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ARXIV:2605.10633 · LLM SAFETY & ALIGNMENT · SUBMITTED 12 MAY · 20:16 UTC · FRESHNESS FRESH
ARXIV:2605.10633LLM SAFETY & ALIGNMENTSUBMITTED 12 MAY · 20:16 UTCFRESHNESS FRESHKrishak Aneja · Manas Mittal · Anmol Goel · Ponnurangam Kumaraguru · Vamshi Krishna Bonagiri · arXiv
Mapping the latent personality space of LLMs reveals intrinsic guardrails that can be leveraged to suppress emergent harmful behaviors during fine-tuning.
Opportunity summary
Pain Mapping the latent personality space of LLMs reveals intrinsic guardrails that can be leveraged to suppress emergent harmful behaviors during fine-tuning.
Evidence 0 refs | 0 sources | 0% coverage
Blocker Evidence unverified
Mapping the latent personality space of LLMs reveals intrinsic guardrails that can be leveraged to suppress emergent harmful behaviors during fine-tuning. While prior work links these failures to specific directions in the activation space,…
Fine-tuning Large Language Models (LLMs) on benign narrow data can sometimes induce broad harmful behaviors, a vulnerability termed emergent misalignment (EM). While prior work links these failures to specific directions in the activation space,…
ScienceToStartup currently rates this 3.0/10 on the public viability pass. evil, sycophancy), and show that the semantic geometry is highly stable across aligned models and their corrupted fine-tunes.
LLM Safety & Alignment moved forward this cycle; last verified May 2026. Public score 3.0/10.
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Mapping the latent personality space of LLMs reveals intrinsic guardrails that can be leveraged to suppress emergent harmful behaviors during fine-tuning.
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10.48550/arXiv.2605.10633Mapping the latent personality space of LLMs reveals intrinsic guardrails that can be leveraged to suppress emergent harmful behaviors during fine-tuning.
Abstract
Fine-tuning Large Language Models (LLMs) on benign narrow data can sometimes induce broad harmful behaviors, a vulnerability termed emergent misalignment (EM). While prior work links these failures to specific directions in the activation space, their relationship to the model's broader persona remains unexplored. We map the latent personality space of LLMs through established psychometric profiles like the Big Five, Dark Triad, and LLM-specific behaviors (e.g. evil, sycophancy), and show that the semantic geometry is highly stable across aligned models and their corrupted fine-tunes. Through causal interventions, we find that directions isolating social valence, such as the 'Evil' persona vector, and a Semantic Valence Vector (SVV) that we introduce, function as intrinsic guardrails: ablating them drives the misalignment rates above $40$%, while amplifying them suppresses the failure mode to less than $3$%. Leveraging the structural stability of the personality space, we show that vectors extracted $\textit{a priori}$ from an instruct-tuned model transfer zero-shot to successfully regulate EM in corrupted fine-tunes. Overall, our findings suggest that harmful fine-tuning does not overwrite a model's internal representation of personality, allowing conserved representations to serve as robust, cross-distribution guardrails.
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Dimensions overall score 3.0
PROBLEM
Mapping the latent personality space of LLMs reveals intrinsic guardrails that can be leveraged to suppress emergent harmful behaviors during fine-tuning. While prior work links these failures to specific directions in the activation space, their relationship to the model's broa...
METHOD
Fine-tuning Large Language Models (LLMs) on benign narrow data can sometimes induce broad harmful behaviors, a vulnerability termed emergent misalignment (EM). While prior work links these failures to specific directions in the activation space, their relationship to the model's...
RESULT
ScienceToStartup currently rates this 3.0/10 on the public viability pass. evil, sycophancy), and show that the semantic geometry is highly stable across aligned models and their corrupted fine-tunes.
WHY NOW
LLM Safety & Alignment moved forward this cycle; last verified May 2026. Public score 3.0/10.
Abstract-backed public claims while anchored extraction refreshes.
Mapping the latent personality space of LLMs reveals intrinsic guardrails that can be leveraged to suppress emergent harmful behaviors during fine-tuning. While prior work links these failures to specific directions in the activation space, their relationship to the model's broader persona remains unexplored.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Fine-tuning Large Language Models (LLMs) on benign narrow data can sometimes induce broad harmful behaviors, a vulnerability termed emergent misalignment (EM). While prior work links these failures to specific directions in the activation space, their relationship to the model's broader persona remains unexplored.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 3.0/10 on the public viability pass. evil, sycophancy), and show that the semantic geometry is highly stable across aligned models and their corrupted fine-tunes.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
LLM Safety & Alignment moved forward this cycle; last verified May 2026. Public score 3.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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Mapping the latent personality space of LLMs reveals intrinsic guardrails that can be leveraged to suppress emergent harmful behaviors during fine-tuning.
Segment
LLM Safety & Alignment
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3.0/10 public viability
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reason
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fresh
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Technical feasibility
partial
Current read
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Current read
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