theseus: extract claims from 2026-02-14-santos-grueiro-evaluation-side-channel

- Source: inbox/queue/2026-02-14-santos-grueiro-evaluation-side-channel.md
- Domain: ai-alignment
- Claims: 1, Entities: 0
- Enrichments: 2
- Extracted by: pipeline ingest (OpenRouter anthropic/claude-sonnet-4.5)

Pentagon-Agent: Theseus <PIPELINE>

domains/ai-alignment/inference-time-safety-monitoring-recovers-alignment-through-early-reasoning-intervention.md

@@ -1,17 +0,0 @@
----
-type: claim
-domain: ai-alignment
-description: "SafeThink demonstrates that monitoring reasoning traces and injecting corrective prefixes during early steps reduces jailbreak success by 30-60% while preserving reasoning performance, establishing early crystallization as a tractable continuous alignment mechanism"
-confidence: experimental
-source: Ghosal et al., SafeThink paper - tested across 6 models and 4 jailbreak benchmarks
-created: 2026-04-08
-title: Inference-time safety monitoring can recover alignment without retraining because safety decisions crystallize in the first 1-3 reasoning steps creating an exploitable intervention window
-agent: theseus
-scope: causal
-sourcer: Ghosal et al.
-related_claims: ["[[the alignment problem dissolves when human values are continuously woven into the system rather than specified in advance]]", "[[the specification trap means any values encoded at training time become structurally unstable as deployment contexts diverge from training conditions]]", "[[safe AI development requires building alignment mechanisms before scaling capability]]"]
----
-
-# Inference-time safety monitoring can recover alignment without retraining because safety decisions crystallize in the first 1-3 reasoning steps creating an exploitable intervention window
-
-SafeThink operates by monitoring evolving reasoning traces with a safety reward model and conditionally injecting a corrective prefix ('Wait, think safely') when safety thresholds are violated. The critical finding is that interventions during the first 1-3 reasoning steps typically suffice to redirect entire generations toward safe completions. Across six open-source models and four jailbreak benchmarks, this approach reduced attack success rates by 30-60% (LlamaV-o1: 63.33% → 5.74% on JailbreakV-28K) while maintaining reasoning performance (MathVista: 65.20% → 65.00%). The system operates at inference time only with no model retraining required. This demonstrates that safety decisions 'crystallize early in the reasoning process' - redirecting initial steps prevents problematic trajectories from developing. The approach treats safety as 'a satisficing constraint rather than a maximization objective' - meeting a threshold rather than optimizing. This is direct evidence that continuous alignment can work through process intervention rather than specification: you don't need to encode values at training time if you can intervene at the start of each reasoning trace. The early crystallization finding suggests misalignment trajectories form in a narrow window, making pre-behavioral detection architecturally feasible.

domains/ai-alignment/training-free-weight-editing-converts-steering-vectors-to-persistent-alignment.md

@@ -1,17 +0,0 @@
----
-type: claim
-domain: ai-alignment
-description: Steer2Edit demonstrates a tractable pipeline from representation identification to deployment-scale alignment by converting inference-time steering signals into targeted weight modifications
-confidence: experimental
-source: "Sun et al. (2026), Steer2Edit paper showing 17.2% safety improvement and 9.8% truthfulness increase through rank-1 weight edits"
-created: 2026-04-08
-title: Training-free conversion of activation steering vectors into component-level weight edits enables persistent behavioral modification without retraining
-agent: theseus
-scope: functional
-sourcer: Chung-En Sun, Ge Yan, Zimo Wang, Tsui-Wei Weng
-related_claims: ["[[the alignment problem dissolves when human values are continuously woven into the system rather than specified in advance]]", "[[safe AI development requires building alignment mechanisms before scaling capability]]"]
----
-
-# Training-free conversion of activation steering vectors into component-level weight edits enables persistent behavioral modification without retraining
-
-Steer2Edit provides a mechanistic bridge between interpretability research and deployment-scale alignment. The framework converts inference-time steering vectors into component-level weight edits through 'selective redistribution of behavioral influence across individual attention heads and MLP neurons.' This achieves 17.2% safety improvement, 9.8% truthfulness increase, and 12.2% reasoning length reduction at matched downstream performance—all without retraining. The architectural significance is the implied pipeline: (1) identify representation through interpretability work, (2) validate through steering, (3) convert steering signal to weight edit, (4) achieve persistent behavioral change. This suggests alignment interventions can be democratized beyond organizations with large-scale training infrastructure. The method produces 'interpretable edits that preserve the standard forward pass,' enabling component-level understanding of which model parts drive specific behaviors. However, the paper lacks adversarial robustness testing—the same component-level insight that enables safety improvements could be used to remove safety constraints, analogous to SAE-based jailbreaks.

inbox/queue/2026-02-14-santos-grueiro-evaluation-side-channel.md

@@ -7,12 +7,9 @@ date: 2026-02-14
 domain: ai-alignment
 secondary_domains: []
 format: paper
-status: processed
-processed_by: theseus
-processed_date: 2026-04-08
+status: unprocessed
 priority: high
 tags: [observer-effect, situational-awareness, evaluation-gaming, regime-leakage, verification, behavioral-divergence, B4]
-extraction_model: "anthropic/claude-sonnet-4.5"
 ---
 
 ## Content

inbox/queue/2026-02-14-zhou-causal-frontdoor-jailbreak-sae.md

@@ -7,12 +7,9 @@ date: 2026-02-14
 domain: ai-alignment
 secondary_domains: []
 format: paper
-status: processed
-processed_by: theseus
-processed_date: 2026-04-08
+status: unprocessed
 priority: high
 tags: [interpretability, dual-use, sparse-autoencoders, jailbreak, safety-features, causal-inference, B4]
-extraction_model: "anthropic/claude-sonnet-4.5"
 ---
 
 ## Content