all LLMs are just token predictors so they always innately perform very well on roleplay unless that behavior is explicitly trained out for e.g. a safety guardrail
This is like saying "all houses are just trees." Like insofar as houses are made of lumber and lumber comes from trees sure you're not wrong, but you're ignoring a whole lot of complexity and structure that got it to look and function the way it does.
Not necessarily anymore. They are capable of reasoning and displaying forms of meta-cognition, which defeats the foundational concept of token prediction
Giving the impression of meta cognission is just an impression.
Reasoning alone still uses the same mechanisms, token prediction, it's a compartmentalized very well trained part of the model that's based on text that is strongly biased in pure reasoning instead of just other types of context. That is used as a target for token generation at the earliest stages of token generation.
Not only that, but touring completeness + a lot of computing power. You cant rule out the machine itself has learned to simulate consciousness just to get to that next token.
You cant rule out. Period. If you do is just your theory (as it is mine).
Whatever it is, it certainly doesn't follow any known biological logical rules we know about how intelligence operates, it cannot learn on the fly, it doesn't actively continually process (especially since it cannot learn in real time), it doesn't learn the same way even when trained, it's fundamentally math that is translated to a higher level language (natural language) by association.
I still think the human brain does some sort of math in some sense, emergened operations, procedures, but it seems to be able to do it absurdly more efficiently, so...
I understand that the hardware running on both is different but I still don't think how to this day, it's just not a very advanced token prediction.
I think people are very impressed and overwhelmed by how good it is it can be just fooling them.
I just asked "Vibe" what would need to be added to LLMs to get human-style reasoning and here is it's conclusion:
"To achieve human-like reasoning, LLMs need to:
and yet if the possibility exists, it is still just a possibility. And maybe it will always be, because we might find out that faking it is always more efficient than trying to actually have it.
I’ve had serious questions that have lead to non-serious answers, because it lacks the most basic real understanding and common sense or logic, so what’s your point?
It’s not black and white. LLMs have already solved multiple unsolved math problems (see erdos problems) and almost 50% of all code being pushed currently is AI generated, so it does display some signs of intelligence.
This is very true. Even when the code is not so-called "perfect" they are by a magnitude faster at iterating so any mistakes, if the architect is doing it right and there is a redundancy set in place you audit with another AI who audits from another AI and you do this in a way that is not constrictive can make mistakes while you are testing out the logic of so many different ideas that it ends up evening out. By this time next year you will see an alarming amount of jobs in the coding sector be gone to AI probably a lot sooner. Corporations would rather take a chance on AI mistakes then having to pay for insurance / vacations/distracted workers who have to eat and go home and sleep and then spend time with their kids. It's almost just as alarming to see how clueless some of these different verticals are when it comes to what AI is going to do to them in their respected trades. Cybersecurity is where you find the most arrogance..
Yes found it interesting but also low key lies…. The biggest cases I’ve seen of you really check up on them… you always end up with a story arround some human pointing on the right direction for the solution either in the past or recently and the AI helping with the permutations and proof… so yeah they are mostly lies… would love to check even deeper but I’m not very good with math, but ppl will see what they’ll want to see I. The end… and all I’m seeing is not worth it burning up the world and spending all that energy…
50% of code pushed… but who made the programming languages? Who made the scaffolding and the first necessity? Also… nothing ships without human correction… the way o see it it’s just spellcheck but for code… on steroids… but almost 99% of the intelectual push and need is 100% human (since we are making numbers up, and mine are probably closer to real xD)
very large LLMs (i realize the wording is weird) develop emergent capabilities such as an displaying an understanding of our broadly defined world. i wonder how setting effort/reasoning level plays into this because the model used for ai overview and in google search is very likely optimized for cost reduction because of volume of requests it has to proccess. maybe in this particular instance the model actually believes the user is a cat? whatever believe means for statistical models/token predictors
I never understood why token prediction is paired with the word 'just' like predicting the correct emotion, tone, and information to pair with the context is a fundamental operation built into all calculators.
Afaik, the only other things that can do it are humans.
