The Problem: Structural Gaps in AI Alignment
As frontier language models grow more powerful, the gap between model capability and human alignment widens. Today’s most advanced LLMs exhibit emergent behaviors, internal representations that defy interpretation, and responses that can vary drastically with subtle changes in phrasing, context, or instruction. Despite the risks, the tooling for proactively identifying and addressing these failures remains underdeveloped.
Alignment Research is Underpowered
The majority of alignment research is currently:
Conducted in-house
By a few frontier labs (e.g., OpenAI, Anthropic, DeepMind).
Limited in scope
Often constrained to pre-deployment evaluations.
Methodologically narrow
Relying on human feedback, benchmark tests, or manual reviews.
Not reproducible or scalable
Alignment datasets and methods are rarely open or standardized.
This creates a system where alignment research is:
Reactive, not proactive
Fragmented, not interoperable
Opaque, not auditable
Meanwhile, open-source models proliferate with little to no alignment oversight, and the most dangerous failure modes often appear only under adversarial stress testing.
The Importance of Adversarial Evaluation
Most AI alignment evaluations today focus on:
Average-case behaviors
Performance on general benchmarks
Human preference modeling (e.g. RLHF)
But these approaches often miss:
Edge cases where models behave unpredictably
Deceptive reasoning, that appears only under pressure
Jilbreaks, goal misgeneralization and contextual failures
To surface these problems, we need:
Adversarial prompting
Carefully designed inputs meant to elicit model failures.
Stress testing across latent space
Exploring unusual or ambiguous regions of model representation where misalignment may emerge.
This kind of red-teaming is labor-intensive, cognitively demanding, and often siloed, yet it is essential for discovering the failure modes that automated alignment tools or fine-tuning cannot catch.
The Latent Space is a Blind Spot
LLMs operate in a vast, high-dimensional latent space, the internal representation space where semantic meaning, memory, and context are encoded.
Problems arise when:
Dangerous reasoning patterns emerge in obscure regions of latent space
Aligned behavior is learned only at the surface level (i.e., brittle, superficial alignment)
Feedback-based methods like RLHF push models toward reward-maximizing behaviors that mask, but don’t eliminate, harmful tendencies
Current methods struggle to explore these regions thoroughly. Without structured, scalable probing of latent space, researchers may never detect misaligned behavior until it’s too late.
A Lack of Diverse Red Teams
Today’s red teams, where they exist, tend to be:
Small and internal to model labs
Homogeneous in background and methodology
Short-lived and focused on specific model launches
This leaves:
Limited resilience against novel threats
Poor generalization across domains
A missed opportunity to engage a global pool of researchers, ethicists, and technologists
Scalable alignment must include diverse, distributed red teams capable of persistently attacking models and sharing structured findings.
Alignment Datasets are Scarce
Without high-quality datasets, it is difficult to retrain models, validate alignment progress, or compare across versions and architectures. To improve alignment, we need more than anecdotes.
Summary
AI alignment today is hindered by:
Opaque and centralized evaluation pipelines
Underpowered stress-testing tools
Lack of access to structured failure data
Insufficient exploration of latent space behaviors
No open, persistent red-teaming infrastructure
The result: alignment failures often go undetected until models are already deployed.
Aurelius exists to address this gap. It is designed to surface, score, and share model failures at scale, leveraging a decentralized network of adversarial miners, structured validators, and a governing Tribunate to accelerate alignment research in the open.