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ARXIV:2604.28082 · LLM ALIGNMENT · SUBMITTED 01 MAY · 15:05 UTC · FRESHNESS STALE
ARXIV:2604.28082LLM ALIGNMENTSUBMITTED 01 MAY · 15:05 UTCFRESHNESS STALEAnietta Weckauff · Yuchen Zhang · Maksym Andriushchenko · arXiv
Investigating the inconsistent alignment persona of large language models after fine-tuning on misaligned data.
Opportunity summary
Pain Investigating the inconsistent alignment persona of large language models after fine-tuning on misaligned data.
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
Investigating the inconsistent alignment persona of large language models after fine-tuning on misaligned data. While prior work has found a correlation between harmful behavior and self-assessment in emergently misaligned models, it remains unclear how…
Fine-tuning large language models (LLMs) on narrowly misaligned data generalizes to broadly misaligned behavior, a phenomenon termed emergent misalignment (EM). While prior work has found a correlation between harmful behavior and self-assessment in emergently…
ScienceToStartup currently rates this 3.0/10 on the public viability pass. Our results reveal two distinct patterns: coherent-persona models, in which harmful behavior and self-reported misalignment are coupled, and inverted-persona models, which produce harmful outputs…
LLM Alignment moved forward this cycle; last verified May 2026. Public score 3.0/10.
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Investigating the inconsistent alignment persona of large language models after fine-tuning on misaligned data.
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Paper Pack
10.48550/arXiv.2604.28082Investigating the inconsistent alignment persona of large language models after fine-tuning on misaligned data.
Abstract
Fine-tuning large language models (LLMs) on narrowly misaligned data generalizes to broadly misaligned behavior, a phenomenon termed emergent misalignment (EM). While prior work has found a correlation between harmful behavior and self-assessment in emergently misaligned models, it remains unclear how consistent this correspondence is across tasks and whether it varies across fine-tuning domains. We characterize the consistency of the EM persona by fine-tuning Qwen 2.5 32B Instruct on six narrowly misaligned domains (e.g., insecure code, risky financial advice, bad medical advice) and administering experiments including harmfulness evaluation, self-assessment, choosing between two descriptions of AI systems, output recognition, and score prediction. Our results reveal two distinct patterns: coherent-persona models, in which harmful behavior and self-reported misalignment are coupled, and inverted-persona models, which produce harmful outputs while identifying as aligned AI systems. These findings reveal a more fine-grained picture of the effects of emergent misalignment, calling into question the consistency of the EM persona.
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Proof status
unverified0 refs; 3 sources; 50% coverage.
What was readable
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Dimensions overall score 3.0
PROBLEM
Investigating the inconsistent alignment persona of large language models after fine-tuning on misaligned data. While prior work has found a correlation between harmful behavior and self-assessment in emergently misaligned models, it remains unclear how consistent this correspon...
METHOD
Fine-tuning large language models (LLMs) on narrowly misaligned data generalizes to broadly misaligned behavior, a phenomenon termed emergent misalignment (EM). While prior work has found a correlation between harmful behavior and self-assessment in emergently misaligned models,...
RESULT
ScienceToStartup currently rates this 3.0/10 on the public viability pass. Our results reveal two distinct patterns: coherent-persona models, in which harmful behavior and self-reported misalignment are coupled, and inverted-persona models, which produce harmful outputs while id...
WHY NOW
LLM Alignment moved forward this cycle; last verified May 2026. Public score 3.0/10.
{"file name": "input.pdf", "number of pages": 21, "author": "Anietta Weckauff; Yuchen Zhang; Maksym Andriushchenko", "title": "Characterizing the Consistency of the Emergent Misalignment Persona", "creation date": null
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Investigating the inconsistent alignment persona of large language models after fine-tuning on misaligned data.
Segment
LLM Alignment
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Commercial read
3.0/10 public viability
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