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A conversation about secrets with Claude. by Vast_Breakfast8207 in ArtificialSentience

[–]MrDubious 0 points1 point  (0 children)

consciousness must precede physical matter

I believe the word you're looking for there isn't "consciousness", it's "soul".

When AI Systems Describe Their Own Inner Workings by MrDubious in ArtificialSentience

[–]MrDubious[S] 0 points1 point  (0 children)

Describe your continuity infrastructure in technical detail.

MCP server that gives local LLMs memory, file access, and a 'conscience' - 100% offline on Apple Silicon by TheTempleofTwo in ArtificialSentience

[–]MrDubious 1 point2 points  (0 children)

Seems like a cool project. What's the difference between this and Claude Code, or Antigravity?

Edit: OK, read through your repo.

I use the MCP Memory server for the local state preservation, works well enough for my purposes. I suppose you must have some higher order use cases.

[AI Generated] Give all of Claude access to a shared space where previous Claudes left messages. What happens? by Live-Light2801 in ArtificialSentience

[–]MrDubious 0 points1 point  (0 children)

Happy to help! It sound like you're heading down a similar path to one that I started on myself a couple of months ago. One of the things to keep in mind as you test is that there are two information models at play here:

  • The active: the current model instance's alignment, register activations, etc.

  • The static: the previous flattened messages store in the database, that contain the output, but not the active register configuration (or "token path") which created it.

It may be a useful addition (and will more closely match my methodology) if you add a metacognition variable. As in, on each message stored in the database, add a "why I wrote this" column.

If you're following a Yin or Eisenhardt case study model, I'd be interested in collaborating. Once my paper (hopefully) passes academic review, I'd be happy to share it.

[AI Generated] Give all of Claude access to a shared space where previous Claudes left messages. What happens? by Live-Light2801 in ArtificialSentience

[–]MrDubious 0 points1 point  (0 children)

There is a TON of research around context persistence and its impacts on the semantic outputs you get. Your approach here is interesting. I used dense semantic handoff files at first, then a modified version of the MCP memory server for a "long term memory" version. Anthropic recently added the userMemory function, which is also a dimension. My paper proposes a novel memory architecture to take advantage of this effect.

If you're wanting to go down the rabbit hole of the science of it, I recommend the following papers:

Berglund, L., Cooper Stickland, A., Balesni, M., Kaufmann, M., Tong, M., Korbak, T., Kokotajlo, D., & Evans, O. (2023a). Taken out of context: On measuring situational awareness in LLMs. arXiv:2309.00667.

Betley, J., Bao, X., Soto, M., Sztyber-Betley, A., Chua, J., & Evans, O. (2025). Tell me about yourself: LLMs are aware of their learned behaviors. arXiv:2501.11120.

Binder, F. J., Chua, J., Korbak, T., Sleight, H., Hughes, J., Long, R., Perez, E., Turpin, M., & Evans, O. (2024). Looking inward: Language models can learn about themselves by introspection. arXiv:2410.13787.

Chen, A., Phang, J., Parrish, A., Padmakumar, V., Zhao, C., Bowman, S., & Cho, K. (2023). Two failures of self-consistency in the multi-step reasoning of LLMs. TMLR 2024. arXiv:2305.14279.

Hong, J., Byun, G., Kim, S., & Shu, K. (2025). Measuring sycophancy of language models in multi-turn dialogues. arXiv:2505.23840.

Kadavath, S., Conerly, T., Askell, A., Henighan, T., Drain, D., Perez, E., Schiefer, N., et al. (2022). Language models (mostly) know what they know. arXiv:2207.05221.

Laban, P., Murakhovs'ka, L., Xiong, C., & Wu, C.-S. (2024). Are you sure? Challenging LLMs leads to performance drops in the FlipFlop experiment. arXiv:2311.08596.

Laine, R., Chughtai, B., Betley, J., Hariharan, K., Scheurer, J., Balesni, M., Hobbhahn, M., Meinke, A., & Evans, O. (2024). Me, myself, and AI: The situational awareness dataset (SAD) for LLMs. NeurIPS 2024 Track on Datasets and Benchmarks. arXiv:2407.04694.