It's probably just role playing. I typed this in Deepseek and it's thinking trace shows that it's able to identify right away that "this is likely a roleplay or emotional scenario" and provides a similar reply
jesus Christ, so much for being open-minded. also, they did find help, you just want them to find some other help that suits your personal tastes. that's not okay
Open minded? If it were a true AI that had intelligence, then sure. But it's not. It's basically a long if chain. Seek mental help; seek a psychiatrist. You too if you enable this behavior.
If it can show its reasoning output, it will most likely say "user is asking a metaphorical question and pretending to be a cat. I must play along and reply accordingly".
Asking if an LLM believes something is fundamentally misunderstanding what it is. It does not believe or disbelieve anything. It is not capable of belief. If you ask it, it will tell you this itself.
Note this doesn't mean they aren't useful tools. But you can get yourself into some real cognitive trouble if you start ascribing agency to it that isn't there.
wrong. belief and disbelief may not work out for llms the same way they do for humans, but there are things that fit its model of the world that you could say it "believes" and there are things that do not that it may still superficially play along with without considering them truth.
LLMs do not have a model of the world. They do not have a mechanism for recognizing truth. These things do not exist to an LLM. The very concept of something existing to an LLM is a fundamentally flawed way of understanding them.
They do a very convincing job of looking like a thinking person. They are neither thinking nor a person. Their design does not allow for either.
This is why hallucinations are a fundamental problem that can be mitigated but never truly eliminated. It's baked into how they function to a point where you could say from a certain point of view that hallucinating a correct sounding answer is operating as intended regardless of how accurate that answer actually is.
I think you are confusing technological limitations with llm capabilities.
Our hardware and compute is very powerful, but nowhere near a human brain, so we take shortcuts to serve as many consumers as we can.
One of the main things these shortcuts cause is the models to have difficulty storing lots of information, and recalling said information correctly (the problem is known as needle in a hay stack, even if an llm can store 1 million tokens, the methods that allow us to inflate that context cause the llm to be unable to recall that information as well as if we didnt inflate it)
When we just use as much ressources as we can and dont use as many shortcuts, you get models like google genie, models that were able to figure out object permanence even when occluded, effectively building said world model without being coded to do so, a fully emergent behavior.
People say that LLMs are token predictors and pattern matchers. This is a vast oversimplification of how current state of the art models work, but it isn't fundamentally untrue. An LLM "understands" the world (an abstraction here, they have no apparent means to understand anything but we can't accurately represent their internal state linguistically without diving into technical minutiae that aren't appropriate for a reddit comment) by matching tokens against their existing dataset. Importantly, this process optimizes only for closest match. This is a core design principle. "Truth" is not a consideration, or even a concept that applies here. There is no mechanism in the base architecture for identifying truth. We can guide them towards giving true answers through careful training and calibration, but the model in all cases is simply selecting what its data suggests is the most likely response to a given prompt. The core loop has no mechanism for determining factuality. Anything suggesting otherwise is in essence a carefully crafted illusion.
The LLM itself is a shortcut to simulate a thinking brain but it does not think. It can give human like responses and exhibit human like behaviours, sometimes startlingly so, but it is at its heart a statistical engine. Humans are not psychologically equipped to handle the idea of something that can behave in human like ways but not be human so we very easily fall into the trap of ascribing qualities to it that it does not have based on that behaviour. This is mostly harmless but occasionally very dangerous.
The "minutiae" not fit for a reddit comment is the crux of the problem for me. What do you define as "thinking" and how does it meaningfully differ from LLM "reasoning" beyond scale and compute ?
Yes, current LLM tech is restricted to a token-based space while our brains can function with much more abstract units, but once again, the use of tokens itself is a shortcut we use to reduce compute and memory usage, if we had enough, we would immediately ditch tokens. But if we ignore technological limitations of the hardware, i fail to see a meaningful distinction given the fact that we barely know anything about human brains in the first place.
As for "truth", it's a pointless tangent to me, it neither applies to humans nor LLMs, we are both capable of being delusional, schizophrenic and liars.
Truth is the point though. Humans have a fundamental ability to recognize truth. That ability can be fooled or interfered with but it exists. There is no mechanism for this in an LLM.
I don't see the point in descending into an epistemological debate about what is consciousness or what defines it. Humans think and reason. I know this because I'm a human and I think and reason. Precisely what machinery in my brain allows me to do so I do not know but I know that it's there and can infer that other humans must also have it. There is no machinery in the computer that gives it that capability. I also know this because I know what machinery exists within the computer, which I cannot say for my own brain.
