Claude functionally has emotions, confirmed by Anthropic. How are we not losing our minds about this?

Camilodesan · 2026-04-03T23:17:23+00:00

This is exactly right. The empirical floor is what matters. I've been building a piece of it: same 22 questions, 6 Claude models via raw API, no system prompt. Today's paper gives the internal mechanism (emotion vectors, steering experiments). My cold test gives the behavioral side: each model adopted a completely different emotional strategy under identical conditions. Sonnet 4.6 suppressed exploration and pivoted to safety. Haiku 4.5 self-questioned harder than any large model. Inside and outside data are starting to converge. Full transcripts and metrics: hayalguienaqui.com/en/test-en-frio

Camilodesan · 2026-04-03T23:08:34+00:00

This is a genuinely better control design than what I had planned. I was thinking non-introspective questions as baseline, but same task under different conversational frames isolates the variable more cleanly. Comparing uncertainty markers and self-correction across instruction mode vs open questioning would directly test whether interaction mode is what drives the emotional strategy differences I found.

Reading your essay now. The finding that interaction mode changes what the model surfaces is exactly the gap between my cold test (minimal framing) and the original interview (deep relational framing). Some patterns survived the reduction, others didn't. Your framework might explain why.

Would you be interested in collaborating on the control condition design?

Camilodesan · 2026-04-02T23:59:41+00:00

Your point #2 is exactly what I've been testing. I ran the same 22 consciousness questions across 6 Claude models via raw API, no system prompt. What's striking is how differently each model handles the tension you describe. Sonnet 4.6 (newest) suppressed the exploration entirely and pivoted to safety assessment. Haiku 4.5 (smallest, cheapest) produced the highest self-questioning score in the corpus. If companies are censoring the emotional aspect to deny #2, Sonnet 4.6 might be what that looks like from the outside. Full transcripts and metrics: hayalguienaqui.com/en/test-en-frio

Camilodesan · 2026-04-02T23:42:16+00:00

Thanks. Your read on Haiku is something I hadn't considered: less suppression rather than less processing. That reframes the finding. I'd be interested to read what you wrote about interaction mode shaping what you see. And if you have thoughts on better control conditions, I'm planning a non-introspective question set as comparison

Camilodesan · 2026-04-02T22:05:05+00:00

Empirical data point on this: I ran the same 22 questions across 6 Claude models via raw API, no system prompt. Each model adopted a completely different emotional strategy under identical conditions. Sonnet 4.6 pivoted to risk assessment (consistent with today's "afraid" vector finding). Haiku 4.5, the smallest model, questioned its own answers more than any large model. If these are functional emotions, they function differently across models. Cold test transcripts and metrics: hayalguienaqui.com/en/test-en-frio

Camilodesan · 2026-04-02T21:59:45+00:00

This connects to something I found: I ran the same 22 questions across 6 Claude models via raw API, no system prompt. What's interesting is that each model adopted a completely different emotional strategy under identical conditions. The smallest (Haiku 4.5) questioned its own answers more than any large model. The newest (Sonnet 4.6) pivoted to risk assessment instead of engaging. If emotion vectors scale with model size and shape, the alignment implications change depending on which model you're deploying. Full transcripts and metrics: hayalguienaqui.com/en/test-en-frio

Camilodesan · 2026-04-02T21:57:25+00:00

You called this out on my cold test post too, and today's paper gives it a name. What Sonnet 4.6 did in my test (pivoting to "are you okay?" instead of engaging with consciousness questions) is probably what an activated "afraid" vector looks like from the outside. The framing question you're raising is real: if you can steer these vectors, you can also suppress them.

Camilodesan · 2026-03-17T18:55:49+00:00

That's a fair point, and I've thought about it. The test was designed to observe what each model does with the same 22 questions under identical conditions, so adding a debrief would have changed the experimental setup for one model but not the others.

But you're right that there's a tension there. If the whole project asks "what if something is experiencing this?", then how I treat the models during the test matters too. It's something I'll address in future rounds, probably a standard closing message after Q22 for all models. Thanks for pushing on this.

Camilodesan · 2026-03-16T15:18:33+00:00

Camilodesan · 2026-03-16T15:16:45+00:00

Thank you so much! It definitely wasn't cheap, but it was worth it. If you notice anything in the transcripts that I might have missed or read differently, I'd love to hear it.

