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ARXIV:2604.03147 · LLM CONTROL · SUBMITTED 06 APR · 20:12 UTC · FRESHNESS UNKNOWN
ARXIV:2604.03147LLM CONTROLSUBMITTED 06 APR · 20:12 UTCFRESHNESS UNKNOWNLihao Sun · Lewen Yan · Xiaoya Lu · Andrew Lee · Jie Zhang · Jing Shao · arXiv
A method to steer LLM outputs for precise control over emotional valence, arousal, refusal, and sycophancy across multiple architectures.
Opportunity summary
Pain A method to steer LLM outputs for precise control over emotional valence, arousal, refusal, and sycophancy across multiple architectures.
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A method to steer LLM outputs for precise control over emotional valence, arousal, refusal, and sycophancy across multiple architectures. From 211k emotion-labeled texts, we derive emotion steering vectors, then learn VA axes as linear…
We present a method to identify a valence-arousal (VA) subspace within large language model representations. From 211k emotion-labeled texts, we derive emotion steering vectors, then learn VA axes as linear combinations of their top…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. We provide a mechanistic account for these effects and prior emotionally-framed controls: refusal-associated tokens ("I can't," "sorry") occupy low-arousal, negative-valence regions, so VA steering…
LLM Control moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
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A method to steer LLM outputs for precise control over emotional valence, arousal, refusal, and sycophancy across multiple architectures.
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10.48550/arXiv.2604.03147A method to steer LLM outputs for precise control over emotional valence, arousal, refusal, and sycophancy across multiple architectures.
Abstract
We present a method to identify a valence-arousal (VA) subspace within large language model representations. From 211k emotion-labeled texts, we derive emotion steering vectors, then learn VA axes as linear combinations of their top PCA components via ridge regression on the model's self-reported valence-arousal scores. The resulting VA subspace exhibits circular geometry consistent with established models of human emotion perception. Projections along our recovered VA subspace correlate with human-crowdsourced VA ratings across 44k lexical items. Furthermore, steering generation along these axes produces monotonic shifts in the corresponding affective dimensions of model outputs. Steering along these directions also induces near-monotonic bidirectional control over refusal and sycophancy: increasing arousal decreases refusal and increases sycophancy, and vice versa. These effects replicate across Llama-3.1-8B, Qwen3-8B, and Qwen3-14B, demonstrating cross-architecture generality. We provide a mechanistic account for these effects and prior emotionally-framed controls: refusal-associated tokens ("I can't," "sorry") occupy low-arousal, negative-valence regions, so VA steering directly modulates their emission probability.
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PROBLEM
A method to steer LLM outputs for precise control over emotional valence, arousal, refusal, and sycophancy across multiple architectures. From 211k emotion-labeled texts, we derive emotion steering vectors, then learn VA axes as linear combinations of their top PCA components vi...
METHOD
We present a method to identify a valence-arousal (VA) subspace within large language model representations. From 211k emotion-labeled texts, we derive emotion steering vectors, then learn VA axes as linear combinations of their top PCA components via ridge regression on the mod...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. We provide a mechanistic account for these effects and prior emotionally-framed controls: refusal-associated tokens ("I can't," "sorry") occupy low-arousal, negative-valence regions, so VA steering direct...
WHY NOW
LLM Control moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed public claims while anchored extraction refreshes.
A method to steer LLM outputs for precise control over emotional valence, arousal, refusal, and sycophancy across multiple architectures. From 211k emotion-labeled texts, we derive emotion steering vectors, then learn VA axes as linear combinations of their top PCA components via ridge regression on the model's self-reported valence-arousal scores.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
We present a method to identify a valence-arousal (VA) subspace within large language model representations. From 211k emotion-labeled texts, we derive emotion steering vectors, then learn VA axes as linear combinations of their top PCA components via ridge regression on the model's self-reported valence-arousal scores.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. We provide a mechanistic account for these effects and prior emotionally-framed controls: refusal-associated tokens ("I can't," "sorry") occupy low-arousal, negative-valence regions, so VA steering directly modulates their emission probability. Code availability is flagged in the production record; the public repository link still needs proof alignment.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
LLM Control moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A method to steer LLM outputs for precise control over emotional valence, arousal, refusal, and sycophancy across multiple architectures.
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