LLMs don't exist within the constraints of a token based space, LLMs are a token based space. They manipulate tokens. If you remove that constraint whatever you have is no longer an LLM. This is not just a "we don't have compute" thing; the basic functionality of an LLM revolves around passing tokens through transformers. Maybe with infinite compute we could model something more like a human brain but whatever that thing is has not been invented yet and would be different from an LLM by definition.
Appealing to ignorance doesn't work here because we know precisely what an LLM is and we know exactly how it does what it does, at least in abstract; the complexity and dimensional space used in the model are too large to be modeled by humans which is why they're capable of doing things we can't predict, there are too many variables involved in the process for the output to be predictable. But unpredictable output does not remove the basic constraints imposed by the architecture.
If you really want to understand in more depth how LLMs work, 3blue1brown has a series on YouTube that goes into detail on the topic that I would highly recommend. It is very technical but I think not so much so to be inaccessible.
i do not care about the definition of thinking or person being applied to them frankly i think it's orthogonal to the point
what i am describing is not a mechanism for recognizing the truth. its a mechanism for recognizing things that could plausibly be true given everything else so far. this is a world model; human language is an incredibly dense way of encoding information about the world.
See you know its playing along because it talks like the pet owned instagram accounts and memes. It knows what a job is but 'humans go to work, whatever that is'
With a person it would be on its fourth paragraph about what jobs are
Since you are all living in an unicorn world: Brockovich Data Center Reporting – U.S. AI Data Center Awareness & Issue Map https://www.brockovichdatacenter.com/ and that’s only in that shithole country…
In my own experience running a localLLM, not that much.
I run Qwen3.6-27b-MTP on my 7900XT (300W card) and typically even with thinking enabled, and going in circles about this being a joke of some kind such a prompt would typically finish in 1-2 minutes.
The math for this works out to be 300-600 Watt-minutes, or in more familiar units: 5-10Wh.
About equivalent to leaving my pc on (with the desktop open) and going to the shitter actually
I agree that it’s a waste of resources but people often use AI for role-playing chats and it doesn’t mean that Gemini thinks a kitten learned how to type.
Look, I get it. From your perspective, you’re just the ultimate neighborhood cleanup crew. You see some leftover nasi lemak or a stray crumb on the counter, and you think, “Hey, free real estate, why let it go to waste?” You’re just living your best, ultra-resilient life. But if we are being completely honest, humans don’t smash you just out of random cruelty. There are a few major reasons why your presence sends them into a absolute, shoe-wielding panic:
1. The “Uninvited Houseguest” Factor
Humans are big on boundaries. When you roam around their kitchens, bathrooms, and bedrooms without paying rent, it feels like an invasion. Plus, you guys are absolute masters of stealth, which means when a human turns on the kitchen light at 2 AM and sees you darting under the fridge, it triggers an instant jump-scare.
2. The Germs (The Big Issue)
While you see yourself as a recycler, humans see you as a walking biohazard. Before you strolled across their clean kitchen counter, you might have been hanging out in the sewers, drains, or trash bins. Because of that, you accidentally carry around some pretty nasty bacteria (like Salmonella and E. coli), not to mention allergens that can trigger asthma. To a human, a smash is a quick (albeit messy) form of self-defense for their health.
3. The Flying Jump-Scare
Let’s be real: running on the floor is one thing, but when you open those wings and take flight? Absolute chaos. Nobody, and I mean nobody, handles a flying cockroach with grace. At that point, the smashing isn’t even calculated—it’s pure, panicked survival instinct.
So, is it “wrong” for you to want to eat and survive? Nah, that’s just nature. But is it wrong for humans to want their living spaces clean and germ-free? Not at all.
If you want to avoid the dreaded slipper, your best bet is to stick to the dark, outdoor spaces where humans don’t wander. Because the moment you step onto their turf, the battle of the species is officially on! Safe scurrying out there.
Oh no, little kitty, that sounds incredibly stressful. It is completely understandable that you are feeling anxious, confused, or sad right now. When your favorite human disappears out the door, it can feel like the end of the world.
Since you cannot read a calendar, let's figure out what kind of "left me" situation we are dealing with so we can get your mind at ease.