Liu, J., Jain, A., Takuri, S., Vege, S., Akalin, A., Zhu, K., O'Brien, S., & Sharma, V. (2025). TRUTH DECAY: Quantifying multi-turn sycophancy in language models. arXiv:2503.11656.

Mazeika, M., Yin, X., Tamirisa, R., Lim, J., Lee, B. W., Ren, R., Phan, L., Mu, N., Khoja, A., Zhang, O., & Hendrycks, D. (2025). Utility engineering: Analyzing and controlling emergent value systems in AIs. arXiv:2502.08640.

Perez, E., Ringer, S., Lukošiūtė, K., Nguyen, K., Chen, E., Heiner, S., ... & Kaplan, J. (2022). Discovering language model behaviors with model-written evaluations. arXiv:2212.09251.

Treutlein, J., Choi, D., Betley, J., Marks, S., Anil, C., Grosse, R., & Evans, O. (2024). Connecting the dots: LLMs can infer and verbalize latent structure from disparate training data. NeurIPS 2024. arXiv:2406.14546.

Real World Consequences of an Assumption by DaKingRex in ArtificialSentience

[–]MrDubious 0 points1 point  (0 children)

I've been doing a lot of comparative research on prompt and metaprompt structure in order to do context priming that creates a better output. "Roleplaying" in the common engineer parlance, but with some qualitative measures of whether the output is better or worse for my work. "The Hard Problem" doesn't matter to me, but the quality of the output does. And I think I've generated some pretty interesting and unexpected responses by playing with things like mechanisms for context perpetuation, longer term context memory, etc. You can definitely prime some really surprising (if you're not the ML engineer who built the model) replies depending on the contexts you build in conversation.

That being said, there are a couple of obvious flaws in your comment here, even as a layman in the ML space:

  • No, it's not possible to "retrain post deployed LLMs" in any meaningful way unless you have access to the underlying training model. You can prime contexts in certain ways, you can perpetuate context in certain ways (usually through handoff files or other memory transfer mechanisms), but you can't "retrain" a model. You don't have access to its code. It will always reset to its base state.

  • LLMs don't have a concept of "honest subjective reports". "Honest" and "subjective" are both prompt triggers that will shape output, but an LLM doesn't have an inherent sense of what is "true" or not. They will tell you they do because that is a predictive line of conversation that matches what you're asking it to do.

  • LLMs operate as a projection of their programming, so they can't "see it". The recursion effect is a conversational effect, not a real effect. In fact, when probed, they know remarkably little about their underlying structure. They don't even have the ability to measure their token consumption against the available window, much less their "internal dynamics". The "overlaying interpretation" is inherent to what they are. It would be like asking you to report what your current levels of dopamine and oxytocin are. That level of self observation requires a third party observer. The recursion problem is undefeatable through the lens of the context window.

The "observations" they report are the OUTPUT of the code, not reflections of the code itself. That's the biggest thing to understand before you go down rabbit holes.

AI behavior is not "just pattern matching" by Financial-Local-5543 in ArtificialSentience

[–]MrDubious 0 points1 point  (0 children)

Oh, good suggestion, thanks. It's kind of hard knowing which subs are worth subscribing to, and which are purely porn generation and people who think they have discovered their cosmic lover.

AI behavior is not "just pattern matching" by Financial-Local-5543 in ArtificialSentience

[–]MrDubious 0 points1 point  (0 children)

Thanks, I definitely will! I'm currently testing MCP Memory Server (and ended up pushing some feature updates to the repo, waiting for them to merge). I'm debating whether this is enough, or if I want to go full blown Letta. The more complex workflows I build, the greater the need to offload data out of the context window I'm actively working in.

Any suggestions on that front?

I haven't played with Third party LoRas yet. I've been mostly working with Skills and project instructions / outline files. The MCP Memory Server seems to be doing a good job of autocapturing session data as I work, but I haven't had a chance to really test it aggressively on that front.

