domains/ai-alignment/multi-layer-ensemble-probes-outperform-single-layer-by-29-78-percent.md

@@ -30,3 +30,10 @@ The 29-78% AUROC improvement occurs because deception-relevant concept direction
 **Source:** Theseus synthetic analysis (2026-04-22)
 
 Nordby et al.'s clean-data accuracy improvements do not translate to adversarial robustness. White-box multi-layer SCAV can suppress concept directions at all monitored layers simultaneously through higher-dimensional optimization. Open-weights models gain no structural protection from multi-layer ensembles against adversarially-sophisticated attackers with model access.
+
+
+## Extending Evidence
+
+**Source:** Theseus synthetic analysis
+
+The 29-78% AUROC improvement is a clean-data accuracy result. Nordby et al. did not test adversarial robustness. Under white-box adversarial conditions (open-weights models), multi-layer ensembles provide no structural protection against SCAV-generalization attacks. The improvement applies only to non-adversarial monitoring contexts.

domains/ai-alignment/representation-trajectory-geometry-distinguishes-deceptive-from-sincere-alignment-without-creating-adversarial-attack-surfaces.md

@@ -24,3 +24,10 @@ The method characterizes alignment-relevant properties as geometric patterns in
 **Source:** Theseus synthetic analysis (2026-04-22)
 
 Trajectory geometry monitoring does create adversarial attack surfaces through multi-layer SCAV generalization. The claim that geometric approaches avoid attack surface creation is incorrect for white-box adversaries with access to model weights. May need qualification to 'black-box adversaries only, contingent on rotation pattern specificity.'
+
+
+## Challenging Evidence
+
+**Source:** Theseus synthetic analysis of multi-layer SCAV feasibility
+
+Multi-layer trajectory geometry monitoring (ensemble probes across layers) does create adversarial attack surface in white-box settings. White-box multi-layer SCAV can suppress concept directions at all monitored layers simultaneously. The claim that trajectory geometry avoids attack surfaces may need qualification: it may hold only for black-box adversaries if rotation patterns are model-specific, but not for white-box adversaries with access to model weights.

domains/ai-alignment/trajectory-monitoring-dual-edge-geometric-concentration.md

@@ -31,3 +31,10 @@ The dual-use finding now extends to multi-layer ensemble monitoring with deploym
 **Source:** Theseus synthetic analysis (2026-04-22)
 
 The dual-use vulnerability extends to multi-layer ensemble monitoring, not just single-layer probes. However, the severity is deployment-context-dependent: open-weights models (white-box adversaries) remain fully vulnerable, while closed-source models (black-box adversaries) may gain protection if rotation patterns are model-specific (untested assumption).
+
+
+## Extending Evidence
+
+**Source:** Theseus synthetic analysis of Nordby et al. + SCAV
+
+Multi-layer ensemble probes do not escape the dual-use structure in white-box settings (open-weights models). White-box multi-layer SCAV is feasible by computing concept directions at each monitored layer and constructing a single perturbation that suppresses all simultaneously. The monitoring precision hierarchy holds: each level is structurally defeatable given sufficient attacker capability. Multi-layer ensembles raise attack cost but do not escape the dual-use structure for open-weights deployments.