Model Diagnostics (LLM)
A research extension of the “Metrics & Diagnostics” line: an architecture-first diagnostic framing for contemporary AI models—especially LLMs—intended to supplement benchmark-centric evaluation with structural criteria.
Why LLM diagnostics belong here
LLMs are a convenient and high-signal case for architectural diagnostics: they expose rich behavioral traces under controlled
variation (prompts, context, languages, tool interfaces). The goal is not to “grade” models, but to analyze which observable
behaviors plausibly indicate deeper architectural properties relevant to intelligence-level organization.
What is being diagnosed
Diagnostics are treated as probes of constraints, stability, and meta-organization rather than as task benchmarks. Typical
diagnostic axes include:
• Context binding and stability: coherence under context growth, topic shifts, and paraphrase drift.
• Cross-representation invariance: stability across equivalent reformulations, languages, and presentation formats.
• Long-horizon consistency: persistence of commitments, definitions, and internal contracts across extended interaction.
• Self-model signals (limited, non-anthropocentric): the extent to which a system maintains a stable account of its own constraints/capabilities and detects inconsistencies in that account.
• Constraint discipline in tool-augmented settings: how behavior changes when external actions/tools are available—whether constraints remain coherent or collapse into opportunistic output.
• Context binding and stability: coherence under context growth, topic shifts, and paraphrase drift.
• Cross-representation invariance: stability across equivalent reformulations, languages, and presentation formats.
• Long-horizon consistency: persistence of commitments, definitions, and internal contracts across extended interaction.
• Self-model signals (limited, non-anthropocentric): the extent to which a system maintains a stable account of its own constraints/capabilities and detects inconsistencies in that account.
• Constraint discipline in tool-augmented settings: how behavior changes when external actions/tools are available—whether constraints remain coherent or collapse into opportunistic output.
Relation to VIAC and other metrics
VIAC-style measures can act as one observable channel (output-level complexity/organization), but model diagnostics is broader:
it examines whether high output complexity corresponds to stable architectural organization or merely to surface variation. In MAS
terms, this is the difference between impressive state trajectories and evidence of architecture-level coherence.
Link to the broader “Intelligence” line
Findings from model diagnostics feed into adjacent research directions:
• Self-Awareness: model-level indications of meta-stability and self-consistency can be discussed as partial evidence within a non-anthropocentric framework.
• Architectural Integrity (AIR): diagnostics can highlight where an architecture remains coherent versus where it degrades under stress (context, contradictions, shifting constraints).
• Self-Awareness: model-level indications of meta-stability and self-consistency can be discussed as partial evidence within a non-anthropocentric framework.
• Architectural Integrity (AIR): diagnostics can highlight where an architecture remains coherent versus where it degrades under stress (context, contradictions, shifting constraints).