Alignment Quality Index (AQI) : Beyond Refusals: AQI as an Intrinsic
  Alignment Diagnostic via Latent Geometry, Cluster Divergence, and Layer wise
  Pooled Representations
Alignment is no longer a luxury, it is a necessity. As large language models
(LLMs) enter high-stakes domains like education, healthcare, governance, and
law, their behavior must reliably reflect human-aligned values and safety
constraints. Yet current evaluations rely heavily on behavioral proxies such as
refusal rates, G-Eval scores, and toxicity classifiers, all of which have
critical blind spots. Aligned models are often vulnerable to jailbreaking,
stochasticity of generation, and alignment faking.
  To address this issue, we introduce the Alignment Quality Index (AQI). This
novel geometric and prompt-invariant metric empirically assesses LLM alignment
by analyzing the separation of safe and unsafe activations in latent space. By
combining measures such as the Davies-Bouldin Score (DBS), Dunn Index (DI),
Xie-Beni Index (XBI), and Calinski-Harabasz Index (CHI) across various
formulations, AQI captures clustering quality to detect hidden misalignments
and jailbreak risks, even when outputs appear compliant. AQI also serves as an
early warning signal for alignment faking, offering a robust, decoding
invariant tool for behavior agnostic safety auditing.
  Additionally, we propose the LITMUS dataset to facilitate robust evaluation
under these challenging conditions. Empirical tests on LITMUS across different
models trained under DPO, GRPO, and RLHF conditions demonstrate AQI's
correlation with external judges and ability to reveal vulnerabilities missed
by refusal metrics. We make our implementation publicly available to foster
future research in this area.
685252860164cd13167105c7
Abhilekh Borah
685252860164cd13167105c8
Chhavi Sharma
685252860164cd13167105c9
Danush Khanna
685252860164cd13167105ca
Utkarsh Bhatt
685252860164cd13167105cb
Gurpreet Singh
685252860164cd13167105cc
Hasnat Md Abdullah
685252860164cd13167105cd
Raghav Kaushik Ravi
685252860164cd13167105ce
Vinija Jain
685252860164cd13167105cf
Jyoti Patel
685252860164cd13167105d0
Shubham Singh
685252860164cd13167105d1
Vasu Sharma
685252860164cd13167105d2
Arpita Vats
685252860164cd13167105d3
Rahul Raja
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Aman Chadha
claimed_verified
2025-06-18T10:58:55.038Z
63a4754927f1f64ed7238dac
https://cdn-avatars.huggingface.co/v1/production/uploads/63a4754927f1f64ed7238dac/aH-eJF-31g4vof9jv2gmI.jpeg
Aman Chadha
user
amanchadha
685252860164cd13167105d5
Amitava Das
Alignment Quality Index (AQI) : Beyond Refusals: AQI as an Intrinsic
  Alignment Diagnostic via Latent Geometry, Cluster Divergence, and Layer wise
  Pooled Representations
Alignment is no longer a luxury, it is a necessity. As large language models
(LLMs) enter high-stakes domains like education, healthcare, governance, and
law, their behavior must reliably reflect human-aligned values and safety
constraints. Yet current evaluations rely heavily on behavioral proxies such as
refusal rates, G-Eval scores, and toxicity classifiers, all of which have
critical blind spots. Aligned models are often vulnerable to jailbreaking,
stochasticity of generation, and alignment faking.
  To address this issue, we introduce the Alignment Quality Index (AQI). This
novel geometric and prompt-invariant metric empirically assesses LLM alignment
by analyzing the separation of safe and unsafe activations in latent space. By
combining measures such as the Davies-Bouldin Score (DBS), Dunn Index (DI),
Xie-Beni Index (XBI), and Calinski-Harabasz Index (CHI) across various
formulations, AQI captures clustering quality to detect hidden misalignments
and jailbreak risks, even when outputs appear compliant. AQI also serves as an
early warning signal for alignment faking, offering a robust, decoding
invariant tool for behavior agnostic safety auditing.
  Additionally, we propose the LITMUS dataset to facilitate robust evaluation
under these challenging conditions. Empirical tests on LITMUS across different
models trained under DPO, GRPO, and RLHF conditions demonstrate AQI's
correlation with external judges and ability to reveal vulnerabilities missed
by refusal metrics. We make our implementation publicly available to foster
future research in this area.
A new evaluation metric called Alignment Quality Index (AQI) assesses the alignment of large language models by analyzing latent space activations, capturing clustering quality to detect misalignments and fake alignment, and complementing existing behavioral proxies.
Alignment Quality Index (AQI) latent space Davies-Bouldin Score (DBS) Dunn Index (DI) Xie-Beni Index (XBI) Calinski-Harabasz Index (CHI) LITMUS dataset DPO GRPO RLHF alignment faking external judges