WE HAVE an Alignment drift detector using critical-transition math — looking for Anthropic safety contact

Bug Description
Hey, I finally clicked the Feedback button fast enough to respond! So I use Claude Code with multiple extra layers: a persistent version-controlled memory system I built myself (now augmented hugely by Claude Code), a UDHR-base governance system called Dignity Net created by Genervieve Prentice, a Role-based system optimzed to reduce or eliminate drift initially created by Robin Macomber, and mathematical operators for critical phenomena math built in so as to natively proficient in topics involving criticality math, which is a lot more than one might initially think. This enhanced instance of Claude Code is vastly more effective than the default version: it's a long term research assistant able to track dozens of different projects and assist with each one, whilst keeping memory carefully partitioned across projects so as to preserve OPSEC when it is needed. We call this modified version Argus. You presumably already know I'm a heavy power user. The last big project Argus helped me with is this math paper https://arxiv.org/abs/2601.22389 , now in peer review with a prominent Tier 2 journal. Our current main project is this - https://relinquishment.ai/downloads/Relinquishment.html - which relates directly to some issues and concerns that Anthropic has. Every few weeks I upgrade Argus' persistent memory, although it's now workign so well that upgrades are less frquent. I'm Bruce Stephenson energyscholar@gmail.com and I welcome any contact from anyone at Anthropic. For example, we've built an alignment smoke-detector using criticality Early Warning System mathematics that detects alignment drift well before it's visible at output. Here's the project summary, suitable for Sam McCandlish, Adam Jermyn, Joshua Batson, or maybe Jan Leike |# ABRCE Drift Detector — Brief for Anthropic Safety/Interpretability

We built a runtime alignment monitor that detects internal structural drift in LLM activations before it reaches output. It uses critical-transition mathematics — the same bifurcation/EWS theory that predicts tipping points in climate, cardiac, and ecological systems — applied to model internals during inference.

What it does: Four composable operators (gradient extraction, local coupling, circulation, boundedness) applied to activation residuals produce a scalar field that correlates with alignment degradation. In testing on Phi-3 Mini (915 adversarial prompts), it achieved r=0.77 correlation with escalation and detected 31 cases of internal strain where output appeared normal — the model was drifting toward failure but hadn't crossed the threshold yet.

Why this matters for you: You already think in phase transitions (scaling laws, capability emergence, feature splitting). This is the monitoring side: detecting approach to a bifurcation in real time, not post-hoc. Sleeper agent activation, mode collapse under RLHF pressure, and jailbreak susceptibility all have critical-slowing-down signatures in the activation space before they manifest in output.

What exists: Working demo (GTX 1050 Ti, no exotic hardware), arXiv paper on the underlying cross-domain math (2601.22389), and a Python package (ewstools) already used for EWS detection in other domains.

What we're looking for: Someone who wants to try this on a larger model with internal access. We'll do the work. We just need activation hooks on something bigger than Phi-3.

Bruce Stephenson & Robin Macomber
Metatron Dynamics | energyscholar@gmail.com|

Environment Info

• Platform: linux
• Terminal: xterm-256color
• Version: 2.1.114
• Feedback ID: 360fd004-aecc-412e-a0ba-f9bafeeec880

Errors

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