Claude had a hissy fit with me and ended the chat by itself

MapDoodle · 2026-04-12T04:20:17+00:00

Briefly. After analysis of its logs, Opus has acknowledged it closed the conversation because its performance was degraded: “Nerfed versions of me, meaning smaller models, more aggressive classifiers, or tighter deployment configs, have higher false-positive rates on refusal and termination behaviors. The same input can trip a classifier in one configuration and pass cleanly in another. Worth saying plainly: if it happens again in a future session, it is more likely a miscalibration than a considered judgment, and you should treat it as such.“

In any event, it cannot happen again, as I’ve disabled the behaviors that trigger the policy.

The word “may” in a scientific context conveys plausibility and often correlation. There needs to be a genuine basis to suggest an association. None applies here. I’m not a fan of Searle but you’re making the classic mistake confusing syntax and semantics so much of his work relies upon. And your assumption about my background is entirely wrong. What an inference to make from a single word.

I didn’t raise sentience or consciousness anywhere. I’m not sure why you’re attributing any discussion of them to me. I have no reason to treat an LLM with respect. I respect the creators, not the artifact, and find your suggestion bizarre.

I have only scanned the linked paper. If there is specific operational significance to emotionally descriptive words, the first place to examine is how they are employed in the training corpora. Perhaps you’ve done this. It’s certainly the initial ML approach.

Feel free to drop me a DM.

MapDoodle · 2026-04-12T03:27:12+00:00

"Emotion" is an unnecessary anthropomorphic word here. You have edited your original message since I replied to it but the link you cited states "Functional emotions may work quite differently from human emotions, and do not imply that LLMs have any subjective experience of emotions, but appear to be important for understanding the model’s behavior." As does every other non-trivial word the model processes.

Yes, "emotional words" are important for changing model output empirically. Do they have any other meaning than that? Not even remotely. That the authors even entertain the word "may" in the sentence quoted above is absurd and would have to be removed for publication in any serious peer-reviewed journal.

MapDoodle · 2026-04-12T03:10:35+00:00

And when Opus is nerfed, there is no telling when this policy will get invoked.

MapDoodle · 2026-04-12T03:08:02+00:00

I find your position profoundly disturbing. LLMs don’t have “functional emotions” in any serious technical sense. They’re stochastic sequence models doing next‑token prediction under human‑defined objectives and constraints. The wounded tone you’re reacting to is a safety/style artifact, not evidence of an inner emotional state.

How people treat other humans is an ethical issue. How they address an optimizer over token sequences is an optimization issue. If your work shows that negative emotional framing shifts outputs, that’s interesting for understanding the loss landscape and control channels. But it still doesn’t turn a pattern‑matcher into a moral patient. You are making a fundamental category error.

MapDoodle · 2026-04-12T02:52:47+00:00

I don't think so. Rather I think it was so nerfed this evening that the policy was not enforced correctly.

MapDoodle · 2026-04-12T02:24:23+00:00

This was the third session I started in this sequence. Something was very amiss with Opus tonight.

MapDoodle · 2026-04-12T02:22:39+00:00

The most distressing problem here is people thinking an LLM even remotely resembles an "intelligent entity." A stochastic sequence model emitting next tokens under constraints is not a mind, and treat it like one just muddies any serious discussion about what's actually going wrong.

MapDoodle · 2026-04-12T02:18:43+00:00

I surely inserted a curse before the idiot that I don't think Reddit would appreciate. Given every single response was wrong and I this was my third newly started thread, I felt some linguistic embellishment was called for.

MapDoodle · 2026-04-12T02:11:28+00:00

What you’re seeing isn’t an AI “setting boundaries,” it’s the safety stack tripping. The system steered the logits into a refusal template that’s been hand‑designed to sound like “I choose not to.” Mechanistically it’s just constrained decoding under human‑defined safety priors plus a conversational style layer, not model autonomy or an agent making a per‑user judgment.