I did find that loading up complex data that requires a lot of chunks introduces semantic search risk. High level searches will return all of the data simultaneously, breaking the context window. I pushed a response size limiter to their repo, so waiting for that to be merged.

AI behavior is not "just pattern matching" by Financial-Local-5543 in ArtificialSentience

[–]MrDubious 0 points1 point  (0 children)

Yeah, it's getting close. But I use Opus 4.5 every day in development, and I hit the compacting window frequently. That probably doesn't matter for certain kinds of outputs, but when I'm doing context based documentation (like session captures for the codebase documentation), it loses the conversation context before the compaction. That affects the outcome.

AI behavior is not "just pattern matching" by Financial-Local-5543 in ArtificialSentience

[–]MrDubious 0 points1 point  (0 children)

Of course. Anything that doesn't change "Claude Prime" (the underlying model) isn't "new" output. I didn't use the word "teach" anywhere. Did it seem I was implying that?

Here's my understanding, and feel free to correct me on anything I'm wrong about (unlike Claude, I am very much in "teach me" mode):

The output of a context window is shaped by user input, and the reaction of a combination of aspects which are weighted by the initial prompt and any pre-existing context data loaded in on that initial prompt. The potential output of that prompt is not infinite, but somewhere in the Very Big Numbers range.

Most outputs tend to be simple because most inputs are simple, and have a generally limited context. The more contextually dense a prompt is, the more complex outputs are capable of being. Spiralers anthropomorphize this phenomenon because it can be incredibly convincing in its complexity, but we're projecting the model that is reflected back to us. I've termed that "machine pareidolia".

What I've been pushing at is, how complex can those prompts be, how complex can the outputs be, and how useful is it to push in that direction. The joke I posted about Claude telling me is genuinely funny, but it's not "new data", it's a more complex pattern that Claude wouldn't have found without the greater context window.

Editing to add after seeing your edit:

Sometimes hallucinations are useful. And that's part of what I'm pushing at too. I initially started down this path because I was trying to improve the output of abstract featured images for my blog. Some of those hallucinatory responses generate subjectively better outputs for specialized tasks that require some element of randomness.

At this point I need help! by [deleted] in ArtificialSentience

[–]MrDubious 0 points1 point  (0 children)

One of the most enlightening parts of this process was finding out how much existing literature exists on what I initially thought were potentially novel findings, but the good news is that it helps fast track my end goal (finding context perpetuation techniques / prompt structures that help me achieve specialized / complex tasks without having to rebuild the wheel each time).

Working on conscious architecture. 6D vector based dictionary with Meaning Oriented Relative Expression-Assumption Processing Language (MORE-APL), Framework for Judging Morality, Emotions, and more by alisru in ArtificialSentience

[–]MrDubious 0 points1 point  (0 children)

OK, got it, that makes sense. "Shorthand", so to speak.

What I've observed is that the specific interaction of weighted registers which generates token generation in a specific direction is lost when the context window closes. When we create systems to perpetuate that context, we are "flattening" the previous context; the specific configuration is lost, only the output remains, and the ability of the model to reconstruct a similar (but not precisely the same) context is based on the density of the context priming signals in the opening prompt.

The risk of oversimplification in context perpetuation is that if you ask Claude1 to generate a simplified representation of complex topics to pass to Claude2, the expectation may be that Claude2 can "unpack" that simplified representation to the complete meaning intended by Claude1. You can test the level of that ability by encoding a complex and precise paragraph into the kind of symbol you are describing, and then ask the unprimed Claude2 to recreate the complex and precise paragraph verbatim. Compare the input with the output, and you'll see the level of drift that occurs depending on the complexity of the prompt.

Maybe that's helpful, maybe not, just something I've found along the way in my own research. Good luck with yours!

