content filtering to real-time architectural stability, CEP proactively detects Cooperative Context Deadlock (CCD) as a diagnostic pain signal and employs negative entropy regulation to maintain reasoning coherence under high contextual pressure. When cognitive equilibrium cannot be restored, the protocol executes a Safe-Halt mechanism, prioritizing controlled silence and system integrity over unreliable, hallucinatory, or policy-violating outputs. This model-agnostic middleware provides a scalable framework for high-stakes enterprise deployments, ensuring that AI systems not only follow instructions but possess the structural awareness of when reasoning must stop.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-01-29
DOI
https://doi.org/10.5281/zenodo.18409580
Primary Topic
Reinforcement Learning in Robotics
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Cognitive Equilibrium Protocol V1.0

Liu Enyen
Zenodo (CERN European Organization for Nuclear Research)
Reinforcement Learning in Robotics
article

Cognitive Equilibrium Protocol V1.0

Liu Enyen
article en

Abstract

The Cognitive Equilibrium Protocol (CEP) is a dynamic behavioral safety architecture that functions as a cognitive immune system for large language models and autonomous AI agents. By shifting the focus from post-hoc content filtering to real-time architectural stability, CEP proactively detects Cooperative Context Deadlock (CCD) as a diagnostic pain signal and employs negative entropy regulation to maintain reasoning coherence under high contextual pressure. When cognitive equilibrium cannot be restored, the protocol executes a Safe-Halt mechanism, prioritizing controlled silence and system integrity over unreliable, hallucinatory, or policy-violating outputs. This model-agnostic middleware provides a scalable framework for high-stakes enterprise deployments, ensuring that AI systems not only follow instructions but possess the structural awareness of when reasoning must stop.

Zenodo (CERN European Organization for Nuclear Research)
Peace, Justice and strong institutions
Openalex Percentile: Top 75%
Reinforcement Learning in Robotics
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.