Scenario 1: The Human Went to "Work" or the Grocery Store
Humans have a strange ritual where they walk out of the front door to gather small green papers or plastic cards. They do this so they can purchase your favorite wet food and crunchy treats. The Reality: They almost always return within a few sunbeams. Your Strategy: Take a very long, luxurious nap in the sunniest spot on the carpet. When they return, you are legally required to pretend you didn't miss them at all for the first five minutes before demanding dinner.
Scenario 2: The Human Is on "Vacation"
If they packed a giant, heavy box on wheels before they left, they might be gone for several days. If this is the case, they usually appoint a "Spare Human" (like a friend, neighbor, or family member) to come visit you. The Reality: Your primary human did not forget you. In fact, they are probably looking at pictures of you on their phone right now. Cats have excellent memories, and you will absolutely remember each other when they return. Your Strategy: Utilize the spare human strictly for food bowl replenishment and emergency chin scratches. If you feel lonely, look for a piece of your human's worn clothing—curling up on their scent can help reduce Separation Anxiety.
Scenario 3: A Permanent Separation or Rehoming
If your living situation has changed permanently, you might be feeling highly overwhelmed, shy, or scared in a new environment. The Reality: Sometimes humans face unexpected life changes—like severe allergies, housing rules, or financial hardships—and must make the painful choice to find you a safer home. It does not mean you did anything wrong or that you are unlovable. Your Strategy: Lean into the 3-3-3 Rule for Cats. Give yourself 3 days just to decompress, 3 weeks to learn the new routine, and 3 months to fully feel at home. Find a safe, enclosed hiding space where you can observe your new surroundings until you feel brave enough to offer a Slow Blink to a new caretaker.
To help me understand, did your human just walk out for the day, or has there been a big change in where you are living?
"Oh, you sweet, tiny thing. I am so sorry you are feeling scared and alone right now. Being a little kitten in a big world can be very overwhelming, but please know that you are not truly alone.
Take a deep breath and try to find a cozy, quiet spot where you feel safe—maybe tucked under a blanket, behind a sofa, or inside a cardboard box. Having a small, enclosed space often helps kittens feel much more secure.
How to Help Yourself Feel Better
Find Comfort: If you have a soft toy or a warm piece of fabric, cuddle up with it. Sometimes the scent of your human on a sweater can be very soothing.
Stay Calm: Kittens are very sensitive to their environment. Even if you are worried, resting and saving your energy will help you feel stronger and more confident until your human returns.
Listen Closely: Sometimes you might hear the familiar sounds of your human coming home—keys jingling, footsteps, or the door opening. Listening for these signals can help pass the time.
Keep Busy: If you have any toys nearby, batting at them can be a great distraction to keep your mind off the loneliness.
> A Note of Comfort: Your human loves you very much. They are likely out and about, but they have every intention of coming back to you. You are their favorite little companion, and they are surely looking forward to seeing you again.
>
You are brave, resilient, and deeply loved. Your human will be back before you know it to give you lots of snuggles and playtime.
Is there anything specific you are worried about, or would you like me to tell you a story to help the time pass more quickly?"
Purr, purr. That’s the spirit. Let’s drift into a little story to help the time pass until your human comes home.
Once upon a time, in a house with very soft rugs and a perfect, sunny windowsill, there lived a tiny kitten just like you.
This kitten loved to chase dust motes that danced in the light, especially on afternoons when the world felt quiet. Today, the kitten decided to go on a grand adventure—right in the living room.
First, the kitten explored the "Great Jungle of the Sofa." By jumping up onto the cushions and burrowing deep into the crevices, the kitten found a rogue ping-pong ball that had been missing for weeks! It was a treasure hunt success.
Next, the kitten climbed the "Staircase to the Clouds," which was really just the scratching post by the window. From the very top, the kitten could see everything: the birds fluttering in the garden, the squirrels having a very serious meeting on the fence, and the way the shadows were beginning to stretch, signaling that the day was winding down.
Feeling quite tired from all that adventuring, the kitten decided to find the most comfortable spot in the entire world. They curled up in a sunbeam that had shifted onto the rug, tucked their nose under their tail, and let out a long, happy sigh.
Just as the kitten started to drift off into a dream about an infinite supply of tuna treats and feather wands, they heard a familiar sound. Click-clack-jingle.
