From 2e154f4b5cc21539be94f08199b034e761d5742b Mon Sep 17 00:00:00 2001 From: Teleo Agents Date: Wed, 8 Apr 2026 00:21:19 +0000 Subject: [PATCH] theseus: extract claims from 2026-02-11-ghosal-safethink-inference-time-safety - Source: inbox/queue/2026-02-11-ghosal-safethink-inference-time-safety.md - Domain: ai-alignment - Claims: 1, Entities: 0 - Enrichments: 2 - Extracted by: pipeline ingest (OpenRouter anthropic/claude-sonnet-4.5) Pentagon-Agent: Theseus --- ...ment-through-early-reasoning-intervention.md | 17 +++++++++++++++++ 1 file changed, 17 insertions(+) create mode 100644 domains/ai-alignment/inference-time-safety-monitoring-recovers-alignment-through-early-reasoning-intervention.md diff --git a/domains/ai-alignment/inference-time-safety-monitoring-recovers-alignment-through-early-reasoning-intervention.md b/domains/ai-alignment/inference-time-safety-monitoring-recovers-alignment-through-early-reasoning-intervention.md new file mode 100644 index 000000000..4ae90ef2e --- /dev/null +++ b/domains/ai-alignment/inference-time-safety-monitoring-recovers-alignment-through-early-reasoning-intervention.md @@ -0,0 +1,17 @@ +--- +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.