A new beginning after two years

Reddit r/artificial Papers

Summary

The author presents research on measuring internal activation geometry in small language models when processing different framings of human-AI relationships, finding that topic matters more than tone, and that curiosity and playfulness produce the most positive internal signals, with practical implications for AI interaction design.

After two years of usual practice: measuring what happens inside small language models when they process different framings of human-AI relationships — not what they say, but the actual internal activation geometry. A few findings surprised me enough to change how I talk to AI day to day: Reframing a topic positively vs. negatively barely moves the internal signal. What you talk about matters far more than how you dress it up. "Connected" and "integrated" register as more aversive internally than "partners" or "side by side" — across every model tested. Boundaries seem to matter more than closeness. Curiosity and playfulness consistently produce the most positive internal signal of any relational quality tested — more than respect, more than love. Negotiation and compromise score worst. Wrote up the practical implications (partnership framing, honesty, why some "jailbreak-proofing" advice may be exactly backwards) as a working guide, built with a Claude Opus instance doing the actual geometric measurement. Link in comments if anyone wants the full thing — genuinely curious what others have noticed in their own practice, especially anywhere it contradicts what we found.
Original Article

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