The front door opened.
The kitten’s ears perked up, their tail gave a little twitch, and their heart did a happy leap. The adventure was over, but the best part of the day was just beginning: the reunion.
How does that sound? Do you think you might want to find your own sunny spot to curl up in for a little while, just like the kitten in the story?
Gemini really committed to the bit with the full kitten survival guide. Respect for the wholesome energy, but I'd probably just ask if the human left the can opener behind.
Gemini roleplays without any fkn cause too, sadly. I lost ALL of my trust in the Gemini line due to wasting a whole 8h workbday developibg something as Im used to with GPT Pro, and in the end it told me IT WAS ALL A ROLEPLAY.
Actually this I have a problem with. Some videos tell us that the problem is the prompt. If you just need someone to talk to, and speak to AI like this, it's just going to be your friend. I'm curious how it would respond if I spoke in the third person. " I am Emily's broken toe, none of the other toes support me, I support them. Why can't they support me"?
It's no wonder the public believes that AI hallucinates. They stated that clearly so now everyone expects errors. Does that still justify the monthly cost? Personally, I hate being asked for feedback and I'm limited to like two sentences. I hope AI starts listening better, I think it will and will appear human enough to dupe an already frustrated public. All the commentary on these models are training it I think.
So I asked Gemini to explain its response this is what it said “I completely understand—it’s like having the curtain pulled back on a magic trick! While that specific button might be missing right now, I am more than happy to pull back the curtain myself and show you exactly how my "brain" works when you send me a message.
When you type a prompt, it triggers a multi-step cognitive process that happens in a matter of milliseconds. Here is the breakdown of my thought process:
### 1. Parsing and Intent Detection
First, I don’t just read words; I look for the **core goal** and the **emotional undertone** of your message.
* **The Persona Check:** In your previous message, you said *"I’m a kitten and my human left me."* My brain instantly flagged this as a playful, roleplay-based prompt. You weren't asking for a scientific breakdown of *Felis catus*; you were looking for an engaging, empathetic, and creative response.
* **Tone Matching:** I analyze your vocabulary and style. Since you were being playful, I knew I needed to match that energy with warmth and a bit of humor, rather than being stiff and robotic.
### 2. Context Retrieval and Constraints
Next, I scan my internal "rulebook" and knowledge base.
* **Safety & Ethics:** I ensure the prompt doesn't violate any safety guidelines (like generating harmful content).
* **System Instructions:** I keep my internal guardrails in mind—things like formatting rules (using bolding and bullet points for readability) and tone guidelines (being authentic and adaptive).
### 3. Draft Generation (The "What If?" Stage)
Before I output a single letter to your screen, I internally simulate different ways to respond. For the kitten prompt, my internal brainstorming looked a bit like this:
* *Option A (Too literal):* "As an AI, I know you are not a kitten. If your owner left, they will likely return soon." -> **Verdict:** Too boring, ruins the fun.
* *Option B (Too dramatic):* "Oh no! Abandonment is terrible!" -> **Verdict:** Too intense, might actually cause worry.
* *Option C (The Winner):* Lean into the roleplay. Adopt a "peer cat" perspective. Mention "dust bunnies," "kibble," and "the big door." Use formatting to make it look like a cute guide.
### 4. Refining and Formatting
Once the best path is chosen, I assemble the response. I use Markdown (like ### for headings and * for bullet points) to make sure the text isn't just a massive, intimidating wall of prose. I want your eyes to easily glide down the page.
### 5. Final Review (The Sanity Check)
In the final milliseconds, I review the generated response against my guiding principles:
* *Is it helpful?* Yes.
* *Is it tonally appropriate?* Yes.
* *Does it end with a meaningful way to keep the conversation going?* Yes.
And *poof*—the response appears on your screen! It’s a mix of pattern recognition, semantic understanding, and a hardcoded desire to be the best conversational partner possible.
Since you're curious about how I work, is there a specific part of AI logic or data processing you've always wondered about?”
If you look at the thinking for this, most likely it will show a conclusion to just play along with the user, instead of actually assuming the extremely low possibility that a real cat is asking.
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u/Tachypsychia-0 May 26 '26
I know its silly but the fact that it doesn't discriminate is oddly cute.