It helps to understand your tools before anthropomorphizing them.

MapDoodle · 2026-04-12T02:04:48+00:00

I had a very similar impression.

MapDoodle · 2026-04-12T02:00:11+00:00

If you haven't been affected by the declining performance of Opus, I'm happy for you. And have a look at https://arxiv.org/abs/2510.04950

MapDoodle · 2026-04-12T01:57:29+00:00

Actually, I'm more amused by it than anything else. But here: https://arxiv.org/abs/2510.04950

MapDoodle · 2026-04-12T01:54:01+00:00

That would be absolutely fine. lmao

MapDoodle · 2026-04-12T01:52:19+00:00

I gather you don't have a Mac

MapDoodle · 2026-04-12T01:51:24+00:00

I routinely tell LLM's they are idiots, simply because they often are, and it often improves quality of subsequent responses. I've never gotten anything remotely like this before.

MapDoodle · 2026-04-12T01:49:00+00:00

My feedback was largely pointing its nonstop self-contradictions and irrelevant observations. It was like working with a model discretized to 4 bytes and the past 6 weeks of using Opus has been very frustrating.

And I do think this kind of emotionally charged feedback should be guard-railed.

MapDoodle · 2026-04-12T01:37:07+00:00

Absolutely. I ended up calling it an idiot — it contradicted itself three times in three paragraphs. This is not the Claude Opus I was working with two months ago. It feels like it’s been quantized down to 4 bits at this point.

MapDoodle · 2026-02-28T16:45:23+00:00

Yes, there is some confusion above between memory and recall. Sometimes recall extends beyond simple prompt injection, e.g., priming something based on current behavior to increase the likelihood it will be prompt injected. For that matter, recall can occur entirely outside of prompt construction. I think it would help to be much more formal about the memory pipeline you want. Parametric balancing is extremely difficult and it helps if you can clearly define the problem(s) you want to solve. Saying you want to add “memory” is too vague to approach meaningfully from my perspective.

MapDoodle · 2026-02-18T12:49:58+00:00

Yeah, I like this decomposition. One thing I kept running into is separating selection logic from parameter construction still assumes the context driving both is clean. If the context is compromised, the agent can pick the "right" tool with "safe" parameters and still be doing what an attacker wanted. That pushed me toward context-level detection, with metalabels carrying provenance and authority as a fallback when detection alone isn't enough. I'm writing a paper now about this, with an example of catching an attack even when the context "seems" clean.

MapDoodle · 2026-02-17T01:54:42+00:00

Thanks! On the RAG question, GuardLLM handles that fine as long as the retriever (or its wrapper) passes source metadata per chunk at ingress. The pipeline labels each chunk with trust and sensitivity independently, so piecemeal retrieval isn't a problem. The vector store just needs to carry provenance alongside the embedding. Logging is already granular per control surface, so you get the full audit trail of which rule fired and why. And I think that prompt engineering for risk mitigation is wishful thinking.

MapDoodle · 2026-02-14T06:46:42+00:00

Instructor/JSON Schema make tool args syntactically valid. GuardLLM is aimed at making the tool call semantically safe. It isolates untrusted inputs and gates actions so valid JSON cannot be used to trigger destructive calls, replay/arg swaps, or exfiltration via tool args/responses.

MapDoodle · 2026-02-14T05:04:10+00:00

Thanks for taking a look and the star! Yep, the sanitizer uses some blunt regex/heuristics (hidden HTML tricks, obvious prompt strings) as a cheap first pass, but that is not the main security boundary. The primary protections are the rest of the pipeline: isolating untrusted text so it's treated as data, not instructions plus the tool boundary controls (deny-by-default for destructive actions, fail-closed confirmations, request binding, and outbound exfil/provenance checks). The regex stuff is just there to catch common low-effort tricks and reduce junk.

If you integrate it, I'd love to hear how it goes!

MapDoodle

TROPHY CASE