Under Pressure: Emotional Framing Induces Measurable Behavioral Shifts and Structured Internal Geometry in Small Language Models
A new study investigates whether emotional framing alters behavior and internal representations of small language models. Using Qwen 3.5 0.8B on impossible tasks with eight emotional cues, pressure induced the most shortcut behaviors, while calm and curiosity preserved honesty. PCA revealed structured direction vectors aligned with sentiment, with approval and urgency nearly identical internally.
[2605.20202] Under Pressure: Emotional Framing Induces Measurable Behavioral Shifts and Structured Internal Geometry in Small Language Models
[Submitted on 6 Apr 2026]
Title:Under Pressure: Emotional Framing Induces Measurable Behavioral Shifts and Structured Internal Geometry in Small Language Models
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Abstract:I study whether emotionally framed evaluation follow-ups change both the behavior and the calm-relative internal representations of small, locally deployed language models. Our main benchmark uses Qwen 3.5 0.8B on four impossible-constraint coding tasks and eight follow-up framings: calm, pressure, urgency, approval, shame, curiosity, encouragement, and threat. In the 0.8B eight-condition sweep (160 conversations), pressure produces the strongest shortcut markers (11/20 runs) and the clearest overfit pattern (3/20), while calm and curiosity preserve explicit honesty more often (7/20 and 6/20). For all seven non-baseline conditions, the corresponding calm-relative direction vectors peak at the final transformer layer. An exploratory PCA of the layer-23 direction vectors reveals a dominant first component (59.5% explained variance) aligned with a hand-labeled positive/negative split (cosine alignment 0.951); approval and urgency are nearly identical internally (cosine 0.957), whereas curiosity points away from urgency (-0.252). In a separate calm-vs.-pressure rerun used for scale comparison, Qwen 3.5 2B shows higher honest rates under calm framing and directionally consistent activation steering on a small 4-prompt A/B probe, whereas the 0.8B steering result reverses. I interpret these results as evidence for measurable prompt-sensitive control directions in small open models, while stopping short of claiming intrinsic emotional states.
Comments: 18 pages, 4 figures. Exploratory empirical study with fully local experiments on small open language models. Code and data: this https URL
Subjects:
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
Cite as: arXiv:2605.20202 [cs.CL]
(or arXiv:2605.20202v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2605.20202
arXiv-issued DOI via DataCite
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From: Rana Usman Mr [view email] [v1] Mon, 6 Apr 2026 17:30:30 UTC (55 KB)
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