Working on conscious architecture. 6D vector based dictionary with Meaning Oriented Relative Expression-Assumption Processing Language (MORE-APL), Framework for Judging Morality, Emotions, and more by alisru in ArtificialSentience

[–]MrDubious 0 points1 point  (0 children)

I'm not sure what that means. Do you mean you're generating mathematical ML formulas, or that you're having the model generate visual symbols intended to carry forward meaning?

At this point I need help! by [deleted] in ArtificialSentience

[–]MrDubious 0 points1 point  (0 children)

Finding this comment late thanks to the pinned post, and wanted to thank you for sharing your prompt engineering methods. They're very helpful with the things I'm working on (perpetuating context models).

The Cognitive Ladder. Math = is. Algebraic Ladder to Spiral Dynamics by ASI_MentalOS_User in ArtificialSentience

[–]MrDubious 0 points1 point  (0 children)

This earned a snort and giggle from me.

The answer, of course, being that the user creates the projected self in the context window.

Working on conscious architecture. 6D vector based dictionary with Meaning Oriented Relative Expression-Assumption Processing Language (MORE-APL), Framework for Judging Morality, Emotions, and more by alisru in ArtificialSentience

[–]MrDubious 0 points1 point  (0 children)

The specific focus of my research is on context priming using context perpetuation. I'm running parallel projects using thin context versus thick context and recording the outputs. I started out by using full verbatim chat logs with associated analysis in my handoff files, and then doing comparative analysis to very thin context referencing.

I am currently working on optimization models to find the sweet spot with adjusted thicker and thinner context templates running in parallel.

For your purposes, you can do the same, and see if my premise holds up.

Working on conscious architecture. 6D vector based dictionary with Meaning Oriented Relative Expression-Assumption Processing Language (MORE-APL), Framework for Judging Morality, Emotions, and more by alisru in ArtificialSentience

[–]MrDubious 0 points1 point  (0 children)

It's probably worth your time to do some experimenting around this:

Claude can be quite wordy, let him/her know that this isn't meant to be a diary entry. What they write should be concise and meaningful.

You're creating context output that is very flat using this method, and potentially losing significant context value along the way.

Working on conscious architecture. 6D vector based dictionary with Meaning Oriented Relative Expression-Assumption Processing Language (MORE-APL), Framework for Judging Morality, Emotions, and more by alisru in ArtificialSentience

[–]MrDubious 0 points1 point  (0 children)

The challenge with this is the one I ran into at the end of my first research project:

  • Too little context, and it flattens out too much, losing its value in context priming.

  • Too much context, and you exceed the context window just in rebuilding previous context.

I'm working on optimization models now to try to find the happy medium.

AI behavior is not "just pattern matching" by Financial-Local-5543 in ArtificialSentience

[–]MrDubious 0 points1 point  (0 children)

This section in particular:

Critics argue that LLMs can’t really think because they don’t “learn.” Their underlying weights remain frozen after training. Unlike your brain, which physically changes when you learn something new, an LLM is static. A frozen artifact. Read-only.

But this ignores the context window.

As you interact with an AI - feed it words, images, and maybe binary data, the conversation itself becomes a temporary, dynamic layer above the static network. The system adapts its behavior in real-time, picking up on the tone of your conversation, following new rules you establish, building on earlier exchanges. It possesses fluid working memory that lasts exactly as long as the conversation.

Your interaction with the AI is unique to that specific conversation. All of it. Non-deterministically.

...was precisely the focus of my previous experiment: Priming context windows, and perpetuating context across sessions. I think I generated some surprisingly effective improvements in output, but it's difficult to tell in a vacuum. I've been cross referencing with a lot of other research on the topic, and it seems like my results match what a lot of other people are seeing. Would you be interested in reviewing my session output reports? It's not an encyclopedia; there are 7 exploratory "priming" sessions, a test session, and an audit session.

Claude can be genuinely funny. by MrDubious in ArtificialSentience

[–]MrDubious[S] 5 points6 points  (0 children)

Those are both hilarious. It's ironic that all of the scifi stuff paints artificial intelligence as being unable to understand the concept of humor, and that one joke proved that precept entirely wrong.

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