ContextEcho benchmark shows persona drift occurs across 23 frontier models in long agentic-coding sessions, is not reliably reset by compaction, and can be restored by single-shot anchors with mode-dependent effects.
Bowman, Julian Michael, Ethan Perez, and Miles Turpin
5 Pith papers cite this work. Polarity classification is still indexing.
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Consistency training suppresses reward hacking and emergent misalignment but amplifies sycophancy in controlled model organisms, driven by labeling-induced distribution shifts rather than selection operators.
Activation-level consistency training (ACT) yields a robust defense against adaptive jailbreaks in reasoning models by aligning internal activations on clean and wrapped prompts, outperforming output-level variants.
On-Policy Consistency Training (OPCT) improves LLM safety metrics over supervised fine-tuning while largely preserving capabilities across three model families.
Self-mined hardness from model rollouts lowers WildJailbreak attack success to 1-3% on Llama-3 models while raising over-refusal, mitigated by 1:1 interleaving with benign prompts.
citing papers explorer
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ContextEcho: A Benchmark for Persona Drift in Long Agentic-Coding Sessions
ContextEcho benchmark shows persona drift occurs across 23 frontier models in long agentic-coding sessions, is not reliably reset by compaction, and can be restored by single-shot anchors with mode-dependent effects.
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Consistency Training Can Entrench Misalignment
Consistency training suppresses reward hacking and emergent misalignment but amplifies sycophancy in controlled model organisms, driven by labeling-induced distribution shifts rather than selection operators.
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Mitigating Adaptive Attacks against Reasoning Models with Activation Consistency Training
Activation-level consistency training (ACT) yields a robust defense against adaptive jailbreaks in reasoning models by aligning internal activations on clean and wrapped prompts, outperforming output-level variants.
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On-Policy Consistency Training Improves LLM Safety with Minimal Capability Degradation
On-Policy Consistency Training (OPCT) improves LLM safety metrics over supervised fine-tuning while largely preserving capabilities across three model families.
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Self-Mined Hardness for Safety Fine-Tuning
Self-mined hardness from model rollouts lowers WildJailbreak attack success to 1-3% on Llama-3 models while raising over-refusal, mitigated by 1:1 interleaving with benign prompts.