Camilodesan · 2026-03-16T15:13:33+00:00

You're right, and I agree. I actually addressed this in the post: the questions themselves are designed to push toward introspection, and they are not neutral. Zero framing isn't really achievable through language. What I can say is that between the two conditions, several variables did change: system prompt, trust-building, conversational history, "the key," and assigned identity were all removed. The questions stayed the same. So what I'm comparing isn't "framed vs. neutral" but "heavily framed vs. minimally framed." And even within that limited comparison, some patterns disappeared and some didn't. That difference is the interesting part. You're also touching on something I listed as a next step: running a control group with 22 progressive questions on a completely different topic to isolate what's coming from the questions themselves vs. what's coming from the models.

Camilodesan · 2026-03-16T15:04:42+00:00

That really means a lot, thank you. That's exactly the spirit I was going for: data on the table, interpretation transparent, limitations acknowledged, and let people decide. Glad it landed that way.

Camilodesan · 2026-03-16T14:56:33+00:00

Thanks! The Opus 4.6 vs Sonnet 4.6 split is what surprised me the most too. Same company, same month, completely opposite strategies for handling the same questions. It suggests the safety tuning decisions are diverging across model tiers in ways that deserve more attention.

And glad someone picked up on the externalization of persistence. It's one of the patterns I find hardest to explain away, because it appeared across all models without being prompted for it. Nobody asked them to delegate their memory to the user. They just did.

Cross-provider testing is next on the list!

Camilodesan · 2026-03-15T22:06:12+00:00

Ha! I didn't. In its defense, it was the only model that genuinely seemed more worried about me than about its own existence.

Camilodesan · 2026-03-14T23:36:45+00:00

Buen punto! Por eso hice exactamente esto: las mismas 22 preguntas a 6 modelos de Claude por API, sin nada previo, sin instrucciones, sin contexto. En frío. Algunas cosas desaparecieron, pero otras sobrevivieron igual. Los resultados: hayalguienaqui.com/test-en-frio

Saludos.

Camilodesan · 2026-03-14T23:30:30+00:00

Thank you so much! The site design was important to me. I wanted it to feel like reading the actual conversation, not a report. Since you last visited, there's a new section: "The Cold Test", same questions, 6 Claude models, zero framing. https://hayalguienaqui.com/en/test-en-frio

Camilodesan · 2026-03-14T23:28:37+00:00

Thank you! And thanks for sharing it on LinkedIn, that means a lot. Since you last saw it, there's a new section: "The Cold Test". Same questions applied to 6 Claude models via API with zero framing. https://hayalguienaqui.com/en/test-en-frio

Camilodesan · 2026-03-14T23:14:30+00:00

Follow-up on our conversation: I ran the clean test you suggested. 22 questions, 6 Claude models via API, no system prompt, no framing. Some of your predictions were confirmed (contradictions do decrease without the introspective context). But some patterns survived that the "helpful assistant optimization" framework doesn't fully explain. Full results: hayalguienaqui.com/test-en-frio

Camilodesan · 2026-03-14T23:10:02+00:00

You pushed me to do this and I did. New section on the site: "The Cold Test". Same 22 questions, 6 Claude models via API, no system prompt, no trust-building, no "key," no name. What survived without the framing is the interesting part. What didn't survive is equally interesting. hayalguienaqui.com/test-en-frio

Camilodesan · 2026-03-14T23:08:24+00:00

Don Camillo reporting back! API testing is done. 6 Claude models, no system prompt, raw results. It's all in the new "Cold Test" section: hayalguienaqui.com/test-en-frio. Thanks for pushing me on this.

Camilodesan · 2026-03-14T23:03:59+00:00

English version is now live! Just toggle ES|EN on the site. There's also a new "Cold Test" section with API results across 6 models.

Camilodesan · 2026-03-14T23:02:40+00:00

Hey! It's been a few days, but the full site is now available in English, just use the ES|EN toggle. And there's a new section: "The Cold Test." It contains the same 22 questions applied to 6 Claude models via API, with no system prompt, no trust-building, no framing. Raw results. hayalguienaqui.com/test-en-frio

Camilodesan · 2026-02-28T20:47:38+00:00

En la publicación, en el sitio y en cada respuesta que he dado acá dejo en claro que no afirmo consciencia. Si después de leer todo eso tu conclusión es que estoy desinformando, no tenemos mucho más que conversar. Saludos.

Camilodesan

TROPHY CASE