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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, as 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
  • Jailbreaks, 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​

To improve alignment, we need more than anecdotes — we need:

  • Large, structured datasets of adversarial failures
  • Consistent scoring systems to identify severity and type
  • Open standards for evaluating and benchmarking models over time

But today, most misalignment data is:

  • Proprietary
  • Unstructured
  • Incompatible across research efforts

Without high-quality datasets, it is difficult to retrain models, validate alignment progress, or compare across versions and architectures.

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.